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Transport for South Hampshire Evidence Base Public Transport Model Calibration and Validation Summary Report 5

Report for Transport for South Hampshire

August 2011

This report, and information or advice which it contains, is provided by MVA Consultancy Ltd solely for internal use and reliance by its Client in performance of MVA Consultancy Ltd’s duties and liabilities under its contract with the Client. Any advice, opinions, or recommendations within this report should be read and relied upon only in the context of the report as a whole. The advice and opinions in this report are based upon the information made available to MVA Consultancy Ltd at the date of this report and on current UK standards, codes, technology and construction practices as at the date of this report. Following final delivery of this report to the Client, MVA Consultancy Ltd will have no further obligations or duty to advise the Client on any matters, including development affecting the information or advice provided in this report. This report has been prepared by MVA Consultancy Ltd in their professional capacity as Consultants. The contents of the report do not, in any way, purport to include any manner of legal advice or opinion. This report is prepared in accordance with the terms and conditions of MVA Consultancy Ltd’s contract with the Client. Regard should be had to those terms and conditions when considering and/or placing any reliance on this report. Should the Client wish to release this report to a Third Party for that party's reliance, MVA Consultancy Ltd may, at its discretion, agree to such release provided that: (a) MVA Consultancy Ltd's written agreement is obtained prior to such release, and (b) by release of the report to the Third Party, that Third Party does not acquire any rights, contractual or otherwise, whatsoever against MVA Consultancy Ltd and MVA Consultancy Ltd, accordingly, assume no duties, liabilities or obligations to that Third Party, and (c) MVA Consultancy Ltd accepts no responsibility for any loss or damage incurred by the Client or for any conflict of MVA Consultancy Ltd's interests arising out of the Client's release of this report to the Third Party.

Document Control

Project Title: Transport for South Hampshire Evidence Base

MVA Project Number: C39344

Document Type: Public Transport Model Calibration and Validation Summary

Directory & File Name: J:\C39344_Transport_For_SOUTH_HAMPSHIRE_Model_Suite\MVA_Docs\Re

ports\R5 PTM Calibration And Validation Report\Tfsh_R5_PTM Calibration

And Validation Report_V1c.Doc

Document Approval

Primary Author: Nick Benbow

Other Author(s): James Blythe, Ann Fenwick, Matt Pollard, Fitsum Teklu, Ian Burden, Dave

Carter

Reviewer(s): Ian Burden, Dave Carter

Formatted by: Sally Watts

Distribution

Issue Date Distribution Comments

0 05/05/11 Ian Burden, Dave Carter Proposed structure

1 10/06/11 Evidence Base Progress Group Draft for comment

2 25/08/11 Evidence Base Progress Group Final Incorporating Comments

Public Transport Model Calibration and Validation Summary Report 5 1

Contents

Foreword i

1 Introduction 1.1 1.1 Background 1.1 1.2 Context and Scope 1.1

2 Model Dimensions 2.1 2.1 Introduction 2.1 2.2 Model Areas 2.1 2.3 Zoning 2.2 2.4 Modes 2.3 2.5 Dimensions 2.3

3 Data Sources 3.1 3.1 Introduction 3.1 3.2 Summary of Data Sources 3.1 3.3 Data Collection Locations and Routes 3.2

4 Network Construction and Calibration 4.4 4.1 Introduction 4.4 4.2 Network and Public Transport Services 4.4 4.3 Fares 4.5

5 Matrix Development 5.1 5.1 Introduction 5.1 5.2 Creation of Matrices from Passenger Survey Data 5.2 5.3 Creation of Matrices from Bus ETM Data 5.3 5.4 Combination ETM and Observed Bus Data 5.3 5.5 Trip Ends 5.3 5.6 Dissagregation to Zones 5.4

6 Calibration and Validation 6.1 6.1 Introduction 6.1 6.2 Assignment Process 6.1 6.3 Network and Assignment Parameter Calibration 6.2 6.4 Strategic Validation 6.3 6.5 Detailed Validation 6.4

7 Fitness for Purpose 7.1 7.1 Objectives 7.1

Contents

Public Transport Model Calibration and Validation Summary Report 5 2

7.2 Modelling Approach 7.1 7.3 Model Inputs 7.1 7.4 Model Outputs 7.2

Tables Table 3.1 Data Sources – Demand Side 3.1 Table 3.2 Data Sources – Supply Side 3.2 Table 6.1 Boarding and Interchange Penalties 6.2 Table 6.2 Strategic Validation 6.3 Table 6.3 Bus Cordon Validation 6.4 Table 6.4 Observed and Modelled Passengers Entering and Leaving TfSH Stations 6.5 Table 6.5 Observed and Modelled Ferry Passengers 6.5

Figures Figure 1.1 TfSH Sub-Regional Transport Model 1.2 Figure 2.1 Study Area of the PTM 2.1 Figure 2.2 SRTM Zone System around the Study Area 2.2 Figure 3.1 Bus OD Survey Cluster & Screenlines : Southampton & surrounding area 3.2 Figure 3.2 Bus OD Survey Cluster & Screenlines: Portsmouth & surrounding area 3.3 Figure 3.3 Location of Bus Journey Time Routes 3.3 Figure 5.1 PTM Sector Map 5.1 Figure 6.1 PTM Wait Curve – All Services 6.2

TfSH Evidence Base: Sub‐Regional Transport

Model

Foreword The TfSH Steering Group was formed in July 2009 and has met every two months to oversee and direct the development of the TfSH Evidence Base. The Steering Group’s first objective is:

• To develop an Evidence Base consisting of WebTAG compliant analysis and forecasting tools to define current and future problems and develop practical solutions of improvement schemes and interventions to resolve them and achieve local, regional and national objectives;

The development of the Evidence Base is centred on the Sub‐Regional Transport Model (SRTM) and has covered to date:

• Tender specification and appointment of Consultants; • 2010 Data Collection and R1 Report of Surveys; • Development of the SRTM Suite consisting of:

o Main Demand Model, o Local Economic Impact Model, o Road Traffic Model, o Public Transport Model, and o Gateway Demand Model.

This Report documents the development, calibration and validation of one or more of these models. An important objective of the consultant’s specification and subsequent role of the Steering Group has been to achieve the successful development of the SRTM to time and budget, and in accordance with current best practice and guidance laid down by the Department for Transport in its Transport Appraisal Guidance (webTAG). In so far as it has been feasible, the Steering Group is satisfied that the development of the SRTM described in this report meets its first objective. Signatories

1

http://www.dft.gov.uk/

http://www.go-se.gov.uk/

Public Transport Model Calibration and Validation Summary Report 5 1.1

1 Introduction

1.1 Background

1.1.1 MVA Consultancy was commissioned, as part of a wider team, to support Transport for South

Hampshire (TfSH) with the development and application of a Sub-Regional Transport Model

Suite (SRTM) for this nationally important area.

1.1.2 The SRTM will be used to support a wide-ranging set of interventions across the TfSH sub-

region, and is specifically required to be capable of:

forecasting changes in travel demand, road traffic, public transport patronage and

active mode use over time as a result of changing economic conditions, land-use

policies and development, and transport improvement and interventions;

testing the impacts of land-use and transport policies and strategies within a relatively

short model run time; and

testing the impacts of individual transport interventions in the increased detail

necessary for preparing submissions for inclusion in funding programmes within

practical (but probably longer) run times.

1.1.3 This report describes the development, calibration and validation of the Public Transport

Model (PTM) within the SRTM.

1.2 Context and Scope

1.2.1 The SRTM is a suite of linked models comprising the following components as shown in Figure

1.1:

the Main Demand Model (MDM) which predicts when (time of day), where (destination

choice) and how (choice of mode) journeys are made;

the Gateway Demand Model (GDM) which predicts demand for travel from ports and

airports;

the Road Traffic Model (RTM) which determines the routes taken by vehicles through

the road network and journey times, accounting for congestion;

the Public Transport Model (PTM) which determines routes and services chosen by

public transport passengers; and

an associated Local Economic Impact Model (LEIM) which uses inputs including

transport costs to forecast the quantum and location of households, populations and

jobs.

1 Introduction


Figure 1.1 TfSH Sub-Regional Transport Model

Main Demand ModelMDM

Road Traffic Model RTM

Public Transport Model PTM

HW

Dem

and

HW speeds

Bus Frequency

Gateway Demand Model

GDM(run once at start)

Sub-Regional Transport ModelSRTMLocal Economic

Impact Model LEIM

HW

Gen

Cos

t

PT G

en C

ost

PT D

eman

d

Costs

Port/Airport demand(HW & PT)

Costs

Population & Employment

Main Demand ModelMDM

Road Traffic Model RTM

Public Transport Model PTM

HW

Dem

and

HW speeds

Bus Frequency

Gateway Demand Model

GDM(run once at start)

Sub-Regional Transport ModelSRTMLocal Economic

Impact Model LEIM

HW

Gen

Cos

t

PT G

en C

ost

PT D

eman

d

Costs

Port/Airport demand(HW & PT)

Costs

Population & Employment

1.2.2 The PTM has been developed to represent the base year demand, route and sub-mode (i.e.

bus, rail or ferry) choices and costs on the public transport network. In terms of future

scenarios, it will represent the network impacts of different policy and infrastructure

interventions. Park and ride mode choice is modelled separately in the MDM, although the PT

leg of park and ride journeys are modelled within the PTM.

1.2.3 Citilabs’ Voyager software was selected to implement the PTM. Voyager replaces Citilabs’

now unsupported TRIPS software, which has historically been the most widely used public

transport modelling tool in the UK, but shares many of the same interfaces. Voyager is

developing an extensive user base in the UK, has user friendly interface facilities, extensive

graphical and reporting capabilities, and represents the key characteristics of public transport

services and how passengers make their route choices.


2 Model Dimensions

2.1 Introduction

2.1.1 This chapter summarises the features of the PTM and includes the following sections:

Geographic scope;

Zoning system;

Modes represented;

Time periods;

Modelled years; and

Demand segmentation.

2.2 Model Areas

2.2.1 The modelled area of the PTM is sub-divided into two regions, shown in Figure 2.1, which

differ by zone aggregation and modelling detail, as follows:

Fully Modelled Area (FMA) - detailed zoning; and

Buffer / External (zones based on wards and districts).

Figure 2.1 Study Area of the PTM

2 Model Dimensions


2.3 Zoning

2.3.1 Travel in the model is aggregated into zones which therefore determine the spatial detail

available. As the PTM zone system must be consistent with that used in other components of

the suite, in particular the RTM, the zone system (shown in Figure 2.2) has been defined

following current guidance1 for highway models but with consideration given to the

requirements for public transport modelling. The definition of zones takes account of:

natural barriers (rivers, railways, motorways or other major roads);

areas of similar land use that have clearly identifiable and unambiguous points of

access onto the road network included in the model;

existing zone boundaries, where an existing model is being used as the basis for the

new model (in this case the Solent Strategic Transport Model (SSTM) and Portsmouth

Western Corridor Study (PWCS));

administrative and planning data boundaries (TfSH zones are aggregations of Census

Output Areas in the fully modelled area and wards elsewhere);

the location of the main parking areas, where town centres are included in the model;

the need for internal screenlines for trip matrix validation;

catchment areas for rail stations and bus stops and fare boundaries are also

considered; and

additional zones are included for the ports and airports.

Figure 2.2 SRTM Zone System around the Study Area

1 Design Manual for Roads and Bridges, Section 12.2.1

2 Model Dimensions


2.4 Modes

2.4.1 Trips by public transport as a main mode are produced by the demand model. The PTM is

used to assign trips to one or more of bus, ferry and rail. In some cases it is possible that

very short distance public transport trips may not be assigned onto a public transport mode

where the generalised cost of the public transport modal journey is higher than the

(weighted) walk time. The PTM has been configured with a maximum walk time of 15

minutes for any journey that does not use a public transport service to ensure long walks do

not replace public transport journeys where frequencies are relatively low.

2.5 Dimensions

2.5.1 In accordance with guidance three weekday periods are modelled in the PTM:

AM peak: busiest hour between 0700 and 1000, 40% of the three hours;

Inter peak: average of 1000 to 1600; and

PM peak: busiest hour between 1600 and 1900, 40% of the three hours.

2.5.2 In line with the Main Demand Model the PTM has a base year of 2010, and forecast years of

2014, 2019, 2026 and 2036. In addition LEIM provides forecasts through to 2041.

2.5.3 Unlike the RTM, the PTM does not use demand matrices for different user class segments.

This is because Employer’s Business trips constitute a very small share of public transport

journeys and the remaining journeys have a similar value of in-vehicle and waiting times. As

such, only one fare structure is modelled.


3 Data Sources

3.1 Introduction

3.1.1 This chapter describes the data used to calibrate and validate the PTM.

3.2 Summary of Data Sources

3.2.1 The data used can be defined as either demand or supply. Demand data is any information

used to calibrate and validate the demand matrices and is shown in Table 3.1. Supply data is

used for building the public transport network and is shown in Table 3.2.

Table 3.1 Data Sources – Demand Side

Travel Pattern Data Source

Passenger surveys at rail stations, ferry ports and major bus stop clusters:

bus survey locations shown in Figures 3.1 and 3.2

for bus passengers self completion postcards issued where face-to-face interviews were

impractical

for rail passengers the National Rail Travel Survey questionnaire was adapted and

distributed for self-completion and return across the study area

accompanying counts of rail passenger boarding and alightings (0700-1900) where

service number, arrival time and departure time were recorded (0700-1100 for less

busy railway stations)

only boarding (waiting) passengers surveyed as alighting passengers are generally

unwilling to be delayed

surveys capture journey purpose, origin and destination postcode, bus service number,

access / egress mode, number of people travelling together including children, single

or return journey, time of day, age, gender, car ownership

also captured for rail: ticket type and location where purchased; origin, destination and

any interchange stations

ferry surveys for the following routes: Southsea to Ryde, Southampton to Hythe,

Southampton to East Cowes, Southampton to West Cowes, Portsmouth to Fishbourne,

Portsmouth Harbour to Ryde and Portsmouth Harbour to Gosport

ferry foot and car (where applicable) passengers were surveyed

Electronic Ticket Machine (ETM) data of bus ticket sales:

two week periods in April and May 2010

for Go-Ahead, Black Velvet (1 week only), First and Stagecoach

includes date and time, ticket type, boarding fare stage and service number

1.1 million journeys recorded

Census Journey To Work data from 2001

National Trip End Mode trip rates

Trip end estimates from TEMPRO for Buffer and External Areas

Outbound / return trip proportions from the National Travel Survey

3 Data Sources


Table 3.2 Data Sources – Supply Side

Data Type Data Source

Network

and route

specification

RTM road network

Public transport timetables (May 2010)and Hampshire’s Bus, Train and

Ferry Travel Guide

Journey

times

Rail and ferry timetables

Bus journey times derived from the RTM network with application of factors

to reflect the difference in speed relative to cars

Journey times on 12 bus routes were used to validate the bus journey

times (see Figure 3.3.)

Fares Operator websites

3.3 Data Collection Locations and Routes

3.3.1 The following figures, referenced in Table 3.1, illustrate the locations of interview sites.

Figure 3.1 Bus OD Survey Cluster & Screenlines : Southampton & surrounding area

3 Data Sources


Figure 3.2 Bus OD Survey Cluster & Screenlines: Portsmouth & surrounding area

3.3.2 Figure 3.3 shows the 12 journey time routes used in the PTM.

Figure 3.3 Location of Bus Journey Time Routes

BS8

FT10

FT7FT3

FT9

FT80

FT82

SC21

FT1

FT41

FT63

BS1


4 Network Construction and Calibration

4.1 Introduction

4.1.1 This chapter summarises the processes used to construct the public transport network and

service pattern description, and the validation of bus speeds.

4.2 Network and Public Transport Services

4.2.1 The road network definition is used by buses and for walking in the PTM and was obtained

from the RTM. Bus lane locations were also taken from the RTM. Links to represent the rail

and ferry networks were added to the road links. Walking is not permitted in the PTM on

motorway, rail or ferry links. Links were also added to represent pedestrianised areas in

town centres.

4.2.2 Bus speeds are based on speeds extracted from the RTM which reflect congestion due to the

assigned highway traffic. For links with bus lanes the free flow speed was extracted from the

RTM and is factored by 0.8 to allow for the slower nature of buses compared to cars. For

motorways the speeds extracted from the RTM were factored by 0.9 to represent buses, and

for all other links a factors of 0.7 was applied.

4.2.3 The resulting bus times were checked against timetabled times for a sample of 12 routes in

both directions and for all three periods (see Figure 3.3 above). Total times for each set of

routes were replicated to within 6%; and all but one route in one direction in each of the

periods were replicated within 25%. This indicates that the model closely reflects actual bus

speeds.

4.2.4 All bus, rail and ferry services within the FMA have been coded into the model, along with key

routes which cross the FMA boundary. The following key attributes were coded for each

service:

service number;

service description;

operator;

mode;

headway;

nodes traversed; and

fare table reference.

4.2.5 All network and service coding was checked by somebody other than the original coder.

4 Network Construction and Calibration


4.3 Fares

4.3.1 Both bus and rail fares were derived using the same methodology. The costs of travelling

between the main urban areas with the study area were obtained, and these were compared

against the journey distances. Relationships between fare and distance were derived for both

bus and rail, and also for peak and off peak periods.

4.3.1 A representation of discounts relating to season tickets and concessionary fares has been

modelled, reducing average modelled fares by 10%, 15% and 10% in the AM, inter-peak and

PM periods respectively.

4.3.2 For rail, the modelled cost of travelling is half of the return fare. For bus, the cost of

travelling was based on a combination of single fares and a relationship between single and

return fares, which varies depending on the price of the single fare. The more expensive a

single fare is, the closer it is likely to be to the cost of a return fare. This relationship was

used to derive return fares from the single fares. As with rail, the modelled cost of travelling

is half of the return fare. In addition bus fares were capped at half of the cost of a one-day

travel card. Ferry fares were costed by each individual service.

4.3.3 In reality fare structures and the availability of general and more specific discounts is

exceptionally complex. The Voyager software model cannot handle specific fare products,

including, for example on the rail network, differences between anytime, off-peak and super-

off peak fares, season ticket discounts and railcard usage. Similarly complex structures on

the bus network and the range of discounts for uni-mode and multi-modal ferry journeys are

simplified to a set of distance based structures for rail and bus and fixed fares for ferry

usage.

4.3.4 In modelling fares, it is important that a reasonable representation of absolute average fares

is used as a deterrence for multiple boarding for single public transport journeys in the

assignment model and for demand model calibration purposes. In future forecasting, the

absolute fare level modelled is of less importance, as it is the marginal change in fare levels

that will affect assignment between modes and within the demand model.


5 Matrix Development

5.1 Introduction

5.1.1 This section describes the methodology for the development of the base year trip matrices.

The demand matrices contain estimates of all travel to, from and within the Core FMA. The

only trips included to and from the Marginal FMA, Buffer and External area are those trips

which cross the Core FMA.

5.1.2 As discussed in Chapter 2 a single demand segment was used during the assignment.

Matrices were developed with the following more disaggregate set of purposes:

home based employers business;

non home based employers business;

home based work;

home based education;

home based other; and

non home based other.

5.1.3 Matrices were created using data from passenger surveys described in Chapter 3, and ETM

data. The ETM data were not available at the level of spatial detail required to allocate

demand to model zones. The observed matrices were therefore initially built for a less

detailed sector system (shown in Figure 5.1) and subsequently disaggregated to zones using

population data, Census Journey to Work information and a destination choice model.

Figure 5.1 PTM Sector Map



5.1.4 The remainder of this chapter is structured as follows:

creation of matrices from the passenger surveys;

creation of matrices from bus ETM data;

combination of ETM and observed bus data;

calculating trip ends of total demand to and from each zone; and

disaggregation to zones.

5.2 Creation of Matrices from Passenger Survey Data

5.2.1 Similar processes were followed to create matrices from the bus, rail and ferry passenger

surveys described in Chapter 3. The locations of surveys were specified in order to capture a

high proportion of public transport movements within the TfSH area.

5.2.2 An initial task was to check whether the origin and destination postcodes recorded in the

surveys were consistent with the direction of the public transport service being used. If this

was not the case the origins and destinations were switched.

5.2.3 Expansion factors were calculated as the ratio of the number of passengers surveyed to the

number of passengers counted boarding services at the survey location. These factors were

calculated separately for combinations of:

age group (over or under 16);

stop, station or ferry port;

public transport route; and

arrival time (grouped by hour).

5.2.4 Following the expansion of records to the boarding counts, duplicate records were created to

represent the return journeys. Records were replicated but with origin and destination

reversed and with the return journey time used in place of the outbound time. Expansion

factors were calculated from alighting counts to apply weights to the reversed records so that

the numbers of alighting passengers were matched. The factors were calculated by bus stop

cluster, station or ferry port, and hour.

5.2.5 A cap of 45 was applied to the expansion factors so that no individual survey could dominate

the matrix. This affected 1.5% of the expanded number of trips, and an uplift factor was

applied to all records to compensate for the capping.

5.2.6 Some journeys could have been observed more than twice and so expansion factors were

reduced by 50% in the following cases:

where the access or egress mode was also public transport and the origin or

destination were also in a surveyed area; and

movements between surveyed areas.



5.2.7 The National Rail Travel Survey (NRTS) data for the TfSH area was also processed into

sectored matrices. It was found that NRTS captured journeys for some sector-pairs which

were not observed in the TfSH surveys. Where the number of journeys for a sector-pair were

90% lower in the TfSH data than the NRTS data the NRTS data were used.

5.3 Creation of Matrices from Bus ETM Data

5.3.1 ETM patronage data were obtained from each of the bus operators in the study area. The

data from Go-Ahead was provided separately for each of 5 depots: Eastleigh, Marchwood,

Salisbury, Lymington and Unilink. For each operator (and for Go-Ahead each depot), the

number of passengers recorded on each day for each route was processed in order to give

estimates of bus patronage on an average weekday.

5.3.2 Each record in the ETM dataset was allocated to origin and destination TfSH sectors based on

the boarding and alighting fare zone, to allow for comparison with the survey data matrices.

Both matrices were compressed into an appropriate sector system, and the totals for each

sector-sector pair were compared in each time period. Since the survey data matrices are

split by purpose, it was necessary to impose a purpose split on the ETM data. Purpose splits

were taken from national average figures based on NTEM data.

5.4 Combination ETM and Observed Bus Data

5.4.1 The observed bus matrix was then built, at a sector level, from a combination of the

expanded survey data and the ETM boarding data, as follows:

cells to or from sectors representing areas where the surveys took place were all taken

from the expanded surveys rather than the ETM data;

trips to or from sectors outside areas where the surveys took place, but possibly

interchanging in survey areas, were taken from a combination of the expanded

surveys (for those interchanging) and the ETM data (for those not travelling via survey

areas);

there was no ETM data for the External areas or the Isle of Wight, so the cells to and

from the External area and Isle of Wight were all taken from the surveys;

it was found that in all periods, there were comparatively fewer ETM trips in the

Gosport area, so the survey results were preferred for cells to and from Gosport; and

remaining sector to sector movements were taken from the ETM data.

5.5 Trip Ends

5.5.1 The home-based purpose origin/destination person trip ends for zones within the FMA were

produced using the following steps:

Home-based production trip ends were estimated for all FMA zones by applying the

NTEM production trip rates to the population data. These trip ends represent the

‘outbound’ trip only;



Home-based attraction trip ends within the FMA were estimated by applying the NTEM

trip attraction trip rates to the employment data, and scaling total attractions to match

total productions for each purpose, mode (including active modes), time period and

car availability across the FMA;

The Outbound/Return factors (derived from the National Travel Survey) were used to

calculate the ratio of from-home and to-home trips in each time period; these ratios

were used to generate return trip ends from the NTEM-based outbound trip ends;

Census Journey to Work data by zone was used to adjust the mode shares (including

active modes) for the home-based production/attraction trip ends so that they reflect

the accessibility of particular zones to public transport services. This was necessary

because NTEM-derived trip ends were not deemed representative at the sub-NTEM-

zone level; and

Origin/Destination trip ends were then derived from the production/attraction trip ends

by re-applying the Outbound/Return factors.

5.5.2 The non-home-based purpose origin/destination trip ends for zones within the FMA were

developed using home-based to non-home based trip rate factors derived from National

Travel Survey (NTS) data.

5.5.3 For all zones outside the FMA, DfT’s TEMPRO software was used to output 2010

origin/destination trip ends by purpose, mode and time period for all zones outside the FMA,

using TEMPRO dataset version 5.4. TEMPRO was also used to generate car availability

splitting factors for these zones.

5.6 Dissagregation to Zones

5.6.1 The distribution of the sectored matrices to model zones in the FMA makes use of a synthetic

pattern of trips derived using a destination choice model (DCM). The DCM took account of:

the number of trips from/to the respective origin/destination pairs;

observed sector-to-sector movements; and

the generalised cost of PT travel between two zones.

5.6.2 The DCM predicts the probability of a trip from an origin travelling to each destination as a

function of the relative generalised costs of travel to each destination. The parameter which

determines the sensitivity of the DCM to generalised cost differences was calibrated using

destination choice profiles from the matrices developed from the passenger surveys and

preliminary generalised cost estimates from the network model. Separate models were

calibrated for each time period and purpose combination. Destination specific constants,

which adjust the generalised cost of travel for all trips to a destination by the same amount,

were included in the models to ensure that the total of distributions matched the trip ends

calculated as described above. Generalised cost adjustments were also calibrated so that the

DCM matched observed demands sector pairs.

5.6.3 Plots of trip cost distributions (TCDs) for zone-pairs which were observed were checked to

ensure that the model closely replicated observed TCDs.


6 Calibration and Validation

6.1 Introduction

6.1.1 This chapter describes:

the assignment process applied using Voyager;

the approach taken to calibrate the PTM;

strategic validation of total demand for each sub-mode against published data; and

detailed validation of passenger flows.

6.2 Assignment Process

6.2.1 Voyager’s frequency and cost-based strategic public transport assignment method is used in

the PTM. The key features of the Cube Voyager public transport software are listed below:

the derivation of a discrete set of routes between zone pairs;

the evaluation of the ‘reasonable’ routes between zone pairs, and the calculation of the

probability of each route being used; and

the provision of methods to model fare levels and different fare structures.

6.2.2 The choice of routes (and sub-modes) is based on a generalised cost based formulation of

travel costs that includes fares, in-vehicle travel times, waiting times, boarding penalties and

interchange penalties. The other components of the generalised cost are calibration

parameters that have been adjusted within reasonable bounds. Generalised cost

adjustments to reflect the discomfort and inconvenience of travelling on crowded services

have not been included. Generalised costs were calculated using the following relationships

and coefficients which were based on advice given in DfT’s Transport Analysis Guidance

(TAG):

values of time derived from TAG Unit 3.5.6;

bus in-vehicle time in the peak periods was factored by 1.1 based on previous studies

reflecting poorer reliability than rail or ferry;

waiting and walking time was doubled;

boarding and interchange penalties were included which represent the perceived

inconvenience of a mode or of changing service (see Table 6.1); and

wait time was calculated as a function of service frequency using the curve shown in

Figure 6.1 which reflects actual time spent waiting for a service and the inconvenience

of infrequent services.



Table 6.1 Boarding and Interchange Penalties

Penalty Mode AM/PM (mins) IP (mins)

Boarding penalty Bus 5.0 5.0

Boarding penalty Rail 2.5 5.0

Boarding penalty Ferry 2.5 5.0

Interchange penalties Bus to Bus

Bus to Rail and v.v

Bus to Ferry and v.v

5.0

7.5

5.0

5.0

7.5

5.0

Figure 6.1 PTM Wait Curve – All Services

0

2

4

6

8

10

12

14

16

0 10 20 30 40 50 60Headway (minutes)

Wai

t Tim

e (m

inut

es)

6.3 Network and Assignment Parameter Calibration

6.3.1 TAG Unit 3.11.2 recommends techniques with which the assignment model may be calibrated

to produce a higher degree of validation. These are:

adjustments to the zone centroid connector times and costs;

adjustments to network and service details;

adjustments to in-vehicle time factors;

adjustments to walk and wait time factors;

adjustments to interchange penalties;

adjustments to trip loading algorithm parameters;

path building and trip loading algorithm changes; and

segmentation of demand.



6.3.2 WebTAG presents these techniques roughly in the order in which they should be considered,

noting that any adjustments must be plausible. A number of development tests were

undertaken to assess whether adjusting any of the parameters, within plausible ranges,

would improve model calibration. A number of adjustments to the zone centroid connectors

were made to correct initial network inaccuracies. A wholesale validation-driven adjustment

programme to adjust line loadings through zone connector review, adjusting network and

service details, and factoring demands has not been undertaken as we feel focusing on

targeting validation counts would not be appropriate, particularly given the variability in

loading data apparent from count data in Southampton.

6.4 Strategic Validation

6.4.1 TAG Unit 3.11.2 recommends that aggregate modelled results, e.g. total boardings or

passenger kilometres for each sub-mode, are compared with statistics compiled by transport

operators, local authorities or other sources.

6.4.2 These published data report annual passenger demand. An estimate of annual demand has

been constructed as a comparator using matrices from the weekday morning, inter- and

evening peak weekday models. A draft set of annualisation factors was developed from ETM

data obtained from the bus operators specifically for this strategic validation exercise. Whilst

these factors will be valid for use with the bus based assignments, it is unlikely that similar

factors for rail or ferry use would be generated were detailed data available to transform

hourly or period demand into annual estimates. However, in the absence of mode specific

annualisations and for the purposes of this strategic validation a single consistent set of

factors has been applied.

6.4.3 The comparison of reported annual patronage data against estimates derived from the model

is summarised in Table 6.2. The comparison for bus and rail indicates a good match. The

match is less close for ferry travel, which may in part be due to the use of annualisation

factors based on bus travel. Ferries would be expected to carry relatively more passengers

than buses on weekends which is consistent with an underestimate of travel when bus based

annualisation factors are applied to weekday ferry travel. In addition there is significantly

more seasonal variation for ferry travel, particularly for the Isle of Wight. The 2006 Cross

Solent Movement Study notes that the number of foot passengers travelling to and from the

island as much as doubles during the summer months.

Table 6.2 Strategic Validation

Bus Rail Ferry

Source of

Observed Data

National Indicator

177 reported by each

local authority

Counts undertaken

for this study

Local authorities and

Hythe Ferry operator

Units Annual Bus Journeys

Annualised boardings

and alightings Annual ferry journeys

Observed 45.5m 14.8m 7.74m

Modelled 43.8m 16.7m 5.22m

% Difference -4% +13% -33%



6.5 Detailed Validation

6.5.1 TAG Unit 3.11.2 states that modelled screenline flows should be within 15% of the observed

flows, while individual flows should be within 25%, except where observed flows are

particularly low (less than 150 passengers per hour).

6.5.2 Modelled and observed bus passenger flows across the screenlines are shown in Table 6.3.

82% of cordons points meet the TAG validation criteria.

Table 6.3 Bus Cordon Validation

Observed Modelled % Diff

AM Peak

Southampton Cordon Inbound 2388 2318 -3%

Outbound 856 1073 25%

Both directions 3244 3391 5%

Portsmouth Cordon Inbound 754 946 25%

Outbound 654 685 5%

Both directions 1409 1631 16%

Interpeak


Outbound 1410 1367 -3%

Both directions 2901 2705 -7%

Portsmouth Cordon Inbound 952 903 -5%

Outbound 1151 949 -18%


PM Peak


Outbound 2558 2141 -16%


Portsmouth Cordon Inbound 788 789 0%

Outbound 1230 1102 -10%


6.5.3 Table 6.4 summarises the overall number of passengers entering and leaving rail stations in

the TfSH area. It should be noted that the boardings and alightings do not balance due to

out–of-area trip origins and destinations generating net out-commuting rail flows in the AM

peak period and vice versa in the PM peak. With the exception of interpeak the results meet

the TAG criteria.



Table 6.4 Observed and Modelled Passengers Entering and Leaving TfSH Stations

Observed Modelled % Difference

AM peak

- boardings

- alightings

4043

2933

4002

3110

-1%

6%

Inter peak

- boardings

- alightings

1394

1172

1664

1536

19%

31%

PM peak

- boardings

- alightings

2988

3598

3268

3662

9%

2%

6.5.4 The modelled interpeak rail demand is proportionately higher than observed. This is believed

to be due to the sub mode choice in the assignment models allocating more demand onto rail

rather than onto bus during the interpeak. The effect is less noticeable for bus as the

number of bus trips is much greater than the number of rail trips in the interpeak. The

assignment parameters affecting the choice between rail and bus in the interpeak (e.g. in

vehicle time factors and boarding and interchange penalties described in Section 5.3) have

been biased towards bus as much as can be achieved within the recommended guidelines. It

is possible that the wider variations in values of times between purposes in the inter peak is

represented less well by the average assignment values than is the case in the peak periods.

However the overall effect is not believed to be significant as the absolute number of rail trips

that are missing in the interpeak is low.

6.5.5 Table 6.5 summarises the validation of ferry journeys and shows a very close match between

modelled and observed flows.

Table 6.5 Observed and Modelled Ferry Passengers

Observed Modelled % Difference

AM peak 1,512 1,599 6%

Inter peak 1,266 1,161 -8%

PM peak 1,804 1,815 1%


7 Fitness for Purpose

7.1 Objectives

7.1.1 The SRTM, of which the PTM is an integral component, will be used to support a wide-ranging

set of interventions across the TfSH sub-region, and is specifically required to be capable of:

forecasting changes in travel demand, road traffic and public transport patronage over

time as a result changing economic conditions, land-use policies and development, and

transport improvement and interventions;

testing the impacts of land-use and transport policies and strategies within a relatively

short model run time; and

testing the impacts of individual transport interventions in the increased detail

necessary for preparing submissions for inclusion in funding programmes within

practical (but probably longer) run times.

7.2 Modelling Approach

7.2.1 The PTM has been developed using state-of-the art software, Citilabs’ Voyager suite, which is

fully supported by the provider and represents all of the key characteristics of public

transport services and how passengers make their route and sub-mode choices.

7.2.2 The PTM has been developed in line with the relevant DfT guidance which encapsulates good

practice. This includes the model approach and parameters. Consistency with this guidance

will be a key requirement should evidence from the SRTM be used to support bids to DfT for

scheme funding.

7.2.3 The PTM links with other elements of the SRTM, including highway assignment, transport

demand and land use models. As such the model system is a comprehensive representation

of the land use and transport systems, and can forecast how this will evolve over time in

response to changes in planning assumptions or transport supply.

7.3 Model Inputs

7.3.1 The PTM has been developed using recent and extensive surveys of public transport usage

including travel patterns and usage of services. These data have been supplemented by

comprehensive data from bus operators’ ETM systems. As such the model includes a full and

up to date representation of the demand for public transport across the TfSH area.

7.3.2 The representation of public transport supply is also comprehensive with all services within

the TfSH area included along with key services to and from the area.

7.3.3 The model has spatially detailed zone and network definitions which allow for accurate

forecasting of travel demand and for accurate assignment to the network. As a result the

transport demand and land use models will receive reliable estimates of travel generalised

costs.

7 Fitness for Purpose


7.4 Model Outputs

7.4.1 The robustness of model outputs has been demonstrated through:

confirming that total journeys for each sub-mode closely accord with published data;

and

confirming that modelled public transport loadings match observed loadings within

tolerances recommended by DfT.

MVA Consultancy provides advice on transport, to central, regional and local government, agencies, developers, operators and financiers. A diverse group of results-oriented people, we are part of a strong team of professionals worldwide. Through client business planning, customer research and strategy development we create solutions that work for real people in the real world. For more information visit www.mvaconsultancy.com

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