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Market Share Analysis of High-Speed Railway and Airline between

Bangkok and Chiang-Mai

– Using Passenger Choice Behavior Model –

March 2012

Transportation Engineering and Socio-Technology

Graduate School of Science and Technology

Master Course

Nihon University

Rapee Parichatrkanont

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ACKNOWLEDGEMENT

This dissertation could not be completed successfully without many suggestions and

incredible support from many people. I would like to thank and dedicate a part of the success

to all of them.

First, I would like to express my profound gratitude to my supervisor, Prof. Dr.

Tomoyuki Todoroki, for his valuable advice and encouragement through my study at Nihon

University. I deeply appreciate his enduring supervision, kind provisions, the unlimited

support, and all the valuable time and effort contributing toward my accomplishment. His

guidance and recommendations are precious in my future professional life.

Special thanks are conveyed to all members of Public Transportation Planning

Laboratory, especially to Asst. Prof. Dr. Hiroaki Nishiuchi, Dr. Taro Aratani, Mr. Akira

Endo, Mr. Takahiro Kazumi, and other members, for their continual support in various

matters during my stay at Nihon University. I also would like to use this opportunity to thank

Asst. Prof. Joseph Falout for his precious time to assist in my English technical writing, and

all of my friends from other laboratories, departments, and universities, especially Prof. Dr.

Mongkut Piantanakulchai, Dr. Jirapat Phormprapha, Mr. Watana Neangchuklin, and other

foreign friends for being with me throughout my academic career.

Last but not least, I would like to express my deepest appreciation to my beloved

parents, my friends in Thailand, and my many other friends in various communities who have

always been the source of my inspiration. Without their constant support, unremitting

encouragement, infinitive love, and every effort made for the accomplishments in my life, I

would not have come this far. I am forever in debt to them. This dissertation is dedicated to

all of them.

Rapee Parichatrkanont

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ABSTRACT

Recently the Association of South East Asian Nations or ASEAN had agreed to relax

immigration policies, allowing people to commute freely across national borders by 2015.

That means the transportation will become an important role to carry massive numbers of

people crossing countries. And Thailand’s government has a project to construct High-speed

Rail for supporting business and tourism industries’ infrastructure.

However, after low-cost airline business was introduced in Thailand, a “mode shift”

phenomenon had occurred as inter-city passengers from rail and road modes are moving to

low-cost airline.

After Thailand’s first High-speed Rail is completed, a “mode shift” from airlines to

high-speed railway should occur. This thesis will introduce forecasted effects of high-speed

railway development in Thailand and analyze market share between both transport modes. It

will be conducted by Multinomial Logit in Discrete Choice Model to analyze passenger

model choice, which is necessary for understanding modal shifts due to High-speed Rail. The

result of a revenue simulation will help fulfill the vision of development strategy for High-

speed Rail in Thailand.

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TABLE OF CONTENTS

Chapter Title Page Title Page i Acknowledgements iii Abstract iv Table of Contents v List of Tables vi List of Figures viii Abbreviations ix 1 INTRODUCTION 1 1.1 Background 1 1.2 Statement of problem 1 1.3 Objective 1 1.4 Scope of the study 2 1.5 Thesis Outline 2 2 LITERATURE REVIEW 5 2.1 Choice Model 5 2.2 Utility function 6 3 INTER-CITY DEVELOPMENT IN THAILAND 9 AND THE WORLD 3.1 High-Speed Rail 9 3.2 Airline 11 3.3 Comparison of Time, Cost, Distance Trilogies 12 3.4 Market surveying 15 4 DEVELOPMENT OF INTER-CITY DEMAND MODEL 21 4.1 Introduction 21 4.2 The Overview 21 4.3 Travel demand modeling 21 4.3.1 Trip Generation model 22 4.3.2 Trip Distribution model 22 4.3.3 Model Choice model 22 4.3.4 Trip Assignment model 22

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4.4 Model Formulation 22 4.4.1 Estimate Algorithm 22 4.4.2 Result of Modeling 25 5 PASSENGER BEHAVIORAL FOR INTER-CITY 39 TRANSPORTIN THAILAND 5.1 Introduction 39 5.2 Overview of Methodology 39 5.3 Choice model 39 5.3.1 Utility function 40 5.3.2 Probability choice 41 5.3.3 Model Estimation 42 5.4 Market Share Analysis 47 5.4.1 Passenger Choice Estimation 47 5.4.2 Operator Revenues 48 5.5 Experimental Scenario 49 5.6 Results and Discussion 50 5.6.1 Passenger choice results 52 5.6.2 Operator profit results 56 5.6 Elasticity of demand 56 5.7 Concluding remarks 57 6 CONCLUSIONS 59 6.1 Conclusion 59 6.2 Recommendation 59 APPENDICES 61 Appendix A: Model Estimation source code 63 Appendix B: Market Surveying 65 Appendix C: Presentation Slides 73 Appendix D: Summary Sheets 81

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LIST OF TABLES

Tables Title Page

2.1 List of reviewed articles 6

3.1 Flight schedules from Bangkok to Chiang Mai 13

3.2 Flight schedules from Chiang Mai to Bangkok 14

3.3 Likert scales 15

4.1 Result of trip generation until 2030 36

5.1 Observed data of inter-city transport mode choice 42

5.2 Estimated parameter and statistical data for utility model of passengers

46

5.3 Route choice conditions 50

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LIST OF FIGURES

Figure Title Page

1.1 Structure of the thesis 3

3.1 Initial planned High-speed Rail map in Thailand and surrounding areas

10

3.2 Chiang-Mai International Airport 11

3.3 Suvarnabhumi airport, Samut Prakan Province 12

3.4 Comparison of passengers’ total traveling time between High-speed Rail and Airline

12

3.5 Passenger choice from questionnaire 15

3.6 Effect of Transit fair on transport choice 16

3.7 Effect of Total traveling time on transport choice 16

3.8 Effect of Frequency of service on transport choice 17

3.9 Effect of Freedom aboard on transport choice 17

3.10 Effect of On-time service on transport choice 18

3.11 Effect of Safety on transport choice 18

3.12 Effect of Reliability on transport choice 19

4.1 Estimation of future demand trips 23

4.2 Relation of Bangkok GPP per capita and number of departures from Bangkok to Chiang-Mai

24

4.3 Relation of Chiang-Mai GPP per capita and number of departures from Chiang-Mai to Bangkok

24

4.4 GPP estimating data for Bangkok 26

4.5 GPP estimating data for Chiang-Mai 27

4.6 Trip generated from Bangkok by rail 28

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4.7 Trip generated from Bangkok by air 29

4.8 Total trip generated from Bangkok 30

4.9 Trip generated from Chiang-Mai by rail 31

4.10 Trip generated from Chiang-Mai by air 32

4.11 Total trip generated from Chiang-Mai 33

4.12 Statistics and Forecasted GPP per capita for Bangkok 34

4.13 Statistics and Forecasted GPP per capita for Chiang-Mai 35

4.14 Statistics and Forecasted passenger for each origin point 37

5.1 Concept of passengers’ choice 40

5.2 Passenger flow structure in each mode 47

5.3 Non-Constructed High-speed Rail Results (BKK to CM) 51

5.4 Non-Constructed High-speed Rail Results (CM to BKK) 51

5.5 Market share if constructed High-speed Rail Results (BKK to CM)

53

5.6 Market share if constructed High-speed Rail Results (CM to BKK)

53

5.7 Comparison of rail mode with and without High-speed Rail in operation (BKK to CM)

54

5.8 Comparison of rail mode with and without High-speed Rail in operation (CM to BKK)

54

5.9 Comparison of Airline mode with and without High-speed Rail in operation (BKK to CM)

55

5.10 Comparison of Airline mode with and without High-speed Rail in operation (CM to BKK)

55

5.11 Total revenue of each operator from 2010 to 2030 56

5.12 Sensitivity of general cost of passenger and market share 57

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ABBREVIATIONS

AIR Airline

ASEAN Association of Southeast Asian Nations

BKK Bangkok

CM Chiang-Mai

Est. Estimated

FY Fiscal Year

GPP Gross Regional and Province Product

GRP Gross Regional Product

HSR High-speed Railway

ML Mixed Logit

MNL Multinomial Logit

Prob. Probability

RAIL Railway

S.E. Standard Error

THB Thai Baht

UIC International Union of Railway

UNESCAP United Nations Economic and Social Commission for Asia and Pacific

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CHAPTER 1

INTRODUCTION

1.1 Background

In the future of inter-city transport in Thailand and Asia region, we have only two

competitors such Airline and High-speed Rail that can satisfy passengers in travelling time,

cost-benefit, and comfort. Furthermore, in comparing travel in the same distances, High-

speed Rail has advantage over Airline in terms of flexibility to change the route and freedom

to move about when on board. The Thai government has planned to promote and support rail

transport by means of an administration for both domestic and international transport. Then

government decided to issue the policies to improve and expand the railway network and

services including High-speed Rail in order to increase the efficiency in the transport sector.

Therefore, the High-speed Rail development plan is considered necessary to make possible

the reduction of travel time by rail with compromise to travel costs, and aiming for reduction

in air pollution and road accidents.

1.2 Statement of Problem

In the inter-city transportation market under an oligopoly, the competition between

operators is inevitable. The satisfaction of passengers will indicate the future survival of

investment on networks. Now, it is a very important opportunity to analyze possible situation

on network, because the High-speed Rail networks in Thailand are in negotiation with

investors. The experimental results will not only help transport authority to regulate as

desired but help operators plan their own future strategies.

1.3 Objective

The purposes of this thesis are (1) to analyze inter-city transportation market share

between Airline and future of the First High-speed Rail operates between Bangkok and

Chiang-Mai, which is necessary for understanding modal shift by High-speed Rail and (2) to

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fulfill vision of inter-city transport development in Thailand by forecasting the growth of

passenger demand in origin-destination points.

1.4 Scope of the study

For the scope of the study, the travel route between Bangkok to Chiang-Mai with

future High-speed Rail and presently established Airline will be clarified. Then will follow by

a forecast of travel demand, taking into consideration actual Airline operation data, and the

government’s planned High-speed Rail operation data. An analysis of the market share of

each operator will be determined by using discrete choice model. Lastly, revenue of each

operator will be determined from the market share.

1.5 Thesis outline

This thesis consists of 6 chapters, as shown in Figure 1.1. Chapter 1 introduces the

background, statement of problem, objective, and scope of study of this thesis. Chapter 2

reviews past studies relating with utility function of transportation modes, transport mode-

choice analysis, and multinomial logit model, then it concludes with a direction of the thesis.

Chapter 3 reviews the current situation of Airline and future High-speed Rail in Thailand.

Chapter 4 explains the process of transport demand estimation for each origin point as a part

of trip generation by using curve estimation. Chapter 5 explains formulation of choice model

for inter-city transport between Bangkok and Chiang-Mai based on the derived situation from

market surveying. Then this chapter analyzes the market share by using choice model, and

determines operator revenues. Chapter 6 concludes the main contributions of the thesis,

including the suggestion for the further study.

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Figure 1.1 Structure of the thesis

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REFERENCES

Office of State Enterprise Policy Office, Ministry of Finance (2010). Development Policies &

Economic Analysis, Brief on Market Sounding Session.

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CHAPTER 2

LITERATURE REVIEW

In Thailand, transport mode shift phenomenon occurred when Low-cost Airline was

introduced. Passengers in an inter-city transport market have changed traveling behavior from

using trains that were replaced by Airline. This phenomenon can be explained by discrete

choice model, showing that decision makers will choose one goods or service from a multiple

choice set which maximizes utility.

2.1 Choice Model

Discrete choice is a general mathematical model used in transportation planning. The

forming of models take varieties of format, such as Binary Logit, Binary Probit, Multinomial

Logit, Multinomial Logistic, Multinomial Probit, Nested Logit, GEV, and Mixed Logit, etc.

Early development of choice models was based on the assumption that the error terms

were either multivariate normal and independently and identically extreme value distributed

(Johnson, 1970). The multivariate normal assumption leads to the multinomial probit model

(Daganzo, 1979), and independently and identically gumbel assumption leads to the

multinomial logit (MNL) model (McFadden, 1973).

The Multinomial logit (MNL) is derived through the application of utility

maximization concepts to a set of alternatives. Decision makers will choose the alternative

with maximum utility.

In Thailand, Chalita Phadungmitr et el. (2009) used Multinomial Logistic model with

Utility model for studying factors that affected passenger mode choice between Airline, bus,

and train. The result showed most passengers had chosen Low-cost Airline according to price

of ticket follow by bus and train. Behavior of passengers has varied according to increase of

fuel price. Political effects, such as the airport closing in 2009, do not seem to influence the

transport choice of passengers.

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For airport choice, Stefano de Luca (2011) used Multinomial Logit (MNL) and Mixed

Multinomial Logit (MMNL) to analyze airport choice behavior for direct flight in multi

airport regions.

Frank S. Koppelman and Chandra Bhat (2006) had introduced Multinomial and

Nested Logit models to U.S. Department of Transportation, Federal Transit Administration

for illustrating the inter-city mode choice model. The travel modes typically include car, rail,

air, and bus modes

2.2 Utility Function

For transportation planning, utility is a way of measuring satisfaction of passengers in

discrete choice modeling. In the following, utility will refer to an index of attractiveness, a

measure of decision makers attempting to maximize their benefits through their choices

(Moshe Ben-Akiva and Steven R. Lerman, 1985).

Table 2.1: List of reviewed articles Author Year Problem Methodology

Frank S. Koppelman 2006 Inter-city transport choice MNL, Probit

Chalita Phadungmitr et el. 2009 Transport mode choice behaviors MNL

Sophie Masson 2009 HSR reinforce tourism attractiveness -

Edoardo Marcucci et el. 2011 Regional airport choice ML

Stefano de Luca 2012 Airport choice behaviors MNL, MMNL

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REFERENCES

Moshe Ben-Akiva, Steven R. Lerman, n.d. Theories of Individual Choice Behavior, in:

Discrete Choice Analysis. The MIT Press, pp. 31–58.

Frank S. Koppenman, Chandra Bhat, n.d. A Self Instructing Course in Mode Choice

Modeling: Multinomial and Nested Logit Models.

Chalita Phadungmitr et el., Factor Affected to Traveling Behavior between Bangkok and

Chiang-Mai. ThaiVCML 2009, 1(9), pp.201-212.

Stefano de Luca, n.d. Modelling airport choice behaviour for direct flights, connecting flights

and different travel plans. Journal of Transport Geography, 22, pp. 148–163.

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CHAPTER 3

INTER-CITY DEVELOPMENT IN THAILAND AND THE WORLD

The Thai government has planned to promote and support rail transport by means of

an administration for both domestic and international transport. The government decided to

issue policies to improve and expand the railway network and services, including the addition

of a new High-speed Rail, in order to increase the efficiency in the transport sector. Therefore,

the High-speed Rail development plan is considered necessary to make possible the reduction

of travel time by rail with alleviations to travel costs, and aiming for reduction of air pollution

and road accidents.

3.1 High-speed Rail

International Union of Railways (UIC) sets High-speed Rail principles upon which a

system of infrastructure can be based, determining the operation speed of trains that starts

from 200 km/hour. To make this system operational, funding is necessary. The average of

operation costs of High-speed Rail in Europe case are separated into (1) Maintenance of 1 km

of track at 70,000 Euro per year, (2) Maintenance of High-speed Train sets is at 1,000,000

Euro per year. The passenger capacity per trips of one set of cars varies from 600 – 1600

passengers, depending on the set and seat configuration.

In the Asia region, development of High-speed Rail matches with United Nations

Economic and Social Commission for Asia and The Pacific (UNESCAP), which has planned

to join rail transport between all Asian countries by what is called the Trans Asia Railway.

Moreover, after 2015 ASEAN communities will allow the people to move freely across

national borders, and then High-speed Rail will become a suitable choice for international

transportation.

In Thailand’s High-speed Rail initial development, the Office of Transport and Traffic

Policy and Planning, Ministry of Transport has purposed a four line developing plan (figure

3.1). The green line runs between Bangkok and Chiang Mai, for the Northern line, and

between Bangkok and Chanthaburi (by the Thai-Cambodia border) for the Eastern line. The

blue line runs between Bangkok and Nong Khai (Thailand-Laos’ border) for the North-

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eastern line, and between Bangkok and Songkhla (the Thai-Malaysia border) for the Southern

line. The red line in Figure 3.1 is located in Laos.

Figure 3.1: Initial planned High-speed Rail map in Thailand and surrounding areas

Chiang2Mai"

Bangkok"

Nong"Khai"

Trat"

Songkhla"

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3.2 Airline

In Thailand inter-city air market has been operating by both traditional Airline and

low-cost carriers. There is not an absolutely sharp difference between them. Most low-cost

carriers could be defined as Airlines that operate on relatively short distances in a certain

region without offering additional services. Presently, a sample route (between Bangkok and

Chiang-Mai) of a research study comprised 60-65 commercial flights operating per day.

The number of passenger that can board depends on size of airplane and seat

configuration. Normally, the mid-size airplane serving the Bangkok-Chiang-Mai route carries

140-375 passengers per trip, and around 12,000 passengers per day. Flight information is

shown in Tables 3.1 and 3.2 for departure from Bangkok flights and departure from Chiang-

Mai flights.

Figure 3.2 Chiang-Mai International Airport

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Figure 3.3 Suvarnabhumi airport, Samut Prakan Province

3.3 Comparison of Time, Costs, Distance Trilogies

For practicality in long distance travelling, Airline and High-speed Rail seem to be

suitable choices when time per distance is considered. In the future of inter-city transport,

there are only two competitors that can satisfy passengers regarding travelling time, cost-

benefit, and comfort. However, due to limitation of technology High-speed Rail will

competitive with Airline in destinations where the total traveling time does not exceed 3 or 4

hours, as shown in Figure 3.4.

Figure 3.4: Comparison of passengers’ total traveling time between High-speed Rail and Airline

Accessing Check2in Waiting Travelling Immigration

Accessing Waiting Travelling Immigration

0 1 2 3

ΣTr"="T

waiting"+"T

travel

ΣTa"="T

check2in"+"T

waiting"+"T

travel

Time%(hrs)

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Table 3.1: Flight schedules from Bangkok to Chiang Mai.

Schedule( Flight(No.( Airline( From( To( Seats(07:25" DD8350" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"07:50" FD3230" AIR"ASIA" Suvarnabhumi"" Chiang2Mai" 180"08:25" DD8352" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"09:15" EY7727" ETIHAD" Suvarnabhumi"" Chiang2Mai" *"09:15" PG215" BANGKOK" Suvarnabhumi"" Chiang2Mai" 170"09:10" TG"102" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 375"09:50" OX8120" 1222GO" Suvarnabhumi"" Chiang2Mai" 170"10:40" TG"104" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 250"11:00" DD8356" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"11:15" AY6291" FINN"AIR" Suvarnabhumi"" Chiang2Mai" *"11:15" PG"223" BANGKOK" Suvarnabhumi"" Chiang2Mai" 170"11:15" EY7729" ETIHAD" Suvarnabhumi"" Chiang2Mai" *"11:40" FD3232" AIR"ASIA" Suvarnabhumi"" Chiang2Mai" 180"11:50" DD8360" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"12:50" TG"106" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 250"13:25" PG217" BANGKOK" Suvarnabhumi"" Chiang2Mai" 170"13:25" AB5840" AIR"BERLIN" Suvarnabhumi"" Chiang2Mai" *"14:05" TG"110" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 250"14:35" DD8362" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"15:00" FD3234" AIR"ASIA" Suvarnabhumi"" Chiang2Mai" 180"15:10" TG112" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 250"15:25" DD8366" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"16:40" OX8124" 1222GO" Suvarnabhumi"" Chiang2Mai" 170"16:40" TG"114" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 300"17:00" FD3236" AIR"ASIA" Suvarnabhumi"" Chiang2Mai" 180"17:10" DD8368" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"18:15" BR3987" EVA"AIR" Suvarnabhumi"" Chiang2Mai" *"18:15" PG"219" BANGKOK" Suvarnabhumi"" Chiang2Mai" 170"18:15" AY6293" FINN"AIR" Suvarnabhumi"" Chiang2Mai" *"18:15" AF8034" AIR"FRANCE" Suvarnabhumi"" Chiang2Mai" *"18:15" KL3735" ROYAL"DUTCH" Suvarnabhumi"" Chiang2Mai" *"18:35" TG"116" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 250"19:40" FD3238" AIR"ASIA" Suvarnabhumi"" Chiang2Mai" 180"19:45" DD8374" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"20:00" TG"120" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 375"20:30" OX8126" 1222GO" Suvarnabhumi"" Chiang2Mai" 170"21:35" DD8378" NOK"AIR" Suvarnabhumi"" Chiang2Mai" 150"21:45" EY7737" ETIHAD" Suvarnabhumi"" Chiang2Mai" *"21:45" PG"221" BANGKOK" Suvarnabhumi"" Chiang2Mai" 170"21:50" FD3240" AIR"ASIA" Suvarnabhumi"" Chiang2Mai" 180"22:40" TG"122" THAI"AIR" Suvarnabhumi"" Chiang2Mai" 250"

"" "" "" "" Total(Seats( 6,340"

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Table 3.2: Flight schedules from Chiang Mai to Bangkok.

Schedule( Flight(No.( Airline( From( To( Seats(06:55" AB5841" AIR"BERLIN" Chiang2Mai" Suvarnabhumi"" *"06:55" PG"222" BANGKOK" Chiang2Mai" Suvarnabhumi"" 144"06:55" BR3968" EVA"AIR" Chiang2Mai" Suvarnabhumi"" *"07:00" TG"123" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 250"07:10" FD3427" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 180"07:55" DD8351" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"08:20" FD3231" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 180"08:55" DD8353" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"10:00" BR3978" EVA"AIR" Chiang2Mai" Suvarnabhumi"" *"10:00" PG"216" BANGKOK" Chiang2Mai" Suvarnabhumi"" 144"10:15" TG"103" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 375"10:30" OX8121" 1222GO" Chiang2Mai" Suvarnabhumi"" 170"11:30" TG"105" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 250"11:30" DD8357" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"12:10" FD3233" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 180"12:20" DD8361" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"14:15" EY7728" ETIHAD" Chiang2Mai" Suvarnabhumi"" *"14:15" BR3988" EVA"AIR" Chiang2Mai" Suvarnabhumi"" *"14:15" PG"218" BANGKOK" Chiang2Mai" Suvarnabhumi"" 144"14:55" TG"111" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 250"15:05" DD8363" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"15:30" FD3235" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 180"15:55" DD8367" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"16:00" TG113" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 250"17:10" OX8125" 1222GO" Chiang2Mai" Suvarnabhumi"" 170"17:30" TG"115" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 250"17:30" FD3237" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 180"17:40" DD8369" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"19:00" AF8035" AIR"FRANCE" Chiang2Mai" Suvarnabhumi"" *"19:00" KL3734" ROYAL"DUTCH" Chiang2Mai" Suvarnabhumi"" *"19:00" PG"220" BANGKOK" Chiang2Mai" Suvarnabhumi"" 162"19:00" AY6292" FINN"AIR" Chiang2Mai" Suvarnabhumi"" *"19:25" TG"117" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 375"20:15" FD3239" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 180"20:20" DD8375" NOK"AIR" Chiang2Mai" Suvarnabhumi"" 150"20:50" TG"121" THAI"AIR" Chiang2Mai" Suvarnabhumi"" 300"21:00" OX8127" 1222GO" Chiang2Mai" Suvarnabhumi"" 170"22:05" DD8379" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 150"22:20" FD3241" AIR"ASIA" Chiang2Mai" Suvarnabhumi"" 180"

"" "" "" "" Total(Seats( 6,014"* Code share with other Airline.

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3.4 Market surveying

The market survey was conducted on an inter-city transport market to obtain the

characteristic of passengers having choice of transport mode. The condition of traveling is

provided in questionnaire (shown in Appendix B) for decision making.

From the questionnaire, passengers who choose High-speed Rail were 64.9% and

Airline were 35.1%, as shown in Figure 3.5. Moreover, for perspective questions about transit

fair, total traveling time, frequency of service, freedom to move around while on board

(freedom aboard), on-time service, safety, and reliability of transport were inquired by using

Likert’s technique, which divided scores of satisfaction by 5 levels, as shown in Table 3.3.

Table 3.3: Likert scales

Perspective Strong effect Effect Neutral Less effect No effect

Score 5 4 3 2 1

The result of surveying for each effect for decision making is shown in Figure 3.6 for

transit fair, Figure 3.7 for total traveling time, Figure 3.8 for frequency of service, Figure 3.9

for freedom aboard, Figure 3.10 for on-time service, Figure 3.11 for safety, and Figure 3.12

for reliability.

Figure 3.5: Passenger choice from questionnaire

HSR"65%"

AIR"35%"

16""

Figure 3.6: Effect of Transit fair on transport choice

Figure 3.7: Effect of Total traveling time on transport choice

26.40%"

42.60%"

23.60%"

7.40%"

0.00%"0.00%"

5.00%"

10.00%"

15.00%"

20.00%"

25.00%"

30.00%"

35.00%"

40.00%"

45.00%"

5" 4" 3" 2" 1"

Percen

tage(of(d

ecision(

Likert(scale(

Transit(Fair(

36.50%"

48.00%"

11.50%"4.10%" 0.00%"

0.00%"

10.00%"

20.00%"

30.00%"

40.00%"

50.00%"

60.00%"

5" 4" 3" 2" 1"

Percen

tage(of(d

ecision(

Likert(scale(

Total(Traveling(Time(

17""

Figure 3.8: Effect of Frequency of service on transport choice

Figure 3.9: Effect of Freedom aboard on transport choice

18.90%"

33.10%" 33.80%"

10.80%"

3.40%"0.00%"

5.00%"

10.00%"

15.00%"

20.00%"

25.00%"

30.00%"

35.00%"

40.00%"

5" 4" 3" 2" 1"

Percen

tage(of(d

ecision(

Likert(scale(

Frequency(of(service(

29.10%"

51.40%"

16.20%"

2.70%" 0.70%"0.00%"

10.00%"

20.00%"

30.00%"

40.00%"

50.00%"

60.00%"

5" 4" 3" 2" 1"

Percen

tage(of(d

ecision(

Likert(scale(

Freedom(aboard(

18""

Figure 3.10: Effect of On-time service on transport choice

Figure 3.11: Effect of Safety on transport choice

54.70%"

30.40%"

11.50%"2.70%" 0.70%"

0.00%"

10.00%"

20.00%"

30.00%"

40.00%"

50.00%"

60.00%"

5" 4" 3" 2" 1"

Percen

tage(of(d

ecision(

Likert(scale(

On@Ame(

66.90%"

23.60%"

6.80%" 2.00%" 0.70%"0.00%"

10.00%"

20.00%"

30.00%"

40.00%"

50.00%"

60.00%"

70.00%"

80.00%"

5" 4" 3" 2" 1"

Percen

tage(of(d

ecision(

Likert(scale(

Safety(

19""

Figure 3.12: Effect of Reliability on transport choice

35.10%"

39.90%"

20.30%"

4.10%" 0.70%"0.00%"

5.00%"

10.00%"

15.00%"

20.00%"

25.00%"

30.00%"

35.00%"

40.00%"

45.00%"

5" 4" 3" 2" 1"

Percen

tage(of(d

ecision(

Likert(scale(

Reliability(

20""

REFERENCES

Sophie Masson, Romain Petiot, n.d. Can the high speed rail reinforce tourism attractiveness?

The case of the high speed rail between Perpignan (France) and Barcelona (Spain).

Technovation 29, pp. 611–617.

Ministry of Transportation, n.d. Development Policies & Economic Analysis.

Ministry of Transportation, n.d. Brief on Market Sounding Session.

Airports of Thailand PCL., n.d. Airport Info - Daily Flight. Airport of Thailand. URL

http://www.airportthai.co.th/chiang_mai/en/airport_daily_flight_departure.php

21""

CHAPTER 4

DEVELOPMENT OF INTER-CITY DEMAND MODEL

4.1 Introduction

For transportation system planning, trip demand analysis plays an essential role. The

demand volume of passengers help transport planners to calculate a variety of planning

results in sequential steps such market share, competition, revenue, profit, economic internal

return rate, possibility of project, etc.

This chapter provides a concise overview of the mechanics of the trip demand model

in this study. It may assume that transportation demand is a process by which measures of

city income per capita are converted into number of trips. From the assumption that people’s

incomes positively influence travel demand, the mathematical model is used to relate

information such as Gross Regional and Province Product (GRP or GPP) to determine

number of departure trips for each origin points.

4.2 The Overview

Firstly, the relationship of personal income in each region, Gross Regional Province

Product (GRP or GPP) and statistics of transport demand are declared. Next, a forecast of the

GPP data in each origin is conducted, and then the forecasted GPP is used to estimate future

departure trips in each origin, and separated by transport mode. The relationship of statistic

GPP per capita and number of trips departing from Bangkok to Chiang-Mai is shown in

Figure 4.2, and for departing from Chiang-Mai to Bangkok is shown in Figure 4.3

4.3 Travel demand modeling

In transportation engineering and planning, conventional transportation demand

modeling is a four-step model, which basically consists of four parts: Trip generation, Trip

distribution, Mode choice, and Trip Assignment model.

22""

4.3.1 Trip Generation Model

In the four-step model, trip generation modeling is the process of estimating the

number of trips produced from an origin zone. In this thesis, this step was processed by

estimate population incomes such Gross Regional and Provincial Product (GPP) and

passenger statistic data of each region.

4.3.2 Trip Distribution Model

After having predicted the number of trips produced in each region, trip distribution

will match passenger’s origin and destination. In this step, it was processed by estimating trip

demand separately for each region.

4.3.3 Mode Choice Model

For the proposal of mode choice model, it indicates boarding passengers in each mode

and region. The most efficient approach is probability-based models such as the logit model,

which is based on the principle of utility maximization. In this thesis, this step is mentioned

in detail in chapter 5.3.1.

4.3.4 Trip Assignment Model

Trip assignment models concern the choice of path between pair of origin and

destination by travel mode. This step may be viewed as the equilibration between the demand

of travel and the supply of transportation. In this study, the travel route is fixed for each

transport mode, thus this step was not considered.

4.4 Model Formulation

4.4.1 Estimate Algorithm

By used curve estimation of regression analysis to find relation of local GPP and local

departure trips, given parameters for use in estimation of trip generation in each location. The

estimation algorithm was shown in Figure 4.3.1. Relation of each local trip generation model

is expressed by,

!! = !(!) (4.1)

!! = !!,!!!,! + !! (4.2)

23""

Where, !!: Number of departure trip from origin i

!!,!: GPP of fiscal year y in origin i

!!,!: Parameter for GPP G in origin i

!!: Constraint for estimation in origin i

For future demand of transport predicting in each location i, trip generation model

needed future of GPP data for each location i. The GPP of each origin is forecast separately

by using a linear equation, as expresses by,

!! = !(!) (4.3)

!!,! = !!,!! + !!,! (4.4)

Where, !!,!: GPP in fiscal year y origin i

Y: Fiscal year y

!!,!: Parameter for fiscal year Y in origin i

!!,!: Constraint for estimation in fiscal year Y in origin i

Figure 4.1: Estimation of future demand trips

24""

Figure 4.2: Relation of Bangkok GPP per capita and number of departures

from Bangkok to Chiang-Mai

Figure 4.3: Relation of Chiang-Mai GPP per capita and number of departures

from Chiang-Mai to Bangkok

2000" 2001" 2002" 2003" 2004" 2005" 2006" 2007" 2008" 2009"

0"

50,000"

100,000"

150,000"

200,000"

250,000"

300,000"

350,000"

400,000"

0"

200,000"

400,000"

600,000"

800,000"

1,000,000"

1,200,000"

1,400,000"

1,600,000"

2000" 2001" 2002" 2003" 2004" 2005" 2006" 2007" 2008" 2009"

GPP(of(Ban

gkok

(Area((THB

)(

Num

ber(o

f(Passenger(

Fiscal(Year(

Trip"from"BKK"by"Rail" "Trip"from"BKK"by"Air"" GPP"of"Bangkok"

2000" 2001" 2002" 2003" 2004" 2005" 2006" 2007" 2008" 2009"

0"

10,000"

20,000"

30,000"

40,000"

50,000"

60,000"

70,000"

80,000"

90,000"

0"

200,000"

400,000"

600,000"

800,000"

1,000,000"

1,200,000"

1,400,000"

1,600,000"

2000" 2001" 2002" 2003" 2004" 2005" 2006" 2007" 2008" 2009"

GPP(og(Chian

g@Mai((T

HB)(

Num

ber(o

f(Passenger((log(sc

ale)(

Fiscal(Year(Trip"from"CM"by"Rail" Trip"from"CM"by"Air" GPP"of"Chiang2Mai"

25""

4.4.2 Result of Modeling

The growth and parameter of GPP per capita are shown in Figure 4.4 and 4.5 for

Bangkok and Chiang-Mai, respectively. The GPP per capita curve was fitted by with liner

function, the square of correlation coefficient (R Square) was 0.987 for both Bangkok and

Chiang-Mai, which is acceptable.

For trip generation respected with GPP per capita for Bangkok origins, square of

correlation coefficient was 0.521 for rail, 0.790 for Airline, and 0.822 for total trip generate

from origin, which acceptable.

For trip generation respected with GPP per capita for Chiang-Mai origins, square of

correlation coefficient was 0.514 for rail with fitted by quadratic function, 0.680 for Airline,

and 0.664 for total trip generate from origin, which acceptable.

From growing of GPP data in linear equation form, this study forecasted number of

trips generated in each origin from 2011 until 2030, as shown in Figure 4.14. These

regressions showing results per year consist of total trips generated in each origin, and

passenger flow through each mode.

26""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.987 353.711 -23188497.440 11713.030 0.000

Quadratic 0.987 353.784 -11449527.340 0.000 2.922

Figure 4.4: GPP estimating data for Bangkok

27""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.987 629.876 -7558448.291 3802.436

Quadratic 0.987 630.988 -3747505.607 0.000 0.948

Figure 4.5: GPP estimating data for Chiang-Mai

28""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.521 8.705 740828.353 -1.041

Quadratic 0.529 3.927 1176147.599 -4.065 5.179E-6

Figure 4.6: Trip generated from Bangkok by rail

29""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.790 30.026 -320130.011 5.253

Quadratic 0.842 18.660 -4988537.220 37.680 -5.554E-5

Figure 4.7: GPP Trip generated from Bangkok by air

30""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.822 36.848 420698.342 4.212

Quadratic 0.891 28.726 -3812389.621 33.615 -5.036E-5

Figure 4.8: Total trip generated from Bangkok

31""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.106 0.945 22733.251 -0.242

Quadratic 0.514 3.708 236000.186 -7.127 5.393E-5

Figure 4.9: Trip generated from Chiang-Mai by rail

32""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.680 16.971 274583.840 15.660

Quadratic 0.688 7.704 -487347.829 40.260 0.000

Figure 4.10: Trip generated from Chiang-Mai by air

33""

Equation Model Summary Parameter Estimates

R Square F !! !!,! !!,!!

Linear 0.664 15.788 297317.091 15.418

Quadratic 0.668 7.038 -251347.643 33.133 0.000

Figure 4.11: Total trip generated from Chiang-Mai

34##

Figure 4.12: Statistics and Forecasted GPP per capita for Bangkok

0#

100,000#

200,000#

300,000#

400,000#

500,000#

600,000#

700,000#

2000#

2001#

2002#

2003#

2004#

2005#

2006#

2007#

2008#

2009#

2010#

2011#

2012#

2013#

2014#

2015#

2016#

2017#

2018#

2019#

2020#

2021#

2022#

2023#

2024#

2025#

2026#

2027#

2028#

2029#

2030#

GPP#Pe

r#Cap

ita#(T

HB)#

Fiscal#Year#Bangkok#

35##

Figure 4.13: Statistics and Forecasted GPP per capita for Chiang-Mai

0#

20,000#

40,000#

60,000#

80,000#

100,000#

120,000#

140,000#

160,000#

180,000#

2000#

2001#

2002#

2003#

2004#

2005#

2006#

2007#

2008#

2009#

2010#

2011#

2012#

2013#

2014#

2015#

2016#

2017#

2018#

2019#

2020#

2021#

2022#

2023#

2024#

2025#

2026#

2027#

2028#

2029#

2030#

GPP#Pe

r#Cap

ita#(T

HB)#

Fiscal#Year#Chiang6Mai#

36##

Table 4.1: Result of trip generation until 2030

FY#Departure#from#BKK# Departure#from#Chiang#Mai#

RAIL# AIR# TOTAL# RAIL# AIR# TOTAL#2000# 422,247# 1,023,041# 1,445,288# 27,896# 1,074,161# 1,102,057#2001# 498,963# 1,020,476# 1,519,439# 11,219# 1,071,116# 1,082,335#2002# 511,260# 933,200# 1,444,460# 180# 992,849# 993,029#2003# 520,992# 894,693# 1,415,685# 329# 951,796# 952,125#2004# 432,533# 1,263,912# 1,696,445# 178# 1,355,850# 1,356,028#2005# 408,907# 1,340,601# 1,749,508# 9,432# 1,390,039# 1,399,471#2006# 394,387# 1,410,895# 1,805,282# 570# 1,482,415# 1,482,985#2007# 379,866# 1,422,975# 1,802,841# 11,012# 1,509,784# 1,520,796#2008# 406,943# 1,336,237# 1,743,180# 9,064# 1,402,956# 1,412,020#2009# 411,096# 1,400,347# 1,811,443# 3,947# 1,464,521# 1,468,468#2010# 371,593# 1,543,072# 1,914,665# 18,739# 1,597,041# 1,599,337#2011# 359,400# 1,604,600# 1,964,000# 27,054# 1,656,587# 1,657,963#2012# 347,207# 1,666,129# 2,013,335# 36,928# 1,716,133# 1,716,589#2013# 335,013# 1,727,657# 2,062,671# 48,361# 1,775,679# 1,775,215#2014# 322,820# 1,789,186# 2,112,006# 61,354# 1,835,225# 1,833,841#2015# 310,627# 1,850,714# 2,161,341# 75,907# 1,894,771# 1,892,467#2016# 298,434# 1,912,243# 2,210,676# 92,019# 1,954,317# 1,951,093#2017# 286,240# 1,973,771# 2,260,012# 109,690# 2,013,864# 2,009,719#2018# 274,047# 2,035,300# 2,309,347# 128,921# 2,073,410# 2,068,345#2019# 261,854# 2,096,829# 2,358,682# 149,712# 2,132,956# 2,126,971#2020# 249,660# 2,158,357# 2,408,017# 172,062# 2,192,502# 2,185,597#2021# 237,467# 2,219,886# 2,457,353# 195,971# 2,252,048# 2,244,223#2022# 225,274# 2,281,414# 2,506,688# 221,440# 2,311,594# 2,302,849#2023# 213,081# 2,342,943# 2,556,023# 248,469# 2,371,141# 2,361,475#2024# 200,887# 2,404,471# 2,605,359# 277,057# 2,430,687# 2,420,101#2025# 188,694# 2,466,000# 2,654,694# 307,204# 2,490,233# 2,478,727#2026# 176,501# 2,527,528# 2,704,029# 338,911# 2,549,779# 2,537,353#2027# 164,308# 2,589,057# 2,753,364# 372,178# 2,609,325# 2,595,979#2028# 152,114# 2,650,585# 2,802,700# 407,004# 2,668,871# 2,654,605#2029# 139,921# 2,712,114# 2,852,035# 443,389# 2,728,417# 2,713,231#2030# 127,728# 2,773,643# 2,901,370# 481,334# 2,787,964# 2,771,857#

Forecasted data

Statistic data

37##

Figure 4.14: Statistics and Forecasted passengers for each origin point

2000#

2001#

2002#

2003#

2004#

2005#

2006#

2007#

2008#

2009#

2010#

2011#

2012#

2013#

2014#

2015#

2016#

2017#

2018#

2019#

2020#

2021#

2022#

2023#

2024#

2025#

2026#

2027#

2028#

2029#

2030#

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2000#

2001#

2002#

2003#

2004#

2005#

2006#

2007#

2008#

2009#

2010#

2011#

2012#

2013#

2014#

2015#

2016#

2017#

2018#

2019#

2020#

2021#

2022#

2023#

2024#

2025#

2026#

2027#

2028#

2029#

2030#

Num

ber'o

f'Trip

s'per'Year'(Pa

ss/Year)'

YEAR'

BKK/>CM#by#Rail# BKK/>CM#by#Air# CM/>BKK#by#Rail# CM/>BKK#by#Air# BKK/>CM#(Total)# CM/>BKK#(Total)#

38##

REFERENCES

Juan de Dios Ortuzar & Juis G. Willumsen, Discrete Choice Models. In Modelling Transport.

A John Wiley and Sons, Ltd., Publication, pp. 139–223.

Ministry of Information and Communication Technology, Chiang-Mai Statistical Database.

National Statistical Office. Available at: http://chiangmai.nso.go.th/nso/project/search_option

/index.jsp?province_id=48.

Office of the National Economic and Social Development Board, Gross Regional and

Provincial Product 2010f 2010th ed.,

Airports of Thailand PCL., Airport Transport Statistics. Airport of Thailand. Available at:

http://www.airportthai.co.th/chiang_mai/th/airport_info_statistic.php.

39##

CHAPTER 5

PASSENGER BEHAVIORAL MODEL

FOR INTER-CITY TRANSPORT IN THAILAND

5.1 Introduction

This chapter will introduce discrete choice modeling by using multinomial logit

(MNL) to analyze inter-city transport market share. First, this thesis will introduce the

concept of the discrete choice model in section 5.3.

5.2 Overview of Methodology

The Multinomial Logit (MNL) model is the most basic member of the family of

General Extreme Value models, and most widely used model forms. The logit formula is

consistent with utility maximization. The MNL model is referred to as the conditional logit

model for multinomial choices with probability for alternatives and decision making. In this

study, alternatives were defined as High-speed Rail and Airline that service Bangkok –

Chiang-Mai routes.

5.3 Choice Models

In discrete choice modeling, decision makers’ choices are among alternatives that are

described. Let the passengers be decision makers, and the alternatives might represent

competing transportation services. To fit within a discrete choice framework, the choice set

of alternatives needs to exhibit three characteristics.

First, the alternatives must be mutually exclusive from the decision makers’

perspective. That means passengers choose only one transport mode from the choice set.

Second, the choice set must be exhaustive, in that all possible alternatives are

included. Therefore passengers necessarily choose only one of the transport modes.

40##

Third, the number of alternatives must be finite. The alternatives in this study are

represented only by the High-speed Rail and Airline, which present suitable alternatives to

competing with inter-city transport.

The concept of passenger choice is shown in Figure 5.1.

Figure 5.1: Concept of passengers’ choice

5.3.1 Utility Function

From the first characteristic of discrete choice model, passenger must choose only one

choice from available alternative of transport mode k such High-speed Rail or Airline. And

the probability of choice will expected utility maximization. In this study, the experiment

assumed passengers choose their transport mode by making decisions from their general cost,

total traveling time (time from origin stations to destination stations), and frequency of

service offered by transport operators. The utility function will expressed by,

!!(!, !, !,!) (5.1)

41##

!! = !!!! + !!!! + !!!! (5.2)

where !!: Utility function of transport mode k

!!: General cost to passenger for transport mode k

!!: Total traveling time for transport mode k

!!: Frequency of service per day for transport mode k

!!: Parameter of general cost to passenger

!!: Parameter of total traveling time

!!: Parameter of frequency of service per day#

5.3.2 Probability choice

Discrete choice models are usually under an assumption of passenger utility-

maximizing behavior. The basic assumption is that passengers make a decision from general

cost of trip, total travel time, and frequency of service, as shown in Figure 5.1. It was

concluded that probability of choice function can be expressed as,

!"#$!,! = !(!!) (5.3)

The probability that individual passenger q chooses transport mode is defined as,

!"#$!,! = !!!(!,!,!,!)!!!(!,!,!,!) ! ∈ ! (5.4)

where !"#$!,!: Probability that individual passenger q choose transport mode k

K: Choice set of transport alternative k

In this study, probability of choice is expanded in an equation easily illustrated by,

!"#$!"#,! = !!!"#(!,!,!,!)!!!"#(!,!,!,!)!!!!"#(!,!,!,!) (5.4a)

!"#$!"#,! = !!!"#(!,!,!,!)!!!"#(!,!,!,!)!!!!"#(!,!,!,!) (5.4b)

42##

5.3.3 Model Estimation

Logit model development consists of formulating model specification and estimating

numerical values of the parameters for the various attributes specified in each utility function

by fitting the modes to the observed choice data. In this research, observed choice data was

done by public questionnaire via Internet. The sample size was 148 from all areas in Thailand

and foreign countries; the observed data is shown in Table 5.1. After providing important

information for making personal decision to samples, the questionnaire asked about personal

information, perspectives, and mode choice.

Table 5.1: Observed data of inter-city transport mode choice

Individual)Mode)Choice) Time) Passengers’)General)Cost)(for)choice)) SERVICE)

HSR) AIR) WAIT) TRAVEL) TOTAL) FAIR) WAIT) TRAVEL) TOTAL) FREQ) DIST)

1# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

2# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

3# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

4# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

5# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

6# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

7# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

8# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

9# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

10# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

11# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

12# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

13# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

14# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

15# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

16# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

17# 0# 1# 2# 1# 3# 2373# 150# 75# 2598# 39# 592#

18# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

19# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

20# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

21# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

22# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

23# 0# 1# 2# 1# 3# 2373# 150# 75# 2598# 39# 592#

24# 0# 1# 2# 1# 3# 2373# 150# 75# 2598# 39# 592#

25# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

26# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

27# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

28# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

29# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

30# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

43##

31# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

32# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

33# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

34# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

35# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

36# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

37# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

38# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

39# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

40# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

41# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

42# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

43# 0# 1# 2# 1# 3# 2373# 150# 75# 2598# 39# 592#

44# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

45# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

46# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

47# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

48# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

49# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

50# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

51# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

52# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

53# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

54# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

55# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

56# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

57# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

58# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

59# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

60# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

61# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

62# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

63# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

64# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

65# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

66# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

67# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

68# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

69# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

70# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

71# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

72# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

73# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

74# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

44##

75# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

76# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

77# 0# 1# 2# 1# 3# 2373# 400# 200# 2973# 39# 592#

78# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

79# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

80# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

81# 0# 1# 2# 1# 3# 2373# 150# 75# 2598# 39# 592#

82# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

83# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

84# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

85# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

86# 0# 1# 2# 1# 3# 2373# 150# 75# 2598# 39# 592#

87# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

88# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

89# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

90# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

91# 1# 0# 0.5# 3.5# 4# 1190# 100# 700# 1990# 30# 751#

92# 1# 0# 0.5# 3.5# 4# 1190# 0# 0# 1190# 30# 751#

93# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

94# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

95# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

96# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

97# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

98# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

99# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

100# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

101# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

102# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

103# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

104# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

105# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

106# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

107# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

108# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

109# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

110# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

111# 0# 1# 2# 1# 3# 2373# 150# 75# 2598# 39# 592#

112# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

113# 1# 0# 0.5# 3.5# 4# 1190# 150# 1050# 2390# 30# 751#

114# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

115# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

116# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

117# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

118# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

45##

119# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

120# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

121# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

122# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

123# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

124# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

125# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

126# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

127# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

128# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

129# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

130# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

131# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

132# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

133# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

134# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

135# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

136# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

137# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

138# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

139# 1# 0# 0.5# 3.5# 4# 1190# 25# 175# 1390# 30# 751#

140# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

141# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

142# 0# 1# 2# 1# 3# 2373# 600# 300# 3273# 39# 592#

143# 1# 0# 0.5# 3.5# 4# 1190# 37.5# 262.5# 1490# 30# 751#

144# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

145# 1# 0# 0.5# 3.5# 4# 1190# 50# 350# 1590# 30# 751#

146# 0# 1# 2# 1# 3# 2373# 100# 50# 2523# 39# 592#

147# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

148# 0# 1# 2# 1# 3# 2373# 200# 100# 2673# 39# 592#

One of the popular methods for estimating parameter of model is Maximum

Likelihood Method. The procedure for maximum likelihood estimation involves two

important steps:

1. Developing a joint probability function of the observed sample.

2. Estimating parameter values which maximize the likelihood function.

Maximizing the value of L by taking the logarithm function, the equation returns the

fitness of parameter. The general expression is described as:

46##

! = (!"#$!,!)!∈!!!!! (5.5)

!! = log!(!) (5.6)

where !"#$: Probability of passenger chooses mode k

!: Likelihood function

!!: Log Likelihood function

The fitness value of parameter is obtained by finding the first derivative of likelihood

function as,

!""!!!

, !""!!! , and !""!!!

(5.7), (5.8), (5.9)

The fitness of parameter for mode choice model with estimated statistical data is

showed in Table 5.1

Table 5.2: Estimated parameter and statistical data for utility model of passengers Parameter Estimate S.E. Est./S.E. Prob. Gradient

!! -0.0025 0.0020 -1.230 0.2188 0.000

!! 0.0032 0.1782 0.018 0.9859 0.000

!! 0.2281 0.2439 0.935 0.3497 0.000

Number of iterations 19

Minutes of convergence 0.00177

Mean Log-likelihood -0.643086

Log Likelihood (0) -102.58575

Log Likelihood (b) -95.176700

Hit value 0.64864865

47##

5.4 Market Share Analysis

From previous section, the study got fitness parameter for analyze market share.

5.4.1 Passenger Choice Estimation

Basically, passengers mainly choose their transport mode by their general cost such

transit fair, and travel time. The frequency of service was derived with flexible departure time

for passengers. By these conditions, passenger flow on each mode might be described by

Figure 5.2.

Figure 5.2: Passenger flow structure in each mode

Based on discrete choice model concepts, passengers in marker will chooses only one

of transport mode from provided choice set, which reach their utility maximization. The flow

rate of passenger for either High-speed Rail or Airline is expressed by,

!!,! = !!!(!,!,!,!)!!!(!,!,!,!) ! ∈ ! (5.10)

!! = !!!! + !!!! + !!!! (5.11)

48##

where !!,!: Passenger flow rate on transport mode k in origin i

!!: Utility function of transport mode k

!!: General cost of passenger for transport mode k

!!: Total traveling time for transport mode k

!!: Frequency of service per day for transport mode k

!!: Parameter of general cost of passenger

!!: Parameter of total traveling time

!!: Parameter of frequency of service per day

In this study, passenger flow equation will expanding for easier illustrate by,

!!"#,! = !(!!!!"#!!!!!"#!!!!!"#)

!(!!!!"#!!!!!"#!!!!!"#)!!(!!!!"#!!!!!"#!!!!!"#) (5.12)

!!"#,! = !(!!!!"#!!!!!"#!!!!!"#)

!(!!!!"#!!!!!"#!!!!!"#)!!(!!!!"#!!!!!"#!!!!!"#) (5.13)

The flow rate equation is given results by ratio. To convert into number of boarding

passenger for each mode expresses by multiplying market ratio with travel demand in each

origin. Estimation of travel demand was expressed in chapter 4. Number of boarding

passenger for each mode expresses by,

!!,! = !!,! ∙ !! (5.14)

where !!,!: Passenger flow on transport mode k in origin i

!!,!: Passenger flow rate on transport mode k in origin i

!!: travel demand volume in origin i#

5.4.2 Operator Revenues

Revenue calculation is a simplest way to measure success of operation performance of

each operator. For transportation planning, revenue from operation strategic is a part of next

49##

step calculation such as profit of operator, economic internal rate of return (EIRR), or

possibility of investment etc. In this thesis, because of insufficient information from each

transport operator, the study will calculate only revenue of each operator.

Revenue of each operator was expressed by,

!! = !!,! ∙ !! (5.15)

where !!: Total revenue of operator k

!!,!: Passenger flow on transport mode k in origin i

!!: General cost of passenger for transport mode k

5.5 Experimental Scenario

This study chooses the travel route between Bangkok and Chiang-Mai, which are the

most favorite tourism and business areas within Thailand, with both accessible through the

international airport and High-speed Rail route. Moreover, total traveling time for both High-

speed Rail and Airline is within approximately 3-4 hours. The condition details for transit fair,

total travel time, and frequency of services are shown in Table 5.3.

The first scenario of this experiment assumes that the High-speed Rail and Airline

operators are offering 100% of transit fair to passengers in 2016. The experiment will apply

transit conditions to passenger choice estimation for forecasting market share ratio. Then, the

forecasted trip demand in each origin will be applied to market share ratio for each operator

in order to estimate number of boarding passengers of High-speed Rail and Airline for each

origin point. Lastly, revenues for each operator will be estimated.

The second scenario assumes that after their operations have been going for 1 year,

the operator which has the lower number of passengers will decrease transit fair by 5%. Then,

the change of market share in each origin and revenue of operator will be estimated the same

as the first scenario process.

The expected result of the first scenario will forecast how much the High-speed Rail

affects the Airline market. The second scenario allows for effects of transit fair change for the

50##

operator using strategies to gain business. Both operators’ policies will likely change year by

year, because sudden changes draw a negative effect for passenger reservation.

Table 5.3: Route choice conditions Mode Distance (km) Fair (THB) Total time (h) Freq.

Airline 592 2,373 3 39

High-speed Rail 751 1,190 4 30

5.6 Results and Discussion

Before going to results of the scenario experiment, the forecasting of trips generate

from each origin will be introduced, as shown in Figures 5.3 and 5.4. The number of

passenger chooses Airline to make their trip is increasing dramatically for both Bangkok and

Chiang-Mai origins. In spite of this, the number of passengers choosing conventional train is

slightly decreasing continually for the Bangkok origin, and slightly increasing with lower

growth rate than Airline for the Chiang-Mai origin.

51##

Figure 5.3: Non-Constructed High-speed Rail Results (BKK to CM)

Figure 5.4: Non-Constructed High-speed Rail Results (CM to BKK)

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)Passenger)

Local#Train# Airline#

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)Passenger)

Local#Train# Airline#

52##

5.6.1 Passenger choice results

Operating results for the first scenario showed the market share of High-speed Rail

and Airline is 0.71:0.29 for each route. Total passenger flow in rail transport model with

High-speed Rail was increased by 428% for departures from Bangkok and increased by

1,411% for departures from Chiang-Mai. On the other hand, total passenger flow in Airline

fell dramatically, which decreased by 67% for departure from Bangkok and by 71% for

departures from Chiang-Mai. The number of boarding passengers is shown in Figures 5.5 and

5.6. The trend line is declared for comparison and illustrates the number of passengers when

operating with the same strategy plan until 2030.

For the second scenario, from the experimental condition for gaining business, Airline

will give 5% discount to the market. After the discount enters the market, the market share

for High-speed Rail and Airline changed to 0.65:0.35. Additionally, the passenger flow for

Airline increased by 25% for departures from Bangkok, and 26% for departures from Chiang-

Mai. Meanwhile, High-speed Rail operator lost 7% of passengers to Airline for departures

from Bangkok and 6.3% for departures from Chiang-Mai.

53##

Figure 5.5: Market share if constructed High-speed Rail Results (BKK to CM)

Figure 5.6: Market share if constructed High-speed Rail Results (CM to BKK)

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)Passenger)

High#Speed#Rail# Airline#

S1#

S2#

S2#

S1#

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)Passenger)

High#Speed#Rail# Airline#

S1#

S2#

S2#

S1#

54##

Figure 5.7: Comparison of rail mode with and without High-speed Rail in operation (BKK to CM)

Figure 5.8: Comparison of rail mode with and without High-speed Rail in operation (CM to BKK)

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)passenger)

Annual)year)Train# HSR#

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)passenger)

Annual)year)

Train#(non#HSR)# HSR#

55##

Figure 5.9: Comparison of Airline mode with and without High-speed Rail in operation (BKK to CM)

Figure 5.10: Comparison of Airline mode with and without High-speed Rail in operation (CM to BKK)

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)passenger)

Annual)year)

Airline#(NonCHSR)# Airline#(with#HSR)#

0#

500,000#

1,000,000#

1,500,000#

2,000,000#

2,500,000#

3,000,000#

2016# 2017# 2018# 2019# 2020# 2021# 2022# 2023# 2024# 2025# 2026# 2027# 2028# 2029# 2030#

Num

ber)o

f)passenger)

Annual)year)

Airline#(NonCHSR)# Airline#(with#HSR)#

56##

5.6.2 Operator Revenue results

For the total revenue results, in the first scenario, Airline revenue had dropped by

68% compared with the year before High-speed Rail opens. After Airline given 5% discount

of transit fair, Airline revenue will increase by19%, as shown in Figure 5.7. The trend line is

declared for comparison and illustrates revenue when operating with the same strategy plan

until 2030.

Figure 5.11: Total revenue of each operator from 2010 to 2030

5.7 Elasticity of demand

In this experiment, sensitivity of general cost of passengers is considered regarding

transit fair, which changed the demand of transport mode. The experiment assumed that

Airline will offer discount rates from 0% to 25% for gaining more market share. The results

show the Airline market share will be closely equal to High-speed Rail when discount rate is

around 15%, with market share for High-speed Rail and Airline at 0.5045:0.4955. Figure 5.12

will show the sensitivity of general cost of passenger and market share (demand). Price

elasticity of demand is 1.4102 on average when transit fair of Airline decreases every 5%

0#

1,000#

2,000#

3,000#

4,000#

5,000#

6,000#

7,000#

8,000#

9,000#

10,000#

2010#

2011#

2012#

2013#

2014#

2015#

2016#

2017#

2018#

2019#

2020#

2021#

2022#

2023#

2024#

2025#

2026#

2027#

2028#

2029#

2030#

Total)reven

ue)fo

r)each)op

erator)(T

HB))

Millions)

Fiscal)year)

HSR#

AIR#

S1#

S1#S2#

57##

Figure 5.12: Sensitivity of general cost of passenger and market share

5.8 Concluding remarks

In this chapter, passenger choice model based on multinomial logit has been proposed.

Model estimation has been explained. The given model is used to estimate the market share

ratio, number of passenger flow, and operator revenue from forecasted trip demand in

Chapter 4.

0%#

10%#

20%#

30%#

40%#

50%#

60%#

70%#

80%#

90%#

100%#

0%# 5%# 10%# 15%# 20%# 25%#

0.7125#

0.6482#

0.5780#

0.5045#

0.4308#

0.3600#

0.2875)

0.3518)

0.4220)

0.4955)

0.5692)

0.6400)

Market)sha

re)ra

Yo)

Airline)discount)rate)(percentage))

AIR#

HSR#

58##

REFERENCES

Frank S. Koppenman & Chandra Bhat, A Self Instructing Course in Mode Choice Modeling:

Multinomial and Nested Logit Models. U.S. Department of Transportation

Frank S. Koppenman, Closed Form Discrete Choice Models. Handbook of Transport

Modelling, (Second Edition), pp. 257–277.

Moshe Ben-Akiva & Steven R. Lerman, Discrete Choice Analysis: Theory and Application to

Travel Demand, The MIT Press.

Juan de Dios Ortuzar & Juis G. Willumsen, Discrete Choice Models. In Modelling Transport.

A John Wiley and Sons, Ltd., Publication, pp. 227–234.

59##

CHAPTER 6

CONCLUSIONS

In this chapter, the author summarizes the thesis’s findings, conclusions, and

recommendations. In a long time that Airline is serving for long-distance such Bangkok and

Chiang-Mai route. However, High-speed Rail comes to share a number of passengers in an

inter-city transport market in 2016. Then, market share and following effects are necessary to

know for business preparation.

6.1 Conclusion

This research proposed and developed a passenger behavior model for an inter-city

transportation market in Thailand. The results by using Multinomial Logit choice models

make the operators understand their market shares and future investments. The coming of the

High-speed Rail will directly and strongly affect the Airline operators due to “mode shifting”

occurring in passenger choice behaviors.

Based on reviewed documents and technical papers, it was discovered that there is no

previous study about “market share in High-speed Rail in Thailand”; moreover, “mode shift

situation” and “operator revenue affects” in Thailand inter-city transport market have never

been discussed before. This leads to the motivation of this thesis.

6.2 Recommendations

For the Airline operators, coming of High-speed Rail is impacted directly to market

share and operation revenues. The falling of revenue will cause of shortage in operation profit,

and then preparation of operating is necessary. Airline operator may reduce transit fair for

gaining a higher market share.

Airline operator may changes target group to longer-distance passengers, such as for

long flights from Chiang Mai to Southern areas, or for international flights.

60##

For authorities, reducing airport fees for domestic flights may help operator to offer

lower transit fare to keep Airline services in Bangkok – Chiang-Mai.

For further research, future frequency of service may change by both High-speed Rail

and Airline operators for reasons of reduce operation costs. Moreover, changing technology

may make total traveling time shorter. For these reasons, the model should be incorporating

adjustments with actual passenger choices and market share, which can be obtained from

passenger statistical data after High-speed Rail operates in 2016.

In addition, like hub and spoke system, which operates in other transport modes

surrounding station areas (such as commuter rail, bus, or private car), can act as a feeder for

moving passengers from outer provinces to the station. It is recommended to include High-

speed Rail demand from surrounding provinces of the station in order to match more closely

to the real world.

61##

APPENDICES

62##

63##

Appendix A: Model Estimation source code

File: MODELING.g // Load DATA file (Excel 97-2003) x = xlsReadM("ANALDATA.xls", "A3:AL150", 1, 0); // Data specification nobs=148; para=3; b0=zeros(para,1); // call maxlik library library maxlik; maxset; _max_Algorithm=4; {b,ff,gg,cov,retcode}=maxprt(maxlik(x,0,&ll,b0)); cp=prob(b,x); print " choice probabiliry "; print cp; print ; print prob(b,x); print "ll(0) =" ll(b0,x); print "ll(b) =" ll(b,x); print "rou^2 =" 1-ll(b,x)/ll(b0,x); print "~rou^2 =" 1-(ll(b,x)-para)/ll(b0,x); hitv=zeros(nobs,1); hit=cp[.,.].==maxc(cp'); tcp=x[.,10:11]; for i(1,nobs,1); if tcp[i,.]==hit[i,.]; hitv[i,1]=1; else; hitv[i,1]=0; endif; endfor; hitv=sumc(hitv)/nobs; print "Hit value=" hitv; //--------------------------------------------------- // choice prob & utilty functon //--------------------------------------------------- proc prob(b0,x); local v,p; v=zeros(nobs,2); /* set of V */ p=zeros(nobs,2); /* set of P */

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// utilty functon v[.,1]=exp(b0[1]*x[.,25] +b0[2]*x[.,14]+b0[3]*x[.,18] ); v[.,2]=exp(b0[1]*x[.,29] +b0[2]*x[.,17]+b0[3]*x[.,19] ); // choice probability p[.,1]=v[.,1]./(v[.,1]+v[.,2]); p[.,2]=v[.,2]./(v[.,1]+v[.,2]); retp(p); endp; //-------------------------------------------------- // Log likelihood functon //-------------------------------------------------- proc ll(b0,x); local pp,al; pp=prob(b0,x); al=sumc(ln(sumc((pp.*x[.,10:11].*x[.,30:31])'))); retp(al); endp;