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DEVELOPING A METHODOLOGY FOR

EVALUATING PRIVATELY OPERATED

TOLL ROAD PROJECTS USING

STOCHASTIC COST-BENEFIT ANALYSIS

Sae Chi

BE (Civil) (Hons)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Civil Engineering and Built Environment

Science and Engineering Faculty

Queensland University of Technology

2018

Developing a Methodology for Evaluating Privately Operated Toll Road Projects Using Stochastic Cost-Benefit

Analysis

i

Abstract

In the project appraisal of a major road project, the host government acts as the

decision-maker. Addressing net impacts and risks to the community is crucial for the

host government to ensure that the benefits of a decision outweigh the costs. Cost-

Benefit Analysis (CBA) is conducted for major road projects for the purpose of

evaluation. It measures the net impacts of the project through monetisation of the

impacts. The outcome of CBA is represented as Benefit-Cost Ratio (BCR).

Tolls are generally treated as financial transfers in CBA. However, it is

questionable whether tolls should be treated as financial transfers when tolls are

collected by the private operator. Moreover, many toll road projects have complex

risk-sharing mechanisms that can be unique to each project. Depending on the risk-

sharing arrangement, a part of the risks can be shifted to the private operator. This

indicates that risk allocations need to be carefully considered in the evaluation of a toll

road project and the treatment of some impacts may need to be altered accordingly, to

properly reflect net impacts and risks to the community in the evaluation.

Reviewing previous studies revealed that limited studies have been conducted

regarding CBA for the purpose of evaluating toll road projects, a lack of empirical

assessment of risks in CBA, and a scarcity of investigation into the treatment of tolls

in CBA.

This study examines the impacts and the risks of a toll road project to the

community, in order to reflect them in CBA. A range of literature, including recent

academic literature and publications of Australian transport authorities are reviewed

to outline the potential barriers in evaluation of toll road projects. Previously

conducted CBA for major road projects are reviewed to highlight any limitations and

difficulties of the existing practices of CBA. A toll road project case and a toll tunnel

project case are synthesised on the basis of the overarching characteristics of recent

existing major road projects. This study uses the stochastic CBA by incorporating the

Monte Carlo simulation approach for the purpose of examining evaluations of toll road

projects. The Monte Carlo simulation is a well-established risk analysis tool. The

synthesised case is evaluated using the stochastic CBA, which represents its outcome

as stochastic BCR distributions. Risk profiles are developed using statistic inferences


Analysis

ii

on the basis of the stochastic BCR distributions. The perspectives of “toll as a financial

transfer” (TT) and “toll as an end-user cost” (TC) are considered. Tolls and other

payments that are often used in toll road projects, such as minimum revenue

guarantees, are examined while considering the perspectives, to determine the

movements of each payment. On the basis of the payment movements, treatments of

some impacts are altered in CBA to better reflect the net impacts and risks to the

community. Examination of the developed risk profiles across perspectives and

scenarios reveals whether the shift risk can be portrayed in the stochastic CBA.

Risk profiles of the synthesised cases across various risk arrangement scenarios

found that treating tolls as the cost to the community is a reasonable and valid

approach. Treatments of various impacts need to be carefully considered on the basis

of the risk-sharing arrangement of the project. Altering impacts appropriately in CBA

better reflects the shift of risk in the outcome risk profile. The outcome risk profile

illustrated the net impacts and risks to the community in an empirical manner using

the stochastic CBA. Various scenarios and perspectives can be examined by analysing

the shift of risk on the basis of the risk profile.

There have been limited studies with regard to CBA for a toll road project in

academic literature. Different perspectives have been studied for various financial

analysis, however are seldom studied using CBA. This study enhanced the knowledge

of CBA for a toll road projects and incorporated different perspectives in CBA.

Moreover, this study determined the appropriate treatment of tolls in CBA through the

considerations of perspectives, which is a significant contribution to academic study

and practices about CBA. The CBA methodology that is proposed in this study can

be implemented in the existing project appraisal process. A detailed framework of the

proposed methodology is developed and shown. The proposed methodology provides

the host government with an effective tool that can support their decision making on

the basis of the net impacts and risks to the community by solely evaluating the project

from the public perspective.

Future study can be conducted to investigate the impacts of using various

discount rates, incorporating ramp-up period, examining various concession

arrangements and payments using the proposed methodology. Additionally,

considering perspectives in the CBA of other types of projects, such as public transport

projects can further extend the knowledge of CBA.


Analysis

iii

Keywords

Cost-Benefit Analysis; transport economics; transport engineering; transport planning;

toll road project; project evaluation; project risk; Monte Carlo simulation; Public-

Private Partnership; community perspective


Analysis

iv

Table of Contents

Abstract ..................................................................................................................................... i

Keywords ................................................................................................................................ iii

Table of Contents .................................................................................................................... iv

List of Figures ....................................................................................................................... viii

List of Tables ............................................................................................................................ x

List of Abbreviations .............................................................................................................. xii

Principal Notation ................................................................................................................. xiv

Statement of Original Authorship ......................................................................................... xvi

Acknowledgements .............................................................................................................. xvii

Chapter 1: Introduction ...................................................................................... 1

1.1 Background .................................................................................................................... 1

1.2 Aim of this Study ........................................................................................................... 2

1.3 Scope and Definitions .................................................................................................... 4

1.4 Study Contributions and their Significance ................................................................... 5

1.5 Thesis Outline ................................................................................................................ 7

1.6 Summary ........................................................................................................................ 9

Chapter 2: Research Methodology ................................................................... 11

2.1 Methodology Framework ............................................................................................. 11

2.2 Literature Review ......................................................................................................... 12

2.3 Comparative Case Study .............................................................................................. 13

2.4 Incorporating Stochastic Approach in Cost-Benefit Analysis ..................................... 13

2.5 Synthesising Toll Road and Tunnel Project Cases....................................................... 14

2.6 Profiling Risk Using Benefit-Cost Ratio Distribution Drawn from Simulation .......... 14

2.7 Altering Project Impacts in the Cost-Benefit Analysis ................................................ 15

2.8 Proposed Methodology for Evaluations of Toll Road Projects .................................... 16

Chapter 3: Literature Review ........................................................................... 17

3.1 Project Evaluation for Public Good ............................................................................. 17

3.2 Measuring Impacts using Cost-Benefit Analysis ......................................................... 20 3.2.1 Impacts of Transport Facilities .......................................................................... 20 3.2.2 Cost-Benefit Analysis Background Theory ....................................................... 21 3.2.3 Critique of Cost-Benefit Analysis ...................................................................... 22 3.2.4 Wider Economic Benefits .................................................................................. 23 3.2.5 Discounting ........................................................................................................ 24 3.2.6 Sensitivity Analysis ........................................................................................... 25 3.2.7 Alternatives to Cost-Benefit Analysis ............................................................... 26

3.3 Risks and Uncertainties of Toll Road Projects............................................................. 26 3.3.1 Project Costs ...................................................................................................... 27


Analysis

v

3.3.2 Traffic and Revenue Forecasts ...........................................................................28 3.3.3 Political Influences .............................................................................................29 3.3.4 Toll Pricing .........................................................................................................30 3.3.5 Risk Allocation ...................................................................................................31

3.4 Monte Carlo Simulation ...............................................................................................32

3.5 Summary .......................................................................................................................33 3.5.1 Research Gaps ....................................................................................................34 3.5.2 Recommendation ................................................................................................35

Chapter 4: A Review of Cost-Benefit Analysis Practices ............................... 37

4.1 The Study Cases ...........................................................................................................37 4.1.1 Non-Tolled Roads ..............................................................................................37 4.1.2 Toll Roads ..........................................................................................................38 4.1.3 Project Proponent and Owners ...........................................................................40

4.2 Economic Parameters ...................................................................................................42

4.3 Project Costs .................................................................................................................43

4.4 Project Benefits .............................................................................................................44 4.4.1 Travel Time Saving ............................................................................................44 4.4.2 Vehicle Operating Cost Saving (VOCS) ............................................................45 4.4.3 Crash Cost Saving (CCS) ...................................................................................46 4.4.4 Environmental and External Cost Saving (EECS) .............................................47

4.5 Residual Value ..............................................................................................................49

4.6 Sensitivity Analysis ......................................................................................................51

4.7 Selection of Preferred Option .......................................................................................53

4.8 Treatment of Tolls ........................................................................................................54

4.9 Discussion .....................................................................................................................54

4.10 Summary .......................................................................................................................58

Chapter 5: Incorporating Stochastic Approach in Cost-Benefit Analysis ... 61

5.1 Cost-Benefit Analysis Calculation ...............................................................................61

5.2 Probability Distributions Used in this Study ................................................................63 5.2.1 Capital Cost of a Toll Road Project ....................................................................63 5.2.2 Case Dependent Input Variables ........................................................................64 5.2.3 Transport Cost Unit Price ...................................................................................64

5.3 Synthesising a Toll Tunnel Project Case ......................................................................65

5.4 Unit Price of Transport Cost .........................................................................................69

5.5 Results and Data Synthesis ...........................................................................................70

5.6 Discussion .....................................................................................................................74

5.7 Summary .......................................................................................................................75

Chapter 6: Evaluating a Toll Tunnel Project .................................................. 77

6.1 Examining Perspectives ................................................................................................77 6.1.1 Hypothesis ..........................................................................................................77 6.1.2 Consideration of Cost Formats of Toll Road Projects ........................................78 6.1.3 Perspectives Considered for this Study ..............................................................81

6.2 Methodology .................................................................................................................82


Analysis

vi

6.2.1 Identifying Concession Payments and Costs ..................................................... 83 6.2.2 Estimation of Benefit ......................................................................................... 83 6.2.3 Evaluation and Decision Making ....................................................................... 84

6.3 Model Development ..................................................................................................... 84 6.3.1 Traffic Volume and Growth .............................................................................. 84 6.3.2 Baseline Toll Price ............................................................................................. 84 6.3.3 Minimum Revenue Guarantee ........................................................................... 86 6.3.4 Premium Toll ..................................................................................................... 87

6.4 Results .......................................................................................................................... 88 6.4.1 Evaluation and Decision making of the Synthesised Toll Tunnel Project

Case .................................................................................................................... 88 6.4.2 Examination of Perspectives .............................................................................. 91 6.4.3 Sensitivity Analysis of Variation in Risk Characteristics .................................. 92

6.5 Discussion .................................................................................................................... 98

6.6 Summary ...................................................................................................................... 99

Chapter 7: Evaluating a Toll Road Project ................................................... 103

7.1 Synthesising a Toll Road Project Case ...................................................................... 103

7.2 Synthesised Toll Road Project Case Characteristics .................................................. 105

7.3 Results ........................................................................................................................ 106 7.3.1 Evaluation and Decision making of the Synthesised Toll Road Project

Case .................................................................................................................. 106 7.3.2 Sensitivity Analysis of Variation in Risk Characteristics ................................ 110

7.4 Comparison between Toll Tunnel and Road Projects ................................................ 116 7.4.1 A Review of Benefit-Cost Ratio ...................................................................... 116 7.4.2 A Review of Risk Profiles and Perspectives.................................................... 117

7.5 Summary .................................................................................................................... 117

Chapter 8: Proposed Methodology for Evaluations of Toll Road Projects 119

8.1 Typical Project Appraisal Process ............................................................................. 119

8.2 Framework of the Proposed Methodology ................................................................. 120

8.3 Practical Considerations ............................................................................................. 122 8.3.1 Conducting the Analysis .................................................................................. 122 8.3.2 Interpreting the Results .................................................................................... 123

8.4 Enhancement and Refinement of the Proposed Methodology ................................... 126

8.5 Suggested Future Studies ........................................................................................... 126

8.6 Summary .................................................................................................................... 127

Chapter 9: Conclusion ..................................................................................... 129

9.1 Summary of Findings ................................................................................................. 129 9.1.1 Literature Review ............................................................................................ 129 9.1.2 A Review of Australian Practice of Cost-Benefit Analysis ............................. 129 9.1.3 Incorporating Stochastic Approach in Cost-Benefit Analysis ......................... 130 9.1.4 Evaluating a Toll Tunnel Project ..................................................................... 130 9.1.5 Evaluating a Toll Road Project ........................................................................ 131 9.1.6 Proposed Methodology .................................................................................... 131

9.2 Review of the Research Questions ............................................................................. 132 9.2.1 Research Question 1 ........................................................................................ 132


Analysis

vii

9.2.2 Research Question 2 .........................................................................................133 9.2.3 Research Question 3 .........................................................................................133 9.2.4 Research Question 4 .........................................................................................133

9.3 Contribution to Theory ...............................................................................................134

9.4 Contribution to Practice ..............................................................................................135

9.5 Recommendations for Future Work ...........................................................................137

9.6 Concluding Remarks ..................................................................................................138

List of Publications and Awards ........................................................................... 139

References ............................................................................................................... 141


Analysis

viii

List of Figures

Figure 2.1 Study methodology flowchart ................................................................... 12

Figure 2.2 Detailed links between outputs of each phase .......................................... 16

Figure 5.1 Box-and-whisker plots of Benefit-Cost Ratio (BCR) with each

variable varied stochastically, and all variables varied stochastically ......... 73

Figure 6.1 Payment movement of when toll roads are delivered and operated

by the host government from the “toll as a transfer payment” (TT)

perspective ................................................................................................... 79

Figure 6.2 Payment movement of when toll roads are delivered and operated

privately from the “toll as an end-user cost” (TC) perspective .................... 80

Figure 6.3 Payment movement of when the private operator charges premium

tolls from the TC perspective ....................................................................... 81

Figure 6.4 Methodology of the evaluation of the synthesised toll tunnel project

case ............................................................................................................... 83

Figure 6.5 Cumulative stochastic Benefit-Cost Ratio (BCR) distributions of the

synthesised toll tunnel project case .............................................................. 90

Figure 6.6 Box-and-whisker plots of stochastic BCR distributions of the

synthesised toll tunnel project case .............................................................. 91


synthesised toll tunnel project case when the annual average daily

traffic (AADT) and the traffic growth rate variables are treated as

stochastic ...................................................................................................... 94


synthesised toll tunnel project case when the AADT and the traffic

growth rate variables are treated as stochastic ............................................. 95

Figure 6.9 Cumulative stochastic BCR distributions of the synthesised toll

tunnel project case when capital cost variable is treated as stochastic ........ 95


synthesised toll tunnel project case when capital cost variable is

treated as stochastic ...................................................................................... 96

Figure 6.11 Cumulative stochastic BCR distributions of the synthesised toll

tunnel project case when vehicle hours travelled saving (VHTS)

variable is treated as stochastic .................................................................... 97


synthesised toll tunnel project case when VHTS variable is treated as

stochastic ...................................................................................................... 98


synthesised toll road project case ............................................................... 109


Analysis

ix


synthesised toll road project case ............................................................... 110

Figure 1.3 Cumulative stochastic BCR distributions of the synthesised toll road

project case when the annual average daily traffic (AADT) and the

traffic growth rate variables are treated as stochastic ................................ 112


synthesised toll road project case when the AADT and the traffic

growth rate variables are treated as stochastic ........................................... 113


project case when the capital cost variable is treated as stochastic ........... 114


synthesised toll road project case when the capital cost variable is

treated as stochastic.................................................................................... 114


project case when the vehicle hours travelled saving (VHTS) variable

is treated as stochastic ................................................................................ 115


synthesised toll road project case when the VHTS variable is treated

as stochastic ............................................................................................... 116

Figure 8.1 Project appraisal process......................................................................... 120

Figure 8.2 The proposed Cost-Benefit Analysis (CBA) framework for major

road projects ............................................................................................... 121


Analysis

x

List of Tables

Table 2.1 Measures of risk profiles and their interpretations .................................... 15

Table 3.1 Evaluation of toll road projects found in academic literature ................... 19

Table 3.2 Use of the Monte Carlo simulation in assessment of transport

projects ......................................................................................................... 33

Table 4.1 Project proponent of the study cases ......................................................... 41

Table 4.2 Toll road operators and owners of the study cases ................................... 42

Table 4.3 Economic parameters used in the study cases ........................................... 43

Table 4.4 Travel time unit price per hour in 2015 dollars ......................................... 45

Table 4.5 Vehicle operating cost saving unit price per km in 2015 dollars .............. 46

Table 4.6 Crash cost saving unit price in 2015 dollars .............................................. 47

Table 4.7 Environmental and external cost types ...................................................... 48

Table 4.8 Environmental and external cost saving (EECS) unit price per km in

2015 dollars .................................................................................................. 49

Table 4.9 Assumed lifespan of the study cases .......................................................... 50

Table 4.10 Sensitivity analysis conducted in the study cases .................................... 52

Table 4.11 Recommended sensitivity analysis in the Australian guidelines ............. 53

Table 5.1 Probability distribution forms and coefficient of variable (CV) of the

case depend input variables .......................................................................... 64

Table 5.2 Probability distribution forms and coefficient of variable (CV) of

transport cost unit prices .............................................................................. 65

Table 5.3 Characteristics of Brisbane toll tunnels ...................................................... 67

Table 5.4 Assumptions made in Cost-Benefit Analysis (CBA) calculation of

the synthesised toll tunnel project case ........................................................ 68

Table 5.5 Transport cost unit price summary ............................................................. 70

Table 5.6 Impacts of the synthesised toll tunnel case when all variables were

deterministically equal to their expected values in present value ................ 71

Table 5.7 Risk profiles of output Benefit-Cost Ratio (BCR) distribution as

input variable was distributed stochastically ............................................... 72

Table 6.1 Costs to the community that are considered in Cost-Benefit Analysis

(CBA) for this study ..................................................................................... 82

Table 6.2 Risk profiles of the synthesised toll tunnel project case across

perspectives and scenarios ........................................................................... 89


Analysis

xi

Table 6.3 Risk profiles of the synthesised toll tunnel project case across

perspectives and scenarios when each variable was treated as

stochastic ...................................................................................................... 93

Table 7.1 Characteristics of Australian toll roads .................................................... 104

Table 1.2 Project costs of West Petrie Bypass project in 2015 dollars (GHD,

2013) .......................................................................................................... 105

Table 1.3 Assumptions made in Cost-Benefit Analysis (CBA) calculation of

the synthesised toll road project case ......................................................... 106

Table 1.4 Impacts of the synthesised toll road project case when all variables

were deterministically equal to their expected values in present value ..... 107

Table 1.5 Risk profiles of the synthesised toll road project case across

scenarios ..................................................................................................... 108

Table 1.6 Risk profiles of the synthesised toll road project case across

scenarios when each variable was treated as stochastic ............................. 111

Table 8.1 Interpretations of the risk profiles in the proposed methodology ............ 124

Table 8.2 Interpretations of cumulative probability distribution graphs, and

box-and-whisker plots in the proposed methodology ................................ 125


Analysis

xii

List of Abbreviations

Abbreviation Definition

APL Airport Link

AADT Annual average daily traffic

BCR Benefit-Cost Ratio

BNG Baseline toll, no guarantee

BRG Baseline toll, minimum revenue guarantee

BOT Build-operate-transfer

CBD Central business district

CYL City Link

CV Coefficient of variation

CBA Cost-Benefit Analysis

CC Crash cost

CCS Crash cost saving

DBFO Design-build-finance-operate

EEC Environmental and external cost

EECS Environmental and external cost saving

GUP Gateway Upgrade Project

GHG Greenhouse gas emission

HSB Horsham Bypass

HV Heavy vehicles

HV% Proportion of heavy vehicles

LGW Legacy Way

LV Light vehicles

MCDA Multi-Criteria Decision Analysis

NSW New South Wales

O&M Operation and maintenance


Analysis

xiii

Abbreviation Definition

PNG Premium toll, minimum revenue guarantee

PPP Public-Private Partnership

RMS Roads and Maritime Services

RV Residual value

SNB Singleton Bypass

TC Toll as an end-user cost

TT Toll as a transfer payment

TWB Toowoomba Bypass

VFM Value for money

VHTS Vehicle hours travelled saving

VKTS Vehicle kilometres travelled saving

VOC Vehicle operating cost

VOCS Vehicle operating cost saving

WEB Wider Economic Benefits

WPB West Petrie Bypass


Analysis

xiv

Principal Notation

Symbol Definition Unit

𝐴𝐴𝐷𝑇𝑖,𝑗 Initial average annual daily traffic for Monte Carlo trial 𝑗 veh

𝐴𝐴𝐷𝑇𝑗,𝑦 Average annual daily traffic at year 𝑦 for trial 𝑗 veh/d

𝐴𝐴𝐷𝑇𝑦,𝑒𝑥𝑝 Expected AADT with expected traffic growth at year 𝑦 veh

𝐵𝑗,𝑦 Total annual project benefit at year 𝑦 for trial 𝑗 $

𝐶𝑎𝑝 Total capital cost over the whole planning horizon in present value $

𝐶𝑎𝑝𝑎𝑣 Expected capital cost $

𝐶𝑎𝑝𝑗 Capital cost in present value for trial 𝑗 $

𝐶𝑎𝑝𝑚𝑖𝑛 Minimum feasible capital cost $

𝐶𝐶𝑗 Crash cost unit price for trial 𝑗 $/veh-

km

𝑑 Discount rate applicable to the project format %

𝐸𝐸𝐶𝑗,𝑘 Environmental and external cost unit price for the vehicle type 𝑘 for

trial 𝑗

$/veh-

km

𝑔𝑗 Traffic growth rate for Monte Carlo trial 𝑗 %

𝐺𝑦,𝑒𝑥𝑝 Expected guarantee payment at year 𝑦 in present value $

𝑖 Annual rate of inflation in the economy %

𝑘 Vehicle type, 𝑘 ∈ (𝐿𝑉,𝐻𝑉) na

𝑙 Number of Monte Carlo trials na

𝑛 Period of planning horizon years

𝑂&𝑀 Total operation and maintenance cost over the whole planning

horizon in present value

$

𝑂&𝑀𝑗 Total O&M cost over the whole planning horizon for trial 𝑗 $

𝑃(𝑘)𝑗 Proportion of vehicle type 𝑘 for trial 𝑗 %

𝑟 Financier’s interest rate on the private entity’s loan %

𝑅𝑉 Residual value of the road $


Analysis

xv

Symbol Definition Unit

𝑅𝑉𝑗 Residual value of the road for trial 𝑗 $

𝑇𝑃𝑏𝑎𝑠𝑒,𝑗 Baseline toll price for Monte Carlo trial 𝑗 $

𝑇𝑇𝑗,𝑘 Travel time unit price for the vehicle type 𝑘 for trial 𝑗 $/veh-h

𝑈𝑃 Upfront payment to capital cost $

𝑉𝐻𝑇𝑆𝑗 Vehicle hours travelled saved by using the road for trial 𝑗 h

𝑉𝐾𝑇𝑆𝑗 Vehicle kilometre travelled saved by using the road for trial 𝑗 km

𝑉𝑂𝐶𝑗,𝑘 Vehicle operating cost unit price for the vehicle type 𝑘 for trial 𝑗 $/veh-

km

𝑦 Corresponding year, 𝑦(0, 1,… , 𝑛) na

𝜙 Probability that capital cost exceeds 𝐶𝑎𝑝𝑚𝑖𝑛 %


Analysis

xvi

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

QUT Verified Signature


Analysis

xvii

Acknowledgements

I would like to express my deepest gratitude to my principal supervisor, Associate

Professor Dr Jonathan Bunker, for his support throughout my PhD study. He has

demonstrated incredible capability as a researcher, a PhD supervisor, a unit

coordinator, and a mentor in the course of my study. His guidance and encouragement

have led me to the early completion of my PhD.

I would like to thank my associate supervisors, Professor Stephen Kajewski and

Dr Melissa Teo for their support.

I would also like to acknowledge the professional editing service that Diane

Kolomeitz has provided for this thesis, in accordance with the Australian Guidelines

for Editing Research Theses.

Finally, I would like to thank my husband, Kuan-Yu Wesley Chi, and my son,

Nathan Chun-Hsiang Akihiro Chi, for their continuous support and understanding.

Chapter 1: Introduction 1

Chapter 1: Introduction

This chapter explains the research motivation and the purpose of this study. The

research aim, hypothesis and research objectives are stated. The methodology of this

study is explained in detail. The scope and significance of this study are then discussed.

The thesis outline is presented in the final section.

1.1 BACKGROUND

Cost-Benefit Analysis (CBA) is conducted for major road projects for the purpose of

evaluations. CBA measures and quantifies project impacts. The decision making using

CBA is mainly based on the Benefit-Cost Ratio (BCR), which is a ratio of benefits to

costs of the project.

Traditionally, tolls are considered as financial transfers between the road users

and the host government in the CBA of a toll road project. However, when the toll

road project is delivered through a form of Public-Private Partnership (PPP) scheme

and the tolls are collected by the private operator, it is questionable whether to treat

the tolls as financial transfers.

Also important is the complex risk arrangement of many toll road projects that

need to be properly assessed in project evaluation. Many recent toll roads, such as

Legacy Way and Clem Jones Tunnel, both in Brisbane, Australia, are delivered

through forms of PPP scheme. This allows the host government to shift a part of project

risks, including the traffic and revenue risks to the private operator. A commonly

known risk-sharing strategy of a toll road project is the minimum revenue or traffic

guarantee. Depending on the project, a number of risk-sharing strategies can be used

to control the proportion of risks that are shifted to the private operator. Quantifying

and measuring risks that are borne by the host government is crucial in the decision

making of a large infrastructure project, such as a toll road, in terms of the public good.

The complex risk-sharing arrangement that can be unique to each toll road project

needs to be properly quantified and assessed. In other words, the shifts of risk need to

be displayed and communicated in the project evaluation outcomes.

For the host government to ensure that the benefits of a decision outweigh its

costs, addressing net impacts and risks to the community are crucial in project

2 Chapter 1: Introduction

evaluation. The representation of the net risk in CBA is limited by point assumptions

using one-way sensitivity analysis.

A number of studies exist with respect to traffic and revenue forecast accuracies

(Bain, 2009; Carpintero, 2010; Li & Hensher, 2010), financial risks (Aldrete, Bujanda,

& Valdez, 2012; Mishra, Khasnabis, & Swain, 2013), toll pricing (Anas & Lindsey,

2011; Welde, 2011; Zhang, 2008) and concession arrangements (Vassallo, Ortega, &

Baeza, 2012) of toll road projects. However, past studies express limited knowledge

with respect to the appropriate treatment of tolls in the CBA for a privately operated

toll road project and measuring risks using CBA.

1.2 AIM OF THIS STUDY

The aim of this study is to examine the impacts and the risks of a toll road project to

the community, in order to reflect them in Cost-Benefit Analysis (CBA). This study

has a particular focus on altering treatments of some of the project impacts of a toll

road project and reflecting them in CBA outcomes. The payment movements between

different entities, such as road users, non-road users, the host government and the toll

operator are considered, in order to explore an appropriate treatment of tolls in CBA.

Examining various payment movements and altering treatments of impacts

accordingly within CBA have not been studied in literature. This study also explores

how risks can be represented in CBA by incorporating a stochastic approach using the

Monte Carlo simulation. This is particularly useful to quantify risks of the projects

with various risk characteristics using various forms of Public-Private Partnership

scheme. The purpose of this study is to propose a methodology that can readily be used

in practice. Therefore, the study outcomes contribute directly to CBA practitioners and

professionals in transport economics. The hypothesis that is to be tested in this study

is the following.

“The estimation of net impacts and risks of a toll road project to the

community for the purpose of project evaluation can be improved by

considering the treatment of project impacts and performing stochastic Cost-

Benefit Analysis.”

To test this hypothesis, the following research questions have been drawn.

1. How have various toll road projects been evaluated using CBA in practice?


2. Does the extant CBA methodology that is used to evaluate toll road projects

properly reflect the net impacts and risks to the community of a toll road?

3. Can CBA results properly reflect the source/s of risks of a toll road project

by incorporating a stochastic approach?

4. How does altering treatments of some impacts of a toll road project in CBA

improve its outcomes in terms of reflecting net impacts and risks to the

community?

The literature review highlighted:

• the limited studies conducted regarding the CBA for the purpose of evaluation

of toll road projects

• the representation of risks in CBA requires improvements with empirical

assessments and representations of risks; and

• a scarcity of investigating the treatment of tolls in CBA.

The research questions are designed to fill these research gaps. The following research

objectives of this study have been developed to answer the above stated questions.

1. Review the extent CBA methodology by studying the current guidelines,

academic studies and previously conducted CBA for the purpose of

evaluation of toll road projects to highlight any limitations and difficulties

of CBA (in response to the first and the second research questions)

2. Examine the outcomes of the stochastic CBA to observe how the source/s

of risks are reflected in the outcomes (in response to the third research

question)

3. Synthesise a project on the basis of existing toll road projects, in order to

determine the methodology that best reflects the net impacts and risks of a

toll road project to the community (in response to the third research

question)

4. Investigate the outcomes of CBA of a toll road project when the treatment

of project impacts varies by examining various payment movements (in

response to the fourth research question)


1.3 SCOPE AND DEFINITIONS

The scope of this study includes project evaluation for public and private major road

projects. Evaluations of other infrastructure types are beyond the scope of this study.

This study uses Cost-Benefit Analysis (CBA) as a project evaluation methodology and

the impacts considered in the study are limited to the ones that can be addressed in

CBA. Evaluations with respect to the financial viability and other non-monetised

impacts that are not addressed in CBA are beyond the scope of this study.

A risk is defined as any uncertain event associated with the work (Kendrick,

2009). This study considers “risks” as measurable factors that may or may not

influence the project. Considering “uncertainties” is beyond of the scope of this study

as these are defined as unmeasurable factors (Knight, 2012).

The scope of the case study conducted as part of this study reviews toll and non-

toll road projects in various states of Australia and UK. The finding of the case study

is not limited to a specific state, however is limited to Australian and UK contexts and

the contexts of the countries with a similar settings. In Australia and UK, transport

projects that are costly, such as tunnel projects, tend to be tolled. Particularly in

Brisbane, Australia, there have been several toll tunnels built in a short time frame.

The purpose of the case study is to examine how CBA is conducted in practice, instead

of identifying the appropriate methodology for a toll road project, which is instead

considered in Chapter 5. Ten study cases are examined and the findings of the case

study indicate general practice of CBA for Australian major road projects. A review

of CBA for the purpose of financial or accounting audits is beyond the scope of this

study.

This study implements the most common CBA methodology for a major road

project and does not address a range of different CBA methodologies. The theory of

CBA is discussed in Chapter 3. The toll road and tunnel project cases are synthesised

on the basis of overarching characteristics of a selection of recent toll road projects in

Australia. Therefore, the characteristics of the synthesised cases represent realistic toll

road project characteristics. The findings of this study do not consider projects with

unordinary characteristics. Notwithstanding, the findings of this study are applicable

to many toll road projects, including both publicly and privately operated roads. The

synthesised cases are not geographically specific, and therefore the findings of this

study are not limited to a specific city or state.


The stochastic representations of risks of a project rely heavily on the probability

distributions used in the Monte Carlo simulation. A range of literature is reviewed, in

order to determine the appropriate probability distribution for each input variable.

However, studies of the probability distributions of annual average daily traffic

(AADT), forecasted traffic growth rate, the proportion of heavy vehicles (HV%),

vehicle kilometres travelled saving (VKTS), and the unit price of environmental and

external cost saving (EECS) have not been addressed in literature. Further statistical

studies are required to determine the appropriate probability distributions of these

variables.

There may be possible methodological limitations, such as limitations with data

analysed and synthesised, and uncertainties with the results of analyses. After

completing the interpretation of the findings, there may be important issues that need

to be analysed but were not included in the analysis. The processes of data analysis

and synthesis can be repeated as necessary so that the important issues can be analysed

through the processes.

1.4 STUDY CONTRIBUTIONS AND THEIR SIGNIFICANCE

The outcomes of this study are listed in Figure 2.1. The key outcome of this study is

the treatments of impacts of toll road projects in CBA that better reflect the net impacts

and risks to the community. This study also proposes the methodology that best reflects

the net impacts and risks of both publicly and privately operated toll road projects to

the community and can readily be used in practice. Moreover, incorporating well-

established methodologies, such as CBA and the Monte Carlo simulation ensures the

reliability of the proposed methodology.

Addressing net impacts and risks to the community in project evaluation are

crucial in decision making by the host government of a large infrastructure project that

is intended for public use. The host government needs to ensure that the benefits of the

decision outweigh its costs. This study will propose the methodology that provides the

host government with a toll to evaluate major PPP projects from the public perspective.

This methodology can be useful to evaluate the project to assess whether the project is

beneficial to the community, instead of solely focussing on its financial viability. This

particularly significant in extant academic study by providing extended knowledge

regarding CBA for the purpose of evaluating toll road projects.


This study has a particular focus on toll tunnel projects. The unique

characteristics of a toll tunnel project that would impact the CBA are the fact that it’s

tolled and its substantial capital costs. Compared to a non-tolled major road project,

economic impacts of a toll road project to the community would be different, because

the road users are responsible in paying to use the road. This difference should be

reflected in its CBA outcomes. Additionally, the economic justification of a toll tunnel

project, which have considerable capital costs is crucial. Therefore, conducting CBA

appropriately for a toll tunnel project to reflect the net impact and risk to the

community is essential in the decision making about a public good.

This study investigates the treatment of tolls in CBA by considering different

perspectives. Previous studies provide limited knowledge regarding the treatment of

tolls. Additionally, consideration of perspectives in CBA has not been studied. The

knowledge that can be obtained from this study can be extremely significant in both

academic studies and CBA practices.

Presenting the evaluation outcomes in an empirical manner allows them to be

communicated effectively in the decision making process. The representation of risks

using the extant CBA-based project evaluation methodology of a toll road project is

limited by point assumptions made when using one-way sensitivity analysis. This

study uses the stochastic CBA by incorporating the Monte Carlo simulation approach,

in order to present the risks in an effective manner. The risks can be represented in an

empirical manner by incorporating the Monte Carlo simulation in the CBA, and

therefore a comprehensive risk profile can be developed for a project. The attempt to

incorporate the Monte Carlo simulation in the CBA of a road project was found to

have been first conducted by Salling and Leleur (2011). In contrast to that study, this

study considers wider variety of risks of all input variables and also attempts to model

the level of each risk by applying variety of probability distributions to each variable.

On the basis of the stochastic representations of risks, various concession payments

that are often included in the concession arrangement of a privately operated toll road

project are further investigated in this study. This leads to appropriate reflection of the

shifts of risk between the public and private sectors in the CBA.

Moreover, reflecting payment movements in financial analysis of privately

operated toll road projects has been studied in Mishra et al. (2013), however it has not

been studied particularly for CBA of a major road project. Considering the payment


movements between road users, non-road users, the host government and the toll

operator of a privately operated toll road project may alter the treatment of some

project impacts in CBA and therefore may alter the outcome of the analysis.

Through the stochastic CBA, the effectiveness of various statistical inferences

to evaluate toll road projects can be investigated. Using various measures, different

PPP scenarios and delivery options can be explored. This can be particularly useful in

practice.

1.5 THESIS OUTLINE

An outline of this thesis is shown in the following.

Chapter 1 details the background, purpose, scope and definitions, and

significance of this study. The research aim, research hypothesis, research questions

and research objectives of this study are stated. Scope, definitions and limitations of

this study are addressed. The importance, key outcomes and the evidence of

significance of this study are then discussed.

Chapter 2 proposes the research methodology of this study. It discusses

methodologies and methods that are used in this study, and linkages to previous

studies. An illustrative presentation of the methodology is also shown. Each phase of

this study is then explained in detail.

Chapter 3 presents the literature review that is conducted for this study in

response to the first research objective. This review first revisits the purpose of project

evaluation and the role of the governments in decision making of major road projects.

On the basis of the purpose of project evaluation, it highlights the limitations of

evaluations conducted recently. The fundamental underpinning of Cost-Benefit

Analysis (CBA) is then summarised and distinctions between CBA and financial

analysis are outlined. The risks that are specific to toll road projects, including traffic

forecasts and toll prices that can significantly impact the outcome of their evaluations

are considered to determine their implications in project evaluation. Incorporating the

Monte Carlo simulation approach in the CBA was recently proposed in Salling and

Leleur (2011, 2017). To extend this knowledge, the chapter reviews the use of the

Monte Carlo simulation to assess transport infrastructure projects in recent literature.

A summary of this review and discussions with regard to gaps that need to be addressed

in future study are presented in the final section.


Chapter 4 discusses the review of CBA of Australian and UK major road projects

in response to the first research objective. Ten study cases, including four non-tolled

roads and six toll roads, are reviewed. A brief background to each study case is

provided. The input variables of the cases, such as planning, horizon, discount rate and

costs are presented. Then, the calculations of benefits are reviewed, including various

unit prices used to monetise impacts. Residual value (RV) calculations and the

variables that are tested in sensitivity analysis are reviewed. The treatment of tolls,

practical issues and further considerations of input variables are discussed.

Chapter 5 explores the influence of risks of each input variable in response to

the second and third research objectives. CBA theory is first reviewed, then the

appropriate probability distributions for capital cost, case dependent variables and

transport unit prices are explored. A toll tunnel project case is then synthesised on the

basis of the overarching characteristics of a selection of toll tunnel projects in Brisbane,

Australia. The synthesised case is then evaluated using the CBA. The risk profile of

the synthesised case is then developed using the stochastic Benefit-Cost Ratio

distribution that is produced using the Monte Carlo simulation. Results and discussions

are then summarised.

Chapter 6 examines payment movements and evaluates the previously

synthesised toll tunnel project case in response to the fourth research objective. The

payment movements are first examined by considering a number of concession

payments that are often used to share the risks, and a number of scenarios, including

when the project is publicly operated and privately operated. On the basis of the

examination, the models for the concession payments are developed. The synthesised

case is then evaluated using the same approach as the one used in Chapter 5. The

outcome BCR distribution is then reviewed to determine how the risk is reflected in

the risk profile. The results and discussions are then summarised.

Chapter 7 explores the CBA of a toll road project using the previously proposed

methodology in response to the fourth research objective. A toll road project case is

synthesised on the basis of the overarching characteristics of a selection of toll road

projects in Australia. The synthesised case is then evaluated using the same approach

as the one used in Chapter 5 and Chapter 6. The risk profiles of both tunnel and road

projects are then compared. The results and discussions are then summarised.


Chapter 8 details the proposed methodology. It reviews the existing Australian

project appraisal process to determine how the proposed methodology can be

incorporated within the existing framework. It also details the framework of the

proposed methodology to show how each step can be incorporated in a typical CBA

framework. Practical considerations and future studies are discussed in the final

sections.

Chapter 9 presents overall conclusion, limitations and recommendations of this

study. The contributions both to the academic study and practice are discussed. Future

study and discussions of where this study may be extended are also discussed in the

final section.

1.6 SUMMARY

This chapter provided background and the aim of this study. The hypothesis that is

tested in this study is stated and research questions were drawn on this basis. Research

objectives were developed to answer those questions. It then discussed the

methodology, scope and significance of this study. An outline of this thesis was

provided in the final section.

Chapter 2: Research Methodology 11

Chapter 2: Research Methodology

This chapter describes the research methodology of the study. The research

methodology is designed to test the previously stated hypothesis and to achieve the

previously stated research objectives. This study revolves around Cost-Benefit

Analysis (CBA) as the most commonly used project evaluation methodology for major

road projects. CBA is a well-established project evaluation methodology that is

commonly used in practice. Sinha and Labi (2007) specify the fundamental

underpinning of the CBA in great detail.

2.1 METHODOLOGY FRAMEWORK

Figure 2.1 depicts this study. The corresponding research objectives are shown in

brackets. The knowledge that can be gained through the literature review and the case

study are shown in dashed boxes, which are incorporated throughout this study. The

key research outputs are shown in thick lined boxes. Each phase is discussed in detail

in the following sections.

12 Chapter 2: Research Methodology

Figure 2.1 Study methodology flowchart

2.2 LITERATURE REVIEW

A range of recent literature is reviewed. These include recent academic literature and

publications of Australian transport authorities, such as Austroads, and Australian

Transport and Infrastructure Council, to review the project evaluation methodologies

that are often used in practice. The aim of this review is to outline the potential barriers

in evaluating toll road projects to address net impacts and risks. This review does not

contain reviews of previously conducted evaluations. Rather, it suggests potential

barriers in the evaluation of toll road projects on the basis of theories and findings of

recent studies, and advice of transport authorities.


2.3 COMPARATIVE CASE STUDY

The comparative case study investigates how major road projects, including toll road

projects in Australia, have been evaluated using CBA in practice, and compares the

analyses of toll road projects with those of non-tolled road projects. The aim of this

case study is to identify the limitations and difficulties in existing practices of CBA.

The calculations of project capital cost, user benefits and residual value (RV), the

treatment of tolls, and methodology of sensitivity analysis are reviewed. Reviewing

and examining CBA cases can highlight the limitations in existing practices of CBA

for major road projects, as well as the most significant factors in the outcomes of the

evaluation. This will allow the complexity of CBA for major road projects in practice

to be explored.

2.4 INCORPORATING STOCHASTIC APPROACH IN COST-BENEFIT

ANALYSIS

The aim of this phase is to develop a stochastic CBA framework. The parameters that

contain some level of risks, such as those that are estimated using some modelling

technique or monetised using market value estimations, are examined using the Monte

Carlo simulation, which is incorporated within the framework. These variables include

capital cost, annual average daily traffic (AADT), traffic growth, proportion of heavy

vehicles (HV%), vehicle kilometres travelled saving (VKTS), vehicle hours travelled

saving (VHTS) and various transport costs. For each variable, the form of probability

distribution, mean, and coefficient of variation (CV) are defined based on the project

characteristics of the synthesised case and inference from a range of literature.

Deterministic values of planning horizon and discount rate are incorporated in this

study. Operation and maintenance (O&M) cost was assumed to be ten percent of the

whole capital cost. The percentage was determined based on the CBA case study

conducted (see Section 4.3). Hence, O&M cost is stochastic when capital cost is

stochastic.

The Monte Carlo simulation is a well-established risk analysis tool. Mun (2010)

explains the fundamental underpinning of the Monte Carlo simulation methodology in

great detail. The Monte Carlo simulation can be used to produce a stochastic Benefit-

Cost Ratio (BCR) set on the basis of a sufficiently large number of trials, and hence a

comprehensive risk profile of the project on the basis of various combinations of input

impacts. In this study, with each trial, the value of each variable was simulated by a


Monte Carlo draw from its predefined probability distribution with predefined mean

and coefficient of variation (CV). When 100,000 trials are conducted, a set of 100,000

BCR values will be obtained to ensure a sufficiently representative variation in output.

This set represents the outcome stochastic BCR distribution for the project of interest.

2.5 SYNTHESISING TOLL ROAD AND TUNNEL PROJECT CASES

This study first considers a toll tunnel project instead of a toll road project due to the

considerable scale of its construction cost. They key difference between a toll road

project and a toll tunnel project is the scale of the project. The lengths of a toll road

and a toll tunnel can vary significantly and this may change their VHTS and VKTS.

Economic justification of a tunnel project is therefore particularly crucial in project

evaluation. A toll tunnel project case and a toll road project case were synthesised to

demonstrate a stochastic approach to project evaluation on the basis of overarching

characteristics of existing toll road projects. This study seeks useful insights through

investigations of both tunnel and road projects. The purpose of studying the

synthesised cases was so that their project characteristics could be adjusted in a

controlled manner to examine various risk scenarios.

2.6 PROFILING RISK USING BENEFIT-COST RATIO DISTRIBUTION

DRAWN FROM SIMULATION

The BCR distribution that was generated using CBA and the Monte Carlo simulation

can be analysed using various statistical inferences. The interpretations of various

measurements provide a comprehensive representation of the risk of a project. Table

2.1 shows the statistical inferences that were used in this study, which are particularly

useful for comparisons of different scenarios or methodologies. For instance, CV of

the outcome BCR distribution can be compared with the predefined CVs of input

variables to assess how the risk of each input variable impacts the risk of the outcome

BCR.


Table 2.1 Measures of risk profiles and their interpretations

Risk

profile Measures used in this study Interpretation

Central

tendency

Mean, reflecting the

expected Benefit-Cost Ratio

(BCR)

A higher value reflects a lower risk profile.

Median, reflecting the

middle BCR

A higher median than mean reflects a lower

risk profile.

Spread Coefficient of variation (CV) CV is a normalised measure of spread. A

higher CV implies a wider distribution, for a

higher risk profile.

Skew Skew Positive skew corresponds to a longer right

hand tail of the BCR, for a lower risk profile.

Percentile

The probability of a specific

BCR

The proportion of BCR trials greater than 1.0

represents the probability of the project being

beneficial. A higher probability reflects a lower

risk profile.

2.7 ALTERING PROJECT IMPACTS IN THE COST-BENEFIT ANALYSIS

Two perspectives of “toll as a transfer payment” and “toll as an end-user cost” are

drawn from the findings of literature review and comparative case study. On the basis

of these perspectives, the treatment of tolls and other payments that are often used in

toll road projects are examined. These payments are examined by observing their

movements between various entities, such as road users, non-road users, the host

government and the private operator. Models of each payment are then developed to

reflect these movements. The synthesised cases are evaluated using the stochastic CBA

and the developed models. Examination of the risk profiles of different payment

scenarios reveals how shifts of risk can be portrayed in the stochastic CBA. This also

determines the treatment of project impacts that best reflects the risk characteristics of

a toll road project across scenarios.


2.8 PROPOSED METHODOLOGY FOR EVALUATIONS OF TOLL ROAD

PROJECTS

The key contribution of this study is the proposed CBA methodology that best reflects

the net impacts and risks to the community. Figure 2.2 illustrates the links between

outputs of each phase of this study, for the purpose of developing the methodology.

The stochastic CBA methodology is developed, and project characteristics are

determined in Chapter 5. Using the methodology developed, and the characteristics,

the synthesised project is evaluated. Its risk profile and the impacts of altering

treatments of project impacts on the risk profile are studied. The appropriate treatments

to best reflect the net impacts and risks to the community can then be determined. The

appropriate treatments are incorporated into the stochastic CBA, in order to propose

the methodology in Chapter 8.

Figure 2.2 Detailed links between outputs of each phase

Chapter 3: Literature Review 17

Chapter 3: Literature Review

Evaluating and making decisions about large-scale investment is a complex and

difficult task. Toll road projects are often large-scale projects. Large-scale investment

can receive significant public scrutiny. An empirical and comprehensive project

evaluation tool is crucial in decision making to ensure that the benefits of the decision

outweigh the costs and risks that the governments and the communities are bearing.

Toll road projects are often delivered through a form of Public-Private

Partnership (PPP) scheme, in part to shift a part of risks to the private operator. These

shifts of risk need to be considered in project evaluation to assess the risks that the

public is bearing. These toll road specific characteristics require that the methodology

is adapted specifically to toll road projects. Using traditional project evaluation

methodology, the risk characteristics that are unique to many toll road projects may be

miscalculated or ignored.

The aim of this review is to outline the potential barriers in evaluating toll road

projects to address net impacts and risks. This chapter reviews academic literature and

publications of Australian road authorities, such as Austroads, and the Australian

Transport and Infrastructure Council, to review the project evaluation methodologies

that are often used in practice. This review does not contain reviews of previously

conducted evaluations. Rather, it suggests potential barriers in evaluation of toll road

projects on the basis of theories and findings of recent studies and guidelines.

3.1 PROJECT EVALUATION FOR PUBLIC GOOD

Governments are responsible for decision making for the good of their constituents.

This includes ensuring that public funds are invested wisely, and that regulation of

private sector activity ensures a net benefit to society. Decision making about

infrastructure investment is based on the net impacts measured by the host government

through project evaluation. The goals of a public project are to increase the well-being

of the community and to maintain or increase overall prosperity (Keating & Keating,

2013).

Project evaluation is a process of measuring impacts and risks of a project for

the purpose of evaluating and prioritising projects. This study considers project

18 Chapter 3: Literature Review

evaluation for the purpose of decision making by the governments for a public good.

Project evaluation is therefore a process to ensure that the project is beneficial with

respect to the public good. Mendel and Brudney (2014) define the public good as the

outcomes of public policy or private actions that create benefit or potential benefit

shared by everyone and arises within the moralistic, mission or values-driven work of

philanthropy. Examples of project outcomes for the public good include strong

economy, freeways, bridges and a healthy civil society (Mendel & Brudney, 2014).

For instance, the road project that is beneficial to the community is the project that

provides shorter travel time, shorter travel distance and fewer environmental impacts,

and serves as part of an effective transport network.

Bertoméu-Sánchez and Estache (2017) claim that the transport investment

decisions are more coherent in terms of providing economic benefits, with the

centralisation objectives than the strict concerns for mobility. For instance, the project

that mainly aims to improve accessibility is inefficient compared to the project that

aims to strength the local economy. This emphasises the importance of evaluating a

project with regard to a public good.

The scope of evaluation defines the impacts and risks that need to be considered

in the evaluation. For instance, Bauer and Szarata (2015) proposed a project evaluation

methodology of a transport corridor, which only reviews travel time and traffic volume

along it. The fewer the number of impacts to be included in project evaluation, the

simpler the methodology will be. However, it is difficult to select appropriate impacts

to be analysed and results of the evaluation can be limited. It is also inefficient to

conduct a number of analyses using different methodologies for a single infrastructure

item.

Table 3.1 summarises recent literature of toll road project evaluations. Toll road

projects have been evaluated in numerous articles, however many focus on traffic and

revenue forecasting (Bain, 2009; Li & Hensher, 2010; Welde, 2011), while there is

limited study in the literature regarding CBA for the purpose of evaluation of toll road

projects.


Table 3.1 Evaluation of toll road projects found in academic literature

Author Study purpose Item evaluated

Aldrete, Bujanda

and Valdez (2012)

The study evaluated public revenue

financial risk exposure when transport

infrastructure is delivered through PPP.

Revenue risk exposure

Anas and Lindsey

(2011)

The study reviewed urban road pricing

theory on the basis of a toll road project.

Benefit and costs, public

transport, public acceptance

Bain (2009) The study reported the results from the

study of toll road forecasting

performance.

Traffic forecasts

Bel and Foote

(2009)

The study explored the implications

with respect to the public interest.

Impacts of toll road

concessions on the public

interest

Carpintero (2010) The study examined the gap between

the expected outcomes and the actual

results of toll roads.

Traffic forecasts, contract

management, government’s

role

Li and Hensher

(2010)

The study compared and discussed

actual traffic levels and forecasts.

Traffic forecasts

Liyanage and

Villalba-Romero

(2015)

The study measured overall success of

PPP toll road projects.

Qualitative measures from

project management,

stakeholder and contract

management perspectives

Mishra, Khasnabis

and Swain (2013)

The study proposed a framework to

analyse measures of effectiveness of

each entity involved in a toll road

project.

Capital cost, operation and

maintenance cost, toll

revenues and other payments

to the toll operator

Odeck (2008) The study evaluated the technical

efficiency of toll companies.

Payments to governments,

operational costs, traffic

volume, number of lanes, and

other productivity measures

Vassallo, Ortega

and de los Ángeles

Baeza (2012)

The study analysed the impact that the

economic recession had on the

performance of toll highway

concessions in Spain and the actions

Risk allocations and traffic

growth


Author Study purpose Item evaluated

that the government adopted to avoid

the bankruptcy of the concessionaires.

Welde (2011) The study examined demand and

operating cost forecasting accuracy for

Norwegian toll projects by comparing

the forecasted and actual levels of

traffic and operating costs.

Traffic forecasts and

operating costs

Zhang (2008) The study developed models of market

entry, price, and capacity choices on

mixed-ownership networks to address

these research needs.

Market entry, price, and

capacity choices

Zhang, Bai, Labi

and Sinha (2013)

The study investigated in the decision

making process: economic efficiency of

privatisation and the protection of

public interest.

Financial transactions and

public interest

3.2 MEASURING IMPACTS USING COST-BENEFIT ANALYSIS

3.2.1 Impacts of Transport Facilities

Development of a new transport facility can impact the community in a number of

ways, directly and indirectly. An adequate transport network provides solutions to

traffic infiltration and high volumes into local areas, delays and congestion at major

intersections, and mixed function roads, where the traffic access function conflicts

with the traffic movement function (VicRoads, 2010). Connecting missing links and

improving accessibility of existing transport network contributes to significant

economic growth and regional development (European Conference of Ministers of

Transport, 2001). The most important influence categories of transport projects are

related to accessibility, safety and the environment which have both economic and

social impacts (van Wee & Tavasszy, 2008). Economic impacts particularly tend to

play a key role in project evaluation of a transport project. The costs of transport

projects are an immediate negative effect to public financial resources. Instead, many

of the benefits of transport improvements tend to gradually grow over the years after

the completion of the project. Additionally, although transport improvements tend to


have large influences on growth patterns, the nature of the effect is significantly

dependent on the context of the investment (Funderburg, Nixon, Boarnet, & Ferguson,

2010).

3.2.2 Cost-Benefit Analysis Background Theory

Cost-Benefit Analysis (CBA) is the most commonly used project evaluation

methodology for major road projects (van Wee & Rietveld, 2014). CBA measures the

net impact of a project by monetising based on its market value and allocating the

impacts to benefit and costs (Rogers & Duffy, 2012; van Wee & Rietveld, 2014). The

project is economically viable when the monetary valuation of economic benefit

outweighs the full cost of the project (Rogers & Duffy, 2012). CBA is well studied in

academic literature, however the guidance on the treatment of tolls in CBA in extant

guidelines (Australian Transport and Infrastructure Council, 2016b; Queensland

Department of Transport and Main Roads, 2011; Rockliffe, Patrick, & Tsolakis, 2012)

is limited. For instance, Queensland CBA manual (Queensland Department of

Transport and Main Roads, 2011) advices to only include tolls as one of the factors

that influence road demands.

Broad impacts, including the impacts to the road users and the non-road users

can be included in CBA (Decorla-Souza, Lee, Timothy, & Mayer, 2013; Mackie,

Worsley, & Eliasson, 2014). This is particularly helpful when decision-makers need

to review the project from diverse viewpoints rather than making decisions based only

upon financial benefit. It is important to distinguish project evaluation from financial

assessment. Zhang, Bai, Labi and Sinha (2013) found in their financial analysis that

the host government is unlikely to gain sufficient benefit from toll road projects unless

traffic growth and toll prices are sufficient to provide financial benefit that is

equivalent to an upfront capital cost contribution. When CBA is used to evaluate a toll

road project, which would include transport impacts, the outcome of the evaluation

may differ from their study outcome. Transport impacts that are generally included in

CBA evaluation for a road project include the following monetised impacts; travel

time saving, vehicle operating cost saving (VOCS), crash cost saving (CCS),

environmental and external cost saving (EECS), capital cost, and operation and

maintenance (O&M) cost.

The result of CBA is generally represented as a Benefit-Cost Ratio (BCR), which

is the ratio of monetised benefit to monetised costs of the project over the planning


horizon, each brought to present value. While a BCR greater than 1.0 theoretically

means that the benefits outweigh the costs and that the project provides a net benefit

to the community, decision-makers typically prefer the project with a higher BCR, in

order to accommodate a buffer against inherent inaccuracies in project evaluation

estimations, as well as the potential for unrealised assumptions and/or unknown

impacts that may arise during the planning horizon. BCR highly depends on the nature

of the project and can range from less than 1.0 to greater than 5.0.

The benefits of major transport projects often require a period of time to be

realised (Vickerman, 2017). This is due to the significant capital spending in early

years and the time required for the traffic demand to grow. When the length of planning

horizon is short, although there may not be enough transport benefits for the project to

be justified, it may still be due to its substantial residual value (RV). The longer the

planning horizon is the less RV and more other benefits to be realised. The justification

of a project should be made on the basis of its benefits to the community, such as travel

time savings, rather than only relying on its RV.

The CBA practices in different countries can vary significantly. This is due to

the differences in economic and financial settings in various countries. For instance, a

major transport project does not affect taxes in Australia, and therefore, taxes are not

captured in CBA of Australian projects. Tax revenue can instead be captured as part

of wider economic benefits (WEB) (Australian Transport and Infrastructure Council,

2017). However, taxes are generally considered in CBA of Danish projects, as seen in

(Manzo & Salling, 2016).

3.2.3 Critique of Cost-Benefit Analysis

Whilst CBA offers various advantages, such as abilities to present the analysis

outcomes in an empirical manner that is easy to interpret, and to capture travel time

saving, a number of limitations have been discussed in academic literature. Commonly

discussed are the issues associated with non-monetised impacts. Hwang (2016)

discussed monetisation issues of project impacts, particularly those non-market goods.

As has been highlighted, impacts are generally monetised on the basis of the market

values. Monetising non-market goods such as environmental impacts can be complex.

Inability to capture equity impacts is discussed in a number of studies (Hwang,

2016; Sumana & Hegde, 2014; Tsolakis, Patrick, & Thoresen, 2005; van Wee &


Roeser, 2013; van Wee & Tavasszy, 2008). Although equity issues need to be taken

into consideration when evaluation a project, there are a number of difficulties in order

to capture equity impacts in CBA. First, equity impacts can be unique to each location

due to variations in the level of wealth of the residents in various locations (van Wee

& Roeser, 2013). Second, extensive data is required in order to capture equity impacts

in CBA (Sumana & Hegde, 2014). In order to effectively capture equity impacts in

project evaluation, tools other than CBA may need to be used. Alternative project

evaluation tools are discussed in a later section.

Other limitations that are highlighted in academic literature are the inability to

capture land use impacts and bias in CBA. For instance, a number of studies (Laird,

Nash, & Mackie, 2014; Tsolakis et al., 2005; van Wee & Tavasszy, 2008) argue that

standard CBA does not account for land use implications in demand forecasting, other

land use issues, overall performance of the economy, level of accessibility,

opportunities for interaction, and overall social functioning of the community. Some

of these impacts are non-monetised impacts, which cannot be captured using CBA,

however, others may be captured as part of wider economic benefits. Issues of wider

economic benefits are discussed in a later section. Moreover, Vejchodská (2015)

claims that many CBA practitioners lack knowledge in CBA and previously conducted

CBA are often biased and misleading. This should be further examined in the context

of CBA of Australian major road projects.

3.2.4 Wider Economic Benefits

As has been discussed, some of the impacts that are generally not accounted using

CBA can be captured through inclusion of WEB. Legaspi, Hensher and Wang (2015)

claim that there is an increasing awareness in the government policy community which

has escalated the focus on WEB to find ways of including them in allocation policy

criteria. The difficulty of estimating WEB is that the majority of WEB outputs are

generated from agglomeration and clustering, which requires a sophisticated land use

transport model to simulate how firms would respond to the transport improvement

and move their locations and how households would choose their residential locations

(Legaspi et al., 2015). To ensure transparency, consistency and robustness in WEB

estimation, reliability of data needs to be peer-reviewed, and rigorous data collection

and analysis are needed (Dobes & Leung, 2015). Vickerman (2017) further discusses

the weaknesses of WEB, including reliability of underlining assumptions. In Australia,


government agencies are actively working on the development of guidelines with

regard to inclusion of WEB in CBA. The Australian Government has commissioned

consultants to work with the Australian Bureau of Statistics to undertake econometric

and economic modelling to obtain the set of parameter value estimates for publication

in the Guidelines (Australian Transport and Infrastructure Council, 2016a).

3.2.5 Discounting

A fundamental principle underlying economic analysis is that the value of money

depreciates over time (Sinha & Labi, 2007). Discount rate reflects the time value of

money as well as the premium that is required by investors to compensate them for the

systematic risk inherent in the project (Australian Department of Infrastructure and

Regional Development, 2013). Inflation and interest rate are generally accounted in

discount rate.

When calculating monetary benefits or costs at the particular point of year, any

past and/or future benefits and costs need to be calculated in present value terms. In a

monetary project evaluation methodology, such as CBA, all benefits and costs are

converted to present values, which represent the values of those benefits and costs at

the time of analysis or when construction has been completed. Year zero refers to the

year when initial costs, including construction costs are fully paid and the facility is

opened. Year one refers to the year when toll revenue of the opening year is counted

as income. The discount rate can have large impacts on benefits and costs that occur

in the long term (Koopmans & Rietveld, 2014; van Wee & Rietveld, 2014). The future

value depreciates exponentially with discount rate, therefore Benefit-Cost Ratio

(BCR) is calculated as:

𝐵𝐶𝑅 =𝑃𝑉(𝐵) + 𝑃𝑉(𝑅𝑉)

𝑃𝑉(𝐶𝑎𝑝 + 𝑂&𝑀)

(3.1)

Where:

𝑃𝑉 = present values, values are discounted using a discount rate

𝐵 = benefits of a project ($)

𝑅𝑉 = residual value of a project ($)

𝐶𝑎𝑝 = total capital cost over the whole planning horizon ($)

𝑂&𝑀= total operation and maintenance cost over the whole planning horizon

($)


Contreras (2014) states that using one single discount rate for projects with

different characteristics is inadequate. Choosing the most appropriate discount rate to

be adopted in economic planning, project evaluation and public policy formulation is

a significant issue for researchers (Simonelli, 2013). In practice, either the discount

rate that the host government through its state treasury indicates at the time of analysis,

or the discount rate that is derived from the discount rate methodology that is

documented in the applicable government guidelines, is used. Discount rate varies for

Public-Private Partnership (PPP) projects. This is because, depending on the risk

allocations between public and private sectors, the systematic risk premium is adjusted

to reflect the proportion of risks that the public sector is bearing (Australian

Department of Infrastructure and Regional Development, 2013). For instance, a risk-

free rate is used when all of the systematic risks are borne by private sector (Australian

Department of Infrastructure and Regional Development, 2013). Estimating the

proportion of the systematic risks that are borne by the public sector can be complex.

Hence, it has been recommended that sensitivity and risks of the discount rate should

be tested. Additionally, Hwang (2016) argues that applications of discount rate become

controversial when the monetary value of human life or environmental value are

discounted.

3.2.6 Sensitivity Analysis

Sensitivity analysis is generally conducted as part of CBA to test the sensitivities of

input variables. Infrastructure Australia (2017) summarises common sensitivity tests

that can be conducted in CBA for business cases. As documented in Infrastructure

Australia (2017), the sensitivity analysis that is generally conducted in Australia is

one-way sensitivity analysis, where each input variable is varied, while others are held

unchanged. One-way sensitivity analysis can show which input variables are more

sensitive than others using tornado diagrams (Clemen & Reilly, 2013). However,

Saltelli and Annoni (2010), and Wang, Dyer and Hahn (2017) highlighted the

weaknesses of the one-way sensitivity analysis. One of the weaknesses is that it

assumes independence among the input variables (Wang et al., 2017). Wang, Dyer and

Hahn (2017) suggest probabilistic or multi-way sensitivity analysis, in order to test

sensitivities of dependent input variables. Additionally, risk profiles can be developed

using probability distributions (Clemen & Reilly, 2013).


3.2.7 Alternatives to Cost-Benefit Analysis

Multiple-Criteria Decision Analysis (MCDA) is often used in practice in project

appraisal process, in order to account for non-monetised impacts. For instance, MCDA

was included in the feasibility assessment of Singleton Bypass (SNB) (AECOM

Australia, 2013). MCDA compares a number of alternatives or scenarios in terms of

specific criteria, which represent the feasibility of the objectives and sub-objectives of

decision makers and stakeholders participating in the decision-making process

(Brucker, Macharis, & Veisten, 2011). MCDA is generally used along with CBA in

practice (Rockliffe et al., 2012) and is also useful to assess transit accessibility at

strategic level (Hawas, Hassan, & Abulibdeh, 2016). The main limitation of MCDA is

the potential inconsistencies of weighting of each criterion due to subjective and

objective perspectives (Chen, Leng, Mao, & Liu, 2014).

There are various MCDA methodologies available depending on the required

inputs (Ishizaka & Nemery, 2012). A variety of MCDA methodologies can be found

in a number of recent studies (Barbosa et al., 2017; Chen et al., 2014; Hawas et al.,

2016; Macharis & Bernardini, 2015; MacHaris, Turcksin, & Lebeau, 2012; Salling &

Pryn, 2015).

A number of authors claim that the joint use of MCDA and CBA can overcome

their mutual weaknesses (Beria, Maltese, & Mariotti, 2012; van Wee & Roeser, 2013)

and the methodologies that combine MCDA and CBA have been proposed in a number

of previous studies (Ambrasaite, Barfod, & Salling, 2011; Gühnemann, Laird, &

Pearman, 2012).

Along with CBA, value for money (VFM) analysis is often conducted for Public-

Private Partnership projects. The usefulness of VFM is investigated in a number of

studies (Aldrete et al., 2012; Decorla-Souza et al., 2013). Key limitations of VFM is

that it only accounts for financial impacts to the host government and can include

irrelevant comparison of the economic resource costs (Decorla-Souza et al., 2013).

3.3 RISKS AND UNCERTAINTIES OF TOLL ROAD PROJECTS

The terms “risks” and “uncertainties” can be used interchangeably, however can also

be defined as different terms in some fields. For instance, Knight (2012) defines

“risks” as measurable factors that may or may not influence the project, while an

“uncertainty” is defined as an unmeasurable factor. This is particularly evident in


capital cost estimation modelling, where project risks are modelled stochastically, and

capital costs are generally shown as P50 and P90 costs. The project risks are measured

and quantified as part of project contingencies in the stochastic cost modelling. This

study adopts Knight’s definitions and only considers “risks”. Considering

“uncertainties” or unmeasurable factors is beyond the scope of this study. The

following provides reviews of both risks and uncertainties of toll road projects.

The risks of toll road projects that need to be assessed in project evaluation can

be borne from many factors, such as cost and benefits estimations and monetisation,

political influence and toll price setting. Although there have been various approaches

in an attempt to address risks of projects in project evaluation in academic literature

(Mouter, Holleman, Calvert, & Annema, 2015; Shiau, 2014; Xu & Lambert, 2014),

Austroads advises addressing risks using one-way sensitivity analysis (Rockliffe et al.,

2012). Sensitivity analysis is a procedure in which the parameters are varied in an

arbitrary manner in an effort to ascertain the extent of changes in the economic

indicators as a result (Rogers & Duffy, 2012) and measures the sensitivities of input

variables. Mouter, Annema and van Wee (2014) claim that risks need to be

communicated more effectively in CBA and suggest testing sensitivities of a wider

variety of variables in CBA. The one-way sensitivity analysis provides a range of point

estimates of BCRs across different scenarios. However, it is unable to represent all

appreciable risks, nor a stochastic form of the output variables without incorporating

probability distributions in the sensitivity analysis.

The following discusses the risks that are particularly relevant to toll road

projects. These include risks in project cost estimations, traffic and revenue forecasts,

political influences and toll pricing. Impacts of risk allocations when a toll project is

delivered through a form of Public-Private Partnership (PPP) scheme are then

discussed.

3.3.1 Project Costs

When the cost of a road project is significantly large, tolling is an effective option to

recover the project costs. For instance, the project cost of the 4.6 km Legacy Way

tunnel in Brisbane, Australia, was AU$1.5 billion (ACCIONA Australia, 2015), while

the construction cost of the 6.8 km Clem Jones Tunnel also in Brisbane was AU$3.0

billion (Go VIA, 2015). Large-scale projects are often difficult to manage due to

complexity, which in turn is a result of large numbers of stakeholders’ involvement,


considerable resources and time required for planning, design and construction, and

close media and political scrutiny. The common issues of large-scale projects include

cost overruns, project delays and benefit shortfalls (Bruijn & Leijten, 2008; Flyvbjerg,

2014).

Large-scale projects can produce significant wider economic benefits that are

greater than those accruing to users (Vickerman, 2008). These benefits can be added

to CBA as additional benefits to reinforce and justify projects that may not be

acceptable on the basis of the user benefits alone in practice (Vickerman, 2008). This

does not indicate that those projects should not proceed, although the greater the

number of wider economic benefits included in the CBA, the greater the risk contained

in the results of the CBA.

3.3.2 Traffic and Revenue Forecasts

Estimations of impacts of a major road project largely depend on traffic forecasts. The

forecasted traffic volume drives the outcomes of both economic and financial

evaluations of toll road projects. Additionally, Asplund and Eliasson (2016) claim that

transport investment and transport demand risks affect the CBA results the most. A

number of authors (Bain, 2009; Flyvbjerg, Holm, & Buhl, 2005; Li & Hensher, 2010)

claimed that traffic forecasts of toll roads tend to be overestimated. Rose and Hensher

(2014) claim that misestimating the value of travel time is the main contributor of

errors in traffic forecasting of toll roads, while Flyvbjerg et al. (2005) claims that

optimism bias is the key contributor of the errors.

Many toll road projects are Public-Private Partnership (PPP) projects that

contain a bidding process. The reported tendency for systematic overestimations was

also suggested by Bain (2009), and Vassallo and Baeza (2007), because of privately

financed toll road concessions being commonly awarded to bidding teams that submit

the highest traffic projections. Vassallo and Baeza (2007) claim that the traffic

forecasts that are produced by bidders contain notable bias towards overestimation,

compared to the projections produced by the host governments.

Accurate traffic volume estimations can only be derived from accurate travel

demand and system supply. Transport economics topics, such as demand modelling,

supply functions, market equilibrium, price elasticity, production costs and pricing are

therefore essential in traffic forecasting (Sinha & Labi, 2007).


3.3.3 Political Influences

The number of political pressures that a project can face during the project appraisal

phase is highlighted by Mackie et al. (2014). Because toll road projects are often

initiated by a government, for large-scale projects, which can be significantly

influenced by political circumstances, elected officials could take opposition to tolling

seriously (Poole, 2011) and could view that the population at large is opposed to

tolling. According to Poole (2011), those who are affected fall into the following

groups:

• Tolled: financial disadvantage

• Tolled-off: those who divert to non-tolled parallel arterials

• Not-tolled: those who will be affected from increased traffic from the tolled-

off groups

For the tolled-off group, this applies when an existing facility is tolled. However, for

a new toll facility, toll road users can benefit from travel time saving by using the toll

road instead of alternative routes that may not be tolled. This is also the same for the

not-tolled group. When a new toll facility is built, some of the traffic that formerly

used the existing non-tolled alternative route will use the new facility; as a result, the

traffic on the existing route ought to decrease. Poole (2011) argues that the opposition

to paying tolls can be strong, as most car owners and road freight operators are opposed

to tolling.

However, this is not always the case for all toll road projects. As part of a large

transport network, toll road projects can provide significant benefits to road users and

non-road users as well as benefits to the surrounding community. Engel and Galetovic

(2014) claim that toll road projects can offer an opportunity to make tolls politically

acceptable, because tolls can reduce road congestion, ensure an adequate mix of public

and private transport, and finance maintenance and new infrastructure. There are

various benefits that a toll road can bring to the community, such as economic, land

use, social, cultural and transport benefits. Additionally, recent study (Mouter, 2017)

claims that many government officials have positive attitude towards CBA.

Offering various benefits of tolling across different types of supporters increases

a number of potential supporters to tolling (Yusuf, O’Connell, & Anuar, 2014). More

residents become supportive of tolls when revenue from tolling can be justified as a


way to improve community life (Yusuf et al., 2014). CBA can be an effective tool to

measure net impacts to the community. The outcomes of CBA need to be

communicated effectively to the community to gain support from the community.

3.3.4 Toll Pricing

As the toll price increases, traffic volume along the toll road theoretically should

decrease (Low & Odgers, 2012). This phenomenon can be explained by price elasticity

of demand. It is rational to postulate that demand would decrease on a toll road when

there is increased traffic density, and therefore a reduced level of service on that road.

Drivers would avoid a congested toll road because it defeats the purpose of paying

tolls to save travel time. The rational motorists would not use the toll road if the

alternative, free route offered shorter travel time. This suggests the complexity of toll

road traffic modelling and the risk of toll price leads to risk in traffic forecasting.

Toll pricing strategies are well discussed in literature. A number of strategies

exist in toll pricing, such as travel time reliability maximising (Tirachini, Hensher, &

Bliemer, 2014), and toll revenue maximising (Joksimovic, Bliemer, & Bovy, 2005).

Beck and Hensher (2015) highlighted that a well-designed toll pricing scheme can

provide demonstrable time savings in the peak-time. Hensher and Bliemer (2014)

suggested a toll pricing scheme that reduces registration charges and peak-time pricing

that is distance-based to ensure sufficient toll revenues to the host government.

The possible objectives of road pricing generally are internalising external costs,

including welfare effects elsewhere in the economy, effective curbing of transport-

related problems, the generation of revenue and financing, and fairness (Verhoef,

2008). These objectives are not always compatible. For instance, welfare maximisation

and profit maximisation can create a significant conflict. In principle, the pricing

strategy that focuses on welfare maximises social surplus in the market (Verhoef,

2008). The successful deployment, both profitability and improvement of social

welfare of toll road pricing schemes, relies on individual project characteristics

(Tsekeris & Voß, 2008).

Pricing policies depend on the project objectives, which can be spawned from

the government’s policies. When a private sector sets the toll price, that price depends

on the market power exercised by the operator and road users (Button, 2010). The

operator can dictate the prices to a large extent when there is a single monopoly


operator and numerous road users (Button, 2010). However, concession arrangements

could also dictate that the host government can regulate prices. Detailed price

modelling techniques are beyond the scope of this review.

3.3.5 Risk Allocation

In some toll road settings, when the toll road is owned, operated and/or maintained by

the private concessionaire, rather than earning revenue directly through receipt of tolls,

it receives payments from the host government using one of various methods

depending on the concession agreement. The following explains those payments

(Brocklebank, 2014):

• Shadow tolls avoid charging the users tolls; instead the host government is

responsible for paying the concessionaire according to traffic volume or

total travel distance along the road, in which case and specifically for traffic

modelling, the road is considered not to be tolled.

• Performance-based public sector payments may be paid by the host

government to the concessionaire.

• The concession can include guarantees of toll revenue. Toll revenue risk can

be shared with the host government through minimum revenue guarantees.

With minimum revenue guarantees, partial or full revenue risk is transferred

to the host government whereby it compensates the concessionaire for

shortfalls when the toll revenue received by the concessionaire is less than

a guaranteed amount.

These payments are forms of risk-sharing strategies. The risk to the public needs to be

properly assessed in project evaluation. However, the concession arrangement of a toll

road project can be complex and unique to each project.

The general principle is that exogenous traffic demand risk should be borne by

the party that is the best able to bear it (Engel, Fischer, & Galetovic, 2014). However,

Chung, Hensher and Rose (2010) suggest that many Australian toll road projects

experienced misallocation of risks due to the perception that certain risks are best left

alone to the party that is known to be “best able” to manage the risks. The assessment

of alternative risk allocations needs to be conducted as part of project evaluation of

toll road projects. Each toll road project is unique and the general principle may not be

applicable to all toll road projects.


3.4 MONTE CARLO SIMULATION

All of the input variables in project evaluation are rarely certain (Rockliffe et al., 2012)

and each input variable contains various risks. Salling and Leleur (2011, 2017)

proposed a methodology that combines CBA and the Monte Carlo simulation to assess

risks of construction maintenance costs, travel time saving and crash cost saving.

The Monte Carlo simulation uses randomly generated values for the purpose of

forecasting, estimating or risk analysis (Mun, 2010). The Monte Carlo simulation of a

particular variable of interest requires a specific form of probability distribution and

its parameters. By applying a random number to the cumulative form of the

distribution, a value of the variable of interest can be generated randomly, in order to

present a particular trial. This algorithm can be repeated for numerous trials. The

Monte Carlo simulation, therefore, allows a predefined level of risk within the variable

to be portrayed.

The Monte Carlo simulation is a well-established methodology for assessing

risks of input variables. Table 3.2 summarises the use of the Monte Carlo simulation

for assessment of transport projects in recent literature. The impact of risks of various

parameters on a Benefit-Cost Ratio (BCR), crash counts, emissions, traffic volumes

and toll revenue was investigated by Fagnant and Kockelman (2012), whose study was

limited to minor road projects.


Table 3.2 Use of the Monte Carlo simulation in assessment of transport projects

Literature

Measure of

economic

outcome

Study purpose Source of risks

modelled

Salling and

Leleur (2011,

2017)

BCR Presenting the Danish

CBA-DK software

model for assessment

of transport

infrastructure projects

Construction and

maintenance costs, travel

time saving and crash cost

saving unit price

Zhang, Bai, Labi,

and Sinha (2013)

Net present

value

Investigating

economic efficiency

of privatisation and

the protection of

public interest of toll

roads

Traffic growth, toll price and

discount rate

Ambrasaite,

Barfod, and

Salling (2011)

Total Rate of

Return

Introducing risk

analysis and the

Monte Carlo

simulation to the

weighting profile in

the MCDA

Weighting of criteria using

Multi-Criteria Decision

making approach

Fagnant and

Kockelman

(2012)

BCR Exploring the impact

of risks with the use of

hundreds of sensitivity

analyses to evaluate

highway capacity

expansion and toll

project scenarios

28 parameter sets of minor

transport projects

Khan (2013) Net present

worth

Identifying risk factors

in the lifecycle

analysis of a toll road

Toll revenue forecasts

3.5 SUMMARY

This review outlined the potential barriers to properly evaluate net impacts of a toll

road project to the community. This review also identified research gaps and

recommendations of how the barriers can be addressed.


Project evaluation is a process to measure the net impacts of a project to the

community to ensure that the benefits of the decision made on the basis of the

evaluation outcomes outweigh the costs. The most commonly used project evaluation

methodology is Cost-Benefit Analysis (CBA). CBA measures and quantifies impacts

of a project and represents its outcome as a Benefit-Cost Ratio (BCR), which is a ratio

of the monetised benefits and monetised costs. It is important to distinguish CBA with

financial analysis. In the calculation of the CBA, the impacts that are not considered

in the financial analysis are considered, including various transport impacts.

Toll road projects may face various project risks that may or may not be similar

to the risks associated with non-toll road projects. For instance, a large-scale toll road

project contains the risks that are associated with any large-scale projects, such as cost

underestimations and benefit shortfalls. Other risks that are specific to toll road

projects include traffic, revenue, political and toll pricing risks. Particularly, the risk

associated with traffic and revenue forecasts have been studied by a number of authors.

These risks can be minimised and mitigated but can never be eliminated. The key to

the successful project evaluation is identifying and properly quantifying the risks so

that they are given appropriate considerations in the decision making.

3.5.1 Research Gaps

This review highlighted the limited studies conducted regarding the CBA for the

purpose of evaluation of toll road projects. CBA is a well-established project

evaluation methodology that can evaluate the project by considering various transport

impacts. As has been discussed, the outcome of CBA can differ from the outcome of

financial analysis. Whether the previous findings differ when CBA was used to

evaluate a toll road project require further investigation.

The sensitivities of project impacts and analysis inputs are generally tested in

CBA. However, one-way sensitivity analysis is limited by point estimations of

Benefit-Cost Ratio (BCR) across different scenarios. The representation of risks in

CBA requires improvements with empirical assessments and representations of risks.

Payment movements between entities of PPP toll road projects have been studied

in financial analysis (Mishra et al., 2013). Although not generally considered in CBA,

investigating payment movements and altering treatments of project impacts in the

CBA may extend the current knowledge. Particularly compelling is when the private


operator collects tolls. Toll revenues are generally only included in CBA by affecting

travel behaviours and efficiencies in the transport system (Decorla-Souza et al., 2013).

This is rational when the host government obtains the toll revenues, because the end-

users who pay those tolls are constituents of the host government and therefore enjoy

the benefits of that toll revenue through government expenditure, including repayment

of project debt. However, the review found that there is a scarcity of investigating the

treatment of tolls in CBA. Along with various concession payments, the movement of

tolls can be considered in the CBA.

3.5.2 Recommendation

Unlike fully publicly delivered toll road projects, the risk-sharing arrangement of a

Public-Private Partnership (PPP) toll road project can be complex and unique to each

project. There are a number of risk-sharing strategies for toll road projects.

Additionally, along with the investigations of risk-sharing arrangements of a toll road

project, the discount rate that varies according to the risks that are borne by the host

government requires consideration. This is because the discount rate depends on the

risk allocations between public and private sectors (Australian Department of

Infrastructure and Regional Development, 2013). Discount rate and risk-sharing

arrangements of a PPP toll road project need to be considered together, as they

interrelate to each other.

The review highlighted the advantages and disadvantages of CBA and compared

it with other alternative project evaluation tools. Whilst CBA cannot account non-

monetised impacts, the comparison indicated that it is the best tool, in order to

effectively assess economic impacts and various delivery options. Literature (Laird et

al., 2014) claim that CBA is the most coherent and robust method available.

The Monte Carlo simulation is a well-established risk assessment methodology

and it may overcome the limitations of one-way sensitivity analysis conducted in the

CBA. An empirical representation of risks is an effective way to present them to the

decision-makers and the communities that may be impacted by the project.

Chapter 4: A Review of Cost-Benefit Analysis Practices 37

Chapter 4: A Review of Cost-Benefit

Analysis Practices

Previously conducted Cost-Benefit Analysis (CBA) for various major road projects

are reviewed in this chapter. This comparative case study compares CBA of toll road

projects with those of non-tolled road projects to highlight the limitations and

difficulties in existing practices of CBA for the purpose of evaluation of toll road

projects. This chapter first provides a brief introduction of the study cases and reviews

economic parameters that were used. It then reviews project cost and benefit

calculations, as well as residual value (RV) calculations. The variables that were tested

in sensitive analysis, selection criteria and the treatment of tolls in CBA are then

reviewed. Finally, discussions and findings are summarised.

4.1 THE STUDY CASES

Eight Australian major road study cases and two international study cases that include

four non-tolled roads and six toll roads were analysed in this study. These cases were

selected based on two key criteria: CBA reports are publicly available, and key

assumptions and parameters used in the analysis are described in the report. The

following describes background of each case.

4.1.1 Non-Tolled Roads

Horsham Bypass (HSB)

A study was commissioned by VicRoads, in order to select preferred route alignment

for a future Western Highway bypass in Horsham, Victoria (AECOM Australia, 2014).

The bypass was planned to allow for the future traffic growth along the Western

Highway that connects Melbourne and Adelaide (AECOM Australia, 2014). A CBA

was conducted to choose the preferred alignment based on the net present value and

the Benefit-Cost Ratio (BCR) (AECOM Australia, 2014). The lengths of option route

alignments were between 22 and 23.8 km (AECOM Australia, 2014).

M4 Corridor around Newport (MCN)

A 23 km new motorway section is proposed to be located in South Wales, UK and

includes a 440 m span cable stayed bridge across the River Usk (Arup, 2015). The

38 Chapter 4: A Review of Cost-Benefit Analysis Practices

proposed route will connect Castleton and Magor and provide an alternative route that

links east and west of Newport (Arup, 2014).

Singleton Bypass (SNB)

A study was commissioned by New South Wales (NSW) Roads and Maritime Services

(RMS), in order to select a preferred route alignment for a future New England

Highway bypass in Singleton, NSW (New South Wales Roads and Maritime Services,

2016). The bypass was planned to allow for future traffic growth along the New

England Highway, which connects Newcastle and the Upper Hunter region (New

South Wales Roads and Maritime Services, 2016). The preferred alignment was

chosen based on the measured economic benefit that was calculated in CBA (AECOM

Australia, 2012). The lengths of option route alignments were between 19.1 and 22.5

km (AECOM Australia, 2013).

West Petrie Bypass (WPB)

The section of Young’s Crossing Road is prone to flooding and is frequently inundated

by flood water (Arup, 2010a). The 1.92 km West Petrie Bypass (WPB) is a proposed

new road connecting Young’s Crossing Road and Dayboro Road to the west of Petrie,

which is a suburb of Moreton Bay Regional Council to the north of Brisbane (Arup,

2010b; GHD, 2013). A business case was produced for the alignment of the WPB that

was selected from the previous study (Arup, 2010a). The business case consists of

CBA of the alignment and the environmental and cultural heritage study (GHD, 2013).

4.1.2 Toll Roads

Airport Link (APL)

The Airport Link (APL) consists of a 6.78 km section of a tunnel and a motorway,

which is located in Brisbane (BrisConnections, 2016). APL is part of the corridor

designated as M7 and A7, which connects the south-west and north-east of Brisbane.

APL connects with other M7 elements of the Clem Jones Tunnel at its southern end

and the East West Arterial Road leading to Brisbane Airport and the Port of Brisbane

at its north-eastern end (BrisConnections, 2016). A major interchange at its southern

end also connects it with the Inner City Bypass expressway and Legacy Way (LGW),

and Bowen Bridge Road. A major interchange mid-tunnel connects it with Gympie

Road and Stafford Road, Kedron. CBA was conducted for the proposed alignment of

APL to assess the viability of the APL project by reviewing BCR calculated in the


CBA and to review the integration of the Interim Northern Busway Project within the

APL project (SKM & Connell Wagner, 2006).

City Link (CYL)

City Link (CYL) is a 22 km motorway located in Melbourne and connects Monash

Freeway, West Gate Freeway and Tullamarine Freeway (Transurban, 2016a). CYL

consists of Western Link and Southern Link and provides access to Melbourne Airport,

Melbourne central business district (CBD) and Eastlink (Transurban, 2016a). A study

was conducted to review economic benefits of the CYL using CBA (The Allen

Consulting Group, 1996).

Gateway Upgrade Project (GUP)

Gateway Upgrade Project (GUP) is a 22.4 km upgrade of Gateway Motorway (M1) in

Brisbane, between Mt Gravatt-Capalaba Road at Wishart and Nudgee Road at Nudgee

(Connell Wagner, 2004). The motorway provides direct access to Brisbane Airport and

to Port of Brisbane Motorway (Connell Wagner, 2004). The GUP project consists of

lane widening of the existing infrastructure, a new bridge crossing, a new section of

motorway and a new interchange along the motorway (Connell Wagner, 2004). A

business case was developed to review economic impacts of the GUP project using

CBA (Connell Wagner, 2004).

Legacy Way (LGW)

Legacy Way (LGW), previously referred to as Northern Link, is located in Brisbane

and is a 4.6 km tunnel designated as part of the M5 corridor passing through the west

of Brisbane. It connects Centenary Motorway (Western Freeway) at Toowong with

the Inner City Bypass expressway at Herston (Brisbane City Council, 2010). The LGW

tunnel provides an access to Brisbane Airport, Royal Brisbane Hospital, Chermside,

Sandgate Road and Toowoomba (Queensland Motorways Management, 2016). CBA

was conducted as part of a business case of the LGW project to assess costs and

benefits of the project (Brisbane City Council, 2010).

Silvertown Tunnel (SLT)

Silvertown Tunnel (SLT) is a tolled tunnel that is about 2 km long and is proposed to

connect Greenwich Peninsula and Silvertown, UK (Jacobs, 2014b). SLT will be

directly connected to Blackwell Tunnel (A2), in order to ease traffic along the existing

tunnels under River Thames, UK. CBA was conducted to assess economic impacts of


the SLT, however, different options, such as “do-nothing option”, were not considered

in the CBA (Jacobs, 2014a).

Toowoomba Bypass (TWB)

Toowoomba is located at the convergence of the Warrego, Gore and New England

Highways (Queensland Government, 2008). The road network in Toowoomba

provides interstate movements from Queensland to NSW, Victoria and Northern

Territory (Queensland Government, 2008). The Toowoomba Bypass (TWB) project

proposed a new, 42 km motorway that bypasses through movements from Toowoomba

City (Queensland Government, 2008). A business case was developed to assess the

needs of the project using CBA (Queensland Government, 2008).

4.1.3 Project Proponent and Owners

Major transport infrastructure projects are generally initiated by host government

bodies. Typically, they commission private consulting firms to undertake CBA. Table

4.1 summarises project proponents, representing their host governments.


Table 4.1 Project proponent of the study cases (AECOM Australia, 2012, 2014; Arup, 2014;

Brisbane City Council, 2010; Connell Wagner, 2004; GHD, 2013; Jacobs, 2014a; Queensland

Government, 2008; SKM & Connell Wagner, 2006, 2008; The Allen Consulting Group, 1996)

Case State or country Project proponent

Horsham Bypass (HSB) VIC VicRoads

M4 Corridor around Newport (MCN) UK Welsh Government

Singleton Bypass (SNB) NSW NSW Roads and Maritime

Services

West Petrie Bypass (WPB) QLD Moreton Bay Regional Council

Airport Link (APL) QLD The State of Queensland and

Brisbane City Council

City Link (CYL) VIC VicRoads

Gateway Upgrade Project (GUP) QLD Queensland Department of

Transport and Main Roads

Legacy Way (LGW) QLD The State of Queensland and

Brisbane City Council

Silvertown Tunnel (SLT) UK Transport for London

Toowoomba Bypass (TWB) QLD The Australian Commonwealth

Government and the State of

Queensland

Table 4.2 summarises the operators of each tolled study case and its owners,

following the conclusion of its concession period. Generally, the infrastructure item is

owned by the operator and will be transferred back to the host government at the

conclusion of the concession period. A number of private firms are usually involved

in a single toll road project to design, build, operate, maintain and/or finance it. The

operators generally are responsible in financing the development of the project,

maintaining and operating the toll road in whole or part, and receiving toll revenues in

return. Toll roads are most often part of the state-controlled motorway network. All of

the study cases aside from LGW will be transferred to a state government at the

conclusions of their concession periods. In contrast, SLT was owned and operated by

public agencies.


Table 4.2 Toll road operators and owners of the study cases (Brisbane City Council, 2015; Jacobs,

2014a; Queensland Department of Transport and Main Roads, 2015; Queensland Treasury, 2016;

Transurban, 2015a; VicRoads, 2015)

Case Operator Owner after the concession

APL BrisConnection The State of Queensland

CYL Transurban The State of Victoria

GUP Transurban The State of Queensland

LGW Transurban Brisbane City Council

SLT Transport for London Mayor of London

TWB Nexus Infrastructure The State of Queensland

4.2 ECONOMIC PARAMETERS

Significant variations in BCR values with different planning horizons was also

highlighted in Contreras’s study (2014). The value of discount rate and the length of

planning horizon can be key inputs in CBA. Table 4.3 summarises the discount rates

and planning horizon used for the study cases. The discount rates used for HSB and

CYL are significantly different, although they both are based in the same state. A three

percent difference of the discount rates can impact Benefit-Cost Ratio (BCR)

dramatically over the planning horizon of 30 years or longer. This suggests that the

risk allocations of HSB and CYL are noticeably different. MCN and SLT used variable

discount rates and the same length of planning horizon. Additionally, the discount rates

used in UK cases were noticeably lower than those used in Australian cases. The

discount rates that need to be used is defined by UK Cabinet Office (2017).


Table 4.3 Economic parameters used in the study cases (AECOM Australia, 2012, 2014; Arup, 2014;

Connell Wagner, 2004; GHD, 2013; Jacobs, 2014a; Queensland Government, 2008; SKM & Connell

Wagner, 2006, 2008; The Allen Consulting Group, 1996)

Case State or

country Discount rate

Planning

Horizon

HSB Victoria 5 % 30 years

CYL Victoria 8 % 30 years

SNB NSW 7 % 30 years

WPB Queensland 7 % 30 years

APL Queensland 6.8 % 45 years

GUP Queensland 6 % 30 years

LGW Queensland 6 % 40 years

TWB Queensland 7.6 % 40 years

MCN UK 3.5 % for year 1 to 30 and 3 % for year 31 to

60

60 years

SLT UK 3.5 % for year 1 to 30 and 3 % for year 31 to

60

60 years

4.3 PROJECT COSTS

The project cost of each study case was estimated by each evaluator as a lump sum

payment and distributed over the construction period. In some of the study cases, the

operation and maintenance (O&M) cost was estimated individually. While for others,

simply one percent of the whole capital cost was entered as the O&M cost for the

whole planning horizon. For projects with relatively higher capital cost, such as tunnel

projects, capital cost and O&M cost, should be individually estimated, because their

proportions can vary between each project. For instance, with LGW, the proportions

of the individually estimated capital cost and O&M cost for the whole of planning

horizon were 81 percent and 19 percent respectively (SKM & Connell Wagner, 2008).

This shows considerable variations from the assumption that the O&M cost is one

percent of the whole capital cost. O&M cost was not considered in MCN case (Arup,

2014).


4.4 PROJECT BENEFITS

Project benefits that was considered in the study cases include travel time saving,

vehicle operating cost saving (VOCS), crash cost saving (CCS) and other

environmental and external costs saving (EECS). Travel time saving was estimated

based on the assumption that by using the proposed infrastructure, travel time can be

saved, which then can be converted to a dollar amount. VOCS, CCS and EECS were

estimated based on the assumptions that by using the proposed infrastructure, travel

distance can be saved. This translates to lower vehicle operating cost, fewer crashes,

and fewer impacts upon the environment. The saved travel distance is then used to

estimate the VOCS, CCS and EESC in dollar amount. Comparisons of sources of these

cost estimates and the methodologies to model travel time and travel distance are

beyond the scope of this study.

Along with travel time saving, VOCS, CCS and EESC, costs of incident delays,

travel time variability and delays during construction were considered for SLT (Jacobs,

2014a). However, the final benefits only included travel time saving and VOCS

(Jacobs, 2014a). Unit prices were not documented in the report for MCN, while travel

time saving and VOC were accounted as benefits (Arup, 2014). Impacts during

construction and maintenance and revenues were also included as benefits for MCN

case (Arup, 2014).

Not all input data were documented in the Cost-Benefit Analysis (CBA) reports

of the study cases. The inputs that were not documented are shown as “unknown” in

the following sections, but this does not indicate that they were excluded in the CBA

calculation. In fact, all of travel time saving, VOCS, CCS and EECS were included for

all of the study cases aside from the WPB. All costs are shown with conversion to 2016

Australian dollars or 2016 British pounds for equitable comparison, using the inflation

methodology of the Reserve Bank of Australia (2017) and the Bank of England (2017)

in the following section. British pounds were converted to Australian dollars using the

exchange rates published by the London Stock Exchange (2017), which are shown in

brackets.

4.4.1 Travel Time Saving

Travel time saving was calculated based on the vehicle hours travelled saving and

travel time unit price. The travel time unit price estimation methodology was not


documented for the study cases. For all of the study cases, the travel time unit price

was estimated for light vehicles (LV) and heavy vehicles (HV) separately, and costs

for private time and working time were also estimated. Table 4.4 shows the travel time

unit prices used in the study cases. The unit prices used in the study cases are relatively

consistent.

Table 4.4 Travel time unit price per hour in 2015 dollars (AECOM Australia, 2012, 2014; Arup,

2014; Connell Wagner, 2004; GHD, 2013; Jacobs, 2014a; Queensland Government, 2008; SKM &

Connell Wagner, 2006, 2008; The Allen Consulting Group, 1996)

Case LV (per veh-h) HV (per veh-h)

HSB $15.49 for non-work trips

$49.54 for in-work trips

$34.82 for rigid HV

$73.60 for articulated HV

MCN Unknown

SNB $37.85 $49.76

WPB $29.85 for both LV and HV

APL $22.57 for non-work trips


$37.45-$42.42 for rigid HV

$58.68-$70.21 for articulated HV

CYL $21.04 for both LV and HV

GUP Unknown

LGW $20.10 for non-work trips


$41.04

SLT £8.01 ($13.03) for both LV and HV

TWB Unknown

4.4.2 Vehicle Operating Cost Saving (VOCS)

In the study cases, various VOCS unit prices were used for different vehicle types and

travel speeds. Table 4.5 summarises VOCS unit price used in the study cases. The unit

prices shown in Table 4.5 are for vehicles travelling at 80 km/h. The CBA conducted

for WPB showed some technical errors and VOCS was excluded from the CBA

calculation (GHD, 2013). The unit prices of vehicle operating cost used in the study

cases are also relatively consistent.


Table 4.5 Vehicle operating cost saving unit price per km in 2015 dollars (AECOM Australia, 2012,

2014; Arup, 2014; Connell Wagner, 2004; GHD, 2013; Jacobs, 2014a; Queensland Government,

2008; SKM & Connell Wagner, 2006, 2008; The Allen Consulting Group, 1996)

Case LV (per veh-km) HV (per veh-km)

HSB $0.25 $1.33

MCN Unknown

SNB $0.33 $1.14

WPB Excluded

APL $0.18 for cars

$0.36 for light commercial vehicles

$1.40

CYL Unknown

GUP Unknown

LGW Unknown

SLT Unknown

TWB Unknown

4.4.3 Crash Cost Saving (CCS)

Crash rate theoretically should be different between urban and rural setting and also

between roads and tunnels. Motorways particularly should have different crash rates

to major arterial roads since they are uninterrupted flow facilities. The variations in the

unit price of crash cost can be due to various methodologies for estimations, but ought

to be similar for the same type of infrastructure in the same state. Table 4.6 summarises

the CCS unit price used for the study cases. The CCS was calculated as the product of

crash cost, crash rate and vehicle kilometres travelled saving in three cases (AECOM

Australia, 2012, 2014; GHD, 2013). The estimation of the unit price of the CCS was

not documented for the rest of the five cases. The unit prices used for HSB, SNB and

WPB showed noticeable variations. The crash cost used for HSB only considered

casualties. The crash rate used for SNB was significantly higher than the other two

cases. HSB and SNB are both rural bypass projects and the crash rate for the two

projects should be similar. The CCS unit price used for APL is significantly lower than

the other cases. This shows either an error with estimation of the CCS unit price or the

fact that crash rate is assumed to be lower for tunnels.


Table 4.6 Crash cost saving unit price in 2015 dollars (AECOM Australia, 2012, 2014; Arup, 2014;



Case Crash cost (per crash) Crash rate (per veh-

million km)

Total CCS unit price

(per veh-million km)

HSB $268,026 0.11 $29,482

MCN Excluded

SNB $2,337,366 3.44 $8,040,538

WPB $3,513,667 0.31 $1,075,610

APL Unknown Unknown $23,920

CYL Unknown

GUP Unknown

LGW Unknown

SLT Excluded

TWB Unknown

4.4.4 Environmental and External Cost Saving (EECS)

There are considerable variations in the types of costs that were included in the EECS

in the study cases. The methodology of estimation of the EECS unit price was not

clearly documented in any of the study cases (AECOM Australia, 2012, 2014; Connell

Wagner, 2004; GHD, 2013; Queensland Government, 2008; SKM & Connell Wagner,

2006, 2008; The Allen Consulting Group, 1996). Table 4.7 summarises the types of

costs that were included in the EECS unit price for each case. Austroads states that all

of these impacts shown in Table 4.7 should be considered in CBA (Tan, Lloyd, &

Evans, 2012). Although GHD (2013) mentioned that the EECS unit price includes air

pollution, greenhouse gas emission (GHG), noise pollution, water pollution, impact on

nature and landscape, urban separation, and upstream and downstream costs, only the

GHG, and air and noise pollutions were accounted in their EEC calculation. The whole

of EECS was not considered in the CYL case (The Allen Consulting Group, 1996).

The types of EECS considered in the GUP case were not documented (Connell

Wagner, 2004). External costs were only considered in the TWB case, however the


types of costs that were considered were not documented (Queensland Government,

2008).

Table 4.7 Environmental and external cost types (AECOM Australia, 2012, 2014; Arup, 2014;



Case GHG Air

pollution

Noise

pollution

Water

pollution

Nature

and

landscape

Urban

separation

Upstream

and

downstream

HSB ✓ ✓ ✓ ✓ ✓ ✓ ✓

MCN Excluded

SNB ✗ ✓ ✓ ✓ ✓ ✓ ✓

WPB ✓ ✓ ✓ ✗ ✗ ✗ ✗

APL ✗ ✓ ✓ ✓ ✗ ✗ ✗

CYL Excluded

GUP Unknown

LGW ✓ ✓ ✓ ✓ ✓ ✓ ✓

SLT Excluded

TWB Only externalities are included

Table 4.8 summarises the unit prices used to estimate EECS. Although the total

EECS unit price for HSB included GHG and other environmental and external costs,

the document prepared for the HSB case (AECOM Australia, 2014) does not specify

the values of other environmental and external costs, which are unknown. Separate

unit price for LV and HV of EECS were estimated in the APL case, however the unit

price for HV was not documented (SKM & Connell Wagner, 2006). There are

noticeable variations in the unit prices used to estimate EECS between all study cases.

The unit price for HV has more significance than those for LV in CBA calculations.


Table 4.8 Environmental and external cost saving (EECS) unit price per km in 2015 dollars (AECOM

Australia, 2012, 2014; Arup, 2014; Connell Wagner, 2004; GHD, 2013; Jacobs, 2014a; Queensland

Government, 2008; SKM & Connell Wagner, 2006, 2008; The Allen Consulting Group, 1996)

Case LV (per veh-km) HV (per veh-km)

HSB $2.89 for GHG

Other EEC Unknown

$6.50 for GHG cost of rigid HV

$3.45 for GHG cost of articulated HV

Other EEC Unknown

MCN Excluded

SNB $0.07 $0.43

WPB $0.0236 for GHG for both LV and HV

$0.0299 for air pollution for both LV and HV

$0.0098 for noise pollution for both LV and HV

APL $0.0102 for noise pollution

$0.0308 for air pollution

$0.0045 for water pollution

Unknown

CYL Excluded

GUP Unknown

LGW $0.116 $8.35

SLT Excluded

TWB Unknown

4.5 RESIDUAL VALUE

There was considerable variation in the treatment and calculation of RV. RV

represents the value of the asset at the end of the planning horizon and is added in

Cost-Benefit Analysis (CBA) as a benefit. The expected economic life of a road is 40

to 60 years, a concrete bridge is 120 years and a tunnel is 100 years (Australian

Transport Council, 2006b). There may be minor variations in these values, however

the lifespan of an asset should be equal to its design life. Table 4.9 summarises

assumed lengths of lifespans for the infrastructure items of the study cases.


Table 4.9 Assumed lifespan of the study cases (AECOM Australia, 2012, 2014; Arup, 2014; Connell

Wagner, 2004; GHD, 2013; Jacobs, 2014a; Queensland Government, 2008; SKM & Connell Wagner,

2006, 2008; The Allen Consulting Group, 1996)

Case Assumed lifespan of the infrastructure

HSB RV was not considered

MCN RV was not considered

SNB RV was not considered

WPB 50 years for road structure and 100 years for bridge structures

APL Lifespan was assumed to be equal to the length of concession period of 45

years

CYL RV was not considered

GUP Unknown

LGW 40 years

SLT RV was not considered

TWB Unknown

The infrastructure values were assumed to depreciate linearly over their lifespans

for WPB, APL and LGW cases. The CBA for APL assumed that the value of the APL

will be zero at the conclusion of its concession period. The CBA for LGW also

assumed that the value of the LGW will be zero after 40 years. This can be true from

the private sector’s perspective, as generally the infrastructure will be transferred back

to the host government at the conclusion of the concession period. From the host

government’s perspective, the infrastructure will be owned by the public and there

should always be some RV at the conclusion of the concession period, unless the life

of the infrastructure item has ended and it needs to be fully replaced. Even so, present

worth of an RV is generally very small. RV was not considered in the CBA of HSB,

MCN, SNB, CYL and SLT (AECOM Australia, 2012, 2014; Jacobs, 2014a; The Allen

Consulting Group, 1996). This can indicate that the RV was assumed to be zero at the

end of the planning horizon. This argument also suggests that when the planning

horizon is shorter than the concession period, the infrastructure item is still owned by

the private concessionaire and the RV at the end of planning horizon would be zero.

When the planning horizon is longer than the concession period, RV still applies at the


end of the planning horizon instead of the conclusion of concession period, as the

depreciation of the value of the infrastructure continues.

4.6 SENSITIVITY ANALYSIS

Table 4.10 summarises the sensitivity analyses that were performed for the study cases.

Sensitivity analysis was not performed for CYL and GUP, and therefore was not

considered in the decision making process. The type of sensitivity analysis that was

conducted for the study cases was one-way. The inputs that were tested in the

sensitivity analysis varied between cases. Theoretically, inputs that form the basis of

calculated impacts, which are traffic growth rate and forecasted traffic volume, should

at least be tested. Capital cost of major transport projects tend to be underestimated

(Flyvbjerg, 2014), and therefore should also be tested in sensitivity analysis. Various

discount rates are commonly tested, as shown in Table 4.10.


Table 4.10 Sensitivity analysis conducted in the study cases (AECOM Australia, 2012, 2014; Connell

Wagner, 2004; GHD, 2013; Jacobs, 2014a; Queensland Government, 2008; SKM & Connell Wagner,

2006, 2008; The Allen Consulting Group, 1996)

Case Traffic

growth rate

Forecasted

traffic

volume

Discount

rates Capital cost Other

HSB ± 10 % ± 10 % Not tested Not tested

MCN Low, high

and no

growths

Not tested Not tested Not tested When tolls along

nearby motorways

were removed

SNB Higher and

lower than

forecasted

± 20 % 4 % and

10 %

± 20 % Recalculated crash

cost saving

WPB Not tested Not tested 4 % and

10 %

Not tested

APL Not tested Not tested 5.5 % Recalculated

with various

risks

1 % higher population

growth

CYL Not performed

GUP Not performed

LGW Not tested Not tested 4 % and 8

%

Not tested na

SLT Not

performed

TWB Not tested Not tested 3.5 % and

6.5 %

± 20 % ± 20 % of travel time

saving and vehicle

operating cost; and ±

50 % of crash rate

Table 4.11 summarises the variables that the extent guidelines recommend

performing sensitivity analysis on. Australian Transport and Infrastructure Council

(2016b) recommends the sensitivity analysis suggested in the Austroads guidelines

(Rockliffe et al., 2012).


Table 4.11 Recommended sensitivity analysis in the Australian guidelines

Guideline

Traffic

growth

rate

Forecasted

traffic

volume

Discount

rates

Capital

cost Other

Austroads

(Rockliffe et

al., 2012)

± 2 % ± 10-20 % Not

tested

± 20 % Proportion of HV,

average car

occupancy rate,

induced traffic

estimate, traffic speed

and crash rate

Infrastructure

Australia

(2017)

± 2 % ± 5 % 4 % and

10 %

± 20 %

and/or P50

and P90

costs

Project specific

benefits, planning

horizon, proportion of

HV, and traffic

generated or diverted

by the initiative

As shown in Table 4.10 and Table 4.11, there are some similarities to the

sensitivity analysis that was conducted for each case and the recommended sensitivity

analysis. However, all cases except SNB, there are large variations between what is

performed and what is recommended. It can be speculated that the variation can be due

to technical limitations and/or differences in the business case requirements in each

state. Additionally, this also suggests that less focus may be placed on the sensitivity

analysis than the key CBA results, such as BCR and NPV. As discussed, the risks of a

project are generally only assessed using sensitivity analysis in CBA in practice, which

indicates the lack of risk assessment in the CBA practices.

4.7 SELECTION OF PREFERRED OPTION

As part of the whole project evaluation for all of the study cases, although other

considerations, such as wider economic benefits and other intangible factors were

considered as part of the whole project evaluation of all of the study cases, the outcome

of Cost-Benefit Analysis (CBA) was represented using net present value and Benefit-

Cost Ratio (BCR). A BCR below 1.0 was determined for HSB (AECOM Australia,

2014), while BCRs below 2.0 were determined for SNB, WPB, APL, LGW and SLT

(AECOM Australia, 2012; GHD, 2013; Jacobs, 2014a; SKM & Connell Wagner,


2006, 2008). A BCR greater than 2.0 was determined for MCN (Arup, 2014). None of

the study cases that resulted in BCR below 2.0 were discontinued, suggesting that other

major factors that were not accounted for in CBA were considered in the justification

process of these projects.

4.8 TREATMENT OF TOLLS

Of the six toll road project cases, tolls were considered as a financial transfer between

the host government and the toll road users and so were excluded in Cost-Benefit

Analysis (CBA) calculations of APL, CYL, GUP and LGW cases. Various toll prices

were applied in traffic forecast modelling and traffic volumes, and proportions of HV

were estimated for each different toll price in the CBA for TWB case (Queensland

Government, 2008). Tolls were considered as the cost to the community in SLT case

(Jacobs, 2014a), although a public agency will be collecting the tolls. Costs associated

with toll collection were accounted in cost calculation as well (Jacobs, 2014a). Tolls

were considered as a financial transfer in the TWB CBA, however they were included

in modelling as a factor that influenced traffic volume forecasts (Queensland

Government, 2008). This is consistent with previous study (Decorla-Souza et al.,

2013). It is reasonable to argue that in all of the study cases, tolls were considered as

a financial transfer because they replace the capital, operating and maintenance costs

of the project that would have otherwise been borne by the public through the host

government if the project were a non-tolled, public road. Philosophically, this manner

of treating cost is the most significant assumption that was made in the CBA-based

project evaluations of the tolled study cases.

4.9 DISCUSSION

Some technical inconsistencies exist between the Cost-Benefit Analysis (CBA)

methodologies followed in the study cases. For instance, CYL was the only case that

included the off-road benefit in its CBA calculation. The methodology of the

estimation of the off-road benefit was not documented (The Allen Consulting Group,

1996). The inconsistencies between Australian cases and UK cases were significant.

This can be due to the differences in guidance of road authorities with regard to CBA

in different countries. The following discusses possible causes of the inconsistencies.

First, every consulting firm that would be conducting CBA for host government

agencies has some level of limitations with their resources, including labour, money


and time. For instance, a practitioner may have to use values derived from other studies

to conduct CBA in practice, as it is common for the practitioner to produce the analysis

in a short time-frame (Soh, 2012). The quality of the analysis can highly depend on

the resources available and time constraints.

Second, the guidelines of project evaluation and CBA range from the complex,

to those lacking in depth or consistency. For instance, the discount rate methodology

shown in the discount rate guidelines (Australian Department of Infrastructure and

Regional Development, 2013; New South Wales Treasury, 2007) suggests the use of

various discount rate methodologies for projects with different risk allocations. It

sometimes can be difficult for the firm to first select the applicable methodology and

then to conduct the required risk analysis while identifying the appropriate discount

rate to use.

Third, there is a possibility that the host government is not necessarily expecting

comprehensive CBA, particularly when there are significant intangible factors

involved in the project. For instance, for a bypass project in a rural region, the Benefit-

Cost Ratio (BCR) that was derived from the CBA may not necessarily completely

decide viability of the project. Some items considered to be intangibles in the CBA,

such as community sentiment, may drive its decision making. This was also evident in

the cases. Despite CBA being capable of capturing many project impacts, monetising

certain impacts, particularly related to community issues, can be difficult.

Fourth, decision making for large-scale projects, such as transport projects can

be complex work, which requires consideration of monetary and non-monetary

impacts as well as various project risks and other intangible factors. A large part of

what makes project evaluation of transport projects difficult is the complexity of

transport planning itself. Forecasting and modelling the impact of adding a new

transport infrastructure into an existing network increases with the complexity of the

network itself. When evaluating a single road, the worthiness of the road depends on

how it acts as part of the whole transport network.

Additionally, economic analysis reports of many study cases had poor

transparencies. Input parameters and detailed calculations were not all documented.

This can be possibly due to the use of computer software to conduct CBA calculations.

Many practitioners may have failed to document key information of the calculation in

the report. For instance, for CBA of a major road project, input parameters, list of


benefits that were accounted, the manuals or guides that were referred, and how

discount rate was determined should at least be documented.

Also important is the opening year of the road. As the time value of money

diminishes with the size of discount rate used, the benefit gained in the first few years

will be more significant than the benefit gained later years with a higher discount rate.

If a road is built as an initial component of a larger transport planning initiative, for

instance a network of toll roads in an urban area, the full benefit of that road in terms

of its use may only be realised once other components of the network are opened and

operating in unison. Unfortunately, the discounted benefit and therefore CBA of that

initial road would be expected to be relatively less than its subsequent partner

component roads due to a longer ramp-up period. For a road to perform at its maximum

capacity, the surrounding transport network needs to be working effectively with the

road. This circumstance is referred as the toll road network founder disadvantage in

this study. The improvement in ability to accurately forecast traffic as the network

develops compounds this circumstance. A case in point is the Brisbane toll tunnel

network. Clem Jones Tunnel opened two years prior to APL and five years prior to

LGW (BrisConnections, 2016; Transurban, 2015b, 2016d). LGW has been the most

successful link in the network since its opening, while the first two components

suffered from limited demands initially.

It has been highlighted that the estimation of RV at the end of the planning

horizon depends on the scope of the evaluation. When the design life of the

infrastructure is larger than the planning horizon, and the infrastructure will be

transferred back to the host government at the conclusion of its concession period, RV

should be included in CBA as a benefit. The review of the study cases revealed that

RV of some of the study cases was treated in this manner. In particular, for tunnel

projects, the longer design life and higher construction cost of the tunnel structure

leaves a larger RV at the end of the planning horizon. Therefore, RV of tunnels can

significantly influence the outcome of CBA.

The treatment of tolls in CBA raises questions when impacts to the community

are concerned. As illustrated by the tolled study cases, tolls are generally considered

as a financial transfer and enter into CBA calculations only to the extent that they cause

a change in micro-economic behaviour (Decorla-Souza et al., 2013). As discussed

above, it is reasonable to argue that this is because they replace the capital, operating


and maintenance costs of the project that would have otherwise been borne by the

public through the host government if the project were a non-tolled, public road.

However, this is only completely truly realistic, if the transfer is internal to the public

purse, for instance when the host government on behalf of the public pays for the

infrastructure item, collects the revenue, and bears all of the project risk during its

lifetime. An example of this case was when Queensland Motorways, as a Queensland

Government, owned and operated a tolled motorway, Gateway Motorway in Brisbane,

before the concession of the motorway was later purchased by Transurban.

If the opposite extreme is considered, whereby the toll road is designed, built,

operated and financed by private sector firms, the influences of the impacts that are

borne by the private sector need to be carefully considered. If such impacts are

recouped in a commercial environment by way of charging the end-users, who in the

case of a toll road are normally members of the community, it can be argued that those

end-user charges should be counted as the societal cost impacts in CBA. Meanwhile,

while the project’s capital, operating and maintenance costs are borne by the private

sector rather than the host government, these latter costs should be excluded from the

CBA because they are financial impositions that are contained within the private sector

entity’s enterprise of offering its product, rather than as an end-user societal cost. The

majority of toll road projects in Australia do not fit into either extreme, which makes

the treatment of tolls in CBA a complex consideration. Further investigation is needed

to explore how tolls should be treated, and how concession arrangements, such as

minimum revenue guarantees should be considered.

The UK CBA manual (UK Department of Transport, 2014) states that user

charges, including tolls, need to be included in CBA calculations, however, toll

revenues require careful considerations in the calculation. As highlighted, tolls were

accounted as costs in the CBA calculation of the SLT case. This indicates that the CBA

practice significantly differ between Australia and UK. Treatments of tolls and toll

revenues may need to differ between projects with various risk-sharing arrangement,

as has been discussed previously.

As has been discussed, discount rate varies depending on the risk-sharing

arrangement between public and private sectors, and significantly influence the

outcome of CBA. As illustrated in the discussion of the treatment of tolls in CBA, the

risk allocation is also directly linked to the allocation of impacts. The risk of a project


can play a key role in decision making and needs to be measured precisely and

empirically. There was limited coverage of risk in all of the study cases.

In contrast, discount rate methodology that is used in UK does not account the

risk-sharing arrangement. For any projects, UK Cabinet Office advises to use the same

discount rates (UK Cabinet Office, 2017). Additionally, RV is generally excluded in

CBA calculation for projects with the planning horizon longer than 60 years in UK

(UK Department of Transport, 2014). The UK CBA manual (UK Department of

Transport, 2014) does not consider when the length of lifespan of the asset is longer

than 60 years. These indicate that discount rate and RV calculation practices differ

between Australia and UK.

This case study revealed a limitation of the extant CBA methodology, which is

to clearly display the range in risk that the project may face. Estimating appropriate

discount rate for Public-Private Partnership (PPP) projects can also be a complex task

due to the complexity of estimating risk-sharing between public and private sectors.

For instance, the sensitivity analysis that was conducted for each case did not show the

project risks in an empirical manner. As an example, representation of the risk of a

Benefit-Cost Ratio (BCR) being below 1.0 that is represented using percentages would

be extremely useful in the decision making process.

4.10 SUMMARY

This case study found that for many toll road projects in Australia, tolls were

considered as a financial transfer and were excluded in Cost-Benefit Analysis (CBA).

However, further consideration showed that tolls should not necessarily simply be

excluded as costs, and the treatment of tolls should differ between different toll road

projects. Tolls were considered as the cost to the community for the UK case, although

the tolls will be collected by a public toll operator (Jacobs, 2014a). Further study is

needed to investigate the mechanisms of the treatment of tolls in CBA and various risk

allocations. The study also identified limitations of the extant CBA methodology to

evaluate toll road projects. The representations of project risks in cases were somewhat

limited. Empirical based representations of project risks would assist effective decision

making. For instance, measuring the risk of Benefit-Cost Ratio (BCR) using statistical

measurements, such as probability of failure would be significantly useful for decision

making.

Chapter 5: Incorporating Stochastic Approach in Cost-Benefit Analysis 61

Chapter 5: Incorporating Stochastic

Approach in Cost-Benefit

Analysis

The risk arrangement of a toll road project can be unique to each project and assessing

risk is crucial in the decision making of the project. Cost-Benefit Analysis (CBA) is

the most commonly used to evaluate major road projects, including tolled and non-

tolled roads (van Wee & Rietveld, 2014). The decision making using CBA relies on a

single, deterministic Benefit-Cost Ratio (BCR) and one-way sensitivity analysis.

However, the interpretation of the impacts and risks using a single, deterministic BCR,

even with one-way sensitivity analysis, is limited by the point assumptions that are

made in the monetisation of impacts. There is a paucity of study in the literature

regarding CBA to measure the net impacts of a toll road project and the representation

of risk in an empirical manner. This chapter examines how risks of various input

variables can be reflected in the CBA outcome. It is hypothesised that the risks that

influence net impacts to the community can be quantified using the Monte Carlo

simulation in CBA. A toll tunnel project case is synthesised based on the overarching

characteristics of a selection of recent toll tunnel projects in Brisbane, Australia, in

order to examine how various risks of a toll road project are quantified using a

stochastic BCR distribution.

5.1 COST-BENEFIT ANALYSIS CALCULATION

Transport benefits that are generally assessed in CBA of a major road project include

travel time, vehicle operating cost saving (VOCS), crash cost saving (CCS), and

environmental and external cost saving (EECS). Each saving is generally estimated

individually for light vehicles (LV) and for heavy vehicles (HV). An annualised

project benefit at year 𝑦 for Monte Carlo trial 𝑗 is given as follows:

62 Chapter 5: Incorporating Stochastic Approach in Cost-Benefit Analysis

𝐵𝑗,𝑦 = (𝐴𝐴𝐷𝑇𝑗,𝑦 × 365)

× {∑(𝑇𝑇𝑗,𝑘 × 𝑃(𝑘)𝑗 × 𝑉𝐻𝑇𝑆𝑗)

+∑(𝑉𝑂𝐶𝑗,𝑘 × 𝑃(𝑘)𝑗 × 𝑉𝐾𝑇𝑆𝑗) + (𝐶𝐶𝑗 × 𝑉𝐾𝑇𝑆𝑗)

+∑(𝐸𝐸𝐶𝑗,𝑘 × 𝑃(𝑘)𝑗 × 𝑉𝐾𝑇𝑆𝑗)}

(5.1)

Where:

𝐵𝑗,𝑦 = total annual project benefit at year 𝑦 for trial 𝑗 ($)

𝐴𝐴𝐷𝑇𝑗,𝑦 = average annual daily traffic at year 𝑦 for trial 𝑗 (veh/d)

𝑇𝑇𝑗,𝑘 = travel time unit price for the vehicle type 𝑘 for trial 𝑗 ($/veh-h)

𝑉𝑂𝐶𝑗,𝑘 = vehicle operating cost unit price for the vehicle type 𝑘 for trial 𝑗

($/veh-km)

𝐶𝐶𝑗 = crash cost unit price for trial 𝑗 ($/veh-km)

𝐸𝐸𝐶𝑗,𝑘= environmental and external cost unit price for the vehicle type 𝑘 for

trial 𝑗 ($/veh-km)

𝑘 = vehicle type, 𝑘 ∈ (𝐿𝑉,𝐻𝑉)

𝑃(𝑘)𝑗 = proportion of vehicle type 𝑘 for trial 𝑗 (%)

𝑉𝐻𝑇𝑆𝑗 = vehicle hours travelled saved by using the road for trial 𝑗 (h)

𝑉𝐾𝑇𝑆𝑗 = vehicle kilometre travelled saved by using the road for trial 𝑗 (km)

A discount rate was applied to all future annualised values of monetised benefits

and costs to accommodate the depreciation of the value of money. A BCR of

monetised and discounted impacts can be calculated for Monte Carlo trial 𝑗 as follows:

𝐵𝐶𝑅𝑗 =∑ [𝐵𝑗,𝑦(1 + 𝑑)

−𝑦]𝑛𝑦=0 + 𝑅𝑉𝑗(1 + 𝑑)

−𝑛

𝐶𝑎𝑝𝑗 + 𝑂&𝑀𝑗

(5.2)

Where:

𝑛 = period of planning horizon (years)

𝑑 = discount rate applicable to the project format (%)

𝑦 = corresponding year, 𝑦(0, 1, … , 𝑛)


𝑅𝑉𝑗 = residual value of the road for trial 𝑗 ($)

𝑂&𝑀𝑗= total O&M cost over the whole planning horizon for trial 𝑗 ($)

5.2 PROBABILITY DISTRIBUTIONS USED IN THIS STUDY

The Monte Carlo simulation requires a careful selection of the form of probability

distribution chosen for each impact, along with nuanced postulation of the distribution

parameters. The distribution for an input variable must be selected with sound

reasoning. Investigating the impacts of applying alternative forms of probability

distribution by variable is beyond the scope of this study. Salling (2008) further

discusses the use of various probability distributions. Coefficient of variable (CV)

needs to be carefully defined as it is a measure of the level of risk in the variable. The

magnitude of risks of various variables have been reviewed previously (Salling &

Leleur, 2011) so are not readdressed here. It is important to note that CV does not

indicate the variety of the variable. For instance, the CV of vehicle hours travelled

saving (VHTS) does not reflect how VHTS may vary between peak-time and off-peak

time. The mean needs to be carefully defined as it indicates the expected value. The

characteristics, including the form of probability distribution, its mean and CV,

predefine the risk of each variable. Therefore, the risks of input variables are inherent

within the risk profile of the outcome Benefit-Cost Ratio (BCR) distribution.

5.2.1 Capital Cost of a Toll Road Project

It is reasonable that a threshold capital cost exists, below which a project’s

development would not be possible, but that higher cost is plausible due to risks. For

this purpose the Cowan’s M3 distribution (Cowan, 1975) was applied. This

dichotomised distribution contains a set proportion of values equal to the minimum,

and the remaining proportion distributed negative-exponentially. It has been

incorporated previously in various transport applications (Bunker & Troutbeck, 2003;

Troutbeck, 1992). Capital cost can be modelled using the Cowan’s M3 distribution in

cumulative form by:

𝐹(𝐶𝑎𝑝𝑗) =

{

1 − 𝜙𝑒− 𝜙(𝐶𝑎𝑝𝑗−𝐶𝑎𝑝𝑚𝑖𝑛)

(𝐶𝑎𝑝𝑎𝑣−𝐶𝑎𝑝𝑚𝑖𝑛) 𝐶𝑎𝑝𝑗 > 𝐶𝑎𝑝𝑚𝑖𝑛1 − 𝜙 𝐶𝑎𝑝𝑗 = 𝐶𝑎𝑝𝑚𝑖𝑛0 𝐶𝑎𝑝𝑗 < 𝐶𝑎𝑝𝑚𝑖𝑛

(5.3)


Where:

𝐶𝑎𝑝𝑗 = capital cost in present value for trial 𝑗 ($)

𝜙 = probability that capital cost exceeds 𝐶𝑎𝑝𝑚𝑖𝑛 (%)

𝐶𝑎𝑝𝑚𝑖𝑛 = minimum feasible capital cost ($)

𝐶𝑎𝑝𝑎𝑣 = expected capital cost ($)

5.2.2 Case Dependent Input Variables

There is a scarcity with regard to identifying the forms of probability distributions of

various traffic modelling outputs. In the Salling and Leleur’s methodology (2011), a

probability distribution was applied to travel time saving as a whole and each risk of

input variables needed to determine the travel time saving was not modelled in their

study. For the purpose of this study, the normal distribution and the CV of 10 percent

were applied to those input variables, because the normal distribution can be used to

describe uncertain variables (Mun, 2010). Table 5.1 summarises the characteristics of

the probability distributions used for annual average daily traffic (AADT), yearly

traffic growth rate, proportion of heavy vehicle (HV%), vehicle kilometres travelled

saving (VKTS) and VHTS in this study.

Table 5.1 Probability distribution forms and coefficient of variable (CV) of the case depend input

variables

Variable Source Probability

distribution form CV

Annual average daily traffic (AADT) NA Normal distribution 10 %

Traffic growth NA Normal distribution 10 %

Proportion of heavy vehicles (HV%) NA Normal distribution 10 %

Vehicle kilometres travelled saving (VKTS) NA Normal distribution 10 %

Vehicle hours travelled saving (VHTS) Salling and

Leleur (2011)

Normal distribution 20 %

5.2.3 Transport Cost Unit Price

A range of literature was reviewed to determine the probability distributions of unit

prices of travel time, vehicle operating cost (VOC), crash cost (CC), and


environmental and external cost (EEC). The EEC includes air pollution, greenhouse

gas, noise, water, nature and landscape, urban separation, and upstream and

downstream impacts. Table 5.2 summarises the probability distributions used in this

study for these unit prices. The probability distribution of EEC unit price is not well

studied. As EEC includes variety of impacts, its unit price tends to contain a large risk.

This is similar to CC unit price. Salling and Leleur (2011) used the uniform distribution

for CC unit price. The same distribution was also applied to the EEC unit price in this

study.

Table 5.2 Probability distribution forms and coefficient of variable (CV) of transport cost unit prices

Variable Source Distribution

form

Coefficient of

variation (CV)

Travel time unit price Hensher (2001) Normal

distribution

33 %

Vehicle operating cost (VOC) unit

price

Berthelot et al.

(1996)

Normal

distribution

15 %

Crash cost (CC) unit price Salling and

Leleur (2011)

Uniform

distribution

10 %

Environmental and external cost

(EEC) unit price

NA Uniform

distribution

10 %

5.3 SYNTHESISING A TOLL TUNNEL PROJECT CASE

The overarching characteristics of recently built toll tunnel projects in Brisbane,

Australia were incorporated to synthesise a toll tunnel project case for the purpose of

demonstrating the analysis of risks within Cost-Benefit Analysis (CBA). Toll tunnels

in Brisbane were reviewed, because toll tunnels in Brisbane have all been opened in a

short span of time between 2010 and 2015, as shown in Table 5.3. They are all located

close to each other in Brisbane City and represent overarching characteristics of recent

Australian toll tunnel projects. The project parameters that are needed to conduct CBA

for this type of major road project include; capital cost, annual average daily traffic

(AADT), traffic growth, proportion of heavy vehicles (HV%), vehicle kilometres

travelled saving (VKTS), vehicle hours travelled saving (VHTS), various transport


costs, operation and maintenance (O&M) cost, planning horizon and discount rate.

Table 5.3 presents these values for Brisbane’s recent urban toll tunnel facilities of

Legacy Way, Clem Jones Tunnel and Airport Link. The amount of capital cost depends

on the size and the type of the infrastructure. For instance, the construction cost of a

tunnel project is usually relatively high. The capital cost of Airport Link was

significantly high, given the fact that its construction was combined with two other

projects, the Northern Busway and the Airport Roundabout Upgrade

(BrisConnections, 2011).


Table 5.3 Characteristics of Brisbane toll tunnels

Characteristic Legacy Way Clem Jones Tunnel Airport Link

Opening year 2015 (Transurban,

2016d)

2010 (Transurban,

2015b)

2012 (Transurban,

2016b)

Capital cost AU$ 1.5 billion

(ACCIONA Australia,

2015)

AU$ 3 billion (Go

VIA, 2015)

AU$ 5.6 billion

(BrisConnections,

2011)

Annual average

daily traffic

(AADT)

18,000 (2016 estimate)

(Morgans Financial,

2016)

27,000 (2015 actual)

(Morgans Financial,

2016)

30,757 (2012 actual)

(BrisConnections,

2012)

Proportion of

heavy vehicles

(HV%)

Unknown 17 % (Transurban,

2014)

Unknown

Vehicle

kilometres

travelled saving

(VKTS)

(Google, 2016)

0.7 km 1.2 km 1.0 km

Vehicle hours

travelled saving

(VHTS)

Between 3 and 18

minutes depending on

the time of the day

(Google, 2016)

Between 8 and 17


the time of the day (Go

VIA, 2016)

Between 10 and 14


the time of the day

(Google, 2016)

Planning

horizon

40 years (SKM &

Connell Wagner, 2008)

Unknown 45 years (SKM &

Connell Wagner, 2006)

Discount rate 6.0 % (SKM & Connell

Wagner, 2008)

Unknown 6.8 % (SKM & Connell

Wagner, 2006)

Table 5.4 summarises the assumptions that were made in order to conduct CBA

of the synthesised case. According to the Australian Bureau of Infrastructure Transport

and Regional Economics (2012), traffic in Queensland, Australia, was estimated to

grow by 2.8 percent annually until 2020. In Queensland, Australia, the discount rate

that was used to evaluate major road projects varies between 6.0 and 7.6 percent

(Connell Wagner, 2004; GHD, 2013; Queensland Government, 2008; SKM & Connell


Wagner, 2006, 2008). All prices were converted to 2015 Australian dollar values using

the Reserve Bank of Australia’s method (Reserve Bank of Australia, 2017).

Table 5.4 Assumptions made in Cost-Benefit Analysis (CBA) calculation of the synthesised toll

tunnel project case

Item Assumption and the expected value

Capital cost Cowan’s M3 distribution with 𝐶𝑎𝑝𝑚𝑖𝑛 = AU$ 1.4 billion and

probability of actual cost being greater than the minimum, 𝜙

= 63 %, while maintaining an expected value of AU$ 1.5

billion and a CV of 10 %.

AADT at year 1 30,000

Yearly traffic growth in

percentage

2.8 %, the same rate as traffic growth rate in Queensland,

Australia

HV% 10 %

VKTS 1.0 km

VHTS 15 min (0.25 h)

Type of project A toll tunnel project in the greater South East Queensland

region, Australia.

Age of facility A newly constructed facility that has never been used before

opening.

The expected economic life

of a tunnel

100 years (Australian Transport Council, 2006b)

Facility type Acts as part of the motorway (freeway) network in Brisbane,

Australia and is connected to other major roads.

Residual value (RV) The value of an asset is assumed to depreciate linearly over its

expected economic life.

Opening year Opening year is assumed to be year 1 and therefore daily

traffic volume of the first year is equal to AADT before

applying any growth. Traffic volume will then be increased

yearly with the traffic growth rate defined.

User benefits User benefits will be accrued from year 1.


Item Assumption and the expected value

Capital cost and, operation

and maintenance (O&M) cost

values

Total cost of the whole of planning horizon in present value.

Capital cost was applied as a lump sum at year 1. O&M cost

was distributed equally over the planning horizon. The

proportions of O&M and capital cost are 10 % and 90 %

respectively over the whole planning horizon.

Maximum AADT Absolute maximum AADT for four lane tunnel is 100,000

(based upon 2,250 pc/h/ln according to the Highway Capacity

Manual (Transportation Research Board, 2010) with two

lanes per direction, peak hour directional split of 55 %, peak

hour to daily ratio of 12 %).

Planning horizon 50 years

Discount rate 7.0 %

5.4 UNIT PRICE OF TRANSPORT COST

Unit prices of travel time, vehicle operating cost (VOC), crash cost (CC), and

environmental and external cost (EEC) are used to monetise transport cost savings.

Table 5.5 summarises the expected values of each unit price, which were determined

on the basis of Austroads guidelines (Tan et al., 2012). All prices were converted to

2015 Australian dollar values using the Reserve Bank of Australia’s method (Reserve

Bank of Australia, 2017). Crash cost of AU$ 0.025 per km was incorporated for both

light vehicles (LV) and heavy vehicles (HV). EEC includes air pollution, greenhouse

gas, noise, water, nature and landscape, urban separation, and upstream and

downstream costs.


Table 5.5 Transport cost unit price summary (Tan et al., 2012)

Transport cost

unit price

Light vehicles

(LV)

Heavy

vehicles (HV) Note

Travel time (/h) AU$ 22.42 AU$ 56.66 Includes freight travel time.

Proportion of private and business

car uses was obtained from

Australian Bureau of Statistics

(Australian Bureau of Statistics,

2015).

Vehicle operating

cost (VOC) (/km)

AU$ 0.69 AU$ 2.50 VOC for the operating speed of 80

km/h (50 mi/h).

Proportions of cars, light

commercial vehicle and heavy

vehicle were obtained from



2015).

Crash cost (CC)

(/km)

AU$ 0.025 for both LV and HV

Environmental and

external cost (EEC)

(/km)

AU$ 0.14 AU$ 1.02 Proportions of cars, light

commercial vehicle and heavy

vehicle were obtained from



2015).

5.5 RESULTS AND DATA SYNTHESIS

Table 5.6 summarises the calculation of deterministic impacts of the synthesised case

when all variables were equal to the expected values of their stochastic distributions.

The benefit that most contributed to the overall benefit was the travel time saving,

which is impacted by the risks of annual average daily traffic (AADT), traffic growth,

travel time unit price and vehicle hours travelled saving (VHTS).


Table 5.6 Impacts of the synthesised toll tunnel case when all variables were deterministically equal to

their expected values in present value

Project impact Amount Proportion

Travel time saving AU$ 1,553,975,403 83.9 %

Vehicle operating cost (VOC) saving AU$ 209,578,588 11.3 %

Crash cost (CC) saving AU$ 5,927,046 0.3 %

Environmental and external cost (EEC) saving AU$ 55,623,491 3.0 %

RV AU$ 27,243,077 1.5 %

Total saving of transport costs AU$ 1,852,347,605 -

Capital cost AU$ 1,500,000,000 90.9 %

Operation and maintenance (O&M) cost AU$ 150,000,000 9.1 %

Total cost AU$ 1,650,000,000 -

Net present value AU$ 202,347,605 -

BCR 1.12 -

For the purpose of determining impacts of each risk, Benefit-Cost Ratio (BCR)

distributions were calculated individually, using a combination of deterministic

variables and stochastic variables. Table 5.7 summarises by row the statistical

measures of risk profile identified previously, as each input variable was varied

according to its defined distribution, while in each case holding all other variables at

their expected value. Note that the bottom row reflects these measures when all input

variables were varied according to their defined distributions.


Table 5.7 Risk profiles of output Benefit-Cost Ratio (BCR) distribution as input variable was

distributed stochastically

All variables held to

expected value aside from: Mean Median CV Skew

Probability of

BCR above 1.0

Capital cost 1.13 1.17 8 % -1.61 90 %

AADT 1.12 1.12 10 % -0.02 88 %

Traffic growth 1.12 1.12 4 % 0.09 100 %

HV% 1.12 1.12 1 % 0.01 100 %

Travel time unit price 1.12 1.12 28 % 0.02 66 %

VOC unit price 1.12 1.12 2 % 0.00 100 %

CC unit price 1.12 1.12 <0 % 0.00 100 %

EEC unit price 1.12 1.12 <0 % 0.00 100 %

VKTS 1.12 1.12 1 % 0.00 100 %

VHTS 1.12 1.12 17 % 0.00 75 %

All 1.13 1.09 36 % 0.61 60 %

When each non-skewed distribution was applied alone, the mean value of the

BCR distribution was found to equal the deterministic BCR of 1.12. However, when

capital cost distribution, which is a skewed Cowan’s M3 model, was applied alone, the

mean value of the BCR value was found to be slightly higher at 1.13. This is correct,

as point BCR is mathematically biased under a ratio distribution containing a skewed

distribution. When all distributions were applied, the mean value of the BCR

distribution was also found to equal 1.13, due to the presence of the skewed capital

cost distribution.

The skew of the BCR distribution of capital cost was noticeably negative due to

the application of Cowan’s M3 distribution. When travel time unit price contains a

high risk, or all variables contain risks, the probability of BCR above 1.0 was

noticeably lower. This indicates that although when mean BCR is higher than 1.0, the

risk can be considerably high when these risks exist. When risks of all input variables

were incorporated, the outcome BCR distribution was slightly skewed.


Figure 5.1 illustrates box plots of BCR distributions with each variable varied

stochastically, and all variables varied stochastically. Medians vary due to the

skewness of the capital cost distribution as discussed above. Coefficient of variation

(CV) noticeably affected the outcome BCR distribution for the following variables in

order of influence; travel time unit price, VHTS, AADT, capital cost, and traffic

growth. CV minimally affected the outcome BCR distribution for the following

variables in order of influence; vehicle operating cost (VOC), proportion of heavy

vehicle (HV%), vehicle kilometres travelled saving (VKTS), environmental and

external cost (EEC), and crash cost (CC). When risks of all input variables were

incorporated, CV was significantly high, which indicates the high risk associated with

the synthesised case.

Figure 5.1 Box-and-whisker plots of Benefit-Cost Ratio (BCR) with each variable varied

stochastically, and all variables varied stochastically


It is apparent from Table 5.7 and Figure 5.1 that, when risks of all input variables

are incorporated into the stochastic analysis, there is considerable spread in the

outcome BCR distribution, and therefore considerable risk exists within the case to

proceed. In fact, there is only a 60 percent chance that the synthesised case would

achieve a net positive impact to the community. Additionally, there are many possible

combinations of input variables that would lead to the project delivering a very poor

BCR. Given the scale of such a project, the results of this analysis demonstrate that it

would be unwise not to undertake stochastic analysis within Cost-Benefit Analysis

(CBA), or detailed multi-way sensitivity analysis at a minimum, to assist in decision

making.

5.6 DISCUSSION

Salling and Leleur (2011) introduced an innovative methodology by combining Cost-

Benefit Analysis (CBA) and the Monte Carlo simulation. In comparison, this study

stochastically modelled a wider variety of input variables of CBA, which allowed the

different characteristics of each variable to be portrayed. Moreover, this study

examined the impacts of each source of risk on the analysis outcome.

Although, Asplund and Eliasson (2016) claim that transport investment and

transport demand risks affect the CBA results the most, the results showed that the

risks of travel time unit price, VHTS, and traffic growth, as well as AADT and capital

cost affected the outcome BCR the most. This is a consistent finding with the finding

that the travel time savings most contributed in the overall benefits.

Capital cost significantly impacts overall cost of the project. This indicates that

when the host government is not responsible for capital cost, such as build-operate-

transfer (BOT) and design-build-finance-operate (DBFO) schemes, a high overall

Benefit-Cost Ratio (BCR) can result. Complex inter-relationship of the length of

planning horizon, risk-sharing arrangements, discount rates and BCR of a toll road

project is the subject of further investigation.

Travel time saving is a multiplication of annual average daily traffic (AADT),

traffic growth, travel time unit price and vehicle hours travelled saving (VHTS). Each

of these variables has a unique risk that needs to be portrayed in its probability

distribution. When risks are underestimated or ignored in the analysis, interpretations

of the BCR would be limited. Moreover, travel time saving resulted in the highest


benefit. This indicates the importance of conducting sensitivity analysis on all the input

variables that influence the travel time saving, such as AADT and VHTS.

The reliability of the results depends on the reliability of predefined probability

distribution applied for each input variable in the Monte Carlo simulation. Further

study is needed to determine appropriate probability distributions for each variable,

particularly for those with significant impacts on the outcome BCR. For instance, Bain

(Bain, 2009) conducted a study of traffic forecast errors of toll road projects and

identified its probability distribution.

Additionally, it was beyond the scope of this study to consider the correlations

between input variables. For instance, there may be some correlations between VHTS

and vehicle kilometres travelled saving (VKTS). Although the impact of the risk of

VKTS was found to be minimal and the correlation between these two variables may

not be a significant consideration for the purpose of this study, further study to

investigate the correlations of various variables can be useful.

For any Public-Private Partnership projects, the appropriate discount rate needs

to be carefully selected when the project risk is shared. Depending on the risk

allocations, in project evaluation, the systematic risk premium may require adjustment

to reflect the proportion of risks that the public sector is bearing (Australian

Department of Infrastructure and Regional Development, 2013). A lower, risk-free

rate should be used when all of the systematic risks are borne by the private sector

(Australian Department of Infrastructure and Regional Development, 2013). In

practice, the planning horizon is the same as the duration of the toll concession period

of the project. This is because the scope of the evaluation is predefined as the viability

of the project over the whole concession period. Depending on the discount rate

applied, the net impact of the project will depreciate to negligible amounts after a

certain number of years. Discount rate and planning horizon are thus inextricably

linked and require careful consideration.

5.7 SUMMARY

This chapter explored the impacts of risks of each input variable on the analysis

outcome. A synthesised toll tunnel project case was examined, in order to explore

various risk scenarios. The Monte Carlo simulation approach was used in the Cost-

Benefit Analysis (CBA) case to quantify the risks of input variables. The distribution


of a stochastic Benefit-Cost Ratio (BCR) set was analysed to develop a risk profile of

the synthesised case. Risk profiles of the outcome BCR distributions under various

risk scenarios were examined to determine the risk that most impacted the net impact

to the community.

The results showed that the risks of capital cost, annual average daily traffic

(AADT), travel time unit price and vehicle hours travelled saving (VHTS)

significantly impacted the outcome BCR distribution, while the effect of other risks

were minimal. This suggests that these risks need to be properly assessed in project

evaluation of a toll road project. Furthermore, this also suggests strong emphasis on

further research in the fields of transport economics and traffic modelling to reduce

risks of these variables, from the viewpoint of conducting reliable project evaluation.

The proportion of BCR trials greater than 1.0 was shown to be useful in the

decision making, however it may not be as useful when BCR is highly likely to be

greater than 1.0 across all scenarios. The probability corresponding to any BCR value

can be found, which would assist decision-makers based upon their objectives and

policies. Mean and coefficient of variation (CV) provided extremely useful means of

profiling risk using the study approach. Box plots effectively illustrated the

characteristics of each risk, including its spread and skew.

Comprehensive risk profiles of the impacts to the community can be formed by

combining CBA and the Monte Carlo simulation approach. This is particularly useful

to the host government as the decision-maker. Moreover, the methodology is highly

practical and can be incorporated by various practitioners involved in project

evaluation and decision making of public investments. The developed risk profiles are

particularly useful when the project is in the planning phase and risks are substantial.

Chapter 6: Evaluating a Toll Tunnel Project 77

Chapter 6: Evaluating a Toll Tunnel Project

The private sector is often involved in toll road projects, including various schemes to

design, build, operate and/or finance the project either in a partnership with a host

government, independently, or in some combination. Involvements of the private

sector require careful allocations of project impacts when conducting Cost-Benefit

Analysis (CBA), in order to properly reflect the net impact to the community. The aim

of this chapter is to investigate whether alternative assumptions in CBA are valid from

differing perspectives, when toll roads are delivered and operated privately rather than

by a host government. Treatments of tolls and other toll road project-related payments

are considered from different perspectives. CBA is conducted for the previously

synthesised toll tunnel project case by alternating treatments of some impacts. This

leads to the exploration of CBA outcomes when the treatment of tolls differ when two

perspectives of “toll as a transfer payment” (TT) and “toll as an end-user cost” (TC)

are considered. The Monte Carlo simulation approach is used to account the risks of

various variables in the CBA.

6.1 EXAMINING PERSPECTIVES

6.1.1 Hypothesis

Traditionally, when Cost-Benefit Analysis (CBA) is used to evaluate toll projects, tolls

have been assumed to be, and therefore treated, as financial transfers and not counted

as societal costs; instead the capital cost, and operation and maintenance (O&M) cost

are those which are treated as the societal cost impacts. As Decorla-Souza (2013)

claimed, toll revenues are generally only included in CBA by affecting travel

behaviours and efficiencies in the transport system. This is rational when the host

government obtains the toll revenues, because the end-users who pay those tolls are

constituents of the host government and therefore enjoy the benefit of that toll revenue

through government expenditure, including repayment of project debt. However,

depending on the perspective taken within decision making, the influences of each

impact need to be considered carefully in CBA, especially when the private sector is

involved in the project. This is particularly so for cost impacts that are borne by the

private sector.

78 Chapter 6: Evaluating a Toll Tunnel Project

If one takes the perspective that the private operator is an element of an overall

economy that bears the costs and reaps the benefit of a project, then the assumption of

the toll as a transfer payment is appropriate. However, for public sector decision

making, an alternative perspective can be that the host government, as the decision-

maker on behalf of the community, should consider how the end-users bear the cost

and reap the benefit of a project. Under such an assumption, cost impacts are

considered to be recouped in a commercial environment by a private operator charging

tolls to the end-users, who in the case of a toll road, are normally the host government’s

constituents. Therefore, from this perspective, those end-user toll charges should be

counted as the societal cost impacts in CBA. Furthermore, it is reasonable to contend

that any capital and/or O&M costs that are borne by the private operator should be

excluded from the CBA, because they are financial impositions that are contained

within the private operator’s enterprise of offering services to consumers, rather than

as an end-user societal cost. The effect of this perspective is that the private operator

is sequestered from the overall economy, such that the host government can evaluate

the project independent of the private operator’s financial interests. Notwithstanding,

these considerations become more entangled when a toll road project is delivered in

some form of Public-Private Partnership (PPP).

The principal rationale of the “toll as an end-user cost” (TC) perspective is that

it may enable the public decision-maker to understand how the risk profile to the end-

user community as expressed by the CBA might differ from that of the risk profile

among the overall economy, including the toll operator, under the “toll as a transfer

payment” (TT) perspective. The remainder of this chapter uses a case-study approach

to examine the extent to which CBA results vary between these two perspectives.

6.1.2 Consideration of Cost Formats of Toll Road Projects

Commonly for a toll road project, concession deeds may include various risk-sharing

arrangements. For instance, a minimum revenue guarantee arrangement enables the

private operator to mitigate its traffic uncertainty risk with its financial obligations,

through a mechanism where the host government effectively acts as a guarantor should

revenue fall short of the private operator’s required debt repayment during a period.

Alternatively, the host government may permit the private operator to charge a higher

toll price, in order to balance its traffic uncertainty risk with its financial obligation of

debt repayment. Additionally, an upfront capital cost contribution may be paid by the


host government to support the start-up of the project; the amount depending upon its

budgetary and political priorities. These arrangements are forms of risk management

strategy and need to be considered carefully in project evaluation. Therefore, the CBA

for the purpose of evaluation of toll road projects needs to appropriately account for

these impacts and their risks.

Figure 6.1 summarises the payment movement when a toll road project is fully

delivered by the host government. When the host government is designing, building,

financing and operating the toll road, including through traditional methods of

purchasing from the private sector, they are responsible for capital cost and O&M cost,

while the road users, who are its constituents, are paying for tolls. In this scenario, the

tolls and any imposed consumption taxation can be considered as a financial transfer

between the public toll operator and the users. Moreover, a part of the tolls paid by

commercial vehicles can be considered as financial transfers as they are paid back by

the end-users. However, these are considered as part of the wider economic impacts

and not considered in this study, as with other wider economic impacts that are

generally excluded from the CBA of major road projects.

Figure 6.1 Payment movement of when toll roads are delivered and operated by the host government

from the “toll as a transfer payment” (TT) perspective

Figure 6.2 summarises the payment movement when a toll road is fully designed,

built, financed and operated by a private sector entity or entities. For purposes of this

study, the private sector entities are collectively termed as the private operator. The

host government may be responsible for an upfront capital cost contribution and

minimum revenue guarantee if included in the concession deed. The private operator

is responsible for the balance of capital cost and O&M cost, while collecting tolls and

receiving any minimum revenue guarantee payments from the host government. In this

scenario, tolls are hypothesised to no longer be a financial transfer under the TC


perspective, because the sum of upfront capital cost contribution and the guarantee

would not be equivalent to the capital and O&M costs. Hence, costs to the community

that need to be accounted in CBA in this scenario are the upfront capital cost

contribution, any minimum revenue guarantees, and the tolls paid as end-users, net of

any imposed consumption taxation.

Figure 6.2 Payment movement of when toll roads are delivered and operated privately from the “toll

as an end-user cost” (TC) perspective

As introduced in the discussion above, another scenario that could apply to a toll

road is that when the private operator charges a premium toll price instead of receiving

a minimum revenue guarantee from the host government. In this scenario, the premium

tolls paid by the users replace the minimum revenue guarantee in CBA. This is

summarised in Figure 6.3.


Figure 6.3 Payment movement of when the private operator charges premium tolls from the TC

perspective

Moreover, a number of other risk-sharing arrangements can be found with toll

road projects, which for brevity are not dealt with in this study. The impacts of the

arrangements need to be considered carefully for each project to determine whether

they need to be counted as costs to the community in CBA.

6.1.3 Perspectives Considered for this Study

As has been previously discussed, a toll road project can be delivered under various

schemes. Two perspectives that consider all of the scenarios that were previously

discussed, and highlight the difference in terms of payment movements between the

host government and the private operator, are examined in this study. The first is the

TT perspective, whereby the toll road project is fully delivered and operated by the

host government at a baseline toll price, therefore the total cost to the community is

the sum of capital cost and O&M cost. Then, three scenarios of the TC perspective are

considered. The first scenario is a “baseline toll with no guarantee” (BNG), whereby

the private operator receives no minimum revenue guarantees and charges users the

baseline toll price, which is further detailed in a later section. The second scenario is a

“baseline toll with minimum revenue guarantee” (BRG), whereby the private operator

charges users the baseline toll price, however receives a minimum revenue guarantee

from the host government, where necessary, in a given period. The third scenario is a

“premium toll with no guarantee” (PNG), whereby the private operator charges a

higher toll price than baseline, instead of receiving the minimum revenue guarantee.

Table 6.1 summarises the costs considered in CBA across the perspectives and

scenarios.


Table 6.1 Costs to the community that are considered in Cost-Benefit Analysis (CBA) for this study

Perspective Scenario Costs considered

Toll as a transfer payment (TT) Capital and O&M costs

Toll as an end-user

cost (TC)

Baseline toll, no guarantee

(BNG)

Baseline tolls paid by end-users and

upfront capital cost contribution

Baseline toll, minimum

revenue guarantee (BRG)

Guarantee payment, baseline tolls paid by

end-users and upfront capital cost

contribution

Premium toll, no guarantee

(PNG)

Premium tolls paid by end-users and

upfront capital cost contribution

For the purpose of CBA, this study presumes that each CBA was undertaken

before the end of construction of the toll road. Therefore, risks exist in the variables

that are determined from traffic modelling and estimations of project-related costs.

These variables include annual average daily traffic (AADT), traffic growth, vehicle

hours travelled saving (VHTS), vehicle kilometres travelled saving (VKTS),

proportion of heavy vehicles (HV%), capital cost, and O&M cost. The risks of these

variables are accounted in CBA using the Monte Carlo approach, as discussed in the

following section.

6.2 METHODOLOGY

Figure 6.4 presents the methodology of the evaluation of the synthesised toll tunnel

project case. It consists of three phases, which are explained in the following section.


Figure 6.4 Methodology of the evaluation of the synthesised toll tunnel project case

6.2.1 Identifying Concession Payments and Costs

The concession payments and costs that are involved in a toll road project need to be

first identified. The movements of these payments and costs can then be examined to

identify the entity that is responsible for each payment and cost. Costs that the host

government and its constituents are responsible for, can be determined on the basis of

the payment movements. The costs that can be considered as financial transfers would

be excluded in this process. Total costs in stochastic forms can be developed using the

Monte Carlo simulation, in order to reflect their risks to be incorporated in the

evaluation. Meanwhile, models can be developed for each payment and cost.

6.2.2 Estimation of Benefit

Input variables of benefits considered in CBA, such as vehicle hours travelled saving

(VHTS), need to be determined based on the characteristics of the toll road. Each

benefit in a stochastic form can then be developed on the basis of probability

distribution forms of input variables using the Monte Carlo simulation, in order to

reflect their risks to be incorporated in the evaluation. The benefits that are generally

considered in CBA of a major road project include travel time saving, vehicle

operating cost saving, crash cost saving, environmental and external cost saving, and

residual value (RV).


6.2.3 Evaluation and Decision Making

The stochastic Benefit-Cost Ratio (BCR) distribution can be developed based on the

stochastic costs and benefits. The outcome BCR distributions represent the net impacts

and risks of the project. Moreover, risk profiles can be developed for the toll road

project on the basis of the stochasticity of the outcome BCR distributions.

6.3 MODEL DEVELOPMENT

6.3.1 Traffic Volume and Growth

For purposes of clarity of this study, traffic volume is calculated yearly, based on the

initial annual average daily traffic (AADT) and traffic growth rate. The traffic growth

rate is presumed to be constant over the whole planning horizon. The AADT at year 𝑦

for Monte Carlo trial 𝑗 is given as follows:

𝐴𝐴𝐷𝑇𝑦,𝑗 = 𝐴𝐴𝐷𝑇𝑖,𝑗 × (1 + 𝑔𝑗)(𝑦−1)

(6.1)

Where:

𝐴𝐴𝐷𝑇𝑖,𝑗 = initial average annual daily traffic for Monte Carlo trial 𝑗 (veh)

𝑔𝑗 = traffic growth rate for Monte Carlo trial 𝑗 (%)

𝑦 = corresponding year, 𝑦(0, 1, … , 𝑛)

It is noted that this case study does not expressly consider a ramp-up period,

which often occurs post-opening of a toll road. During the ramp-up period, the traffic

growth rate can be different from, and typically less than, the annual growth rate as the

road matures as a component of the greater road network. However, a ramp-up period

may also be readily incorporated in future research.

6.3.2 Baseline Toll Price

The baseline toll price is determined on the basis of the expected traffic volume, while

the project cost contains risks as the baseline toll price is determined before the

completion of the construction. This study presumes that this baseline toll price would

be incorporated into traffic modelling to estimate AADT, traffic growth, VHTS, VKTS

and HV%, therefore the risks of these variables are not accounted in the baseline toll

price model.


The baseline toll price is that which equates the sum of the net capital cost after

upfront capital cost contribution is deducted, and the operating and maintenance

(O&M) cost, to the expected value of all collected tolls when brought to net present

value. It is important to note that this is not a financial analysis and does not ensure

that the private operator will recover its cost and yield a profit during the planning

horizon. Rather, it is that particular toll price which, under the expected opening year

AADT and the expected traffic growth rate, would yield the same project cost to the

community as a public road with the same total capital plus O&M cost. That is, under

the “toll as an end-user cost” (TC) perspective, the present value of its expected cost

would the same as that under the “toll as a transfer payment” (TT) perspective. It is a

starting point for consideration of various payments considered in the scenarios, which

are detailed later. The baseline toll price is calculated as follows:

∑[𝑇𝑃𝑏𝑎𝑠𝑒,𝑗 × 𝐴𝐴𝐷𝑇𝑦,𝑒𝑥𝑝 × 365 × (1 + 𝑑)(1−𝑦)]

𝑛

𝑦=1

= (𝐶𝑎𝑝𝑗 + 𝑂&𝑀𝑗 − 𝑈𝑃)

(6.2)

𝑇𝑃𝑏𝑎𝑠𝑒,𝑗 =(𝐶𝑎𝑝𝑗 + 𝑂&𝑀𝑗 − 𝑈𝑃)

∑ [𝐴𝐴𝐷𝑇𝑦,𝑒𝑥𝑝 × 365 × (1 + 𝑑)(1−𝑦)] 𝑛

𝑦=1

(6.3)

𝑇𝑃𝑏𝑎𝑠𝑒 =∑ (𝑇𝑃𝑏𝑎𝑠𝑒,𝑗) 𝑙𝑗=1

𝑙

(6.4)

Where:

𝑛 = number of years in planning horizon

𝑇𝑃𝑏𝑎𝑠𝑒,𝑗 = baseline toll price for Monte Carlo trial 𝑗 ($)

𝐴𝐴𝐷𝑇𝑦,𝑒𝑥𝑝 = expected AADT with expected traffic growth at year 𝑦 (veh)

𝑑 = discount rate applicable to the project format (%)

𝐶𝑎𝑝𝑗 = total capital cost in present value for Monte Carlo trial 𝑗 ($)


𝑂&𝑀𝑗= total O&M cost over the whole planning horizon for Monte Carlo trial

𝑗 as a present year cost ($)

𝑈𝑃 = upfront payment to capital cost ($)

𝑙 = number of Monte Carlo trials

When the host government contributes a set upfront capital cost contribution

towards the gross capital cost, the risk of capital cost is borne by the private operator

through the net capital cost that it contributes after the upfront capital cost contribution

is deducted. This study presumes the upfront capital cost contribution to be

deterministic and one third of the expected capital, and operation and maintenance

(O&M) costs. Notwithstanding, the Cost-Benefit Analysis (CBA) presented here could

be modified to allow for a variable upfront capital cost contribution to be made by the

host government.

As shown in Equation 6.2, the baseline toll price is not influenced by the price

that the toll operator or the host government would charge. Rather, it is an optimal toll

price under the assumptions of this study for the purposes of CBA. Sensitivity analysis

could readily be performed on the impact of different baseline toll prices on the

stochastic Benefit-Cost Ratio (BCR) distribution.

6.3.3 Minimum Revenue Guarantee

For purposes of this case study, the minimum revenue guarantee is defined as a

payment paid by the host government to the private operator in any year of the planning

horizon, when the toll revenue of that year is less than the payment that is required of

the private operator to meet its obligations to its financier. For the illustrative purposes

of this study, these are limited to its principal plus interest repayments. The following

annual finance repayment by the private operator for Monte Carlo trial 𝑗 is assumed:

𝑃𝑎𝑛𝑛𝑢𝑎𝑙,𝑗 =𝑟(𝐶𝑎𝑝𝑗 + 𝑂&𝑀𝑗 − 𝑈𝑃)

1 − (1 + 𝑟)−𝑛

(6.5)

Where:

𝑟 = financier’s interest rate on the private entity’s loan (%)


This study presumes the financier’s interest rate on the private entity’s loan as

5.0 %. Under the minimum revenue guarantee scenario, any necessary amount of

guarantee payment is calculated uniquely for each period (year) of a Monte Carlo trial

of traffic volume to incorporate the risk of the guarantee payment and the traffic

uncertainty risk in its CBA. This allows the development of a stochastic distribution

of the guarantee payment. The guarantee payment at year 𝑦 for Monte Carlo trial 𝑗 is

given as follows:

𝐺𝑦,𝑗 = 𝑚𝑖𝑛 {(𝐴𝐴𝐷𝑇𝑦,𝑗 × 365 × 𝑇𝑃𝑏𝑎𝑠𝑒) −

𝑃𝑎𝑛𝑛𝑢𝑎𝑙,𝑗(1 + 𝑖)(𝑦−1)

0

(6.6)

Where:

𝑖 = annual rate of inflation in the economy (%)

This study presumes 1.3 % (Reserve Bank of Australia, 2016) as the annual rate

of inflation in the economy.

6.3.4 Premium Toll

The premium toll price is defined in this study as that which the host government

permits the private operator to charge, such that it is expected to earn sufficient revenue

that a minimum revenue guarantee by the host government is not warranted during any

year of the planning horizon, irrespective of traffic uncertainty risk. In this scenario,

stochastically simulated traffic volumes of each Monte Carlo trial, which were

produced under the minimum revenue guarantee scenario, are unknown, as evaluation

of these two scenarios are conducted independently. However, the expected guarantee

payment amount is known, because it can easily be estimated before the completion

of construction of the road. A number of approaches exist to estimate the expected

guarantee payment. For purposes of this study, the stochastic guarantee payment

distribution that was developed under the minimum revenue guarantee scenario was

implemented as the expected guarantee payment. The price of premium toll is

therefore determined on the basis of the expected guarantee payment amount, while

incorporating the traffic uncertainty risk.


It is first necessary to calculate the premium toll coefficient. This is the ratio of

the cost that is borne by both the host government and the private operator, to that

which is borne by the private operator alone, under the minimum revenue guarantee

scenario. The premium toll coefficient for Monte Carlo trial 𝑗 is given as follows:

𝑅𝑗 =𝑇𝑃𝑏𝑎𝑠𝑒∑ [𝐴𝐴𝐷𝑇𝑦,𝑗×365×(1+𝑑)

(1−𝑦)]𝑛𝑦=1 +∑ [𝐺𝑦,𝑒𝑥𝑝×(1+𝑑)

(1−𝑦)]𝑛𝑦=1

𝑇𝑃𝑏𝑎𝑠𝑒 ∑ [𝐴𝐴𝐷𝑇𝑦,𝑗×365×(1+𝑑)(1−𝑦)]𝑛

𝑦=1

(6.7)

Where:

𝐺𝑦,𝑒𝑥𝑝 = expected guarantee payment at year 𝑦 in present value ($)

Using this premium toll coefficient, the premium toll price for Monte Carlo trial

𝑗 that would compensate for the absence of minimum revenue guarantee, is therefore

given as follows:

𝑇𝑃𝑝,𝑗 = 𝑇𝑃𝑏𝑎𝑠𝑒 × 𝑅𝑗 (6.8)

Literature (Poole, 2011) has identified that toll price may influence traffic

volume and growth, as some users may be tolled off as toll price increases. For clarity,

this study does not address this micro-economic behaviour, however, elasticity

between toll price and traffic volume may also be considered in future research.

Further, the premium toll is not necessarily that which the private operator would

wish to charge, or which the host government would regulate as a cap. Rather, it is an

optimal toll price under the assumptions of this study, for the purposes of CBA.

Sensitivity analysis could also readily be performed on the impact of different toll

prices on the stochastic BCR distribution.

6.4 RESULTS

6.4.1 Evaluation and Decision making of the Synthesised Toll Tunnel Project

Case

Table 6.2 summarises the risk profiles of the synthesised case. The perspectives of

“toll as a transfer payment” (TT) and “toll as an end-user cost” (TC), and the scenarios

of “baseline toll with no guarantee” (BNG), “baseline toll with minimum revenue


guarantee” (BRG) and “premium toll with no guarantee” (PNG) were considered. All

perspectives and scenarios showed similar results of the expected Benefit-Cost Ratio

(BCR) between 1.06 and 1.03. This similarity can be explained by the assumptions

used in the model development in this study. The outcome BCR may differ when

different assumptions are applied. The expected BCR and medians of BCR across

perspectives and scenarios showed great similarities, which indicate that sufficient

Monte Carlo trials were conducted for each scenario. The TT perspective showed the

highest coefficient of variation (CV) and the scenario BNG showed the highest

probability of BCR being greater than 1.0.

Table 6.2 Risk profiles of the synthesised toll tunnel project case across perspectives and scenarios

Perspective Scenario Expected

BCR Median CV

Proportion

of BCR

trials

greater than

1.0

Toll as a transfer payment (TT) 1.06 1.05 21% 59%

Toll as an end-

user cost (TC)

Baseline toll, no guarantee

(BNG)

1.05 1.05 17% 61%

Baseline toll, minimum

revenue guarantee (BRG)

1.03 1.03 18% 57%

Premium toll, no guarantee

(PNG)

1.03 1.03 17% 57%

Figure 6.5 shows the cumulative stochastic BCR distributions of the synthesised

case. The cumulative graph clearly illustrates the wider spread in the BCR distribution

under the TT perspective.


Figure 6.5 Cumulative stochastic Benefit-Cost Ratio (BCR) distributions of the synthesised toll tunnel

project case

Figure 6.6 shows box-and-whisker plots of the stochastic BCR distributions of

the synthesised case. The 9th and 91st percentiles were used as the minimum and the

maximum of the whiskers to effectively highlight the characteristics of each

distribution.


Figure 6.6 Box-and-whisker plots of stochastic BCR distributions of the synthesised toll tunnel project

case

6.4.2 Examination of Perspectives

Comparison of the perspectives of TT and TC under the scenario BNG reveals nearly

identical expected values of BCR. However, from the perspective of the TC, the CV

is noticeably lower. This is also evident in Figure 6.5 and Figure 6.6. This indicates

that by sequestering the toll operator from the overall economy, there is less volatility

in BCR, and therefore less risk borne by the remainder of the community. It follows

that the risk represented by the difference in CV is borne by the toll operator.

Comparison of the perspectives of TT and TC under the scenario BRG reveals a

slightly lower expected value of BCR. This is because of the additional payments made

by the host government to guarantee the minimum revenue. Again, from the

perspective of the TC, the CV is noticeably lower. This is also evident in Figure 6.5

and Figure 6.6. This indicates that by sequestering the toll operator from the overall

economy, there is less volatility in BCR, and therefore less risk borne by the remainder


of the community. While it follows that the risk represented by the difference in CV is

borne by the toll operator, the minimum revenue guarantee payments to some extent

mitigate the toll operator’s risk. On the other hand, the reduction in the expected BCR

indicates that the project is less attractive to the remainder of the community than if

there were no minimum revenue guarantee.

Comparison of the perspectives of TT and TC under the scenario PNG also

reveals a slightly lower expected value of BCR. This is also because of the higher tolls

paid by the end-users. Again, from the perspective of the TC, the CV is noticeably

lower. This is also evident in Figure 6.5 and Figure 6.6. This indicates that by

sequestering the toll operator from the overall economy, there is less volatility in BCR,

and therefore less risk borne by the remainder of the community. While it follows that

the risk represented by the difference in CV is borne by the toll operator, the premium

toll to some extent mitigates the toll operator’s risk. On the other hand, the reduction

in the expected BCR indicates that the project is less attractive to the remainder of the

community than if there were no premium toll.

6.4.3 Sensitivity Analysis of Variation in Risk Characteristics

The previous chapter found that the impacts of the risks of annual average daily traffic

(AADT), traffic growth rate, capital cost and savings on vehicle hours travelled

(VHTS) on the outcome BCR are considerable. BCR distributions were calculated

individually, using a combination of deterministic variables and each of these variables

treated as stochastic to show the vulnerability across perspectives and scenarios against

the risks of the variables. For instance, a scenario with larger CV than other scenarios

when a particular variable is treated as stochastic indicates that the scenario is more

vulnerable to the risk of the variable.

Table 6.3 summarises risk profiles across perspectives, as each input variable

was varied according to its defined distribution, while in each case holding all other

variables at their expected value. When the AADT and the traffic growth rate variables

were treated as stochastic, the difference of CV between the perspective TT and TC

became more apparent. This indicates that the perspective TT is more vulnerable to

the AADT and the traffic growth risks than other risks. When the capital cost variable

was treated as stochastic, the expected BCR for BRG and PNG scenarios significantly

decreased in comparison to when all variables were treated as stochastic. Scenarios

BNG and PNG showed no spread and this indicates that the capital cost risk does not


influence the outcome BCR under these scenarios. When the VHTS variable was

treated as stochastic, again, the expected BCR for BRG and PNG scenarios

significantly decreased in comparison to when all variables were treated as stochastic.

CV across perspectives and scenarios showed equal values when the VHTS variables

was treated as stochastic.

Table 6.3 Risk profiles of the synthesised toll tunnel project case across perspectives and scenarios

when each variable was treated as stochastic

Stochastic

variable Perspective Scenario

Expected

BCR Median CV

Proportion of BCR

trials greater than

1.0

Annual

average

daily

traffic

(AADT)

and traffic

growth

rate

TT 1.05 1.05 9% 70%

TC BNG 1.05 1.05 3% 92%

BRG 1.04 1.04 4% 82%

PNG 1.04 1.04 3% 87%

Capital

cost

TT 1.06 1.10 8% 80%

TC BNG 1.05 1.05 <0% 100%

BRG 0.93 0.95 5% <0%

PNG 0.92 0.92 <0% <0%

Vehicle

hours

travelled

saving

(VHTS)

TT 1.05 1.05 17% 62%

TC BNG 1.05 1.05 17% 62%

BRG 0.92 0.92 17% 32%

PNG 0.92 0.92 17% 32%

Figure 6.7 to Figure 6.12 show the cumulative stochastic BCR distributions, and

box-and-whisker plots of the synthesised case when each variable is treated as

stochastic. The 9th and 91st percentiles were used as the minimum and the maximum

of the whiskers to effectively highlight the characteristics of each distribution. The

cumulative graph and the box-and-whisker plots clearly illustrate the wider spread in


the BCR distribution under the TT perspective. The perspective TT and the scenario

BNG showed the same distribution characteristics when VHTS is treated as stochastic.

Scenarios BRG and PNG showed the same distribution characteristics that are

different from the perspective TT and the scenario BNG when VHTS is treated as

stochastic.

Figure 6.7 Cumulative stochastic Benefit-Cost Ratio (BCR) distributions of the synthesised toll tunnel

project case when the annual average daily traffic (AADT) and the traffic growth rate variables are

treated as stochastic

Figure 6.8 shows larger CV under the TT perspective. This indicates that the TT

perspective is most vulnerable compared to the TC perspective, when AADT and

traffic growth rate contain risks.


Figure 6.8 Box-and-whisker plots of stochastic BCR distributions of the synthesised toll tunnel project

case when the AADT and the traffic growth rate variables are treated as stochastic

Figure 6.9 Cumulative stochastic BCR distributions of the synthesised toll tunnel project case when

capital cost variable is treated as stochastic


Figure 6.10 shows large CV under the TT perspective and minimal CV under

the BNG and PNG scenarios. This indicates that the TT perspective is most vulnerable

compared to the TC perspective, when capital cost contains risks. Additionally, the

BNG and PNG scenarios are insensitive to the capital cost risks.

Figure 6.10 Box-and-whisker plots of stochastic BCR distributions of the synthesised toll tunnel

project case when capital cost variable is treated as stochastic


Figure 6.11 Cumulative stochastic BCR distributions of the synthesised toll tunnel project case when

vehicle hours travelled saving (VHTS) variable is treated as stochastic

Figure 6.12 shows equivalent sizes of CV under all perspectives and scenarios.

This indicates that the VHTS risks influence the outcomes across all perspectives and

scenarios.


Figure 6.12 Box-and-whisker plots of stochastic BCR distributions of the synthesised toll tunnel

project case when VHTS variable is treated as stochastic

The TT perspective resulted in high CV across all variables tested. This indicates

that the TT perspective is vulnerable to AADT, traffic growth rate, capital cost and

VHTS risks. While, CV varied across all variables tested under the TC perspective.

This indicates that the outcome risk profile varies depending on the sources of the risks

that the project contains.

6.5 DISCUSSION

The results suggested that treating tolls as an end-user cost in Cost-Benefit Analysis

(CBA) of a privately operated toll road project is a reasonable and valid approach

under the “toll as an end-user cost” (TC) perspective. Notwithstanding this, the

treatment of other payments of the toll road project need to be considered carefully.

When a toll road project is fully delivered by the host government, the public is

bearing the whole project risk. In contrast, when a toll road project is designed, built,

operated and/or financed by a private operator, a part of risks is shifted to the private

operator. When evaluating a toll road project with respect to a public good, the impacts

and their risks that are borne by the host government on behalf of its constituents

should be considered in the evaluation. In this regard, the risk that is borne by the


private operator can effectively be sequestered from the evaluation under the TC

perspective. Therefore, the evaluation of a privately operated toll road project can

reflect this shift of the risk between the host government and the private operator under

the TC perspective.

Risk profiles of “toll as a financial transfer” (TT) and TC perspectives

appropriately reflected the shift of risk in two scenarios. Coefficient of variation (CV)

was lowered in the TC risk profile, which indicates the shift of the risk.

Due to the assumptions applied in the models used in this study, the risk profiles

of three scenarios of TC perspective did not show significant variations with respect

to the risk. Applications of various assumptions, in order to model toll road project

specific payments would further extend the knowledge in terms of how the risk varies

with each assumption.

The synthesised case was found to benefit the community between 57% and 61%

of trials, depending on perspectives and scenarios. The decision-maker may consider

it risky to proceed due to the risk that is quantified, which indicated a reasonably high

probability of BCR being less than 1.0.

The calculated baseline toll price for this synthesised case was $ 4.89. This is

slightly lower than the car toll prices of the existing toll road tunnels in Brisbane,

Australia, the Clem Jones Tunnel and Legacy Way, which are $ 4.93 to $ 4.94,

including goods and services tax (Australian Department of Infrastructure and

Regional Development, 2013). This difference can be explained by the difference

between the theoretical assumptions and the higher complexities of the real toll roads.

The one-way probabilistic sensitivity analysis of variation in risk characteristics

further investigated the vulnerability across perspectives and scenarios towards each

source of risks. The findings of the sensitivity analysis highly rely on the case synthesis

and model assumptions. However, the analysis carried out in this study demonstrated

the methodology to assess the vulnerability of the project.

6.6 SUMMARY

This study examined alternative treatments of tolls and other toll road project-related

payments in Cost-Benefit Analysis (CBA) for a privately operated toll road project

when two perspectives of “toll as a financial transfer” (TT) and “toll as an end-user


cost” (TC) were considered. The previously synthesised toll tunnel project case was

evaluated using CBA across various scenarios. In those scenarios, various payments

were considered and their treatments were explored under the two perspectives. The

risk of the synthesised case was quantified by incorporating the Monte Carlo

simulation approach.

Stochastic representations of risk profiles across perspectives and scenarios

illustrated detailed profile, including the sources of risks the most influence the

outcome and the perspectives or scenarios that are most vulnerable to certain risks.

These key considerations related to risks were displayed visually using probability

distribution graphs and box-and-whisker plots. This allowed the results to be easily

interpreted.

This study enhanced the knowledge in terms of the treatment of tolls and other

payments by examining different perspectives in CBA. Furthermore, this study

considered the impacts that are not accounted in financial analysis. This allowed an

exploration of the overall impacts to the community, including transport benefits and

concession payments to the community of a privately operated toll road project.

The assumptions used in this study to develop the models of various payments

can differ between projects in practice. One of the key contributions of the

methodology presented in this study is that it can incorporate various assumptions to

model the payments. Additionally, the risks of input variables, including traffic

forecasts and toll price, can be quantified in the methodology. These risks tend to

reduce towards the opening of the toll road. As has been previously discussed, the risk

that is borne by the host government varies between projects. Various risk-sharing

arrangements and shifts of risk can also be quantified using the methodology. The

quantified risk is one of the key pieces of information that assists in decision making

for a major project. The contributions of this study include the methodology that can

readily be used in practice and are not limited to academic contributions.

The risk of discount rate was not considered in this study. Discount rate depends

on the risk shared between public and private sectors for any Public-Private and

Partnership (PPP) projects. This is because the systematic risk premium is adjusted to

reflect the proportion of risks that the public sector is bearing (Australian Department

of Infrastructure and Regional Development, 2013). The impact of the risk of discount

rate can be explored in the future study. Detailed case studies of existing toll road


projects can also be conducted to further explore the impacts of the ramp-up period,

discount rates and various risk-sharing arrangements.

Chapter 7: Evaluating a Toll Road Project 103

Chapter 7: Evaluating a Toll Road Project

This chapter provides an evaluation of a toll road project using Cost-Benefit Analysis

(CBA) for the purposes of comparison and contrast with the findings from previous

chapters. The previous chapters studied a tunnel project case, instead, this chapter

examines an at-grade road project case. A toll road project case is synthesised on the

basis of the overarching characteristics of Australian toll road projects. The

synthesised case is evaluated using the methodologies and the models presented in

Chapter 5 and Chapter 6.

This chapter first summarises the overarching characteristics of Australian toll

road projects, and presents the characteristics of the synthesised case. It then provides

the results of the CBA. The findings are summarised in the final section.

7.1 SYNTHESISING A TOLL ROAD PROJECT CASE

The key difference between an at-grade toll road (termed toll road from hereon) and a

toll tunnel project is the scale of the project. Due to the large construction cost of a

tunnel, the length of the proposed tunnel is often shorter than many other proposed

roads. Therefore, vehicle hours travelled saving (VHTS) and vehicle kilometres

travelled saving (VKTS) of a toll road project can differ from those of a tunnel project.

Table 7.1 summarises the characteristics of three Australian urban toll road

facilities of Hills M2, Logan Motorway and M5 South West. These toll roads were

reviewed, because, first, they are all located in Australian urban area, and second, the

lengths of each road section are all similar, ranging between 21 and 28 kilometres, as

shown in Table 7.1. There are large variations in VKTS between toll roads. This can

be due to the longer length of the roads. VHTS is also noticeably larger than other

tunnels. M5 South West occasionally fails to provide shorter travel distance. This can

be explained by the road network in Sydney, NSW. There are major arterials parallel

to M5 South West that are not tolled, which can provide shorter travel distance

depending on the origin and the destination of an itinerary. However, travel time along

these arterials tends to be significantly higher than M5 South West, which can also

been seen in Table 7.1. This argument suggests that travel time saving depends on the

104 Chapter 7: Evaluating a Toll Road Project

typology of the surrounding transport network and does not necessary depend on the

length of the road.

Table 7.1 Characteristics of Australian toll roads

Characteristic Hills M2 Logan Motorway M5 South West

VKTS (Google,

2016)

Between 0.7 and 1.8

km

Between 5.6 and 6.4

km

Between - 2.7 and 2.3

km (negative shows

that the travel distance

was longer)

VHTS (Google,

2016)

12 minutes depending

on the time of the day

17 minutes depending

on the time of the day

Between 19 and 23


the time of the day

State VIC QLD NSW

Length 21 km (Transurban,

2016c)

28.9 km (Transurban,

2016e)

22 km (Transurban,

2016f)

Many recent major toll road projects in Australia are tunnel projects. Australian

toll road projects, Hills M2, Logan Motorway and M5 South West all have been built

many years ago and their cost figures do not represent appropriate cost figures for the

synthesis of the case for this study. It is also difficult to gain access to the original

project cost calculations of old road projects. Construction costs of tolled and non-

tolled roads do not particularly differ. Therefore, the costs of West Petrie Bypass

(WPB) were reviewed as a model project. The WPB is not a toll road, however is one

of the recent major road projects in Brisbane, Australia. The business case of the WPB

was developed in 2013 (GHD, 2013) and provides the most recent cost figures. Table

7.2 summarises the project costs of the WPB. All costs are shown with conversion to

2015 dollars for equitable comparison, using the inflation methodology of the Reserve

Bank of Australia (2017).


Table 7.2 Project costs of West Petrie Bypass project in 2015 dollars (GHD, 2013)

Cost item Cost

Capital cost AU$ 130,480,078

Operation and maintenance (O&M) cost AU$ 68,000 per year

State QLD

Length 1.9 km

7.2 SYNTHESISED TOLL ROAD PROJECT CASE CHARACTERISTICS

Input variables, including annual average daily traffic (AADT), traffic growth rate,

proportion of heavy vehicles (HV%), unit prices of transport costs, and other

assumptions that are not discussed below are the same as the previously synthesised

toll tunnel project case. Table 7.3 summarises the input variables and the assumptions

that are different from the previously synthesised tunnel case. The synthesised case is

a 25 km toll road. The capital cost was assumed to be AU$ 1.7 billion on the basis of

the capital cost per km of the West Petrie Bypass (WRB). The characteristics of the

synthesised case only represents a typical Australian toll road project that provides

shorter travel time and distance. The projects with unusual characteristics are beyond

the scope of this study.


Table 7.3 Assumptions made in Cost-Benefit Analysis (CBA) calculation of the synthesised toll road

project case

Item Assumption and distribution characteristic

VKTS Normal distribution with an expected value of 4.0 km and a

CV of 10 %.

VHTS Normal distribution (Salling & Leleur, 2011) with an

expected value of 15 min = 0.25 h and a CV of 20 % (Salling

& Leleur, 2011).

Type of project A toll road project in the greater South East Queensland

region, Australia.

The expected economic life of

a road

50 years (Australian Transport Council, 2006b)

Capital cost Cowan’s M3 distribution with 𝐶𝑎𝑝𝑚𝑖𝑛 = AU$ 1.58 billion

and probability of actual cost being greater than the

minimum, 𝜙 = 65 %, while maintaining an expected value of

AU$ 1.7 billion and a CV of 10 %. Capital cost was assumed

to be AU$ 69 million per kilometre on the basis of the West

Petrie Bypass project (GHD, 2013).

Proportion of capital cost and

operation and maintenance

(O&M) cost

The proportions of O&M and capital cost are 3 % and 97 %

respectively over the whole planning horizon. This was

assumed on the basis of the O&M cost of AU$ 900,000 per

year. O&M cost per kilometre was assumed to be AU$ 36

million per year on the basis of the West Petrie Bypass

project (GHD, 2013).

Upfront capital cost

contribution

One third of the expected capital, and operation and

maintenance (O&M) costs (AU$ 580 million).

7.3 RESULTS

7.3.1 Evaluation and Decision making of the Synthesised Toll Road Project

Case

Table 7.4 summarises the calculation of the point Benefit-Cost Ratio (BCR) when all

variables were equal to the expected values of their stochastic distributions. The

measure that most contributed to the overall benefit was the travel time saving. It is

noted that this would be impacted by the risks of annual average daily traffic (AADT),


traffic growth, travel time unit price, and vehicle hours travelled saving (VHTS). The

resultant BCR was 1.41, which reflects a net positive impact of the synthesised toll

road case.

Table 7.4 Impacts of the synthesised toll road project case when all variables were deterministically

equal to their expected values in present value

Project impact Amount Proportion

Travel time saving AU$ 1,452,313,461 58.9 %

Vehicle operating cost (VOC)

saving

AU$ 783,471,356 31.8 %

Crash cost (CC) saving AU$ $22,157,181 0.9 %

Environmental and external cost

(EEC) saving

AU$ 207,938,285 8.4 %

RV AU$ 0 0 %

Total saving of transport costs AU$ 2,465,880,283 -

Capital cost AU$ 1,700,000,000 97.1 %

Operation and maintenance (O&M)

cost

AU$ 51,000,000 2.9 %

Total cost AU$ 1,751,000,000 -

Net present value AU$ 714,880,283 -

BCR 1.41 -

Table 7.5 summarises the risk profiles of the synthesised case. The perspectives

of “toll as a transfer payment” (TT) and “toll as an end-user cost” (TC), and the

scenarios of “baseline toll with no guarantee” (BNG), “baseline toll with minimum

revenue guarantee” (BRG) and “premium toll with no guarantee” (PNG) were

considered. All perspectives and scenarios showed similar results of the expected

Benefit-Cost Ratio (BCR) between 1.36 and 1.42. This similarity can be explained by

the assumptions used in the model development in this study. The outcome BCR may

differ with the different assumptions used. The expected and medians of BCR across

perspectives and scenarios showed great similarities, which indicated that sufficient

Monte Carlo trials were conducted for each scenario. The TT perspective showed the


highest coefficient of variation (CV). All scenarios under the TC perspective resulted

in similarly high proportions of BCR trials greater than 1.0.

Table 7.5 Risk profiles of the synthesised toll road project case across scenarios

Perspective Scenario Expected

BCR Median CV

Proportion of BCR

trials greater than 1.0

Toll as a transfer payment (TT) 1.42 1.41 18% 96%

Toll as an

end-user cost

(TC)

Baseline toll, no

guarantee (BNG)

1.40 1.40 13% 99%

Baseline toll,

minimum revenue

guarantee (BRG)

1.37 1.36 14% 98%

Premium toll, no

guarantee (PNG)

1.36 1.36 13% 99%

Figure 7.1 shows the cumulative stochastic BCR distributions of the synthesised

case. The cumulative graph clearly illustrates the wider spread in the BCR distribution

under the TT perspective.


Figure 7.1 Cumulative stochastic Benefit-Cost Ratio (BCR) distributions of the synthesised toll road

project case

Figure 7.2 shows box-and-whisker plots of the stochastic BCR distributions of

the synthesised case. The 9th and 91st percentiles were used as the minimum and the

maximum of the whiskers to effectively highlight the characteristics of each

distribution.


Figure 7.2 Box-and-whisker plots of stochastic BCR distributions of the synthesised toll road project

case

7.3.2 Sensitivity Analysis of Variation in Risk Characteristics

Similar to Chapter 6, the impacts of the risks of annual average daily traffic (AADT),

traffic growth rate, capital cost and vehicle hours travelled saving (VHTS) were

investigated. Again, BCR distributions were calculated individually, using a

combination of deterministic variables and each of these variables were treated as

stochastic.

Table 7.6 summarises risk profiles across perspectives, as each input variable

was varied according to its defined distribution, while in each case holding all other

variables at their expected value. When the AADT and the traffic growth rate variables

were treated as stochastic, the difference in CV between scenarios BNG, BRG and C

became apparent, compared to when all variables were treated as stochastic. This

indicates that the scenario BRG is more vulnerable to the AADT and the traffic growth

rate risks than scenarios BNG and PNG. When the capital cost variable was treated as

stochastic, the expected BCR for BRG and PNG scenarios significantly decreased in

comparison to when all variables were treated as stochastic. Scenarios BNG and PNG

showed no spread and this indicates that the capital cost risk does not influence the


outcome BCR under these scenarios. When the VHTS variable was treated as

stochastic, again, the expected BCR for BRG and PNG scenarios significantly

decreased in comparison to when all variables were treated as stochastic. The

proportion of BCR trials greater than 1.0 across perspectives and scenarios were

sufficiently high, apart from scenarios BRG and PNG, which indicates the

vulnerability of these scenarios against the VHTS risk.

Table 7.6 Risk profiles of the synthesised toll road project case across scenarios when each variable

was treated as stochastic

Stochastic

variable Perspective Scenario

Expected

BCR Median CV

Proportion of

BCR trials

greater than

1.0

Annual average

daily traffic

(AADT) and

traffic growth

rate

TT 1.41 1.41 10% 100%

TC BNG 1.40 1.41 3% 100%

BRG 1.37 1.38 5% 100%

PNG 1.37 1.38 3% 100%

Capital cost TT 1.42 1.47 9% 100%

TC BNG 1.41 1.41 <0% 100%

BRG 1.20 1.22 5% 99%

PNG 1.19 1.19 <0% 100%

Vehicle travelled

hours saving

(VHTS)

TT 1.41 1.41 12% 100%

TC BNG 1.41 1.41 12% 100%

BRG 1.19 1.19 12% 92%

PNG 1.19 1.19 12% 92%

Figure 7.3 to Figure 7.8 show the cumulative stochastic BCR distributions, and

box-and-whisker plots of the synthesised case when each variable is treated as

stochastic. The 9th and 91st percentiles were used as the minimum and the maximum

of the whiskers to effectively highlight the characteristics of each distribution. The

cumulative graph and the box-and-whisker plots clearly illustrate the wider spread in

the BCR distribution under the TT perspective. The perspective TT and the scenario


BNG showed the same distribution characteristics when VHTS is treated as stochastic.

Scenarios BRG and PNG showed the same distribution characteristics that are

different from the perspective TT and the scenario BNG when VHTS is treated as

stochastic.

Figure 7.3 Cumulative stochastic BCR distributions of the synthesised toll road project case when the

annual average daily traffic (AADT) and the traffic growth rate variables are treated as stochastic



case when the AADT and the traffic growth rate variables are treated as stochastic

Figure 7.4 indicates that the TT perspective is most vulnerable to AADT and

traffic growth rate risks compared to the TC perspective. This indicates that a toll road

project is vulnerable to traffic risk the most when it is fully delivered the host

government.



capital cost variable is treated as stochastic


case when the capital cost variable is treated as stochastic


Figure 7.6 showed that the scenarios BNG and BRG resulted in minimal CV

when capital cost risk exists. This indicates that the capital cost risk does not impact

the project outcome when the project is delivered by the private operator and the host

government does not provide the minimum revenue guarantees.


vehicle hours travelled saving (VHTS) variable is treated as stochastic



case when the VHTS variable is treated as stochastic

Figure 7.8 showed that all perspectives and scenarios resulted in equivalent CV

when vehicle hours travelled saving (VHTS) risk exists. This indicates that the

delivery method does not influence the level of VHTS risk that the host government is

bearing. The TT perspective resulted in higher CV compared to the TC perspective

across all variables tested. While, the CV of the TC perspective varied across all

variables tested. This indicates that the outcome risk profile varies depending on the

sources of risks that the project contains.

7.4 COMPARISON BETWEEN TOLL TUNNEL AND ROAD PROJECTS

7.4.1 A Review of Benefit-Cost Ratio

The synthesised toll road project case resulted in a higher expected Benefit-Cost Ratio

(BCR) than the previously synthesised toll tunnel project case, although the total cost

of the tunnel case was lower. This is due to higher total benefits accrued from larger

vehicle kilometre travelled saving (VKTS). The proportions of travel time saving and

vehicle operating cost saving values also reflected the increase of VKTS. The savings

in crash cost (CC) and environmental and external cost (EEC) were insignificant

similarly to the synthesised toll tunnel project case.


7.4.2 A Review of Risk Profiles and Perspectives

An overall increase of Benefit-Cost Ratio (BCR) of the synthesised road case was

observed and this resulted in the higher expected BCR, the higher medians and larger

proportions of BCR trials greater than 1.0 across perspectives and scenarios. For both

of the synthesised tunnel and road cases, the “toll as a transfer payment” (TT)

perspective showed a higher coefficient of variation (CV) than the “toll as an end-user

cost” (TC) perspective. This indicates that the shift of risk was successfully illustrated

in the risk profiles for both cases. An overall CV of the synthesised tunnel case showed

higher values than the overall CV values of the synthesised road case across

perspectives and scenarios. This indicates that the risk of the synthesised road case is

less than the one of the synthesised tunnel case.

7.5 SUMMARY

This chapter detailed the evaluation of a synthesised toll road project for the purposes

of comparison and contrast with the findings of previous chapters. A toll road project

case was synthesised on the basis of the overarching characteristics of existing

Australian major road projects. Previously proposed Cost-Benefit Analysis (CBA) and

the Monte Carlo simulation methodologies were used for the purpose of the evaluation.

Similar to Chapter 6, the perspectives of “toll as a financial transfer” (TT) and “toll as

an end-user cost” (TC) were examined in the evaluation.

This chapter assesses the applicability of the proposed CBA methodology by

directly applying it to the synthesised toll road project case. The results were consistent

with the findings in Chapter 6, which examined the evaluation of the synthesised toll

tunnel case, and indicated that the findings of this study are applicable to both toll road

projects and toll tunnel projects.

The synthesised case resulted in an expected Benefit-Cost Ratio (BCR) of 1.41

and was therefore found to benefit the community. The significant benefits of the

synthesised case were travel time saving and vehicle operating cost saving (VOCS),

which showed proportions of 58.9 % and 31.8 % respectively. The risk profile

measures were found to be useful, however due to the high outcome BCR, the

proportion of BCR trials greater than 1.0 failed to indicate the level of risk across

perspectives and scenarios. Instead, coefficient of variation (CV) effectively

represented the risk of the synthesised case. This indicates that CV and stochastic


results better represent the risk profile, especially when the outcome BCR is highly

likely to meet the required cut-off value.

The calculated baseline toll price was $5.21 for the synthesised road case, which

is a little higher than the calculated baseline toll price of the synthesised tunnel case.

This increase is due to the higher capital cost and the higher capital cost contribution

as a result. However, this price is only slightly higher than the existing toll prices in

Brisbane, Australia, which indicates that the baseline toll price model assumptions

used in this study are valid and realistic for both tunnel and road cases.


Chapter 8: Proposed Methodology for

Evaluations of Toll Road

Projects

This chapter proposes the Cost-Benefit Analysis (CBA) methodology for toll road

projects that was developed in this study. A typical project appraisal process that is

commonly used by Australian government bodies is reviewed, then how the proposed

methodology can be implemented in the existing process is discussed. The framework

of the methodology is developed on the basis of the existing CBA methodology that is

documented in the Australian Transport Council’s guideline (Australian Transport

Council, 2006a). Practical considerations are also discussed. Further research that is

needed to refine the methodology is discussed in the final section.

8.1 TYPICAL PROJECT APPRAISAL PROCESS

The project appraisal process can vary greatly between jurisdictions. This study refers

to the project appraisal process that is presented by the Australian Transport Council,

which is a transport authority within the Australian Department of Infrastructure and

Regional Development (Australian Department of Infrastructure and Regional

Development, 2016). Austroads also refers to the Australian Transport Council

Guidelines (Tsolakis, Preski, & Patrick, 2009) and these guidelines can be referred to

as typical Australian practice. Figure 8.1 shows the project appraisal process that is

advised in the Australian Transport Council’s guideline (Australian Transport Council,

2006a). CBA is abbreviated as “BCA” in that guideline. The steps with a dashed box

represent where the proposed methodology can be implemented. The proposed

methodology is suitable as a detailed CBA methodology as it involves numerical risk

assessment, which is not typically needed in the indicative assessment that only

considers main benefits and costs (Australian Transport Council, 2006a).

120 Chapter 8: Proposed Methodology for Evaluations of Toll Road Projects

Figure 8.1 Project appraisal process (Australian Transport Council, 2006a)

Particularly in Queensland, Australia, CBA is included in the phases of

preliminary evaluation and business case development within the project assurance

framework of Queensland Government (Queensland Department of Infrastructure and

Planning, 2009, 2011). Detailed procedures of CBA is documented in the CBA manual

(Queensland Department of Transport and Main Roads, 2011).

8.2 FRAMEWORK OF THE PROPOSED METHODOLOGY

The Australian Transport Council (2006a) provides guidance on Cost-Benefit Analysis

(CBA) methodology for transport projects. Figure 8.2 illustrates the framework of the

proposed CBA for major road projects that was developed in this study on the basis of


the Australian Transport Council’s CBA methodology. The thick lined boxes represent

the steps that are added or changed. This framework complements the framework that

was used in this study previously, which was shown in Figure 6.4.

Figure 8.2 The proposed Cost-Benefit Analysis (CBA) framework for major road projects

First, the initiative is specified and analysis options are defined. Then, the

required data are acquired. For major road project CBA, the required data include

annual average daily traffic (AADT), traffic growth rate, proportion of heavy vehicles


(HV%), vehicle hours travelled saving (VHTS), vehicle kilometres travelled saving

(VKTS) and various transport cost unit prices. Many of these variables can be

determined through traffic modelling and forecasting. Then the risk allocations are

examined, in order to determine the movements of various payments. The risk

allocations need to align with the previously defined analysis options (see Figure 8.2).

The identified payment movements determine how project impacts need to be treated

in the CBA. Probability distributions then can be applied to each input variable. This

can be determined by the traffic modellers, or otherwise the probability distributions

used in this study can be implemented. However, it is more appropriate to implement

the probability distributions that are determined by the modellers for accurate analysis

outcomes. The probability distributions used in this study were selected on the basis

of a number of previous studies, which may not directly be relevant to a particular

project. Stochastic CBA is then conducted on the basis of the probability distributions

applied to input variables and the treatment of project impacts. Through the stochastic

CBA, risk profiles can be developed. Benefit-Cost Ratio (BCR) and coefficient of

variation (CV) represent the net impacts and risks of the project in the risk profiles.

An option with a higher BCR represents more beneficial option to the community than

other options. An option with a lower CV represents the option with less risk that is

borne by the host government than other options.

The key contribution of the proposed methodology is that the CBA outcome

represents the impacts to the community. This is particularly useful to evaluate a

project solely from the public perspective. The host government acts as the decision-

maker in the project appraisal of major road projects and the decision needs to be made

based on the impacts to the community.

8.3 PRACTICAL CONSIDERATIONS

8.3.1 Conducting the Analysis

The required numerical calculations for the proposed methodology can be conducted

easily using a spreadsheet platform, such as Microsoft Excel, which was used for the

purpose of this study. A spreadsheet is capable of incorporating various probability

distributions. This allows a number of the Monte Carlo simulations to be conducted

rapidly. The simulated values can be used in the Cost-Benefit Analysis (CBA)

calculation, which only involves additions and multiplications. Calculation of


concession payments and other financial analysis that may be needed as part of the

CBA calculation can also easily be conducted using a spreadsheet.

Practitioners could use tools other than spreadsheet to conduct the stochastic

CBA, however their preferred tool needs to be able to simulate or incorporate

stochastic input variables and analyse the stochastic Benefit-Cost Ratio (BCR) for the

purpose of developing risk profiles.

Practitioners may also use other tools to assist with the analysis, such as various

traffic modelling or financial analysis tools. These tools may produce outcomes in

stochastic forms or percentages representing confidence levels. These can be

incorporated into the analysis using a spreadsheet, when practitioners wish to use these

forms.

Additionally, it is important to note that, when software is used to conduct

analyses, every step of the calculations needs to be shown transparently. For instance,

Microsoft Excel has a feature called “macro” or “visual basic for applications (VBA)”

to automate tasks within the Excel workbook. Although VBA is an efficient tool to

simplify the analysis, the practitioner needs to be able to effectively communicate the

analysis outcomes and calculations to the decision-makers when explanations are

needed.

8.3.2 Interpreting the Results

A spreadsheet can be used to present the outcome stochastic BCR using various

measures and graphs. This study used the expected BCR, median, coefficient of

variation (CV), the proportion of BCR trials greater than 1.0, the cumulative

probability distribution graph, and the box-and-whisker plots as the measures to

develop risk profiles. The interpretations of these measures were developed on the

basis of the interpretations of various statistical inferences that are detailed by Mun

(2010).

Table 8.1 summarises the risk profiles that can be developed using the proposed

Cost-Benefit Analysis (CBA) methodology and their interpretations. This summary

can maintain consistencies of interpretations of the analysis outcomes. As has been

highlighted previously, the probability of Benefit-Cost Ratio (BCR) greater than 1.0 is

not an effective measure when all of analysis options are highly likely to result with

BCR greater than 1.0.


Table 8.1 Interpretations of the risk profiles in the proposed methodology

Measure Outcome Interpretation

Expected Benefit-Cost

Ratio (BCR)

Below 1.0 Not economically viable, on average

Greater than 1.0 Economically viable, on average

Higher than other

options

More beneficial to the community than

other options

Lower than other

options

More costly to the community than other

options

Median Similar to the

expected BCR

A sufficient number of Monte Carlo trials

have been conducted

Varies from the

expected BCR

An insufficient number of Monte Carlo

trials have been conducted

Coefficient of variation

(CV)

Higher than other

options

The option involves greater risk than other

options

Lower than other

options

The option involves less risk than other

options

Proportion of BCR trials

greater than 1.0

Higher than other

options

The option is more likely to benefit the

community than other options

Lower than other

options

The option is less likely to benefit the


Table 8.2 summarises the interpretation of the cumulative probability

distribution graph, and box-and-whisker plots. These graph and plots are useful tools

to represent the risk profiles visually.


Table 8.2 Interpretations of cumulative probability distribution graphs, and box-and-whisker plots in

the proposed methodology

Item Outcome Interpretation

The cumulative

probability

distribution graph

Wide/flatter Larger coefficient of variation (CV)

therefore the option involves greater

risk than other options

Vertical/steeper Smaller CV therefore the option

involves less risk than other options

Skewed to right compared to

other options

Higher expected Benefit-Cost Ratio

(BCR) than other options therefore

more beneficial to the community

than other options

Skewed to left compared to

other options

Lower expected Benefit-Cost Ratio

(BCR) than other options

Box-and-whisker

plots

Longer box Larger CV therefore the option

involves greater risk than other

options

Shorter box Smaller CV therefore the option


Distance between the top

whisker end and bottom

whisker end is wider than other

options

Larger CV therefore the option

involves greater risk than other

options

Distance between the top

whisker end and bottom

whisker end is narrower than

other options

Smaller CV therefore the option


Centre of box is located higher

than other options

Higher expected BCR than other

options therefore more beneficial to

the community than other options

Centre of box is located lower

than other options

Lower expected BCR than other

options therefore more costly to the



8.4 ENHANCEMENT AND REFINEMENT OF THE PROPOSED

METHODOLOGY

As has been discussed in previous chapters, the key issue that needs to be studied

further is about determination of the appropriate forms of probability distributions of

input variables, particularly of those that have not been studied previously. Statistical

studies can be conducted by reviewing past projects, as the available data increases, to

generalise the probability distributions that should be used for each input variable.

However, different types of modelling or estimation techniques can be used between

different projects for the same variable. For instance, there are a number of traffic

forecasting modelling techniques that traffic modellers can use to forecast traffic

demand. The forecasted traffic produced from different techniques can have different

probability distributions depending on the assumptions incorporated in the models

used. This suggests that, when modellers produce the forecasts, they should also be

able to determine the appropriate distribution form for the variables that were

forecasted.

For the input variable with well-established estimation methodology, such as

project cost, previously estimated costs of various projects can be studied statistically

for the purpose of determining the appropriate form of probability distribution. There

are studies found in the literature (Berthelot et al., 1996; Hensher, 2001; Salling &

Leleur, 2011) with regard to appropriate form of probability distribution of some input

variables, which were considered in this study.

8.5 SUGGESTED FUTURE STUDIES

Future studies can be conducted to investigate the impacts of variation in discount rate

on the analysis outcome. Additionally, various concession arrangements and payment

models that were not considered in this study can be studied, using the proposed

methodology. These studies would further extend the knowledge of the CBA of toll

road projects.

Studies can also be conducted on the Cost-Benefit Analysis (CBA) of other types

of transport infrastructure projects, while considering the perspectives and payment

movements. For instance, many public transport projects are often delivered through

a form of Public-Private Partnership (PPP) scheme (Bonnafous, 2012). Types of

benefits and costs may differ from the benefits and costs of a major road project,


however, considering the perspectives in the CBA of public transport projects would

further extend the knowledge of CBA. Similar studies can also be conducted for other

types of PPP projects intended for the public good, such as hospital projects and other

larger health care initiatives, social welfare initiatives, knowledge precinct projects

and the like.

8.6 SUMMARY

This chapter discussed how the proposed methodology can be incorporated in the

existing Australian project appraisal process. Well-regarded guideline was reviewed

to determine the phase in which the proposed methodology can be used. The

framework of the proposed methodology was presented, which was developed on the

basis of the existing Cost-Benefit Analysis methodology. Practical considerations

included how the analysis can be conducted and how the analysis outcomes can be

interpreted. The analysis can be conducted by using a spreadsheet platform such as

Microsoft Excel, however modelling and estimations of input variables may need to

be conducted using various other tools. This chapter also summarised appropriate

interpretations of risk profiles, in order to maintain the interpretations of the analysis

consistent. The issues that need to be further studied were then discussed. Further study

to determine the appropriate forms of probability distributions of input variables,

particularly of those that have not been studied previously, will contribute to

enhancement and refinement of the methodology proposed in this study.

Chapter 9: Conclusion 129

Chapter 9: Conclusion

This chapter first provides a brief summary of the findings of this study. Contributions

to the academic study and practice are then discussed. This is followed by discussion

of the limitations and recommendations of this study. The aim of this study is then

reviewed to examine whether it was achieved and the conclusion of this study is drawn.

9.1 SUMMARY OF FINDINGS

9.1.1 Literature Review

The literature review discussed the key issues of the project evaluation of a toll road

project. It particularly focused on highlighting potential barriers against proper

evaluations and identifying research gaps. Cost-Benefit Analysis (CBA) is the most

commonly used and well-established project evaluation methodology (van Wee &

Rietveld, 2014). The review highlighted limited studies found with regard to CBA of

a toll road project. The representation of risk, using one-way sensitivity analysis in

CBA, is limited by point assumptions of Benefit-Cost Ratio (BCR). Effectively

illustrating risks of a toll road project is particularly important, because toll road

projects can have complex risk characteristics that differ from the risk characteristics

of a general major road project. The risk allocations of a toll road project can also be

unique to each project. This review suggested further examinations of the treatment of

tolls in CBA, because tolls may be collected by the private operator, depending on the

concession arrangement of the project.

9.1.2 A Review of Australian Practice of Cost-Benefit Analysis

Chapter 4 reviewed previously conducted CBA of a number of Australian and UK

major road projects. The review highlighted limitations and difficulties in the CBA

practices, such as time and resource limitations, the complexity of CBA, the level of

comprehensiveness of the CBA that the host government may be expecting, and the

complexity of transport planning. The review also identified a number of technical

inconsistencies, such as the residual value (RV) calculation and the treatment of tolls

in CBA. Further study was suggested to examine the complex mechanisms of risk

allocations, discount rate, and the treatment of tolls in CBA.

130 Chapter 9: Conclusion

9.1.3 Incorporating Stochastic Approach in Cost-Benefit Analysis

Chapter 5 presented the methodology to incorporate the stochastic approach in CBA.

The proposed methodology represents the outcome BCR in a stochastic form, using

the Monte Carlo simulation approach. A toll tunnel project case was synthesised on

the basis of the overarching characteristics of recent toll road projects. The sources of

risks that the most influence the outcome BCR were capital cost, annual average daily

traffic (AADT), travel time unit price, and vehicle hours travelled saving (VHTS). The

risk profile that is developed using the stochastic BCR was found to be extremely

useful in decision making by providing detailed illustration of the project risk.

9.1.4 Evaluating a Toll Tunnel Project

Chapter 6 examined the treatment of tolls and other impacts in CBA of a privately

operated toll road project. Payment movements of various impacts, including tolls and

other concession payments were examined to explore appropriate treatments of the

impacts. Two perspectives of “toll as a financial transfer” (TT) and “toll as an end-

user cost” (TC) were considered in this examination. The previously synthesised toll

tunnel project case was evaluated using the stochastic CBA methodology, in order to

observe shifts of risk across perspectives and scenarios. It was concluded that the

evaluation of the project can reflect the shift of risk between the host government and

the private operator under the TC perspective. The analysis outcomes also suggested

that treating tolls as the cost to the community is a reasonable and valid approach under

the TC perspective.

The expected BCR of the synthesised case was found to be beneficial between

57 % and 61 % of trials, depending on the perspectives and scenarios. The calculated

baseline toll price indicated that the baseline toll price assumption was realistic.

The stochastic CBA illustrated detailed risk profiles across perspectives and

scenarios using. The risk profiles provided insightful CBA outcomes, including the

sources of risks the most influence the outcome and the perspectives or scenarios that

are most vulnerable to certain risks. These key considerations related to risks were

displayed visually, which allowed the results to be easily interpreted.

This evaluation highlighted some of the key contributions of the proposed

methodology. One is the ability to incorporate various assumptions within the model

development. For instance, various financial models can be incorporated within the


baseline toll price model. Different types of concession payments that were not

considered in this study can also be incorporated into the proposed CBA methodology.

Also important is the ability to quantify the risk. Quantifying risks enables comparison

of the risk across perspectives and scenarios. This also allows the shift of risk to be

represented in an empirical manner.

9.1.5 Evaluating a Toll Road Project

Chapter 7 investigated the evaluation of a toll road project, which was synthesised on

the basis of the overarching characteristics of major road projects. Similar to the

previous chapter, the perspectives of TT and TC were considered.

The evaluation resulted in findings consistent with the previous findings. The

shift of risk was observed under the TC perspective for the synthesised case.

Additionally, the analysis outcomes indicated that treating toll as the cost to the

community is a reasonable and valid approach for a privately operated toll road project

under the TC perspective.

The toll road project resulted in a higher BCR due to greater vehicle kilometres

travelled saving (VKTS). Risk profiles across perspectives and scenarios showed that

the coefficient of variation (CV) was useful to observe the level of risk. However, the

proportion of BCR trials greater than 1.0 failed to represent the risk effectively. This

is because the expected BCR across perspectives and scenarios were highly likely to

result in BCR greater than 1.0. The calculated baseline toll price was only slightly

higher than existing toll prices in Australia. This indicates that the baseline toll price

model assumption was also realistic for a road project case, along with a tunnel project

case.

9.1.6 Proposed Methodology

Chapter 8 presented the framework of the proposed CBA methodology and discussed

how it can be used in practice. The existing project appraisal process (Australian

Transport Council, 2006a) was reviewed to determine how the proposed methodology

can be incorporated into the existing process. Detailed framework of the proposed

methodology was then presented and the added steps to the existing CBA methodology

were highlighted. Practical considerations, such as how the analysis can be conducted

and how the outcomes can be interpreted, were discussed. Future research needed to

improve the proposed methodology was discussed in the final section. The key future


research will be to determine the appropriate forms of probability distributions of input

variables, particularly of those that have not been previously studied.

9.2 REVIEW OF THE RESEARCH QUESTIONS

The following sections address the research questions, which are restated as follows:

1. How have various toll road projects been evaluated using CBA in practice?

2. Does the extant CBA methodology that is used to evaluate toll road projects

properly reflect the net impacts and risks to the community of a toll road?

3. Can CBA results properly reflect the source/s of risks of a toll road project

by incorporating a stochastic approach?

4. How does altering treatments of some impacts of a toll road project in CBA

improve its outcomes in terms of reflecting net impacts and risks to the

community?

9.2.1 Research Question 1

The first research question was addressed in Chapter 4, by reviewing previously

conducted CBA for the purpose of evaluation of toll road projects. The Cost-Benefit

Analysis (CBA) of toll road projects generally include travel time saving, vehicle

operating cost saving, crash cost saving, environmental and external cost saving,

capital cost, and operation and maintenance cost. These impacts are monetised using

transport cost unit prices. The outcome of the CBA is represented as Benefit-Cost

Ratio (BCR), which is a ratio of benefits to costs.

There are often technical inconsistencies between the CBA that is conducted for

different projects. This can be explained by time and resource availabilities at the time

of the analysis. Many projects with insufficient BCR have still proceeded, which

indicates that other intangible factors that were not considered in CBA have been given

substantial consideration in the decision making. Also highlighted was the complexity

of evaluating a single transport infrastructure item while its performance depends on

its surrounding transport network. For instance, when several roads are opening within

a short span of time within the same network, their performance will be at their best

when all are opened.

Tolls are generally treated as financial transfers in practice. Further

considerations revealed that whether tolls should be treated as financial transfers for


privately operated toll road projects, needs to be further examined. Additionally, the

calculation of RV, estimation of discount rate and the impacts of various risk-sharing

arrangements also needs to be investigated for Public-Private Partnership (PPP) toll

road projects.


The second research question was addressed in Chapter 3 and Chapter 4. CBA is a

well-established project evaluation methodology (van Wee & Rietveld, 2014), which

considers project impacts from wide perspectives, including road users, non-road users

and the road operator (Decorla-Souza et al., 2013; Mackie et al., 2014). BCR reflects

the net impact of the project. However, with the variations of various delivering

strategies and risk-sharing arrangements, the treatment of some impacts need to be

considered carefully. For instance, tolls are generally treated as financial transfers by

assuming that the public operator collects the tolls. For many of the recent toll road

projects, tolls are instead collected by the private operator. The treatment of tolls

traditionally does not differ between a publicly operated toll road and a privately

operated toll road in CBA. Whether treatments of impacts need to be altered to

properly reflect the net impacts and risks in CBA need to be investigated.


The third research question was addressed in Chapter 5. The Monte Carlo simulation

approach was incorporated into CBA to demonstrate stochastic representation of BCR.

Risk profiles can be developed using statistical inferences on the basis of the stochastic

BCR. The risk profile demonstrated the risk effectively in an empirical manner. The

risk can be assessed across perspectives and scenarios using the stochastic BCR.

Sources of risks can be identified by observing the outcome BCR for

combinations of deterministic input variables and stochastic input variables. This study

revealed that annual average daily traffic (AADT), vehicle hours travelled saving

(VHTS), travel time unit price and capital cost influence the outcome BCR most

significantly.


The fourth research question was addressed in Chapter 6 and Chapter 7. A toll road

project case and a toll tunnel project case were synthesised on the basis of existing

major road projects and evaluated using CBA. Altering treatments of impacts were


investigated by considering the perspectives of “toll as a financial transfer” (TT) and

“toll as an end-user cost” (TC). This revealed that treating tolls as the cost to the

community is a reasonable and valid approach. Other payments that are often used in

toll road projects, such as minimum revenue guarantee, were also considered and the

treatments of impacts were altered accordingly to each scenario across the

perspectives. The shift of risk when the project is operated by the private operator was

observed in the risk profile when treatments were altered. This indicates that

considering the perspectives of TT and TC and altering treatments of some impacts in

CBA appropriately reflects the net impacts and risks of a toll road project in the

outcome BCR and risk profiles.

9.3 CONTRIBUTION TO THEORY

Many past studies (Aldrete et al., 2012; Anas & Lindsey, 2011; Bain, 2009; Bel &

Foote, 2009; Carpintero, 2010; Li & Hensher, 2010; Liyanage & Villalba-Romero,

2015; Mishra et al., 2013; Odeck, 2008; José Manuel Vassallo et al., 2012; Welde,

2011; L. Zhang, 2008; Z. Zhang et al., 2013) evaluated toll road projects from different

points-of-view than CBA. Various studies exist with regard to financial analysis and

traffic forecasting studies of toll road projects (Bain, 2009; Li & Hensher, 2010;

Welde, 2011). In comparison, CBA evaluates a project with respect to transport

benefits and costs. This allows assessment of whether the project is beneficial to the

community, instead of evaluating projects solely based on financial impacts. This

study extended the knowledge of evaluation of a toll road project by considering

transport impacts using CBA.

Considering “toll as a financial transfer” (TT) and “toll as an end-user cost” (TC)

perspectives in CBA has not been studied, although different perspectives have

previously been examined in financial analysis (Mishra et al., 2013). Treatments of

some project impacts in CBA were altered by considering these perspectives in this

study. This allowed exploration of whether the TC perspective is a valid approach. The

outcomes of the evaluation confirmed that the shift of risk of a privately delivered

project can be observed using the proposed methodology under the TC perspective.

How CBA can be conducted by solely evaluating the project from the public

perspective was examined by considering the TT and TC perspectives. This is a

significant contribution to the academic study, which can suggest a number of future


studies on incorporating different perspectives into CBA of various infrastructure

types.

Generally, tolls are considered as a financial transfer and enter into CBA

calculations only to the extent that they cause a change in micro-economic behaviour

(Decorla-Souza et al., 2013). This study determined that treating tolls as the cost to the

community is a valid approach under the TC perspective. This is a significant

contribution to the extent knowledge.

This study found that by incorporating a stochastic approach into CBA of a toll

road project, the shift of risk can be analysed empirically, which demonstrated

applications of the stochastic CBA. This study also examined the effectiveness of

various measures to evaluate toll road projects. Coefficient of variation (CV) was

found to be an effective measure of risk of projects with any Benefit-Cost Ratio (BCR).

Percentiles and cumulative probability distribution graphs can also represent the

spread of the outcome stochastic BCR, while CV quantifies the level of risk in an

empirical manner, which can easily be compared across scenarios.

Although, Asplund and Eliasson (2016) claim that transport investment and

transport demand risks affect the CBA results the most, this study showed that the risks

of travel time unit price, VHTS, and traffic growth, as well as AADT and capital cost

affected the outcome BCR the most. This is consistent with that the travel time savings

most contributed in the overall benefits.

The one-way probabilistic sensitivity analysis of variation in risk characteristics

demonstrated the assessment in terms of the vulnerability towards particular sources

of risks across perspectives and scenarios. The outcome of the assessment heavily

relies on the model assumptions, however, various sensitivity analyses can be

conducted to further investigate the relationship between the risk that the host

government is bearing and various risk-sharing arrangements using the proposed

methodology. This would extend knowledge, particularly in terms of better allocating

traffic and revenue risks of a toll road project through various risk-sharing

arrangements.

9.4 CONTRIBUTION TO PRACTICE

The key contribution of the proposed methodology is the representation of impacts to

the community by solely evaluating the project from the public perspective, using


Cost-Benefit Analysis (CBA). Not only contributing to the academic study

significantly, this can also provide significant contribution to practice. The host

government acts as the decision-maker in the project appraisal of major road projects

and is responsible to ensure that the benefits of the decision outweigh the costs. Many

recent major road projects, such as those examined in this study (AECOM Australia,

2013, 2014; Arup, 2010a; Brisbane City Council, 2010; Connell Wagner, 2004;

Queensland Government, 2008; SKM & Connell Wagner, 2006; The Allen Consulting

Group, 1996), have been delivered through Public-Private Partnership (PPP) schemes.

The proposed methodology provides the host government with a tool to evaluate major

PPP projects from the public perspective. For the purpose of equitable decision

making, the proposed methodology can be used by government agencies, transport

economists and transport planners, in order to present CBA outcomes to the decision-

makers.

The proposed methodology can be implemented into the existing project

appraisal process by incorporating it into CBA phase. The framework of the proposed

methodology (see Figure 8.2) clearly displays the added steps and how it can be

conducted as part of the existing framework.

Decorla-Souza et al. (2013) argue that a broad view of the public as a whole,

including users and nonusers of the project, need to be considered, in order to assess

various delivery options including PPP schemes. The proposed methodology assesses

various delivery options by using CBA. This potentially provides a link between CBA

and procurement.

CBA calculation does not require any rigorous statistical or modelling

techniques and can be conducted easily using spreadsheet analysis. The Monte Carlo

simulation can also be conducted using a spreadsheet, such as Excel. Tools other than

spreadsheet can also be used, however they need to be able to conduct the Monte Carlo

simulations and analyse stochastic representation of the analysis outcome.

Interpretations of statistical inferences can be a complex task without knowledge of

statistics. Guidance on interpretations of risk profiles was therefore provided in this

thesis.

This study demonstrated graphical representations of the net impacts and risks

of the project using cumulative probability distributions, and box-and-whisker plots.

Interpretations of these graphs are summarised in Table 8.2. Visually representing the


analysis outcome is particularly useful for the decision-maker. For instance, the length

and the position of each box in box-and-whisker plots represent the net impacts and

risks of the project visually across scenarios. The decision-makers can efficiently make

a well-informed decision without having to read and interpret numerously represented

outcomes.

9.5 RECOMMENDATIONS FOR FUTURE WORK

➢ Exploring variety of probability distributions

The impacts of using a range of different probability distributions for input variables

were not explored in this study. The level of impacts of using different probability

distributions on the CBA outcome can be explored. This allows to identify the input

variables that the most impact the outcome when types of probability distributions are

changed. Additionally, past data can be statistically studied to determine the types of

probability distributions that the real data follows.

➢ Incorporating ramp-up period

The length of ramp-up period can differ between projects. The impacts of

incorporating a range of ramp-up periods using the proposed methodology, in order to

investigate their influences on the CBA outcome.

➢ Testing the sensitivity of a range of discount rates

A wide range of discount rates are used in different countries. The sensitivity of using

different discount rates can be investigated, in order to explore their influences on the

CBA outcome.

➢ Conducting multi-way sensitivity

One-way probabilistic sensitivity was conducted in this study. Multi-way probabilistic

sensitivity can be conducted on a number of input variables. Particularly, the input

variables with some correlations can further be investigated through the multi-way

sensitivity analysis. For instance, this study showed that travel time saving is the major

benefit of a major road project. Therefore, multi-way probabilistic sensitivity analysis

is recommended for those input variables that influence the travel time saving.

Additionally, the elasticity of the toll price and traffic volume can be incorporated into

the analysis.

➢ Applying the proposed methodology to other types of projects


Similar studies on various transport infrastructure projects other than toll road projects

are also recommended as future studies. Applications on various types of Public-

Private Partnership (PPP) projects would further extend the knowledge of Cost-Benefit

Analysis (CBA).

➢ Incorporating WEB

As has been highlighted in the literature review, the guidelines that provide guidance

on accounting wider economic benefits (WEB) in CBA will be published in Australia

in the near future. The WEB methodology needs to be reviewed when ready, in order

to incorporate the WEB in the proposed methodology.

9.6 CONCLUDING REMARKS

This study examined the impacts and risks of a toll road project to the community, in

order to reflect them in Cost-Benefit Analysis (CBA). Literature and previously

conducted CBA were reviewed to highlight any limitations and difficulties of CBA.

The outcomes of the stochastic CBA were examined to observe how the sources of

risks are reflected in the outcomes. A toll road project case and a toll tunnel project

case were synthesised on the basis of existing toll road projects, in order to determine

the methodology that best reflects the net impacts and risks of a toll road project to the

community. The outcomes of CBA of a toll road project, when treatments of project

impacts vary, were investigated by examining various payment movements. This study

concludes that the estimation of net impacts and risks of a toll road project to the

community for the purpose of project evaluation can be improved by considering the

treatment of project impacts and performing stochastic CBA. Additionally, this study

proposed the CBA methodology that incorporates considerations of multiple

perspectives, in order to best reflect the net impacts and risks to the community, and

demonstrates them in an empirical manner in risk profiles.

List of Publications and Awards 139

List of Publications and Awards

Journal publications:

Chi, Sae, Bunker, Jonathan M., & Teo, Melissa (2017) Measuring impacts and risks

to the public of a privately operated toll road project by considering perspectives

in cost-benefit analysis. Journal of Transportation Engineering, 143(12), Article

number-04017060

Conference publications:

Chi, Sae, Bunker, Jonathan M., & Kajewski, Stephen L. (2016) Comparative case

study on cost-benefit analysis for toll road projects. In 27th ARRB Conference

2016, 16-18 November 2016, Melbourne, Vic. (Peer reviewed conference paper)

Chi, Sae, Bunker, Jonathan M., & Kajewski, Stephen L. (2016) A review of project

evaluation methodologies to address net impacts and risks of toll road projects

to the community. In Conference of Australian Institutes of Transport Research

2016, 11-12 February 2016, Brisbane, Qld. (Non-peer reviewed conference

paper)

Awards:

• Young Research Award 2016 from ARRB Academy

• Regional Student Researcher Prize (Queensland and Northern Territory)

2016 from ARRB and Roads Australia

• Australasian Prize 2016 (Best Student Researcher at an Australian

University) from ARRB and Roads Australia

• High Degree Research Student Award for December 2016 from Science and

Engineering Faculty at Queensland University of Technology

References 141

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