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
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Contents
Page
Chapter 1 Introduction
1.1 Introduction
1.2 Principle and Reason
1.3 Objectives
1-1
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1-2
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 The Study and Survey Data of Commodity Flow and Freight Transport Logistics
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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
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Chapter 4 Improvement and maintenance of transport and traffic of National Mode (NAM)
4.1
4.2
4.3
4.4
Introduction
Study and review of NAM
Improvement and development of NAM
Development of innovation for the application of model
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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
5.2
5.3
5.4
5.5
Introduction
Study and review of eBUM
Improvement and development of eBUM
Development of innovation for the application of model
Update of Software for Cube of OTP
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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
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Transport Data and Model Integrated with Multimodal and Logistics (TDL)
List of Tables
Page
Table 2.2-1
Table 2.2-2
Table 2.3-1
Table 2.4-1
Table 2.5-1
Table 2.5-2
Review of policy and plan in the projects of organizations or departments involved
Review of the study results
Additional data collected and updated into the project database
Secondary data of goods transport and the sources
Names of borders in the surveys
The places of passenger survey at the main bus terminals, main train stations,
and main airports
Table 2.5-3 The survey points along the North-South Screen line
Table 2.5-4 List of traffic volume survey points along the East-West Screen line
Table 2.5-5 Summary of traffic volume and Volume/Capacity – V/C at the survey sites
along the North-South Screen line
Table 2.5-6 Summary of traffic volume and Volume/Capacity – V/C at the survey points
along the East-West Screen line
Table 2.5-7 Analysis results of PCU for different kinds of vehicles
Table 2.5-8 Roads chosen for case study
Table 2.6-1 The summary of import, export and domestic freight transport
from Survey Data in Project
Table 2.6-2 The Proportion of Goods Volume and Unit Transport Cost from Survey Data
in Project categorized by Mode
Table 2.6-3 The Proportion of Goods Volume (including Dummy) categorized
by Mode from Transport and Traffic Model
Table 4.2-1 Summary of data review on transport network in the model
Table 4.3-1 Zonal Data for current model development
Table 4.3-2 Coefficient of utility equation from modal split analysis of passengers’ behavior
Table 4.3-3 Parameters used in Modal Split Model
Table 4.3-4 Points of data survey along Screen Line nationwide
Table 4.3-5 Results of NAM validation along Screen Line in 2012
Table 4.3-6 Estimation of people’s travel based on Transport modes
Table 4.3-7 Results from NAM
Table 4.3-8 Freight Transport Results from NAM (Year 2012)
Table 4.3-9 Results of NAM validation along Screen Line in 2013
Table 4.3-10 Freight volume in 2013 (1,000 tons/year)
Table 4.3-11 Estimation of people’s travel based on transport modes
Table 4.3-12 Freight Transport Volume from NAM
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
List of Tables (Continued-1)
Page
Table 4.3-13
Table 4.3-14
Table 4.3-15
Table 4.4-1
Table 4.4-2
Table 5.2-1
Table 5.3-1
Table 5.3-2
Table 5.3-3
Table 5.3-4
Table 5.3-5
Table 5.3-6
Table 5.3-7
Table 5.3-8
Table 5.3-9
Table 5.3-10
Table 5.3-11
Table 5.3-12
Table 5.3-13
Table 5.3-14
Table 5.3-15
Table 5.3-16
Table 5.3-17
Table 5.3-18
Table 5.3-19
Table 5.3-20
Table 5.3-21
Table 5.3-22
Table 5.3-23
Table 5.3-24
Travelling Data Results from NAM
Domestic Freight Transport Results from NAM
Results from NAM, Freight, Average distance of freight Transport
Forecasted amount of pollutions Emitted categorized by vehicle type
Fuel Consumption
Summary of improvement and development of eBUM
Population and household in eBUM compared with population census in 2010
Summary of Mean Trip Length from the synthesis of HIS 2546
between the survey results and model results
Survey Locations for Modal Split Model
Example of Analysis results of Modal Split Model (0 Veh.) – HBW
The results of updating VOC and VOT
Acceptable deviation based on road types
Model Calibration for Traffic Volume along the North-South Screen Line in 2012
Model Calibration for Average Traffic Volume on Expressway System in 2012
Model Calibration for Average MRT Ridership in 2012
Model Calibration for Average BTS Ridership in 2012
Model Calibration for Average Airport Rail Link - ARL Ridership in 2012
Average Travel Speed in BMR by area in 2012
Numbers of Trips in each area in 2012
Modal Splits in 2012
Trip volume categorized by type of vehicle ownership and trip
purposes with no transfer to public transport system in 2012
Major trip proportion including transfer and no transfer to public transport system,
categorized by type of travel in 2012
Numbers of passengers using public transport in 2012
(including transfer to public transport system)
Model Calibration for Traffic Volume along the North-South Screen Line in 2013
Model Calibration for Traffic Volume along the East-West Screen line in 2013
Model Calibration for Average Traffic Volume on Expressway System in 2013
Model Calibration for Average MRT Ridership in 2013
Model Calibration for Average BTS Ridership in 2013
Model Calibration for average Airport Rail Link- ARL Ridership in 2013
Average Travel Speed in BMR by area in 2013
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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
Table 5.3-26 Trip volume categorized by type of vehicle ownership and trip purposes
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,
categorized by type of travel in 2013
Table 5.3-28 Numbers of passengers using public transport in 2013
(including transfer to public transport system)
Table 5.3-29 Forecasted traffic during morning peak
Table 5.3-30 Forecasted traffic during evening peak
Table 5.3-31 Forecasted traffic all day
Table 5.3-32 Forecasted proportion of main trip with no transfer to public transport system
Table 5.3-33 Forecasted proportion of main trip including transfer to public transport system
Table 5.3-34 Forecasted numbers of passengers using major public transport system (Person Trips)
Table 5.3-35 Forecasted numbers of passengers using major public transport system
(including transfer to public transport system)
Table 5.3-30 Average Speed in each area
Table 5.3-31 Numbers of trips in each area
Table 5.4-1 Analysis results of evacuation model in case of emergency
in Bang Pa-in industrial estate of Ayutthaya
Table 5.4-2 Fuel Consumption categorized by vehicle type from eBUM in 2013
Table 5.4-3 Fuel Consumption by province from Model eBUM in 2013
Table 5.4-4 Statistics of fuel distribution at gas stations in Bangkok and metropolitan areas in 2013
Table 5.4-5 Average Fuel Sales at Gas station in Bangkok Metropolitan and Surrounding Area in 2013
Table 5.4-6 Proportion of Fuel Consumption between eBUM and
Department of Energy Business Statistics
Table 5.4-7 Pollution emitted from the model based on different provinces in 2013
Table 5.4-8 Pollution emitted categorized by vehicle type
Table 6.2-1 Daily ridership in rail transit in 2014-2032
Table 6.2-2 Average speed all day (km/hr) on private vehicle network
Table 6.2-3 Traffic volume in PCU per day entering the areas of Ratchadaphisek Ring Road 2012
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
Table 6.2-5 Daily Ridership of rail transit in 2022-2037
Table 6.2-6 Average speed all day (km/hr) on private vehicle network in 2022-2037
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
List of Tables (Continued-3)
Page
Table 6.2-7
Table 6.2-8
Table 6.2-9
Table 6.2-10
Table 6.2-11
Table 6.2-12
Table 6.2-13
Expected import - export in case of AEC in 2015
Volume of commodity passing borders
Analysis results
Traffic volume (V/C Ratio)
High-Speed Train Projects
Numbers of passengers (person-trip/day)
Average speed on network
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
List of Figures
Page
Figure 2.5-1
Figure 2.5-2
Figure 2.5-3
Figure 2.5-4
Figure 2.5-5
Figure 2.5-6
Figure 2.5-7
Figure 2.5-8
Figure 2.5-9
Figure 2.5-10
Figure 2.5-11
Figure 2.5-12
Figure 2.5-13
Figure 2.5-14
Figure 2.5-15
Figure 2.5-16
Figure 2.5-17
Figure 2.5-18
Figure 2.5-19
Figure 2.5-20
Figure 2.5-21
Figure 2.5-22
Figure 2.5-23
Figure 2.5-24
Figure 2.5-25
Figure 2.5-26
Figure 2.5-27
Figure 2.5-28
Figure 2.5-29
Figure 2.5-30
Figure 2.5-31
Screen Lines Survey Locations
Data survey along the Screen Line SL1, SL3, SL4 in 2012
Data survey along the Screen Line SL2, SL5, SL6 in 2013
Survey Results for Screen Lines 1 (SL 1)
Survey Results for Screen Lines 2 (SL 2)
Survey Results for Screen Lines 3 (SL 3)
Survey Results for Screen Lines 4 (SL 4)
Survey Results for Screen Lines 5 (SL 5)
Survey Results for Screen Lines 6 (SL 6)
Roadside Interview Survey Results for Screen Lines 1 : SL 1
Roadside Interview Survey Results for Screen Lines 2 : SL 2
Roadside Interview Survey Results for Screen Lines 3 : SL 3
Roadside Interview Survey Results for Screen Lines 4 : SL 4
Roadside Interview Survey Results for Screen Lines 5 : SL 5
Roadside Interview Survey Results for Screen Lines 6 : SL 6
Locations of main borders between Thailand and neighboring countries
Data survey at main borders between Thailand and neighboring countries
Interviews with passengers at the main bus terminals, main train stations,
and main airports in different regions
The proportion of the interviewers based on the distance and objective of travel
The relationship between distance and modes of travels
The locations of traffic volume and travel condition
survey along the North-South Screen line
The locations of traffic volume and travel condition survey
along the East-West Screen line
The division of sub-areas in eBUM within Ayutthaya and Chachoengsao
The locations of Roadside Interview Survey at the 4 truck terminal
Points of travel condition survey in Ayutthaya
Points of travel condition survey in Chachoengsao
Areas of study for this survey
Samples of roads chosen for case study
Results of Speed-Flow Curve Analysis on the road with 2 lanes (inner areas)
Results of Speed-Flow Curve Analysis on the road with 2 lanes (outer areas)
Results of Speed-Flow Curve Analysis on the road with more than 2 lanes (inner areas)
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
List of Figures (Continued-1)
Page
Figure 2.5-32
Figure 2.6-1
Figure 2.6-2
Figure 3.1-1
Figure 3.4-1
Figure 3.4-2
Figure 3.4-3
Figure 3.4-4
Figure 3.4-5
Figure 3.4-6
Figure 3.4-7
Figure 3.4-8
Figure 3.4-9
Figure 3.4-10
Figure 3.4-11
Figure 3.5-1
Figure 4.3-1
Figure 4.3-2
Figure 4.3-3
Results of Speed-Flow Curve Analysis on the road with more than 2 lanes (outer areas)
Supply Chain Relationship with Transport.
Relationships of Transport Mode Selection
Maintenance of Transport and Traffic Database System
Structure of presentation of Executive Information System of OTP
Overall information of OTP
Information of important projects and budget monitoring
Fundamental information of 2-trillion project
Project information about bridges
Information of average traffic speed and traffic volume
Names of Plan/Project under the strategy of Ministry of Transport
Information of logistics survey
Information for integration with Ministry Operating Center (MOC/DOC)
Statistic information of transport and logistics of National Statistical Office
Information of OTP Strategic Plans
Main page of transport and traffic publication system after update
Details of Coarse Traffic Analysis Zone
Additional sub-district zones
Comparison of provincial population from civil registration database
and population and housing census in 2010
Figure 4.3-4 Road network after update
Figure 4.3-5 Routes of high-speed train as to the master plan
Figure 4.3-6 Overall view of access volume to the bus terminals in 2005-2012
Figure 4.3-7 Overall passengers at the airports in 2007-2012
Figure 4.3-8 Trip Length distribution
Figure 4.3-9 Structure of modal split proposed by the consultants to improve NAM
Figure 4.3-10 Structure of Modal Split Model in case of high-speed train (Added-mode Structure)
Figure 4.3-11 Steps of checking for correction in NAM
Figure 4.4-1 Steps of transport model development to analyze the emission
Figure 4.4-2 Commands for analysis of fuel consumption and emission
Figure 4.4-3 Analysis results of emission in 2013
Figure 4.4-4 Analysis results of fuel consumption in vehicle
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
List of Figures (Continued-2)
Page
Figure 5.3-1
Figure 5.3-2
Figure 5.3-3
Figure 5.3-4
Figure 5.3-5
Figure 5.3-6
Figure 5.3-7
Figure 5.3-8
Figure 5.3-9
Figure 5.3-10
Figure 5.4-1
Figure 5.4-2
Figure 5.4-3
Figure 5.4-4
Locations of survey for Modal Split Model
Trip distribution based on distance
Trip distribution based on trip duration
Desired line within Bangkok and Vicinity Area in 2012 (including 2 provinces)
Desired line within Bangkok and Vicinity Area in 2013 (including 2 provinces)
Desired line within Bangkok and Vicinity Area in 2017 (including 2 provinces)
Desired line within Bangkok and Vicinity Area in 2022 (including 2 provinces)
Desired line within Bangkok and Vicinity Area in 2027 (including 2 provinces)
Desired line within Bangkok and Vicinity Area in 2032 (including 2 provinces)
Desired line within Bangkok and Vicinity Area in 2037 (including 2 provinces)
Traffic zones in Samutprakarn
Structure of Land Use Model
Example of analysis result of eBUM in TRANUS Program
Comparative Results between TRANUS Program and traffic survey Data
along Screen Line morning peak (unit : PCU/hour)
Figure 5.4-5 Comparative Results between TRANUS Program and traffic survey Data
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
Figure 5.4-7 Fuel Consumption Analysis Flow Chart for eBUM Development
Figure 5.4-8 Emission Analysis Flow Chart for eBUM Development
Figure 5.4-9 Emission of Hydrocarbon (HC) from eBUM
Figure 5.4-10 Emission of Carbon Monoxide (CO) from eBUM
Figure 5.4-11 Emission of Nitrogen Oxide (NOx) from eBUM
Figure 5.4-12 Emission of Carbon Dioxide (CO2) from eBUM
Figure 5.4-13 Emission of Particle Matter (PM) from eBUM
Figure 5.4-14 Proportion of emission of different pollution types based on different provinces
Figure 5.4-15 Concept of Cloud Computing
Figure 5.4-16 The concept of transport and traffic model development on Cube Cloud
Figure 5.4-17 NAM in Cube Cloud
Figure 6.2-1 Test areas of Congestion Charging
Figure 6.2-2 Development of express train/high-speed train as to the master plan
Figure 6.3-1 Atmosphere of the 1st Workshop Seminar
Figure 6.3-2 Atmosphere of the 2nd Workshop Seminar
Figure 6.3-3 Atmosphere of training "Application of Model in Cube Cloud"
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List of Figures (Continued-3)
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Figure 6.3-4
Atmosphere of transport and traffic technology and logistics system field trip
at Chiang Rung-Sib Song Panna
Figure 6.3-5 Atmosphere of the 2nd transport and traffic technology and logistics system field trip
Figure 6.3-6 Sample of “Homepage” of website promoting the project
Figure 6.3-7 The 1st and 2nd interviews with OTP executives
Figure 6.3-8 Homepage of TDL website linking to the learning materials about analysis
Figure 6.3-9 Details of self-learning contents (4 classrooms)
Figure 6.3-10 Samples of self-learning materials (Classroom 3)
Figure 6.3-11 Samples of self-learning materials (Classroom 4)
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Chapter 1
Introduction
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Chapter 1 Introduction
1.1
1.2
1.3
1.1
Introduction
Principle and Reason
Objectives
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
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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
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Chapter 2
Survey, Study, Review and Analysis of
the recent information
Executive Summary Report
Chapter 2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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 (20122016)
 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
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2.3
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
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
2.4
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
 Demand of travel for 7 groups
 Data of Commodity Flow in 2010 fot 6 groups with total 52 types of goods
 Data of transport volume based on mode of transportin 2010(O-D Report)
for 4 groups
 Data of Logistic Nodes in 2010 for30 groups
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
Source
The Development of
Multimodal Transport and
Logistics Supply Chain
Management for
Implementation of Action
Plan, (OTP)
Ministry of Commerce
(MOC)
Customs Department
Imported products based on their values within the period of 5 years
Exported products based on their values within the period of 5 years
Statistics of the first 35 imported products based on their weight
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
Primitive data of Truck Terminal
Primitive data of Inland Container Depots (ICD)
Volume of vehicles in and out of each truck terminal
Primitive data Industrial Zones
Primitive data of Inland Container Depots
Primitive data of Container Yards
Primitive data of River Ports
Primitive data of Private Ports
Primitive data of International Sea Ports
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
Primitive data of Cold Storage
Primitive data of Silos
Primitive data of PWO’s Warehouses

Annual volume of import and export through international airports
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2.5
Source
Department of Land
Transport (DLT)
IEAT
SRT
Marine Department (MD)
PAT
Department of Internal
Trade of Thailand
Public Warehouse
Organization Ministry of
Commerce
Department of Air
Transport
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
RS04
RS05
RS01
RS02
RS06
RS09
RS10
RS08
RS11
Screen Line SL3
RS13
RS14
RS07
RS12
Screen Line SL4
Screen Line SL5
RS16
RS15
Screen Line SL6
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)
RS01
13,247
2,806
9,942
A
B
RS03
RS04
14,282
RS01
3,136
RS03
RS02
9,342
16,029
RS04
SL1
40,277
RS02
14,193
42,700
PCU
2,000
8,130
7,406
7,455
6,960
7,211
6,564
6,038
6,673
3,914
4,000
6,376
6,000
7,247
8,000
9,005
10,000
0
่วงเว า
Total
(PCU)
ปรมาTraffic
รา รVolume
รวม 2 ท-2-way
ทาง PCU)
_SLSL1 1(รวม)_
(All)
Compositionาon
1 : SL
1 SL1
สัดส่วTraffic
นยานพาหนะประเภทต่
งๆ Screen
ตามแนวLine
Screen
Line:
_RS02_
_RS01_
15.9%
4.4%
1.8%
8.5%
2.7%
6.8%
10.1%
0.6%
4.8%
_RS04_
_RS03_
74.4%
65.0%
22.2%
13.8%
6.0%
2.9%
69.4%
66.8%
66.6%
6.7%
2.1%
0.5%
3.4%
0.4%
2.5%
10.8%
13.7%
6.6%
2.6%
1.3%
6.4%
รBicycle/Motorcycle
ั รยาน ร ั รยานยนต
ะ
2 & 3 แwheel
รPrivate
ยนตนังส่
Carวน
รBusดยสาร
รLightรรทTruck
นาดเ
รMedium
รรท Truck
นาด าง
รHeavy
รรทTruck
นาด incl.
ห ่ รวมทั
งร &พ่วSemi-trailer
งแ ะ งพ่วง
Trailer
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)
RS05
3,470
4,169
19,443
RS06
13,805
8,129
RS08
9,381
2,382
RS05
3,640
RS07
SL2
33,425
B A
Screen Line SL2
30,995
RS06
(SL2)
4,764
RS07
5,429
6,003
5,729
5,685
5,557
5,652
5,472
5,423
5,120
4,471
5,116
PCU
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
RS08
่วงเว า
TotalปรTraffic
มา ราVolume
ร รวม 2 -2-way
ท ทาง (PCU)
PCU)
สัดส่วนยานพาหนะประเภทต่างๆ ตามแนว Screen Line: SL2
(All)
_SLSL22 (รวม)_
_RS06_
_RS05_
78.9%
87.3%
65.3%
25.5%
1.2%
1.6%
3.3%
4.0%
2.6%
1.4% 5.0%
_RS08_
_RS07_
74.2%
67.2%
22.0%
2.8%
1.9%
1.2%
1.6%
17.0%
3.6%
2.0%
0.2%
6.0%
2.0%
1.2%
11.7%
3.4%
2.2%
1.8%
1.9%
รBicycle/Motorcycle
ั รยาน ร ั รยานยนต
ะ
2 & 3 แwheel
รPrivate
ยนตนังส่
Carวน
รBusดยสาร
รLightรรทTruck
นาดเ
รMedium
รรท Truck
นาด าง
Trailer
รHeavy
รรทTruck
นาดincl.
ห ่ รวมทั
งร &พ่วSemi-trailer
งแ ะ 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)
B
A
18,372
RS09
20,250
58,934
RS10
59,329
SL3
79,579
77,363
RS09
10,028
14,252
12,217
14,333
14,535
11,816
12,972
12,343
12,632
16,075
15,006
RS10
10,735
PCU
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
่วงเว า
Total
Traffic
(PCU)
ปรมา
รา Volume
ร รวม 2 ท-2-way
ทาง PCU)
Compositionางๆ
สัดส่Traffic
วนยานพาหนะประเภทต่
ตามแนว
Screen
on
Screen
LineLine:
3 : SLSL33
_RS09_
SL 3 (รวม)_
(All)
_SL
60.5%
5.7%
26.9%
33.9%
1.8%
6.8%
5.2%
21.6%
_RS10_
68.6%
2.9%
16.8%
0.6%
8.9%
2.1%
3.8%
17.9%
13.1%
2.8%
รBicycle/Motorcycle
ั รยาน ร ั รยานยนต
ะ
2 & 3 แwheel
รPrivate
ยนตนังส่
Carวน
รBusดยสาร
รLightรรทTruck
นาดเ
รMedium
รรท Truck
นาด าง
รHeavy
รรทTruck
นาด incl.
ห ่ รวมทั
งร &พ่วSemi-trailer
งแ ะ งพ่วง
Trailer
Figure 2.5-6 Survey Results for Screen Lines 3 (SL 3)
PCBK / SEA / CMCL / SYSTRA MVA
2-11
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Traffic Volume on Screen Line: SL4
during 6.00-18.00 hrs. (PCU)
40,407
SL4
17,185
19,739
23,103
7,644
7,246
7,955
7,413
6,791
7,055
6,880
6,868
6,782
RS12
6,469
RS11
42,842
PCU
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
5,179
23,223
6,970
Executive Summary Report
่วงเว า
Total
(PCU)
ปรมาTraffic
รา รVolume
รวม 2 ท -2-way
ทาง PCU)
RS12
RS11
B
A
Screen Line 4 (SL4)
SL 4 (รวม)_
(All)
_SL
Compositionางๆ
สัดส่Traffic
วนยานพาหนะประเภทต่
ตามแนว
Screen
on
Screen
LineLine:
4 : SLSL44
68.3%
_RS11_
66.5%
13.2%
19.6%
2.4%
2.7%
4.5%
5.1%
4.2%
4.3%
_RS12_
70.8%
3.7%
9.1%
6.5%
1.6%
8.4%
6.0%
3.2%
รBicycle/Motorcycle
ั รยาน ร ั รยานยนต
ะ
2 & 3 แwheel
รPrivate
ยนตนังส่
Carวน
รBusดยสาร
รLightรรทTruck
นาดเ
รMedium
รรท Truck
นาด าง
รHeavy
รรทTruck
นาด incl.
ห ่ รวมทั
งร &พ่วSemi-trailer
งแ ะ งพ่วง
Trailer
Figure 2.5-7 Survey Results for Screen Lines 4 (SL 4)
PCBK / SEA / CMCL / SYSTRA MVA
2-12
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Traffic Volume on Screen Line: SL5
during 6.00-18.00 hrs. (PCU)
SL5
1,000
500
12,151
2,265
2,121
1,863
1,825
1,950
1,276
1,500
1,947
2,325
RS13
2,038
2,000
2,020
12,151
PCU
2,500
2,051
11,481
11,481
1,954
Executive Summary Report
0
่วงเว า
ปรมา รา ร รวม 2 ท ทาง PCU)
Compositionางๆ
สัดส่Traffic
วนยานพาหนะประเภทต่
ตามแนว
Screen
on
Screen
LineLine:
5 : SLSL55
55.0%
A
B
12.8%
16.4%
1.7%
8.7%
5.2%
รBicycle/Motorcycle
ั รยาน ร ั รยานยนต
ะ
2 & 3 แwheel
รPrivate
ยนตนังส่
Carวน
รBusดยสาร
รLightรรทTruck
นาดเ
รMedium
รรท Truck
นาด าง
Trailer
รHeavy
รรทTruck
นาด incl.
ห ่ รวมทั
งร &พ่วSemi-trailer
งแ ะ งพ่วง
Figure 2. 5-8 Survey Results for Screen Lines 5 (SL 5)
PCBK / SEA / CMCL / SYSTRA MVA
2-13
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Traffic Volume on Screen Line: SL6
during 6.00-18.00 hrs. (PCU)
11,224
2,000
5,053
4,728
4,632
4,744
4,715
4,678
4,248
4,686
3,000
7,003
3,119
4,000
4,629
5,000
4,935
26,567
PCU
6,000
RS16
RS15
RS14
SL6
8,846
10,589
8,482
27,917
4,319
Executive Summary Report
1,000
0
8,340
่วงเว า
ปรมา รา ร รวม 2 ท ทาง PCU)
(All)
_SLSL66 (รวม)_
Compositionาon
Screen Line
6 : SL
6 SL6
สัดส่วTraffic
นยานพาหนะประเภทต่
งๆ ตามแนว
Screen
Line:
_RS14_
62.8%
73.5%
18.3%
18.5%
2.5%
2.2%
_RS15_
2.4%
1.1%
_RS16_
59.0%
55.5%
25.2%
12.9%
16.0%
1.4%
6.2%
4.5%
5.2%
4.4%
1.8%
7.9%
8.2%
3.7%
1.9%
5.0%
รBicycle/Motorcycle
ั รยาน ร ั รยานยนต2 &แ3 ะwheel
รPrivate
ยนตนังส่Car
วน
รBusดยสาร
รLight
รรทTruck
นาดเ
Truckาง
รMedium
รรท นาด
Trailer
รHeavy
รรท Truck
นาด หincl.
่ รวมทั
งร พ่ว&งแSemi-trailer
ะ งพ่วง
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
Industrial Goods
Energy
Construction Materials
Others
Vacant
¼ Loaded
½ Loaded
¾ Loaded
Fully loaded
Figure 2.5-10 Roadside Interview Survey Results for Screen Lines 1 : SL 1
PCBK / SEA / CMCL / SYSTRA MVA
2-15
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
RS05
Trip Purpose
RS06
Work / Business Trip
(2-way)
Tour
School Trip (2-way)
Freight Transport
Others
Screen Line 2 (SL2)
Executive Summary Report
RS07
RS08
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
Agricultural Goods
Processed Agricultural Goods
Industrial Goods
Energy
Construction Materials
Others
Freight Volume loaded
Vacant
¼ Loaded
½ Loaded
¾ Loaded
Fully loaded
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
School Trip (2-way)
Freight Transport
Others
RS10
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
Industrial Goods
Energy
Construction Materials
Others
Vacant
¼ Loaded
½ Loaded
¾ Loaded
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
RS11
RS12
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
Industrial Goods
Energy
Construction Materials
Others
Vacant
¼ Loaded
½ Loaded
¾ Loaded
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
Agricultural Goods
Processed Agricultural Goods
Industrial Goods
Energy
Construction Materials
Others
Freight Volume loaded
Vacant
¼ Loaded
½ Loaded
¾ Loaded
Fully loaded
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
Agricultural Goods
Processed Agricultural Goods
Industrial Goods
Energy
Construction Materials
Others
Freight Volume loaded
Vacant
¼ Loaded
½ Loaded
¾ Loaded
Fully loaded
Figure 2.5-15 Roadside Interview Survey Results for Screen Lines 6 : SL 6
PCBK / SEA / CMCL / SYSTRA MVA
2-20
Executive Summary Report
2.5.1.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
Phanom
10
Mae Sod customs house, Tak
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
8
9
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
Railway
station
Place of survey
 Chiangmai Bus Terminal
 Pissanulok Bus Terminal
 Chiangmai Train Station
 Pissanulok Train Station
Airport
 Chiangmai Airport
North
Bus terminal
Region
Central part (Bangkok)
Region
 Pissanulok Airport
Railway
station
Airport
Railway
station
airport
Bus terminal
 Nakonratchasima
PCBK / SEA / CMCL / SYSTRA MVA
 Trat Bus Terminal
East
Bus Terminal
 Udonthani Bus Terminal
 Nakonratchasima
Railway Station
 Udonthani Railway Station
 Ubonratchathani Airport
 Udonthani Airport
South
Northeast
Bus terminal
Bus terminal
Place of survey
 Mochit Bus Terminal
 Akkamai Bus Terminal
 South Bus Terminal
 Bangkok Railway Station
(Hua Lam Pong)
 Don Muang Airport
 Rayong Bus Terminal
 Pattaya Bus Terminal
Railway
station
Airport
Bus terminal
Railway
station
 Pattaya Railway Station
 Pattaya Airport (U Tapao)
 Hatyai Bus Terminal
 Hatyai Railway station
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
(2-way)
> 600 km.
Others 5%
Work /
business
< 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 highspeed 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
2.5.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
MB – NS01 Bridge over Chao Phraya River –
Western Ring Road
MB – NS02 Pathum Thani Bridge
(Highway No. 346)
MB – NS03 Pathum Thani Bridge II
(Highway No. 3100)
MB – NS04 Nondhaburi Bridge
(Highway No. 345)
MB – NS05 Rama IV Bridge (Highway No. 304)
MB – NS06 Phra Nangklao Bridge (New)
(Highway No. 302)
MB – NS07 Phra Nangklao Bridge (old)
(Highway No. 302)
MB – NS08 Rama V Bridge
MB – NS09 Rama VII Bridge
MB – NS10 Rama VI Bridge (Railway only)
MB – NS11 Krungthon Bridge
PCBK / SEA / CMCL / SYSTRA MVA
Point
Zone
MB – NS12 Rama VIII Bridge
MB – NS13 Phra Pinklao Bridge
MB – NS14 Memorial Bridge
MB – NS15 Phra Pokklao Bridge
MB – NS16 Taksin Bridge
MB – NS17 Krungthep Bridge
MB – NS18 Rama III Bridge
MB – NS19
MB – NS20
MB – NS21
MB – NS22
Rama IX Bridge
Bhumipol Bridge
Bhumipol Bridge II
Kanchana Pisek Bridge
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
NS04
NS03
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
PCBK / SEA / CMCL / SYSTRA MVA
Executive Summary Report
Figure 2.5-22 The locations of traffic volume and travel condition survey along the East-West Screen line
2-29
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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 06:0009.00 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 NorthSouth Screen line
Survey Site
MB – NS01
MB – NS02
MB – NS03
MB – NS04
MB – NS05
MB – NS06
MB – NS07
MB – NS08
MB – NS09
MB – NS10
Traffic Volume
Traffic Volume
Inbound
Outbound
V/C Inbound
V/C Outbound
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
Bridge over Chao Phraya
1,115
970
2,064
1,426
0.37
0.32
0.69
0.48
River – Western Ring Road
Pathum Thani Bridge
2,090
1,882
2,455
2,383
0.46
0.42
0.55
0.53
Pathum Thani Bridge II
2,367
2,333
2,114
2,141
0.53
0.52
0.47
0.48
Nondhaburi Bridge
1,518
1,670
1,307
2,053
0.51
0.56
0.44
0.68
Rama IV Bridge
2,998
2,158
2,075
2,038
0.67
0.48
0.46
0.45
Phra Nangklao Bridge
4,631
4,443
4,170
3,654
1.03
0.99
0.93
0.81
(New)
Phra Nangklao Bridge
1,432
1,479
1,397
1,376
0.48
0.49
0.47
0.46
(old)
Rama V Bridge
2,078
2,157
1,750
2,203
0.46
0.48
0.39
0.49
Rama VII Bridge
2,797
2,857
2,698
2,906
0.62
0.63
0.60
0.65
Rama VI Bridge (Railway
only)
MB – NS11
MB – NS12
MB – NS13
MB – NS14
MB – NS15
MB – NS16
MB – NS17
MB – NS18
MB – NS19
MB – NS20
MB – NS21
MB – NS22
Krungthon Bridge
Rama VIII Bridge
Phra Pinklao Bridge
Memorial Bridge
Phra Pokklao Bridge
Taksin Bridge
Krungthep Bridge
Rama III Bridge
Rama IX Bridge
Bhumipol Bridge
Bhumipol Bridge II
Kanchana Pisek Bridge
Total
PCBK / SEA / CMCL / SYSTRA MVA
2,500
3,032
6,989
2,578
6,962
6,958
3,259
7,961
6,447
3,132
2,958
2,991
76,793
2,642
2,494
5,816
1,788
5,483
3,658
1,925
4,466
4,341
2,287
2,121
2,267
59,237
2,884
2,109
3,070
1,385
6,063
4,349
1,695
3,019
2,868
1,457
1,885
2,264
53,078
2,594
2,826
9,519
1,977
6,834
4,397
1,317
2,597
5,320
3,791
2,531
2,455
66,338
0.56
1.01
1.16
0.86
1.29
1.29
1.09
1.47
1.19
052
0.49
0.66
-
1.47
0.83
1.62
1.19
1.02
0.81
0.64
0.99
0.96
0.51
0.47
0.50
-
1.60
0.70
1.02
0.92
1.12
0.97
0.57
0.67
0.64
0.32
0.42
0.50
-
0.58
0.94
1.32
0.66
1.27
0.98
0.44
0.58
0.99
0.63
0.42
0.55
-
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 EastWest 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
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Executive Summary Report
Survey Site
MB – EW36
MB – EW37
MB – EW38
MB – EW39
2.5.2.2
Location
Charan Sanitwong
Sirindhorn
Ratchapruek
Outer Ring Road (West)
Total
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Traffic Volume
Traffic Volume
Inbound
Outbound
V/C Inbound
V/C Outbound
(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
3,867
2,941
2,536
3,133
0.86
0.65
0.88
0.98
9,783
9,077
10,358
8,845
1.22
1.13
1.29
1.11
2,183
3,433
1,698
3,082
0.49
0.76
0.38
0.68
8,063
7,253
8,567
7,877
1.08
0.97
1.14
1.05
91,899 96,318 80,338 86,817
-
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
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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
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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
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
xx Turning Movement Count (16 Intersections)
yy
1
Mid-block Count (15 sites)
Z Roadside Interview Survey (8 sites)
1
1
Travel Speed Survey Route (6 Routes)
2
2
3
4
2
6
13
7
6
5
4
10
3
8
3
5
11
9
7
15
8
12
7
9
6
12
4
13
15
16
14
5
14
8
11
10
Figure 2.5-25 Points of travel condition survey in Ayutthaya
PCBK / SEA / CMCL / SYSTRA MVA
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
4
1
22
1
3
3 4 13
13 5
1
14
2
15
2
15
12
3
55
7
7
16 6
10
6
8
7
6
14
9
8
9
8
11
4
12
11
10
xx Turning Movement Count (16 Intersections)
yy
5
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
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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)
Type
Mid-Block
Intersection
eBUM
Count
Motorcycle
0.44
0.53
0.25
Three-wheel taxi
0.80
0.80
0.70
Taxi
1.00
1.00
1.00
Private car
1.00
1.00
1.00
Small bus
1.10
1.20
1.50
Large bus
2.30
1.80
2.00
Pick-up car and small truck (4 wheels)
1.10
1.10
1.00
Medium truck (6 wheels)
1.00
1.00
2.00
Large truck (10 wheels), trailer, and semi1.10
1.20
2.50
trailer
PCBK / SEA / CMCL / SYSTRA MVA
Difference
(%)
76–112
14
0
0
20-27
10-15
10
0-5
8
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Executive Summary Report
2.5.4
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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)
2 Lanes 2 ways with median
4 Lanes 2 ways with median
More than 4 Lanes 2 ways with
median
2 Lanes 2 ways without median
4 Lanes 2 ways without median
More than 4 Lanes 2 ways without
median
Outer Urban Area (without footpath)
2 Lanes 2 ways with median
4 Lanes 2 ways with median
PCBK / SEA / CMCL / SYSTRA MVA
Road Code
CBD-01-1
CBD-01-2
CBD-02-1
CBD-02-2
CBD-03-1
CBD-03-2
CBD-04-1
CBD-04-2
CBD-05-1
CBD-05-2
CBD-06-1
CBD-06-2
Road Code
OCOR-01-1
OCOR-01-2
OCOR-02-1
OCOR-02-2
Name
Ramkamhaeng 60
Ekcharoen Rd.
Nontburi Bypass
Art Narong Rd.
Pahonyothin 51 (Infront of Army 11)
Tiwanond Rd.
Kampangpetch 3 Rd.
Suksawad 70 Rd. (Krunai)
Charoenkrung Rd. (Near PTT station)
Soi Arun Amarin 31 (Pedestrian Bridge)
Soi Pahonyothin 30
Rajdamnoen Nai Rd.
(In front of Supreme Court)
Name
Highway 3477
Highway 3190
Dechatungkha Rd.
Rangsit-Pathumthani Rd. (Soi 23/3)
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Inner Urban Area (with footpath)
More than 4 Lanes 2 ways with
median
Road Code
OCOR-03-1
OCOR-03-2
2 Lanes 2 ways without median
OCOR-04-1
OCOR-04-2
OCOR-05-1
OCOR-05-2
OCOR-06-1
OCOR-06-2
4 Lanes 2 ways without median
Moer than 4 Lanes 2 ways without
median
Name
Rangsit-Nakorn Nayok Rd. (Wat Khiankhet)
Pahonyothin Rd.
(Thammasat University - Rangsit)
Bung Kumproi Rd.
Kitmani Rd.
Kampangpetch 6 Rd.
Ha-thaimit Rd.
Highway 3036
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 SpeedFlow 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
SPEED (km/hr)
80
eBUM, 1997
Ramkamhaeng
ถ.รามคาแหง 6060
60
Ekcharoen
ถ.เอกเจริญ Rd.
40
Kampangpetch
ถ.กาแพงเพชร 3 3 Rd.
Suksawad
ถ.สุขสวัสดิ์ 70
70 Rd.
20
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
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
2-lane Rd. (Outer Urban Area)
120
100
SPEED (km/hr)
80
eBUM, 1997
60
Highway 3477 3477
ทางหลวงหมายเลข
Highway 3190 3190
ทางหลวงหมายเลข
40
ถ.บึ
งคาพร้
อย Rd.
Bung
Kumproi
ถ.กิ
จมณี Rd.
Kitmani
20
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
SPEED (km/hr)
Nontburi
ถ.เลี่ยงเมือBypass
งนนทบุรี
80
Art
Narong Rd.
ถ.อาจณรงค์
Pahonyothin
ถ.พหลโยธิน 5151
60
Tiwanond
ถ.ติวานนท์ Rd.
40
Charoenkrung
ถ.เจริญกรุง Rd.
20
Arun
ถ.อรุณAmarin
อมรินทร์3131Rd.
Pahonyothin
ถ.พหลโยธิน 3030 Rd.
0
0
0.2
0.4
0.6
0.8
1
1.2
Rajdamnoen Rd.
ถ.ราชดาเนิน
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
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
More than 2-lane Rd. (Outer Urban Area)
120
eBUM, 1997
SPEED (km/hr)
100
Dechatungkha
ถ.เดชะตุงคะ Rd.
80
Rangsit-Pathumthani Rd.
ถ.รังสิต-ปทุมธานี 23
(Soi 23/3)
60
ถ.รังสิต-นครนายกNayok Rd.
Rangsit-Nakorn
40
Pahonyothin
Rd. (TU-Rangsit)
ถ.พหลโยธิน (ธรรมศาสตร์
รังสิต)
Kampangpetch
ถ.กาแพงเพชร 6 6 Rd.
20
Ha-thaimit
ถ.หทัยมิตร Rd.
0
0
0.2
0.4
0.6
V/C RATIO
0.8
1
1.2
Highway
3036 3036
ทางหลวงหมายเลข
Tanyaburi
ถ.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
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Executive Summary Report
2.6
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
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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
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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.
The unit transport cost =
𝑨
𝐁𝐱𝐂
(Baht/Ton-Kilometer)
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.
The average unit transport cost of exported rice by road =
𝟏𝟎,𝟎𝟎𝟎
𝟐𝟎 𝐱 𝟒𝟏𝟕
= 1.20 (Baht/Ton-Kilometer)
Thus, the average unit transport cost of each mode will be calculated from the amount of TonKilometer 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
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
𝑻𝒉𝒆 𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒖𝒏𝒊𝒕 𝒕𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒊𝒐𝒏 𝒄𝒐𝒔𝒕 𝒐𝒇 𝒓𝒐𝒂𝒅 𝒕𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕
∑𝟏𝟖𝟎
𝒊=𝟏 (𝒕𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒕𝒊𝒐𝒏 𝒄𝒐𝒔𝒕 (𝑩𝒂𝒉𝒕)𝒊 𝒙 𝒗𝒐𝒍𝒖𝒎𝒆 (𝑻𝒐𝒏)𝒊 𝒙 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆 (𝑲𝒎)𝒊 ) 𝐨𝐟 𝐞𝐚𝐜𝐡 𝐠𝐨𝐨𝐝𝐬 𝐢𝐭𝐞𝐦 𝐛𝐲 𝐫𝐨𝐚𝐝 𝐭𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭
=
𝑺𝒖𝒎 𝒐𝒇 𝒕𝒉𝒆 𝒒𝒖𝒂𝒏𝒕𝒊𝒕𝒊𝒆𝒔 (𝒗𝒐𝒍𝒖𝒎𝒆 (𝑻𝒐𝒏)𝒙 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆(𝑲𝒎)) 𝐨𝐟 𝐚𝐥𝐥 𝐠𝐨𝐨𝐝𝐬 𝐢𝐭𝐞𝐦 𝐛𝐲 𝐫𝐨𝐚𝐝 𝐭𝐫𝐚𝐧𝐬𝐩𝐨𝐫𝐭
𝑻𝒉𝒆 𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒖𝒏𝒊𝒕 𝒕𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕𝒂𝒊𝒐𝒏 𝒄𝒐𝒔𝒕 𝒐𝒇 𝒓𝒐𝒂𝒅 𝒕𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕 =
∑(𝟏.𝟐𝟎 𝒙 𝟔𝟓,𝟑𝟒𝟖 𝒙 𝟒𝟏𝟕)+(𝟐.𝟓𝟎 𝒙 𝟓𝟎,𝟎𝟎𝟎 𝒙 𝟑𝟎𝟎)+(… )
𝟏𝟐𝟗,𝟗𝟒𝟕,𝟓𝟖𝟖,𝟎𝟑𝟒
= 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
Goods Item
1*
2
3*
4
5*
6
7
8
9
10
11
12
13*
14*
15
16
17*
18*
19
20*
21
22*
23
24
25
26
Rice
Corn
Cassava products
Longans
Durians
Mangosteens
Apples and pears
Grapes
Citrus fruits
Onion, small onion, garlic, fresh or chilled
Leguminous vegetables, chilled or frozen
Spices
Orchid
Rubber
Coffee
Palm
Soybean
Oil cake
Wheat
Fresh, chilled or frozen shrimp
Frozen squid
Chilled or frozen fish
Dried fish
Snapping turtle
Chilled or frozen processed chicken
Swine, fresh, chilled or frozen
PCBK / SEA / CMCL / SYSTRA MVA
Volume of Freight Transport (Ton)
Import
26,948
180,000
646,407
0
0
0
133,096
83,104
157,623
124,625
3,894
52,101
0
0
29,064
0
1,961,015
2,814,917
2,581,987
425
84,697
1,454,218
1,000
0
0
15,860
Export
6,500,000
290,000
7,799,081
581,047
339,760
130,100
3,208
177
2,490
43,935
37,713
0
15,427
2,800,000
2,085
13,247
2,030
0
0
174,360
69,756
293,259
35,000
21,070
500,000
13,500
Domestic
Total
13,960,000 20,486,948
4,360,000 4,830,000
4,770,740 13,216,228
236,776
817,823
164,920
504,680
45,321
175,421
158,906
295,210
17,080
100,361
180,000
340,113
331,260
499,820
15,380
56,987
447,780
499,881
29,159
44,586
530,000 3,330,000
39,370
70,519
12,512,684 12,525,931
105,449 2,068,494
3,961,849 6,776,766
595 2,582,582
305,640
480,425
5,912
160,365
45,727 1,793,204
465,000
501,000
600
21,670
947,458 1,447,458
953,000
982,360
2-46
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Volume of Freight Transport (Ton)
Goods Item
Import
27
28
29
30
31*
32
33*
34
35
36
37*
38
39
40
41
42
43
44
45
46
47
48
49
50*
51*
52*
53*
54
55
56*
57*
58
59
60*
61*
62
63
64
65
Fresh eggs
Fresh, chilled and frozen beef cattle
Canned tuna
Canned sardine
Processed shrimp
Processed fish
Sugar
Molasses
Canned fruit
Fruit Juice
Dried fruit
Vegetables and vegetable preparations
Sweet corn
Flour
Starch
Rice noodle
Instant noodles and instant food
Dog and Cat Food
Soy sauce, chili sauce, tomato sauce
Fish sauce
Milk and milk products
Drinks and beverage
Palm oil
Garments made of knitted fabrics
Garments made with fabrics
Fabrics made of cotton
Fabrics made of artificial fibers
Cotton Yarn
Filament
Fibers
Gemstone
Jewelry made of gold
Gold unwrought
Television receiver and parts
Cathode Ray Tube (CRT)
Refrigerators, freezers and components
Air conditioning and components
Compressors of refrigeration
Circuit breaker
PCBK / SEA / CMCL / SYSTRA MVA
0
25,165
8,713
0
0
72,039
0
21,486
79,206
30,924
7,134
67,056
0
0
0
10,667
67,058
11,698
0
0
243,807
152,623
44,194
9,020
86,755
53,499
132,994
16,726
0
106,320
16,973
2
335
84,030
11,449
394,078
1,000
1,000
4,380
Export
5,948
19,824
411,872
82,177
163,177
964,014
7,545,002
979,637
780,367
407,449
80,936
93,065
172,187
35,103
89,793
142,066
474,185
346,275
48,190
44,076
131,639
1,422,965
292,830
14,172
183,814
56,660
89,400
46,648
211,919
395,766
8,562
53
371
416,870
19,500
1,082,016
40,000
30,000
3,000
Domestic
Total
655,432
661,380
133,170
178,159
20,048
440,633
22,600
104,777
285,499
448,676
150,900 1,186,954
2,695,246 10,240,248
300,000 1,301,123
124,530
984,103
80,300
518,673
20,000
108,070
436,935
597,056
151,350
323,537
30,000
65,103
94,000
183,793
73,000
225,732
345,897
887,140
121,554
479,526
171,810
220,000
218,420
262,496
940,000 1,315,446
1,045,380 2,620,968
11,037,170 11,374,194
93,228
116,420
47,100
317,669
23,450
133,609
65,280
287,674
80,551
143,925
153,800
365,719
35,000
537,086
8,698
34,233
4
59
0
706
5,627
506,527
8,030
38,979
450,000 1,926,094
15,000
56,000
10,000
41,000
2,000
9,380
2-47
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Goods Item
66
67
68*
69*
70*
71
72*
73
74*
75*
76
77
78
79*
80*
81
82
83
84
85
86
87*
88
89
90
91
92
93
94
95
96*
97
98*
99
100
101
102
Washer, washing machine and components
Electrical apparatus for providing voice signals and components
Computer
Computer components
Circuit boards
Motors and Generators
Telephone equipment and components
Semiconductor transistors and diodes
Transformers and Components
Wooden Furniture
Lumber
Plywood
Fiberboard
Cold steel
Hot steel
Tube connection installation
Steel frame for construction
Nails, bolts, screws
Wire Rope Slings Wire Cable
Appliances, tableware and household stainless steel
Semi-finished products of iron or stainless steel
Aluminum
Structures and components made of aluminum
Aluminum products used in the industry
Appliances, tableware and household made of aluminum
Taps, valves and components
Ethylene
Propylene
Styrene
Vinyl chloride
Polyacetal
Sheets, film, foil and strip
Plastic packing
Appliances, tableware and household plastic
Carbon
Hydrogen, rare gases and other non-metals
Ammonia
PCBK / SEA / CMCL / SYSTRA MVA
Volume of Freight Transport (Ton)
Import
1,000
88,000
43,402
73,532
84,755
270,000
673,736
15,600
2,654,281
1,464
5,800,400
4,932
22,883
4,309,123
857,801
0
0
20,900
20,000
18,500
437,192
521,666
7,373
254,068
0
47,025
245
30,000
20,000
30,000
304,540
200,000
111,272
25,053
145,349
76,974
357,403
Export
33,000
20,000
80,508
33,165
72,164
330,000
3,439
25,000
12,900
19,558
2,051,960
1,106,604
146,558
37,306
34,822
1,367,827
166,566
120,000
158,619
23,000
3,640,510
42,764
67,910
28,396
26,449
38,644
61,989
800,000
280,533
390,840
962,102
350,000
357,315
53,483
116,746
46,244
0
Domestic
Total
7,000
41,000
20,000
128,000
37,106
161,016
8,087
114,784
86,586
243,505
270,000
870,000
670,297 1,347,472
7,800
48,400
3,641,471 6,308,652
55
21,076
0 7,852,360
661,396 1,772,932
1,765,542 1,934,983
7,328,745 11,675,174
183,590 1,076,212
2,051,741 3,419,568
6,763,387 6,929,953
8,500
149,400
95,000
273,619
7,400
48,900
2,184,000 6,261,702
809,680 1,374,110
32,090
107,373
121,604
404,068
5,900
32,349
161,356
247,025
1,300
63,534
480,000 1,310,000
160,000
460,533
230,000
650,840
56,000 1,322,642
150,000
700,000
892,769 1,361,356
10,148
88,684
388,254
650,349
273,756
396,974
0
357,403
2-48
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Goods Item
103 Carbonate, Peroxide carbonate, Ammonium carbonate used
in trade
104 Acyclic hydrocarbons
105 Cyclic hydrocarbons
106 Terephthalic acid
107* Acyclic alcohols and derivatives
108 Phenol and phenol-alcohol
109 Cyclic monocarboxylic acid & Acyclic monocarboxylic acid
110 Nitrile compounds–function
111* Chemical Fertilizers
112 Paint and Varnish
113 Colored objects
114 Lubricant additives used as catalyst
115 Insecticides, pesticides and animals
116 Shoes and pieces
117 Leather, leather products and compressed leather
118* Tire
119 Gloves
120 Tube and Pipe
121 Conveyor and power transmission
122 Vulcanized rubber
123 Synthetic rubber
124 Floor tile and wall mosaic
125 Sanitary ware made of ceramics
126 Ceramic tableware
127* Car
128* Car parts and accessories
129 Motorcycles and parts
130 Bicycles and parts
131 Reciprocating internal combustion engines and components
132 Lens
133 Ingredients for makeup and body cleansing
134 Essential oils and fragrant mixture of compounds. The
flavored lubricants are used. Artificial waxes and prepared
waxes. Organic compounds that reduce surface tension
135* Medical equipment
136 Pharmaceutical products
137* Cement
138 Glass and Glazing
PCBK / SEA / CMCL / SYSTRA MVA
Volume of Freight Transport (Ton)
Import
659,294
Export
79,367
Domestic
790,633
Total
1,529,294
0
0
0
913,092
196,000
91,085
71,539
5,759,108
47,559
99,664
32,876
134,480
3,290
160,474
36,103
779
731,455
13,900
85,937
3,200
0
22,922
9,997
60,000
24,000
245,842
16,750
248,583
61,895
80,701
175,210
295,550
1,438,664
1,381,399
118,286
113,000
119,360
29,980
315,636
34,124
22,010
11,641
22,165
556
80,636
145,585
34,350
4,146,726
20,000
1,400,815
2,000
165,000
56,976
54,048
1,343,783
537,513
1,210,993
33,910
197,866
172,422
521,335
272,328
7,404,450
1,161,336
1,085,601
82,000
121,000
70,000
170,020
2,404,200
355,876
22,010
488,359
127,835
2,980
27,364
256,044
51,162
279,360
7,300
290,000
4,000
750,000
35,000
10,050
1,200,535
480,214
156,000
33,000
203,000
27,578
253,647
437,672
7,700,000
2,600,000
2,467,000
1,113,378
340,000
280,445
271,539
8,478,944
437,559
143,684
532,876
284,480
6,826
268,474
437,732
86,291
5,157,541
41,200
1,776,752
9,200
915,000
114,898
74,095
2,604,318
1,041,727
1,612,835
83,660
649,450
261,895
855,683
885,210
5,616,325
78,191
17,068
453,693
1,136,741
35,013
13,092,615
428,547
10,658,421 17,411,487
26,783
139,987
27,989,160 41,098,843
2,011,533 2,893,773
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Goods Item
139
140
141
142*
143
144*
145
146*
147
148
149
150
151
152*
153
154
155
156
157
158*
159
160
161
162
163*
164*
165*
166*
167*
168*
169*
170*
171*
172
173
174
175
176
177
Toothbrush
Lighters
Zinc products
Paper and paper products
Pulp and waste paper
Molding boxes for metal foundry
Machinery for processing rubber or plastics
Fuel pumps for liquids and pneumatic pumps
Turbines
Liquid or gas separation machines
Machinery used in the construction and components
Shafts and crank
Machinery for metal processing and components
Removable or interchangeable tools including mold
Copper and copper products
Gypsum
Petroleum gas
Natural gas
Feldspar
Crude oil
Refined oil
Coal
Marble and granite
Kaolin and other soil used in industry
Coffee, tea and spices
Wood and wood products
Ingredients from vegetables, fruit, nuts
Shoes and accessories
Automobiles and equipment (vehicles)
Beverages, whiskey, vinegar
Products of grains and malts
Grains
Electrical machinery, electrical equipment, and components
Live animals
Sugarcane
Stone, sand, soil
Construction materials
Construction metals
Animal feeds
PCBK / SEA / CMCL / SYSTRA MVA
Volume of Freight Transport (Ton)
Import
3,160
600
184,746
1,033,589
1,592,342
27,372
480,000
100,000
134,000
15,000
220,000
90,000
320,000
19,473
436,025
5,247
1,522,945
8,965,161
41,544
48,944,305
4,181,648
18,578,230
220,728
199,366
102,493
497,590
184,668
64,975
84,000
132,863
574,497
51,247
9,521,112
0
0
0
0
0
0
Export
3,345
8,000
52,523
1,056,405
137,028
10,167
41,400
253,000
61,000
50,800
150,400
25,000
54,000
6,104
173,594
8,955,860
134,069
0
690,320
2,523,710
14,907,143
3,547
12,267
71,511
35,306
5,458,170
1,534,006
77,540
1,881,296
841,120
2,275,616
96,358
5,864,236
0
0
0
0
0
0
Domestic
6,655
32,000
125,645
24,000,000
1,985,000
350,000
0
124,000
0
49,200
0
50,000
12,600
40,000
0
6,178,191
5,672,863
7,454,180
60,000
0
33,769,521
1,000,000
800,231
1,270,781
100,480
78,768
97,102
101,324
1,680,749
994,236
218,632
10,672,234
2,957,649
2,110,000
100,002,515
78,254,119
1,300,000
387,000
5,920,000
Total
13,160
40,600
362,914
26,089,994
3,714,371
387,539
521,400
477,000
195,000
115,000
370,400
165,000
386,600
65,576
609,619
15,139,298
7,329,877
16,419,341
791,864
51,468,014
52,858,312
19,581,777
1,033,226
1,541,658
238,279
6,034,528
1,815,776
243,839
3,646,045
1,968,218
3,068,745
10,819,839
18,342,997
2,110,000
100,002,515
78,254,119
1,300,000
387,000
5,920,000
2-50
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Volume of Freight Transport (Ton)
Goods Item
Import
178 Asphalt
179 Consumer Products/Modern Trade
180 Paddy
Total
Proportion (%)
0
0
0
144,229,717
18.27
Export
Domestic
Total
0
783,000
783,000
0
38,710,601 38,710,601
0
37,400,000 37,400,000
126,621,475 518,535,574 789,386,766
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
Transport Mode
Road
Rail
Water
Air
Total
Goods Volume Surveyed in
Project (Million Tons)
710.151
9.646
69.554
0.036
789.387
Proportion
(%)
89.962
1.222
8.811
0.004
100.00
Unit Transport Cost
(Baht/Ton-Km).
2.12
0.95
0.65
10.00
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
Transport Mode
Road
Rail
Water
Air
Total
Goods Volume Surveyed in
Project (Million Tons)
710.151
9.646
69.554
0.036
789.387
Total Goods Volume
(Million Tons)
704.013
11.253*
89.125*
0.130*
804.521
Proportion
(%)
87.51
1.40
11.08
0.02
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
Chapter 3
Maintenance of transport and traffic
database system
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Chapter 3 Maintenance of transport and traffic database system
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.1
Introduction
Study and review of transport and traffic database system development process
Update of data in the database system
Development of Executive Information System
Improvement of data presentation from the database system
Support for maintenance of transport and traffic database system
Improvement of Computer’s Equipment and Network System
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)
3. Development of
Executive Information
System
4. Presentation
Improvement of Data
and Information from
Database System
5. Support for
Maintenance of Transport
and Traffic’s Database and
Information System
2. Update Data in
Database System
1. Study and Review
System Development
Process of Transport
and Traffic’s Database
and Information System
Maintenance of
Transport and
Traffic’s Database
and Information
System
6. Improvement of
Computer's Equipments
and Network 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
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3.3
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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.
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3.4
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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.
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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
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Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Figure 3.4-2 Overall information of OTP
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(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
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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.
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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).
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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)
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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
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Transport Data and Model Integrated with Multimodal and Logistics (TDL)
2) Information of freight transport
Figure 3.4-8 Information of logistics survey
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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)
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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
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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
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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.
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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
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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
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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
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Chapter 4
Improvement and maintenance of
transport and traffic NAM
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
4.2
4.3
4.4
4.1
Introduction
Study and review of NAM
Improvement and development of NAM
Development of innovation for the application of model
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.
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4.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
Road transport
Format of data
Line file that can be output and changed
in *.shp file.
Water transport
Rail transport
Air transport
Schedule and routes of
public bus
Schedule and routes of
domestic flights
Schedule and routes of
public train
4.3
Public Transport (TRIPS)
Requirement
Network fineness needs to be added
and updated to be compatible with
the model analysis
Check for update of network and routes
of present water transport
The present rail transport network is not
compatible with the network of highspeed transport
Check for update of network and routes
of present air transport
 Update of schedule and routes
 Correct the input format to be
compatible with analysis from TRIPS,
and Cube Voyager
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
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(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
Population
Highest accuracy
Sub-district
Population and housing census
GPP
Sub-district
Province
Survey data of travel behavior
Province group
Source
Department of Provincial
Administration, Ministry of Interior
National Statistical Office
Office of the National Economics and Social
Development Board
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.
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4.3.1.1
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
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4.3.1.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
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4.3.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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.
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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
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4.3.3
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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.
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Figure 4.3-5 Routes of high-speed train as to the master plan
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4.3.3.3
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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:
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4.3.5.1
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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)
้โดยสำร เข้ำIn-ออกสถำนี
วย:
จำนวนผู
of Airport
of Passengers
and Out (หน่
Number
ล้ำนคน)
(Million)
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 20072012.
200.00
เหนืNorthern
อ
150.00
ใต้ Southern
กลำง
Central
100.00
ตะวัWestern
นตก
50.00
ตะวัEastern
นออก
ตะวัNortheastern
นออกเฉียงเหนือ
0.00
25482006
25492007
2550 2008
2551 2009
2552 2010
2553 2011
2554 2555
2005
2012
Figure 4.3-6 Overall view of access volume to the bus terminals in 2005-2012
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จำนวนผู้โดยสำร เข้ำ-ออก สนำมบิน
(Million)
(หน่วย: ล้ำนคน)
Number of Passengers In and Out of Airport
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
6.00
เหนืNorthern
อ
5.00
ใต้ Southern
4.00
3.00
ตะวัNortheastern
นออกเฉียงเหนือ
2.00
ตะวั
นออก
Eastern
1.00
กลำง
Central
0.00
2550
2007
2551
2008
2552
2009
2553
2010
2554
2011
2555
2012
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
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Number of Trips
Executive Summary Report
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.
PC
BUS
Trip Matrix
Conventional Train
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 = 𝛽𝑇 𝑇𝑎 + 𝛽𝐶 𝐶𝑎 +𝛽𝐹 𝐹𝑎 + 𝐴𝑆𝐶𝑎
Whereby
(1)
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
Type
Train
Distance
(km.)
< 300
300-600
> 600
< 300
300-600
> 600
< 300
300-600
> 600
300-600
> 600
Bus
Car
Air
Variables
βC
(Cost)
βT
(Time)
βF
(Frequency)
ASC
(Constant)
-5.60E-03
-3.60E-03
-2.32E-03
-1.61E-02
-5.23E-03
-4.36E-03
-7.46E-03
-2.36E-03
-1.47E-03
-3.66E-03
-3.54E-03
-3.31E-03
-2.13E-03
-1.11E-03
-1.47E-02
-1.09E-03
-9.69E-04
-1.36E-02
-3.31E-03
-1.87E-03
-1.02E-02
-5.54E-03
2.17E-03
5.91E-02
2.89E-02
1.55E-02
1.55E-02
3.58E-02
4.85E-02
4.62E-02
1.10E-02
7.53E-02
4.28E-02
-0.85565
-1.14898
-1.74486
-3.24747
-3.05223
-2.46734
0.543874
6.90E-02
-0.82332
1.304418
1.385444
Value of Time
(Baht/hour)
35
35
29
55
13
13
109
84
76
167
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
Private car
Bus
Train
High-speed train
Plane
4.3.5.4
Ai
0.00
-0.03
-1.70
-1.50
-3.50
Bi
-0.0015
-0.0015
-0.0015
-0.0015
-0.0015
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
4.3.6
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
Screen Line 1 (SL 1)
Site
RS01
RS02
RS03
RS04
RS05
RS06
RS07
RS08
RS09
RS10
RS11
RS12
RS13
RS14
RS15
RS16
Screen Line 2 (SL 2)
Screen Line 3 (SL 3)
Screen Line 4 (SL 4)
Screen Line 5 (SL 5)
Screen Line 6 (SL 6)
Highway Number
12
117
115
1
12
2
33
304
32
4
3
304
4
4
41
401
Section
Pitsanuloke – Wang Thong
Nakorn Sawan - Pitsanuloke
Pluak Sung – Kampaeng Petch
Kampaeng Petch - Nakorn Sawan
Lomsak - Chumpae
Si Kiu – Nakorn Ratchasima
Prachin Buri – Sa Kaew
Nakorn Ratchasima - Prachin Buri
Singburi – Ang Thong
Nakorn Pathom - Bangkok
Samut Prakarn - Rayong
Prachin Buri 0 Chachoengsao
Prachuab Kirikhan - Chumporn
Khlong Thom – Wang Wiset
Ban Nasarn – Thung Song
Si Chon – Tha Sala
Table 4.3-5 Results of NAM validation along Screen Line in 2012
Vehicle type
Screen Line
Screen Line 1 (SL 1)
Survey
Model
Difference (%)
Screen Line 2 (SL 2)
Survey
Model
Difference (%)
Screen Line 3 (SL 3)
Survey
Model
Difference (%)
Screen Line 4 (SL 4)
Survey
Model
Difference (%)
Screen Line 5 (SL 5)
Survey
Model
Difference (%)
Screen Line 6 (SL 6)
Survey
Model
Difference (%)
Overall Difference (%)
PCBK / SEA / CMCL / SYSTRA MVA
Private car
63,958
72,537
13.41
71,912
62,063
-13.70
72,824
81,628
12.09
42,048
45,712
8.71
17,250
18,504
7.27
46,512
52,390
12.64
5.83
Bus
2,147
2,168
0.98
3,277
3,208
-2.11
7,937
7,848
-1.12
3,701
3,772
1.92
897
948
5.69
2,639
2,436
-7.69
-1.06
Truck
10,031
11,174
11.39
19,615
17,757
-9.47
25,600
22,683
-11.39
9,783
11,436
16.90
6,759
7,348
8.71
14,570
13,882
-4.72
-2.41
Total (unit)
76,136
85,879
12.80
94,804
83,028
-12.42
106,361
112,159
5.45
55,532
60,920
9.70
24,906
26,800
7.60
63,721
68,708
7.83
3.80
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
Year 2012
1,267
45
912
150
91
2,465
Modal Split
Private car
Van
Bus
Train
Air
Total
Source: NAM
Table 4.3-7 Results from NAM
Year
Million PCU-km.
Million PCU-hr.
Speed (km. /hr.)
336.67
4.24
79.40
2012
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
(1,000 tons/year)
405,934
10,848
88,074
57
504,913
Road
Train
Water
Air
Average for all modes
Million tons-km/year
186,772,872
2,442,189
4,920,272
34,038
194,169,370
Average Distance
(km.)
460.1
225.1
55.9
597.2
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
Screen Line
Screen Line 1 (SL 1)
Difference (%)
Screen Line 2 (SL 2)
Survey
Model
Survey
Model
Difference (%)
PCBK / SEA / CMCL / SYSTRA MVA
Private car
66,516
62,885
-5.46
74,788
81,305
8.71
Vehicle type
Bus
Truck
2,233
10,432
2,419
11,462
8.33
9.87
3,408
20,400
3,008
17,751
-11.74
-12.99
Total (vehicle)
79,181
76,766
-3.05
98,596
102,064
3.52
4-20
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Screen Line
Screen Line 3 (SL 3)
Private car
75,737
69,236
-8.58
43,730
37,110
-15.14
17,940
16,344
-8.90
48,372
43,843
-9.36
-5.00
Survey
Model
Difference (%)
Screen Line 4 (SL 4)
Survey
Model
Difference (%)
Screen Line 5 (SL 5)
Survey
Model
Difference (%)
Screen Line 6 (SL 6)
Survey
Model
Difference (%)
Overall Difference (%)
Vehicle type
Bus
Truck
8,254
26,624
7,440
27,097
-9.86
1.78
3,849
10,174
3,840
9,171
-0.23
-9.86
933
7,029
1,012
7,579
8.47
7.82
2,745
15,153
2,506
14,296
-8.71
-5.66
-5.59
-2.73
Total (vehicle)
110,615
103,773
-6.19
57,753
50,121
-13.21
25,902
24,935
-3.73
66,270
60,644
-8.49
-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
1
2
3
4
5
6
7
8
Commodity
Agricultural products
Fishery products
Live stocking products
Agricultural industry products
Industrial products
Mineral and fuel products
Cross-border products
Addition products for domestic transport
Total
Transport volume (1,000 tons/year)
67,816
1,138
2,492
32,824
192,979
181,981
49,005
276,287
804,521
Table 4.3-11 Estimation of people’s travel based on transport modes
Mode
Private car
Van
Bus
Train
Air
Total
Source: NAM
2012
1,267
45
912
150
91
2,465
PCBK / SEA / CMCL / SYSTRA MVA
2013
1,358
46
978
145
93
2,620
2017
1,452
50
1,048
156
100
2,806
2022
1,594
56
1,154
173
111
3,087
2027
1,766
61
1,284
193
123
3,426
Unit: 1,000 person-trip/day
2032
2037
1,973
2,220
68
75
1,439
1,625
217
246
137
153
3,834
4,320
4-21
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 4.3-12 Freight Transport Volume from NAM
Mode
Road
Train
Water
Air
Total
Source: NAM
2012
405,934
10,848
88,074
57
504,913
2013
704,013
11,252
89,126
130
804,521
2017
743,463
11,882
94,120
137
849,602
2022
795,024
12,706
100,647
147
908,525
Unit: 1,000 tons/year
2032
2037
910,010
973,597
14,544
15,561
114,520
120,273
168
180
1,039,243 1,109,611
2027
850,576
13,594
107,680
157
972,008
Table 4.3-13 Travelling Data Results from NAM
Year
Mil. Veh-Km
336.67
304.63
324.53
353.29
386.94
426.38
472.73
Mil. Veh-Hr
4.24
3.80
4.08
4.50
5.00
5.61
6.36
2012
2013
2017
2022
2027
2032
2037
Source: NAM
Remark: Veh-Km: vehicle x distance
Veh-Hrs: vehicle x travel time
Speed (km/hr)
79.40
80.13
79.48
78.50
77.36
75.98
74.34
Table 4.3-14 Domestic Freight Transport Results from NAM
Mode
2012
Unit: million tons-km/year
Road
186,772
2013
228,200
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
34
194,169
77
246,567
82
260,797
87
279,142
93
299,084
100
320,560
107
343,208
Air
Total
2017
241,400
2022
258,400
2027
276,900
2032
296,900
2037
318,200
Table 4.3-15 Results from NAM, Freight, Average distance of freight Transport
Mode
2012
Unit: km
Road
460.1
2013
324.2
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
Average of all types
597.2
384.6
594.5
306.5
594.5
307.0
594.5
307.3
594.5
307.8
594.5
308.5
594.5
309.4
PCBK / SEA / CMCL / SYSTRA MVA
2017
324.8
2022
325.1
2027
325.7
2032
326.3
2037
326.9
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)
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
Year
Vehicle Type
Dioxide
(CO2)
2012 Passenger Car
15.613
Bus
3.240
Truck
10.384
Total
29.237
2013 Passenger Car
16.717
Bus
3.469
Truck
11.118
Total
31.304
2017 Passenger Car
17.808
Bus
3.887
Truck
11.823
Total
33.518
2022 Passenger Car
19.456
Bus
4.493
Truck
12.768
Total
36.717
2027 Passenger Car
21.481
Bus
5.202
Truck
13.821
Total
40.504
2032 Passenger Car
23.891
Bus
6.024
Truck
14.970
Total
44.885
2037 Passenger Car
26.868
Bus
7.000
Truck
16.246
Total
50.114
Source: Estimated by the Consultants
PCBK / SEA / CMCL / SYSTRA MVA
Carbon
Monoxide
(CO)
0.083
0.042
0.034
0.159
0.089
0.045
0.036
0.170
0.093
0.050
0.038
0.181
0.102
0.057
0.041
0.200
0.111
0.065
0.044
0.220
0.126
0.076
0.048
0.250
0.141
0.087
0.052
0.280
Nitrogen Oxide Hydro Carbon
(NOX)
(HC)
0.066
0.029
0.077
0.172
0.073
0.032
0.085
0.190
0.080
0.037
0.093
0.210
0.087
0.043
0.100
0.230
0.094
0.049
0.107
0.250
0.105
0.057
0.118
0.280
0.117
0.066
0.127
0.310
0.012
0.001
0.007
0.020
0.012
0.001
0.007
0.020
0.012
0.001
0.007
0.020
0.018
0.002
0.010
0.030
0.018
0.003
0.009
0.030
0.018
0.003
0.009
0.030
0.024
0.004
0.012
0.040
Particle
Matters
(PM)
0.001
3.206
6.752
9.959
0.001
3.461
7.290
10.752
0.001
3.863
7.744
11.608
0.001
4.435
8.351
12.787
0.001
5.095
9.022
14.118
0.001
5.855
9.761
15.617
0.001
6.740
10.576
17.317
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
2012
Vehicle Type
Passenger Car
Bus
Truck
Total
2013 Passenger Car
Bus
Truck
Total
2017 Passenger Car
Bus
Truck
Total
2022 Passenger Car
Bus
Truck
Total
2027 Passenger Car
Bus
Truck
Total
2032 Passenger Car
Bus
Truck
Total
2037 Passenger Car
Bus
Truck
Total
Source: Estimated by the Consultants
PCBK / SEA / CMCL / SYSTRA MVA
Benzene
4.99
0.47
0.02
5.48
5.59
0.56
0.02
6.17
6.26
0.67
0.02
6.95
7.28
0.82
0.02
8.12
8.46
1.00
0.02
9.48
9.89
1.22
0.02
11.13
11.71
1.49
0.03
13.23
Diesel
11.47
5.57
16.01
33.05
12.84
6.58
17.18
36.60
14.38
7.77
18.44
40.59
16.71
9.56
20.31
46.58
19.41
11.67
22.39
53.47
22.70
14.19
24.77
61.66
26.87
17.36
27.58
71.81
LPG
0.15
0.06
0.02
0.23
0.17
0.07
0.02
0.26
0.19
0.08
0.03
0.30
0.22
0.09
0.03
0.34
0.26
0.12
0.04
0.42
0.30
0.14
0.05
0.49
0.35
0.17
0.06
0.58
CNG
0.03
0.02
0.03
0.08
0.03
0.02
0.03
0.08
0.03
0.03
0.04
0.10
0.03
0.03
0.04
0.10
0.04
0.04
0.04
0.12
0.05
0.05
0.05
0.15
0.06
0.06
0.06
0.18
4-29
Chapter 5
Improvement and maintenance of
transport and traffic eBUM
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
5.2
5.3
5.4
5.5
5.1
Introduction
Study and review of eBUM
Improvement and development of eBUM
Development of innovation for the application of model
Update of Software Licenses for Cube Program of OTP
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
5.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
1. Information about planning,
e.g. population, household,
employment, household income


2. Household size Distribution Model


3. Vehicle Ownership Model


PCBK / SEA / CMCL / SYSTRA MVA
Points of Development
According to the recent data of
population census, Bangkok and
metropolitan areas have the total
population of 14.6 million and 4.8
million households. This is
significantly different from the
planning data in the recent traffic
model, which has the population
of 11.5 million and 3.9 million
households. This affects all
aspects of the model, so the
model, as well as its variations,
must be reviewed.
Review and improve the main
traffic sources
Improvement and validation in
planning database, which has
different population and
household from those in the
recent data of population census
Have impacts on Trip Production
Model; if HH Size is modeled
incorrectly, it will affect the
estimations of Total Trip
Improvement and validation in
planning database, which has
different population and
household from those in the
recent data of population census
Have significant impacts on all
process of the model since it is
the model of Market
Segmentation, in which different
groups have completely different
travel behavior






Major trends of development
Adjust planning database to be in
line with the recent data of
population census
Review and examine the
estimations of population growth,
household, and in come in the
model’s database and make them
updated as much as possible
Review the data of employment,
especially in the new industrial
zones or particular areas such as
hospitals, airports, etc.
Review the number of students
because the education at present
is extending.
Review and improve the model
with existing HIS and the new one
to derive from this study
Validation with HIS, which is
updated based on the recent data
of population census
 Review and improve the model
with existing HIS and the new one
to derive from this study
 Validation with HIS, which is
updated based on the recent data
of population census and the
statistics of registered cars from
the Department of Land Transport
5-2
Executive Summary Report
Details
4. Trip Production Model
5. Trip Attraction Model
6. Trip Distribution Model
PCBK / SEA / CMCL / SYSTRA MVA
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Points of Development
 Improvement and validation in
planning database, which has
different population and
household from those in the
recent data of population census
 Have significant impacts especially
on Trip rate in each Household
Segment, leading to the deviation
of the estimated car number on
road network; and impacts on the
analysis of traffic condition as well
as the following steps in the
model since it requires the input
data of speed and time
 Improvement and validation in





Major trends of development
Review and improve the model
with existing HIS and the new one
to derive from this study
Validation with HIS, which is
updated based on the recent data
of population census and the
statistics of registered cars from
the Department of Land Transport
Examine with the number of cars
on road network passing Strategic
Screen line after the Assignment
Model
Check for the necessity of
O-D Table in Bangkok and
metropolitan areas
Review and improve the model to
comply with the recent data of
population census, as well as the
employment data of National
Statistical Office; e.g.
the ratio of employment and
population is at 45%-50%, etc.
Check for the necessity of
O-D Table in Bangkok and
metropolitan areas
planning database, which has
different population and
household from those in the
recent data of population census
 Not many impacts since the
Control is Trip Production Model;
however, there should be

improvement to clearly reflect the
development of high employment
or high number of students
 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
5-3
Executive Summary Report
5.3
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
Area
Bangkok
Nonthaburi
Samutprakarn
PathumThani
Samutsakorn
NakonPathom
Total
(1)
Census (‘000)
Population Household
8,305
2,882
1,334
474
1,829
646
1,327
519
887
328
944
286
14,626
5,135
Model eBUM(1) (‘000)
Population Household
6,915
2,272
910
313
1,075
393
717
243
430
146
790
244
10,837
3,612
Difference (‘000)
Population Household
-1,390
-610
-424
-161
-754
-253
-610
-276
-457
-182
-154
-42
-3,789
-1,524
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
Purpose
Vehicle
Ownership
0 VEH
Mean Trip Length
Observed
Estimated
HBW
58.90
56.70
HBE
48.40
49.70
HBO
42.40
42.40
NHB
53.00
50.90
MC
HBW
25.60
26.70
HBE
19.80
20.40
HBO
17.20
17.80
NHB
19.10
19.40
1 CAR
HBW
69.40
71.20
HBE
53.70
53.50
HBO
48.20
48.70
NHB
47.60
46.10
Multi-Vehicle
HBW
72.90
71.70
HBE
57.70
57.70
HBO
48.90
48.60
NHB
58.90
61.30
Remark: Observed – from the survey HIS 2546
Estimated– from the synthesis of Gravity Model
5.3.3
% Intrazonal
% Diff
(Est./Orbs)
-3.74%
2.69%
0.00%
-3.96%
4.30%
3.03%
3.49%
1.57%
2.59%
-0.37%
1.04%
-3.15%
-1.65%
0.00%
-0.61%
4.07%
Observed
Estimated
28.10
23.70
41.90
20.50
39.40
33.70
53.90
42.50
29.50
28.60
40.20
22.20
23.50
21.50
36.90
19.60
24.30
29.70
39.30
31.50
27.40
48.30
60.50
37.40
17.00
33.00
41.40
41.50
16.00
39.20
41.50
22.10
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
1
2
3
4
5
6
7
8
Place
Future Park Rangsit
Don Mueang Railway Station
Mo Chit 2 Bus Terminal
Bang Sue Railway Station
Ratchadapisek Road
Sapan Hua Chang Pier
Hua Lampong
Si Phraya Pier
Location
9
10
11
12
13
14
15
16
Place
Bearing Station
Bang Na – Trad Highway
Samut Prakarn City Hall
Charan Sanitwong Road
New Southern Bus Terminal
Bang Yai
Ban Phachi Railway Junction
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
Value
Variable
Symbol
สัมประสิทธิ์
Alternative Specific Constant - ASC
ASC bus
ASCBUS
1.3368
ASC van
ASCVAN
1.0096
ASC taxi
ASCPARA
1.4553
ASC urban rail transit
ASCRRT
2.8459
ASC urban and suburban rail transit
ASCSRT
3.1030
ASC passenger boat
ASCVES
Variables of travel characteristics
Access time to the transport (minutes)
ACCTIME
-0.0194
Access cost to the transport (Baht)
ACCCOST
-0.0120
Waiting time for the transport (minute)
WTIME
-0.0386
On board time (minute)
OBTIME
-0.0061
Cost while on board (Baht)
FARE
-0.0098
Variables of economic and social characteristics
Monthly income (1,000 Baht) bus
INCBUS
-0.0787
Monthly income (1,000 Baht) van
INCVAN
-0.0171
Monthly income (1,000 Baht) taxi
INCPARA
-0.1272
Monthly income (1,000 Baht) urban rail transit
INCRRT
-0.1939
Monthly income (1,000 Baht) urban and suburban
rail transit
LL
LL(0)
Likelihood Ratio Index (2)
% Correct
INCSRT
-0.3007
t value
Sig.
1.855
1.450
2.026
3.619
3.480
-
0.064
0.147
0.043
0.000
0.001
-
-4.302
-2.752
-4.345
-1.618
-2.103
0.000
0.006
0.000
0.106
0.036
-1.635
-0.371
-3.068
-3.681
0.102
0.711
0.002
0.000
-4.651
0.000
-652.781
-871.537
0.251
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 SpeedFlow 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.)
Vehicle type
2012
2.995
0.240
Private Car (PC)
Motorcycle (MC)
5.3.5
VOC (Baht/min.)
2013
2.977
0.239
2012
1.25
0.61
2013
1.28
0.62
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
Survey Site
Location
MB – NS12
MB – NS13
MB – NS14
MB – NS15
MB – NS16
MB – NS17
MB – NS18
MB – NS19
MB – NS20
MB – NS21
MB – NS22
Rama VIII Bridge
Phra Pinklao Bridge
Memorial Bridge
Phra Pokklao Bridge
Taksin Bridge
Krungthep Bridge
Rama III Bridge
Rama IX Bridge
Bhumipol Bridge
Bhumipol Bridge II
Kanchana Pisek Bridge
Total
Source: Survey by the Consultants
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
From Survey
From Survey
From Model Difference
From Model Difference
am peak
pm peak
(PCU/hour)
(%)
(PCU/hour)
(%)
(PCU/hour)
(PCU/hour)
5,141
5,863
14.04
5,320
4,883
-8.21
10,059
8,815
-12.37
15,335
13,493
-12.01
3,963
3,650
-7.89
3,765
3,645
-3.18
13,025
11,835
-9.14
12,317
10,472
-14.98
11,307
9,826
-13.10
8,055
6,942
-13.82
4,954
5,542
11.88
3,242
2,855
-11.94
10,980
9,735
-11.34
7,063
6,034
-14.56
9,315
9,833
5.56
9,661
10,620
9.92
4,589
5,027
9.55
6,078
5,772
-5.04
4,843
5,344
10.34
4,652
5,120
10.06
5,255
4,513
-14.11
4,722
5,213
10.40
129,871
121,478
-6.46
125,575
121,397
-3.33
Table 5.3-8 Model Calibration for Average Traffic Volume on Expressway System in 2012
Expressway System
PCU/day
Chalong Rat (Ram Indra – At Narong)
170,969
Burapa Withi (Bangna – Chonburi)
123,710
Ram Indra – Outer Ring road
16,000
nd
SriRat Expressway (2 Stage)
664,781
Kanchana Pisek Expressway (Bang Pli-Suk Sawad)
226,597
Udorn Ratthaya (Bang Pa-In – Pak Kred)
65,126
st
Chalerm Mahanakorn (1 Stage)
380,053
Total
1,647,236
Source: Revenue Unit, Bangkok Expressway Company Limited-BECL
PCBK / SEA / CMCL / SYSTRA MVA
From Model (PCU/day)
147,248
142,837
18,208
633,877
203,874
64,656
307,339
1,518,039
Difference (%)
-13.87
15.46
13.80
-4.65
-10.03
-0.72
-19.13
-7.84
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
(person-trip/day)
(person-trip/day)
Bangsue
8,118
8,898
Kampangpetch
3,648
3,756
Chatuchak
14,139
13,876
Paholyothin
14,859
16,396
Ladprao
13,582
15,255
Ratchada Pisek
6,582
7,044
Sutthisarn
11,146
9,431
Huay Kwang
16,900
13,589
Thai Cultural Center
13,070
13,381
Rama IX
17,689
14,634
Petchburi
15,989
15,679
Sukhumwit
30,403
24,496
Sirikitti National Conference Center
11,345
11,922
Klong Toei
1,841
1,873
Lumpini
8,434
8,470
Silom
15,834
13,545
Samyan
8,746
7,627
Hualampong
13,074
13,941
Total
225,399
228,340
Source: Annual Report of Bangkok Metro Public Company Limited - BMCL
Station
Difference (%)
9.62
2.95
-1.86
10.34
12.32
7.02
-15.39
-19.59
2.38
-17.27
-1.94
-19.43
5.08
1.76
0.43
-14.46
-12.80
6.64
1.30
Table 5.3-10 Model Calibration for Average BTS Ridership in 2012
Secondary Data
(person-trip/day)
From Model
(person-trip/day)
Difference (%)
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
(person-trip/day)
From Model
(person-trip/day)
Difference (%)
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
Period
Area
PCU-Km.
Whole Day
Inner Ring Road
Outer Ring Road
Bangkok Metropolitan and Surrounding Area
AM peak
Inner Ring Road
Outer Ring Road
Bangkok Metropolitan and Surrounding Area
PM peak
Inner Ring Road
Outer Ring Road
Bangkok Metropolitan and Surrounding Area
Source: eBUM base year 2012
24,741,068
73,246,269
183,630,687
2,173,221
6,202,978
14,403,210
1,929,545
5,706,175
13,576,436
PCU-Hr.
832,310
1,958,335
4,666,342
176,301
338,392
694,170
124,950
265,589
566,714
Average
Speed
(km. /hr.)
29.7
37.4
39.4
12.3
18.3
20.8
15.4
21.5
24.0
Table 5.3-13 Numbers of Trips in each area in 2012
Trip Area
Within Inner Ring Road (IRR)
In-Out of Inner Ring Road (IRR)
Within Outer Ring Road (ORR)
In-Out of Outer Ring Road (IRR)
Between IRR and out-off Ring Road
Out of Ring Road
Source: eBUM base year 2012
Number of trips (1,000 person-trip/day)
1,316
3,592
10,314
6,468
2,186
5,236
Table 5.3-14 Modal Splits in 2012
Type of vehicle ownership
Non vehicle
1 motorcycle
1 private car
More than 1 vehicle
Total
External Trips
Special Generators
HBW
884
2,189
4,904
3,725
11,703
HBE
418
763
1,511
1,569
4,261
Total
HBO
724
787
1,512
999
4,022
Unit: 1,000 person-trip/day
NHB
Total
51
2,077
341
4,080
658
8,585
625
6,918
1,675
21,660
284
852
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
Type of vehicle
ownership
Non vehicle
1 motorcycle
1 private car
More than 1 vehicle
Total
External Trips
Special Generators
Total
Source: eBUM base year 2012
Total
Private
Proportion of
private (%)
2,077
4,080
8,585
6,918
21,660
284
852
22,796
224
2,302
5,865
5,554
13,945
284
418
14,647
10.78
56.41
68.32
80.28
64.38
100.00
49.06
64.25
Unit: 1,000 person-trip/day
Proportion
Public
of public
transport
transport (%)
1,853
89.22
1,778
43.58
2,720
31.68
1,364
19.72
7,715
35.62
434
50.94
8,149
35.75
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
Trip volume
%
14,647
48.02
2012
Transfer System
No Transfer System
14,647
64.25
Public Transport
Trip volume
%
15,856
51.98
8,149
35.75
Total
30,503
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)
Mode
Green Line BTS
Blue Line MRT
Red Line Airport Rail Link
Boat
Bus
Train
Van
Total
Source: eBUM base year 2012
PCBK / SEA / CMCL / SYSTRA MVA
Number of passengers
(1,000 person-trip/day)
677
243
49
112
13,999
75
701
15,856
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
Site
Location
MB – NS01 Bridge over Chao Phraya River
– Western Ring Road
MB – NS02 Pathum Thani Bridge
MB – NS03 Pathum Thani Bridge II
MB – NS04 Nondhaburi Bridge
MB – NS05 Rama IV Bridge
MB – NS06 Phra Nangklao Bridge (New)
MB – NS07 Phra Nangklao Bridge (Old)
MB – NS08 Rama V Bridge
MB – NS09 Rama VII Bridge
MB – NS11 Krungthon Bridge
MB – NS12 Rama VIII Bridge
MB – NS13 Phra Pinklao Bridge
MB – NS14 Memorial Bridge
MB – NS15 Phra Pokklao Bridge
MB – NS16 Taksin Bridge
MB – NS17 Krungthep Bridge
MB – NS18 Rama III Bridge
MB – NS19 Rama IX Bridge
MB – NS20 Bhumipol Bridge
MB – NS21 Bhumipol Bridge II
MB – NS22 Kanchana Pisek Bridge
Total
Source: Expand from Survey in 2012
PCBK / SEA / CMCL / SYSTRA MVA
Am peak (pcu/hr)
Survey
Model
3,274
2,761
4,681
4,615
2,910
5,225
9,065
2,914
3,943
5,660
5,546
5,295
10,361
4,082
13,416
11,646
5,103
11,309
9,594
4,727
4,988
5,413
133,767
3,947
3,891
2,454
6,100
10,583
3,402
4,603
6,462
6,332
6,046
11,830
4,661
15,318
13,297
4,644
10,291
8,731
4,302
4,539
4,926
139,120
Difference
(%)
-15.70
-15.70
-15.70
-15.70
16.70
16.70
16.70
16.70
14.20
14.20
14.20
14.20
14.20
14.20
14.20
-9.00
-9.00
-9.00
-9.00
-9.00
-9.00
4.00
Pm peak (pcu/hr)
Survey
Model
2,468
2,566
4,393
4,608
3,835
4,322
8,340
2,941
4,491
5,936
5,393
5,480
16,166
3,878
12,687
8,297
3,339
7,275
9,951
6,260
4,792
4,864
129,713
4,380
4,080
3,868
4,662
9,320
2,849
4,844
6,403
5,487
5,576
16,449
4,280
11,427
7,742
3,116
6,789
9,286
7,408
5,427
4,719
130,678
Difference
(%)
4.00
-0.30
-11.50
0.90
7.90
7.90
7.90
7.90
7.90
1.70
1.80
1.80
10.40
-9.90
-6.70
-6.70
-6.70
-6.70
18.30
13.30
-3.00
0.70
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
Site
MB – EW 01
MB – EW 02
MB – EW 03
MB – EW 04
MB – EW 05
MB – EW 06
MB – EW 07
MB – EW 08
MB – EW 09
MB – EW 10
MB – EW 11
MB – EW 12
MB – EW 13
MB – EW 14
MB – EW 15
MB – EW 16
MB – EW 17
MB – EW 18
MB – EW 19
MB – EW 20
MB – EW 21
MB – EW 22
MB – EW 23
MB – EW 24
MB – EW 25
MB – EW 26
MB – EW 27
MB – EW 28
MB – EW 29
MB – EW 30
MB – EW 31
MB – EW 32
MB – EW 33
MB – EW 34
MB – EW 35
Location
Outer Ring Road (Eastern)
Sri Burapa
Puang Siri
Sri Nakarin
Soi Lad Prao 130
Soi Mahad Thai
Soi Ramkamhaeng 53
Soi Ramkamhaeng 43/1
Soi Wat Thep Lila
Soi Ramkamhaeng 21
Soi Ramkamhaeng 9
Chalong Rat Expressway
Rama 9
Kampangpetch 7
Petchburi
Prasert Manukit Road
Petchburi 38/1
Sukhumwit 55
Asoke Montri
Sukhumwit 3
Chalerm Mahanakorn
Expressway
Witthayu
Chidlom
Ratchadamri
Phaya Thai
Banthat Thong
Sri Rat Expressway
Rama 6
Krung Kasem
Chakkrapaddipong
Raj Damnoen Klang
Prachathipatai
Samsen
Phra Pinklao
Arun Amarin
PCBK / SEA / CMCL / SYSTRA MVA
Am peak (pcu/hr)
Survey
Model
6,198
6,638
606
618
1,518
1,472
3,730
3851
673
590
1,163
1,300
772
795
871
854
1,097
1,070
649
601
194
180
5,926
5760
4,626
4497
1,000
972
2,567
2914
3,623
4113
2,617
2971
1,692
1,448
1,621
1,636
791
798
10,601
10,696
2,299
1,930
5,267
5,473
3,057
11,083
4,738
2,024
7,446
3,817
4,573
4,488
10,059
6,384
1,937
1646
5,521
6168
3,314
10065
4,522
2,273
7,660
3,927
4,705
3559
10,562
6145
Difference
(%)
7.10
2.00
-3.00
3.20
-12.30
-19.40
3.00
-2.00
-2.50
-7.40
-7.20
-2.80
-2.80
-2.80
13.50
13.50
13.50
-14.40
0.90
0.90
0.90
-15.70
-14.70
4.80
12.70
8.40
-9.20
-4.60
12.30
2.90
2.90
2.90
-20.70
5.00
-3.70
Pm peak (pcu/hr)
Survey
Model
7,187
7,646
678
669
1,372
1,335
3,739
4,293
802
734
1,142
1,250
869
763
958
820
1,269
1,360
754
839
215
189
7,632
6,718
5,697
5,015
1,256
1,106
3,550
4,152
3,689
4,335
2,761
2,214
1,588
1,304
1,665
1,848
791
878
9,692
10,755
2,550
1,930
6,908
4,732
3,687
11,002
4,952
2,504
7,042
4,093
3,918
4,177
9,246
6,784
2,233
1,837
6,384
5,499
4,284
9,825
4,440
2,330
8,077
4,141
4,961
3,196
10,338
5,905
Difference
(%)
6.40
-1.30
-2.70
14.80
-8.50
9.50
-12.20
-14.40
7.20
11.30
-12.10
-12.10
-12.10
-11.90
17.00
17.50
-19.80
-17.90
11.00
11.00
11.00
-12.40
-4.80
-7.60
16.20
16.20
-10.70
-10.30
-6.90
8.50
8.50
8.50
-23.50
11.80
-13.00
5-16
Executive Summary Report
Site
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Location
MB – EW 36
MB – EW 37
MB – EW 38
MB – EW 39
Charan Sanitwong
Sirindhorn
Ratchapruek
Outer Ring Road (West)
Total
Source: Survey by the Consultants
Am peak (pcu/hr)
Survey
Model
6,403
6,311
20,141
19192
3,881
4797
16,630
16,429
172,228 172,507
Pm peak (pcu/hr)
Survey
Model
5,477
6,256
19,435
18,892
5,131
5,168
15,820
16,217
176,694 178,206
Difference
(%)
-1.40
-4.70
23.60
-1.20
-0.10
Difference
(%)
14.20
-2.80
0.70
2.50
0.90
Table 5.3-20 Model Calibration for Average Traffic Volume on Expressway System in 2013
Expressway System
PCU/day
Chalong Rat (Ram Indra – At Narong)
181,520
Burapa Withi (Bangna – Chonburi)
137,266
Ram Indra – Outer Ring road
17,600
nd
SriRat Expressway (2 Stage)
670,832
Kanchana Pisek Expressway (Bang Pli-Suk Sawad)
207,227
Udorn Ratthaya (Bang Pa-In – Pak Kred)
72,764
st
Chalerm Mahanakorn (1 Stage)
373,889
Total
1,661,098
Source: Revenue Unit, Bangkok Expressway Company Limited-BECL
From Model
(PCU/day)
185,232
95,262
18,253
625,893
260,948
77,048
406,006
1,668,642
Difference (%)
2.00
-30.60
3.70
-6.70
25.90
5.90
8.60
0.50
Table 5.3-21 Model Calibration for Average MRT Ridership in 2013
Station
Bangsue
Kampangpetch
Chatuchak
Paholyothin
Ladprao
Ratchada Pisek
Sutthisarn
Huay Kwang
Thai Cultural Center
Rama IX
Petchburi
Sukhumwit
Sirikitti National
Conference Center
Klong Toei
Lumpini
PCBK / SEA / CMCL / SYSTRA MVA
Secondary Data
(person-trip/day)
9,199
1,997
14,912
15,330
15,371
7,714
13,457
18,955
14,745
19,529
19,782
34,236
9,176
From Model
(person-trip/day)
9,172
2,111
15,038
15,376
15,555
7,723
13,417
18,940
14,723
19,557
19,413
34,703
9,181
2,142
10,647
2,155
10,640
Difference (%)
-0.29
5.71
0.84
0.30
1.20
0.12
-0.30
-0.08
-0.15
0.14
-1.87
1.36
0.05
0.61
-0.07
5-17
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Secondary Data
From Model
(person-trip/day)
(person-trip/day)
Silom
19,732
19,857
Samyan
10,058
10,555
Hualampong
14,800
14,790
Total
251,782
252,406
Source: Annual Report of Bangkok Metro Public Company Limited - BMCL
Station
Difference (%)
0.63
-0.03
-0.07
0.25
Table 5.3-22 Model Calibration for Average BTS Ridership in 2013
Station
Mo Chit
Saphan Khwai
Ari
Sanam Pao
Victory Monument
Phaya Thai
Ratchathewi
Siam
Chit Lom
Phloen Chit
Nana
Asok
Phrom Phong
Thong Lo
Ekkamai
Phra Khanong
On Nut
Bang Chak
Punnawithi
Udom Suk
Bang Na
Bearing
National Stadium
Ratchadamri
Sala Daeng
Chong Nonsi
Surasak
Saphan Taksin
PCBK / SEA / CMCL / SYSTRA MVA
Secondary Data
(person-trip/day)
From Model
(person-trip/day)
Difference (%)
44,711
9,665
13,400
4,134
51,601
25,536
12,317
67,257
30,483
19,175
18,154
51,952
24,743
14,261
15,701
11,649
30,271
7,585
7,150
17,040
4,274
21,001
21,802
5,740
32,465
23,491
12,344
19,441
44,327
9,593
13,306
4,100
51,243
24,971
12,195
66,653
30,160
18,955
18,139
51,277
24,616
14,157
15,547
11,554
29,971
7,514
7,049
16,966
4,536
20,330
21,612
5,716
32,172
23,354
12,224
19,208
-0.86
-0.75
-0.70
-0.83
-0.69
-2.21
-0.99
-0.90
-1.06
-1.15
-0.08
-1.30
-0.51
-0.73
-0.98
-0.81
-0.99
-0.94
-1.41
-0.43
6.13
-3.20
-0.87
-0.41
-0.90
-0.58
-0.97
-1.20
5-18
Executive Summary Report
Station
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Secondary Data
(person-trip/day)
From Model
(person-trip/day)
Difference (%)
10,799
18,383
3,116
6,474
6,434
16,955
673,606
-0.73
-1.01
-0.65
-0.43
-0.80
-0.76
-0.94
Krung Thon Buri
10,878
Wongwian Yai
18,570
Pho Nimit
3,136
Talat Phlu
6,502
Wutthakat
6,486
Bang Wa
17,084
Total
680,000
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
(person-trip/day)
Phaya Thai
12,787
Ratchaprarop
3,213
Makkasan
4,190
Ramkamhaeng
4,458
Huamark
5,064
Ban Thapchang
1,860
Ladkrabang
6,051
Suvannabhumi
8,342
Total
45,965
Source: Revenue Collecting Division, SRT
From Model
(person-trip/day)
12,960
3,162
4,137
4,571
5,116
1,910
6,179
8,539
46,574
Difference (%)
1.35
-1.59
-1.26
2.53
1.03
2.69
2.12
2.36
1.32
Table 5.3-24 Average Travel Speed in BMR by area in 2013
Period
Whole Day
Area
Inner Ring Road
Outer Ring Road
Bangkok Metropolitan and Surrounding Area
Bangkok Metropolitan and Surrounding Area + 2 provinces
AM peak
Inner Ring Road
Outer Ring Road
Bangkok Metropolitan and Surrounding Area
Bangkok Metropolitan and Surrounding Area + 2 provinces
PM peak
Inner Ring Road
Outer Ring Road
Bangkok Metropolitan and Surrounding Area
Bangkok Metropolitan and Surrounding Area + 2 provinces
Source: eBUM base year 2013
PCBK / SEA / CMCL / SYSTRA MVA
PCU-Km.
PCU-Hr.
26,247,976
80,367,315
203,936,979
233,781,354
2,274,853
6,770,371
16,537,740
18,923,014
2,017,599
6,193,621
15,340,444
17,460,370
923,648
2,236,024
5,333,793
5,963,105
193,080
394,121
827,916
886,666
136,970
304,204
664,850
718,592
Average Speed
(km./hr.)
28.4
35.9
38.2
39.2
11.8
17.2
20.0
21.3
14.7
20.4
23.1
24.3
5-19
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 5.3-25 Modal Splits in 2013
Type of vehicle ownership
Non vehicle
1 motorcycle
1 private car
More than 1 vehicle
Total
External Trips
Special Generators
HBW
876
2,336
5,228
4,049
12,489
HBE
415
825
1,623
1,710
4,573
HBO
725
981
1,850
1,150
4,706
Total
Unit: 1,000 person-trip/day
NHB
total
58
2,074
598
4,740
1,056
9,757
765
7,674
2,477
24,245
297
882
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
Type of vehicle
ownership
Non vehicle
1 motorcycle
1 private car
More than 1 vehicle
Total
External Trips
Special Generators
Total
Source: eBUM base year 2013
PCBK / SEA / CMCL / SYSTRA MVA
Total
Private
Proportion of
private (%)
2,074
4,740
9,757
7,674
24,245
297
882
25,424
225
2,947
6,959
6,212
16,343
298
432
17,073
10.85
62.17
71.32
80.96
67.41
100.00
48.98
67.15
Unit: 1,000 person-trip/day
Proportion
Public
of public
transport
transport (%)
1,848
89.15
1,793
37.83
2,798
28.68
1,461
19.04
7,901
32.59
0
0.00
450
51.02
8,351
32.85
5-20
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 5.3-27 Major trip proportion including transfer and no transfer to public transport system, categorized
by type of travel in 2013
Area
2013
Bangkok and
Transfer System
metropolitan No Transfer System
areas
Bangkok and
Transfer System
metropolitan No Transfer System
areas + 2
provinces
Source: eBUM base year 2013
Private
Trip volume
%
15,195
51.90
15,195
67.15
17,073
17,073
51.90
67.15
Unit: 1,000 person-trip/day
Public Transport
Total
Trip volume
%
14,082
48.10
29,277
7,432
32.85
22,627
15,822
8,351
48.10
32.85
32,895
25,424
Table 5.3-28 Numbers of passengers using public transport in 2013 (including transfer to public transport
system)
Mode
Green Line BTS
Blue Line MRT
Red Line Airport Rail Link
Boat
Bus
Train
Van
Total
Source: eBUM base year 2013
PCBK / SEA / CMCL / SYSTRA MVA
Number of passengers
(1,000 person-trip/day)
707
263
49
115
13,941
82
665
15,822
5-21
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
1,000,000
900,000
No. of trip (person-trip/day)
800,000
700,000
600,000
500,000
400,000
300,000
200,000
100,000
0
Travel Distance (kilometer)
Figure 5.3-2 Trip distribution based on distance
No. of trip (person-trip/day)
300,000
250,000
200,000
150,000
100,000
50,000
0
Travel Time (minute)
Figure 5.3-3 Trip distribution based on trip duration
PCBK / SEA / CMCL / SYSTRA MVA
5-22
Executive Summary Report
5.3.6
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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)
14,403,210
18,923,014
21,653,492
24,600,003
27,457,154
30,457,445
33,679,692
2012
2013
2017
2022
2027
2032
2037
Source: eBUM
Remark: Veh-km = vehicles x distance of travel
Veh-Hr = vehicles x time of travel
(Veh-Hr)
694,170
886,666
1,197,014
1,491,608
1,755,838
2,181,860
2,672,468
Speed (km/hr)
20.8
21.3
18.1
16.5
15.6
14.0
12.6
(Veh-Hr)
566,714
718,592
933,898
1,166,771
1,366,411
1,709,719
2,129,595
Speed (km/hr)
24.0
24.3
21.4
19.4
18.5
16.4
14.6
Table 5.3-30 Forecasted traffic during evening peak
PM Peak Traffic
Year
(Veh-Km)
13,576,436
17,460,370
19,942,745
22,648,795
25,292,018
28,057,431
31,047,970
2012
2013
2017
2022
2027
2032
2037
Source: eBUM
Remark: Veh-km = vehicles x distance of travel
Veh-Hr = vehicles x time of travel
PCBK / SEA / CMCL / SYSTRA MVA
5-23
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 5.3-31 Forecasted traffic all day
Daily Traffic
Year
(Veh-Km)
183,630,687
233,781,354
267,036,238
305,539,025
340,427,690
378,486,075
421,271,012
2012
2013
2017
2022
2027
2032
2037
Source: eBUM
Remark: Veh-km = vehicles x distance of travel
Veh-Hr = vehicles x time of travel
(Veh-Hr)
4,666,342
5,963,105
7,357,397
9,008,141
10,083,075
12,001,841
14,660,999
Speed (km/hr)
39.4
39.2
36.3
33.9
33.8
31.5
28.7
2012
Figure 5.3-4 Desired line within Bangkok and Vicinity Area in 2012 (including 2 provinces)
PCBK / SEA / CMCL / SYSTRA MVA
5-24
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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)
PCBK / SEA / CMCL / SYSTRA MVA
5-25
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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)
PCBK / SEA / CMCL / SYSTRA MVA
5-26
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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)
PCBK / SEA / CMCL / SYSTRA MVA
5-27
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 5.3-32 Forecasted proportion of main trip with no transfer to public transport system
Unit: 1,000 person-trip/day
Year
Total
Private
(percentage)
64.25%
67.16%
69.19%
70.16%
70.97%
71.44%
72.03%
Private
2012
22,796
14,647
2013
25,424
17,074
2017
27,618
19,110
2022
30,320
21,272
2027
32,986
23,410
2032
35,764
25,550
2037
38,570
27,780
Source: eBUM
Remark: With no transfer to public transport system
Public Transport
8,151
8,351
8,508
9,047
9,576
10,214
10,790
Public Transport
(percentage)
35.75%
32.84%
30.81%
29.84%
29.03%
28.56%
27.97%
Table 5.3-33 Forecasted proportion of main trip including transfer to public transport system
Unit: 1,000 person-trip/day
Year
Total
Private
2012
30,503
14,647
2013
32,895
17,074
2017
35,669
19,110
2022
39,987
21,272
2027
43,411
23,410
2032
46,937
25,550
2037
50,531
27,780
Source: eBUM
Remark: Including transfer to public transport system
Private
(percentage)
48.01%
51.90%
53.58%
53.20%
53.93%
54.43%
54.98%
Public Transport
15,856
15,822
16,559
18,715
20,001
21,387
22,751
Public Transport
(percentage)
51.98%
48.10%
46.42%
46.80%
46.07%
45.57%
45.02%
Table 5.3-34 Forecasted numbers of passengers using major public transport system (Person Trips)
Mode
MRT
Public Bus
Boats
Others
Total
Source: eBUM
2012
969
13,999
112
776
15,856
PCBK / SEA / CMCL / SYSTRA MVA
2013
1,019
13,941
115
747
15,822
Passengers (1,000 person-trip/day)
2017
2022
2027
2,070
3,518
3,950
13,733
14,727
15,566
107
111
127
649
359
358
16,559
18,715
20,001
2032
4,769
16,120
156
342
21,387
2037
5,425
16,765
213
348
22,751
5-28
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 5.3-35 Forecasted numbers of passengers using major public transport system (including transfer
to public transport system)
Mode
2012
677
243
49
112
13,999
75
701
15,856
2013
707
263
49
Passengers (1,000 person-trip/day)
2017
2022
2027
857
1,298
1,450
757
883
991
217
274
319
239
454
502
233
262
173
198
203
228
107
111
127
13,733
14,727
15,566
87
90
88
562
269
270
16,559
18,715
20,001
BTS
MRT
ARL
Purple Line
Orange Line
Pink Line
Yellow Line
Gray Line
Light Blue Line
Boats
115
Public Bus
13,941
Trains
82
Van
665
Total
15,822
Source: eBUM
Remark: Number of trips including transfer to public transport system
2032
1,622
1,065
680
538
261
241
240
110
12
156
16,120
88
254
21,387
2037
1,869
1,205
760
618
293
276
266
124
14
213
16,765
89
259
22,751
Table 5.3-30 Average Speed in each area
Time period
All day
AM Peak
PM Peak
Area
Inner Ring Road
Outer Ring Road
Bangkok and Vicinity Area
Bangkok and Vicinity Area
+ 2 provinces
Inner Ring Road
Outer Ring Road
Bangkok and Vicinity Area
Bangkok and Vicinity Area
+ 2 provinces
Inner Ring Road
Outer Ring Road
Bangkok and Vicinity Area
Bangkok and Vicinity Area
+ 2 provinces
PCBK / SEA / CMCL / SYSTRA MVA
2012
29.7
37.4
39.0
39.4
2013
28.4
35.9
38.2
39.2
Average Speed (km/hr)
2017
2022
2027
25.1
21.5
20.4
32.7
29.9
29.9
35.3
32.9
32.9
36.3
33.9
33.8
12.3
18.3
20.5
20.7
11.8
17.2
20.0
21.3
9.6
14.3
16.9
18.1
8.3
12.9
15.4
16.5
7.2
12.5
14.6
15.6
6.4
10.9
13.0
14.0
5.6
9.8
11.7
12.6
15.4
21.5
23.6
24.0
14.7
20.4
23.1
24.3
12.8
17.5
20.2
21.4
10.8
15.5
18.3
19.4
9.3
15.0
17.5
18.5
7.8
13.3
15.4
16.4
6.7
11.5
13.6
14.6
2032
18.1
27.2
30.4
31.5
2037
15.5
24.1
27.6
28.7
5-29
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 5.3-31 Numbers of trips in each area
Trips in each area
Within Inner Ring Road (IRR)
In-Out of Inner Ring Road (IRR)
Within Outer Ring Road (ORR)
In-Out of Outer Ring Road (IRR)
Between IRR and out-off Ring Road
Out of Ring Road
5.4
2012
1,316
3,592
10,313
6,468
2,186
5,236
Number of trips (1,000 person-trip/day)
2013
2017
2022
2027
2032
1,296
1,254
1,283
1,345
1,422
3,576
3,732
4,013
4,333
4,660
10,524
10,823
11,648
12,562
13,503
7,303
8,228
9,231
10,153
11,099
2,482
2,698
2,982
3,268
3,571
6,870
7,606
8,502
9,311
10,139
2037
1,505
4,976
14,357
12,062
3,922
11,061
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
PCBK / SEA / CMCL / SYSTRA MVA
5-30
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
5.4.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
PCBK / SEA / CMCL / SYSTRA MVA
5-32
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Model data (pcu/hr)
Executive Summary Report
Survey data (pcu/hr)
Remark: Details of survey data along Screen Line can be read more in Final Report
Model data (pcu/hr)
Figure 5.4-4 Comparative Results between TRANUS Program and traffic survey Data
along Screen Line morning peak (unit : PCU/hour)
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)
PCBK / SEA / CMCL / SYSTRA MVA
5-33
Executive Summary Report
5.4.3
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Application of MATSim in the planning of emergency response plan in the industrial estate
of Ayutthaya
PCU/hr.
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
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
from DOH and EXAT
Year 2013
External Matrix OD
Adjustment for eBUM
Daily Traffic Volume
by Link
Model eBUM
Fuel Consumption
Analysis
Gasoline
Gasohol 91&95
Link Base Fuel
Consumption Calculation
from
8 types of vehicles and
Average Link Speed
E20
LPG
Parameters a and b
for Consumption
Equation
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
Vehicle
91/95
Motorcycle
(MC)
SAMLOR
(TAXI
Passenger
973,721
car (PC)
BUS
Pick up
Truck
VAN
Total
973,721
Remark: * unit kg. /day
Gasohol
91/95
4,442,997
CNG*
LPG
Diesel
E20 & E85
Total
-
-
-
-
4,442,997
4,126,425
825,632
201,181
810,585
1,305,742
1,958,536
3,031,007
1,414,075
810,585
2,131,374
11,704,946
8,569,422
246,448
190,903
1,464,165
4,074,863
1,463,526
13,522,484
6,271,061
880,340
25,168,418
1,414,075
1,709,975
13,522,484
6,461,964
880,340
41,664,661
Table 5.4-3 Fuel Consumption by province from Model eBUM in 2013
Unit: litre/day
Item
Province
1
Bangkok
Metropolitan
2
Nomthaburi
3
Samut Prakarn
4
Pathumthani
5
Nakorn Pathom
6
Samut Sakorn
7
Ayudhya
8
Chachoengsao
Total
Remark: * unit kg. /day
Gasoline
91/95
480,707
Gasohol
91/95
4,030,114
CNG*
LPG
Diesel
E20 & E85
753,538
2,061,443
10,420,243
699,319
94,549
62,557
98,712
96,351
50,105
35,372
55,367
973,721
784,690
521,631
1,004,311
889,338
425,077
384,816
529,443
8,569,421
132,534
87,744
150,974
140,059
73,726
49,170
76,416
1,464,165
381,085
254,373
418,696
393,120
203,695
141,135
221,318
4,074,863
2,276,807
1,523,726
3,134,611
3,301,588
1,443,631
1,336,474
1,731,340
25,168,418
137,087
90,712
142,877
139,889
72,731
51,187
80,273
1,414,075
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
Item
Province
1
2
3
4
Bangkok
Samut Prakan
Nonthaburi
Pathum
Thani
Nakhon
Pathom
Samut
Sakhon
Phra Nakhon
Si Ayutthaya
Chachoengsao
Total
5
6
7
8
Gasoline Gasoline
91
95
58,370 194,274
11,697
11,127
1,028
12,131
1,225
8,576
Gasohol
91
1,061,323
104,145
91,952
87,217
Gasohol
95
1,157,469
74,789
77,054
66,531
Gasohol
E20
211,433
31,973
38,850
33,752
Gasohol
E85
57,269
6,716
7,353
4,869
Basic
Diesel
101,209
18,656
3
0
Diesel
LPG*
NGV**
6,169,646
575,590
327,058
440,885
255,677
108,762
80,716
79,405
-
1,219
8,570
49,807
38,971
19,462
4,216
0
275,587
53,187
-
506
4,577
42,089
33,458
15,594
4,185
7,975
282,021
94,724
-
936
7,389
59,482
40,285
24,439
3,031
3,970
390,922
41,270
-
430
75,411
5,960
252,604
48,406
1,544,421
30,727
1,519,284
14,821
390,323
611
88,250
2,993
134,806
254,587
8,716,297
49,791
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
Item
Province
1
2
3
4
5
6
7
Bangkok
Samut Prakan
Nonthaburi
Pathum Thani
Nakhon Pathom
Samut Sakhon
Phra Nakhon Si
Ayutthaya
Chachoengsao
Gasoline
91/95
Gasohol
91/95
CNG*
LPG
Diesel
E20 & E85
692,176
62,533
36,053
26,851
26,817
13,927
6,078,882
490,230
463,030
421,228
243,229
206,976
-
1,260,873
536,358
398,049
391,588
262,294
467,133
16,903,140
1,576,959
896,049
1,207,905
755,033
772,660
736,170
105,996
126,584
105,811
64,873
54,188
7,826,071
203,522
245,547
3,765,364
1,071,020
697,499
23,880,265
75,260
42,280
1,311,161
22,809
273,333
17,507
216,804
8
898,673
8,393,713
Total
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
Item
Province
1
2
3
4
5
6
7
Bangkok
Samut Prakan
Nonthaburi
Pathum Thani
Nakhon Pathom
Samut Sakhon
Phra Nakhon Si
Ayutthaya
Chachoengsao
8
Gasoline
91/95
Gasohol
91/95
CNG*
LPG
Diesel
E20 & E85
1.44
0.66
0.58
0.27
0.28
0.28
0.64
1.51
0.62
0.89
0.42
0.27
0.49
0.71
-
0.61
1.41
1.56
0.94
0.67
2.29
1.44
1.62
0.69
0.59
0.39
0.23
0.54
0.80
1.05
0.77
1.40
0.74
0.46
0.75
1.47
0.32
0.92
0.41
0.98
5.35
1.11
0.92
0.40
0.95
0.53
0.93
Total
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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Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Model eBUM
Base Year 2013
Traffic Volume
from DOH and EXAT
Year 2013
External Matrix OD
Adjustment for eBUM
Daily Traffic Volume
by Link
Model eBUM
Emission Analysis
for Green Transport
HC
Link Base Emission Calculation
from 8 types of vehicles and
Average Link Speed
CO
NOX
Parameters a and b for
Emission Equation
CO2
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.
1
Province
Bangkok
2
Nonthaburi
3
Samut Prakan
4
Pathum Thani
5
Samut Sakhon
6
Nakorn Prathom
7
Phra Nakhon Si
Ayutthaya
Chachoengsao
8
Total
PCBK / SEA / CMCL / SYSTRA MVA
Portion
Volume
%
Volume
%
Volume
%
Volume
%
Volume
%
Volume
%
Volume
%
Volume
%
Volume
%
HC
52.5
51.20
8.3
8.10
5.5
5.30
10.9
10.60
10.7
10.40
4.9
4.70
4.1
4.00
5.8
5.60
102.5
100.00
CO
319.5
46.40
61.0
8.90
40.6
5.90
84.7
12.30
72.6
10.50
33.6
4.90
33.1
4.80
43.8
6.40
689.0
100.00
NOX
93.6
48.40
17.8
9.20
11.6
6.00
21.3
11.00
20.1
10.40
9.9
5.10
7.8
4.00
11.4
5.90
193.4
100.00
CO2
PM
16,820.9 1,871.4
48.10
55.70
3,201.1 248.1
9.20
7.40
2,101.7 137.2
6.00
4.10
3,796.5 341.0
10.90
10.10
3,725.0 310.4
10.70
9.20
1,796.7 152.7
5.10
4.50
1,419.3 120.3
4.10
3.60
2,079.4 178.5
6.00
5.30
34,940.6 3,359.6
100.00 100.00
Total
19,157.8
48.80
3,536.2
9.00
2,296.7
5.80
4,254.4
10.80
4,138.8
10.50
1,997.8
5.10
1,584.5
4.00
2,318.9
5.90
39,285.1
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
1
Passenger Car
2
Taxi
3
Pick up
4
Van
5
Motorcycle
6
Bus
7
Truck
8
Samlor
Total (kilogram/day)
Total per year (million ton)
Source: eBUM
5.4.6
CO2
15,560,000
1,099,311
9,823,816
336,906
3,500,286
2,630,683
1,434,277
550,094
34,935,372
12.75
CO
154,235
2,464
28,642
1,294
461,146
22,251
4,638
14,339
689,009
0.25
NOX
75,550
19,671
44,273
2,193
12,298
25,124
11,880
2,394
193,382
0.07
HC
20,479
4,222
6,539
337
54,357
1,739
995
13,868
102,537
0.04
PM
1,364
4,453
2,438,963
914,778
3,359,559
1.23
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
Developed
with Cube
P
u
b
l
i
s
h
Model
Run with
Cube
Cloud
Services
TRAVEL MODEL
Figure 5.4-16 The concept of transport and traffic model development on Cube Cloud
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Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
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Chapter 6
Application of transport and traffic model and
enhancement of the staff's potential
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
6.2
6.3
6.4
6.1
Introduction
Application of transport and traffic model
Enhancement of the staff's potential
summary of operation
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)
Base case
Mission test case
2557
959,010
971,960 (case 1)
2558
979,825
993,290 (case 2)
2559
1,085,265
1,100,735 (case 3)
2565
3,861,835
3,931,350 (case 3)
2575
4,673,270
4,758,800 (case 3)
Source: Analysis from the Consultants
Year
Difference (%)
1.33%
1.36%
1.41%
1.77%
1.80%
Table 6.2-2 Average speed all day (km/hr) on private vehicle network
Daily ridership (person/day)
Base case
Mission test case
2557
31.35
31.38 (case 1)
2558
31.05
31.08 (case 2)
2559
31.18
31.21 (case 3)
2565
29.45
29.48 (case 3)
2575
27.81
27.84 (case 3)
Source: Analysis from the Consultants
Year
PCBK / SEA / CMCL / SYSTRA MVA
Difference (%)
0.10%
0.10%
0.10%
0.10%
0.11%
6-2
Executive Summary Report
6.2.2
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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.
PCBK / SEA / CMCL / SYSTRA MVA
6-3
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 6.2-3 Traffic volume in PCU per day entering the areas of Ratchadaphisek Ring Road 2012
Test case
Base case
20 Baht: no exemption
20 Baht: with exemption for residents
in Ratchadaphisek Ring Road
40 Baht: no exemption
40 Baht: with exemption for residents
in Ratchadaphisek Ring Road
60 Baht: no exemption
60 Baht: with exemption for residents
in Ratchadaphisek Ring Road
Source: Analysis from the Consultants
* Change from base case
Traffic volume in Ratchadaphisek Ring
Road (PCU/day)
Normal
From
Total
road
expressway
1,065,462
137,612
1,203,074
853,915
76,849
930,764
1,011,890
91,065
1,102,955
Difference*
(%)
Income
(million
Baht/day)
-22.63
-8.32
18.62
17.98
766,485
908,285
47,204
55,935
813,689
964,220
-32.37
-19.85
32.55
31.43
618,833
733,317
25,007
29,633
643,840
762,950
-46.48
-36.58
38.63
37.60
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
Case
Base case
20 Baht: no exemption
20 Baht: with exemption for residents
in Ratchadaphisek Ring Road
40 Baht: no exemption
40 Baht: with exemption for residents
in Ratchadaphisek Ring Road
60 Baht: no exemption
60 Baht: with exemption for residents
in Ratchadaphisek Ring Road
Source : Analysis from the Consultants
Remark : * Change from base case
PCBK / SEA / CMCL / SYSTRA MVA
Average speed during rush hour in the morning (km/hr)
and percentage of change
Bangkok and
Difference*
Ratchadaphisek
Difference*
metropolitan
(%)
Ring Road
(%)
22.89
16.50
23.15
1.12
16.61
0.71
23.08
0.83
16.56
0.42
23.22
23.16
1.43
1.13
16.73
16.67
1.42
1.12
23.36
23.30
2.04
1.74
16.87
16.82
2.27
1.97
6-4
Executive Summary Report
6.2.3
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
Year
Case 1
(Base case)
Case 2
No fee
2565
3,861,830
3,919,767
2575
4,673,275
4,752,715
2580
5,267,160
5,356,700
Source: Analysis from the Consultants
* compared with base case
PCBK / SEA / CMCL / SYSTRA MVA
Daily Ridership of rail transit (person/day)
Case 3
Difference
Difference
Flat rate
%*
%*
20 Baht
1.48%
4,634,200
16.67%
1.67%
5,664,000
17.49%
1.67%
6,399,600
17.70%
Case 4
New line
20 Baht
4,441,115
5,383,610
6,078,300
Difference
%*
13.04%
13.19%
13.34%
6-5
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 6.2-6 Average speed all day (km/hr) on private vehicle network in 2022-2037
Year
Case 1
(Base case)
2565
29.45
2575
27.81
2580
26.95
Source: Analysis from consultants
6.2.4
Average speed all day (km/hr)
Case 2
Case 3
No fee
Flat rate 20 Baht
29.47
29.50
27.83
27.86
26.97
27.00
Case 4
New line 20 Baht
29.49
27.85
26.99
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
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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
Border
Links
Value of imports
(million Baht)
Value of exports
(million Baht)
Chiang
Khong
AH3
AH2
AH1
Nong
Khai
AH12
8,800
80
1,530
5,000
35,000
4,600
6,300
166,000
Padang
Besar
ASEAN
Railway
52,000
8,600
17,000
36,000
65,000
16,000
4,000
48,000
263,000
320,000
Mae Sai Mae Sod
Mukda
harn
AH16
Nokhon
Panom
AH15
Aranya
prathet
AH1
Sadao
AH2
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
Coffee, tea, spices
Wood and wood products
Garments
Shoes and accessories
Seasoning made from vegetable, fruits, nuts
Land vehicle, except that running on rail or track, and its accessories
Beverage, liquor, vinegar
Products from painting industry, malt grain
Cereal
Electric machine, electric appliance, and accessories
Total
Weight (ton/day)
128,500
3,947,000
25,500
6,500
739,000
166,500
4,000
2,462,000
2,717,000
543,500
10,739,500
%
1.20
36.75
0.24
0.06
6.88
1.55
0.04
22.92
25.30
5.06
100
Table 6.2-9 Analysis results
Whole
country
Case 1
Case 2
Case 3
PCU-KM
PCU-HR
Without
With
With project Change No project
project
project
304,154,266 306,416,339 0.74% 3,796,584 3,829,120
304,154,266 308,661,743 1.48% 3,796,584 3,861,998
304,154,266 313,059,983 2.93% 3,796,584 3,929,010
PCBK / SEA / CMCL / SYSTRA MVA
Speed (km/hr)
Without With
Change
Change
project project
0.86%
80.11
80.02 -0.11%
1.72%
80.11
79.92 -0.24%
3.49%
80.11
79.68 -0.54%
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
LOC1
LOC2
LOC3
LOC4
LOC5
LOC6
LOC7
With project
0.440
0.799
0.908
0.251
0.150
0.260
0.413
Case 1
0.462
0.804
0.923
0.253
0.151
0.264
0.415
% Change
5.08
0.62
1.63
0.75
0.76
1.26
0.51
Case 2
0.479
0.815
0.938
0.254
0.153
0.265
0.420
% Change
8.94
1.91
3.31
1.19
2.18
1.96
1.87
Case 3
0.506
0.863
0.970
0.257
0.156
0.270
0.424
% Change
15.02
7.98
6.80
2.31
4.29
3.78
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
PCBK / SEA / CMCL / SYSTRA MVA
6-8
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
PCBK / SEA / CMCL / SYSTRA MVA
6-9
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Table 6.2-11 High-Speed Train Projects
Line Name
Northern
Year in operation
2019 Bangkok - Chiangmai
Number of station
12












Northeastern
2019 Bangkok – Nakorn
Ratchasima
2021 Bangkok - Nongkhai
11










Eastern
2021 Bangkok – Rayong
8








Southern
2019 Bangkok – Padang Besar
16







PCBK / SEA / CMCL / SYSTRA MVA
Station name
Bang Sue
Ayutthaya
Lopburi
Ban Takhli
Nakornsawan
Taphanhin
Pitsanulok
Ban Dara
Uttaradit
Denchai
Lampang
Chiang Mai
Bang Sue
Ayutthaya
Saraburi
Pakchong
Nakorn Ratchasima
Bua Yai
Khon Kaen
Khao Suan Kwang
Udon Thani
Nong Khai
Bang Sue
Makasan
Lad Krabang
Chachoengsao
Chonburi
Sriracha
Pattaya
Rayong
Bang Sue
Nakorn Pathom
Ratchaburi
Petchburi
Huahin
Prachuab Kirikhan
Bang Sapan
6-10
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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)
Line
Northern
Northeastern
Eastern
Southern
Route
Bangkok - Chiangmai
Bangkok - Nong Khai
Bangkok - Rayong
Bangkok - Padang Besar
2022
26,980
30,310
27,570
28,890
Year
2027 2032
28,780 30,770
33,060 36,160
33,380 40,750
31,650 34,730
2037
32,910
39,640
52,910
38,210
Table 6.2-13 Average speed on network
Year
2022
2027
2032
2037
Case
Without project
With project
Difference
Without project
With project
Difference
Without project
With project
Difference
Without project
With project
Difference
PCBK / SEA / CMCL / SYSTRA MVA
PCU-KM
349,351,174
342,413,094
-1.99%
382,610,768
374,851,142
-2.03%
421,663,523
412,937,472
-2.07%
467,642,069
457,688,403
-2.13%
PCU-HR
4,440,101
4,336,790
-2.33%
4,934,867
4,817,195
-2.38%
5,535,237
5,396,710
-2.50%
6,270,123
6,103,995
-2.65%
Speed (km/hr)
78.68
78.96
0.36%
77.53
77.82
0.37%
76.18
76.52
0.45%
74.58
74.98
0.54%
6-11
Executive Summary Report
6.3
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
PCBK / SEA / CMCL / SYSTRA MVA
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
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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
PCBK / SEA / CMCL / SYSTRA MVA
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
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
PCBK / SEA / CMCL / SYSTRA MVA
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Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Figure 6.3-9 Details of self-learning contents (4 classrooms)
Figure 6.3-10 Samples of self-learning materials (Classroom 3)
PCBK / SEA / CMCL / SYSTRA MVA
6-16
Executive Summary Report
Transport Data and Model Integrated with Multimodal and Logistics (TDL)
Figure 6.3-11 Samples of self-learning materials (Classroom 4)
PCBK / SEA / CMCL / SYSTRA MVA
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