6.2 Application of Transport and Traffic Model 6-1 6.3 Enhancement of the Staff's Potential 6-12

Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Introduction

The Transport Data and Model Integrated with Multimodal and Logistics Project (TDL) in 2012-2014 (TDL II) is aimed to develop and maintain the transport and traffic database models. The said project has been conducted consecutively from the projects of UTDM, TDMC I-VI, TDML I-II, to TDL. The present project has integrated the results of the former projects so that the database, the information, and the transport and traffic models of Office of Transport and Traffic Policy and Planning (OTP) could be more comprehensive and updated all the time. Office of Transport and Traffic Policy and Planning (OTP) has assigned a group of consultant companies, i.e. PCBK INTERNATIONAL CO., LTD. SEA Consult Engineering Co., Ltd. Chotichinda Mouchel Consultants Limited and Systra MVA (Thailand) Ltd, to carry out this project with an operation term of 18 months. This Executive Summary Report represents the summary of all operation results in this project, including survey, study, review, recent data analysis, maintenance of transport and traffic database, improvement and maintenance of National Model (NAM) and extended Bangkok Urban Model (eBUM), application of transport and traffic model, and enhancement of staff’s potential. The consultants would like to express our appreciation to the executives and other officials involved in Office of Transport and Traffic Policy and Planning for cooperation and provision of information which is very indispensable in the study. Also, the consultants would like to declare our gratitude to the Steering Committee for precious opinions that are very beneficial to the conduct of this project until it is finally successful as expected.

Consultants PCBK INTERNATIONAL CO., LTD. SEA Consult Engineering Co., Ltd. Chotichinda Mouchel Consultants Limited Systra MVA (Thailand) Ltd

August 2015

PCBK / SEA / CMCL / SYSTRA MVA Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Contents

Page

Chapter 1 Introduction

1.1 Introduction 1-1 1.2 Principle and Reason 1-2 1.3 Objectives 1-2

Chapter 2 Survey, Study, Review and Analysis of the recent information

2.1 Introduction 2-1 2.2 Study and review of management and operation policy of the organizations 2-1 and departments relevant to transport and traffic as well as logistics in Thailand 2.3 Collection and update of data on the travel characteristics of people in the country 2-3 2.4 Collection and update of data on the freight transport and commodity flow 2-5 2.5 Study, survey and collection of data on the travel characteristics of people 2-6 and vehicles in order to improve the transport models of NAM and eBUM 2.6 The Study and Survey Data of Commodity Flow and Freight Transport Logistics 2-42 Chapter 3 Maintenance of transport and traffic database system

3.1 Introduction 3-1 3.2 Study and review of transport and traffic database 3-2 system development process 3.3 Update of data in the database system 3-3 3.4 Development of Executive Information System 3-4 3.5 Improvement of data presentation from the database system 3-16 3.6 Support for maintenance of transport and traffic database system 3-17 3.7 Improvement of Computer’s Equipment and Network System 3-18

Chapter 4 Improvement and maintenance of transport and traffic of National Mode (NAM)

4.1 Introduction 4-1 4.2 Study and review of NAM 4-2 4.3 Improvement and development of NAM 4-2 4.4 Development of innovation for the application of model 4-23

PCBK / SEA / CMCL / SYSTRA MVA 1 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Contents (Continued)

Page Chapter 5 Improvement and maintenance of transport and traffic eBUM

5.1 Introduction 5-1 5.2 Study and review of eBUM 5-2 5.3 Improvement and development of eBUM 5-4 5.4 Development of innovation for the application of model 5-30 5.5 Update of Software for Cube of OTP 5-48

Chapter 6 Application of transport and traffic model and enhancement of the staff's potential

6.1 Introduction 6-1 6.2 Application of transport and traffic model 6-1 6.3 Enhancement of the staff's potential 6-12

PCBK / SEA / CMCL / SYSTRA MVA 2 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Tables Page Table 2.2-1 Review of policy and plan in the projects of organizations or departments involved 2-2 Table 2.2-2 Review of the study results 2-3 Table 2.3-1 Additional data collected and updated into the project database 2-4 Table 2.4-1 Secondary data of goods transport and the sources 2-5 Table 2.5-1 Names of borders in the surveys 2-21 Table 2.5-2 The places of passenger survey at the main bus terminals, main train stations, 2-23 and main airports Table 2.5-3 The survey points along the North-South Screen line 2-26 Table 2.5-4 List of traffic volume survey points along the East-West Screen line 2-27 Table 2.5-5 Summary of traffic volume and Volume/Capacity – V/C at the survey sites 2-30 along the North-South Screen line Table 2.5-6 Summary of traffic volume and Volume/Capacity – V/C at the survey points 2-31 along the East-West Screen line Table 2.5-7 Analysis results of PCU for different kinds of vehicles 2-37 Table 2.5-8 Roads chosen for case study 2-38 Table 2.6-1 The summary of import, export and domestic freight transport 2-46 from Survey Data in Project Table 2.6-2 The Proportion of Goods Volume and Unit Transport Cost from Survey Data 2-51 in Project categorized by Mode Table 2.6-3 The Proportion of Goods Volume (including Dummy) categorized 2-51 by Mode from Transport and Traffic Model Table 4.2-1 Summary of data review on transport network in the model 4-2 Table 4.3-1 Zonal Data for current model development 4-3 Table 4.3-2 Coefficient of utility equation from modal split analysis of passengers’ behavior 4-16 Table 4.3-3 Parameters used in Modal Split Model 4-17 Table 4.3-4 Points of data survey along Screen Line nationwide 4-19 Table 4.3-5 Results of NAM validation along Screen Line in 2012 4-19 Table 4.3-6 Estimation of people’s travel based on Transport modes 4-20 Table 4.3-7 Results from NAM 4-20 Table 4.3-8 Freight Transport Results from NAM (Year 2012) 4-20 Table 4.3-9 Results of NAM validation along Screen Line in 2013 4-20 Table 4.3-10 Freight volume in 2013 (1,000 tons/year) 4-21 Table 4.3-11 Estimation of people’s travel based on transport modes 4-21 Table 4.3-12 Freight Transport Volume from NAM 4-22

PCBK / SEA / CMCL / SYSTRA MVA 3 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Tables (Continued-1) Page Table 4.3-13 Travelling Data Results from NAM 4-22 Table 4.3-14 Domestic Freight Transport Results from NAM 4-22 Table 4.3-15 Results from NAM, Freight, Average distance of freight Transport 4-22 Table 4.4-1 Forecasted amount of pollutions Emitted categorized by vehicle type 4-27 Table 4.4-2 Fuel Consumption 4-29 Table 5.2-1 Summary of improvement and development of eBUM 5-2 Table 5.3-1 Population and household in eBUM compared with population census in 2010 5-4 Table 5.3-2 Summary of Mean Trip Length from the synthesis of HIS 2546 5-6 between the survey results and model results Table 5.3-3 Survey Locations for Modal Split Model 5-7 Table 5.3-4 Example of Analysis results of Modal Split Model (0 Veh.) – HBW 5-8 Table 5.3-5 The results of updating VOC and VOT 5-10 Table 5.3-6 Acceptable deviation based on road types 5-10 Table 5.3-7 Model Calibration for Traffic Volume along the North-South Screen Line in 2012 5-10 Table 5.3-8 Model Calibration for Average Traffic Volume on Expressway System in 2012 5-11 Table 5.3-9 Model Calibration for Average MRT Ridership in 2012 5-12 Table 5.3-10 Model Calibration for Average BTS Ridership in 2012 5-12 Table 5.3-11 Model Calibration for Average Airport Rail Link - ARL Ridership in 2012 5-12 Table 5.3-12 Average Travel Speed in BMR by area in 2012 5-13 Table 5.3-13 Numbers of Trips in each area in 2012 5-13 Table 5.3-14 Modal Splits in 2012 5-13 Table 5.3-15 Trip volume categorized by type of vehicle ownership and trip 5-14 purposes with no transfer to public transport system in 2012 Table 5.3-16 Major trip proportion including transfer and no transfer to public transport system, 5-14 categorized by type of travel in 2012 Table 5.3-17 Numbers of passengers using public transport in 2012 5-14 (including transfer to public transport system) Table 5.3-18 Model Calibration for Traffic Volume along the North-South Screen Line in 2013 5-15 Table 5.3-19 Model Calibration for Traffic Volume along the East-West Screen line in 2013 5-16 Table 5.3-20 Model Calibration for Average Traffic Volume on Expressway System in 2013 5-17 Table 5.3-21 Model Calibration for Average MRT Ridership in 2013 5-17 Table 5.3-22 Model Calibration for Average BTS Ridership in 2013 5-18 Table 5.3-23 Model Calibration for average Airport Rail Link- ARL Ridership in 2013 5-19 Table 5.3-24 Average Travel Speed in BMR by area in 2013 5-19

PCBK / SEA / CMCL / SYSTRA MVA 4 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Tables (Continued-2) Page Table 5.3-25 Modal Splits in 2013 5-20 Table 5.3-26 Trip volume categorized by type of vehicle ownership and trip purposes 5-20 with no transfer to public transport system in 2013 Table 5.3-27 Major trip proportion including transfer and no transfer to public transport system, 5-21 categorized by type of travel in 2013 Table 5.3-28 Numbers of passengers using public transport in 2013 5-21 (including transfer to public transport system) Table 5.3-29 Forecasted traffic during morning peak 5-23 Table 5.3-30 Forecasted traffic during evening peak 5-23 Table 5.3-31 Forecasted traffic all day 5-24 Table 5.3-32 Forecasted proportion of main trip with no transfer to public transport system 5-28 Table 5.3-33 Forecasted proportion of main trip including transfer to public transport system 5-28 Table 5.3-34 Forecasted numbers of passengers using major public transport system (Person Trips) 5-28 Table 5.3-35 Forecasted numbers of passengers using major public transport system 5-29 (including transfer to public transport system) Table 5.3-30 Average Speed in each area 5-29 Table 5.3-31 Numbers of trips in each area 5-30 Table 5.4-1 Analysis results of evacuation model in case of emergency 5-34 in Bang Pa-in industrial estate of Ayutthaya Table 5.4-2 Fuel Consumption categorized by vehicle type from eBUM in 2013 5-36 Table 5.4-3 Fuel Consumption by province from Model eBUM in 2013 5-36 Table 5.4-4 Statistics of fuel distribution at gas stations in Bangkok and metropolitan areas in 2013 5-37 Table 5.4-5 Average Fuel Sales at Gas station in Bangkok Metropolitan and Surrounding Area in 2013 5-37 Table 5.4-6 Proportion of Fuel Consumption between eBUM and 5-38 Department of Energy Business Statistics Table 5.4-7 Pollution emitted from the model based on different provinces in 2013 5-42 Table 5.4-8 Pollution emitted categorized by vehicle type 5-44 Table 6.2-1 Daily ridership in rail transit in 2014-2032 6-2 Table 6.2-2 Average speed all day (km/hr) on private vehicle network 6-2 Table 6.2-3 Traffic volume in PCU per day entering the areas of Ratchadaphisek Ring Road 2012 6-4 Table 6.2-4 Average speed during a.m. peak (km/hr) and percentage of change in 6-4 Ratchadaphisek Ring Road and Bangkok and metropolitan areas in 2012 Table 6.2-5 Daily Ridership of rail transit in 2022-2037 6-5 Table 6.2-6 Average speed all day (km/hr) on private vehicle network in 2022-2037 6-6

PCBK / SEA / CMCL / SYSTRA MVA 5 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Tables (Continued-3) Page Table 6.2-7 Expected import - export in case of AEC in 2015 6-7 Table 6.2-8 Volume of commodity passing borders 6-7 Table 6.2-9 Analysis results 6-7 Table 6.2-10 Traffic volume (V/C Ratio) 6-8 Table 6.2-11 High-Speed Train Projects 6-10 Table 6.2-12 Numbers of passengers (person-trip/day) 6-11 Table 6.2-13 Average speed on network 6-11

PCBK / SEA / CMCL / SYSTRA MVA 6 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Figures Page

Figure 2.5-1 Screen Lines Survey Locations 2-7 Figure 2.5-2 Data survey along the Screen Line SL1, SL3, SL4 in 2012 2-8 Figure 2.5-3 Data survey along the Screen Line SL2, SL5, SL6 in 2013 2-8 Figure 2.5-4 Survey Results for Screen Lines 1 (SL 1) 2-9 Figure 2.5-5 Survey Results for Screen Lines 2 (SL 2) 2-10 Figure 2.5-6 Survey Results for Screen Lines 3 (SL 3) 2-11 Figure 2.5-7 Survey Results for Screen Lines 4 (SL 4) 2-12 Figure 2.5-8 Survey Results for Screen Lines 5 (SL 5) 2-13 Figure 2.5-9 Survey Results for Screen Lines 6 (SL 6) 2-14 Figure 2.5-10 Roadside Interview Survey Results for Screen Lines 1 : SL 1 2-15 Figure 2.5-11 Roadside Interview Survey Results for Screen Lines 2 : SL 2 2-16 Figure 2.5-12 Roadside Interview Survey Results for Screen Lines 3 : SL 3 2-17 Figure 2.5-13 Roadside Interview Survey Results for Screen Lines 4 : SL 4 2-18 Figure 2.5-14 Roadside Interview Survey Results for Screen Lines 5 : SL 5 2-19 Figure 2.5-15 Roadside Interview Survey Results for Screen Lines 6 : SL 6 2-20 Figure 2.5-16 Locations of main borders between Thailand and neighboring countries 2-22 Figure 2.5-17 Data survey at main borders between Thailand and neighboring countries 2-23 Figure 2.5-18 Interviews with passengers at the main bus terminals, main train stations, 2-24 and main airports in different regions Figure 2.5-19 The proportion of the interviewers based on the distance and objective of travel 2-24 Figure 2.5-20 The relationship between distance and modes of travels 2-25 Figure 2.5-21 The locations of traffic volume and travel condition 2-28 survey along the North-South Screen line Figure 2.5-22 The locations of traffic volume and travel condition survey 2-29 along the East-West Screen line Figure 2.5-23 The division of sub-areas in eBUM within Ayutthaya and Chachoengsao 2-33 Figure 2.5-24 The locations of Roadside Interview Survey at the 4 truck terminal 2-34 Figure 2.5-25 Points of travel condition survey in Ayutthaya 2-35 Figure 2.5-26 Points of travel condition survey in Chachoengsao 2-36 Figure 2.5-27 Areas of study for this survey 2-37 Figure 2.5-28 Samples of roads chosen for case study 2-38 Figure 2.5-29 Results of Speed-Flow Curve Analysis on the road with 2 lanes (inner areas) 2-39 Figure 2.5-30 Results of Speed-Flow Curve Analysis on the road with 2 lanes (outer areas) 2-40 Figure 2.5-31 Results of Speed-Flow Curve Analysis on the road with more than 2 lanes (inner areas) 2-40

PCBK / SEA / CMCL / SYSTRA MVA 7 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Figures (Continued-1) Page

Figure 2.5-32 Results of Speed-Flow Curve Analysis on the road with more than 2 lanes (outer areas) 2-41 Figure 2.6-1 Supply Chain Relationship with Transport. 2-43 Figure 2.6-2 Relationships of Transport Mode Selection 2-44 Figure 3.1-1 Maintenance of Transport and Traffic Database System 3-2 Figure 3.4-1 Structure of presentation of Executive Information System of OTP 3-5 Figure 3.4-2 Overall information of OTP 3-6 Figure 3.4-3 Information of important projects and budget monitoring 3-7 Figure 3.4-4 Fundamental information of 2-trillion project 3-8 Figure 3.4-5 Project information about bridges 3-9 Figure 3.4-6 Information of average traffic speed and traffic volume 3-10 Figure 3.4-7 Names of Plan/Project under the strategy of Ministry of Transport 3-11 Figure 3.4-8 Information of logistics survey 3-12 Figure 3.4-9 Information for integration with Ministry Operating Center (MOC/DOC) 3-13 Figure 3.4-10 Statistic information of transport and logistics of National Statistical Office 3-14 Figure 3.4-11 Information of OTP Strategic Plans 3-15 Figure 3.5-1 Main page of transport and traffic publication system after update 3-16 Figure 4.3-1 Details of Coarse Traffic Analysis Zone 4-4 Figure 4.3-2 Additional sub-district zones 4-5 Figure 4.3-3 Comparison of provincial population from civil registration database 4-7 and population and housing census in 2010 Figure 4.3-4 Road network after update 4-8 Figure 4.3-5 Routes of high-speed train as to the master plan 4-9 Figure 4.3-6 Overall view of access volume to the bus terminals in 2005-2012 4-11 Figure 4.3-7 Overall passengers at the airports in 2007-2012 4-12 Figure 4.3-8 Trip Length distribution 4-13 Figure 4.3-9 Structure of modal split proposed by the consultants to improve NAM 4-13 Figure 4.3-10 Structure of Modal Split Model in case of high-speed train (Added-mode Structure) 4-14 Figure 4.3-11 Steps of checking for correction in NAM 4-18 Figure 4.4-1 Steps of transport model development to analyze the emission 4-24 Figure 4.4-2 Commands for analysis of fuel consumption and emission 4-25 Figure 4.4-3 Analysis results of emission in 2013 4-28 Figure 4.4-4 Analysis results of fuel consumption in vehicle 4-28

PCBK / SEA / CMCL / SYSTRA MVA 8 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Figures (Continued-2) Page

Figure 5.3-1 Locations of survey for Modal Split Model 5-7 Figure 5.3-2 Trip distribution based on distance 5-22 Figure 5.3-3 Trip distribution based on trip duration 5-22 Figure 5.3-4 Desired line within Bangkok and Vicinity Area in 2012 (including 2 provinces) 5-24 Figure 5.3-5 Desired line within Bangkok and Vicinity Area in 2013 (including 2 provinces) 5-25 Figure 5.3-6 Desired line within Bangkok and Vicinity Area in 2017 (including 2 provinces) 5-26 Figure 5.3-7 Desired line within Bangkok and Vicinity Area in 2022 (including 2 provinces) 5-26 Figure 5.3-8 Desired line within Bangkok and Vicinity Area in 2027 (including 2 provinces) 5-26 Figure 5.3-9 Desired line within Bangkok and Vicinity Area in 2032 (including 2 provinces) 2-27 Figure 5.3-10 Desired line within Bangkok and Vicinity Area in 2037 (including 2 provinces) 5-27 Figure 5.4-1 Traffic zones in Samutprakarn 5-31 Figure 5.4-2 Structure of Land Use Model 5-31 Figure 5.4-3 Example of analysis result of eBUM in TRANUS Program 5-32 Figure 5.4-4 Comparative Results between TRANUS Program and traffic survey Data 5-33 along Screen Line morning peak (unit : PCU/hour) Figure 5.4-5 Comparative Results between TRANUS Program and traffic survey Data 5-33 along Screen Line evening peak (unit : PCU/hour) Figure 5.4-6 Analysis results of traffic in case of emergency in the industrial estate of Ayutthaya 5-34 Figure 5.4-7 Fuel Consumption Analysis Flow Chart for eBUM Development 5-35 Figure 5.4-8 Emission Analysis Flow Chart for eBUM Development 5-39 Figure 5.4-9 Emission of Hydrocarbon (HC) from eBUM 5-40 Figure 5.4-10 Emission of Carbon Monoxide (CO) from eBUM 5-40 Figure 5.4-11 Emission of Nitrogen Oxide (NOx) from eBUM 5-41 Figure 5.4-12 Emission of Carbon Dioxide (CO2) from eBUM 5-41 Figure 5.4-13 Emission of Particle Matter (PM) from eBUM 5-42 Figure 5.4-14 Proportion of emission of different pollution types based on different provinces 5-43 Figure 5.4-15 Concept of Cloud Computing 5-45 Figure 5.4-16 The concept of transport and traffic model development on Cube Cloud 5-45 Figure 5.4-17 NAM in Cube Cloud 5-47 Figure 6.2-1 Test areas of Congestion Charging 6-3 Figure 6.2-2 Development of express train/high-speed train as to the master plan 6-9 Figure 6.3-1 Atmosphere of the 1st Workshop Seminar 6-12 Figure 6.3-2 Atmosphere of the 2nd Workshop Seminar 6-12 Figure 6.3-3 Atmosphere of training "Application of Model in Cube Cloud" 6-13

PCBK / SEA / CMCL / SYSTRA MVA 9 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

List of Figures (Continued-3) Page

Figure 6.3-4 Atmosphere of transport and traffic technology and logistics system field trip at Chiang Rung-Sib Song Panna 6-13 Figure 6.3-5 Atmosphere of the 2nd transport and traffic technology and logistics system field trip 6-14 Figure 6.3-6 Sample of “Homepage” of website promoting the project 6-14 Figure 6.3-7 The 1st and 2nd interviews with OTP executives 6-15 Figure 6.3-8 Homepage of TDL website linking to the learning materials about analysis 6-15 Figure 6.3-9 Details of self-learning contents (4 classrooms) 6-16 Figure 6.3-10 Samples of self-learning materials (Classroom 3) 6-16 Figure 6.3-11 Samples of self-learning materials (Classroom 4) 6-17

PCBK / SEA / CMCL / SYSTRA MVA 10 ChapterChapter 1 IntroductionIntroduction Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Chapter 1 Introduction 1.1 Introduction 1.2 Principle and Reason 1.3 Objectives

1.1 Introduction

The Transport Data and Model integrated with Multimodal and Logistics (TDL II) Project in 2012-2014 is aimed to develop and maintain the models and database of transport and traffic. The said projects have been conducted consecutively from the projects of UTDM, TDMC I-VI, TDML I-II, to TDL. The present project has integrated the results of the former projects so that the database, the information and the transport and traffic models of Office of Transport and Traffic Policy and Planning (OTP) could be more comprehensive and updated. Also, the project can be applied to establish policy and plan, as well as the measures of transport and traffic more efficiently.

1.2 Principle and Reason

Office of Transport and Traffic Policy and Planning (OTP) is a government agency under the Ministry of Transport. The responsibility thereof is to suggest the policies, measures, standards and integration of transport and traffic plans; to take action and enhance safety and environments in the transport and traffic system; to develop and apply technologies so as to create and propagate the information and knowledge in terms of transport and traffic of the country. Referring to the conduct of the development and maintenance project of database system, information, and model in order to integrate and develop transport, distribution and logistics, OTP has developed and maintained the database, the information, and the transport and traffic models from Urban Transport Database and Model Development Project (UTDM), Transport Database and Management Center (TDMC I-VI), Transport Data and Model Integrated with Multimodal Transport and Logistics (TDML I-II), and Transport Data and Model integrated with Multimodal and Logistics (TDL). In addition, OTP has performed distribution and logistics database development project, which led to the integrated database development and efficient development tools for transport and traffic models. Thereby, the said tools can be employed effectively to set up the overall transport and traffic policies and plans, which will in turn bring about the better workflow. Moreover, OTP was commissioned by the Ministry of Transport to create the logistics database system of Thailand. So far, OTP has surveyed 52 kinds of import and export products, but there has been no survey about the domestic consumer products, the data of which can be used to estimate the nationwide logistics data (the Ministry of Transport now uses the estimation from the database of the year 1997). The said data will enable OTP to be a central unit to analyze and forecast the transport and traffic by means of

PCBK / SEA / CMCL / SYSTRA MVA 1-1 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

the models that OTP has developed. Also, the said data can be used as mutual information that is necessary for the ministerial units and the executives to make any significant decisions. OTP suggested that the related projects should be integrated into the development and maintenance project of database system, information, and model in order to integrate and develop transport, distribution and logistics. The said project includes the input and update of data as well as the links of geographic information system, all of which are practically and effectively used in the transport and traffic analysis and the strategic planning.

1.3 Objectives

(1) To study the transfer and the features of logistics, which are significant to the national economy, from the upstream to the end stream of domestic transport in order to create the central information of logistics database for the Ministry of Transport. (2) To maintain and update the existing transport and traffic database of OTP, which can produce the executive reports and operational reports in a quick and convenient manner, leading to the integral establishment of national logistics policies and strategic plans. (3) To develop, improve and maintain National Model (NAM) and Extended Bangkok Urban Model (eBUM) (covering Bangkok and Metropolitan areas), which will suitably and efficiently be applied in the evaluation of transport policies and projects; and to make a standard manual of transport and traffic model development. (4) To have transport and traffic models as a tool to make suitable decisions on the policy and development of transport and traffic as followed: 1) Application of NAM :  Analyze the volume and the routes of both transport and people’s travelling  Test the transport policies for goods and people, develop the concepts of rest areas, and improve the infrastructure (e.g. high-speed train or Bridge over the Mekong River)  Test the transport trends for goods and people when Thailand steps into ASEAN Economic Community : AEC 2) Application of eBUM :  Test the transport and traffic policies, e.g. the fares of mass rail transit in different cases (e.g. distance-based fares, zone-based fares and fixed fares for all distances)  Test the visions and missions in the public transport system

PCBK / SEA / CMCL / SYSTRA MVA 1-2 ChapterChapter 2 Survey,Survey, SStudy,tudy, RRevieweview aandnd AAnalysisnalysis ooff thethe rrecentecent iinformationnformation Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Chapter 2 Survey, Study, Review and Analysis of the recent information 2.1 Introduction 2.2 Study and review of management and operation policy of the organizations and departments relevant to transport and traffic as well as logistics in Thailand 2.3 Collection and update of data on the travel characteristics of people in the country 2.4 Collection and update of data on the freight transport and commodity flow 2.5 Study, survey and collection of data on the travel characteristics of people and vehicles in order to improve the transport models of NAM and eBUM 2.6 Study, survey, and collection of overall domestic freight transport and import- export volume that reflects current freight transport situation; and route analysis of main freight transport within the country 2.7 Summary

2.1 Introduction

The study, development, and maintenance of database system, information and model in order to integrate and develop transport, distribution and logistics are the consecutive operation from the former studies. So, it is necessary to study and review the policies as well as the operation results of the organizations and units involved; and to collect and update the existing data on the travelling of people in the country and the transport and commodity flow. Besides, it is important to study, survey and collect the additional data on the travelling of both people and vehicles in order to enhance transport and traffic models of NAM and eBUM. Regarding the data on transport of goods, this research has studied, surveyed and collected 180 items of transport volume, up from 52 items (import and export), including domestic transport and import-export volume that can reflect the overall pictures of recent transport, and can analyze the main routes of transport within the country. In this chapter, the main contents of study shall be presented while all of the detailed information and contents have been separately collected and bound into 3 interviewers: 1) Report of transport and traffic conditions, 2) Report of data analysis on the surveyed transport and traffic in the project, and 3) Report of commodity flow that is significant to the national economy.

2.2 Study and review of management and operation policy of the organizations and departments relevant to transport and traffic as well as logistics in Thailand

In this section, the consultants divided the review study into 2 parts: 1) study and review of policy and plan in the development project of transport and traffic within organizations or departments involved in order to receive the input data for improving transport and traffic models of NAM and eBUM, and 2) review of the study results in the former projects with an emphasis on methods and outcome of model and database development; and review of the surveyed information in those projects in order to further improve the

PCBK / SEA / CMCL / SYSTRA MVA 2-1 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL) secondary data and database of Management Information System (MIS) on Transport and Traffic and use for development of Executive Information System (EIS). The collected and reviewed information about the policy and plan of development projects in transport and traffic, logistics, and services of government and private sectors, as well as the study of former project operation in transport and traffic has been summarized and presented in Table 2.2-1 and Table 2.2-2 respectively. Table 2.2-1 Review of policy and plan in the projects of organizations or departments involved Plan/Project  Study framework to prepare Thailand for ASEAN Economic Community: AEC, in the field of transport and logistics  Land Bridge: The Dawei Deep Sea Port and Industrial Estate Development Project of Union of Myanmar  Government Administrative Plan 2012-2015  The 11th National Economic and Social Development Plan (2012-2016)  The trends of development during the 11th National Economic and Social Development Plan (2012- 2016)  Thailand Vision 2027  Thailand's Logistics Development Strategy 2007-2011  (Draft) Thailand's Logistics Development Strategic Plan 2012-2016  Progress Report of Thailand's Logistics Development Strategy of the year 2011  Report of border trade between Thailand and the neighboring countries of the year 2001-2010, and the situation and report of border trade between Thailand and the neighboring countries of the year 2011  Strategy and development operation plan for economic development in regional areas  Administration and Government Plan, Ministry of Transport 2012-2015  Other plans of Ministry of Transport  National Industrial Development Master Plan 2012-2031  National Tourism Development Plan 2012-2016  International Trade: Trends and measures under the master plan of Ministry of Commerce 2012-2021  Greater Mekong Subregion-GMS  Study of strategy and relation of economy and logistics along the East-West Economic Corridor(EWEC), case study of route no.12  Case study: Integration of ASEAN logistics and logistics strategy of Thailand  Development study of logistics network to accommodate the North-South Economic Corridor (NSEC) and the East-West Economic Corridor (EWEC)  National Statistical System Master Plan 2011-2015, National Statistical Office (NSO)

PCBK / SEA / CMCL / SYSTRA MVA 2-2 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 2.2-2 Review of the study results Report of study results  Traffic Data Survey for Regional Master Plan Preparation Project (TDL), OTP, 2011  The Evaluate Performance of Transport and Traffic Model in each OTP Administration Level, OTP, 2011  Transport Data and Model Integrated with Multimodal Transport and Logistics (TDML), OTP, 2008  Transport Data and Model Integrated with Multimodal Transport and Logistics (TDML II), OTP, 2009  Transport Data and Model integrated with Multimodal and Logistics (TDL), OTP, 2010-2011  The Development of Multimodal Transport and Logistics Supply Chain Management for Implementation of Action Plan, OTP, 2004  The Pilot Project of Developing Management System of Freight Transport and Service by Railway, OTP, 2008  The Study on Strategy of Strengthening Transport Linkages Capability in order to accommodate an Expansion of Economic, Trade and Investment Route, OTP, 2008  Master Plan for Track Development and High Speed Train, OTP, 2010  Commodity Flow Survey, OTP, 2007  The Study on Transport Cost Structure and Logistics System, OTP, 2009  The Bus System Development in Bangkok and its Vicinities, OTP, 2009  The Study to Develop Master Plan for Sustainable Transport System and Mitigation of Climate Change Impacts, OTP 2012  Master Plan for Transport and Traffic System Development, 2011-2020, OTP, 2011  The Study on Master Plan for Integrating of Road Networks, Cross River Bridge and Traffic Volume in Bangkok and its Vicinities, OTP, 2012  Report on Thailand’s Connectivity 2012, OTP, 2012

2.3 Collection and update of data on the travel characteristics of people in the country

In addition to the update of MIS database of Office of Transport and Traffic Policy and Planning (OTP), i.e. traffic and transport data in the regions and traffic data from the intelligent traffic and transport system, there has been collection of data on people travelling from other agencies such as Bangkok Metropolitan Administration (BMA), Office of Transport and Traffic Policy and Planning (OTP), Department of Highways (DOH), Expressway Authority of Thailand (EXAT) and another studies to update and add to the project database as shown in Table 2.3-1.

PCBK / SEA / CMCL / SYSTRA MVA 2-3 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 2.3-1 Additional data collected and updated into the project database Data Source  Survey data of travel schedule (O-D) in 2004 conducted by King Mongkut's University of Office of Technology Thonburi (KMUTT) Transport and  Survey data of traffic volume and travel speed in the critical areas, derived from the study Traffic Policy and projects to make emergency plans and phasing for the improvement of main roads Planning (OTP)  Survey data of traffic volume, travel speed, Home Interview, and Road Side Interview from the projects of TDMC IV, TMDC V, TDMC VI, TDML, TDMLI, TDMLII and TDL  Survey data of Trip Table (O-D) in 2001 and 2003 Department of  Survey data of Average Annual Daily Traffic (AADT) Highways (DOH)  Ratio of Directional Distribution and Seasonal Factor of AADT on the highways  Survey data of traffic volume for planning, survey and designs of various projects  Survey data of travel speed for planning, survey and designs of various projects  Survey data of traffic volume in the master plans of Department of Highways  Survey data of traffic volume in provincial areas (covering Ayutthaya and Chachoengsao)  Survey data of traffic volume passing the check points (in 1998-2010) Inter-City  Traffic volume, hourly and daily basis Motorway, Department of Highways (DOH)  Volume of cars at the toll booths on monthly, annually and daily basis based on the Expressway types of vehicles (4 wheels, 6-10 wheels, and over 10 wheels) Authority of  Survey data of Trip Table (O-D Matrix) Thailand (EXAT)  Survey data of traffic volume for planning, survey and designs of various projects Department of  Survey data of travel speed for planning, survey, and designs of various projects Rural Roads (DRR)  The number of passengers for the ferries Marine  The number of passengers for Chao Phraya Express Boat Department (MD)  The number of passengers for San Saeb Canal Boat and Chao Phraya  The number of passengers for Phra Khanong Canal Boat Express Boat  Ridership for each MRT station based on the date, time, ticket types, origin and destination MRT  Ridership for each BTS station based on the date, time, ticket types, origin and destination BTS  The number of passengers for buses on a daily basis (estimated from the income) BMTA

PCBK / SEA / CMCL / SYSTRA MVA 2-4 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2.4 Collection and update of data on the freight transport and commodity flow

The secondary data of freight transport collected and updated herein is derived from the sources of different ministries, departments, and other concerned units as shown in Table 2.4-1. Table 2.4-1 Secondary data of goods transport and the sources Data of goods transport Source  Demand of travel for 7 groups The Development of  Data of Commodity Flow in 2010 fot 6 groups with total 52 types of goods Multimodal Transport and  Data of transport volume based on mode of transportin 2010(O-D Report) Logistics Supply Chain for 4 groups Management for  Data of Logistic Nodes in 2010 for30 groups Implementation of Action Plan, (OTP)  Imported products based on their values within the period of 5 years Ministry of Commerce  Exported products based on their values within the period of 5 years (MOC)  Statistics of the first 35 imported products based on their weight Customs Department  Statistics of the first 35 exported products based on their weight  12 types of exported products with the highest net value  5 products with highest volume travelling from Laos to Thailand and to the third country  5 products with highest volume travelling from the third country to Thailand and to Laos  Primitive data of Container Freight Stations  Primitive data of Custom houses  Primitive data of Bonded Warehouse for oil depot  Primitive data of Bonded Warehouse for cargo display  Primitive data of Bonded Warehouse for duty free shops  Primitive data of Bonded Warehouse for repairing or building ships  Primitive data of Bonded Warehouse for factory  Primitive data of Bonded Warehouse for free trade with no tax and duty  Primitive data of factories located in the areas of Bonded Warehouse for free trade with no tax and duty  Survey project of commodity flow in 2007 NSO  Volume of road transport based on groups of commodity Ministry of Transport (MOT)  Volume of rail transport based on groups of commodity  Volume of water transport based on groups of commodity  Volume of sea transport based on groups of commodity  Primitive data of Airports

PCBK / SEA / CMCL / SYSTRA MVA 2-5 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Data of goods transport Source  Primitive data of Truck Terminal Department of Land  Primitive data of Inland Container Depots (ICD) Transport (DLT)  Volume of vehicles in and out of each truck terminal  Primitive data Industrial Zones IEAT  Primitive data of Inland Container Depots SRT  Primitive data of Container Yards  Primitive data of River Ports Marine Department (MD)  Primitive data of Private Ports  Primitive data of International Sea Ports PAT  Annual volume of vehicles in and out of International Sea Ports  Volume of import and export through the main International Sea Ports  Primitive data of Public Warehouses Department of Internal  Primitive data of Cold Storage Trade of Thailand  Primitive data of Silos  Primitive data of PWO’s Warehouses Public Warehouse Organization Ministry of Commerce  Annual volume of import and export through international airports Department of Air Transport

2.5 Study, survey and collection of data on the travel characteristics of people and vehicles in order to improve the transport models of NAM and eBUM

Study, survey and collection of data on the travelling and vehicles in this study have an aim to enhance the performance of transport and traffic models of OTP both in the area of NAM and eBUM 2.5.1 The data survey for the improvement of transport and traffic models in the level of NAM The data survey for the improvement of transport and traffic models in the level of NAM includes the survey of travelling conditions along the borders between Thailand and its neighboring countries, and the survey of passengers at major bus terminals, railway stations and airports. 2.5.1.1 The survey of travelling conditions It is the survey of 6 Screen Lines, which is divided into 2 sessions: 1) The survey of North (SL1), Central (SL3), and East (SL4) in 2012 2) The survey of Northeast (SL2), Upper South (SL5), and Lower South (SL6) in 2013 The locations of all 6 Screen lines are shown in Figure 2.5-1

PCBK / SEA / CMCL / SYSTRA MVA 2-6 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Screen Line SL1 Screen Line SL2

RS03 RS05 RS04 RS01 RS02 RS06 RS09 RS07 RS10 RS08

RS12 RS11 Screen Line SL4 Screen Line SL3

RS13 Screen Line SL5

RS16 RS14 Screen Line SL6 RS15 Screen Line SL1 – Northern Corridor Screen Line SL2 – Northeastern Corridor Screen Line SL3 – Central Corridor Screen Line SL4 – Eastern Corridor Screen Line SL5 – Upper South Corridor

Screen Line SL6 – Lower South Corridor

RS xx –Roadside Interview Location

Figure 2.5-1 Screen Lines Survey Locations

PCBK / SEA / CMCL / SYSTRA MVA 2-7 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

The survey conducted along the screen lines includes the data of traffic volume and Roadside Interview Survey: RIS (Figure 2.5-2 and Figure 2.5-3). The data obtained consists of objective of travel, volume of passengers on the vehicles, volume of commodity transport, choice of transport mode, etc. The data is applied to compare and test for Model Calibration & Validation so as to insure the application of the said models.

Figure 2.5-2 Data survey along the Screen Line SL1, SL3, SL4 in 2012

Figure 2.5-3 Data survey along the Screen Line SL2, SL5, SL6 in 2013 The results of the surveys of traffic volume along the 6 screen lines are shown from Figure 2.5-4 to Figure 2.5-9. The results of RIS along the screen lines are shown from Figure 2.5-10 to 2.5-15.

PCBK / SEA / CMCL / SYSTRA MVA 2-8 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Traffic Volume on Screen Line: SL1 during 6.00-18.00 hrs. (PCU) 13,247 RS01 14,193

2,806 RS03 3,136

9,942 RS02 9,342 A

RS03 B 14,282 RS04 16,029 RS04 RS01 40,277 SL1 42,700 RS02 PCU 10,000

8,000

005

130

455 8

6,000 406

247

211

960

673

564

376

038

, 6

4,000 6 914

2,000 , 3 0 ่วงเว า

Totalปร มา Traffic รา รVolumeรวม 2 ท - 2ทาง-way PCU)(PCU)

สัดส่วนยานพาหนะTraffic Compositionประเภทต่างๆ on Screenตามแนว Line Screen 1 : SLLine 1 : SL1 _SLSL 1 1( รวม(All))_ _RS01_ _RS02_ 69.4% 66.6% 66.8%

15.9% 0.6% 6.8% 13.7% 4.4% 2.7% 10.1% 10.8% 1.8% 8.5% 4.8% 6.6% 1.3% _RS03_ _RS04_ 2.6% 6.4% รBicycle/Motorcycle ั รยาน ร ั รยานยนต 2 & 3 แwheel ะ 65.0% 74.4% รPrivate ยนต นั งส่วนCar รBus ดยสาร 22.2% รLight รรท Truck นาดเ 13.8% รMedium รรท Truckนาด าง 6.0% 0.5% 6.7% 0.4% 2.9% 3.4% 2.1% 2.5% รHeavy รรท Truck นาด incl.ห ่ รวมทัTrailer งร &พ่วงแ Semi ะ- trailer งพ่วง

Figure 2.5-4 Survey Results for Screen Lines 1 (SL 1)

PCBK / SEA / CMCL / SYSTRA MVA 2-9 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Traffic Volume on Screen Line: SL2 during 6.00-18.00 hrs. (PCU) 3,470 RS05 4,169

19,443 RS06 13,805 B 8,129 RS08 9,381 A

RS05 2,382 RS07 3,640 Screen Line SL2 33,425 SL2 30,995 RS06 PCU (SL2) 7,000 6,000 RS07

5,000 003

729

685

652

557

472

429

423

120

116

, 5

4,000 5

764

471

, 4 3,000 4 RS08 2,000 1,000 0 ่วงเว า

Totalปร Traffic มา รา Volume ร รวม 2 -ท2 -wayทาง (PCU)PCU)

สัดส่วนยานพาหนะประเภทต่างๆ ตามแนว Screen Line: SL2 _SLSL 2 2 ((All)รวม) _ _RS05_ _RS06_

87.3% 78.9% 65.3% 25.5%

3.3% 1.2% 11.7% 4.0% 1.2% 1.6% 1.4% 5.0% 2.6% 1.6% 3.4% 1.8% 2.2% _RS07_ _RS08_ 1.9% รBicycle/Motorcycle ั รยาน ร ั รยานยนต 2 & 3 แwheel ะ รPrivate ยนต นั งส่วนCar 67.2% 74.2% รBus ดยสาร 22.0% รLight รรท Truck นาดเ 17.0% รMedium รรท Truckนาด าง 2.8% 0.2% 3.6% 2.0% 1.9% 6.0% 2.0% 1.2% รHeavy รรท Truck นาด incl.ห ่ รวมทัTrailer งร &พ่วงแ Semi ะ- trailer งพ่วง

Figure 2.5-5 Survey Results for Screen Lines 2 (SL 2)

PCBK / SEA / CMCL / SYSTRA MVA 2-10 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Traffic Volume on Screen Line: SL3 during 6.00-18.00 hrs. (PCU)

18,372 RS09 20,250

58,934 RS10 59,329 B A 77,363 SL3 79,579

RS09 PCU 18,000 16,000

14,000

075

006

535 333

12,000 252

972

632

, 14

10,000 343

, 14

217

816

735

12 ,

8,000 12

028 11 RS10 ,

6,000 10 4,000 10 2,000 0 ่วงเว า

Totalปร มาTraffic รา Volumeร รวม 2 ท-2 -ทางway PCU)(PCU)

สัดส่วนยานพาหนะประเภทต่างๆTraffic Composition _SLSL 33 ((All)รวม )_ onตามแนว Screen Screen Line Line: 3 : SL SL3 3 _RS09_ 60.5% 5.7%

33.9% 26.9% 1.8% 13.1% 6.8% 5.2% 3.8% 21.6% 17.9% 2.8% _RS10_ รBicycle/Motorcycle ั รยาน ร ั รยานยนต 2 & 3 แwheel ะ 68.6% รPrivate ยนต นั งส่วนCar รBus ดยสาร รLight รรท Truck นาดเ 0.6% รMedium รรท Truckนาด าง 2.9% 8.9% รHeavy รรท Truck นาด incl.ห ่ รวมทัTrailer งร &พ่วงแ Semi ะ- trailer งพ่วง 16.8% 2.1% Figure 2.5-6 Survey Results for Screen Lines 3 (SL 3)

PCBK / SEA / CMCL / SYSTRA MVA 2-11 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Traffic Volume on Screen Line: SL4 40,407 during 6.00-18.00 hrs. (PCU) SL4 17,185 PCU 42,842 8,000

23,223 7,000

955

644

413

7 246

RS12 ,

7 ,

6,000 055

970

880

868

791

782

, 7

469

, 6

5,000 6

6 179

4,000 , RS11 3,000 5 19,739 2,000 1,000 23,103 0 ่วงเว า

Totalปร มา Traffic รา ร Volumeรวม 2 ท -ทาง2-way PCU) (PCU)

RS12 RS11 B A Screen Line 4 (SL4)

สัดส่วนยานพาหนะประเภทต่างๆTraffic Composition _SLSL 44 ((All)รวม )_ onตามแนว Screen Screen Line Line: 4 : SL SL4 4 _RS11_ 68.3%

66.5%

19.6% 13.2%

2.4% 4.3% 5.1% 3.2% 2.7% 4.5% 4.2% 6.0% _RS12_ รBicycle/Motorcycle ั รยาน ร ั รยานยนต 2 & 3 แwheel ะ 70.8% รPrivate ยนต นั งส่วนCar รBus ดยสาร รLight รรท Truck นาดเ รMedium รรท Truckนาด าง 3.7% 1.6% 8.4% รHeavy รรท Truck นาด incl.ห ่ รวมทัTrailer งร &พ่วงแ Semi ะ- trailer งพ่วง 9.1% 6.5%

Figure 2.5-7 Survey Results for Screen Lines 4 (SL 4)

PCBK / SEA / CMCL / SYSTRA MVA 2-12 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Traffic Volume on Screen Line: SL5 11,481 during 6.00-18.00 hrs. (PCU) SL5

PCU 12,151 11,481 2,500

2,000 325

265

121

051

038

020

954

950

947

863 2

1,500 825

1 ,

RS13 1 1

1,000

276

, 1 500 12,151 0 ่วงเว า

ปร มา รา ร รวม 2 ท ทาง PCU)

สัดส่วนยานพาหนะประเภทต่างๆTraffic Composition onตามแนว Screen Screen Line Line: 5 : SL SL5 5

55.0%

A 1.7% B 12.8% 8.7% 5.2% 16.4%

รBicycle/Motorcycle ั รยาน ร ั รยานยนต 2 & 3 แwheel ะ รPrivate ยนต นั งส่วนCar รBus ดยสาร รLight รรท Truck นาดเ รMedium รรท Truckนาด าง รHeavy รรท Truck นาด incl.ห ่ รวมทัTrailer งร &พ่วงแ Semi ะ- trailer งพ่วง

Figure 2. 5-8 Survey Results for Screen Lines 5 (SL 5)

PCBK / SEA / CMCL / SYSTRA MVA 2-13 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Traffic Volume on Screen Line: SL6 27,917 during 6.00-18.00 hrs. (PCU) SL6 8,846 10,589 PCU 6,000 26,567

RS16 5,000

053

935 ,

4,000 ,

744

728

715

686

678

632

629

, ,

8,482 RS15 ,

319

248

, 4 3,000 4

119

7,003 2,000 , 3 RS14 11,224 1,000 0 ่วงเว า 8,340 ปร มา รา ร รวม 2 ท ทาง PCU)

สัดส่วนยานพาหนะประเภทต่างๆTraffic Composition on ตามแนวScreen LineScreen 6 : SLLine: 6 SL6 _SLSL 66 ((All)รวม) _ _RS14_ 62.8% 73.5%

18.3% 18.5% 2.5% 2.4% 2.2% 1.1% _RS15_ _RS16_ 8.2% 1.9% 3.7% 5.0% รBicycle/Motorcycle ั รยาน ร ั รยานยนต 2 & แ 3 ะwheel 59.0% 55.5% รPrivate ยนต นั งส่วน Car รBus ดยสาร 25.2% รLight รรท Truck นาดเ 12.9% 1.4% รMedium รรท นาดTruck าง 6.2% 5.2% 1.8% รHeavy รรท Truck นาด ห incl. ่ รวมทั Trailer งร พ่วงแ & Semi ะ งพ่วง-trailer 16.0% 4.5% 4.4% 7.9%

RS16 RS14 B RS15 A

Figure 2.5-9 Survey Results for Screen Lines 6 (SL 6)

PCBK / SEA / CMCL / SYSTRA MVA 2-14 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

RS03 RS04 RS01 Trip Purpose RS02

Work / Business Trip (2-way) Tour School Trip (2-way) Freight Transport Others

Bus Occupancy

Light Bus (4-wheel) Medium & Heavy Bus ( > 4 wheels )

Vacant ¼ Occupied ½ Occupied ¾ Occupied Fully Occupied

Freight Transport by Types of Freight Freight Volume loaded

Agricultural Goods Processed Agricultural Goods Vacant Industrial Goods ¼ Loaded Energy ½ Loaded Construction Materials ¾ Loaded Others Fully loaded

Figure 2.5-10 Roadside Interview Survey Results for Screen Lines 1 : SL 1

PCBK / SEA / CMCL / SYSTRA MVA 2-15

Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

RS05 Trip Purpose RS06

Work / Business Trip RS07 (2-way) Tour School Trip (2-way) RS08 Screen(SL2) 2 Line Freight Transport Others

Bus Occupancy

Light Bus (4-wheel) Medium & Heavy Bus ( > 4 wheels )

Vacant ¼ Occupied ½ Occupied ¾ Occupied Fully Occupied

Freight Transport by Types of Freight Freight Volume loaded

Agricultural Goods Vacant Processed Agricultural Goods ¼ Loaded Industrial Goods ½ Loaded Energy ¾ Loaded Construction Materials Fully loaded Others

Figure 2.5-11 Roadside Interview Survey Results for Screen Lines 2 : SL 2

PCBK / SEA / CMCL / SYSTRA MVA 2-16 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Trip Purpose RS09

Work / Business Trip (2-way) Tour RS10 School Trip (2-way) Freight Transport Others

Bus Occupancy

Light Bus (4-wheel) Medium & Heavy Bus (> 4 wheels )

Vacant ¼ Occupied ½ Occupied ¾ Occupied Fully Occupied

Freight Transport by Types of Freight Freight Volume loaded

Agricultural Goods Processed Agricultural Goods Vacant Industrial Goods ¼ Loaded Energy ½ Loaded Construction Materials ¾ Loaded Others Fully loaded

Figure 2.5-12 Roadside Interview Survey Results for Screen Lines 3 : SL 3

PCBK / SEA / CMCL / SYSTRA MVA 2-17 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Trip Purpose

Work / Business Trip (2-way) Tour School Trip (2-way) Freight Transport Others

RS12 RS11

Bus Occupancy

Light Bus (4-wheel) Medium & Heavy Bus (> 4 wheels )

Vacant ¼ Occupied ½ Occupied ¾ Occupied Fully Occupied

Freight Transport by Types of Freight Freight Volume loaded

Agricultural Goods Processed Agricultural Goods Vacant Industrial Goods ¼ Loaded Energy ½ Loaded Construction Materials ¾ Loaded Others Fully loaded

Figure 2.5-13 Roadside Interview Survey Results for Screen Lines 4 : SL 4

PCBK / SEA / CMCL / SYSTRA MVA 2-18 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Trip Purpose

Work / Business Trip (2-way) Tour School Trip (2-way) Freight Transport Others

Bus Occupancy

Light Bus (4-wheel) Medium & Heavy Bus (> 4 wheels)

Vacant ¼ Occupied ½ Occupied ¾ Occupied Fully Occupied

RS13

Freight Transport by Types of Freight Freight Volume loaded

Agricultural Goods Vacant Processed Agricultural Goods ¼ Loaded Industrial Goods ½ Loaded Energy ¾ Loaded Construction Materials Fully loaded Others

Figure 2.5-14 Roadside Interview Survey Results for Screen Lines 5 : SL 5

PCBK / SEA / CMCL / SYSTRA MVA 2-19 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Trip Purpose

Work / Business Trip (2-way) Tour School Trip (2-way) Freight Transport Others

Bus Occupancy

Light Bus (4-wheel) Medium & Heavy Bus (> 4 wheels)

Vacant ¼ Occupied ½ Occupied ¾ Occupied Fully Occupied

RS16 RS14 RS15

Freight Transport by Types of Freight Freight Volume loaded

Agricultural Goods Vacant Processed Agricultural Goods ¼ Loaded Industrial Goods ½ Loaded Energy ¾ Loaded Construction Materials Fully loaded Others

Figure 2.5-15 Roadside Interview Survey Results for Screen Lines 6 : SL 6

PCBK / SEA / CMCL / SYSTRA MVA 2-20 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2.5.1.2 Surveys at main borders between Thailand and neighboring countries Surveys at the borders are aimed to gain the data of travel and transport passing through the main borders of Thailand neighboring countries. The surveys were conducted by means of Roadside Interview Survey: RIS at 10 borders as shown in Table 2.5-1 and Figure 2.5-16. The data derived here is used for External OD Matrix preparation of NAM. The details in the interviews include personal information of the travelers, origin and destination of trips, trip purposes, number of passengers, types and quantity of goods, frequency of travel, choice of transport modes/commodity transport after AEC, and other necessary information needed for the application of the model. The Figure 2.5-17 illustrates the process of survey at different borders. Table 2.5-1 Names of borders in the surveys No. Name No. Name 1 Mae Sai customs house, Chiang Rai 6 Mukdaharn customs house, Mukdaharn 2 Chiang Saen customs house, Chiang Rai 7 Pibulmangsaharn customs house, Ubonratchthani

3 Chiang Khong customs house, Chiang Rai 8 Aranyaprathes customs house, Sa Kaew 4 Nong Kai customs house, Nong Kai 9 Sadao customs house, Songkhla 5 Nakorn Phanom customs house, Nakorn 10 Mae Sod customs house, Tak Phanom

The results of RIS and data analysis along the 10 main borders in the figures can be summarized below: 1) The data received from the interviews at 10 borders comes from 7,849 interview forms, whereby 2,754 forms are from the inbound, and 5,095 forms are from the outbound. 2) 4,281 interviewers are men, 54.50%; and 3,568 interviewers are women, 45.50%. 3) Most of the interviewers are at the age of 31-50 years old, 56.70%. 4) Most of the interviewers are business owners, followed by employees, 33.10% and 28.10%, respectively. 5) Most of the interviewers, 29.50%, have an income of 20,001-30,000 baht/month, followed by 27.80% of the interviewers who have income of 10,001-20,000 baht/month. 6) Most of the interviewers are Thai with their residence in Thailand, 73.50% and 73.40% respectively. 7) Most of the travels through borders, 32.70% – 39.60%, use private cars and public transport.

PCBK / SEA / CMCL / SYSTRA MVA 2-21 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

8) Trip purpose is mostly recreation trip, 32.20%, followed by commodity transport, 21.30%. 9) Most of the interviewers, 53.00%, travel occasionally, with the frequency of once or twice a month, 19.00%, followed by 1-3 times a week, 17.10%. The results of interviews about the attitudes and opinions of travelers and commodity transport in terms of transport mode selection and demand for future infrastructure can be summarized as follows: 1) Most of the interviewers, 67.10%, agree that the present transport modes and routes are good. 2) The desired infrastructure is high-speed train and motorways, 43.40% and 37.10%, respectively. 3) After AEC, most of the interviewers, 86.30%, think that there will be more frequency of travel/transport, and 95.00% of them think that there will be more transport volume. 4) The modes of travel/transport after AEC are private cars, 31.50%, and high-speed train 28.10%.

3 2 4

1 5 6

10 7

Figure 2.5-16 Locations of main borders between Thailand and neighboring countries

PCBK / SEA / CMCL / SYSTRA MVA 2-22 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 2.5-17 Data survey at main borders between Thailand and neighboring countries

2.5.1.3 Survey of passengers at the main bus terminals, main Railway stations, and main airports The survey is in the form of interview about the travel of passengers at the main bus terminals, main railway stations, and main airports in all regions of the country as seen in Table 2.5-2. The questionnaire is in the form of Stated Preference (SP) for the data of Special generator in the models as seen in the Figure 2.5-18.

Table 2.5-2 The places of passenger survey at the main bus terminals, main train stations, and main airports Region Place of survey Region Place of survey

Bus terminal  Chiangmai Bus Terminal Bus terminal  Mochit Bus Terminal  Pissanulok Bus Terminal  Akkamai Bus Terminal

  Railway Chiangmai Train Station South Bus Terminal station  Pissanulok Train Station Railway  Bangkok Railway Station North station (Hua Lam Pong)

Airport  Chiangmai Airport Centralpart (Bangkok) airport  Don Muang Airport  Pissanulok Airport Bus terminal  Rayong Bus Terminal Bus terminal  Nakonratchasima  Pattaya Bus Terminal

Bus Terminal

 Udonthani Bus Terminal  Trat Bus Terminal East Railway  Nakonratchasima Railway  Pattaya Railway Station station Railway Station station

Northeast  Udonthani Railway Station Airport  Pattaya Airport (U Tapao)   Airport Ubonratchathani Airport Bus terminal Hatyai Bus Terminal  Udonthani Airport Railway  Hatyai Railway station South station

PCBK / SEA / CMCL / SYSTRA MVA 2-23 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 2.5-18 Interviews with passengers at the main bus terminals, main train stations, and main airports in different regions The interviewers are divided into 3 groups according to the distance of travel: less than 300 kilometres, 300-600 kilometres, and over 600 kilometres. The data analysis herein reflects the overall travel behavior of each group as followed: (1) The proportion of those who travel less than 300 kilometres and 300-600 kilometres is the same, 36%, while those who travel over 600 kilometres are made up to 28%. (2) Most of the objective or travel, 47%, is for personal trip, followed by Recreation Trip and Business Trip, 21% and 20%, respectively as seen in Figure 2.5-19.

Education Others 5% Work / (2-way) business

> 600 km. < 300km. Recreation

Private Business 300 – 600 km.

Figure 2.5-19 The proportion of the interviewers based on the distance and objective of travel

PCBK / SEA / CMCL / SYSTRA MVA 2-24 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(3) Considering the relationship between distance and modes of travels, it is found that: 1) Those who travel less than 300 kilometres prefer buses at the most, 36.10%, followed by high-speed train, 30.03%. 2) Those who travel 300-600 kilometres prefer buses at the most, 23.09%, followed by high- speed train, 21.31%. 3) Those who travel over 600 kilometres prefer high-speed train at the most, 30.37%, followed by plain, 22.88%. (4) Most of the interviewers who travel less than 300 kilometres usually prefer travelling by car, 12.71%, which is rather low. It may be because there was not enough data derived from the interview at gas stations. (5) The proportion of each group when selecting public transport modes can be summarized as below:

Distance less than 300 km. 300-600 km. over 600 km. Road (Bus & Bus-VIP) 48.07% 42.65% 25.85% Rail (Rail & HSR) 39.02% 37.21% 46.00% Plain (Air) 0.00% 13.10% 22.88%

The relationship thereof is in Figure 2.5-20

> 600 km.

300 - 600 km.

< 300 km.

< 300 km. 300 - 600 km. > 600 km.

Figure 2.5-20 The relationship between distance and modes of travels

PCBK / SEA / CMCL / SYSTRA MVA 2-25 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2.5.2 The data survey for the improvement of transport and traffic models in the level of eBUM The data survey for the improvement of transport and traffic models in the level of eBUM includes the survey of traffic volume and conditions along the screen lines, Home Interview Survey: HIS, Roadside Interview Survey: RIS at the bus terminals, and the data survey or traffic in Ayutthaya and Chachoengsao. 2.5.2.1 Survey of traffic volume and conditions along the Screen Line The survey was conducted along the 2 Screen Lines and the data derived herein will be applied improve the eBUM models. The survey is divided into 2 sessions as below: (1) The survey in 2012 along the North-South Screen line, thereby the locations of survey were on 22 bridges over the Chaopraya River as shown in Table 2.5-3 and Figure 2.5-21. (2) The survey in 2013 along the East-West Screen line, thereby the locations of survey were on the 39 intersections of main and minor roads as shown in Table 2.5-4 and Figure 2.5-22. Table 2.5-3 The survey points along the North-South Screen line Point Zone Point Zone MB – NS01 Bridge over Chao Phraya River – MB – NS12 Rama VIII Bridge Western Ring Road MB – NS02 Pathum Thani Bridge MB – NS13 Phra Pinklao Bridge (Highway No. 346) MB – NS03 Pathum Thani Bridge II MB – NS14 Memorial Bridge (Highway No. 3100) MB – NS04 Nondhaburi Bridge MB – NS15 Phra Pokklao Bridge (Highway No. 345) MB – NS05 Rama IV Bridge (Highway No. 304) MB – NS16 Taksin Bridge MB – NS06 Phra Nangklao Bridge (New) MB – NS17 Krungthep Bridge (Highway No. 302) MB – NS07 Phra Nangklao Bridge (old) MB – NS18 Rama III Bridge (Highway No. 302) MB – NS08 Rama V Bridge MB – NS19 Rama IX Bridge MB – NS09 Rama VII Bridge MB – NS20 Bhumipol Bridge MB – NS10 Rama VI Bridge (Railway only) MB – NS21 Bhumipol Bridge II MB – NS11 Krungthon Bridge MB – NS22 Kanchana Pisek Bridge

PCBK / SEA / CMCL / SYSTRA MVA 2-26 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 2.5-4 List of traffic volume survey points along the East-West Screen line Point Zone Point Zone MB – EW 01 Outer Ring Road (Eastern) MB – EW 21 Chalerm Mahanakorn Expressway MB – EW 02 Sri Burapa MB – EW 22 Witthayu MB – EW 03 Puang Siri MB – EW 23 Chidlom MB – EW 04 Sri Nakarin MB – EW 24 Ratchadamri MB – EW 05 Soi Lad Prao 130 MB – EW 25 Phaya Thai MB – EW 06 Soi Mahad Thai MB – EW 26 Banthat Thong MB – EW 07 Soi Ramkamhaeng 53 MB – EW 27 Sri Rat Expressway MB – EW 08 Soi Ramkamhaeng 43/1 MB – EW 28 Rama 6 MB – EW 09 Soi Wat Thep Lila MB – EW 29 Krung Kasem MB – EW 10 Soi Ramkamhaeng 21 MB – EW 30 Chakkrapaddipong MB – EW 11 Soi Ramkamhaeng 9 MB – EW 31 Raj Damnoen Klang MB – EW 12 Chalong Rat Expressway MB – EW 32 Prachathipatai MB – EW 13 Rama 9 MB – EW 33 Samsen MB – EW 14 Kampangpetch 7 MB – EW 34 Phra Pinklao MB – EW 15 Petchburi MB – EW 35 Arun Amarin MB – EW 16 Prasert Manukit Road MB – EW 36 Charan Sanitwong MB – EW 17 Petchburi 38/1 MB – EW 37 Sirindhorn MB – EW 18 Sukhumwit 55 MB – EW 38 Ratchapruek MB – EW 19 Asoke Montri MB – EW 39 Outer Ring Road (West) MB – EW 20 Sukhumwit 3

PCBK / SEA / CMCL / SYSTRA MVA 2-27 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

NS01

NS02

NS03 NS04

NS06 NS05

NS08 NS07

NS09 NS10

NS12 NS11

NS13 NS15 NS14

NS16 NS17

NS20& NS21

NS18

NS22 NS19

Figure 2.5-21 The locations of traffic volume and travel condition survey along the North-South Screen line

PCBK / SEA / CMCL / SYSTRA MVA 2-28 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 2.5-22 The locations of traffic volume and travel condition survey along the East-West Screen line

PCBK / SEA / CMCL / SYSTRA MVA 2-29 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

The types of data survey along the North-South and East-West screen lines include:  Mid-Block Count  Vehicle Occupancy Count The time to conduct these two surveys is during rush hour in the morning and evening, at 06:00- 09.00 AM and 04:00-07:00 PM, totally 6 hours. Table 2.5-5 Summary of traffic volume and Volume/Capacity – V/C at the survey sites along the North- South Screen line Traffic Volume Traffic Volume Inbound Outbound V/C Inbound V/C Outbound Survey Site Location (pcu/hr) (pcu/hr) a.m. peak p.m. peak a.m. peak p.m. peak a.m. peak p.m. peak a.m. peak p.m. peak MB – NS01 Bridge over Chao Phraya 1,115 970 2,064 1,426 0.37 0.32 0.69 0.48 River – Western Ring Road MB – NS02 Pathum Thani Bridge 2,090 1,882 2,455 2,383 0.46 0.42 0.55 0.53 MB – NS03 Pathum Thani Bridge II 2,367 2,333 2,114 2,141 0.53 0.52 0.47 0.48 MB – NS04 Nondhaburi Bridge 1,518 1,670 1,307 2,053 0.51 0.56 0.44 0.68 MB – NS05 Rama IV Bridge 2,998 2,158 2,075 2,038 0.67 0.48 0.46 0.45 MB – NS06 Phra Nangklao Bridge 4,631 4,443 4,170 3,654 1.03 0.99 0.93 0.81 (New) MB – NS07 Phra Nangklao Bridge 1,432 1,479 1,397 1,376 0.48 0.49 0.47 0.46 (old) MB – NS08 Rama V Bridge 2,078 2,157 1,750 2,203 0.46 0.48 0.39 0.49 MB – NS09 Rama VII Bridge 2,797 2,857 2,698 2,906 0.62 0.63 0.60 0.65 MB – NS10 Rama VI Bridge (Railway ------only) MB – NS11 Krungthon Bridge 2,500 2,642 2,884 2,594 0.56 1.47 1.60 0.58 MB – NS12 Rama VIII Bridge 3,032 2,494 2,109 2,826 1.01 0.83 0.70 0.94 MB – NS13 Phra Pinklao Bridge 6,989 5,816 3,070 9,519 1.16 1.62 1.02 1.32 MB – NS14 Memorial Bridge 2,578 1,788 1,385 1,977 0.86 1.19 0.92 0.66 MB – NS15 Phra Pokklao Bridge 6,962 5,483 6,063 6,834 1.29 1.02 1.12 1.27 MB – NS16 Taksin Bridge 6,958 3,658 4,349 4,397 1.29 0.81 0.97 0.98 MB – NS17 Krungthep Bridge 3,259 1,925 1,695 1,317 1.09 0.64 0.57 0.44 MB – NS18 Rama III Bridge 7,961 4,466 3,019 2,597 1.47 0.99 0.67 0.58 MB – NS19 Rama IX Bridge 6,447 4,341 2,868 5,320 1.19 0.96 0.64 0.99 MB – NS20 Bhumipol Bridge 3,132 2,287 1,457 3,791 052 0.51 0.32 0.63 MB – NS21 Bhumipol Bridge II 2,958 2,121 1,885 2,531 0.49 0.47 0.42 0.42 MB – NS22 Kanchana Pisek Bridge 2,991 2,267 2,264 2,455 0.66 0.50 0.50 0.55 Total 76,793 59,237 53,078 66,338 - - - -

PCBK / SEA / CMCL / SYSTRA MVA 2-30 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 2.5-6 Summary of traffic volume and Volume/Capacity – V/C at the survey points along the East- West Screen line Traffic Volume Traffic Volume Inbound Outbound V/C Inbound V/C Outbound Survey Site Location (pcu/hr) (pcu/hr) a.m. peak p.m. peak a.m. peak p.m. peak a.m. peak p.m. peak a.m. peak p.m. peak MB – EW01 Outer Ring Road (Eastern) 3,147 4,136 3,216 3,052 0.52 0.69 0.51 0.60 MB – EW02 Sri Burapa 311 383 295 283 0.31 0.38 0.30 0.28 MB – EW03 Puang Siri 522 376 996 830 0.26 0.19 0.50 0.42 MB – EW04 Sri Nakarin 1,753 1,762 1,977 2,071 0.58 0.59 0.66 0.69 MB – EW05 Soi Lad Prao 130 294 423 379 490 0.29 0.42 0.38 0.49 MB – EW06 Soi Mahad Thai 494 473 669 627 0.49 0.47 0.67 0.63 MB – EW07 Soi Ramkamhaeng 53 353 450 419 515 0.35 0.45 0.42 0.52 MB – EW08 Soi Ramkamhaeng 43/1 460 547 411 456 0.46 0.55 0.41 0.46 MB – EW09 Soi Wat Thep Lila 498 670 599 804 0.25 0.34 0.30 0.40 MB – EW10 Soi Ramkamhaeng 21 302 391 352 449 0.30 0.40 0.35 0.45 MB – EW11 Soi Ramkamhaeng 9 103 124 91 121 0.10 0.12 0.09 0.12 MB – EW12 Chalong Rat Expressway 2,360 4,066 3,566 2,244 0.52 0.90 0.79 0.50 MB – EW13 Rama 9 1,326 2,397 3,300 3,273 0.33 0.60 0.83 0.82 MB – EW14 Kampangpetch 7 312 568 688 575 0.16 0.28 0.34 0.29 MB – EW15 Petchburi 1,555 2,538 1,054 762 0.38 0.63 0.26 0.19 MB – EW16 Prasert Manukit Road 1,568 1,604 1,820 1,862 0.42 0.44 0.49 0.50 MB – EW17 Petchburi 38/1 2,062 2,206 555 491 0.69 0.74 0.37 0.33 MB – EW18 Sukhumwit 55 721 617 971 879 0.36 0.31 0.49 0.44 MB – EW19 Asoke Montri 775 819 846 787 0.78 0.41 0.28 0.39 MB – EW20 Sukhumwit 3 one-way 791 856 one-way 0.40 0.43 MB – EW21 Chalerm Mahanakorn 6,188 5,279 4,413 4,974 1.03 0.88 0.74 0.83 Expressway MB – EW22 Witthayu 2,299 2,550 one-way 0.57 0.64 one-way MB – EW23 Chidlom one-way 1,930 1,526 one-way 0.48 0.38 MB – EW24 Ratchadamri 2,598 4,239 2,669 4,307 0.58 0.94 0.59 0.96 MB – EW25 Phaya Thai 3,552 2,811 1,921 1,327 1.18 0.94 0.64 0.44 MB – EW26 Banthat Thong 1,514 2,144 1,543 2,106 0.50 0.71 0.51 0.70 MB – EW27 Sri Rat Expressway 5,709 5,628 5,374 5,631 1.06 1.04 1.00 1.04 MB – EW28 Rama 6 4,738 4,952 one-way 0.79 0.83 one-way MB – EW29 Krung Kasem 1,303 1,782 753 726 0.64 0.88 0.37 0.36 MB – EW30 Chakkrapaddipong 3,745 3,341 3,701 3,293 0.83 0.74 0.82 0.73 MB – EW31 Raj Damnoen Klang 2,011 2,287 1,806 2,069 0.67 0.76 0.60 0.69 MB – EW32 Prachathipatai 2,992 2,337 1,581 1,480 1.00 0.78 0.53 0.49 MB – EW33 Samsen 2,242 1,931 2,246 1,980 0.75 0.64 0.75 0.66 MB – EW34 Phra Pinklao 6,989 6,176 3,070 9,519 0.84 1.54 0.77 1.19 MB – EW35 Arun Amarin 3,207 3,607 3,177 3,515 0.89 1.00 0.88 0.98

PCBK / SEA / CMCL / SYSTRA MVA 2-31 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Traffic Volume Traffic Volume Inbound Outbound V/C Inbound V/C Outbound Survey Site Location (pcu/hr) (pcu/hr) a.m. peak p.m. peak a.m. peak p.m. peak a.m. peak p.m. peak a.m. peak p.m. peak MB – EW36 Charan Sanitwong 3,867 2,941 2,536 3,133 0.86 0.65 0.88 0.98 MB – EW37 Sirindhorn 9,783 9,077 10,358 8,845 1.22 1.13 1.29 1.11 MB – EW38 Ratchapruek 2,183 3,433 1,698 3,082 0.49 0.76 0.38 0.68 MB – EW39 Outer Ring Road (West) 8,063 7,253 8,567 7,877 1.08 0.97 1.14 1.05 Total 91,899 96,318 80,338 86,817 - - - -

2.5.2.2 Home Interview Survey: HIS Home Interview Survey: HIS is a kind of survey on the travel of those who live in the city. The survey is conducted by randomly interviewing people at home and the data from this will be subjected to analysis and comparison in order to create the travel schedule for models in base year. The number of homes to be surveyed is 4,500 which are divided as followed:  Home Interview in Ayutthaya 1,000 forms  Home Interview in Chachoengsao 1,000 forms  Home Interview in Bangkok and Metropolitan areas 2,500 forms Ayutthaya covers 40 sub-areas; Chachoengsao covers 74 sub-areas; while Bangkok and Metropolitan areas have 1,656 sub-areas. Once accumulated, the number of sub-areas is 1,771 as shown in Figure 2.5-23. The home interview is made randomly in the sub-areas by asking about the details of travel of all family members who are over 6. The information to survey in this interview is:  starting point and destination of the travel  personal information of the travelers  objective of the travel  modes of transport  details of the travel  data from Stated Preference so as to improve the selection models of transport modes  data for the application of models, e.g. Road Pricing, Public Transit Fare, etc.

PCBK / SEA / CMCL / SYSTRA MVA 2-32 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Ayutthaya 40 traffic zones

Bangkok and Metropolitan Area (1,656 traffic zones) Chachoengsao (74 traffic zones)

Figure 2.5-23 The division of sub-areas in eBUM within Ayutthaya and Chachoengsao 2.5.2.3 Roadside Interview Survey: RIS at truck terminals Roadside Interview Survey: RIS at truck terminal is a kind of survey on the travel as well as the transport within the areas of study. The survey is conducted by means of roadside interview for one day from 06:00 AM to 06:00 PM at 4 truck terminals.  Klong Luang Truck Terminal  Buddha Monthon Truck Terminal  Romklao Truck Terminal  Latkrabang Inland Container Depot Figure 2.5-24 shows the locations of Roadside Interview Survey at the 4 truck terminal. The information to survey in this interview includes starting point and destination of the travel/transport, personal information of the travelers, objective of the travel, types and quantity of the goods. The data from this survey is used to create OD Table of the goods, and to update the transport models.

PCBK / SEA / CMCL / SYSTRA MVA 2-33 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Klong Luang

Puttha Monthon Romklao ICD Ladkrabang

Figure 2.5-24 The locations of Roadside Interview Survey at the 4 truck terminal 2.5.2.4 Survey of traffic data in Ayutthaya and Chachoengsao The survey of travel conditions in Ayutthaya and Chachoengsao is conducted with an attempt to use the said data to create and improve eBUM so that it could match with the extending areas within these two provinces. Thereby, the points of traffic volume survey on the intersections, Mid-Block Count, Roadside Interview Survey, and the routes of speed survey on the main roads in Ayutthaya and Chachoengsao are illustrated in Figure 2.5-25 and Figure 2.5-26, respectively.

PCBK / SEA / CMCL / SYSTRA MVA 2-34 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

xx Turning Movement Count (16 Intersections) 1 yy Mid-block Count (15 sites) Z Roadside Interview Survey (8 sites) 1 Travel Speed Survey Route (6 Routes) 1

2 3 2 4 2 6 13 3 8 5 7 6 5 4 10 7 15 3 11 9 8

12 7 9 16 15 6 14 12 4 5 13 14 8 11

10 Figure 2.5-25 Points of travel condition survey in Ayutthaya

PCBK / SEA / CMCL / SYSTRA MVA 2-35 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

1 4 8 1 2 2 6 14 2 3 7 9 8 7 3 4 13 6 9 13 16 1 5 8 15 14 6 10 12 15 2 11 11 3 4 55 7 12 10 xx Turning Movement Count (16 Intersections) 5

yy Mid-block Count (15 sites) Z Roadside Interview Survey (8 sites) Travel Speed Survey Route (4 Routes)

Figure 2.5-26 Points of travel condition survey in Chachoengsao 2.5.3 Data survey for the improvement if Passenger Car Unit (PCU) In this study, there has been a survey to examine Passenger Car Equivalent: PCE or Passenger Car Unit: PCU, which has been in use in the transport and traffic model since 1995 (UTDM Project), to see whether it is still appropriate to the present transport and traffic conditions or not. The survey relies on Time Headway Method. The areas of study are 2 intersections and 2 Mid-Block Counts, both of which cover the inner and outer areas of Bangkok, as in Figure 2.5-27. (1) Intersections: Urupong Junction, and the intersection in front of Kasembundit University, Romklao Campus (2) Mid-Block Count: Rajdamri Road, between Ratchaprasong Junction and Sarasin Junction, and Suksawas Road, between Prapradaeng Junction and Bangpli-Suksawas Express Way Junction

PCBK / SEA / CMCL / SYSTRA MVA 2-36 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Urupong Jt. Kasembundit University

Rajdamri Rd. Suksawad Rd.

Figure 2.5-27 Areas of study for this survey The analysis results of PCU for different kinds of vehicles derived from the survey at intersections and Mid-Block Count, as shown in Table 2.5-7, are a little different from the PCU in eBUM. This is except for the motorcycles, which had high fluctuation during the time of survey. Therefore, the consultants see that the existing PCU is still appropriate and there is no need to change it. Table 2.5-7 Analysis results of PCU for different kinds of vehicles Passenger Car Unit (PCU) Difference Type Mid-Block Intersection eBUM (%) Count Motorcycle 0.44 0.53 0.25 76–112 Three-wheel taxi 0.80 0.80 0.70 14 Taxi 1.00 1.00 1.00 0 Private car 1.00 1.00 1.00 0 Small bus 1.10 1.20 1.50 20-27 Large bus 2.30 1.80 2.00 10-15 Pick-up car and small truck (4 wheels) 1.10 1.10 1.00 10 Medium truck (6 wheels) 1.00 1.00 2.00 0-5 Large truck (10 wheels), trailer, and semi- 1.10 1.20 2.50 8 trailer

PCBK / SEA / CMCL / SYSTRA MVA 2-37 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2.5.4 The improvement of Speed-Flow according to the physical features of different roads Besides the data survey to improve the Passenger Car Unit, this study also surveys the speed-flow of travel on the main road network in both inner and outer metropolitan areas according to the physical features of different roads. The data herein will be used to analyze and enhance the relation of speed-flow used in the models. The survey is conducted by means of video camera recording the traffic conditions during 6:00 AM - 6:00 PM on the roads selected as case study as seen in Figure 2.5-28. The lists of roads in this survey are provided in Table 2.5-9.

Nontburi Bypass Art Narong Rd. Pahonyothin / Soi Pahonyothin 30

Figure 2.5-28 Samples of roads chosen for case study Table 2.5-8 Roads chosen for case study Inner Urban Area (with footpath) Road Code Name 2 Lanes 2 ways with median CBD-01-1 Ramkamhaeng 60 CBD-01-2 Ekcharoen Rd. 4 Lanes 2 ways with median CBD-02-1 Nontburi Bypass CBD-02-2 Art Narong Rd. More than 4 Lanes 2 ways with CBD-03-1 Pahonyothin 51 (Infront of Army 11) median CBD-03-2 Tiwanond Rd. 2 Lanes 2 ways without median CBD-04-1 Kampangpetch 3 Rd. CBD-04-2 Suksawad 70 Rd. (Krunai) 4 Lanes 2 ways without median CBD-05-1 Charoenkrung Rd. (Near PTT station) CBD-05-2 Soi Arun Amarin 31 (Pedestrian Bridge) More than 4 Lanes 2 ways without CBD-06-1 Soi Pahonyothin 30 median CBD-06-2 Rajdamnoen Nai Rd. (In front of Supreme Court) Outer Urban Area (without footpath) Road Code Name 2 Lanes 2 ways with median OCOR-01-1 Highway 3477 OCOR-01-2 Highway 3190 4 Lanes 2 ways with median OCOR-02-1 Dechatungkha Rd. OCOR-02-2 Rangsit-Pathumthani Rd. (Soi 23/3)

PCBK / SEA / CMCL / SYSTRA MVA 2-38 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Inner Urban Area (with footpath) Road Code Name More than 4 Lanes 2 ways with OCOR-03-1 Rangsit-Nakorn Nayok Rd. (Wat Khiankhet) median OCOR-03-2 Pahonyothin Rd. (Thammasat University - Rangsit) 2 Lanes 2 ways without median OCOR-04-1 Bung Kumproi Rd. OCOR-04-2 Kitmani Rd. 4 Lanes 2 ways without median OCOR-05-1 Kampangpetch 6 Rd. OCOR-05-2 Ha-thaimit Rd. Moer than 4 Lanes 2 ways without OCOR-06-1 Highway 3036 median OCOR-06-2 Tanyaburi Rd.

Results of Speed-Flow Curve Analysis on different types of roads Comparing the Speed-Flow Curve data on different types of roads used in eBUM with the Speed- Flow Curve data on different types of roads in this study, the results come out as shown in Figure 2.5-29 - Figure 2.5-31.

2-lane Rd.(Inner Urban Area) 120

100

80 eBUM, 1997 60 Ramkamhaengถ.รามค าแหง 60 60 Ekcharoenถ.เอกเจริญ Rd. SPEED SPEED (km/hr) 40 Kampangpetchถ.ก าแพงเพชร 3 3 Rd.

20 Suksawadถ.สุขสวัสดิ์ 7070 Rd.

0 0 0.2 0.4 0.6 0.8 1 1.2 V/C RATIO

Figure 2.5-29 Results of Speed-Flow Curve Analysis on the road with 2 lanes (inner areas)

PCBK / SEA / CMCL / SYSTRA MVA 2-39 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2-lane Rd. (Outer Urban Area) 120

100

80 eBUM, 1997 60 ทางหลวงหมายเลขHighway 3477 3477 ทางหลวงหมายเลขHighway 3190 3190 SPEED SPEED (km/hr) 40 ถBung.บึงค าพร้อยKumproi Rd. 20 ถKitmani.กิจมณี Rd.

0 0 0.2 0.4 0.6 0.8 1 1.2 V/C RATIO

Figure 2.5-30 Results of Speed-Flow Curve Analysis on the road with 2 lanes (outer areas)

More than 2-lane Rd. (Inner Urban Area) 120 eBUM, 1997 100 Nontburiถ.เลี่ยงเมืองนนทบุรี Bypass

80 Artถ.อาจณรงค์ Narong Rd. 60 Pahonyothinถ.พหลโยธิน 51 51 Tiwanondถ.ติวานนท์ Rd. SPEED SPEED (km/hr) 40 Charoenkrungถ.เจริญกรุง Rd. 20 Arunถ.อรุณอมรินทร์ Amarin 31 31 Rd. 0 Pahonyothinถ.พหลโยธิน 30 30 Rd. 0 0.2 0.4 0.6 0.8 1 1.2 Rajdamnoen Rd. ถ.ราชด าเนิน V/C RATIO

Figure 2.5-31 Results of Speed-Flow Curve Analysis on the road with more than 2 lanes (inner areas)

PCBK / SEA / CMCL / SYSTRA MVA 2-40 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

More than 2-lane Rd. (Outer Urban Area) 120 eBUM, 1997

100 Dechatungkhaถ.เดชะตุงคะ Rd. Rangsit-Pathumthani Rd. 80 ถ.รังสิต-ปทุมธานี 23 (Soi 23/3) 60 Rangsitถ.รังสิต--นครนายกNakorn Nayok Rd. Pahonyothin Rd. (TU-Rangsit) SPEED SPEED (km/hr) 40 ถ.พหลโยธิน (ธรรมศาสตร์ รังสิต) Kampangpetchถ.ก าแพงเพชร 6 6 Rd. 20 Haถ.หทัยมิตร-thaimit Rd. 0 0 0.2 0.4 0.6 0.8 1 1.2 Highwayทางหลวงหมายเลข 3036 3036

V/C RATIO Tanyaburiถ.ธัญบุรี Rd.

Figure 2.5-32 Results of Speed-Flow Curve Analysis on the road with more than 2 lanes (outer areas)

Once comparing the Speed-Flow Curve on different types of roads used in eBUM with the results of this study, it is found that Speed-Flow Curve has changed a lot. The roads with 2 lanes (inner areas), the roads with 2 lanes (outer areas), the roads with more than 2 lanes (inner areas), and the roads with more than 2 lanes (outer areas) have higher relation between speed-flow and Volume/Capacity or V/C Ratio, increasing by 59%, 16%, 48%, and 36%, respectively. It is clearly seen that the existing data has been used for such a long time that it needs to have their parameters in the model revised/examined seriously. Then, the model to be used will be able to reflect the real situations as much as possible, and have more efficiency and precision in the estimation. However, the number of roads in this study is not high enough to cover or represent each different roads. So, the results of the data analysis may not be accurate and have high deviation. Hence, it is advisable that there should be study projects to particularly survey more data. This is to receive enough representative data of each type of road in order to analyze for suitable parameters, which are appropriate to the current traffic conditions and can be used in place of the old ones in the models.

PCBK / SEA / CMCL / SYSTRA MVA 2-41 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2.6 The Study and Survey Data of Commodity Flow and Freight Transport Logistics

To Study and survey data for commodity flow and freight transport logistics analysis, the Consultants conducted survey for a total of 180 goods items (which 52 goods items were previously studied in the previous TDL by reviewing and updating the most current flow and including analysis of domestic freight transport) by updating goods according to current HS Code (Year 2012) and also updates the accuracy of the flow to reduce the redundancy. The Consultants gathered more data from several sources and relevant report study from the Ministry of Commerce, Customs Department, Ministry of Transport, and Office of Agricultural Economics, Ministry of Industry, Trade Association, OTP, Department of Land Transport, Marine Department and the State Railway of Thailand, etc. The selected goods in transport with certain volumes are chosen to cover at least 90% of both imports and exports and then are back calculated by extending the total amount originally included. 2.6.1 The Concept of the Study Project. The commodity flow study in this TDL project has studied transport mode, transport cost, and the behavior of freight transport from the origin to the destination (Line Haul Origin-Destination Transport), which will lead to the development of transport network in the form of Thailand Layout. This transport system network distribution to build the capacity of transport must be analyzed and optimized in terms of volume and speed. Moreover, optimized transport network can be used as transport logistics tools to reduce the total product costs which lead to the creation of competitive advantage for the country by linking the Provincial level-Regional-Country-ASEAN. Consequently, the development of multi-modal transport patterns according to strategic locations of freight generates advantages of distribution to bring products to consumers as well as values added for its transport. Therefore, strategic infrastructure, trade, and logistics, to support the transport of goods and services within the country must be determined and linked to the key partners that contribute to the operational efficiency. It is needed to understand behaviors of the transport and movement of goods from the origin to final destination (Origin-Destination) as well as transport system, which will directly affect the cost of transport, modes of transport (road, rail, water and air), and choices of the transport system. Modes/Modal shift, the structure and behavior in the supply chain are shown in Figure 2.6-1.

PCBK / SEA / CMCL / SYSTRA MVA 2-42 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 2.6-1 Supply Chain Relationship with Transport. Therefore, the study on movement of goods in terms of volumes and costs is able to contribute to the logistics policy and strategic planning of the Ministry of Transport. This study involves supply chain and demand & supply integrations, business needs, seasonal transport volumes for the year, infrastructures, and estimates the logistical costs. 2.6.2 Theoretical Calculation of Unit Transport Cost. The charge for freight transport costs has put the principles of the survey. "The price of the actual cost incurred in shipping". The transport cost derives from characteristics of O-D transport supply chain of the product itself that will reflect the cost of the transport industry (transport price) and led to the indexed cost of Thailand (Transport Price Index) for infrastructure planning in the future. This method refers to the analysis of the supply chain of freight that upstream to downstream activities, as shown in Figure 2.6-2 and leads to the transport mode selection from producers, freight operators, and customers. It can also affect the analysis of the actual cost of shipping since trades based on the cost of transport operators calculated and/or determined by buyers, which will lead to the set up of indicators of changing conditions of the country's freight costs more suitable than the use of the calculation of Activity-Based Costing (ABC) that can measure only the cost of the supply.

PCBK / SEA / CMCL / SYSTRA MVA 2-43 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 2.6-2 Relationships of Transport Mode Selection The concept of unit transport cost analysis is expressed as following. The analysis method obtained the transport cost (A) (Baht) from survey data. Then, the amount and distance in terms of Ton-Kilometer is calculated by using the volumes of goods (B) (Tons) and the distance from the origin-destination point (C) (Km), a Ton–Km. Then, the amount of Ton-Kilometer is used to divide the transport cost to obtain the unit transport cost, as Baht per Ton–Kilometer. The simple equation is shown as below. 푨 The unit transport cost = (Baht/Ton-Kilometer) 퐁 퐱 퐂 For example, the unit transport cost of exported rice is 1.20 Baht/Ton-Kilometer. It is calculated from cost of rice for export from the origin Surin to the destination Laem Chabang Port, Chonburi with a distance of 417 Kilometers, which is about 10,000 Baht per trip for a truck with capacity 20 Tons. ퟏퟎ,ퟎퟎퟎ The average unit transport cost of exported rice by road = = 1.20 (Baht/Ton-Kilometer) ퟐퟎ 퐱 ퟒퟏퟕ Thus, the average unit transport cost of each mode will be calculated from the amount of Ton- Kilometer by using the same method mentioned above and then multiply by unit transport cost in terms of Baht/Ton-Kilometer in each of item and then average by weight average with total amount of Ton-Kilometer of 180 goods items. For example,

PCBK / SEA / CMCL / SYSTRA MVA 2-44 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

푻풉풆 풂풗풆풓풂품풆 풖풏풊풕 풕풓풂풏풔풑풐풓풕풂풊풐풏 풄풐풔풕 풐풇 풓풐풂풅 풕풓풂풏풔풑풐풓풕 ퟏퟖퟎ ∑ (풕풓풂풏풔풑풐풓풕풂풕풊풐풏 풄풐풔풕 (푩풂풉풕)풊 풙 풗풐풍풖풎풆 (푻풐풏)풊 풙 풅풊풔풕풂풏풄풆 (푲풎)풊) 퐨퐟 퐞퐚퐜퐡 퐠퐨퐨퐝퐬 퐢퐭퐞퐦 퐛퐲 퐫퐨퐚퐝 퐭퐫퐚퐧퐬퐩퐨퐫퐭 = 풊=ퟏ 푺풖풎 풐풇 풕풉풆 풒풖풂풏풕풊풕풊풆풔 (풗풐풍풖풎풆 (푻풐풏)풙 풅풊풔풕풂풏풄풆(푲풎)) 퐨퐟 퐚퐥퐥 퐠퐨퐨퐝퐬 퐢퐭퐞퐦 퐛퐲 퐫퐨퐚퐝 퐭퐫퐚퐧퐬퐩퐨퐫퐭

∑(ퟏ.ퟐퟎ 풙 ퟔퟓ,ퟑퟒퟖ 풙 ퟒퟏퟕ)+(ퟐ.ퟓퟎ 풙 ퟓퟎ,ퟎퟎퟎ 풙 ퟑퟎퟎ)+(… ) 푻풉풆 풂풗풆풓풂품풆 풖풏풊풕 풕풓풂풏풔풑풐풓풕풂풊풐풏 풄풐풔풕 풐풇 풓풐풂풅 풕풓풂풏풔풑풐풓풕 = ퟏퟐퟗ,ퟗퟒퟕ,ퟓퟖퟖ,ퟎퟑퟒ = 2.12 (Baht/Ton-Kilometer) The average unit transport cost of each transport mode will be calculated by using the same method above. 2.6.3 Methodology of Goods and the Entrepreneurs Selection, Survey and Data Collection 2.6.3.1 How to select goods Criteria for goods selected of the study and data collection are set as follows: (1) The first 52 goods items from the original database development system for multimodal transport and ongoing management systems, logistics, in order to bring the plan into action (Logistics) of OTP. While cross border products will be separated products from the others because of the redundancy in our list. Also, amount of products is collected through the border of Laos (Lao PDR) as well as through Thailand to third countries or from third countries via Thailand to Lao PDR. (2) Additional import and export goods items are selected from the Ministry of Commerce by choosing products with high transport volumes and/or shipping values. (3) Considering and selecting the goods items which commodity flow by various transport modes (road, rail, water and air transport) (4) Additional domestic goods items that volume of transport is high and it is not on the imports and exports listed above. By considering the lists of the Ministry of Transport and reviewing data from the study of freight transport costs by trucks, Department of Land Transport. From criteria mentioned above, lists of goods must cover the volume of exports and imports more than 90% of overall import and export weights and cover all items listed of the Ministry of Transport. 2.6.3.2 How to select the entrepreneurs There are two methods in selection of entrepreneurs to survey data. The method 1 is the case when the volume of goods is known while the method 2 is the case when the volume of goods is unknown, but production capacity is recorded. In each method, if the top five producers produces (the method 1.1) or have production capacity (the method 2.1) covers 80% of the production volumes or manufacturing capacity all the country, the Consultants will survey the respective producers to cover at least 80% of the production or manufacturing capacity in the country. However, if the top five producers or production capacity do not cover 80% of the total production in the country, the Consultants will survey the factory with the highest production volumes

PCBK / SEA / CMCL / SYSTRA MVA 2-45 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(the method 1.2) or the highest production capacity (the method 2.2), at least one factory in each regional of the country to cover as many transport routes as possible. 2.6.3.3 The Survey and Data Collection In surveys and data collection of goods transported from entrepreneurs, data are collected in two ways – from mailed questionnaires and direct interviews with entrepreneurs. 2.6.4 The Results of Study The Consultants have summarized the results from survey data in this study as shown in Table 2.6-1 and also concluded the proportion of goods volume and unit transport cost categorized by transport mode shown in Table 2.6-2. Table 2.6-1 The summary of import, export and domestic freight transport from Survey Data in Project

Volume of Freight Transport (Ton) Goods Item Import Export Domestic Total 1* Rice 26,948 6,500,000 13,960,000 20,486,948 2 Corn 180,000 290,000 4,360,000 4,830,000 3* Cassava products 646,407 7,799,081 4,770,740 13,216,228 4 Longans 0 581,047 236,776 817,823 5* Durians 0 339,760 164,920 504,680 6 Mangosteens 0 130,100 45,321 175,421 7 Apples and pears 133,096 3,208 158,906 295,210 8 Grapes 83,104 177 17,080 100,361 9 Citrus fruits 157,623 2,490 180,000 340,113 10 Onion, small onion, garlic, fresh or chilled 124,625 43,935 331,260 499,820 11 Leguminous vegetables, chilled or frozen 3,894 37,713 15,380 56,987 12 Spices 52,101 0 447,780 499,881 13* Orchid 0 15,427 29,159 44,586 14* Rubber 0 2,800,000 530,000 3,330,000 15 Coffee 29,064 2,085 39,370 70,519 16 Palm 0 13,247 12,512,684 12,525,931 17* Soybean 1,961,015 2,030 105,449 2,068,494 18* Oil cake 2,814,917 0 3,961,849 6,776,766 19 Wheat 2,581,987 0 595 2,582,582 20* Fresh, chilled or frozen shrimp 425 174,360 305,640 480,425 21 Frozen squid 84,697 69,756 5,912 160,365 22* Chilled or frozen fish 1,454,218 293,259 45,727 1,793,204 23 Dried fish 1,000 35,000 465,000 501,000 24 Snapping turtle 0 21,070 600 21,670 25 Chilled or frozen processed chicken 0 500,000 947,458 1,447,458 26 Swine, fresh, chilled or frozen 15,860 13,500 953,000 982,360

PCBK / SEA / CMCL / SYSTRA MVA 2-46 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Volume of Freight Transport (Ton) Goods Item Import Export Domestic Total 27 Fresh eggs 0 5,948 655,432 661,380 28 Fresh, chilled and frozen beef cattle 25,165 19,824 133,170 178,159 29 Canned tuna 8,713 411,872 20,048 440,633 30 Canned sardine 0 82,177 22,600 104,777 31* Processed shrimp 0 163,177 285,499 448,676 32 Processed fish 72,039 964,014 150,900 1,186,954 33* Sugar 0 7,545,002 2,695,246 10,240,248 34 Molasses 21,486 979,637 300,000 1,301,123 35 Canned fruit 79,206 780,367 124,530 984,103 36 Fruit Juice 30,924 407,449 80,300 518,673 37* Dried fruit 7,134 80,936 20,000 108,070 38 Vegetables and vegetable preparations 67,056 93,065 436,935 597,056 39 Sweet corn 0 172,187 151,350 323,537 40 Flour 0 35,103 30,000 65,103 41 Starch 0 89,793 94,000 183,793 42 Rice noodle 10,667 142,066 73,000 225,732 43 Instant noodles and instant food 67,058 474,185 345,897 887,140 44 Dog and Cat Food 11,698 346,275 121,554 479,526 45 Soy sauce, chili sauce, tomato sauce 0 48,190 171,810 220,000 46 Fish sauce 0 44,076 218,420 262,496 47 Milk and milk products 243,807 131,639 940,000 1,315,446 48 Drinks and beverage 152,623 1,422,965 1,045,380 2,620,968 49 Palm oil 44,194 292,830 11,037,170 11,374,194 50* Garments made of knitted fabrics 9,020 14,172 93,228 116,420 51* Garments made with fabrics 86,755 183,814 47,100 317,669 52* Fabrics made of cotton 53,499 56,660 23,450 133,609 53* Fabrics made of artificial fibers 132,994 89,400 65,280 287,674 54 Cotton Yarn 16,726 46,648 80,551 143,925 55 Filament 0 211,919 153,800 365,719 56* Fibers 106,320 395,766 35,000 537,086 57* Gemstone 16,973 8,562 8,698 34,233 58 Jewelry made of gold 2 53 4 59 59 Gold unwrought 335 371 0 706 60* Television receiver and parts 84,030 416,870 5,627 506,527 61* Cathode Ray Tube (CRT) 11,449 19,500 8,030 38,979 62 Refrigerators, freezers and components 394,078 1,082,016 450,000 1,926,094 63 Air conditioning and components 1,000 40,000 15,000 56,000 64 Compressors of refrigeration 1,000 30,000 10,000 41,000 65 Circuit breaker 4,380 3,000 2,000 9,380

PCBK / SEA / CMCL / SYSTRA MVA 2-47 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Volume of Freight Transport (Ton) Goods Item Import Export Domestic Total 66 Washer, washing machine and components 1,000 33,000 7,000 41,000 67 Electrical apparatus for providing voice signals and components 88,000 20,000 20,000 128,000 68* Computer 43,402 80,508 37,106 161,016 69* Computer components 73,532 33,165 8,087 114,784 70* Circuit boards 84,755 72,164 86,586 243,505 71 Motors and Generators 270,000 330,000 270,000 870,000 72* Telephone equipment and components 673,736 3,439 670,297 1,347,472 73 Semiconductor transistors and diodes 15,600 25,000 7,800 48,400 74* Transformers and Components 2,654,281 12,900 3,641,471 6,308,652 75* Wooden Furniture 1,464 19,558 55 21,076 76 Lumber 5,800,400 2,051,960 0 7,852,360 77 Plywood 4,932 1,106,604 661,396 1,772,932 78 Fiberboard 22,883 146,558 1,765,542 1,934,983 79* Cold steel 4,309,123 37,306 7,328,745 11,675,174 80* Hot steel 857,801 34,822 183,590 1,076,212 81 Tube connection installation 0 1,367,827 2,051,741 3,419,568 82 Steel frame for construction 0 166,566 6,763,387 6,929,953 83 Nails, bolts, screws 20,900 120,000 8,500 149,400 84 Wire Rope Slings Wire Cable 20,000 158,619 95,000 273,619 85 Appliances, tableware and household stainless steel 18,500 23,000 7,400 48,900 86 Semi-finished products of iron or stainless steel 437,192 3,640,510 2,184,000 6,261,702 87* Aluminum 521,666 42,764 809,680 1,374,110 88 Structures and components made of aluminum 7,373 67,910 32,090 107,373 89 Aluminum products used in the industry 254,068 28,396 121,604 404,068 90 Appliances, tableware and household made of aluminum 0 26,449 5,900 32,349 91 Taps, valves and components 47,025 38,644 161,356 247,025 92 Ethylene 245 61,989 1,300 63,534 93 Propylene 30,000 800,000 480,000 1,310,000 94 Styrene 20,000 280,533 160,000 460,533 95 Vinyl chloride 30,000 390,840 230,000 650,840 96* Polyacetal 304,540 962,102 56,000 1,322,642 97 Sheets, film, foil and strip 200,000 350,000 150,000 700,000 98* Plastic packing 111,272 357,315 892,769 1,361,356 99 Appliances, tableware and household plastic 25,053 53,483 10,148 88,684 100 Carbon 145,349 116,746 388,254 650,349 101 Hydrogen, rare gases and other non-metals 76,974 46,244 273,756 396,974 102 Ammonia 357,403 0 0 357,403

PCBK / SEA / CMCL / SYSTRA MVA 2-48 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Volume of Freight Transport (Ton) Goods Item Import Export Domestic Total 103 Carbonate, Peroxide carbonate, Ammonium carbonate used 659,294 79,367 790,633 1,529,294 in trade 104 Acyclic hydrocarbons 0 295,550 7,404,450 7,700,000 105 Cyclic hydrocarbons 0 1,438,664 1,161,336 2,600,000 106 Terephthalic acid 0 1,381,399 1,085,601 2,467,000 107* Acyclic alcohols and derivatives 913,092 118,286 82,000 1,113,378 108 Phenol and phenol-alcohol 196,000 113,000 121,000 340,000 109 Cyclic monocarboxylic acid & Acyclic monocarboxylic acid 91,085 119,360 70,000 280,445 110 Nitrile compounds–function 71,539 29,980 170,020 271,539 111* Chemical Fertilizers 5,759,108 315,636 2,404,200 8,478,944 112 Paint and Varnish 47,559 34,124 355,876 437,559 113 Colored objects 99,664 22,010 22,010 143,684 114 Lubricant additives used as catalyst 32,876 11,641 488,359 532,876 115 Insecticides, pesticides and animals 134,480 22,165 127,835 284,480 116 Shoes and pieces 3,290 556 2,980 6,826 117 Leather, leather products and compressed leather 160,474 80,636 27,364 268,474 118* Tire 36,103 145,585 256,044 437,732 119 Gloves 779 34,350 51,162 86,291 120 Tube and Pipe 731,455 4,146,726 279,360 5,157,541 121 Conveyor and power transmission 13,900 20,000 7,300 41,200 122 Vulcanized rubber 85,937 1,400,815 290,000 1,776,752 123 Synthetic rubber 3,200 2,000 4,000 9,200 124 Floor tile and wall mosaic 0 165,000 750,000 915,000 125 Sanitary ware made of ceramics 22,922 56,976 35,000 114,898 126 Ceramic tableware 9,997 54,048 10,050 74,095 127* Car 60,000 1,343,783 1,200,535 2,604,318 128* Car parts and accessories 24,000 537,513 480,214 1,041,727 129 Motorcycles and parts 245,842 1,210,993 156,000 1,612,835 130 Bicycles and parts 16,750 33,910 33,000 83,660 131 Reciprocating internal combustion engines and components 248,583 197,866 203,000 649,450 132 Lens 61,895 172,422 27,578 261,895 133 Ingredients for makeup and body cleansing 80,701 521,335 253,647 855,683 134 Essential oils and fragrant mixture of compounds. The 175,210 272,328 437,672 885,210 flavored lubricants are used. Artificial waxes and prepared waxes. Organic compounds that reduce surface tension 135* Medical equipment 5,616,325 1,136,741 10,658,421 17,411,487 136 Pharmaceutical products 78,191 35,013 26,783 139,987 137* Cement 17,068 13,092,615 27,989,160 41,098,843 138 Glass and Glazing 453,693 428,547 2,011,533 2,893,773

PCBK / SEA / CMCL / SYSTRA MVA 2-49 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Volume of Freight Transport (Ton) Goods Item Import Export Domestic Total 139 Toothbrush 3,160 3,345 6,655 13,160 140 Lighters 600 8,000 32,000 40,600 141 Zinc products 184,746 52,523 125,645 362,914 142* Paper and paper products 1,033,589 1,056,405 24,000,000 26,089,994 143 Pulp and waste paper 1,592,342 137,028 1,985,000 3,714,371 144* Molding boxes for metal foundry 27,372 10,167 350,000 387,539 145 Machinery for processing rubber or plastics 480,000 41,400 0 521,400 146* Fuel pumps for liquids and pneumatic pumps 100,000 253,000 124,000 477,000 147 Turbines 134,000 61,000 0 195,000 148 Liquid or gas separation machines 15,000 50,800 49,200 115,000 149 Machinery used in the construction and components 220,000 150,400 0 370,400 150 Shafts and crank 90,000 25,000 50,000 165,000 151 Machinery for metal processing and components 320,000 54,000 12,600 386,600 152* Removable or interchangeable tools including mold 19,473 6,104 40,000 65,576 153 Copper and copper products 436,025 173,594 0 609,619 154 Gypsum 5,247 8,955,860 6,178,191 15,139,298 155 Petroleum gas 1,522,945 134,069 5,672,863 7,329,877 156 Natural gas 8,965,161 0 7,454,180 16,419,341 157 Feldspar 41,544 690,320 60,000 791,864 158* Crude oil 48,944,305 2,523,710 0 51,468,014 159 Refined oil 4,181,648 14,907,143 33,769,521 52,858,312 160 Coal 18,578,230 3,547 1,000,000 19,581,777 161 Marble and granite 220,728 12,267 800,231 1,033,226 162 Kaolin and other soil used in industry 199,366 71,511 1,270,781 1,541,658 163* Coffee, tea and spices 102,493 35,306 100,480 238,279 164* Wood and wood products 497,590 5,458,170 78,768 6,034,528 165* Ingredients from vegetables, fruit, nuts 184,668 1,534,006 97,102 1,815,776 166* Shoes and accessories 64,975 77,540 101,324 243,839 167* Automobiles and equipment (vehicles) 84,000 1,881,296 1,680,749 3,646,045 168* Beverages, whiskey, vinegar 132,863 841,120 994,236 1,968,218 169* Products of grains and malts 574,497 2,275,616 218,632 3,068,745 170* Grains 51,247 96,358 10,672,234 10,819,839 171* Electrical machinery, electrical equipment, and components 9,521,112 5,864,236 2,957,649 18,342,997 172 Live animals 0 0 2,110,000 2,110,000 173 Sugarcane 0 0 100,002,515 100,002,515 174 Stone, sand, soil 0 0 78,254,119 78,254,119 175 Construction materials 0 0 1,300,000 1,300,000 176 Construction metals 0 0 387,000 387,000 177 Animal feeds 0 0 5,920,000 5,920,000

PCBK / SEA / CMCL / SYSTRA MVA 2-50 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Volume of Freight Transport (Ton) Goods Item Import Export Domestic Total 178 Asphalt 0 0 783,000 783,000 179 Consumer Products/Modern Trade 0 0 38,710,601 38,710,601 180 Paddy 0 0 37,400,000 37,400,000 Total 144,229,717 126,621,475 518,535,574 789,386,766 Proportion (%) 18.27 16.04 65.69 100.00 Remark: * 52 goods items studied in Project TDL phase 1 (OTP) Table 2.6-2 The Proportion of Goods Volume and Unit Transport Cost from Survey Data in Project categorized by Mode Goods Volume Surveyed in Proportion Unit Transport Cost Transport Mode Project (Million Tons) (%) (Baht/Ton-Km). Road 710.151 89.962 2.12 Rail 9.646 1.222 0.95 Water 69.554 8.811 0.65 Air 0.036 0.004 10.00 Total 789.387 100.00

Source: Survey Data in Project TDL, 2013 (OTP) The goods volume shown in Table 2.6-2 does not include the remained unknown items transported by rail, water, and air. Some items are also not in the list of this study (Dummy). When quantity of such goods include amount of volume from this study project will estimate the total quantity of all transport modes. These products are implemented in the NAM model to achieve suitable distribution. Results obtained from the model represent quantities as shown in Table 2.6-3. Table 2.6-3 The Proportion of Goods Volume (including Dummy) categorized by Mode from Transport and Traffic Model Goods Volume Surveyed in Total Goods Volume Proportion Transport Mode Project (Million Tons) (Million Tons) (%) Road 710.151 704.013 87.51 Rail 9.646 11.253* 1.40 Water 69.554 89.125* 11.08 Air 0.036 0.130* 0.02 Total 789.387 804.521 100.00 Source: NAM Model base year 2013 Remark: * Data from Ministry of Transport, 2013

PCBK / SEA / CMCL / SYSTRA MVA 2-51 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

From study behaviors of goods transport, it can be classified 3 characteristics as follows. (1) Regular basis. These goods are moved in each region or yearly consumption such as consumer products, construction materials, fuels, processed agricultural products, and etc. When these goods were analyzed on commodity flow per month or annum, the amount of goods movement are relatively constant and are adjusted according to the consumption. (2) Seasonal basis. Amounts of goods are varied according to the seasonal demands. These goods are agricultural products such as fruits, durians, mangosteens (Eastern region, producing in March to May; Southern region, producing in May to July), Longans, and etc. During the harvest period, these goods movements were relatively high compared to during the off season. (3) Irregular basis. Goods are transported on subcontract. This could happen case by case or with valued products such as vehicles.

PCBK / SEA / CMCL / SYSTRA MVA 2-52 ChapterChapter 3 MaintenanceMaintenance ooff ttransportransport aandnd ttrafficraffic databasedatabase ssystemystem Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Chapter 3 Maintenance of transport and traffic database system

3.1 Introduction 3.2 Study and review of transport and traffic database system development process 3.3 Update of data in the database system 3.4 Development of Executive Information System 3.5 Improvement of data presentation from the database system 3.6 Support for maintenance of transport and traffic database system 3.7 Improvement of Computer’s Equipment and Network System

3.1 Introduction

In order to comply with the work scope in the TDL project about maintenance of transport and traffic database system and to support the operation of Office of Transport and Traffic Policy and Planning (OTP) throughout the project term, the consultants have carried out the maintenance of transport and traffic database system by dividing the operation into 6 sessions as followed: (1) Study and Review of System Development Process of Transport and Traffic Database and Information System (2) Update Data in the Database System (3) Development of Executive Information System (EIS) (4) Presentation Improvement of Data and Information from Database system (5) Support for Maintenance of Transport and Traffic Database and Information System (6) Improvement of Computer’s Equipment and Network System The overall information of the maintenance of transport and traffic database system is illustrated in Figure 3.1-1

PCBK / SEA / CMCL / SYSTRA MVA 3-1 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4. Presentation 3. Development of Improvement of Data Executive Information and Information from System Database System 5. Support for 2. Update Data in Maintenance of Transport Database System and Traffic’s Database and Information System

1. Study and Review Maintenance of System Development Transport and 6. Improvement of Process of Transport Traffic’s Database Computer's Equipments and Traffic’s Database and Information and Network System and Information System System

Figure 3.1-1 Maintenance of Transport and Traffic Database System

3.2 Study and review of transport and traffic database system development process

The consultants have studied and reviewed the development process of transport and traffic database system as below: (1) Study and review the 13 projects involved (2) Study and review the current database system and set up the guidelines for system development as followed:  Database system in TDL (Fiscal year 2010-2011)  Trends of system development  Data collection and input to the system  Steps of system development  Test of system (3) System development to accommodate the standard data exchange

PCBK / SEA / CMCL / SYSTRA MVA 3-2 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

3.3 Update of data in the database system

To update the data in database system, the following data updates have been conducted: (1) Management Information System (MIS) : with 6 group updates Group 1 : Socio Economic Group 2 : Travel Characteristics Group 3 : Supply Group 4 : Demand /analysis results of transport and traffic Group 5 : Effect Group 6 : Studied Projects of OTP There are totally 57 items that have been updated. The consultants have collected and examined 21 items from the units outside OTP, and presented the information in Group 2: Travel Characteristics of MIS in the form of Link so that the users can read or download as appropriate. (2) Publication of MIS: the Final Report files of studied projects of OTP, which were accomplished in 2010-2014, have been documented into the system (3) Information system of multi-modal transport and logistics is composed of 4 main data groups: 1) Demand 2) Commodity Flows 3) OD Report 4) Logistics Nodes The consultants have developed the application programs to process the data of import and export products, derived from Customs Department, in the form of Text File; and input the data groups of Demand, Commodity Flows and OD Report into the system. In addition, the Logistics Nodes have been examined and updated in compliance with the sources.

PCBK / SEA / CMCL / SYSTRA MVA 3-3 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

3.4 Development of Executive Information System

The data in Executive Information System includes: (1) Information for the operations within OTP – This means the information that is directly necessary for the operations in OTP. The said information includes overall information of OTP, the follow-up of important projects and budget, overall information of transport infrastructure (Bangkok and Metropolitan Areas), and overall information of logistics. (2) Information in OTP Strategic Plans- this means the information from the sources of Office/Department/Center which complies with the OTP Strategic Plans 2013-2016. Thereby the said information is derived from the interview with Office/Department/Center. (3) Information to integrate with Ministry Operating Center and Department Operating Center (MOC/DOC) – this means the information from OTP, which must be sent to Office of the Permanent Secretary, Ministry of Transport, in order to be kept in the MOC. Thereby the said information is derived from the interview with Office/Department/Center. (4) Statistic information of transport and logistics of National Statistical Office - this means the information from OTP, which is collected and sent for data process at National Statistical Office. The operations to develop Executive Information System include: (1) Interview with representatives from Office/Department/Center (2) Study at MOC, Ministry of Transport (3) Development of Executive Information System of OTP (4) Confirmation of Department Operating Center Information to be integrated with Ministry Operating Center Information (MOC/DOC) (5) Preparation of official statistic information of transport and traffic of National Statistical Office 3.4.1 Interview with representatives from Office/Department/Center There have been interviews with 10 Offices/Departments/Center to explain about how to do the questionnaire and to accumulate information as well as opinions after the interview. Each of the Office/ Department/Center has sent their data to the consultants, 34 items in total. 3.4.2 Guidelines for Development of Executive Information System to comply with Ministry Operating Center, Ministry of Transport The consultants and the staff of Transport and Traffic Information and Technology Center have visited and learned about operations in Ministry Operating Center, Ministry of Transport; and have applied the guidelines for development of the center to the presentation of Executive Information System of OTP. Thereby, the said presentation is illustrated in Figure 3.4-1.

PCBK / SEA / CMCL / SYSTRA MVA 3-4 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 3.4-1 Structure of presentation of Executive Information System of OTP (1) Overall information of OTP (Figure 3.4-2) includes: 1) Information in the strategic plans of OTP 2) Overall information of OTP’s projects 3) Overall information of OTP’s budget 4) Overall information of OTP’s staffs 5) Indicators as to the OTP’s affirmative government

PCBK / SEA / CMCL / SYSTRA MVA 3-5 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 3.4-2 Overall information of OTP

PCBK / SEA / CMCL / SYSTRA MVA 3-6 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(2) Information of important projects and budget monitoring (Figure 3.4-3) includes: 1) The percentage of overall progress in all OTP’s projects 2) The percentage of overall progress in the budget allocated for all OTP’s projects 3) The proportion of projects based on status 4) Performance status of all OTP projects (Planned - Actual) 5) Budget allocation status of all OTP projects (Planned - Actual) 6) Lists of Project Names

Figure 3.4-3 Information of important projects and budget monitoring

PCBK / SEA / CMCL / SYSTRA MVA 3-7 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(3) Overall information of Transport infrastructure (Figure 3.4-4 to Figure 3.4-6) includes: 1) Two-trillion Project, reports on basic project transport infrastructure information, i.e. sectors or types of transport system, ministry and units responsible for the projects, name of project, objectives, scope of work, project sites, etc.

Figure 3.4-4 Fundamental information of 2-trillion project 2) The Bridge-projects illustrate the information about bridges comprising physical features, current bridge network over Chaophraya River, traffic flow rates on the bridges, level of services of urban roads and expressways, and average speed and level of services of all bridge sections.

PCBK / SEA / CMCL / SYSTRA MVA 3-8 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 3.4-5 Project information about bridges 3) Traffic speed and volume consists of information on average traffic speed in Bangkok Area and average traffic speed from transport and traffic model (eBUM).

PCBK / SEA / CMCL / SYSTRA MVA 3-9 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 3.4-6 Information of average traffic speed and traffic volume (4) Overall information of logistics (Figure 3.4-7 to Figure 3.4-8) includes: 1) Names of Plan/Project under the strategy of Ministry of Transport to support Thailand's logistics system development No. 2 (2013-2017)

PCBK / SEA / CMCL / SYSTRA MVA 3-10 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 3.4-7 Names of Plan/Project under the strategy of Ministry of Transport

PCBK / SEA / CMCL / SYSTRA MVA 3-11 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2) Information of freight transport

Figure 3.4-8 Information of logistics survey

PCBK / SEA / CMCL / SYSTRA MVA 3-12 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(5) Other relevant information (Figure 3.4-9 to Figure 3.4-11) includes: 1) Information for integration with Ministry Operating Center (MOC/DOC)

Figure 3.4-9 Information for integration with Ministry Operating Center (MOC/DOC)

PCBK / SEA / CMCL / SYSTRA MVA 3-13 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

2) Statistic information of transport and logistics of National Statistical Office

Figure 3.4-10 Statistic information of transport and logistics of National Statistical Office

PCBK / SEA / CMCL / SYSTRA MVA 3-14 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

3) Internal Information of OTP such as Operation Information within OTP in accordance with OTP Strategic Plans 2013 - 2016

Figure 3.4-11 Information of OTP Strategic Plans

PCBK / SEA / CMCL / SYSTRA MVA 3-15 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

3.5 Improvement of data presentation from the database system

3.5.1 Improvement of main page structure The consultants have updated the main page of transport and traffic publication system to make it compliant with the current situation. The icons of work system are also changed into links, which indicate more clearly what they are linking to, as shown in Figure 3.5-1.

Figure 3.5-1 Main page of transport and traffic publication system after update 3.5.2 Reduce redundancy of OTP’s project output OTP’s website has 2 channels to display the reports of studied projects, i.e. main website of OTP and transport and traffic publication system “Report of studied projects and relevant reports”. So, the consultants advised that there should be only one channel and Back Office of transport and traffic publication system should be used to manage the publication of project information, OTP journals, and annual report.

PCBK / SEA / CMCL / SYSTRA MVA 3-16 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

3.6 Support for maintenance of transport and traffic database system

3.6.1 Preparation of Test Server The consultants have prepared the server, provided by OTP, as a Test Server for installation and test of system prior to installing the developed system into the Production Server. Whereby, the consultants have performed the following operations: (1) Install and set up Windows 2003 Server (2) Install and set up database system MySQL Server 5.1 (3) Install and set up MySQL Query Browser 1.2 (4) Test of database restoration and database query (5) Test the connection to OTP’s Network 3.6.2 Staff Training The consultants have held a workshop, in which the OTP’s staff have to operate the System themselves with closely advised by Consultants (On-the-Job Training). The issues in this workshop include: (1) Development of Web Application with ASP.NET (C#) and AJAX (2) Improvement and maintenance of Management Information System (MIS) (3) Improvement and maintenance of transport and traffic publication system (4) Improvement and maintenance of information system on transport multimodal and logistics (5) Improvement and maintenance of Geographic Information System (GIS) It took about 5 days to accomplish the aforementioned training. 3.6.3 Creation of E-book The consultants have created E-book as publication documents as below: (1) Final report (2) Executive Summary Report - Thai (3) Executive Summary Report - English (4) Maintenance of transport and traffic database system report (5) Transport and traffic travel characteristics report (6) Commodity-flow report (7) Transport model development report

PCBK / SEA / CMCL / SYSTRA MVA 3-17 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(8) 5 Working Papers: 1) Transport and traffic data analysis Report derived from the survey in the Project 2) Land use model development Report 3) Report on Development of transferring traffic assignment data from eBUM to run in TRANUS Program 4) Report on application of MATSim in planning to manage emergency situation in Ayutthaya Industrial Estate 5) Report of development and application of NAM in Cube Cloud (9) Executive Information System Report (10) Information report of OTP’s Data Operating Center (11) Model Application Report: 1) Test of Vision and Mission in public transport System 2) Test of Road Pricing or Congestion Charging Measure 3) Test of Fares on rail public transport 4) Test of impacts on road transport after AEC is effective 5) Test of High-speed Train

3.7 Improvement of computer and network system

The consultants have provided hardware and software to support the operation, all of which have already been installed and submitted to OTP. (1) Server 1 unit (2) External Hard disk 3.5" 5 units (3) External Hard disk 2.5" 5 units (4) Notebook 2 units (5) Ram 4 GB 2 units (6) Tablet PC 6 units (7) Desktop PC 4 units (8) Software for eBook 1 unit (9) Photocopy machine with accessories 1 unit

PCBK / SEA / CMCL / SYSTRA MVA 3-18 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Apart from Improvement of Computer’s Equipment and Network System mentioned above, there are other important outputs obtained from such development, improvement, maintenance of database and transport and traffic information which have been prepared in 4 more reports, i.e. (1) Report on Maintenance of Transport and Traffic Database and Information (2) Report on Executive Information System (3) Report on Information of Data Operating Center of OTP (4) Summary Report on Transport and Traffic Information

PCBK / SEA / CMCL / SYSTRA MVA 3-19 ChapterChapter 4 ImprovementImprovement aandnd mmaintenanceaintenance ooff transporttransport aandnd ttrafficraffic NNAMAM Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Chapter 4 Improvement and maintenance of transport and traffic of National Mode (NAM)

4.1 Introduction 4.2 Study and review of NAM 4.3 Improvement and development of NAM 4.4 Development of innovation for the application of model

4.1 Introduction

National Model (NAM) is a strategic Model developed by Office of Transport and Traffic Policy and Planning (OTP) as a Base Model and using as a tool to analyze and forecast transport and traffic situation results in case there are some changes in transport network in the study area. The model is also used to test traffic management measures proposed by responsible agencies. NAM consists of fundamental data which are different from data in eBUM (extended Bangkok Urban Model) such as Highway Network & Public Transport Network, Socio-economic Data, Traffic Zones, and Traffic Volume Data, etc. There should be clear understanding of which type of study area, which level of accuracy needed prior to applying the model. Another important issue is updating of planning data which needs survey and data collection to use the data collected in model calibration and validation process. Types of data to be surveyed and collected depend on application purpose, for instance, to consider Motorway construction, data to be surveyed are Roadside Interview Survey, Traffic Volume on primary and secondary highway in the surrounding area, etc. This will result in more accuracy from model output. The improvement and maintenance of transport and traffic NAM have been conducted successively from TDL project, phase 1 (2011). The improvement and maintenance herein include the improvement of Traffic Analysis Zone: TAZ, improvement of Socio-Economic Planning Data using the population census in 2010 from National Statistical Office as database, improvement of transport network, improvement of Vehicle Operating Cost: VOC and Value of Time: VOT, improvement of model structure, i.e. Trip Generation Model, Modal Split Model, and Freight Model in which there are 180 items of goods categorized into 8 groups. Then the model of base year 2012 and 2013 is validated and the model derived will be used in prediction for the next future years (2017, 2022, 2027, 2032, and 2037). In addition, the consultants have applied the model to analyze Fuel Consumption and Emission. Thereby, the details of each topic are as belows.

PCBK / SEA / CMCL / SYSTRA MVA 4-1 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.2 Study and review of NAM

The consultants have reviewed the mechanism of the model as well as its input data as followed: (1) Review of mechanism and process of NAM (2) Review of various data used in the model 1) Model network (road transport, water transport, rail transport, air transport and public transport) 2) Data of Traffic Analysis Zoning 3) Socio-Economic Planning Data, e.g. population census, employment data, income, etc. (3) Review of parameters and mathematic relations used in the model The details of operation results are illustrated on Table 4.2-1 Table 4.2-1 Summary of data review on transport network in the model Data Format of data Requirement Road transport Line file that can be output and changed Network fineness needs to be added in *.shp file. and updated to be compatible with the model analysis Water transport Check for update of network and routes of present water transport Rail transport The present rail transport network is not compatible with the network of high- speed transport Air transport Check for update of network and routes of present air transport Schedule and routes of Public Transport (TRIPS)  Update of schedule and routes public bus  Correct the input format to be Schedule and routes of compatible with analysis from TRIPS, domestic flights and Cube Voyager Schedule and routes of public train

4.3 Improvement and development of NAM

The improvement and development of NAM conducted in this project include: (1) Improvement of Traffic Analysis Zone: TAZ (2) Improvement of Socio-Economic Data (3) Improvement of transport network

PCBK / SEA / CMCL / SYSTRA MVA 4-2 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(4) Improvement of VOC and VOT (5) Improvement of model structure 1) Improvement of Trip Generation Model 2) Improvement of Modal Split Model 3) Improvement of Freight Model (6) Validation of model in base year 2012 and 2013 (7) Development of model to analyze Fuel Consumption and Emission. 4.3.1 Improvement of Traffic Analysis Zone: TAZ The main objective of NAM is to analyze the travel between cities and the data used in the analysis of existing model, some of which is the detailed regional or provincial data, especially the data of Gross Provincial Product: GPP and travel behavior as well as trip volume in the country (survey data). Table 4.3-1 shows the analysis zone data accuracy for the development of present model. Table 4.3-1 Zonal Data for current model development Data Highest accuracy Source Population Sub-district Department of Provincial Administration, Ministry of Interior Population and housing census Sub-district National Statistical Office GPP Province Office of the National Economics and Social Development Board Survey data of travel behavior Province group Data survey of the project

Regarding the results in Table 4.3-1, it is found that the data from the survey of travel behavior has accuracy of Traffic Analysis Zone at provincial level only. Hence, to comply with the input data, the appropriate Traffic Analysis Zone for model should be at provincial level. At present, however, there is the need of more accurate NAM analysis data on the model network; that means the need of Traffic Analysis Zone at district or sub-district level. Accordingly, the consultants have updated and divided the data of Traffic Analysis Zone into 2 categories: (1) Coarse Traffic Analysis Zone (2) Fine Traffic Analysis Zone The Coarse Traffic Analysis Zone is used with initial parts of the model such as Trip Generation, Trip Distribution and Modal Split. Meanwhile, the Fine Traffic Analysis Zone is used in Traffic Assignment. The details of Coarse Traffic Analysis Zone and Fine Traffic Analysis Zone are summarized as below.

PCBK / SEA / CMCL / SYSTRA MVA 4-3 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.3.1.1 Coarse Traffic Analysis Zone The consultants have updated the Coarse Traffic Analysis Zone to comply with Traffic Analysis Zone of Cube Cargo Model, which is currently at provincial level. For the Trip Generation Model, accuracy of existing data is still at provincial level, of which the zones have already been updated by Office of the National Economics and Social Development Board and divided into 20 zones as displayed in Figure 4.3-1.

Figure 4.3-1 Details of Coarse Traffic Analysis Zone

PCBK / SEA / CMCL / SYSTRA MVA 4-4 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.3.1.2 Fine Traffic Analysis Zone Fine Traffic Analysis Zone refers to 928 district areas in Thailand and, in this TDL project, the consultants have added more zones in Muang districts of 7 provinces in the eBUM: (1) Nonthaburi (10 sub-districts in Muang Nonthaburi District) (2) Pathumthani (14 sub-districts in Muang Pathumthani District) (3) Samutprakarn (13 sub-districts in Muang Samutprakarn District) (4) Nakhon Pathom (25 sub-districts in Muang Nakhon Pathom District) (5) Samutsakorn (18 sub-districts in Muang Samutsakorn District) (6) Ayutthaya (21 sub-districts in Muang District) (7) Chachoengsao (19 sub-districts in Muang District) This makes the existing 928 zones (districts) become 1,041 zones (districts and sub-districts) as seen in Figure 4.3-2. Anyway, these zones do not include 34 points along the borders and 38 logistics nodes. Overall, the number of zones in the updated NAM is 1,113 zones.

Figure 4.3-2 Additional sub-district zones

PCBK / SEA / CMCL / SYSTRA MVA 4-5 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.3.2 Improvement of Socio-Economic Data The factors used in NAM for analysis and estimation of trip demand within domestic zones are mostly major variables indicating economic and social conditions of the zones, e.g. population census, population density per area, employment rate, average household income, etc. The Trip Generation Model in this project employs two main variables: Gross Product and population census. 4.3.2.1 Gross Product Data Gross Regional Product: GRP and Gross Provincial Product: GPP are statistics data created by Office of the National Economics and Social Development Board by means of Top-Down Approach. The data are used as indicators of economic and social situation in regional and provincial scales every year. Office of the National Economics and Social Development Board (NESDB) has studied, analyzed and improved data process techniques so that the data reports are precise and updated. In the latest publicized data, at present, of the year 2011, the calculation methods of Gross Regional Product and Gross Provincial Product at Real Term have been changed according to Chain Volume Measure: CVMs. The consultants use the data updated from 2004 to 2011 by Office of the National Economics and Social Development Board to estimate the Gross Product to be used in this project. 4.3.2.2 Population census The consultants have checked the difference between the population from civil registration database, Department of Provincial Administration, Ministry of Interior, and that from population and housing census of National Statistical Office in 2010. It is found, from the comparison, that the number of population from population and housing census (65,981,660 people) is higher than that from civil registration database (63,878,267) by 2,103,393 people (3.29 %). Thereby, the highest difference is in Samutsakorn, 80.36%. Bangkok has the number of population from population and housing census higher than that in the civil registration database by 45.67%. The differences of these two data are shown in Figure 4.3-3.

PCBK / SEA / CMCL / SYSTRA MVA 4-6 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 4.3-3 Comparison of provincial population from civil registration database and population and housing census in 2010

PCBK / SEA / CMCL / SYSTRA MVA 4-7 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.3.3 Improvement of transport network The consultants have improved 2 data of transport network, i.e. road transport and rail transport. Furthermore, the consultants have collected the OD and the costs of travel via air transport and public transport. The summary thereof is as below: 4.3.3.1 Improvement of road transport network The addition of details about transport network in the model must be done with consideration of suitability and fineness that the model can accept and yield the most efficient data. The database Transport FGDS has divided transport routes into 9 types. Primarily, the consultants would like to increase the existing highway network in the model, which has 1-3 digits, to 4 digits, as well as some well-chosen rural highways suitable to the fineness for analysis and its process in the model. Whereby, the consultants have improved the road transport network based on the said data as seen in Figure 4.3-4: Road network after updated from the former one.

Existing Road Network Updated Road Network

Figure 4.3-4 Road network after update 4.3.3.2 Improvement of rail transport network In this case, the consultants have reviewed the secondary data concerning the high-speed train project. The said data will be applied to develop the model input data, as seen in Figure 4.3-5.

PCBK / SEA / CMCL / SYSTRA MVA 4-8 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 4.3-5 Routes of high-speed train as to the master plan

PCBK / SEA / CMCL / SYSTRA MVA 4-9 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.3.3.3 Improvement of air transport network The consultants have collected the data of schedule services and air fares of recent domestic flights, both conventional and low cost Airlines. 4.3.3.4 Improvement of public transport network The consultants have collected the fare rates of buses in Category 2 and 3 for use in the improvement of public transport network in NAM. The collected data is as below: (1) Bus Category 2 refers to the buses starting from Bangkok bus terminals and ending in other regional provinces, e.g. Bangkok-Chiangmai and Bangkok-Hatyai, etc. (2) Bus Category 3 refers to the buses starting from any provinces and ending in other regional provinces. Thereby, on their ways, the buses may pass one or more provinces, such as Saraburi-Lom Sak and Chiangmai-Tak, etc. 4.3.4 Improvement of Vehicle Operating Cost: VOC and Value of Time: VOT 4.3.4.1 Improvement of Vehicle Operating Cost: VOC The Vehicle Operating Cost is a main factor to evaluate economic interest of the project since it is the concrete data of benefits. In this case, the consultants have applied the guidelines of Highway Development and Management (HDM-4), which is developed by World Bank. The HDM-4 has been continuously studied and developed; especially there has been a research to compare Vehicle Operating Cost directly for Thailand. Besides the analysis module for Vehicle Operating Cost, HDM-4 has other modules to analyze the conditions of roads, maintenance, and impacts on environment, etc. Nevertheless, this study applies only the analysis module for Vehicle Operating Cost. 4.3.4.2 Improvement of Value of Time: VOT Value of Time means the value (equivalent to money) wasted in travel. The Value of Time is significant to evaluate economic interest in terms of transport projects. These transport projects/measures help save the time of everybody in the society. The time saved from these projects can be utilized in other activities, which can create added value to both economy and society. Not only that, this Value of Time can be used to study the travel behavior. In other words, the commuters will choose the transport modes that match with their Value of Time. So, the consultants have updated the data to comply with the real situation and with the economic and social conditions in the studied zones. 4.3.5 Improvement of model structure NAM is a model with successive 4 parts including Trip Generation Model, Trip Distribution Model, Modal Split Model, and Traffic Assignment Model. Also, it has Cube Cargo Model for freight transport. All of these can be summarized as followed:

PCBK / SEA / CMCL / SYSTRA MVA 4-10 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.3.5.1 Improvement of Trip Generation Model Trip Generation and Attraction Model estimates the travel volume in and out of the zones in the form of Trip ends. The future forecasting equation used in the past was: TRIP = 0.0262*Pop + 0.0763*GPP + 2373 Whereby: TRIP is trip volume of people, (person-trip/day) Pop is number of population in each zone, (people) GPP is gross provincial product, static value in 1988 (Office of the National Economics and Social Development Board uses year 1988 as reference base to process national income at base year price) of each zone (Baht) According to additional data and secondary data collection it was found that the fineness of the data to analyze and create the Trip Generation Model is only at provincial level. Figure 4.3-6 summarizes the trip volume in and out of bus terminals, and overall view of access volume to the terminals in 2005-2012. Figure 4.3-7 is the summary of the trip in and out of airports, and overall passengers at the airports in 2007- 2012.

200.00

: : เหนือNorthern

หน่วย

(

150.00 ใต้ Southern

)

ออกสถำนี กลำงCentral

- In and Out of Airport Airport of Out and In

100.00

เข้ำ

ล้ำนคน

(Million) ตะวันตกWestern 50.00

ตะวันออกEastern จ ำนวนผู้โดยสำรจ 0.00 ตะวันออกเฉียงเหนือNortheastern

Number of Passengers of Number 2548 2549 2550 2551 2552 2553 2554 2555 2005 2006 2007 2008 2009 2010 2011 2012

Figure 4.3-6 Overall view of access volume to the bus terminals in 2005-2012

PCBK / SEA / CMCL / SYSTRA MVA 4-11 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

6.00

เหนือNorthern

5.00

สนำมบิน

) ใต้ Southern ออก

- 4.00

ล้ำนคน

เข้ำ

: :

ตะวันออกเฉียงเหนือNortheastern

In and Out of Airport Out Airport of and In 3.00

หน่วย

(

(Million) 2.00 ตะวันออกEastern จ ำนวนผู้โดยสำรจ 1.00 กลำงCentral 0.00 Numberof Passengers 25502007 20082551 25522009 25532010 25542011 20122555 NOTE: The number of passengers in central region excludes the passengers in Suvarnabhumi airport and Don Muang airport Figure 4.3-7 Overall passengers at the airports in 2007-2012 4.3.5.2 Improvement of Trip Distribution Model Trip Distribution Model is used to distribute trips between traffic zones. This model relies on Gravity Model with the following equation: Tij = aibj Pi AjF(Cij) Kij Whereby: Tij : number of trips from zone i to zone j Pi : number of trips generates from zone i Aj : number of trips to zone j aibj : multiplying factor F(Cij) : function of travel cost from zone i to zone j Kij : adjustment factor of trips from zone i to zone j F(Cij) has the following format: F(Cij) = Cij 1.556 exp (-0.000635Cij ) Whereby: Cij : Generalised Cost of trips from zone i to zone j Thereby, travel demand between provinces and Trip Length distribution are shown in Figure 4.3-8.

PCBK / SEA / CMCL / SYSTRA MVA 4-12 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Number of Trips of Number

Distance (km.)

Figure 4.3-8 Trip Length distribution 4.3.5.3 Improvement of Modal Split Model In this case, the consultants have planned to improve Modal Split Model according to the process of Nested Logit, with 4 modes of transport: (1) By private car (2) By bus (3) By train : Conventional Train and High Speed Train (4) By air : Conventional Airline and Low Cost Airline The hypothesis of the model is: the travelers will decide on modes of transport before types of services in case of select to travel by train or by air, as seen in Figure 4.3-9.

BUS Conventional Train Trip Matrix TRAIN High Speed Train

Conventional Airline Airline Low Cost Airlinne

Figure 4.3-9 Structure of modal split proposed by the consultants to improve NAM

PCBK / SEA / CMCL / SYSTRA MVA 4-13 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

The model structure in this project is Binary Logit, comparing transport modes at present. Upon choosing the present transport modes, if there is a choice of high-speed train, the travelers will choose between conventional train and high-speed train only, not choosing other modes of transport. The details thereof are presented in Figure 4.3-10, which is the concept of Modal Split Model.

Figure 4.3-10 Structure of Modal Split Model in case of high-speed train (Added-mode Structure) Moreover, the consultants have separated travel utility analysis based on different passenger groups or different modes. Then, the results thereof are used to create Modal Split Model in order to forecast number of trips traveled by high-speed train in each O-D pairs. The three main factors are taken into account: travel time calculated by average speed, travel cost calculated from the fare per kilometer, and the daily service frequency. These are volatile strategic factors, which are easily perceived by the travelers and have influence on the decision to use high-speed train. At the meantime, this analysis has a hypothesis: SRT or the rail service providers will control and set other factors, e.g. punctuality, reliability, safety and convenience, in an acceptable level for the users. So, the equation (1) can be explained as below:

Um = 훽푇푇푎 + 훽퐶퐶푎+훽퐹퐹푎 + 퐴푆퐶푎 (1)

Whereby Ua = utility of mode a

Ta = travel time and links of mode a, minutes

Ca = travel cost and links of mode a, Baht

Fa = service frequency of mode a, Baht

ASCa = constant for mode a

βT,βC, βF = parameters for time, cost and frequency respectively

PCBK / SEA / CMCL / SYSTRA MVA 4-14 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

The minimum fare rates in this test starts from 1.60 Baht as to the government policy. Meanwhile, the consultants have considered the ability to pay maximum fares of people based on average fare rate at 3.70 Baht of KTX high-speed train in South Korea. The establishment of price policy is quite a sensitive issue. So, it is important to understand the response of users that keep changing with different prices. The consultants, therefore, set up 2 mean values at 2.30 and 2.70 Baht as choices in steps 2 and 3, respectively, in order to test the influence of fares on the decision to use high-speed train. The study framework defines the maximum speeds at 3 levels, i.e. 250 km/hr, 300 km/hr, and 350km/hr. Anyway, the train usually must stop at every station for the passengers to get on and off, and it has to accelerate the speed and reduce it before reaching the next station. Thus, the real travel time must be evaluated from the average speed of train. According to the data referred in the primary suitability study of high-speed train by OTP in 2010, the consultants has changed these maximum speed to 180 km/hr., 230 km/hr. and 280 km/hr. respectively, to comply with the average speed on network. This study has a hypothesis in which passenger groups of each transport mode respond differently to different travel factors. The passengers of short distance have different decisions from those who travel for medium and long distance. As a result, the analysis will separate the different data of transport modes from each other and divide into 3 distance patterns: short (less than 300 km), medium (300-600 km), and long (over 600 km). The data from interviews is analyzed for Regression in LIMDEP program to find out the influence of different factors on the modal split. The results of parameter analysis used for equation (1) are shown in Figure 4.3-3. The said Table does not display t-statistics or any influence values of each variable on the decision of passengers. Yet, it is noticeable that the coefficients of fare (βC) and of travel time (βT) are almost the same. However, the fare value of these two choices always differs from each other at three digits while the travel time is different at only two digits of minutes. It is summarized that the decision of passengers depends mainly upon the fare rates, followed by travel time, whereas the service frequency hardly has any influence on the choice decision. Also, the consultants have studied the physical features of lines, construction costs, and other engineering suitability such as structure format, running plan, signals and telecommunication system. It is found that the high-speed train Bangkok-Hua Hin should have maximum speed at 250 km/hr. or average speed at 200 km/hr. In order to correlate with the government policy defining that there must be initial fee of high-speed train for VIP passengers, 1st class passengers, and 2nd class passengers, the hypothesis of passenger forecast of high-speed train, Bangkok-Hua Hin, is set up according to government policy so that it will be primary hypothesis for further engineering, economy, and environment feasibility study.

PCBK / SEA / CMCL / SYSTRA MVA 4-15 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 4.3-2 Coefficient of utility equation from modal split analysis of passengers’ behavior Variables Distance Value of Time Type β β β ASC (km.) C T F (Baht/hour) (Cost) (Time) (Frequency) (Constant) Train < 300 -5.60E-03 -3.31E-03 2.17E-03 -0.85565 35 300-600 -3.60E-03 -2.13E-03 5.91E-02 -1.14898 35 > 600 -2.32E-03 -1.11E-03 2.89E-02 -1.74486 29 Bus < 300 -1.61E-02 -1.47E-02 1.55E-02 -3.24747 55 300-600 -5.23E-03 -1.09E-03 1.55E-02 -3.05223 13 > 600 -4.36E-03 -9.69E-04 3.58E-02 -2.46734 13 Car < 300 -7.46E-03 -1.36E-02 4.85E-02 0.543874 109 300-600 -2.36E-03 -3.31E-03 4.62E-02 6.90E-02 84 > 600 -1.47E-03 -1.87E-03 1.10E-02 -0.82332 76 Air 300-600 -3.66E-03 -1.02E-02 7.53E-02 1.304418 167 > 600 -3.54E-03 -5.54E-03 4.28E-02 1.385444 94

In the development of parameters for modal split for TDL II project, the consultants have collected parameters for modal split from different projects studied by OTP, including the survey data in this project. The parameters are then tested with variables in modal split equation, and used as input data for parameter development in TDL II project. The results thereof are finally used to validate the parameters in TDL II project. Modal Split Model is used to explain the selection of transport modes based on Utility Theory. It is hypothesized that travel generates from behavioral decision of an individual; the traveler has several ways of decision whether to travel or not, which mode of transport to use, etc. Generally, the traveler makes a decision in the way that will give him or her ultimate utility, which may be in the form of appropriate travel cost or travel time. The Modal Split Model of NAM has the following Utility Function.

Ui = Ai + (Bi *GCi)

Whereby GCi : Generalised Cost of travel mode i

Bi : coefficient of Generalised Cost variables

Ai : Specific Mode Constant of mode i The parameters thereof are as displayed in Table 4.3-3

PCBK / SEA / CMCL / SYSTRA MVA 4-16 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 4.3-3 Parameters used in Modal Split Model

Vehicle type Ai Bi Private car 0.00 -0.0015 Bus -0.03 -0.0015 Train -1.70 -0.0015 High-speed train -1.50 -0.0015 Plane -3.50 -0.0015

4.3.5.4 Improvement of Freight Model Citilabs developed Cube Cargo program under the project with an attempt to support national freight model of Germany. After that, its functions and variables used in Cube Cargo Model have been updated to apply with urban and regional areas of other countries. Thereby, Cube Cargo program has been adjusted to be compatible with trip forecast model Cube so that it is easier to use. Also, it can be linked with different Cube Cargo Modules within Cube Model , which is used to forecast the number of passengers (applied with Cube Voyager and other conventional programs such as TP+TRANPLAN and TRIPS), to simulate the traffic flow (applied with Cube Dynasim), and to estimate the travel schedule (applied with Cube Me). Cube Cargo can also be utilized in urban and regional zones, or in distant transport. Thereby, the program will calculate to find out OD Matrix in the format of weight per year (ton/year) of various commodities, categorized according to transport modes. It also calculates and finds out the OD Matrix of the travel by all types of trucks. In addition, Cube Cargo can calculate to find out the OD Matrix of transport in urban areas in order to fully forecast the freight by trucks. The primary input data for use in Cube Cargo program consists of: (1) Socio-Economic data in zones, e.g. population, number of households and different types of employment (2) Zone-to-zone service levels, e.g., time and costs of door-to-door service for each mode of transport (3) Recent freight schedule to be used as a basis of model forecast The consultants have improved Cube Cargo structure model by dividing import-export goods into 8 categories, and domestic goods into 8 categories.

PCBK / SEA / CMCL / SYSTRA MVA 4-17 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.3.6 Validation of model in base year 2012 and 2013 The consultants have performed validation of model in base year 2012 in sections of travel and freight by examining the results from the model in order to compare the traffic volume data at Screen Line in the latest project. This is to ensure that the model has more fineness and more accuracy. All points surveyed at every Screen Line must have acceptable deviation for road transport regarding UTP Highway Network Development Guide, U.S. Department of Transport, 1983. The checking for correction, a step of NAM improvement to estimate the future trip volume, is illustrated in Figure 4.3-11. In this validation of model, the consultants use the latest survey data of 3 Screen Lines in 2012 (central, north, east), while the other 3 Screen Lines (northeast, upper south, and lower south) were surveyed in 2013. The said Screen Lines are shown in Table 4.3-4. The results of validation of model in 2012 are presented in Table 4.3-5.

Figure 4.3-11 Steps of checking for correction in NAM

PCBK / SEA / CMCL / SYSTRA MVA 4-18 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 4.3-4 Points of data survey along Screen Line nationwide Screen Line Site Highway Number Section Screen Line 1 (SL 1) RS01 12 Pitsanuloke – Wang Thong RS02 117 Nakorn Sawan - Pitsanuloke RS03 115 Pluak Sung – Kampaeng Petch RS04 1 Kampaeng Petch - Nakorn Sawan Screen Line 2 (SL 2) RS05 12 Lomsak - Chumpae RS06 2 Si Kiu – Nakorn Ratchasima RS07 33 Prachin Buri – Sa Kaew RS08 304 Nakorn Ratchasima - Prachin Buri Screen Line 3 (SL 3) RS09 32 Singburi – Ang Thong RS10 4 Nakorn Pathom - Bangkok Screen Line 4 (SL 4) RS11 3 Samut Prakarn - Rayong RS12 304 Prachin Buri 0 Chachoengsao Screen Line 5 (SL 5) RS13 4 Prachuab Kirikhan - Chumporn Screen Line 6 (SL 6) RS14 4 Khlong Thom – Wang Wiset RS15 41 Ban Nasarn – Thung Song RS16 401 Si Chon – Tha Sala

Table 4.3-5 Results of NAM validation along Screen Line in 2012 Vehicle type Screen Line Private car Bus Truck Total (unit) Screen Line 1 (SL 1) Survey 63,958 2,147 10,031 76,136 Model 72,537 2,168 11,174 85,879 Difference (%) 13.41 0.98 11.39 12.80 Screen Line 2 (SL 2) Survey 71,912 3,277 19,615 94,804 Model 62,063 3,208 17,757 83,028 Difference (%) -13.70 -2.11 -9.47 -12.42 Screen Line 3 (SL 3) Survey 72,824 7,937 25,600 106,361 Model 81,628 7,848 22,683 112,159 Difference (%) 12.09 -1.12 -11.39 5.45 Screen Line 4 (SL 4) Survey 42,048 3,701 9,783 55,532 Model 45,712 3,772 11,436 60,920 Difference (%) 8.71 1.92 16.90 9.70 Screen Line 5 (SL 5) Survey 17,250 897 6,759 24,906 Model 18,504 948 7,348 26,800 Difference (%) 7.27 5.69 8.71 7.60 Screen Line 6 (SL 6) Survey 46,512 2,639 14,570 63,721 Model 52,390 2,436 13,882 68,708 Difference (%) 12.64 -7.69 -4.72 7.83 Overall Difference (%) 5.83 -1.06 -2.41 3.80

PCBK / SEA / CMCL / SYSTRA MVA 4-19 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Analysis of people’s travel volume and freight in 2012 is shown in Table 4.3-6 to 4.3-8, respectively. Table 4.3-6 Estimation of people’s travel based on Transport modes Unit: 1000 person-trip/day Modal Split Year 2012 Private car 1,267 Van 45 Bus 912 Train 150 Air 91 Total 2,465 Source: NAM Table 4.3-7 Results from NAM Year Million PCU-km. Million PCU-hr. Speed (km. /hr.) 2012 336.67 4.24 79.40 Source: NAM NOTE: PCU-Kms: vehicle (pcu) x distance PCU-Hrs: vehicle (pcu) x travel time Table 4.3-8 Freight Transport Results from NAM (Year 2012) Year 2012 Transport mode Transport volume Average Distance Million tons-km/year (1,000 tons/year) (km.) Road 405,934 186,772,872 460.1 Train 10,848 2,442,189 225.1 Water 88,074 4,920,272 55.9 Air 57 34,038 597.2 Average for all modes 504,913 194,169,370 384.6

Results of model validation in 2013 are shown in Table 4.3-9. Table 4.3-9 Results of NAM validation along Screen Line in 2013 Vehicle type Screen Line Private car Bus Truck Total (vehicle) Screen Line 1 (SL 1) Survey 66,516 2,233 10,432 79,181 Model 62,885 2,419 11,462 76,766 Difference (%) -5.46 8.33 9.87 -3.05 Screen Line 2 (SL 2) Survey 74,788 3,408 20,400 98,596 Model 81,305 3,008 17,751 102,064 Difference (%) 8.71 -11.74 -12.99 3.52

PCBK / SEA / CMCL / SYSTRA MVA 4-20 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Vehicle type Screen Line Private car Bus Truck Total (vehicle) Screen Line 3 (SL 3) Survey 75,737 8,254 26,624 110,615 Model 69,236 7,440 27,097 103,773 Difference (%) -8.58 -9.86 1.78 -6.19 Screen Line 4 (SL 4) Survey 43,730 3,849 10,174 57,753 Model 37,110 3,840 9,171 50,121 Difference (%) -15.14 -0.23 -9.86 -13.21 Screen Line 5 (SL 5) Survey 17,940 933 7,029 25,902 Model 16,344 1,012 7,579 24,935 Difference (%) -8.90 8.47 7.82 -3.73 Screen Line 6 (SL 6) Survey 48,372 2,745 15,153 66,270 Model 43,843 2,506 14,296 60,644 Difference (%) -9.36 -8.71 -5.66 -8.49 Overall Difference (%) -5.00 -5.59 -2.73 -4.57

Analysis of people’s travel volume and freight in 2013 is shown in Table 4.3-10 to 4.3-15, respectively. Table 4.3-10 Freight volume in 2013 (1,000 tons/year) Group Commodity Transport volume (1,000 tons/year) 1 Agricultural products 67,816 2 Fishery products 1,138 3 Live stocking products 2,492 4 Agricultural industry products 32,824 5 Industrial products 192,979 6 Mineral and fuel products 181,981 7 Cross-border products 49,005 8 Addition products for domestic transport 276,287 Total 804,521

Table 4.3-11 Estimation of people’s travel based on transport modes Unit: 1,000 person-trip/day Mode 2012 2013 2017 2022 2027 2032 2037 Private car 1,267 1,358 1,452 1,594 1,766 1,973 2,220 Van 45 46 50 56 61 68 75 Bus 912 978 1,048 1,154 1,284 1,439 1,625 Train 150 145 156 173 193 217 246 Air 91 93 100 111 123 137 153 Total 2,465 2,620 2,806 3,087 3,426 3,834 4,320 Source: NAM

PCBK / SEA / CMCL / SYSTRA MVA 4-21 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 4.3-12 Freight Transport Volume from NAM Unit: 1,000 tons/year Mode 2012 2013 2017 2022 2027 2032 2037 Road 405,934 704,013 743,463 795,024 850,576 910,010 973,597 Train 10,848 11,252 11,882 12,706 13,594 14,544 15,561 Water 88,074 89,126 94,120 100,647 107,680 114,520 120,273 Air 57 130 137 147 157 168 180 Total 504,913 804,521 849,602 908,525 972,008 1,039,243 1,109,611 Source: NAM Table 4.3-13 Travelling Data Results from NAM Year Mil. Veh-Km Mil. Veh-Hr Speed (km/hr) 2012 336.67 4.24 79.40 2013 304.63 3.80 80.13 2017 324.53 4.08 79.48 2022 353.29 4.50 78.50 2027 386.94 5.00 77.36 2032 426.38 5.61 75.98 2037 472.73 6.36 74.34 Source: NAM Remark: Veh-Km: vehicle x distance Veh-Hrs: vehicle x travel time

Table 4.3-14 Domestic Freight Transport Results from NAM Unit: million tons-km/year Mode 2012 2013 2017 2022 2027 2032 2037 Road 186,772 228,200 241,400 258,400 276,900 296,900 318,200 Train 2,442 2,569 2,713 2,901 3,103 3,320 3,552 Water 4,920 15,721 16,603 17,754 18,987 20,240 21,348 Air 34 77 82 87 93 100 107 Total 194,169 246,567 260,797 279,142 299,084 320,560 343,208

Table 4.3-15 Results from NAM, Freight, Average distance of freight Transport Unit: km Mode 2012 2013 2017 2022 2027 2032 2037 Road 460.1 324.2 324.8 325.1 325.7 326.3 326.9 Train 225.1 228.3 228.3 228.3 228.3 228.3 228.3 Water 55.9 176.4 176.4 176.4 176.5 176.5 177.5 Air 597.2 594.5 594.5 594.5 594.5 594.5 594.5 Average of all types 384.6 306.5 307.0 307.3 307.8 308.5 309.4

PCBK / SEA / CMCL / SYSTRA MVA 4-22 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

4.4 Development of innovation for the application of model

4.4.1 Development of model to analyze Fuel Consumption and Emission The consultants have developed innovation of Fuel Consumption and Emission Analysis Model in NAM as per steps shown in Figure 4.4-1. The first step thereof begins with the classification of vehicle types based on the results of Trip Assignment Model from TDL project. Nevertheless, the results of the existing model are in PCU (Passenger Car Unit), so the said data must be converted into "unit" of vehicle types classified in the project, mainly considering the ways of emission. Then, the number of vehicles derived from the model is validated with survey data of traffic volume along the Screen Line of TDL project. Next, the number of vehicles on network derived from the validation is calculated to find out the average speed of each type, and the results of this calculation are used as input data for emission analysis. Thereby, the travel speed has an effect on the emission volume once considering the equation EF = aVb whereby V is travel speed (km/hr). After that, the fuel of all vehicles are classified, based on the secondary data from other departments and the results are used as input data together with commands in order to calculate fuel consumption and emission. Finally, the amount of emission from the model is validated to comply with estimated results of departments involved.

PCBK / SEA / CMCL / SYSTRA MVA 4-23 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 4.4-1 Steps of transport model development to analyze the emission

PCBK / SEA / CMCL / SYSTRA MVA 4-24 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

To develop model with ability to analyze emission of different vehicles, in the emission analysis, there is a function used to analyze the pollution volume caused by transport and traffic, i.e. carbon dioxide

(CO2, hydro carbon (HC), carbon monoxide (CO), nitrogen oxide (NOx), and particle matte (PM). The process begins with the creation of commands to analyze the pollutions caused by transport and traffic. The commands for emission analysis in the model, illustrated in Figure 4.4-2, consists of Matrix program, which will input the data of fuel consumption proportion, trip assignment results, average speed of all vehicle types, vehicle distance on individual road, and the value of Emission Factors: EF derived from laboratory tests of the project, in the form of EF=a.Vb whereby a and b are constants, V is average speed (km/hr). Then, the traffic data from network received from trip assignment is converted into DBF format to analyze the pollutions caused by transport and traffic. Finally, Network commands are used to show the results of pollution emitted from different types of vehicles on the road network.

Figure 4.4-2 Commands for analysis of fuel consumption and emission

Figure 4.4-2 Commands for analysis of fuel consumption and emission (continued)

PCBK / SEA / CMCL / SYSTRA MVA 4-25 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 4.4-2 Commands for analysis of fuel consumption and emission (continued)

However, the results of Fuel Consumption and Emission Analysis still have limitations because the NAM is a calculation of average number of trips on traffic network on a daily basis, which is an average of overall estimation. The results of travel analysis in particular areas, for instance, the provincial fuel consumption and pollutions, may not be accurate. Furthermore, NAM has limitations on the completeness of traffic network data since NAM is typically used to analyze the travel of people and freight transport between cities (intercity). Therefore, there is no travel within the cities (intra city), or Zone, which will have effect on the amount of trip and pollution in the network. Estimated amount of pollutions emitted are shown in Table 4.4-1 whereas the results of emission analysis in base year 2013 are shown in Figure 4.4-3.

PCBK / SEA / CMCL / SYSTRA MVA 4-26 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 4.4-1 Forecasted amount of pollutions Emitted categorized by vehicle type Carbon Carbon Particle Nitrogen Oxide Hydro Carbon Year Vehicle Type Dioxide Monoxide Matters (NOX) (HC) (CO2) (CO) (PM) 2012 Passenger Car 15.613 0.083 0.066 0.012 0.001 Bus 3.240 0.042 0.029 0.001 3.206 Truck 10.384 0.034 0.077 0.007 6.752 Total 29.237 0.159 0.172 0.020 9.959 2013 Passenger Car 16.717 0.089 0.073 0.012 0.001 Bus 3.469 0.045 0.032 0.001 3.461 Truck 11.118 0.036 0.085 0.007 7.290 Total 31.304 0.170 0.190 0.020 10.752 2017 Passenger Car 17.808 0.093 0.080 0.012 0.001 Bus 3.887 0.050 0.037 0.001 3.863 Truck 11.823 0.038 0.093 0.007 7.744 Total 33.518 0.181 0.210 0.020 11.608 2022 Passenger Car 19.456 0.102 0.087 0.018 0.001 Bus 4.493 0.057 0.043 0.002 4.435 Truck 12.768 0.041 0.100 0.010 8.351 Total 36.717 0.200 0.230 0.030 12.787 2027 Passenger Car 21.481 0.111 0.094 0.018 0.001 Bus 5.202 0.065 0.049 0.003 5.095 Truck 13.821 0.044 0.107 0.009 9.022 Total 40.504 0.220 0.250 0.030 14.118 2032 Passenger Car 23.891 0.126 0.105 0.018 0.001 Bus 6.024 0.076 0.057 0.003 5.855 Truck 14.970 0.048 0.118 0.009 9.761 Total 44.885 0.250 0.280 0.030 15.617 2037 Passenger Car 26.868 0.141 0.117 0.024 0.001 Bus 7.000 0.087 0.066 0.004 6.740 Truck 16.246 0.052 0.127 0.012 10.576 Total 50.114 0.280 0.310 0.040 17.317 Source: Estimated by the Consultants

PCBK / SEA / CMCL / SYSTRA MVA 4-27 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 4.4-3 Analysis results of emission in 2013 In the process of analysis, the consultants have added a function to calculate the fuel consumption as in Figure 4.4-4.

Figure 4.4-4 Analysis results of fuel consumption in vehicle The analysis of Fuel Consumption in NAM and the calculation of fuel demand from vehicle trip on network (Vehicle Kilometers, Veh-Km). The calculation of fuel consumption for benzene, LPG and NGV relies on the data of Department of Energy Business, Ministry of Energy. There are 7 cars in this test, running in and out of urban areas, as well as on expressways for the distance of 5,200 kilometers. The results of average consumption rate are: Gasohol 13.08 km/litre, LPG 11.10 kg./litre and NGV 15.26 km/kg.

PCBK / SEA / CMCL / SYSTRA MVA 4-28 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Consultants compared the fuel data from all business type between Department of Energy Business (DOEB), Ministry of Energy and NAM Model. In base year 2013, the Diesel consumption from DOEB and NAM Model were 47.03 and 36.60 Million litres/day, respectively. The NAM Model’s result is shown the different ratio about 22.18% The fuel consumption from the analysis are shown in Table 4.4-2. Table 4.4-2 Fuel Consumption Unit : Million litres/day Year Vehicle Type Benzene Diesel LPG CNG 2012 Passenger Car 4.99 11.47 0.15 0.03 Bus 0.47 5.57 0.06 0.02 Truck 0.02 16.01 0.02 0.03 Total 5.48 33.05 0.23 0.08 2013 Passenger Car 5.59 12.84 0.17 0.03 Bus 0.56 6.58 0.07 0.02 Truck 0.02 17.18 0.02 0.03 Total 6.17 36.60 0.26 0.08 2017 Passenger Car 6.26 14.38 0.19 0.03 Bus 0.67 7.77 0.08 0.03 Truck 0.02 18.44 0.03 0.04 Total 6.95 40.59 0.30 0.10 2022 Passenger Car 7.28 16.71 0.22 0.03 Bus 0.82 9.56 0.09 0.03 Truck 0.02 20.31 0.03 0.04 Total 8.12 46.58 0.34 0.10 2027 Passenger Car 8.46 19.41 0.26 0.04 Bus 1.00 11.67 0.12 0.04 Truck 0.02 22.39 0.04 0.04 Total 9.48 53.47 0.42 0.12 2032 Passenger Car 9.89 22.70 0.30 0.05 Bus 1.22 14.19 0.14 0.05 Truck 0.02 24.77 0.05 0.05 Total 11.13 61.66 0.49 0.15 2037 Passenger Car 11.71 26.87 0.35 0.06 Bus 1.49 17.36 0.17 0.06 Truck 0.03 27.58 0.06 0.06 Total 13.23 71.81 0.58 0.18 Source: Estimated by the Consultants

PCBK / SEA / CMCL / SYSTRA MVA 4-29 ChapterChapter 5 ImprovementImprovement aandnd mmaintenanceaintenance ooff transporttransport aandnd ttrafficraffic eeBUMBUM Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Chapter 5 Improvement and maintenance of transport and traffic of Extend Bangkok Urban Model (eBUM) 5.1 Introduction 5.2 Study and review of eBUM 5.3 Improvement and development of eBUM 5.4 Development of innovation for the application of model 5.5 Update of Software Licenses for Cube Program of OTP

5.1 Introduction

Extended Bangkok Urban Model (eBUM) is a strategic Model developed by Office of Transport and Traffic Policy and Planning (OTP) as a Base Model and using as a tool to analyze and forecast transport and traffic situation results in case there are some changes in transport network in the study area. The model is also used to test traffic management measures proposed by responsible agencies. An eBUM consists of fundamental data which are different from data in National Model (NAM) such as Highway Network & Public Transport Network, Socio-economic Data, Traffic Zones, and Traffic Volume Data, etc. There should be clear understanding of which type of study area, which level of accuracy needed prior to applying the model. Another important issue is updating of planning data which needs survey and data collection to use the data collected in model calibration and validation process. Types of data to be surveyed and collected depend on application purpose, for instance, to consider Transfer / Common Ticket System, data to be surveyed are Passenger Interview Survey at Mass Transit Station, Bus stops, Public piers, Home Interview Survey, etc. This will result in more accuracy from model output. The scope of improvement and maintenance of Extended Bangkok Urban Model or eBUM in this study has been extended to cover area of Ayutthaya and Chachoengsao. According to the requirements of study, it is necessary to increase the number of traffic zones from 1,657 to 1,771 and to survey the transport and traffic information so as to update the travel information as much as possible. The said information will also be used in model validation for base year 2012 and 2013. Furthermore, the Socio-Economic Planning Data used in the model has been updated based on population and housing census in 2010 provided by National Statistical Office. So, the said data can be used in validation of the model more precisely and efficiently. Then, eBUM can be used to forecast the transport and traffic conditions in the future, and can be applied to test other projects as well.

PCBK / SEA / CMCL / SYSTRA MVA 5-1 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

5.2 Study and review of eBUM

The study and review of eBUM relies on the survey data and study results accumulated from the former studied projects. The trends of improvement and development thereof are shown in Table 5.2-1. Table 5.2-1 Summary of improvement and development of eBUM Details Points of Development Major trends of development 1. Information about planning,  According to the recent data of  Adjust planning database to be in e.g. population, household, population census, Bangkok and line with the recent data of employment, household income metropolitan areas have the total population census population of 14.6 million and 4.8  Review and examine the million households. This is estimations of population growth, significantly different from the household, and in come in the planning data in the recent traffic model’s database and make them model, which has the population updated as much as possible of 11.5 million and 3.9 million  Review the data of employment, households. This affects all especially in the new industrial aspects of the model, so the zones or particular areas such as model, as well as its variations, hospitals, airports, etc. must be reviewed.  Review the number of students  Review and improve the main because the education at present traffic sources is extending. 2. Household size Distribution Model  Improvement and validation in  Review and improve the model planning database, which has with existing HIS and the new one different population and to derive from this study household from those in the  Validation with HIS, which is recent data of population census updated based on the recent data  Have impacts on Trip Production of population census Model; if HH Size is modeled incorrectly, it will affect the estimations of Total Trip 3. Vehicle Ownership Model  Improvement and validation in  Review and improve the model planning database, which has with existing HIS and the new one different population and to derive from this study household from those in the  Validation with HIS, which is recent data of population census updated based on the recent data  Have significant impacts on all of population census and the process of the model since it is statistics of registered cars from the model of Market the Department of Land Transport Segmentation, in which different groups have completely different travel behavior

PCBK / SEA / CMCL / SYSTRA MVA 5-2 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Details Points of Development Major trends of development 4. Trip Production Model  Improvement and validation in  Review and improve the model planning database, which has with existing HIS and the new one different population and to derive from this study household from those in the  Validation with HIS, which is recent data of population census updated based on the recent data  Have significant impacts especially of population census and the on Trip rate in each Household statistics of registered cars from Segment, leading to the deviation the Department of Land Transport of the estimated car number on  Examine with the number of cars road network; and impacts on the on road network passing Strategic analysis of traffic condition as well Screen line after the Assignment as the following steps in the Model model since it requires the input  Check for the necessity of data of speed and time O-D Table in Bangkok and metropolitan areas 5. Trip Attraction Model  Improvement and validation in  Review and improve the model to planning database, which has comply with the recent data of different population and population census, as well as the household from those in the employment data of National recent data of population census Statistical Office; e.g.  Not many impacts since the the ratio of employment and Control is Trip Production Model; population is at 45%-50%, etc. however, there should be  Check for the necessity of improvement to clearly reflect the O-D Table in Bangkok and development of high employment metropolitan areas or high number of students 6. Trip Distribution Model  Improvement and validation in  Review and improve the model planning database, which has with existing HIS and the new one different population and to derive from this study household from those in the  Validation with HIS, which is recent data of population census updated based on the recent data of population census

PCBK / SEA / CMCL / SYSTRA MVA 5-3 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

5.3 Improvement and development of eBUM

National Statistical Office has surveyed and publicized the data of population census. It is found that in 2010 Bangkok and metropolitan areas have the population of 14.6 million with 5.1 million households. The number is higher than that in eBUM of TDL Project in 2011, in which populations of 3.8 million and 1.5 million households approximately are found as shown in Table 5.3-1. Table 5.3-1 Population and household in eBUM compared with population census in 2010 Census (‘000) Model eBUM(1) (‘000) Difference (‘000) Area Population Household Population Household Population Household Bangkok 8,305 2,882 6,915 2,272 -1,390 -610 Nonthaburi 1,334 474 910 313 -424 -161 Samutprakarn 1,829 646 1,075 393 -754 -253 PathumThani 1,327 519 717 243 -610 -276 Samutsakorn 887 328 430 146 -457 -182 NakonPathom 944 286 790 244 -154 -42 Total 14,626 5,135 10,837 3,612 -3,789 -1,524 (1) Model from the project TDL in 2011 The differences of these databases have significant effects on every step of traffic forecast especially the first step i.e. Trip Generation Model. As to the primary test, it is found that the model has total trips in Bangkok and metropolitan areas up to 6 million person-trips/day, affecting the results of traffic forecast on all road network and public transport system in Bangkok and metropolitan areas. As a consequence, if the said eBUM is to be applied to do analysis, forecasting, or planning on transport, its planning data must be adjusted to comply with the latest census as well as reviewing and checking of parameters used in eBUM. 5.3.1 Trip Generation Model Trip Generation Model in this study still uses the same structure of UTDM, as used in the latest eBUM (TDL 2011). The updated sub-models include: (1) Household Size Distribution Model: HHSD: It is used to divide Market Segment of households based on the average size of household in each zone, and to estimate HH Size to be used as Input in Trip Production Model. Once comparing with the data of census 2010 after the update, it is found that the model has almost the same provincial household distribution rate as shown in census data. (2) Household Vehicle Distribution Model: HHVD: It is used to divide Market Segment of households based on the average income of household in each traffic zone. The model gives the proportion of household vehicle distribution in each traffic zone in order to be used as in Trip Production Model. Once comparing with the data of census 2010 after the update, it is found that the model has almost the same provincial household vehicle distribution rate as shown in census data.

PCBK / SEA / CMCL / SYSTRA MVA 5-4 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

(3) Trip Production Model : It is used to calculate the trips generated in each zone by the following trip purposes: 1) Home-based Work (HBW) – Trips between home and workplace 2) Home-based Education (HBE) - Trips between home and education institute 3) Home-based Other (HBO) - Trips between home and other places such as department store, restaurant, etc. 4) Non-Home-based (NHB) – Trips not relevant to home (neither origin nor destination) The structure of model is in the form of Cross Classification model, presenting the trip rate related to the variables of trip purposes, household size, and household vehicle distribution. Thereby, the model will be updated by adjust the trip rate of HBO and NHB with factor (under-reporting factors) so that the proportion of overall trip purposes are reasonable as much as possible. This is because the said data is usually recorded less than the real scenario once compared with HBW and HBE, which are routines. The analysis results show that people in Bangkok and metropolitan areas have higher Mechanized Trip Rate in 2011, though not so high as the rates in the past. This is partly due to the change of travel behavior in accordance with the recent advanced technology. Regarding the comparative results of trip rate after the update and that of provincial and overall UTDM, it is seen that the trip rate per capita is increasing in almost all provinces whereas the trip rate per household is decreasing. This in compliant with the real situation at present, in which the household size in Bangkok and metropolitan areas is decreasing compared with that in 1995. (4) Trip Attraction Model: It is a sub model used to calculate the number of trips into each of the zones. This study still relies on structure and related equation used in UTDM project and in the latest eBUM (TDL 2011). This is because there is no data for the improvement, e.g. employment data at each destination zone. 5.3.2 Trip Distribution Model It is the model to distribute all trips in each zone obtained from Trip Generation Model into each pair of trips co-related zones. The form of model is still Gravity Model as in UTDM and the latest eBUM (TDL 2011). This study has improved Friction Factor as to the trip purpose and vehicle ownership of some trip makers of 16 groups so that it can simulate the travel pattern like that in HIS in 2003 as much as possible (differences not over ±5% as expected). The results of Mean Trip length and the proportion of Intrazonal trip compared with total trips of all zones after the update of Friction Factor are presented in Table 5.3-2.

PCBK / SEA / CMCL / SYSTRA MVA 5-5 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 5.3-2 Summary of Mean Trip Length from the synthesis of HIS 2546 between the survey results and model results Mean Trip Length % Intrazonal Vehicle Purpose % Diff Ownership Observed Estimated Observed Estimated (Est./Orbs) 0 VEH HBW 58.90 56.70 -3.74% 28.10 24.30 HBE 48.40 49.70 2.69% 23.70 29.70 HBO 42.40 42.40 0.00% 41.90 39.30 NHB 53.00 50.90 -3.96% 20.50 31.50 MC HBW 25.60 26.70 4.30% 39.40 27.40 HBE 19.80 20.40 3.03% 33.70 48.30 HBO 17.20 17.80 3.49% 53.90 60.50 NHB 19.10 19.40 1.57% 42.50 37.40 1 CAR HBW 69.40 71.20 2.59% 29.50 17.00 HBE 53.70 53.50 -0.37% 28.60 33.00 HBO 48.20 48.70 1.04% 40.20 41.40 NHB 47.60 46.10 -3.15% 22.20 41.50 Multi-Vehicle HBW 72.90 71.70 -1.65% 23.50 16.00 HBE 57.70 57.70 0.00% 21.50 39.20 HBO 48.90 48.60 -0.61% 36.90 41.50 NHB 58.90 61.30 4.07% 19.60 22.10 Remark: Observed – from the survey HIS 2546 Estimated– from the synthesis of Gravity Model 5.3.3 Modal Split Model Nowadays, there have been a number of public transport development projects in Bangkok and metropolitan areas, e.g. high-speed train, extensions of BTS and MRT. In addition, the existing public transport, e.g. vans, motorcycle taxis, etc., has also extended its service areas and units. As a result, the structure of transport services in Bangkok and metropolitan areas has changed substantially, leading to the more complicated travel behavior. So, the Modal Split Model should be updated in order to match with the current situation, and in order that eBUM could be used to analyze and plan the transport in Bangkok and metropolitan areas in a more reliable manner. This study uses the structure of Multinomial logit to examine the modal split behavior of the target groups. Thereby, the 16 survey points are chosen to cover the areas of both Bangkok and its suburban areas, and to receive the desired target groups of all transport modes, as seen in Figure 5.3-1 and Table 5.3-3.

PCBK / SEA / CMCL / SYSTRA MVA 5-6 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Figure 5.3-1 Locations of survey for Modal Split Model Table 5.3-3 Survey Locations for Modal Split Model Location Place Location Place 1 Future Park Rangsit 9 Bearing Station 2 Don Mueang Railway Station 10 Bang Na – Trad Highway 3 Mo Chit 2 Bus Terminal 11 Samut Prakarn City Hall 4 Bang Sue Railway Station 12 Charan Sanitwong Road 5 Ratchadapisek Road 13 New Southern Bus Terminal 6 Sapan Hua Chang Pier 14 Bang Yai 7 Hua Lampong 15 Ban Phachi Railway Junction 8 Si Phraya Pier 16 Chachoengsao Railway Station

The data that has high influence on the modal split of the target groups, such as trip purpose, travel time, income, number of possessed cars, travel cost, waiting time for services, time spent on board, etc., will be subjected to modal split analysis according to the type of vehicle ownership, and trip purpose. The analysis results will then be used to update the Modal Split Model. The sample of analysis results is illustrated in Table 5.3-4.

PCBK / SEA / CMCL / SYSTRA MVA 5-7 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 5.3-4 Example of Analysis results of Modal Split Model (0 Veh.) - HBW Variable Symbol Value t value Sig. Alternative Specific Constant - ASC สัมประสิทธิ์

ASC bus ASCBUS 1.3368 1.855 0.064 ASC van ASCVAN 1.0096 1.450 0.147

ASC taxi ASCPARA 1.4553 2.026 0.043

ASC urban rail transit ASCRRT 2.8459 3.619 0.000

ASC urban and suburban rail transit ASCSRT 3.1030 3.480 0.001

ASC passenger boat ASCVES - - - Variables of travel characteristics Access time to the transport (minutes) ACCTIME -0.0194 -4.302 0.000 Access cost to the transport (Baht) ACCCOST -0.0120 -2.752 0.006 Waiting time for the transport (minute) WTIME -0.0386 -4.345 0.000 On board time (minute) OBTIME -0.0061 -1.618 0.106 Cost while on board (Baht) FARE -0.0098 -2.103 0.036 Variables of economic and social characteristics

Monthly income (1,000 Baht) bus INCBUS -0.0787 -1.635 0.102

Monthly income (1,000 Baht) van INCVAN -0.0171 -0.371 0.711

Monthly income (1,000 Baht) taxi INCPARA -0.1272 -3.068 0.002

Monthly income (1,000 Baht) urban rail transit INCRRT -0.1939 -3.681 0.000 Monthly income (1,000 Baht) urban and suburban INC -0.3007 -4.651 0.000 rail transit SRT LL -652.781 LL(0) -871.537 Likelihood Ratio Index (2) 0.251 % Correct 78.09 0 Veh – HBW

VBUS = 1.3368 - 0.0194 x ACCTIME– 0.0120 x ACCCOST - 0.0386 x WTIME- 0.0061 x OBTIME- 0.0098 x FARE- 0.0787 x INCBUS VVAN = 1.0096 - 0.0194 x ACCTIME– 0.0120 x ACCCOST - 0.0386 x WTIME- 0.0061 x OBTIME- 0.0098 x FARE- 0.0171 x INCVAN

VPARA = 1.4553 - 0.0194 x ACCTIME– 0.0120 x ACCCOST - 0.0386 x WTIME- 0.0061 x OBTIME - 0.0098 x FARE – 0.1272 x INCPARA VRRT = 2.8459 - 0.0194 x ACCTIME– 0.0120 x ACCCOST - 0.0386 x WTIME- 0.0061 x OBTIME - 0.0098 x FARE- 0.1939 x INCRRT VSRT = 2.8459 - 0.0194 x ACCTIME– 0.0120 x ACCCOST - 0.0386 x WTIME- 0.0061 x OBTIME- 0.0098 x FARE- 0.3007 x INCSRT VVES = - 0.0194 x ACCTIME– 0.0120 x ACCCOST - 0.0386 x WTIME- 0.0061 x OBTIME - 0.0098 x FARE

PCBK / SEA / CMCL / SYSTRA MVA 5-8 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

5.3.4 Traffic Assignment Model 5.3.4.1 The update of parameters for the model In the development, improvement, and maintenance of transport and traffic model of this study, the parameters used in eBUM have been examined to see whether they are still appropriate to current traffic conditions or not. Any parameters that have been changed so much that they cannot be used in the analysis and forecast of the model will be adjusted and updated so that they are suitable for the current use. The results of parameters in eBUM after the examination are summarized as follows: (1) Passenger Car Unit (PCU): PCU factors received from survey and PCU used in eBUM are slightly different (except for the motorcycle, which has high deviation during surveyed). So, it is advisable that the PCU factors in this model are still appropriate and do not need any update. (2) Speed-Flow Curve on different types of roads: According to the comparison of results in eBUM on different types of roads with the results of survey in this study, it is found that both Speed- Flow Curves are quite different. Considering the number of roads to be surveyed to represent each type of roads in this study, it is found to be inadequate. Relationships derived from this survey, if applied in the Model directly, may not result in proper traffic situation accordingly. Hence, it is advisable that there should be a specific survey project to collect more data in order to receive enough Speed-Flow data that can represent each type of roads in the study area. However, it is recommended in this study to use existing Speed-Flow curves in eBUM until appropriate data on Speed-Flow has been collected. (3) The update of Vehicle Operating Cost (VOC): Since the fuel cost is a main factor that has great effect on the structure of VOC, the update of VOC is conducted based on the comparison of prices of each fuel type in 2013 and those in 2012. Referring to the data, it is found that the average prices of fuel in 2013 are lower than those in 2012. Once calculating all average differences of each fuel type, it is obvious that the fuel prices decrease about 0.60%. Therefore, the consultants use the said data to update VOC for the base year 2013. (4) The update of Value of Time (VOT): The update in this case is conducted by comparing secondary data about economy with that of the year 2012, e.g. Consumer Price Index (CPI) from Bureau of Trade and Economic Indices, inflation rate from Bank of Thailand, together with economic factors used in eBUM. The said factors are multiplied by VOT of each future year, and the average results from this indicate that the rate is increasing by 2.25%. Thus, the said rate is used to update VOT for the base year 2013. The results of updated VOC and VOT are shown in Table 5.3-5.

PCBK / SEA / CMCL / SYSTRA MVA 5-9 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 5.3-5 The results of updating VOC and VOT VOC (Baht/km.) VOC (Baht/min.) Vehicle type 2012 2013 2012 2013 Private Car (PC) 2.995 2.977 1.25 1.28 Motorcycle (MC) 0.240 0.239 0.61 0.62

5.3.5 The Model Calibration The Consultants have calibrated the model by comparing the data obtained from the model along Screen Line with the data derived from the survey, i.e. traffic volume along the North-South corridor and traffic volume along the East-West corridor. Whereby, the calibration of model in the base year 2012 is the calibration only along the North-South corridor since in 2012 there was a survey only in that area. The deviation as to the types of roads is acceptable as shown in Table 5.3-6. Meanwhile, the calibration of model in the base year 2012 shows that the differences are still acceptable as seen from Table 5.3-7 to Table 5.3-17, respectively. Table 5.3-6 Acceptable deviation based on road types Road type % Acceptable deviation Expressway +/- 10 Major Arterial +/- 15 Minor Arterial +/- 25 Source: Travel Model Improvement Program, Federal Highway Administration, U.S. Department of Transport Table 5.3-7 Model Calibration for Traffic Volume along the North-South Screen Line in 2012 From Survey From Survey From Model Difference From Model Difference Survey Site Location am peak pm peak (PCU/hour) (%) (PCU/hour) (%) (PCU/hour) (PCU/hour) MB – NS01 Bridge over Chao Phraya 3,179 2,871 -9.70 2,396 2,668 11.34 River – Western Ring Road MB – NS02 Pathum Thani Bridge 4,545 4,099 -9.81 4,265 4,704 10.29 MB – NS03 Pathum Thani Bridge II 4,481 4,124 -7.98 4,474 4,965 10.98 MB – NS04 Nondhaburi Bridge 2,825 3,212 13.68 3,723 4,235 13.75 MB – NS05 Rama IV Bridge 5,073 4,474 -11.81 4,196 4,096 -2.37 MB – NS06 Phra Nangklao Bridge 8,801 7,576 -13.92 8,097 6,857 -15.32 (New) MB – NS07 Phra Nangklao Bridge 2,829 3,117 10.19 2,855 3,143 10.07 (Old) MB – NS08 Rama V Bridge 3,828 4,252 11.07 4,360 4,931 13.09 MB – NS09 Rama VII Bridge 5,495 4,745 -13.65 5,763 5,774 0.19 MB – NS11 Krungthon Bridge 5,384 5,616 4.31 5,236 4,977 -4.95

PCBK / SEA / CMCL / SYSTRA MVA 5-10 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

From Survey From Survey From Model Difference From Model Difference Survey Site Location am peak pm peak (PCU/hour) (%) (PCU/hour) (%) (PCU/hour) (PCU/hour) MB – NS12 Rama VIII Bridge 5,141 5,863 14.04 5,320 4,883 -8.21 MB – NS13 Phra Pinklao Bridge 10,059 8,815 -12.37 15,335 13,493 -12.01 MB – NS14 Memorial Bridge 3,963 3,650 -7.89 3,765 3,645 -3.18 MB – NS15 Phra Pokklao Bridge 13,025 11,835 -9.14 12,317 10,472 -14.98 MB – NS16 Taksin Bridge 11,307 9,826 -13.10 8,055 6,942 -13.82 MB – NS17 Krungthep Bridge 4,954 5,542 11.88 3,242 2,855 -11.94 MB – NS18 Rama III Bridge 10,980 9,735 -11.34 7,063 6,034 -14.56 MB – NS19 Rama IX Bridge 9,315 9,833 5.56 9,661 10,620 9.92 MB – NS20 Bhumipol Bridge 4,589 5,027 9.55 6,078 5,772 -5.04 MB – NS21 Bhumipol Bridge II 4,843 5,344 10.34 4,652 5,120 10.06 MB – NS22 Kanchana Pisek Bridge 5,255 4,513 -14.11 4,722 5,213 10.40 Total 129,871 121,478 -6.46 125,575 121,397 -3.33 Source: Survey by the Consultants Table 5.3-8 Model Calibration for Average Traffic Volume on Expressway System in 2012 Expressway System PCU/day From Model (PCU/day) Difference (%) Chalong Rat (Ram Indra – At Narong) 170,969 147,248 -13.87 Burapa Withi (Bangna – Chonburi) 123,710 142,837 15.46 Ram Indra – Outer Ring road 16,000 18,208 13.80 SriRat Expressway (2nd Stage) 664,781 633,877 -4.65 Kanchana Pisek Expressway (Bang Pli-Suk Sawad) 226,597 203,874 -10.03 Udorn Ratthaya (Bang Pa-In – Pak Kred) 65,126 64,656 -0.72 Chalerm Mahanakorn (1st Stage) 380,053 307,339 -19.13 Total 1,647,236 1,518,039 -7.84 Source: Revenue Unit, Bangkok Expressway Company Limited-BECL

PCBK / SEA / CMCL / SYSTRA MVA 5-11 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 5.3-9 Model Calibration for Average MRT Ridership in 2012 Secondary Data From Model Station Difference (%) (person-trip/day) (person-trip/day) Bangsue 8,118 8,898 9.62 Kampangpetch 3,648 3,756 2.95 Chatuchak 14,139 13,876 -1.86 Paholyothin 14,859 16,396 10.34 Ladprao 13,582 15,255 12.32 Ratchada Pisek 6,582 7,044 7.02 Sutthisarn 11,146 9,431 -15.39 Huay Kwang 16,900 13,589 -19.59 Thai Cultural Center 13,070 13,381 2.38 Rama IX 17,689 14,634 -17.27 Petchburi 15,989 15,679 -1.94 Sukhumwit 30,403 24,496 -19.43 Sirikitti National Conference Center 11,345 11,922 5.08 Klong Toei 1,841 1,873 1.76 Lumpini 8,434 8,470 0.43 Silom 15,834 13,545 -14.46 Samyan 8,746 7,627 -12.80 Hualampong 13,074 13,941 6.64 Total 225,399 228,340 1.30 Source: Annual Report of Bangkok Metro Public Company Limited - BMCL

Table 5.3-10 Model Calibration for Average BTS Ridership in 2012 Secondary Data From Model Difference (%) (person-trip/day) (person-trip/day) 530,422 578,165 9.00 Source: http://bts-th.listedcompany.com/bts_ridership.html

Table 5.3-11 Model Calibration for Average Airport Rail Link - ARL Ridership in 2012 Secondary data From Model Difference (%) (person-trip/day) (person-trip/day) 40,811 43,761 7.23 Source: Revenue Collecting Division, SRT

PCBK / SEA / CMCL / SYSTRA MVA 5-12 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 5.3-12 Average Travel Speed in BMR by area in 2012 Average Period Area PCU-Km. PCU-Hr. Speed (km. /hr.) Whole Day Inner Ring Road 24,741,068 832,310 29.7 Outer Ring Road 73,246,269 1,958,335 37.4 Bangkok Metropolitan and Surrounding Area 183,630,687 4,666,342 39.4 AM peak Inner Ring Road 2,173,221 176,301 12.3 Outer Ring Road 6,202,978 338,392 18.3 Bangkok Metropolitan and Surrounding Area 14,403,210 694,170 20.8 PM peak Inner Ring Road 1,929,545 124,950 15.4 Outer Ring Road 5,706,175 265,589 21.5 Bangkok Metropolitan and Surrounding Area 13,576,436 566,714 24.0 Source: eBUM base year 2012 Table 5.3-13 Numbers of Trips in each area in 2012 Trip Area Number of trips (1,000 person-trip/day) Within Inner Ring Road (IRR) 1,316 In-Out of Inner Ring Road (IRR) 3,592 Within Outer Ring Road (ORR) 10,314 In-Out of Outer Ring Road (IRR) 6,468 Between IRR and out-off Ring Road 2,186 Out of Ring Road 5,236 Source: eBUM base year 2012 Table 5.3-14 Modal Splits in 2012 Unit: 1,000 person-trip/day Type of vehicle ownership HBW HBE HBO NHB Total Non vehicle 884 418 724 51 2,077 1 motorcycle 2,189 763 787 341 4,080 1 private car 4,904 1,511 1,512 658 8,585 More than 1 vehicle 3,725 1,569 999 625 6,918 Total 11,703 4,261 4,022 1,675 21,660 External Trips 284 Special Generators 852 Total 22,796 Source: eBUM base year 2012 Note: HBW: Home Based Work HBE: Home Based Education HBO: Home Based Other NHB: Non Home Based

PCBK / SEA / CMCL / SYSTRA MVA 5-13 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 5.3-15 Trip volume categorized by type of vehicle ownership and trip purposes with no transfer to public transport system in 2012 Unit: 1,000 person-trip/day Proportion Type of vehicle Proportion of Public Total Private of public ownership private (%) transport transport (%) Non vehicle 2,077 224 10.78 1,853 89.22 1 motorcycle 4,080 2,302 56.41 1,778 43.58 1 private car 8,585 5,865 68.32 2,720 31.68 More than 1 vehicle 6,918 5,554 80.28 1,364 19.72 Total 21,660 13,945 64.38 7,715 35.62 External Trips 284 284 100.00 - - Special Generators 852 418 49.06 434 50.94 Total 22,796 14,647 64.25 8,149 35.75 Source: eBUM base year 2012 Table 5.3-16 Major trip proportion including transfer and no transfer to public transport system, categorized by type of travel in 2012 Unit: 1,000 person-trip/day Private Public Transport 2012 Total Trip volume % Trip volume % Transfer System 14,647 48.02 15,856 51.98 30,503 No Transfer System 14,647 64.25 8,149 35.75 22,796 Source: eBUM base year 2012 Table 5.3-17 Numbers of passengers using public transport in 2012 (including transfer to public transport system) Number of passengers Mode (1,000 person-trip/day) Green Line BTS 677 Blue Line MRT 243 Red Line Airport Rail Link 49 Boat 112 Bus 13,999 Train 75 Van 701 Total 15,856 Source: eBUM base year 2012

PCBK / SEA / CMCL / SYSTRA MVA 5-14 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

The calibration results with the model of base year 2013 will be calibrated with the survey data from two lines. The survey data along the North-South in 2012 is updated to be the data of the year 2013 with the increase of traffic volume across the Chao Phraya River, the data of which was surveyed and collected by such agencies as OTP, SMC, BMA, DRR, etc. Anyway, the survey data along the East-West was derived from the survey in July 2013, of which the calibration results of model in base year 2013 are in Table 5.3-18 and Table 5.3-19. It is clearly seen that the differences between the model and the survey data are acceptable. Thereby, the calibration results and evaluation of model are shown from Table 5.3-20 to Table 5.3-28, and from Figure 5.3-2 and Figure 5.3-3, respectively. Table 5.3-18 Model Calibration for Traffic Volume along the North-South Screen Line in 2013 Am peak (pcu/hr) Difference Pm peak (pcu/hr) Difference Site Location Survey Model (%) Survey Model (%) MB – NS01 Bridge over Chao Phraya River 3,274 2,761 -15.70 2,468 2,566 4.00 – Western Ring Road MB – NS02 Pathum Thani Bridge 4,681 3,947 -15.70 4,393 4,380 -0.30 MB – NS03 Pathum Thani Bridge II 4,615 3,891 -15.70 4,608 4,080 -11.50 MB – NS04 Nondhaburi Bridge 2,910 2,454 -15.70 3,835 3,868 0.90 MB – NS05 Rama IV Bridge 5,225 6,100 16.70 4,322 4,662 7.90 MB – NS06 Phra Nangklao Bridge (New) 9,065 10,583 16.70 8,340 9,320 7.90 MB – NS07 Phra Nangklao Bridge (Old) 2,914 3,402 16.70 2,941 2,849 7.90 MB – NS08 Rama V Bridge 3,943 4,603 16.70 4,491 4,844 7.90 MB – NS09 Rama VII Bridge 5,660 6,462 14.20 5,936 6,403 7.90 MB – NS11 Krungthon Bridge 5,546 6,332 14.20 5,393 5,487 1.70 MB – NS12 Rama VIII Bridge 5,295 6,046 14.20 5,480 5,576 1.80 MB – NS13 Phra Pinklao Bridge 10,361 11,830 14.20 16,166 16,449 1.80 MB – NS14 Memorial Bridge 4,082 4,661 14.20 3,878 4,280 10.40 MB – NS15 Phra Pokklao Bridge 13,416 15,318 14.20 12,687 11,427 -9.90 MB – NS16 Taksin Bridge 11,646 13,297 14.20 8,297 7,742 -6.70 MB – NS17 Krungthep Bridge 5,103 4,644 -9.00 3,339 3,116 -6.70 MB – NS18 Rama III Bridge 11,309 10,291 -9.00 7,275 6,789 -6.70 MB – NS19 Rama IX Bridge 9,594 8,731 -9.00 9,951 9,286 -6.70 MB – NS20 Bhumipol Bridge 4,727 4,302 -9.00 6,260 7,408 18.30 MB – NS21 Bhumipol Bridge II 4,988 4,539 -9.00 4,792 5,427 13.30 MB – NS22 Kanchana Pisek Bridge 5,413 4,926 -9.00 4,864 4,719 -3.00 Total 133,767 139,120 4.00 129,713 130,678 0.70 Source: Expand from Survey in 2012

PCBK / SEA / CMCL / SYSTRA MVA 5-15 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 5.3-19 Model Calibration for Traffic Volume along the East-West Screen line in 2013 Am peak (pcu/hr) Difference Pm peak (pcu/hr) Difference Site Location Survey Model (%) Survey Model (%) MB – EW 01 Outer Ring Road (Eastern) 6,198 6,638 7.10 7,187 7,646 6.40 MB – EW 02 Sri Burapa 606 618 2.00 678 669 -1.30 MB – EW 03 Puang Siri 1,518 1,472 -3.00 1,372 1,335 -2.70 MB – EW 04 Sri Nakarin 3,730 3851 3.20 3,739 4,293 14.80 MB – EW 05 Soi Lad Prao 130 673 590 -12.30 802 734 -8.50 MB – EW 06 Soi Mahad Thai 1,163 1,300 -19.40 1,142 1,250 9.50 MB – EW 07 Soi Ramkamhaeng 53 772 795 3.00 869 763 -12.20 MB – EW 08 Soi Ramkamhaeng 43/1 871 854 -2.00 958 820 -14.40 MB – EW 09 Soi Wat Thep Lila 1,097 1,070 -2.50 1,269 1,360 7.20 MB – EW 10 Soi Ramkamhaeng 21 649 601 -7.40 754 839 11.30 MB – EW 11 Soi Ramkamhaeng 9 194 180 -7.20 215 189 -12.10 MB – EW 12 Chalong Rat Expressway 5,926 5760 -2.80 7,632 6,718 -12.10 MB – EW 13 Rama 9 4,626 4497 -2.80 5,697 5,015 -12.10 MB – EW 14 Kampangpetch 7 1,000 972 -2.80 1,256 1,106 -11.90 MB – EW 15 Petchburi 2,567 2914 13.50 3,550 4,152 17.00 MB – EW 16 Prasert Manukit Road 3,623 4113 13.50 3,689 4,335 17.50 MB – EW 17 Petchburi 38/1 2,617 2971 13.50 2,761 2,214 -19.80 MB – EW 18 Sukhumwit 55 1,692 1,448 -14.40 1,588 1,304 -17.90 MB – EW 19 Asoke Montri 1,621 1,636 0.90 1,665 1,848 11.00 MB – EW 20 Sukhumwit 3 791 798 0.90 791 878 11.00 MB – EW 21 Chalerm Mahanakorn 10,601 10,696 0.90 9,692 10,755 11.00 Expressway MB – EW 22 Witthayu 2,299 1,937 -15.70 2,550 2,233 -12.40 MB – EW 23 Chidlom 1,930 1646 -14.70 1,930 1,837 -4.80 MB – EW 24 Ratchadamri 5,267 5,521 4.80 6,908 6,384 -7.60 MB – EW 25 Phaya Thai 5,473 6168 12.70 4,732 5,499 16.20 MB – EW 26 Banthat Thong 3,057 3,314 8.40 3,687 4,284 16.20 MB – EW 27 Sri Rat Expressway 11,083 10065 -9.20 11,002 9,825 -10.70 MB – EW 28 Rama 6 4,738 4,522 -4.60 4,952 4,440 -10.30 MB – EW 29 Krung Kasem 2,024 2,273 12.30 2,504 2,330 -6.90 MB – EW 30 Chakkrapaddipong 7,446 7,660 2.90 7,042 8,077 8.50 MB – EW 31 Raj Damnoen Klang 3,817 3,927 2.90 4,093 4,141 8.50 MB – EW 32 Prachathipatai 4,573 4,705 2.90 3,918 4,961 8.50 MB – EW 33 Samsen 4,488 3559 -20.70 4,177 3,196 -23.50 MB – EW 34 Phra Pinklao 10,059 10,562 5.00 9,246 10,338 11.80 MB – EW 35 Arun Amarin 6,384 6145 -3.70 6,784 5,905 -13.00

PCBK / SEA / CMCL / SYSTRA MVA 5-16 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Am peak (pcu/hr) Difference Pm peak (pcu/hr) Difference Site Location Survey Model (%) Survey Model (%) MB – EW 36 Charan Sanitwong 6,403 6,311 -1.40 5,477 6,256 14.20 MB – EW 37 Sirindhorn 20,141 19192 -4.70 19,435 18,892 -2.80 MB – EW 38 Ratchapruek 3,881 4797 23.60 5,131 5,168 0.70 MB – EW 39 Outer Ring Road (West) 16,630 16,429 -1.20 15,820 16,217 2.50 Total 172,228 172,507 -0.10 176,694 178,206 0.90 Source: Survey by the Consultants Table 5.3-20 Model Calibration for Average Traffic Volume on Expressway System in 2013 From Model Expressway System PCU/day Difference (%) (PCU/day) Chalong Rat (Ram Indra – At Narong) 181,520 185,232 2.00 Burapa Withi (Bangna – Chonburi) 137,266 95,262 -30.60 Ram Indra – Outer Ring road 17,600 18,253 3.70 SriRat Expressway (2nd Stage) 670,832 625,893 -6.70 Kanchana Pisek Expressway (Bang Pli-Suk Sawad) 207,227 260,948 25.90 Udorn Ratthaya (Bang Pa-In – Pak Kred) 72,764 77,048 5.90 Chalerm Mahanakorn (1st Stage) 373,889 406,006 8.60 Total 1,661,098 1,668,642 0.50 Source: Revenue Unit, Bangkok Expressway Company Limited-BECL Table 5.3-21 Model Calibration for Average MRT Ridership in 2013 Secondary Data From Model Station Difference (%) (person-trip/day) (person-trip/day) Bangsue 9,199 9,172 -0.29 Kampangpetch 1,997 2,111 5.71 Chatuchak 14,912 15,038 0.84 Paholyothin 15,330 15,376 0.30 Ladprao 15,371 15,555 1.20 Ratchada Pisek 7,714 7,723 0.12 Sutthisarn 13,457 13,417 -0.30 Huay Kwang 18,955 18,940 -0.08 Thai Cultural Center 14,745 14,723 -0.15 Rama IX 19,529 19,557 0.14 Petchburi 19,782 19,413 -1.87 Sukhumwit 34,236 34,703 1.36 Sirikitti National 9,176 9,181 0.05 Conference Center Klong Toei 2,142 2,155 0.61 Lumpini 10,647 10,640 -0.07

PCBK / SEA / CMCL / SYSTRA MVA 5-17 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Secondary Data From Model Station Difference (%) (person-trip/day) (person-trip/day) Silom 19,732 19,857 0.63 Samyan 10,058 10,555 -0.03 Hualampong 14,800 14,790 -0.07 Total 251,782 252,406 0.25 Source: Annual Report of Bangkok Metro Public Company Limited - BMCL Table 5.3-22 Model Calibration for Average BTS Ridership in 2013 Secondary Data From Model Station Difference (%) (person-trip/day) (person-trip/day) Mo Chit 44,711 44,327 -0.86 Saphan Khwai 9,665 9,593 -0.75 Ari 13,400 13,306 -0.70 Sanam Pao 4,134 4,100 -0.83 Victory Monument 51,601 51,243 -0.69 Phaya Thai 25,536 24,971 -2.21 Ratchathewi 12,317 12,195 -0.99 Siam 67,257 66,653 -0.90 Chit Lom 30,483 30,160 -1.06 Phloen Chit 19,175 18,955 -1.15 Nana 18,154 18,139 -0.08 Asok 51,952 51,277 -1.30 Phrom Phong 24,743 24,616 -0.51 Thong Lo 14,261 14,157 -0.73 Ekkamai 15,701 15,547 -0.98 Phra Khanong 11,649 11,554 -0.81 On Nut 30,271 29,971 -0.99 Bang Chak 7,585 7,514 -0.94 Punnawithi 7,150 7,049 -1.41 Udom Suk 17,040 16,966 -0.43 Bang Na 4,274 4,536 6.13 Bearing 21,001 20,330 -3.20 National Stadium 21,802 21,612 -0.87 Ratchadamri 5,740 5,716 -0.41 Sala Daeng 32,465 32,172 -0.90 Chong Nonsi 23,491 23,354 -0.58 Surasak 12,344 12,224 -0.97 Saphan Taksin 19,441 19,208 -1.20

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Secondary Data From Model Station Difference (%) (person-trip/day) (person-trip/day) Krung Thon Buri 10,878 10,799 -0.73 Wongwian Yai 18,570 18,383 -1.01 Pho Nimit 3,136 3,116 -0.65 Talat Phlu 6,502 6,474 -0.43 Wutthakat 6,486 6,434 -0.80 Bang Wa 17,084 16,955 -0.76 Total 680,000 673,606 -0.94 Source: http://bts-th.listedcompany.com/bts_ridership.html Table 5.3-23 Model Calibration for average Airport Rail Link- ARL Ridership in 2013 Station Secondary Data From Model Difference (%) (person-trip/day) (person-trip/day) Phaya Thai 12,787 12,960 1.35 Ratchaprarop 3,213 3,162 -1.59 Makkasan 4,190 4,137 -1.26 Ramkamhaeng 4,458 4,571 2.53 Huamark 5,064 5,116 1.03 Ban Thapchang 1,860 1,910 2.69 Ladkrabang 6,051 6,179 2.12 Suvannabhumi 8,342 8,539 2.36 Total 45,965 46,574 1.32 Source: Revenue Collecting Division, SRT Table 5.3-24 Average Travel Speed in BMR by area in 2013 Average Speed Period Area PCU-Km. PCU-Hr. (km./hr.) Whole Day Inner Ring Road 26,247,976 923,648 28.4 Outer Ring Road 80,367,315 2,236,024 35.9 Bangkok Metropolitan and Surrounding Area 203,936,979 5,333,793 38.2 Bangkok Metropolitan and Surrounding Area + 2 provinces 233,781,354 5,963,105 39.2 AM peak Inner Ring Road 2,274,853 193,080 11.8 Outer Ring Road 6,770,371 394,121 17.2 Bangkok Metropolitan and Surrounding Area 16,537,740 827,916 20.0 Bangkok Metropolitan and Surrounding Area + 2 provinces 18,923,014 886,666 21.3 PM peak Inner Ring Road 2,017,599 136,970 14.7 Outer Ring Road 6,193,621 304,204 20.4 Bangkok Metropolitan and Surrounding Area 15,340,444 664,850 23.1 Bangkok Metropolitan and Surrounding Area + 2 provinces 17,460,370 718,592 24.3 Source: eBUM base year 2013

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Table 5.3-25 Modal Splits in 2013 Unit: 1,000 person-trip/day Type of vehicle ownership HBW HBE HBO NHB total Non vehicle 876 415 725 58 2,074 1 motorcycle 2,336 825 981 598 4,740 1 private car 5,228 1,623 1,850 1,056 9,757 More than 1 vehicle 4,049 1,710 1,150 765 7,674 Total 12,489 4,573 4,706 2,477 24,245 External Trips 297 Special Generators 882 Total 25,424 Source: eBUM base year 2013 Remark: HBW: Home Based Work HBE: Home Based Education HBO: Home Based Other NHB: Non Home Based Table 5.3-26 Trip volume categorized by type of vehicle ownership and trip purposes with no transfer to public transport system in 2013 Unit: 1,000 person-trip/day Proportion Type of vehicle Proportion of Public Total Private of public ownership private (%) transport transport (%) Non vehicle 2,074 225 10.85 1,848 89.15 1 motorcycle 4,740 2,947 62.17 1,793 37.83 1 private car 9,757 6,959 71.32 2,798 28.68 More than 1 vehicle 7,674 6,212 80.96 1,461 19.04 Total 24,245 16,343 67.41 7,901 32.59 External Trips 297 298 100.00 0 0.00 Special Generators 882 432 48.98 450 51.02 Total 25,424 17,073 67.15 8,351 32.85 Source: eBUM base year 2013

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Table 5.3-27 Major trip proportion including transfer and no transfer to public transport system, categorized by type of travel in 2013 Unit: 1,000 person-trip/day Private Public Transport Area 2013 Total Trip volume % Trip volume % Bangkok and Transfer System 15,195 51.90 14,082 48.10 29,277 metropolitan No Transfer System 15,195 67.15 7,432 32.85 22,627 areas Bangkok and Transfer System 17,073 51.90 15,822 48.10 32,895 metropolitan No Transfer System 17,073 67.15 8,351 32.85 25,424 areas + 2 provinces Source: eBUM base year 2013

Table 5.3-28 Numbers of passengers using public transport in 2013 (including transfer to public transport system) Number of passengers Mode (1,000 person-trip/day) Green Line BTS 707 Blue Line MRT 263 Red Line Airport Rail Link 49 Boat 115 Bus 13,941 Train 82 Van 665 Total 15,822 Source: eBUM base year 2013

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1,000,000

900,000

800,000

700,000

- 600,000

trip/day)

500,000

400,000

300,000

200,000

No. of trip (person trip of No. 100,000

Travel Distance (kilometer)

Figure 5.3-2 Trip distribution based on distance

300,000

250,000

200,000

trip/day)

150,000

100,000

50,000

No. of trip (person trip of No.

Travel Time ( minute )

Figure 5.3-3 Trip distribution based on trip duration

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5.3.6 Forecasting of future transport Transport plans and projects of relevant agencies as well as future mass rapid transit projects have been collected in order to analyze future traffic situation. Analysis results are illustrated in Table 5.3-29 to Table 5.3-37 whereas desired lines between each sectors are displayed in Figure 5.3-4 to Figure 5.3-10. Table 5.3-29 Forecasted traffic during morning peak AM Peak Traffic Year (Veh-Km) (Veh-Hr) Speed (km/hr) 2012 14,403,210 694,170 20.8 2013 18,923,014 886,666 21.3 2017 21,653,492 1,197,014 18.1 2022 24,600,003 1,491,608 16.5 2027 27,457,154 1,755,838 15.6 2032 30,457,445 2,181,860 14.0 2037 33,679,692 2,672,468 12.6 Source: eBUM Remark: Veh-km = vehicles x distance of travel Veh-Hr = vehicles x time of travel Table 5.3-30 Forecasted traffic during evening peak PM Peak Traffic Year (Veh-Km) (Veh-Hr) Speed (km/hr) 2012 13,576,436 566,714 24.0 2013 17,460,370 718,592 24.3 2017 19,942,745 933,898 21.4 2022 22,648,795 1,166,771 19.4 2027 25,292,018 1,366,411 18.5 2032 28,057,431 1,709,719 16.4 2037 31,047,970 2,129,595 14.6 Source: eBUM Remark: Veh-km = vehicles x distance of travel Veh-Hr = vehicles x time of travel

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Table 5.3-31 Forecasted traffic all day Daily Traffic Year (Veh-Km) (Veh-Hr) Speed (km/hr) 2012 183,630,687 4,666,342 39.4 2013 233,781,354 5,963,105 39.2 2017 267,036,238 7,357,397 36.3 2022 305,539,025 9,008,141 33.9 2027 340,427,690 10,083,075 33.8 2032 378,486,075 12,001,841 31.5 2037 421,271,012 14,660,999 28.7 Source: eBUM Remark: Veh-km = vehicles x distance of travel Veh-Hr = vehicles x time of travel

2012

Figure 5.3-4 Desired line within Bangkok and Vicinity Area in 2012 (including 2 provinces)

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2013

Figure 5.3-5 Desired line within Bangkok and Vicinity Area in 2013 (including 2 provinces)

2017

Figure 5.3-6 Desired line within Bangkok and Vicinity Area in 2017 (including 2 provinces)

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2022

Figure 5.3-7 Desired line within Bangkok and Vicinity Area in 2022 (including 2 provinces)

2027

Figure 5.3-8 Desired line within Bangkok and Vicinity Area in 2027 (including 2 provinces)

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2032

Figure 5.3-9 Desired line within Bangkok and Vicinity Area in 2032 (including 2 provinces)

2037

Figure 5.3-10 Desired line within Bangkok and Vicinity Area in 2037 (including 2 provinces)

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Table 5.3-32 Forecasted proportion of main trip with no transfer to public transport system Unit: 1,000 person-trip/day Private Public Transport Year Total Private Public Transport (percentage) (percentage) 2012 22,796 14,647 64.25% 8,151 35.75% 2013 25,424 17,074 67.16% 8,351 32.84% 2017 27,618 19,110 69.19% 8,508 30.81% 2022 30,320 21,272 70.16% 9,047 29.84% 2027 32,986 23,410 70.97% 9,576 29.03% 2032 35,764 25,550 71.44% 10,214 28.56% 2037 38,570 27,780 72.03% 10,790 27.97% Source: eBUM Remark: With no transfer to public transport system Table 5.3-33 Forecasted proportion of main trip including transfer to public transport system Unit: 1,000 person-trip/day Private Public Transport Year Total Private Public Transport (percentage) (percentage) 2012 30,503 14,647 48.01% 15,856 51.98% 2013 32,895 17,074 51.90% 15,822 48.10% 2017 35,669 19,110 53.58% 16,559 46.42% 2022 39,987 21,272 53.20% 18,715 46.80% 2027 43,411 23,410 53.93% 20,001 46.07% 2032 46,937 25,550 54.43% 21,387 45.57% 2037 50,531 27,780 54.98% 22,751 45.02% Source: eBUM Remark: Including transfer to public transport system Table 5.3-34 Forecasted numbers of passengers using major public transport system (Person Trips) Passengers (1,000 person-trip/day) Mode 2012 2013 2017 2022 2027 2032 2037 MRT 969 1,019 2,070 3,518 3,950 4,769 5,425 Public Bus 13,999 13,941 13,733 14,727 15,566 16,120 16,765 Boats 112 115 107 111 127 156 213 Others 776 747 649 359 358 342 348 Total 15,856 15,822 16,559 18,715 20,001 21,387 22,751 Source: eBUM

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Table 5.3-35 Forecasted numbers of passengers using major public transport system (including transfer to public transport system) Passengers (1,000 person-trip/day) Mode 2012 2013 2017 2022 2027 2032 2037 BTS 677 707 857 1,298 1,450 1,622 1,869 MRT 243 263 757 883 991 1,065 1,205 ARL 49 49 217 274 319 680 760 Purple Line - 239 454 502 538 618 Orange Line - - - 233 262 261 293 Pink Line - - - 173 198 241 276 Yellow Line - - - 203 228 240 266 Gray Line - - - - - 110 124 Light Blue Line - - - - - 12 14 Boats 112 115 107 111 127 156 213 Public Bus 13,999 13,941 13,733 14,727 15,566 16,120 16,765 Trains 75 82 87 90 88 88 89 Van 701 665 562 269 270 254 259 Total 15,856 15,822 16,559 18,715 20,001 21,387 22,751 Source: eBUM Remark: Number of trips including transfer to public transport system

Table 5.3-30 Average Speed in each area Average Speed (km/hr) Time period Area 2012 2013 2017 2022 2027 2032 2037 All day Inner Ring Road 29.7 28.4 25.1 21.5 20.4 18.1 15.5 Outer Ring Road 37.4 35.9 32.7 29.9 29.9 27.2 24.1 Bangkok and Vicinity Area 39.0 38.2 35.3 32.9 32.9 30.4 27.6 Bangkok and Vicinity Area 39.4 39.2 36.3 33.9 33.8 31.5 28.7 + 2 provinces AM Peak Inner Ring Road 12.3 11.8 9.6 8.3 7.2 6.4 5.6 Outer Ring Road 18.3 17.2 14.3 12.9 12.5 10.9 9.8 Bangkok and Vicinity Area 20.5 20.0 16.9 15.4 14.6 13.0 11.7 Bangkok and Vicinity Area 20.7 21.3 18.1 16.5 15.6 14.0 12.6 + 2 provinces PM Peak Inner Ring Road 15.4 14.7 12.8 10.8 9.3 7.8 6.7 Outer Ring Road 21.5 20.4 17.5 15.5 15.0 13.3 11.5 Bangkok and Vicinity Area 23.6 23.1 20.2 18.3 17.5 15.4 13.6 Bangkok and Vicinity Area 24.0 24.3 21.4 19.4 18.5 16.4 14.6 + 2 provinces

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Table 5.3-31 Numbers of trips in each area Number of trips (1,000 person-trip/day) Trips in each area 2012 2013 2017 2022 2027 2032 2037 Within Inner Ring Road (IRR) 1,316 1,296 1,254 1,283 1,345 1,422 1,505 In-Out of Inner Ring Road (IRR) 3,592 3,576 3,732 4,013 4,333 4,660 4,976 Within Outer Ring Road (ORR) 10,313 10,524 10,823 11,648 12,562 13,503 14,357 In-Out of Outer Ring Road (IRR) 6,468 7,303 8,228 9,231 10,153 11,099 12,062 Between IRR and out-off Ring Road 2,186 2,482 2,698 2,982 3,268 3,571 3,922 Out of Ring Road 5,236 6,870 7,606 8,502 9,311 10,139 11,061

5.4 Development of innovation for the application of model

In addition to the transport and traffic model developed and updated for general use, this study has developed innovation for various applications of model; that is: (1) Development of Land Use Model (2) Development of Traffic Assignment Model Transfer from eBUM to be used in TRANUS Program (3) Application of MATSim in the planning of emergency response plan in the industrial estate of Ayutthaya (4) Model Development for Emission Analysis (5) Model Development for Fuel Consumption Analysis 5.4.1 Development of Land Use Model The Land Use Model is the simulation of interaction among households, company/firm, land developers, and the relevant government agencies within the property market, labor section, and products/services. Thereby, the relationship is in the scenario in which the land developer precedes the housing development projects and non-housing projects such as office building, department store, etc., all of which are needed by households and company/firm. The company/firm has relationship with the labor section by providing products and services, whereas the government agencies will supply the infrastructure and relevant services, supervise and define the prices of land and, if any, some infrastructure services. The model of this relationship structure enables the researchers to examine the impacts from various government policies. This study uses Cube Land program for analysis, in which the principles of Land Use Model in Cube Land will simulate the relationship between demand of people or organizations in the study areas, who want to buy pieces of land for housing or for business, and base their demand on Utility Function. Meanwhile, the people or organizations that have property for sale or rent (supply) will have the idea of Maximize profit. According to the said interactive structure, it is necessary to create Market Equilibrium

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between demand and supply of land use. This study employs Equilibrium model, a part of Cube Land, to create equilibrium. In the development of innovation for the application of model, the consultants have developed a land use model for Samutprakarn, which one of the eBUM covering 148 zones as shown in Figure 5.4-1. The model structure is illustrated in Figure 5.4-2.

Figure 5.4-1 Traffic zones in Samutprakarn

Figure 5.4-2 Structure of Land Use Model

PCBK / SEA / CMCL / SYSTRA MVA 5-31 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

5.4.2 Development of Traffic Assignment Model Transfer from eBUM to be used in TRANUS This innovation development herein is an examination of data transfer from Traffic Assignment Model of eBUM in order to do an analysis on transport and traffic by means of an open source program, TRANUS. This will greatly broaden the application of eBUM of the project. The data transfer from model emphasizes on the data in OD, which is analyzed from Traffic Assignment Model and Highway Network and Public Transport Network. The step after completion of inputting data into the model is efficiency analysts of traffic assignment model in TRANUS program. Figure 5.4-3 displays traffic analysis on preliminary network of TRANUS and Figure 5.4-4 shows comparative results with Screen Line data collected by the Project. Analysis results in Figure 5.4-4 and Figure 5.4-5 indicate that traffic data analysis of TRANUS compared with Survey data is nearly the same and acceptable reliability ( R square is greater than 0.90). In order to increase reliability of TRANUS Program. These are 2 ways to be conducted as follows: (1) Additional improvement of trip matrix either by using model in TRANUS itself or by using trip matrix, from eBUM of OTP. This trip matrix is then assigned on to, which derived the network for verification of data. (2) Develop model data in Land use Model, but this task is beyond of objectives and goals of this report. It is necessary to develop the model from the beginning step and, in TRANUS style of analysis, land use model is needed and requires more additional data than data from eBUM.

Figure 5.4-3 Example of analysis result of eBUM in TRANUS Program

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Model data (pcu/hr) data Model

Survey data (pcu/hr)

Remark: Details of survey data along Screen Line can be read more in Final Report Figure 5.4-4 Comparative Results between TRANUS Program and traffic survey Data

along Screen Line morning peak (unit : PCU/hour)

Model(pcu/hr) data

Survey data (pcu/hr)

Remark: Details of survey data along Screen Line can be read more in Final Report Figure 5.4-5 Comparative Results between TRANUS Program and traffic survey Data along Screen Line evening peak (unit : PCU/hour)

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5.4.3 Application of MATSim in the planning of emergency response plan in the industrial estate of Ayutthaya This innovation applies MATSim, an open source program, to analyze and set up emergency response plan in the industrial estate of Ayutthaya. This study will do analysis and comparison of traffic management on the main roads with an aim to evacuate people from Bang Pa In Industrial Estate in the shortest time. The traffic management herein is divided into 2 ways: 1) general evacuation through a single exit of the industrial estate, and 2) evacuation with the other emergency exit behind the industrial estate. Regarding the comparative results, it is found that the evacuation from Bang Pa-In Industrial Estate via method 2 took less time than via method 1, as seen in Figure 5.4-6 and Table 5.4-1.

Guideline 2 PCU/hr.

Guideline 1

Figure 5.4-6 Analysis results of traffic in case of emergency in the industrial estate of Ayutthaya

Table 5.4-1 Analysis results of evacuation model in case of emergency in Bang Pa-in industrial estate of Ayutthaya Time to pass Time to pass Traffic management guideline Time of Evacuation the front exit the rear exit Guideline 1 Maintain the recent 1 hour 20 minutes 45 minutes 30 - traffic line seconds Guideline 2 Add emergency exit 45 minutes 37 minutes 37 minutes behind the estate

The test of MATSim model application is a kind of simple analysis with the estimated information from secondary data. The results derived from performance and analysis test of the program are very useful especially for the emergency response plan; for example, the time used in evacuation of a great many people and the routes which are expected to have traffic congestion. The said data is very important for the traffic management plan and for the evacuation of victims upon any disaster.

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Nonetheless, the data in this test is just from estimation. With additional number of transport and appropriate update of data that matches with the real situation of Bang Pa-In Industrial Estate, the results from this analysis could be more accurate and almost similar to the real scenario. 5.4.4 Model Development for Fuel Consumption Analysis The development of Fuel Consumption Analysis Model is somewhat similar to the emission analysis in eBUM. Thereby, the emission calculation commands are changed to fuel consumption calculation commands for different types of vehicles (8 types) and 6 types of fuel. The operation process is illustrated in Figure 5.4-7. The summary of fuel consumption calculation from the model based on type of vehicle in different provinces is shown in Table 5.4-2 to Table 5.4-6, respectively.

Model eBUM Base Year 2013

Traffic Volume External Matrix OD from DOH and EXAT Year 2013 Adjustment for eBUM

Daily Traffic Volume by Link Model eBUM Fuel Consumption Link Base Fuel Analysis Consumption Calculation Parameters a and b from for Consumption 8 types of vehicles and Equation Average Link Speed

Gasoline Gasohol 91&95 E20 LPG Diesel NGV

Figure 5.4-7 Fuel Consumption Analysis Flow Chart for eBUM Development

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Table 5.4-2 Fuel Consumption categorized by vehicle type from eBUM in 2013 Unit: litre/day Type of Gasoline Gasohol CNG* LPG Diesel E20 & E85 Total Vehicle 91/95 91/95 Motorcycle - 4,442,997 - - - - 4,442,997 (MC) SAMLOR - - - 810,585 - - 810,585 (TAXI - - 825,632 1,305,742 - - 2,131,374 Passenger 973,721 4,126,425 201,181 1,958,536 3,031,007 1,414,075 11,704,946 car (PC) BUS 246,448 - 1,463,526 1,709,975 Pick up - - - - 13,522,484 - 13,522,484 Truck - - 190,903 - 6,271,061 - 6,461,964 VAN - - - - 880,340 - 880,340 Total 973,721 8,569,422 1,464,165 4,074,863 25,168,418 1,414,075 41,664,661 Remark: * unit kg. /day

Table 5.4-3 Fuel Consumption by province from Model eBUM in 2013 Unit: litre/day Gasoline Gasohol Item Province CNG* LPG Diesel E20 & E85 91/95 91/95 1 Bangkok 480,707 4,030,114 753,538 2,061,443 10,420,243 699,319 Metropolitan 2 Nomthaburi 94,549 784,690 132,534 381,085 2,276,807 137,087 3 Samut Prakarn 62,557 521,631 87,744 254,373 1,523,726 90,712 4 Pathumthani 98,712 1,004,311 150,974 418,696 3,134,611 142,877 5 Nakorn Pathom 96,351 889,338 140,059 393,120 3,301,588 139,889 6 Samut Sakorn 50,105 425,077 73,726 203,695 1,443,631 72,731 7 Ayudhya 35,372 384,816 49,170 141,135 1,336,474 51,187 8 Chachoengsao 55,367 529,443 76,416 221,318 1,731,340 80,273 Total 973,721 8,569,421 1,464,165 4,074,863 25,168,418 1,414,075 Remark: * unit kg. /day

The Consultants have collected the statistics of fuel distribution in Bangkok and metropolitan areas in 2013, provided by Department of Energy Business (Table 5.4-4), and converted them into daily average values as seen in Table 5.4-5. This is to compare with the numbers derived from the model, and the comparative results thereof are summarized in Table 5.4-6.

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Table 5.4-4 Statistics of fuel distribution at gas stations in Bangkok and metropolitan areas in 2013 Unit: 1,000 litres/year Gasoline Gasoline Gasohol Gasohol Gasohol Gasohol Basic Item Province Diesel LPG* NGV** 91 95 91 95 E20 E85 Diesel 1 Bangkok 58,370 194,274 1,061,323 1,157,469 211,433 57,269 101,209 6,169,646 255,677 - 2 Samut Prakan 11,697 11,127 104,145 74,789 31,973 6,716 18,656 575,590 108,762 - 3 Nonthaburi 1,028 12,131 91,952 77,054 38,850 7,353 3 327,058 80,716 - 4 Pathum 1,225 8,576 87,217 66,531 33,752 4,869 0 440,885 79,405 - Thani 5 Nakhon 1,219 8,570 49,807 38,971 19,462 4,216 0 275,587 53,187 - Pathom 6 Samut 506 4,577 42,089 33,458 15,594 4,185 7,975 282,021 94,724 - Sakhon 7 Phra Nakhon 936 7,389 59,482 40,285 24,439 3,031 3,970 390,922 41,270 - Si Ayutthaya 8 Chachoengsao 430 5,960 48,406 30,727 14,821 611 2,993 254,587 49,791 - Total 75,411 252,604 1,544,421 1,519,284 390,323 88,250 134,806 8,716,297 763,532 2,856,516 Source: 2013 Statistics of Department of Energy Business * Unit : 1,000 kg. ** Unit : 1,000 kg (for the whole country) Table 5.4-5 Average Fuel Sales at Gas station in Bangkok Metropolitan and Surrounding Area in 2013 Unit: litre/day Gasoline Gasohol Item Province CNG* LPG Diesel E20 & E85 91/95 91/95 1 Bangkok 692,176 6,078,882 - 1,260,873 16,903,140 736,170 2 Samut Prakan 62,533 490,230 - 536,358 1,576,959 105,996 3 Nonthaburi 36,053 463,030 - 398,049 896,049 126,584 4 Pathum Thani 26,851 421,228 - 391,588 1,207,905 105,811 5 Nakhon Pathom 26,817 243,229 - 262,294 755,033 64,873 6 Samut Sakhon 13,927 206,976 - 467,133 772,660 54,188 7 Phra Nakhon Si Ayutthaya 22,809 273,333 - 203,522 1,071,020 75,260 8 Chachoengsao 17,507 216,804 - 245,547 697,499 42,280 Total 898,673 8,393,713 7,826,071 3,765,364 23,880,265 1,311,161 Remark: * unit : kg. /day (for the whole country)

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Table 5.4-6 Proportion of Fuel Consumption between eBUM and Department of Energy Business Statistics Gasoline Gasohol Item Province CNG* LPG Diesel E20 & E85 91/95 91/95 1 Bangkok 1.44 1.51 - 0.61 1.62 1.05 2 Samut Prakan 0.66 0.62 - 1.41 0.69 0.77 3 Nonthaburi 0.58 0.89 - 1.56 0.59 1.40 4 Pathum Thani 0.27 0.42 - 0.94 0.39 0.74 5 Nakhon Pathom 0.28 0.27 - 0.67 0.23 0.46 6 Samut Sakhon 0.28 0.49 - 2.29 0.54 0.75 7 Phra Nakhon Si 0.64 0.71 - 1.44 0.80 1.47 Ayutthaya 8 Chachoengsao 0.32 0.41 - 1.11 0.40 0.53 Total 0.92 0.98 5.35 0.92 0.95 0.93

According to Table 5.4-6, the overall figures of fuel consumption for all types obtained from eBUM are nearly the same as statistics from Department of Energy Business - DEB (ranges between 0.92 – 0.98) except for CNG which is a proportion figure of fuel consumption within BMA and surrounded provinces (8 provinces) comparing with national figure since DEB has statistics for whole country only, no data for each province. However, from such figure it could be concluded that CNG consumption in BMA and surrounded provinces (8 provinces) is 18.71 percent of national consumption for CNG. Proportional details of each type of fuel consumption between model outputs and statistics from DEB are as follows:

 Benzene 91/95: have proportion of 0.92  Gasohol 91/95: have proportion of 0.98  CNG* or NGV : have proportion of 5.35 which is a proportion of CNG consumption within BMA and surrounded provinces (8 provinces) comparing with consumption for the whole country  LPG: have proportion of 0.92  Diesel: have proportion of 0.95  Gasohol E20 and E85: have proportion of 0.93 5.4.5 Model Development for Emission Analysis This innovation is intended to enhance the potential of eBUM so that it can analyze the emission caused by transport. This is conducted by using validated eBUM of the base year 2013 to analyze the traffic volume, average speed, and the emission on road network from 8 types of vehicles: motorcycle (MC), samlor taxi (Samlor), taxi (Taxi), private car (PC), bus (Bus), pickup (Pickup), truck (Truck), and van (Van). The types of pollution to be analyzed herein are hydrocarbon (HC), carbon monoxide (CO), nitrogen oxide (NOx), carbon dioxide (CO2), and particle matter (PM). The flow chart of this development is shown in Figure 5.4-8.

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Model eBUM Base Year 2013

Traffic Volume External Matrix OD from DOH and EXAT Year 2013 Adjustment for eBUM

Daily Traffic Volume by Link Model eBUM Emission Analysis for Green Transport Link Base Emission Calculation

from 8 types of vehicles and Parameters a and b for Average Link Speed Emission Equation

HC CO NO X CO 2 PM

Figure 5.4-8 Emission Analysis Flow Chart for eBUM Development

The samples of emission analysis for each type from eBUM are displayed in Figure 5.4-9 to Figure 5.4-13. Furthermore, there is an analysis of emission in terms of quantity and proportion based on different areas (province). The results that are presented in Table 5.4-7 show that Bangkok has the highest pollution of all types, 48.80%, among the 8 areas of study. This is followed by Pathum Thani, 10.80%, and Nakon Pathom, 10.50%. The province that emits the least pollution from transport is Phra Nakhon Si Ayutthaya, 4.00%, as illustrated in Figure 5.4-14. In addition, the result of pollution categorized by vehicle type was shown in

Table 5.4-8. It found that passenger car and pick-up were highest CO2 emitted amount 15.6 and 9.8 million tons/day, respectively.

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Figure 5.4-9 Emission of Hydrocarbon (HC) from eBUM

Figure 5.4-10 Emission of Carbon Monoxide (CO) from eBUM

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Figure 5.4-11 Emission of Nitrogen Oxide (NOx) from eBUM

Figure 5.4-12 Emission of Carbon Dioxide (CO2) from eBUM

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Figure 5.4-13 Emission of Particle Matter (PM) from eBUM

Table 5.4-7 Pollution emitted from the model based on different provinces in 2013

No. Province Portion HC CO NOX CO2 PM Total 1 Bangkok Volume 52.5 319.5 93.6 16,820.9 1,871.4 19,157.8 % 51.20 46.40 48.40 48.10 55.70 48.80 2 Nonthaburi Volume 8.3 61.0 17.8 3,201.1 248.1 3,536.2 % 8.10 8.90 9.20 9.20 7.40 9.00 3 Samut Prakan Volume 5.5 40.6 11.6 2,101.7 137.2 2,296.7 % 5.30 5.90 6.00 6.00 4.10 5.80 4 Pathum Thani Volume 10.9 84.7 21.3 3,796.5 341.0 4,254.4 % 10.60 12.30 11.00 10.90 10.10 10.80 5 Samut Sakhon Volume 10.7 72.6 20.1 3,725.0 310.4 4,138.8 % 10.40 10.50 10.40 10.70 9.20 10.50 6 Nakorn Prathom Volume 4.9 33.6 9.9 1,796.7 152.7 1,997.8 % 4.70 4.90 5.10 5.10 4.50 5.10 7 Phra Nakhon Si Volume 4.1 33.1 7.8 1,419.3 120.3 1,584.5 Ayutthaya % 4.00 4.80 4.00 4.10 3.60 4.00 8 Chachoengsao Volume 5.8 43.8 11.4 2,079.4 178.5 2,318.9 % 5.60 6.40 5.90 6.00 5.30 5.90 Volume 102.5 689.0 193.4 34,940.6 3,359.6 39,285.1 Total % 100.00 100.00 100.00 100.00 100.00 100.00

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Figure 5.4-14 Proportion of emission of different pollution types based on different provinces

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Table 5.4-8 Pollution emitted categorized by vehicle type Unit : Ton/day

No. Vehicle Type CO2 CO NOX HC PM 1 Passenger Car 15,560,000 154,235 75,550 20,479 1,364 2 Taxi 1,099,311 2,464 19,671 4,222 - 3 Pick up 9,823,816 28,642 44,273 6,539 4,453 4 Van 336,906 1,294 2,193 337 - 5 Motorcycle 3,500,286 461,146 12,298 54,357 - 6 Bus 2,630,683 22,251 25,124 1,739 2,438,963 7 Truck 1,434,277 4,638 11,880 995 914,778 8 Samlor 550,094 14,339 2,394 13,868 - Total (kilogram/day) 34,935,372 689,009 193,382 102,537 3,359,559 Total per year (million ton) 12.75 0.25 0.07 0.04 1.23 Source: eBUM

5.4.6 Development and application of model in Cube Cloud The transport and traffic model of OTP, which has been developed since the projects of UTDM, TDMCI–VI, TDMLI–II and TDL, is designed to be used with personal computer. The model will be more sophisticated in order to accommodate the demand of more analyses. Therefore, the computer must be of higher performance; even so, it cannot run the model immediately when testing any case studies (it takes about 8 hours for a computer with to CPU Intel Core i7 2.7 MHz to run eBUM in the project TDL 1). Moreover, while using the model to analyze the transport and traffic conditions in the study projects of OTP or of other relevant departments under Ministry of Transport, the consultants have often updated the parameters of such models as Modal Split Model. Thus, it is impossible to compare the data from different studies directly. Anyway, thanks to the advancement of today's technology Cloud Computing, this problem has already been solved. 5.4.6.1 Cloud Computing Technology The concept of Cloud Computing is the access to services from the computer on internet networks, which are available in different areas through browser. The users have no need to invest in IT infrastructure as shown in Figure 5.4-15.

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Figure 5.4-15 Concept of Cloud Computing 5.4.6.2 Cube Cloud Cube Cloud is the latest service by Citilabs with an aim to solve the problem of model running time and inability to compare the said analysis data as mentioned above. Cube Cloud was developed on Amazon EC2, a service of Amazon, with the concept of letting users access and use this service conveniently anywhere and anytime. The concept of transport and traffic model development on Cube Cloud is shown in Figure 5.4-16.

TRAVEL MODEL Amazon’s EC2 Cloud Computing Environment

Model Model P Run with Developed u b Cube with Cube l Cloud i s Services h

TRAVEL MODEL

Figure 5.4-16 The concept of transport and traffic model development on Cube Cloud

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According to the figure, it can be summarized that: (1) The developers of the model develop it with Cube program on Desktop Computer (2) Publish the model to Cube Cloud service (3) The users can analyze the models in different formats from everywhere with internet, and can use any equipment as long as there is a web browser. 5.4.6.3 Budget of operation The source of budget for the development of transport and traffic model is a normal government budget for the development of model to use with Cube Cloud service. The consultants would like to suggest another way to raise the budget on the basis that the users should be responsible for the real proportion of usage. The users herein may refer to other government agencies or the consultants. This can be carried out in two ways: (1) Establish a fund for development of transport and traffic model, which will be operated in the same way as Bid of License Plate by Department of Land Transport. There must be a draft law to protect this; thereby the details of law about Bid of License Plate are included in section 10/1 and 10/2 in Motor Cars Bill (no. 12) BE 2546. (2) Users pay the charge directly to Citilabs 5.4.6.4 Test of Cube Cloud Citilabs offers OTP to use Cube Cloud free of charge for a year from January to December 2014, with 1,000 Core-Hours so that OTP could evaluate the performance of Cube Cloud in the analysis of transport and traffic structure. OTP announced that it would share the Core-Hours with other departments in Ministry of Transport, e.g. Department of Highways, Department of Rural Roads, and EXAT, etc. The universities with the course of transport and traffic will join this test. (1) OTP 600 Core-Hours (2) Departments 200 Core-Hours (3) Universities 200 Core-Hours The test is divided into 2 sessions: (1) First session from January to April 2014: First, OTP uploaded NAM into Cube Cloud. Second, check out NAM in Cube Cloud. Third, hold the meeting for discussion and offering training for the officials involved about the application of NAM in Cube Cloud. Fourth, use the feedback from the third step to improve the NAM in Cube Cloud, preparing for test in the second session. (2) Second session from October to November 2014: Other departments were allowed to test the system freely and meanwhile OTP would perform utilization monitor of the said departments.

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After the test, OTP would assess the utilization of data from the second test in order to set up annual usage requirement and then the operation plan. NAM in Cube Cloud system is in Figure 5.4-17.

Figure 5.4-17 NAM in Cube Cloud The aforementioned development and test of NAM on Cube Cloud represents the potential of Cube Cloud; in other words, this operation is successful with the following advantages: (1) Device and location independence: Once online, anyone can use this system. (2) Multi-tenancy: Since the users have different demands, it helps save the costs of equipment. (3) The system is working all the time though some servers are out of order. (4) Scalability: Accommodate the quantity and demands of users (5) Security system that assure the users. (6) Maintainability: All management is from the central unit. There are still some obstacles and most of them are limitations of NAM. For example, the transport network of NAM is not designed as Geo-database; so Cube Cloud cannot display the results in the form of graphic. However, NAM on Cube Cloud is designed to apply the model with the same standard, and it can change the running performance to satisfy the need of users. It is not designed to replace the Cube Cloud program on desktop computer.

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To make the development of NAM on Cube Cloud more successful, the consultants have the following suggestions: (1) Update transport network of NAM to be Geo-database so that Cube Cloud can display the results of maps or graphs in the form of graphic. (2) Develop NAM so that it can run on CPU with multiple Cores, helping Cube Cloud reduce running time significantly. (3) Periodically hold the training about the comprehensive use of NAM for the relevant departments.

5.5 Update of Software for Cube of OTP

The software (User Licenses) for CUBE that OTP has been using for the development of transport and traffic models, NAM and eBUM, have already been updated and renewed with the duration of 2 years (October 2012-2014). The details of CUBE that have been updated include: (1) Cube Base 19 units (2) Cube Voyager 19 units (3) Cube Analyst 19 units (4) Cube Cargo 2 units (5) Cube Avenue 1 units (6) Cube Dynasim 4 units (7) Cube Land 1 units (8) TRIPS 19 units

PCBK / SEA / CMCL / SYSTRA MVA 5-48 ChapterChapter 6 ApplicationApplication ooff ttransportransport aandnd ttrafficraffic mmodelodel aandnd enhancementenhancement ooff tthehe sstaff'staff's ppotentialotential Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Chapter 6 Application of transport and traffic model and enhancement of the staff's potential 6.1 Introduction 6.2 Application of transport and traffic model 6.3 Enhancement of the staff's potential 6.4 summary of operation

6.1 Introduction

The validated and examined transport and traffic model will be used not only to analyze and simulate the transport and traffic conditions in different areas regarding to the various characteristics, but also to test the impacts caused by measures and future projects to be launched based on ideas, proposals, or other annual plans. In this study, the updated model has been used to examine the measures and projects in Bangkok (eBUM) and in national scale (NAM). The results thereof are collected in the format of report so that those who are interested could study it in details. Besides the aforementioned application of model, this study includes the enhancement of the staff's potential, e.g. Training, workshop, technology transfer, etc. This is to increase their knowledge, experiences and work skills, which will in turn improve their competence.

6.2 Application of transport and traffic model

The updated transport and traffic model, NAM and eBUM, have been used to test the following 5 measures and projects: (1) Visions and missions in public transport system (2) Road Pricing or Congestion Charging (3) Fares of public rail transport (4) Impacts on road transport after AEC (5) High-speed train 6.2.1 Test of visions and missions in public transport system The test herein is about the development of common ticket system in the present rail transport and the future networks, as well as the interchange to other modes of transport such as bus, boat, BRT, including the payment of services through the passenger card. The consultants have hypotheses in the test of common ticket system as below:

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(1) Case1: Test the common ticket system for BTS, BRT and BMTA's bus by offering discount of initial fare; the test was done for 2014 (2) Case2: Test the common ticket system for BTS, BRT, BMTA's bus and boat by offering discount of initial fare; the test was done for 2015 (3) Case3: Test the common ticket system for all public transport (except paratransit) by offering discount of initial fare; the test was done for 2016 (4) In the year 2022 and 2032, only Case 3 will be tested (5) The base case of this project is that the fare of rail transport is at 15 Baht + 2.5 Baht/km Results The results show that the application of common ticket system leads to the increasing in ridership of rail transport, and the number will increase along with other public transport modes joining the system. In other words, ridership in case 2 is higher than that in case 1. The ridership in case 3 is higher than that in case 2, as summarized in Table 6.2-1. In addition, the use of common ticket system leads to the change of travel patterns; some of the passengers turning to mass transit are those who used private car. When the volume of private car decreases, the average travel speed on the network is better, according to the analysis results of private car's average speed shown in Table 6.2-2. Table 6.2-1 Daily ridership in rail transit in 2014-2032 Daily ridership (person/day) Year Difference (%) Base case Mission test case 2557 959,010 971,960 (case 1) 1.33% 2558 979,825 993,290 (case 2) 1.36% 2559 1,085,265 1,100,735 (case 3) 1.41% 2565 3,861,835 3,931,350 (case 3) 1.77% 2575 4,673,270 4,758,800 (case 3) 1.80% Source: Analysis from the Consultants Table 6.2-2 Average speed all day (km/hr) on private vehicle network Daily ridership (person/day) Year Difference (%) Base case Mission test case 2557 31.35 31.38 (case 1) 0.10% 2558 31.05 31.08 (case 2) 0.10% 2559 31.18 31.21 (case 3) 0.10% 2565 29.45 29.48 (case 3) 0.10% 2575 27.81 27.84 (case 3) 0.11% Source: Analysis from the Consultants

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6.2.2 Test of Road Pricing or Congestion Charging Measure Congestion Charging is a measure to collect toll when using road system (and/or other system) to enter the some or all of the inner areas of a city, which have chronic problems of traffic congestion, leading to the delay of travel and economic damage. The objective of Congestion Charging is to reduce the number of vehicles heading to the said areas, to relieve the traffic jams and to utilize the revenue in other activities. The target group of this measure is those who have private cars and paratransit, excluding pedestrian, bicycle, or other public transport such as bus or MRT. The consultants have employed an eBUM Model of base year 2012 to test the areas with Congestion Charge, with the following hypotheses of admission fee into Ratchadaphisek Ring Road. (1) The private cars must pay the fee to enter the areas of Ratchadaphisek Ring Road regardless of the ways they use, normal roads or expressways. (2) Test the sensitivity of admission fee 20, 40, and 60 Baht/car (3) Offer exemption for those who reside in the areas of Ratchadaphisek Ring Road The areas for Congestion Charging are shown in figure 6.2-1.

Ratchadaphisek Ring Road

Figure 6.2-1 Test areas of Congestion Charging Results The results show that the use of Congestion Charging leads to the decreasing number of vehicles entering the inner areas, whereby the number of vehicles will vary according to admission fee. That is the higher prices, the lower number of vehicles. As to the test of exemption for those who reside in the areas of Ratchadaphisek Ring Road, it is found that the number of vehicles entering the areas of Ratchadaphisek Ring Road is higher than the case with no exemption, as seen in Table 6.2-3. The average traveling speed within the areas of Ratchadaphisek Ring Road and Bangkok and Metropolitan areas decreases slightly when there is exemption for those who reside in the areas of Ratchadaphisek Ring Road, as shown in Table 6.2-4.

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Table 6.2-3 Traffic volume in PCU per day entering the areas of Ratchadaphisek Ring Road 2012 Traffic volume in Ratchadaphisek Ring Income Road (PCU/day) Difference* Test case (million Normal From (%) Total Baht/day) road expressway Base case 1,065,462 137,612 1,203,074 - - 20 Baht: no exemption 853,915 76,849 930,764 -22.63 18.62 20 Baht: with exemption for residents 1,011,890 91,065 1,102,955 -8.32 17.98 in Ratchadaphisek Ring Road 40 Baht: no exemption 766,485 47,204 813,689 -32.37 32.55 40 Baht: with exemption for residents 908,285 55,935 964,220 -19.85 31.43 in Ratchadaphisek Ring Road 60 Baht: no exemption 618,833 25,007 643,840 -46.48 38.63 60 Baht: with exemption for residents 733,317 29,633 762,950 -36.58 37.60 in Ratchadaphisek Ring Road Source: Analysis from the Consultants * Change from base case Table 6.2-4 Average speed during a.m. peak (km/hr) and percentage of change in Ratchadaphisek Ring Road and Bangkok and metropolitan areas in 2012 Average speed during rush hour in the morning (km/hr) and percentage of change Case Bangkok and Difference* Ratchadaphisek Difference* metropolitan (%) Ring Road (%) Base case 22.89 - 16.50 - 20 Baht: no exemption 23.15 1.12 16.61 0.71 20 Baht: with exemption for residents 23.08 0.83 16.56 0.42 in Ratchadaphisek Ring Road 40 Baht: no exemption 23.22 1.43 16.73 1.42 40 Baht: with exemption for residents 23.16 1.13 16.67 1.12 in Ratchadaphisek Ring Road 60 Baht: no exemption 23.36 2.04 16.87 2.27 60 Baht: with exemption for residents 23.30 1.74 16.82 1.97 in Ratchadaphisek Ring Road Source : Analysis from the Consultants Remark : * Change from base case

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6.2.3 Test of fares of public rail transport At present, the network of mass rail transit has covered the inner areas and suburban of Bangkok, e.g. BTS Green Line, MRT Blue Line, Suvarnabhumi Line (ARL), etc. They collect the fares based on distance at 15 Baht + 2.50 Baht/km, which does not cover all target groups. So, it is advisable that the transport and traffic model should be applied to test the rates of rail transport fares so that the results thereof could be a guideline to define the reasonable fares in the future. The consultants have tested the rail transport fares in the future year 2022, 2032, and 2037 as below: (1) Test of fares at 15 Baht + 2.50 Baht/km (2) Test of common ticket system of rail transport (Free Transfer) (3) Test of flat rate at 20 Baht (4) Test of flat rate at 20 Baht only for the new line, and normal rates for the tendered lines The investment plans of future Mass Transit are according to Mass Rapid Transit Master plan 2010. Results The results from the test of different fares show that Case 3 (flat rate at 20 Baht) has the highest number of passengers, followed by Case 4 (flat rate at 20 Baht only for the new line), Case 2 (common ticket system of rail transport (Free Transfer)), and Case 1 (fares at 15 Baht + 2.50 Baht/km), respectively, which is quite similar to the analysis data in the future year 2022, 2032 and 2037 as seen in Table 6.2-5. It is also found that the average travel speed in Bangkok and metropolitan areas increases a little from Case 1 (base case) because some of the passengers turning to mass rail transit are those who ever used private cars. When the volume of private cars decrease, the average travel speed on the network is increasing, as seen in Table 6.2-6. Table 6.2-5 Daily Ridership of rail transit in 2022-2037 Daily Ridership of rail transit (person/day)

Case 3 Case 4 Year Case 1 Case 2 Difference Difference Difference Flat rate New line (Base case) No fee %* %* %* 20 Baht 20 Baht 2565 3,861,830 3,919,767 1.48% 4,634,200 16.67% 4,441,115 13.04% 2575 4,673,275 4,752,715 1.67% 5,664,000 17.49% 5,383,610 13.19% 2580 5,267,160 5,356,700 1.67% 6,399,600 17.70% 6,078,300 13.34% Source: Analysis from the Consultants * compared with base case

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Table 6.2-6 Average speed all day (km/hr) on private vehicle network in 2022-2037 Average speed all day (km/hr) Year Case 1 Case 2 Case 3 Case 4 (Base case) No fee Flat rate 20 Baht New line 20 Baht 2565 29.45 29.47 29.50 29.49 2575 27.81 27.83 27.86 27.85 2580 26.95 26.97 27.00 26.99 Source: Analysis from consultants 6.2.4 Test of impacts on road transport after AEC By the year 2015, ASEAN community will have developed the Southeast Asia to be more secured and competitive in the world arena. There will be economic cooperation among 10 ASEAN members, i.e. Thailand, Indonesia, Malaysia, Philippines, Singapore, Brunei, Cambodia, Laos, Vietnam and Myanmar. This will lead to the free trade and permission for the registered vehicles to carry the cargo or passengers through the member nations. Thus, the traffic volume on the domestic road network is increasing. The consultants, therefore, advise to study, analyze and forecast the traffic conditions; and test the impacts on road transport in the future. The objective thereof is to set up guidelines about supervision and measures to prepare for the upcoming changes of contexts in ASEAN. Thereby, the following two case studies are examined. (1) Base case: No AEC in 2015 This case is the traffic condition analysis in 2015, where there is no AEC. (2) Test case: AEC in 2015 This case is the traffic condition analysis in 2015, where there is AEC. The hypotheses in this case are: The export and import of goods through 9 major borders are increasing as shown in Table 6.2-7. The following 3 cases will be tested: (1) The number of trucks passing borders: 500 vehicles/day (2) The number of trucks passing borders: 1,000 vehicles /day (3) The number of trucks passing borders: 2,000 vehicles /day

PCBK / SEA / CMCL / SYSTRA MVA 6-6 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 6.2-7 Expected import - export in case of AEC in 2015 Chiang Nong Mukda Nokhon Aranya Padang Border Mae Sai Mae Sod Sadao Khong Khai harn Panom prathet Besar Links AH3 AH2 AH1 AH12 AH16 AH15 AH1 AH2 ASEAN Railway Value of imports 8,800 80 1,530 5,000 35,000 4,600 6,300 166,000 52,000 (million Baht) Value of exports 8,600 17,000 36,000 65,000 16,000 4,000 48,000 263,000 320,000 (million Baht) Analysis data by NAM The test results in case of AEC in 2015 show that volume of commodity transport are increasing as displayed in Table 6.2-8 to 6.2-10. Table 6.2-8 Volume of commodity passing borders Type Weight (ton/day) % Coffee, tea, spices 128,500 1.20 Wood and wood products 3,947,000 36.75 Garments 25,500 0.24 Shoes and accessories 6,500 0.06 Seasoning made from vegetable, fruits, nuts 739,000 6.88 Land vehicle, except that running on rail or track, and its accessories 166,500 1.55 Beverage, liquor, vinegar 4,000 0.04 Products from painting industry, malt grain 2,462,000 22.92 Cereal 2,717,000 25.30 Electric machine, electric appliance, and accessories 543,500 5.06 Total 10,739,500 100 Table 6.2-9 Analysis results PCU-KM PCU-HR Speed (km/hr) Whole Without With Without With country With project Change No project Change Change project project project project Case 1 304,154,266 306,416,339 0.74% 3,796,584 3,829,120 0.86% 80.11 80.02 -0.11% Case 2 304,154,266 308,661,743 1.48% 3,796,584 3,861,998 1.72% 80.11 79.92 -0.24% Case 3 304,154,266 313,059,983 2.93% 3,796,584 3,929,010 3.49% 80.11 79.68 -0.54%

PCBK / SEA / CMCL / SYSTRA MVA 6-7 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Table 6.2-10 Traffic volume (V/C Ratio) Point With project Case 1 % Change Case 2 % Change Case 3 % Change LOC1 0.440 0.462 5.08 0.479 8.94 0.506 15.02 LOC2 0.799 0.804 0.62 0.815 1.91 0.863 7.98 LOC3 0.908 0.923 1.63 0.938 3.31 0.970 6.80 LOC4 0.251 0.253 0.75 0.254 1.19 0.257 2.31 LOC5 0.150 0.151 0.76 0.153 2.18 0.156 4.29 LOC6 0.260 0.264 1.26 0.265 1.96 0.270 3.78 LOC7 0.413 0.415 0.51 0.420 1.87 0.424 2.88

The analysis indicates that after AEC the border trade will increase, leading to the higher number of trucks. As to the case study along Laos’s border, the policy there allows trucks to pass the border with the number of 500, 1,000, and 2,000 vehicles per day. This results in increasing of pcu-km and pcu-hr, too. So, the overall average travel speed on network (km/hr) decreases as per number of trucks. Thereby, the route with highest impact is AH12 between Udon Thani and Khon Kaen, causing the V/C Ratio to increase by 15%. 6.2.5 Test of high-speed train The development of high-speed train seems very interesting for all sectors since it is a part of the infrastructure policy to develop the mass rail transit and transport management for goods and services. The projects relevant to the development of high-speed train include:  Study and development of high-speed train for Bangkok-Chiangmai line, Bangkok- Nakonratchasima line, Bangkok-Hua Hin line and other lines to be connected with neighboring countries.  Study and development of extension for Airport Rail Link from Suvarnabhumi Airport to Chonburi and Pattaya Thereby, the analysis of ridership for high-speed train has already been conducted primarily in the study of rail and high-speed train master plan by OTP in 2010. Figure 6.2-2 illustrates the development plan of high-speed train from the said study. However, the analysis of passenger number mentioned above employs the estimation of the modal change to high-speed train based on the sensitivity of travel cost and travel time. There is no update of Modal Split Model to include the new high-speed train in the Logit model analysis. Therefore, the particular development on modal split may help analyze and estimate ridership of high-speed train more precisely. The consultants use NAM to test the following 2 cases: 1) Base case: No high-speed train It is the analysis of traffic conditions in different years in case of no high-speed train. 2) Test case: With high-speed train

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It is the analysis of traffic conditions in different years with high-speed train as seen in Table 6.2-11.

Source: The study of rail and high-speed train master plan by OTP 2010 Figure 6.2-2 Development of express train/high-speed train as to the master plan

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Table 6.2-11 High-Speed Train Projects Line Name Year in operation Number of station Station name Northern 2019 Bangkok - Chiangmai 12  Bang Sue  Ayutthaya  Lopburi  Ban Takhli  Nakornsawan  Taphanhin  Pitsanulok  Ban Dara  Uttaradit  Denchai  Lampang  Chiang Mai Northeastern 2019 Bangkok – Nakorn 11  Bang Sue Ratchasima  Ayutthaya 2021 Bangkok - Nongkhai  Saraburi  Pakchong  Nakorn Ratchasima  Bua Yai  Khon Kaen  Khao Suan Kwang  Udon Thani  Nong Khai Eastern 2021 Bangkok – Rayong 8  Bang Sue  Makasan  Lad Krabang  Chachoengsao  Chonburi  Sriracha  Pattaya  Rayong Southern 2019 Bangkok – Padang Besar 16  Bang Sue  Nakorn Pathom  Ratchaburi  Petchburi  Huahin  Prachuab Kirikhan  Bang Sapan

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Line Name Year in operation Number of station Station name  Chumporn  Lang suan  Tha Chana  Surat Thani  Wiang sa  Thung Song  Pattalung  Hadyai  Padang Besar

Analysis results The analysis results in case of high-speed train in 2021 show that the number of passengers in different future years is increasing continually as shown in Table 6.2-12. Meanwhile, Table 6.2-13 shows that traffic volume in PCU and average speed (km/hr) changes slightly compared with no high-speed train case. Table 6.2-12 Numbers of passengers (person-trip/day) Year Line Route 2022 2027 2032 2037 Northern Bangkok - Chiangmai 26,980 28,780 30,770 32,910 Northeastern Bangkok - Nong Khai 30,310 33,060 36,160 39,640 Eastern Bangkok - Rayong 27,570 33,380 40,750 52,910 Southern Bangkok - Padang Besar 28,890 31,650 34,730 38,210

Table 6.2-13 Average speed on network Year Case PCU-KM PCU-HR Speed (km/hr) 2022 Without project 349,351,174 4,440,101 78.68 With project 342,413,094 4,336,790 78.96 Difference -1.99% -2.33% 0.36% 2027 Without project 382,610,768 4,934,867 77.53 With project 374,851,142 4,817,195 77.82 Difference -2.03% -2.38% 0.37% 2032 Without project 421,663,523 5,535,237 76.18 With project 412,937,472 5,396,710 76.52 Difference -2.07% -2.50% 0.45% 2037 Without project 467,642,069 6,270,123 74.58 With project 457,688,403 6,103,995 74.98 Difference -2.13% -2.65% 0.54%

PCBK / SEA / CMCL / SYSTRA MVA 6-11 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

6.3 Enhancement of the staff's potential

Enhancement of staff's potential which are completely and successfully performed including: 6.3.1 Workshop Seminar and training 6.3.1.1 The 1st Workshop Seminar was held on 18-19 July 2013 at Caribbean Room, The Tide Resort, Chonburi. The purpose thereof was to present the project study results in fields of NAM and eBUM development to OTP’s staffs and related departments, and to enhance the staff's capabilities. Figure 6.3-1 shows the atmosphere of the 1st Workshop. 6.3.1.2 The 2nd Workshop Seminar was held on 25 February 2014 during 8.30-12.00 a.m., at Kamoltip Room, The Sukosol Hotel, Bangkok, to present the main project study results to the staffs of OTP and the representatives from relevant departments. The objective herein was to increase knowledge and experiences, enhance the staff's potential, and share the suggestions and opinions among the participants, all of which are to be taken into account for Final Report preparation. Figure 6.3-2 shows atmosphere of the 2nd Workshop.

Figure 6.3-1 Atmosphere of the 1st Workshop Seminar

Figure 6.3-2 Atmosphere of the 2nd Workshop Seminar

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6.3.1.3 The training was held after the accomplishment of development, update, and maintenance of transport and traffic model, as well as the maintenance of transport and traffic database system. The consultants provided the training, for staffs of OTP and other relevant departments, about the application and utilization of database and model in order to increase the potential and knowledge of trainees according to the objectives of the project. The training of "Application of Model in Cube Cloud" was held at Conference Room 401, OTP Building, on Wednesday 5 March 2014, as illustrated in Figure 6.3-3.

Figure 6.3-3 Atmosphere of training "Application of Model in Cube Cloud" 6.3.2 Transfer of transport and traffic technology and logistics system 6.3.2.1 The 1st transfer of transport and traffic technology and logistics system The 1st transfer of transport and traffic technology and logistics system is an academic field trip at Chiang Rung-Sib Song Panna on 21-24 February 2013. The staffs visited and saw the operation of Jing Hong, or Chiang Rung Port, Jing Hong Airport, both the new compound for domestic travel and the old one for international flight. The group also made a field trip to study the condition of R3A, a 2-lane highway linking the transport and logistics among Thailand, Laos and Southern China with Kunming. The trip started with the study of 4th bridge across Mekhong River in Chiang Khong, Chiangrai (in use at present), and the learning of living conditions and cultures of Tai Lue and Yunnan people, economic condition of the town, and the expected impacts after AEC is effective. The atmosphere of the said field trip is shown in Figure 6.3-4.

Figure 6.3-4 Atmosphere of transport and traffic technology and logistics system field trip at Chiang Rung-Sib Song Panna 6.3.2.2 The 2nd transfer of transport and traffic technology and logistics system The 2nd transfer of transport and traffic technology and logistics system was a field trip in Europe, the United Kingdom, where there was development of transport and logistics systems, as well as the transport and traffic model database, on 9-17 August 2013. The group studied the operation of both government and private sectors, i.e. 1) Chartered Institute of Logistics and Transport (CILT), a government agency supervising the transport and logistics system, with more than 30 members worldwide, 2) ARUP

PCBK / SEA / CMCL / SYSTRA MVA 6-13 Executive Summary Report Transport Data and Model Integrated with Multimodal and Logistics (TDL)

Group, a private consultant company planning and designing of grand engineering projects in many countries and 3) Citilabs, an organization developing and distributing CUBE software and other applications relevant to the application of transport and traffic model. The advantages from this trip are the increasing knowledge and experiences of staffs in OTP and other departments involved and the ability to apply this knowledge in their tasks. The atmosphere of the trip at the three places are shown in Figure 6.3-5.

Figure 6.3-5 Atmosphere of the 2nd transport and traffic technology and logistics system field trip 6.3.3 Project promotion The periodical project promotion along the operation of this project is well cooperated and facilitated by the staffs of OTP. So, the performance of operation is very satisfying, for instance, a video for the publication of project study (Thai/English with the duration of 5-6 minutes) presented in the 2nd Workshop, brochures, exhibition boards, executive interviews, website to promote the project (http://tdl.otp.go.th/), etc. Figure 6.3-6 shows the homepage of the said website, while the two interviews with OTP executives are shown in Figure 6.3-7.

Figure 6.3-6 Sample of “Homepage” of website promoting the project

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Figure 6.3-7 The 1st and 2nd interviews with OTP executives 6.3.3 Development of knowledge about analysis by means of database and model To develop the knowledge about analysis by means of database and model, the consultants have established a transport and traffic analysis course by using database and model, which have been developed and updated in this study. The self-learning materials have also been produced for use in the training and knowledge development, and learning media are created in the form of website so as to publicize the information and be used in self-learning via the website of OTP (http://tdl.otp.go.th/), as shown in Figure 6.3-8 and 6.3-9. The contents of self-learning are divided into 4 parts or 4 classrooms as follows: Classroom 1: Fundamental knowledge and theories for the application of model Classroom 2: Principles of analysis and test of model Classroom 3: Sample of the study of eBUM Application (Figure 6.3-10) Classroom 4: Sample of the study of NAM Application (Figure 6.3-11)

Figure 6.3-8 Homepage of TDL website linking to the learning materials about analysis

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Figure 6.3-9 Details of self-learning contents (4 classrooms)

Figure 6.3-10 Samples of self-learning materials (Classroom 3)

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Figure 6.3-11 Samples of self-learning materials (Classroom 4)

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