INLAND CARGO FLOW MODELING UNDER SHIPMENT TIMEVARIABILITY IN LANDLOCKED DEVELOPING COUNTRIES㸦ෆ㝣㛤Ⓨ㏵ୖᅜ࡟࠾ࡅࡿ
㍺㏦᫬㛫ኚືࢆ⪃៖ࡋࡓ㈌≀ὶࣔࢹࣝࡢ㛤Ⓨ㸧

byTOMOYA KAWASAKI
Submitted to the Department of International Development Engineeringin Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF ENGINEERINGat
Tokyo Institute of Technology
Tokyo, Japan

Examination Committee:
Associate Professor, Shinya Hanaoka (chairperson)
Professor, Nobuaki Otsuki
Professor, Tetsuo Yai
Associate Professor, Yasunori Muromachi
Associate Professor, Daisuke Fukuda

JUNE 2012© 2012 Tokyo Institute of Technology – All rights reserved

Abstract
As several international organizations recognize, the shipment time variability on crossborder transport adversely contributes on extra cost of the shipment. Nowadays in thisglobalization world, maritime transport is likely to be one of the most important transportmodes for trade activities due to its high economies of scale. Focusing on landlocked
countries, no direct access to seaport is allowed due to geographical nature. In order toaccess to seaport from landlocked countries, one needs to pass across at least one borderthat is a first bottleneck in the route. Once arrived at seaport, it is appeared as a second
bottleneck. As several literatures mentioned, seaport is to be sometimes more severebottleneck in terms of the scale of shipment time variability. Therefore, border and seaport
would be a source of additional cost for logistics companies, particularly for landlocked
developing countries.
In an initial stage of this dissertation, the bottlenecks on freight transport in landlocked
developing countries are confirmed through field survey in several landlocked countries,
Central Asia and Lao PDR. As literature reviews identified, the bottlenecks of inlandfreight transport are also identified as shipment time variability at border and seaport infield survey. Subsequently, impact of shipment time variability on estimating expected
shipment time for logistics decision maker in Lao PDR is examined in order to clarify
whether shipment time variability could be an additional cost of the shipment. The resultshows that more highly variable route requires more number of information source for
estimating next shipment time. In general, it can be postulated that people requires more
information sources as situation becomes unstable. If this hypothesis is true, it is highly
expected that there is additional cost due to shipment time variability, which should beconsidered in the route and departure time choice problem.
Finally, inland cargo flow model considering cost of shipment time variability is developed
so that the impact of increase in reliability of two bottlenecks is observed. This model
basically consists of two sub-models, valuation of shipment time variability and schedulevariability cost model. Using developed model, the scenario analysis was conducted. Itshows that improving seaport reliability receives more impact on the cost reduction
comparing to reliability increase at the border. For seaport choice, as reliability of thebottlenecks increases, decrease in cargo volume in Laem Chabang seaport and increase in
Vietnamese seaport are observed.

ii

Acknowledgments
I would like to express my profound gratitude and sincere appreciation to my supervisor,
Associate Professor, Shinya Hanaoka, for his patient guidance, constructive suggestions,precious time, encouragement, trust and all valuable efforts so as to support my research
activities during my stay in Tokyo Institute of technology. I am also in his debt for building
my studying foundation at Asian Institute of Technology. His patient guidance to me iscontinuous from when I was a master student at Asian Institute of Technology.
I would like to thank my committee members, Professor Nobuaki Otsuki, Professor, Tetsuo
Yai, Associate Professor, Yasunori Muromachi, and Associate Professor, Daisuke Fukuda
for their kind support and valuable comments and guidance.
Great appreciation and sincere thanks also goes to Mr. Long Xuan Nguyen and all myfriends in Tokyo Institute of Technology. Besides, I would like to express deep gratitude todoctoral friends who belong to universities outside of Tokyo Tech for encouraging eachother for higher study.

iii

Table of Contents
Title page ................................................................................................................................... i
Abstract .................................................................................................................................... ii
Acknowledgments ................................................................................................................... iii
Table of Contents .................................................................................................................... iv
List of Tables .......................................................................................................................... vii
List of Figures........................................................................................................................ viii
Abbrebiation ............................................................................................................................. x
1

INTRODUCTION
1.0
1.1
1.2
1.3

2

LITERATURE REVIEW ON FREIGHT TRANSPORT IN LANDLOCKED COUNTRIES
2.0
2.1
2.2
2.3
2.4

3

Background....................................................................................................................... 2
Purpose and Objectives .................................................................................................... 5
Study Area, Scope, and Limitations ................................................................................ 5
Dissertation Outline ......................................................................................................... 6

Introduction .................................................................................................................... 10
Reports of International Organization ............................................................................ 10
Literature on Development Economics .......................................................................... 17
Literature on Econometrics ............................................................................................ 18
Chapter Conclusion ........................................................................................................ 21

FIELD SURVEY IN CENTRAL ASIA AND LAO PDR
3.0 Introduction .................................................................................................................... 23
3.1 Field Survey in Central Asia .......................................................................................... 23
3.1.1 Overview of the Survey ....................................................................................... 23
3.1.2 Basic Data of Uzbekistan ..................................................................................... 24
3.1.3 Basic Data of Kazakhstan .................................................................................... 26
3.1.4 Routes of Inalnd Transport .................................................................................. 27
3.1.5 Treaties Related to Freight Transport .................................................................. 30
3.1.6 Summary of Freight Transport Problems ............................................................ 32
3.1.7 Findings ............................................................................................................... 34
3.2 Field Survey in Lao PDR................................................................................................ 34
3.2.1 Overview of the Survey ....................................................................................... 34
3.2.2 Basic Data of Lao PDR ........................................................................................ 34iv

3.2.3 Cross-Border Transpor ........................................................................................ 36
3.2.4 Seaport Choice ..................................................................................................... 37
3.3 Summary and Findings ................................................................................................... 39

4

IMPACT OF SHIPMENT TIME VARIABILITY ON ESTIMATION OF EXPECTED
SHIPMENT TIME
4.0
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8

5

Introduction .................................................................................................................... 41
Literature Review for Methodology ............................................................................... 41
Hypothesis ...................................................................................................................... 42
Study Area ...................................................................................................................... 43
Model Formulation ......................................................................................................... 44
Data Collection ............................................................................................................... 46
Correlation Anlaysis ....................................................................................................... 47
Model Specification and Discussion .............................................................................. 49
Chapter Conclusion ........................................................................................................ 53

VALUATION OF SHIPMENT TIME VARIABILITY
5.0 Introduction .................................................................................................................... 55
5.1 Literature Review ........................................................................................................... 56
5.1.1 Valuation on Travel Time Variability.................................................................. 56
5.1.2 Mean-Variabnce Approach .................................................................................. 57
5.1.3 Scheduling Approach ........................................................................................... 58
5.1.4 Integrated Approach ............................................................................................ 60
5.2 Model Formulation ......................................................................................................... 61
5.3 SP Questionnaire Survey ................................................................................................ 63
5.3.1 Estimation of Shipment Time Distribution .......................................................... 63
5.3.2 SP Questionnaire Design ..................................................................................... 66
5.3.3 Questionnaire Survey ........................................................................................... 69
5.4 Specification of the Model ............................................................................................. 69
5.5 Results and Discussion ................................................................................................... 70
5.6 Chapter Conclusion ........................................................................................................ 71

6

SCHEDULE VARIABILITY COST AND SCENARIO ANALYSIS
6.0 Introduction .................................................................................................................... 73
6.1 Overview of the Model ................................................................................................... 74
6.1.1 Study Area and Target ......................................................................................... 74v

6.1.2 Freight Forwarder’s Behavior .............................................................................. 77
6.1.3 Formulation of Scheudle Variability Cost ........................................................... 78
6.2 Estimation of Schedule Variability Cost ........................................................................ 81
6.2.1 Specification of Distribution of Arrival Time ..................................................... 81
6.2.2 Determination of Arrival Buffer Time ................................................................. 86
6.3 Specification of Scale Parameter θ ................................................................................. 87
6.3.1 Setting Input Values............................................................................................. 87
6.3.2 Estimation of Cargo Generation and Attraction .................................................. 89
6.3.3 Specification of Scale Parameter θ ...................................................................... 90
6.4 Scenario Analysis ........................................................................................................... 93
6.4.1 Improvement in Reliability of Border.................................................................. 93
6.4.2 Improvement in Reliability of Seaport ................................................................ 97
6.4.3 Results and Discussion ...................................................................................... 101
6.5 Chapter Conclusion ...................................................................................................... 102

7

CONCLUSION
7.0 Introduction .................................................................................................................. 104
7.1 Objective Based Conclusions ....................................................................................... 105
7.1.1 Objective 1 ......................................................................................................... 105
7.1.2 Objective 2 ......................................................................................................... 105
7.1.3 Objective 3 ......................................................................................................... 105
7.1.4 Objective 4 ......................................................................................................... 106
7.2 Future Scope ................................................................................................................. 106
7.2.1 Improving the Model Considering Other Factors .............................................. 106
7.2.2 Improving to an Analytical Method ................................................................... 107
7.2.3 Examning Lognormal Distribution in Lao Context ........................................... 107
References ............................................................................................................................ 108
Appendix A .......................................................................................................................... 114
Appendix B........................................................................................................................... 122
Appendix C........................................................................................................................... 126
Appendix D .......................................................................................................................... 130

vi

List of Tables
Table 1.1 Seaport Development Projects in Vietnam and Thailand .................................................................. 5
Table 2.1 LPI Score of Top 10 Countries and LLCs in Asia in 2010 ................................................................11
Table 2.2 Shipment Time and Cost to Export from Landlocked Country .........................................................14
Table 2.3 Export and Import Data for ASEAN+3. ............................................................................................15
Table 2.4 Estimated Benefits in Transport Cost by Implementing Transport Policy ........................................20
Table 3.1 Regional Framework Surrounding CIS Countries Related to Freight Transport ...............................32
Table 3.2 Reasons for Choosing a Thai Seaport ................................................................................................38
Table 4.1 Mean Operation by Equation (4.3) ....................................................................................................42
Table 4.2 Routes Profile ....................................................................................................................................47
Table 4.3 Result of Correlation Analysis ...........................................................................................................48
Table 4.4 Expected Shipment Time Model .......................................................................................................49
Table 4.5 Expected Shipment Time Model by Multiple Regression Model ......................................................52
Table 5.1 Scenarios for Questionnaire ...............................................................................................................67
Table 5.2 Result of Parameter Estimation .........................................................................................................70
Table 5.3 Results of Each Value of Shipment Time ..........................................................................................71
Table 6.1 Distribution Characteristics Value for Each Bottleneck ....................................................................82
Table 6.2 Distribution Characteristics Value for Convoluted Distribution ........................................................84
Table 6.3 Difference between Arrival Restriction Time and Departure Time, and Average Shipment Time ...87
Table 6.4 Input Values Exogenously Given ......................................................................................................88
Table 6.5 Result of Cargo Generation Estimation .............................................................................................89
Table 6.6 Comparison of Actual and Estimated Cargo Volume ........................................................................92vii

List of Figures
Figure 1.1 Eight Economic Corridors in GMS ........................................................................................................ 4
Figure 1.2 Organization of Dissertation .................................................................................................................. 8
Figure 2.1 Shipment Time and Cost Required for Export for the Countries ..........................................................13
Figure 2.2 Dwelling Time at Mombasa Seaport in Kenya .....................................................................................16
Figure 2.3 Distance-Time Model for Vientiane-Laem Chabang Seaport...............................................................16
Figure 3.1 Change in GDP Per Capita and Economic Growth Rate in Uzbekistan ...............................................24
Figure 3.2 Trade Partners of Uzbekistan (Export) .................................................................................................25
Figure 3.3 Trade Partners of Uzbekistan (Import) .................................................................................................25
Figure 3.4 Change in GDP Per Capita and Economic Growth Rate in Kazakhstan ...............................................26
Figure 3.5 Trade Partners of Kazakhstan (Export) .................................................................................................27
Figure 3.6 Trade Partners of Kazakhstan (Import) .................................................................................................27
Figure 3.7 Location of Each Point of Asia .............................................................................................................29
Figure 3.8 Location of Each Point of Central Asia and Europe .............................................................................30
Figure 3.9 Change in GDP Per Capita and Economic Growth Rate in Lao PDR ..................................................35
Figure 3.10 Trade Partners of Lao PDR (Export) ..................................................................................................35
Figure 3.11 Trade Partners of Lao PDR (Import) ..................................................................................................36
Figure 3.12 Road Map from Vientiane and Savannakhet to Seaports ....................................................................37
Figure 4.1 Study Range of Chapter 4 .....................................................................................................................44
Figure 4.2 Result of Estimated Weighted Coefficient ............................................................................................51
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Figure 5.7

Work Flow Diagram of Chapter 5 in Inland Cargo Flow Model ..........................................................56
Typical Schedule Disutility...................................................................................................................59
Steps to Obtain 1st and 2nd Moments of Lognormal Distribution ..........................................................63
Concept of Minimum and Maximum Time in Standard Normal Distribution ......................................64
Presentation of SP Questionnaire ..........................................................................................................65
Relationship among SD, ti, and st .........................................................................................................68
Presentation of Choice Set ....................................................................................................................69

Figure 6.1 Study Range for Inland Cargo Flow Model ..........................................................................................75
Figure 6.2 Road Network of Study Area ................................................................................................................75
Figure 6.3 Expected Cost Function for Early and Late Arrival ..............................................................................80
Figure 6.4 Steps to Obtain 1st and 2nd Moments of Convoluted Lognormal Distribution ......................................84
Figure 6.5 Graphical Example for Convolution of Lognormal Distribution ..........................................................85
Figure 6.6 Actual and Estimated Cargo Volume ....................................................................................................92
Figure 6.7 Change in Schedule Variability Cost due to Decrease in Border Variability .......................................94
Figure 6.8 Change in Seaport Choice from/to Luang Phabang due to Decrease in Border Variability .................95
Figure 6.9 Change in Seaport Choice from/to Vientiane due to Decrease in Border Variability ...........................95
Figure 6.10 Change in Seaport Choice from/to Savannakhet due to Decrease in Border Variability ....................96
Figure 6.11 Change in Seaport Choice from/to Champassak due to Decrease in Border Variability ....................96
Figure 6.12 Change in Seaport Choice from/to Lao PDR due to Decrease in Border Variability .........................97viii

Figure 6.13
Figure 6.14
Figure 6.15
Figure 6.16
Figure 6.17
Figure 6.18
Figure 6.19

Change in Schedule Variability Cost due to Decrease in Seaport Variability ....................................98
Change in Seaport Choice from/to Luang Phrabang due to Decrease in Seaport Variability .............99
Change in Seaport Choice from/to Vientiane due to Decrease in Seaport Variability ........................99
Change in Seaport Choice from/to Savannakhet due to Decrease in Seaport Variability .................100
Change in Seaport Choice from/to Champassak due to Decrease in Seaport Variability .................100
Change in Seaport Choice from/to Lao PDR due to Decrease in Seaport Variability ......................101
Change in Average Schedule Variability Cost for Each Scenario ....................................................101

Figure B.1
Figure B.2
Figure B.3
Figure B.4
Figure B.5
Figure B.6

Change in Schedule Variability Cost due to Decrease in Shipment Time at Border..........................123
Change in Seaport Choice from/to Luang Phabang due to Decrease in Shipment Time at Border ...123
Change in Seaport Choice from/to Vientiane due to Decrease in Shipment Time at Border .............124
Change in Seaport Choice from/to Savannakhet due to Decrease in Shipment Time at Border ........124
Change in Seaport Choice from/to Champassak due to Decrease in Shipment Time at Border ........125
Change in Seaport Choice from/to Lao PDR due to Decrease in Shipment Time at Border .............125

Figure C.1
Figure C.2
Figure C.3
Figure C.4
Figure C.5
Figure C.6

Change in Schedule Variability Cost due to Decrease in Shipment Time at Seaport ........................127
Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment Time at Seaport ....127
Change in Seaport Choice from/to Vientiane due to Decrease in Shipment Time at Seaport............128
Change in Seaport Choice from/to Savannakahet due to Decrease in Shipment Time at Seaport .....128
Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment Time at Seaport ....129
Change in Seaport Choice from/to Lao PDR due to Decrease in Shipment Time at Seaport ............129

Figure D.1 Change in Schedule Variability Cost due to Decrease in Shipment Time Variability at Nameo/Nam Xoi
and Da Nang Seaport ..............................................................................................................................................131
Figure D.2 Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment Time Variability at
Nameo/Nam Xoi and Da Nang Seaport ..................................................................................................................131
Figure D.3 Change in Seaport Choice from/to Vientiane due to Decrease in Shipment Time Variability at
Nameo/Nam Xoi and Da Nang Seaport ..................................................................................................................132
Figure D.4 Change in Seaport Choice from/to Savannakahet due to Decrease in Shipment Time Variability at
Nameo/Nam Xoi and Da Nang Seaport ..................................................................................................................132
Figure D.5 Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment Time Variability at
Nameo/Nam Xoi and Da Nang Seaport ..................................................................................................................133
Figure D.6 Change in Seaport Choice from/to Lao PDR due to Decrease in Shipment Time Variability at
Nameo/Nam Xoi and Da Nang Seaport ..................................................................................................................133ix

Abbreviation
3PL
ADB
AH
ASEAN
CACO
CAF
CAREC
CBTA
CIA
CIF
CIS
CBA
CLB
CPS
DAN
ECO
EEC
ERIA
ESCAP
FITA
FNEM
FEU
FOB
GSPEM
GTAP
GMS
GDP
HAI
ICT
IMF
JETRO
JICA
LCB
LLCs
LLDCs
LOS
LPB
LPI
MICS
OD
PAT

Third Party Logistics
Asian Development Bank
Asian Highway
Association of South-East Asian Nations
Central Asia Cooperation Organization
Central Asia Forum
Central Asia Regional Economic Cooperation
Cross-Border Transport Agreement
Central Intelligence Agency
Cost, Insurance and Freight
Commonwealth of Independent States
Cost Benefit Analysis
China Land Bridge
Champassak
Da Nang
Economic Cooperation Organization
Eurasian Economic Community
Economic Research Institute for ASEAN and East Asia
United Nations Economic and Social Commission for Asia and the PacificFederation of International Trade Associations
Freight Network Equilibrium Model
Forty-Foot Equivalent Unit
Free on Board
Generalized Spatial Price Equilibrium Model
Global Trade Analysis ProjectGreater Mekong Subregion
Gross Domestic ProductHai PhongInformation and Communications and Technology
International Monetary FundJapan External Trade Organization
Japan International Cooperation Agency
Laem Chabang
Landlocked Countries
Landlocked Developing Countries
Level of Service
Luang Phabang
Logistics Performance Index
Model for International Cargo Simulation
Origin and Destination
Preferred Arrival Time

x

SAV
SCO
SD
SDE
SDL
SPECA
SP
ST
STAN
SVC
TIR
TRACECA
TEU
USSR
USD
UNECE
VTE
WB

Savannakhet
Shanghai Cooperation Organization
Standard Deviation
Schedule Delay Early
Schedule Delay Late
UN Special Programme for the Economies of Central Asia
Stated Preferences
Shipment Time
Strategic Planning of National Freight Transportation
Schedule Variability Cost
Transport International Routier
Transport Europe Caucasus Central Asia
Twenty-Foot Equivalent Unit
Union of Soviet Socialist Republics
United States Dollar
United Nations Economic Commission for Europe
Vientiane
World Bankxi

CHAPTER 1
INTRODUCTION

11.0 Background
Landlocked Developing Countries (LLDCs) are usually marked by low economic
development. Sachs and Warner (1997) revealed that, compared to coastal countries,
Landlocked Countries (LLCs) have lower steady-state incomes, and hence lower economic
growth from an initial level of Gross Domestic Product (GDP). Gallup et al. (1999)
concluded that being landlocked reduces a country’s growth by at least half a percentage
point. According to the World Bank (2008), most LLDCs have two problems: poor
neighbors and remoteness from large economic markets such as the United States, Europe,
or Japan. This remoteness results in long and variable shipment times and, consequently,
high shipment costs because the shipment included need for passing through border andseaport as bottlenecks of cross-border transport (Arvis et al., 2007). Furthermore, it resultsin LLDC products being less competitive in the global market, which has an adverse impact
on the economic growth of these countries. Raballand (2003) found that being landlocked
reduces trade by more than 80% because border crossing is a major part of the extra cost of
freight transport.
Nowadays in the globalization, maritime transport is one of the most important transportmodes for international trade due to its high economies of scale. The importance ofmaritime transport is not only for coastal and developed countries, but also for LLDCs forfurther economic growth. Nevertheless, LLDCs cannot directly access maritime transport intheir own territories because of their geography nature. In order to transport goods bymaritime transport, these countries need to use land transport to access to seaports inneighboring coastal countries. At least one border crossing is also needed for that shipment,
however, border crossings are widely recognized as bottlenecks which violating shipment
time and cost due to delay (Arvis et al., 2007). For instance, Mackellar et al. (2002)
revealed that the cost of a border crossing in Africa is equivalent to approximately 1,600
km of land or 11,000 km of maritime transport. Shipment time of cross-border transport indeveloping countries is highly variable. Hanaoka and Kawasaki (2010) reported that 10–20
days are required for a 1,000 km cross-border haulage between Tashkent (the capital of
Uzbekistan) and Ashgabat (the capital of Turkmenistan). The main contributor to thisvariability is the border crossing, because the truck operates at almost free-flow speed oninter-city and inter-national roads. In general, traffic volume in LLDCs and surrounding
countries are very few. This fact is supported by existing survey result by Banomyong(2000), which shows slight difference of transport time between the best and worst case I
several routes in Greater Mekong Subregion (GMS).
In addition to border crossings, Arvis et al. (2007) pointed out that the use of seaports also
generates a considerable delay. For example, waiting time at the seaport in Mombasa, thegateway seaport of Uganda, is variable, sometimes more than 30 days. Banomyong (2000)
2

surveyed best- and worst-case shipment times of cross-border transport between Vientiane(the capital of the Lao People’s Democratic Republic [PDR]) and Laem Chabang seaport(the busiest seaport in Thailand). Huge differences were generated at the border (15 hours)
and seaport (10 hours). Consequently, time reduction in border and seaport shall largely
contribute on total transport time reduction.
Two bottlenecks adversely contribute on the variability of shipment time on cross-bordertransport between LLDCs and seaport in coastal countries. In order to solve or alleviate
problems violating shipment time reliability, several investments on transport infrastructureis undergone in GMS region. For example, eight economic corridors (Figure 1.1) are being
developed rapidly as well as several seaports as shown in Table 1.1. The three economic
corridors, North-South, East-West, and Southern Economic Corridor, are identified to beinvested intensively for facilitating cross-border transport in 8th GMS Ministerial Meetingin 1998. Investment is not only physical issues, but also institutional issues, for example,
single stop of the trucks at the border, mutual truck use between the countries, etc (ERIA,
2010).
Here, one question arises that how much this type of investment affect on cargo flow in theregion? In order to quantitatively understand the impact of several investment projects,
inland cargo flow model on the basis of capturing characteristics of cross-border transport,such as two bottlenecks above is needed to be developed. In the field of cargo flow
modeling, the most widely known model at this moment is Model for International Cargo
Simulation (MICS) developed by Shibasaki and Watanabe (2009). The notablecharacteristic of this model is that interdependency between shipping lines and shippers isincorporated for seaport choice. The MICS considers border resistance as bottlenecks;
however, shipment time variability is not explicitly included in the model. Since fluctuationof shipment time in developing countries is considerable large, shipment time variability
might have big impact on the generalized cost of the haulage, which determines route
choice of freight forwarders. Besides, seaport is not treated as a bottleneck in the model of
Shibasaki and Watanabe (2009).

3

Figure 1.1 Eight Economic Corridors in GMS
Source: ADB (2011)4

Table 1.1 Seaport Development Projects in Vietnam and Thailand
Country
VietnamThailand

Project

Implementation Schedule

Type

Da Nang seaport at Tien Sa

2001 - 2004

UpgradeHai Phong seaport

1994 - ongoingUpgradeCai Lan seaport

1996 - ongoingUpgradeDa Nang seaport (Phase 2)

2007 - 2008 (proposed)

UpgradeLien Chieu seaport

Proposed

New seaport

Vung Tau seaport rehabilitation

Ongoing

UpgradeCan Tho seaport

ProposedUpgradeCai Mep-Thi Vai deepwater seaport

Ongoing

UpgradeVan Phong seaport in Khanh Hoa
Laem Chabang seaport, Phase 2,
Construction of C and D terminals

Proposed2006 - 2010 (proposed)

New seaport
UpgradeIwata et al. (2010)

1.1 Purpose and Objectives
The primary purpose of this dissertation is to observe how decrease in shipment time
variability at two bottlenecks impacts on inland cargo flow and total shipment time cost
reduction. This is observed by developing an inland cargo flow model considering cost of
shipment time variability. For fulfilling the purpose, the problems lied in the field of freighttransport in LLCs are firstly investigated through field survey for confirming factors
contributes on route choice. In addition, due to the highly variable shipment time, one
hypothesis has been established regarding the impact of shipment time variability forlogistics decision maker for estimating next shipment time. The hypothesis will be
examined as additional study in this dissertation.

1.2 Study Area, Scope, and Limitations
The study area of this dissertation in the model part is Lao PDR, with focus on surrounding
countries, mainly Thailand and Vietnam. Regarding the transport mode, truck is only
assumed in the region although railway development plan exists (ERIA, 2010). However, it
is very likely to be rational assuming only truck since very few amount of goods currently
transported in the region and for cargo volume from/to Lao PDR, it is negligible due to its
low volume.

5

Regarding the cargo flow scenario analysis, transit cargo and potential demand generated
due to improving the level of service are not considered in the model. For transit cargo, it isexpected to be increased as border resistance diminished. Here transit cargo is mainly
mentioned for between Thailand and Vietnam where cargo volume in the region isrelatively high. In this case, cargo diversion from maritime transport since currently,
relatively large number of cargo volume is transported by maritime transport between two
capitals, Bangkok and Hanoi.

Objective 1:
To clarify problems on freight transport of LLDCs- The important factors for both physical and institutional issues adversely contributing
on freight transport of LLDCs are identified through field survey.
- Field survey is conducted in Central Asia and Lao PDR.
Objective 2:
To identify impact of shipment time variability on estimation of expected shipmenttime- Estimation of next shipment time is modeled for different level of variability routes forcomparison purpose.
- Generalized mean concept and multiple regression model as a supplemental analysis areapplied for model development.
Objective 3:
To develop inland cargo flow model for LLDCs- Cost of shipment time variability is incorporated distinguishing the impact of early andlate arrival.
- The model should be dynamic with respect to change in the level of variability.
Objective 4:
To show an impact of improvement in shipment time variability on cargo flow
- The impact of cross-border transport facilitation projects on inland cargo flow andshipment time cost reduction is observed.
- The impact of seaport improvement is also observed.

1.3 Dissertation Outline
The dissertation has been organized into eight groups and organization of dissertation ispresented in Figure 1.2.
6

Chapter 1 has explained the background, problem statement, and purpose and objective.
Chapter 2 has been illustrates the literature reviews regarding to freight transport issues of
LLDCs. Reviews are separated into three groups, such as reviews based on internationalorganization reports by United Nations, World Bank, etc., the field of development
economics, and econometrics. In the review section for econometrics, it also illustrates thenotable point of the model developed in this dissertation.
Chapter 3 dedicates to illustrate the problems related to freight transport learnt from fieldsurvey in Central Asia and GMS. Problems are examined in terms of not only physicalissues but also institutional issues. Looking the system of international inland freighttransport such as binomial agreement, multinomial agreement, etc, we qualitatively find apotential countermeasure for facilitating cross-border transport. The finding of this chapter
serves as the basics to the rest of the dissertation.
Chapter 4 examines the impact of shipment time variability on estimating expected
shipment time. In this analysis, the set up hypothesis is discussed using generalized mean
formula and multiple regression model.
Chapter 5 deals with initial part of the inland cargo flow model, which is the valuation of
shipment time variability distinguishing early arrival and late arrival. On the basis ofvaluation in this chapter, schedule variability cost in chapter 6 can be estimated.
Chapter 6 deals with core part of inland cargo flow model, which is the modeling ofschedule variability cost. This cost is typical issue in terms of problems on freight transportof LLDCs accessing seaport in coastal countries. Several scenario analysis are conductedunder several scenarios using developed model.
Chapter 7 has dedicated to mention the conclusions and recommendations and future scopeof the dissertation.

7

Chapter 1
Background, Problem Statement, Objectives
Chapter 2
Literature Review for issues of freighttransport of Landlocked Countries

Preliminary Analysis(Objective 1 and 2)
Chapter 3
Current Condition of freight transport inlandlocked Countries

Chapter 4
Impact of shipment time variability on
estimating expected shipment time

Chapter 5
Valuation of shipment time variability

Chapter 6
Schedule variability Cost andScenario Analysis

Inland Cargo Flow Model(Objective 3 and 4)
Chapter 7
Conclusion

Figure 1.2 Organization of Dissertation

8

CHAPTER 2
LITERATURE REVIEW ON FREIGHT TRANSPORT IN
LANDLOCKED COUNTRIES

9

2.0 Introduction
Issues on freight transport in LLDCs cannot be completed only within the field of transport
engineering and transport economics. Because LLDCs are normally categorized into poor
country, several international organizations tackling with freight transport problem as
poverty reduction research. Their motivation is one of the factors to improve life levelstandard is improving freight transport condition since tremendous impact is expected on
goods price. From the economics point of view, main two research areas are involved inthis issue: development economics and econometrics. In development economics, most of
the papers examining the disadvantage of being landlocked using simple regression
analysis in regard to transport cost, time, etc. Some papers focus on the import and export
volume as explanatory variable employing gravity model. In the field of econometrics,
existing cargo flow model, MICS as briefly introduced in chapter 1, analyzed the economic
impact of transport infrastructure development in the context of Asia including Lao PDR.
This chapter addresses on the review on existing study and survey result on freighttransport.

2.1 Reports of International Organization
The World Bank (WB) periodically releases Logistics Performance Index (LPI) which isone of the indicators of country’s comparative logistics performance. LPI is scored on thebasis of questionnaire survey to logistics related organization and individuals, such as
freight forwarders, shipping liners, etc. by scaling 1(worst) to 5(best) relevant to thepossible comparison groups. The following six factors are used as key dimensions;(1) Efficiency of the clearance process(2) Quality of infrastructure(3) Easiness of arranging competitively priced shipments(4) Competence and quality of logistics services(5) Ability to track and trace consignments(6) Timeliness of shipments
From the LPI, we can understand the broad condition of freight transport for each country.
Table 2.1 shows the LPI of LLCs in Asia and top 10 countries in the world. Previous resultof LPI told us that the best country in terms of logistics performance was Singapore,
however, Germany receives the top rank in 2010.Comparing LPI score of top 10 countries
an Asian LLCs, the gap is approximately 1.5 points in the range of 1 to 5. Among the LLCs,
Central Asian nations are relatively in good score.

10Table 2.1 LPI Score of Top 10 Countries and LLCs in Asia in 2010
Top 10Landlocked Country in Asia

Rank

Country

LPI

Rank

Country

LPI

1

Germany

4.11

62

Kazakhstan

2.83

2

Singapore

4.09

68

Uzbekistan

2.79

3

Sweden4.08

91

Kyrgyzstan

2.62

4

Netherlands

4.07

114

Turkmenistan

2.49

5

Luxembourg

3.98

118

Lao PDR

2.46

6

Switzerland

3.97

128

Bhutan

2.38

7

Japan

3.97

131

Tajikistan

2.35

8

United Kingdom

3.95

141

Mongolia

2.25

9Belgium

3.94

143

Afghanistan

2.24

10

Norway

3.93

147

Nepal

2.20

Source: World Bank (2010)

The World Bank (2011) estimated procedural requirements for exporting and importing
standardized cargo, including maritime transport, based on Djankov et al. (2006). The timeand cost of maritime transport are not included in shipment time and cost in Table 1; inother words, shipment time and cost are for land transport only. For exports, the procedureincludes all steps, from packing the goods at the warehouse to shipping them from the portof exit. Shipment time and cost are estimated beginning from concluding a contract of tradetill completing a shipment. This is estimated based on 20ft container of dry cargo. In export
case, days accounted from packing the goods at the warehouse till seaport are estimated,
whereas for import case, days from arriving at seaport till arriving at warehouse or logistics
center are estimated as shipment time. The shipment cost does not include customs duties,
international maritime transport cost, and illegal charging such as bribes on the road, whichis unfortunately commonly found in developing countries (NELTI, 2009). This data is also
estimated on the basis of questionnaire survey to trade-related companies, low office, andaccounting office aggregating the shipment cost and time of following four processes.
1. Documents preparation (Documents preparation required for trade)
2. Customs clearance and technical control (Tasks done by middleman for inspection andcustoms clearance)
3. Ports and terminal handling (Transport cost in seaport, loading/unloading of goods, andnecessary commission)
4. Inland transportation and handling (Transport cost from seaport to major city andunloading process)11

Target companies of the model are assumed as followings;- Companies located at the city with the highest population density in the countries- More than 60 employees- Private companies running based on commercial law of their countries- Operation based on domestic capital- Exporting more than 10% value of total sales- Not operating in special zone for exporting or industrial park where not privilege forexporting granted
Transport conditions are assumed as followings;- Full loading of 20ft dry cargo container, 10 tons, and 20,000USD for total trade value- dangerous and military goods are not included and special transport equipment such as
cold storage are not included- Questions are asked in terms of USD or local currency

Using the data of LPI, logistics performance of both groups of countries, LLDCs andcoastal countries is compared To maintain the equity of the comparison, we adopt thecountries which are lower than Lower-Middle Income Countries (1,006 to 3,975 USD)
defined by WB (2011). In total, the numbers of data are aggregated 20 for LLDCs and 45
for coastal counties, respectively. In average, LPI of LLDCs has been received as 2.43 and2.55 for non-LLDCs. From this result, even though the difference is slight, which accounts
for 0.12, the logistics performance gap between LLDCs and non-LLDCs is observed.
Figure 2.1 shows shipment time and cost for export case for the countries, which arecompared LLCs with the non-LLCs (coastal countries). Here, long shipment time and highshipment cost of landlocked countries can be observed. In coastal countries, most of them
are in the condition of shorter shipment time and lower cost required for import and export,
whereas LLCs are widely distributed in the figure, and variation between countries is verylarge.

12

7,000

Cost to Export (USD/TEU)

6,000
5,000
4,000
3,000
2,000
Landlocked Countries

1,000

Coastal Countries

0
0

10

20

30

40

50

60

70

80

90

Time to Export (days/TEU)

Figure 2.1 Shipment Time and Cost Required for Export for the Countries
Data Source: World Bank Doing Business (2010)

Export and import shipment time and costs in Europe are in higher ranking. Looking atexport time, Luxembourg needs six days whereas Tajikistan is 82 days. There is a huge
disparity. Also on shipment cost, Czech Republic is 1,060 USD/TEU whereas Chad bears
5,902 USD/TEU. A comparison among landlocked countries, countries located in Central
Asia and Africa are in bad situation. It is considered that a number of trading processes areto be bottlenecks for trading. We can understand that most of the countries grouped into
“the best” is European countries. On the other hand, European countries are not included in
the group of “the worst” rankings in terms of transport time and cost. Most of the countries
grouped into “the worst” are comprised of Central Asia, Africa and Lao PDR. Focusing on
countries in Central Asia and Africa, despite the high rate of pavement Central Asia, themore time-consuming import and export can be observed. This may be due to the long
distance of inland transport. In addition, in African countries, major problems are not foundin terms of distance to seaport, however, underdeveloped transport infrastructure road andrailway can be considered as causes of problems.

13

Table 2.2 Shipment Time and Cost to Export from Landlocked Country
Top 10 Countries
Time to export(days/TEU)

Worst 10 Countries

Cost to export(USD/TEU)

Time to export(days/TEU)

Cost to export(USD/TEU)

Luxembourg

6

Czech

1,060

Tajikistan

82

Chad

5,902

Austria

7

Austria

1,180

Kazakhstan

81

Central Africa

5,491

Switzerland

8

Hungary

1,225

Chad

75

Afghanistan

3,865

Macedonia

12

Bhutan1,352

Afghanistan

74

Niger

3,545

Serbia12

Macedonia

1,376

Uzbekistan

71

Tajikistan

3,350

Armenia13

Serbia1,398

Kyrgyzstan

63

Zimbabwe

3,280

Belarus

15

Luxembourg

1,420

Niger

59

Rwanda

3,275

Czech

17

Bolivia

1,425

Central Africa

54

Uzbekistan

3,150

Kosovo17Paraguay

1,440

Zimbabwe

53

Botswana

3,010

Slovakia

17

Slovakia

1,530

Lao PDR

48

Kyrgyz Rep.

3,010

Data Source: World Bank Doing Business (2010)

Lao PDR, an LLDC and study target country of this dissertation, suffers from longshipment times and high shipment costs in international trade. To make matters worse,
more documents are needed for trade than in surrounding coastal countries except
Cambodia, as shown in Table 2.3. Generally speaking, countries with lower GDPs have
longer shipment times and higher costs. However, the shipment time and cost for Cambodia
is much lower than that for Lao PDR, even though the Lao GDP per capita in 2010 was 189
USD higher than that of Cambodia (IMF, 2010). Compared to developed countries, forinstance Japan, the Lao shipment cost is approximately 2 times higher, even though mostgoods price in Lao PDR is less than that of Japan. The need for a border crossing must
contribute greatly to this relationship. Obviously, the need for a border crossing is one of
the reasons for increasing LLDCs’ documentation requirements. When exporting goods
from Lao PDR through the Thai seaport, a customs declaration form, a bonded application
form, and so on, should be submitted. The bonded form is required in order to avoid
smuggling. After passing through the border, cargo is checked in the seaport of Thailand.
These processes are, needless to say, not required in coastal countries.

14

Table 2.3 Export and Import Data for ASEAN+3.

CountryaaShipment Time(days)
Exports
Imports

Shipment Cost
(USD/TEU)
Exports
Imports

No. of Documents
Exports

Imports

Lao PDR

50

50

1,750

1,930

9

10

Cambodia

37

46

722

852

11

11

Indonesia

21

27

667

623

5

6

Malaysia

18

14

432

385

7

7

Philippines

17

18

800

800

8

8

Singapore

5

3

416

367

4

4

Thailand

17

14

615

786

7

9

Vietnam

24

23

669

881

6

8

Japan

10

11

989

1,047

4

5

South Korea

8

8

742

742

3

3

China

21

24

390

430

7

6

Data on Myanmar and Brunei are not available.

Source: World Bank (2011)

Arvis et al. (2007) concludes that the condition of roads is not a main reason for costly
shipment. Most important problem lies in seaport as delay in coastal countries as well asborder crossing point. As a result of delay at the seaport and border, increase in shipment
cost would be generated as serious problem. Arvis et al. (2007) also mentioned that bribetaking (unofficial payment) is also serious problem for long cross-border haulage indeveloping countries. As a research showing the disadvantage of landlocked country is
Mackellar et al. (2002) mention that average cost of border crossing in Africa is equivalent
to cost of inland transport of 1,600km or sea transport of 11,000km.

15

0.09
0.08
0.07

Probability

0.06
0.05

30+

0.04
0.03

N=1,939

0.02
0.01
0.00
1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

Dwelling Time at Mombasa Seaport (Days)

Figure 2.2 Dwelling Time at Mombasa Seaport in Kenya
Data Source: Arvis (2004)

Figure 2.3 Distance-Time Model for Vientiane-Laem Chabang Seaport
Source: Banomyong (2001)

Cargo movement from Vientiane till the port of Laem Chabang seaport with shipment timeis shown in Figure 2.3. As seen in Figure 2.3, the main contributors for the gap between thebest and worst cases are border and seaport.

16

2.2 Literature on Development Economics
Several studies regarding landlocked countries related to freight transport applies gravity
model for international trade in order to examine their economic disadvantage. Radelet andSachs (1998), Stone (2001), and Mackellar et al. (2002) are focus on transport cost of LLCs
and Limao and Venables (2001), Raballand (2003), and Grigoriou (2007) examined how
being landlocked affects on trade volume. The researched comparing transport cost oflandlocked country to coastal countries, higher transport cost of landlocked countries arehighlighted. In Radelet and Sachs (1998), using CIF/FOB data for 97 developing countries
including 17 LLCs, shipment cost and insurance cost of LLCs are approximately twice ofthat of coastal countries. Besides, it is proved that there is strong relationship between
shipment cost and economic development. In the research of Stone (2001), shipment
sharing issues are examined trade activities for 64 comparable LLCs and transit countries.
It is revealed that shipment cost of transit countries is 75% lower comparing to that of
LLCs. In 18 LLCs out of 30 LLCs, more than 10% of total exporting value occupies
shipment cost. The situation in African LLCs is more serious situation, that is, 13 LLCs
among 15 LLCs suffer from more than 10% shipment cost occupying total exporting value.
Among 15 LLCs indicated above, 7 LLCs exceed 20% of total exporting value. There areseveral researches for analyzing determinants of shipment cost, for example, Brun et al.
(2005), Micco and Perez (2002) and Martinez-Zarsoso (2003). In overall trend, following
factors are defined as important factor to determine shipment cost of LLCs, such as
distance to the countries of trade partner, type of goods transported, distance to the large
economical market, for example US, Europe, and Japan, level of infrastructuredevelopment, total shipment cost and number of alternative routes.
Limao and Venables (2001) analyze both shipment cost and trade volume. Comparing
LLCs and coastal countries, LLCs bears 50% more than that of coastal countries in average.
The impact of infrastructure development on shipment cost is also analyzed. In caseinfrastructure condition in the countries of trade partner improve up to 75 percentile levelfrom average LLCs, 12% cost reduction can be achieved. In case infrastructure condition intransit countries is increased till same level of that of trade partner, 7% improvement isobserved. Besides, trade volume is estimated using gravity model. It is clarified that tradevolume of LLCs is 30% lower comparing to coastal countries. The level of infrastructuredevelopment is very influential in trade activities. In case infrastructure quality goes down
by one standard deviation, it is equivalent to the incensement in 6,500km of maritime
transport and 1,000km of land transport. Moreover, shipment cost of land transport isrevealed as to be 7 times higher than maritime transport. From this result, they also pointed
out that one of the adverse factors for high shipment cost is to be long inland transportdistance.

17

It is also stated that another reason to increase land shipment cost is due to the bordercrossing in several literatures. Grigoriou (2007) also highlighted that strong correlation
between infrastructure condition and trade volume. Raballand (2003) revealed that the
number of border crossing point for the trade highly influence on the trade volume of LLCs.
As the border crossing points increases, the trade volume decreases. Thus, the number ofborder crossing required and trade volume has inverse proportional relationship. The
problem of border crossing is not only for LLCs but also non-LLCs. Hence, there areseveral literature related to border crossing can be found, for example, MacCullum (1995)
and Helliwell (2001). They develop a trade volume model by employing gravity modelusing trade data between US and Canada, and within US and Canada, respectively. As aresult of comparing trade beyond the border with trade within the country under the
standardized economies scale and shipment distance, domestic trade is 22 times more thaninternational trade in terms of trade volume in Macllum (1995). Similarly, Helliwell (1996)
also obtained the same trend, which domestic trade volume is much more than that ofinternational trade. From this result, we found that the impact of the border crossing is
negligible even in developed countries. We can postulate that the border impact on the
trade in developing counties might be higher comparing to developed countries since tradesystem in terms of both physical and institutional issues in developing countries is lessdeveloped.
Another approach to the study of LLCs is Kawai et al (2011). They categorized LLCs into
several groups because each LLC have country specific problems, which affect on the
shipment time differently. Some country is in good condition of infrastructure condition,
but some country is not. In this case, LLCs should be categorized into the groups based on
their own problems. For example, Raballand (2003) developed trade volume model using
gravity model as following Equation (2.1). Nevertheless, LLCs are treated as homogenous.
It should be considered disparity among LLCs.
ln IMPijt - + , * ln GDPit , ) ln GDP jt , ( ln Dist ij , ' ln Instit it , & ln Access i, %Landlocked ij , $ ln Infra i , #inInfra j , " ln Tariffit , ! ijt(2.1)

2.3 Literature on Econometrics
The most traditional method for forecasting cargo volume at several points such as seaportand borders is method which uses historical trend. In terms of accuracy of model result, it is
not always bad. However, an explanatory variable in this method is normally to besocioeconomic data such as GDP. Thus, this method is not suitable for policy simulation
study, which analyzes the impact of change in road network, etc. The same problems can befound in the forecasting model of multivariate analysis that is based on the econometrics
18

theory. This problem originates in that the change in behavior of shipper and other related
logistics actors corresponding to policy change are not considered.
The trip distribution model is developed for considering behavior of each individual. The
simplest distribution model is the shortest path problem, which all cargo is distributed to
the route that is the cost or time is the minimum among the all sets. The shortest path
problem is sometimes called as all-or-nothing or deterministic distribution because this idea
resulted in all cargo or zero for two routes. The disadvantage of this model is that the only
slight difference leads the huge differences between the routes. Thus, it is also not suitablefor policy simulation because the slight inversion in terms of lowest cost of the routes,
extreme change in cargo distribution is generated.
Stochastic distribution model such as logit model and sacrifice model are developed inorder to avoid extreme change in cargo flow pattern by policy changing. Important propertyof the stochastic model is that cargoes are assumed as flowing not only lowest cost route
but also flowing routes of comparatively higher cost due to unobserved factors foranalyzers. Sacrifice model considers variability between the routes in terms of freight valueof time. To do this, several patterns for route choice is generated. These models are applied
mainly practical purpose, for example, demand forecasting of seaport choice in Japan.
However, the disadvantage of this model is that alternative potential routes are needed to begiven to the model. In this case, two concerns arise study area would be limited to local, notglobal. However, all routes from large transport network cannot be provided because of thecomputational complexity.
On the basis of the problems above, several international cargo flow pattern model havebeen developed. The representative model considering interdependency between shippers
and transport carriers are Strategic Planning of National Freight Transportation (STAN)(Crainic et al., 1990a, 1990b, 1997, 1999, 2007), Freight Network Equilibrium Model(FNEM) (Friesz et al., 1985a, 1985b, 1986) which is an incremental model of seaportchoice and shipping line choice, and Generalized Spatial Price Equilibrium Model(GSPEM) (Harker et al., 1986, 1987). According to Friez and Kwon (2008), FNEM and
STAN is currently used in the government of the US and Canada. However, in regard to the
GSPEM is only applied to the case study of coal industry and railway transport because theenormous calculation process is required.
The notable characteristic of this model is that interdependency between shipping lines andshippers is incorporated for seaport choice. Shipping lines are assumed to be in theoligopoly market, thus, calculation process is somewhat small. The cost of border crossing,
they divide into two; such as “border resistance in narrow sense” and “border resistance inbroad sense”. In narrow sense, it is defined as resistance encountered in real border crossing
19

point, on the other hand, in broad scene, it considers cost incurred at the stage ofpreparation of the shipment since large number of the documents are required particularlyin the developing world. Border resistance is set by five grades which are determined basedon the literature review and field survey. Subsequently, the estimated cost is included into
the generalized cost of the haulage. However, this method does not explicitly include theshipment time variability which affects the route choice under heavily variable route.
Normally, these models are developed so as to analyze the cargo flow pattern by changing
route availability or level of service. However, in this study, interest of this research is only
for the change in cargo flow pattern in Lao PDR and surrounding countries. In the areasurrounding LLDCs, routes for accessing seaport are very limited. Thus, relatively higher
computational process is acceptable.
One paper regarding to the analysis of the impacts of international infrastructuredevelopment in GMS (Iwata et al., 2010) is found. This paper covers two types of impacts;
the economic impacts on the GDP in the country and the impacts on interregional freighttraffic flows crossing the country and results are estimated as reduction in transport cost as
shown in Table 2.4.

Table 2.4 Estimated Benefits in Transport Cost by Implementing Transport Policy
Country

Rate of decreased transport cost
All Projects

Maritime

Land

CBTA

Philippines

3.7%

2.3%

2.5%

0.9%

Vietnam

12.3%

1.8%

7.1%

5.4%

Laos

22.6%

2.1%

-0.2%

19.2%

Cambodia

4.3%

0.3%

0.4%

2.8%

Thailand

12.9%

7.1%

10.6%

4.0%

Malaysia

6.6%

1.4%

2.2%

6.2%

Singapore

6.8%

2.0%

1.9%

4.2%

Myanmar

5.6%

1.3%

1.5%

3.6%

Indonesia

12.8%

8.3%

2.2%

5.7%

Brunei

9.0%

0%

0.9%

7.8%

ASEAN Subtotal

8.8%

3.9%

4.5%

4.2%

Source: Iwata et al. (2010)

Two econometric models are used for analyzing the impacts. The one is the standard
Global Trade Analysis Project (GTAP) model. This is one of the spatial computable general20equilibrium models with which the change in economic activities caused by transport
projects is estimated. It covers multiple sectors in multiple regions with the assumptions of
the perfect competition and constant returns to scale. Another is a MICS. Using MICS, themodal choice and route choice of freight transport is analyzed. The results of the impact
analysis provide two insights. Firstly, the international transport projects in the GMS
increases substantially GDP in Lao PDR in addition to the GDPs in other countries in the
GMS. Secondly, although the current projects may increase slightly the land freight
through-traffic crossing Lao PDR, they may not give the critical impacts on the local
community in Lao PDR. Thirdly, the further cross-border trade facilitation would cause adrastic modal shift from maritime transport to land transport. Finally, the land freight traffic
to and from China could increase substantially in the GMS if the further cross-borderfacilitation would be realized.

2.4 Chapter Conclusion
There are three main groups of the literature in this chapter. The first group (section 2.1) isabout to describe works and surveys generally related to freight transport issues from themacro-scope of view in terms of shipment time and cost. They are introduced for better
understandings for this dissertation. In addition to shipment time and cost, its variability isshown using two evidences, variability at Mombasa seaport (Figure 2.3) and on the route
between Vientiane and Laem Chabang seaport (Figure 2.4). This is the main problem
behind the motivation to build the model in this dissertation.
The second group of the literature (section 2.2) discusses the papers from the field ofdevelopment economics. They lead to the conclusion that the problems on freight transportof cross-border transport exists and adversely affect on increase in shipment cost anddecrease in trade volume using regression analysis and gravity model in principle. However,no study had been done as seaport is one of the bottlenecks for landlocked countries. In thefirst group of literature, several literatures highlight the adverse effect of shipment time
variability at seaport. Nevertheless, in the field of development economics, cross-bordereffect was examined as prior concern.
The last group of the literature (section 2.3) describes the papers about cargo flow
simulation model with brief history regarding forecasting model for seaport cargo volume.
The most prominent model for this field is Shibasaki and Watanabe (2008). In that model,
shipment time variability at border is roughly considered but rigorous theoreticalbackground is somewhat in doubt. Besides, seaport is not treated as a bottleneck in his
model.

21

CHAPTER 3
FIELD SURVEY IN CENTRAL ASIA AND LAO PDR

22

3.0 Introduction
Chapter 3 dedicates to illustrate the clarifying and confirming the problems related tohaulage of LLDCs learnt from field surveys (interview surveys and site visits) in Central
Asia and Lao PDR in order to fulfill objective 1. Problems extracted from the surveys
would not be only physical issues but also institutional issues that include binomialagreement between countries, multinomial agreement, other treaties facilitating smoothinland cargo flow, etc. The finding of this chapter serves as the motivation and fundamental
problem statements to the rest of the dissertation. Inland cargo flow model for LLDCs aregoing to be developed considering problems found in this chapter. As for basic data of
Kyrgyzstan, it is omitted due to the reason that the main survey locations are Uzbekistan
and Kazakhstan.

3.1 Field Survey in Central Asia
3.1.1 Overview of the Survey
The field surveys including interview survey and site visit in Central Asia were conducted
between the period of September 27th and October 3rd, 2009 in three Central Asian
countries, Uzbekistan, Kazakhstan, and Kyrgyzstan. Interview surveys were conductedwith seven state-owned companies and governmental organizations from Uzbekistan, two
private companies from Kazakhstan, and one governmental organization from Kyrgyzstan.
In addition to interview survey, site visits were conducted, such as Uzbekistan/Kazakhstan
Border (Yallama), Railway Container Terminal in Tashkent, Kyrgyzstan/Kazakhstan
Border, Almaty I Railway Container Terminal, and logistics center of private company in
Almaty.
Uzbekistan is one of the most severe-conditioned countries in terms of inland freighttransport because of the geographical situation that the country are forced to pass across theborder at least twice to access to seaport. Such countries are called as doubly landlocked
country. Currently, doubly landlocked countries exist only two in the world, Uzbekistan
and Liechtenstein. As reviewed several papers in chapter 2, the number of border crossing
has negative impact on shipment time, cost, and trade volumes. Considering above factors,
field survey in Uzbekistan were conducted. Accompanying with Uzbek survey, twoneighboring LLCs, Kazakhstan and Kyrgyzstan were visited for field survey.

23

3.1.2

Basic Data of Uzbekistan

Prior to proceeding to detail discussion of the field survey, several basic data are reviewed
in order to understand fundamental situation of Uzbekistan. The population of Uzbekistan
is 27,606,007, which ranks 44th in the world, as of 2009 (CIA, 2009). Recently, economic
condition looks somewhat good in terms of GDP growth rate as shown in Figure 3.1.
Immediately after achieving independence from the former Union of Soviet Socialist
Republics (USSR), Uzbekistan’s level of economic growth declined. After passing throughthese difficult times, the country began, after 1996, to experience positive economic growth,and Uzbekistan finally achieved a +10% GDP growth rate in 2007. The GDP per capita in2009 is estimated to have reached 2,805 USD (IMF, 2009). This indicates that the growthrate in 2009 against the previous year is +6.52%, which implies that Uzbekistan has notbeen widely affected by the recent economic crisis. On the other hand, the GDP growth rate
in 2009 in Kazakhstan is expected to be negative because of the economic crisis.

12

3,000
GDP Growth Rate

8

GDP per Capita

2,000

4

1,500
0

1,000

GDP Growth Rate (%)

GDP per Capita (USD)

2,500

-4

500

-8
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009 est

0

Figure 3.1 Change in GDP Per Capita and Economic Growth Rate in Uzbekistan
Data Source: IMF (2009)

Trade partners of Uzbekistan for both exporting and importing case are shown in Figure 3.2
and 3.3, respectively. Russia is the most trading country for both cases of export and importof Uzbekistan (25.3% and 27.6% of total trade). Total exporting value in 2008 accounts for10.37 Billion USD whereas import value in 2008 is 7.07 Billion USD (CIA, 2009). Types24

of goods exporting in 2008 are cotton, gold, energy products, mineral fertilizers, ferrous
and non-ferrous metals, textiles, food products, machinery and automobiles. Among them,
primary exporting goods are cotton and natural resources (CIA, 2009). As for importing
goods, machinery and equipment, foodstuffs, chemicals, ferrous and non-ferrous metals aretreated. Recently, importation volume from China is rapidly increasing.

RussiaTurkeyKazakhstan

Bangladesh
China
UkraineJapanUSTajikistan
Others
0

5

10

15

20

25

30
(%)

Figure 3.2 Trade Partners of Uzbekistan (Export)
Source: CIA (2009)

RussiaChina
S. Korea

Germany
Kazakhstan
TurkeyUS
Others

0

5

10

15

20

25

Figure 3.3 Trade Partners of Uzbekistan (Import)
Source: CIA (2009)

25

30
(%)

3.1.3 Basic Data of Kazakhstan
The population of Uzbekistan is 15,399,437, which ranks 64th in the world, as of 2009 (CIA,2009). In a trend that is similar to Uzbekistan, in the years immediately after gaining
independence from the former USSR, Kazakhstan’s economic growth in terms of GDP wasrecorded as negative as shown in Figure 3.4. Subsequently, the country managed to achieve
continuously high economic growth because of its abundant natural resources, particularly
oil. In 2000, it reached the 10% mark (12.44%), and maintained an economic growth rate ofover 10% until 2007. Nevertheless, the growth rate drastically decreased until it reached5.30% in 2008 and finally, a negative rate is expected to have occurred in 2009. One of thereasons for this decline in economic prosperity could be that their industrial sectors, which
are highly dependent upon oil production, have been affected by the relatively low price of
crude oil recently.

14,00020
GDP Growth Rate

10,000

10

8,000

5

6,000

0

4,000

2009 est

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

-10

1995

0

1994

-5

1993

2,000

GDP Growth Rate (%)

15

GDP per Capita

1992

GDP per Capita (USD)

12,000Figure 3.4 Change in GDP Per Capita and Economic Growth Rate in Kazakhstan
Source: IMF (2009)

Trade partners of Kazakhstan for both exporting and importing case are shown in Figure
3.5 and 3.6, respectively. Russia and China are the most trading country for total trade ofimport and export. Total exporting value in 2008 is 71.97 Billion USD and import value in2008 is 38.45 Billion USD (CIA, 2009). Differing Uzbekistan, trade of Kazakhstan isexport surplus. Types of goods exporting in 2001 are oil and oil products (59%), ferrous
metals (19%), chemicals (5%), machinery (3%), grain, wool, meat, coal. The main
exporting goods of Kazakhstan are natural resources (CIA, 2009). As for types of importing26

goods of Kazakhstan are machinery and equipment, metal products, foodstuffs, foodstuffs,
chemicals, ferrous and non-ferrous metals.
China

RussiaGermany

Italy
Romania
FranceTurkeyOthers

0

5

10

15

20

25

30

35

40
(%)

Figure 3.5 Trade Partners of Kazakhstan (Export)
Source: CIA (2009)
RussiaChina
Germany
UkraineOthers
0

10

20

30

40
(%)

Figure 3.6 Trade Partners of Kazakhstan (Import)
Source: CIA (2009)

3.1.4 Routes of Inland Transport
In this section, routes for freight transport from/to Uzbekistan and Kazakhstan are
addressed. Accompanying with the routes, several problems lied on the routes and haulage
related treaties are presented. The haulage from Uzbekistan to Russia, which occupies thelargest portion of trading amount in terms of monetary term for Uzbekistan is normallyoperated by trucks through Kazakhstan. For transit in Kazakhstan and Russia by Uzbek
truck, permission for truck operation is not necessary. Types of exporting goods transported
to Russia are daily goods, cotton, agricultural products, and automobiles whereas goods ofimport from Russia are wooden material and goods that are unavailable in Uzbekistan.
Shipment time normally takes 2-5 days, 3 days can be variable for nearly 4,200km journey.
27

The haulage to China is normally conducted by rail transport through Dostyk/Alashankou
border which is shown in Figure 3.7. In this route, total shipment time between Tashkent
and Lianyungang seaport takes 14-20 days, which implies that 6 days can be varied. The
type of export goods to China are mainly secondary materials such as oil, silk material,
plastic, bottle, etc., whereas import goods from China is mainly daily goods, electrical
goods, construction material, machinery, etc. At the border crossing point, cargo inspection
is required since China is not a member of the Federation of International Trade
Associations (FITA) but Uzbekistan is. Consequently, delay usually occurs at the borderdue to the several processes including cargo inspections. Regarding the haulage from/toEuropean countries, it is dominantly conducted by truck through Kazakhstan and Russia.
Railway operation for freight transport in this route is minor. The permission for Russiantransit is not required according to the bilateral agreement. The attractive point of haulage
by trucks is shorter shipment time, which takes 5days-1week, whereas shipment cost takes
4,000-5,000USD per a truck. The possible maximum variability of this route is 2days fortwo border crossing route. In case of seaport access from Uzbekistan, the route through Iranis normally chosen. In this case, Bandar Abbas seaport in Iran is normally used, andCaspian sea route is also rarely used. In case Iranian route, shipment time gets longer,
approximately 20 days to Europe, however, shipment cost is lower than Russian route,which takes 2,500-3,000US per a truck. This is rational since maritime transport is lower
cost in general. The final destinations of Iranian route are; Greece, Bulgaria, and Italy ingeneral. In case of Caspian sea route, goods rarely transported till France but it is very few
amount. Karachi seaport in Pakistan is not used mainly due to the reason that Afghanistantransit is impossible so far. In case goods are transported to/from South East Asia, the routeis normally passing through Bandar Abbas seaport using maritime transport. The reason to
use this route is that the shipment cost of land transport is relatively higher through China.
The shipment time till Bandar Abbas seaport is to be approximately 7-10 days by truck.
Between Uzbekistan and East Asia, Japan and Korea, it is normally relayed Vostochny(Vladivostok) seaport or Lianyungang seaport through Kazakhstan using China Land
Bridge (CLB). The route through Bandar Abbas seaport is not used. The haulage to
Afghanistan, which is neighbor country, is operated for transporting the goods such as daily
goods, military goods, metal, oil, etc. The border is limitedly opened between 8am till 5pm.
Next, the route from/to Kazakhstan is addressed. As for from/to Russia, railway transport
through Novorossiysk is mainly used for food products. The final destinations are normallylarge cities such as Moscow and Saint Petersburg. The distance transported between
Almaty and Moscow is approximately 4,000km and shipment time is accounted as 3-4 days
for trucks. In this case, shipment time variation is 1 day. Shipment cost is approximately
1.5 USD/km in average, however, it is varied depending on insurance, scale of companies,
etc. The shipment time between Almaty and Moscow by railway takes approximately 10-12
days. Regarding shipment cost using container, 1.5 times higher than non-container
28haulages. The shipment time between Shanghai and Dostyk by railway transport, it requires
approximately 1 week. One needs to take 1 day-3 days for transshipment for 500-600
rolling-stocks at the border Dostyk/Alashankou due to the differences of gauge size. The
opening time of the border is limited between 8 am and 8 pm. In regard to haulage from/toEurope, Russian route by truck is the most common. To Japan, it is same route as haulage
from/to Uzbekistan. They are using CLB for accessing to Lianyungang seaport. As a resultof all interview survey, focusing on shipment time variability, it happens at border andalmost no delay can be observed on the road. Thus, for developing inland cargo flow model,
we can assume that the link is operated at almost free-flow speed.

RussiaKazakhstan

Caspian Sea

Uzbekistan

Turkmenistan

Iran

Vostochny (Vladivostok)
SeaportDostyk/Alashankou
Kyrgyzstan

Nakhodka
SeaportTajikistan

CLB

Afghanistan

China
Pakistan

Lianyungang Seaport
Shanghai Seaport

Bandar Abbas Seaport
Karachi Seaport
India

South East Asia

Figure 3.7 Location of Each Point of Asia

29

Saint Petersburg Seaport
Riga Seaport

Black Sea

Moscow
Tula

Novorossiysk
Caspian Sea

Bandar Abbas Seaport

Figure 3.8 Location of Each Point of Central Asia and Europe
3.1.5 Treaties Related to Freight Transport
Treaty and agreement related to freight transport of Uzbekistan and Kazakhstan
surrounding CIS countries are summarized as following (1)-(6). As supplemental
information, several regional frameworks enforced are obtained from JICA (2007). Theseframeworks will contributes on further facilitation of freight transport between the
countries.
(1) TIR (Transport International Routier)
United Nations Economic Commission for Europe (UNECE) manages TIR. After cargo areinspected and bonded at the departure location of the number countries of TIR, TIR sticker
is put on the trucks. The trucks with TIR sticker are no longer take cargo inspection within
the member countries. In general, customs clearance is separately prepared for such trucks.
TIR would contribute on the decrease in unnecessary delay at the border.
(2) Agreement on Freight Transport
By this convention, cargo inspection process in the CIS country can be omitted in casemore than two border crossing for one haulage. In the route of Uzbekistan-KazakhstanChina, cargo inspection upon arrival at Kazakhstan is exempted. However, because China30

is not acceded to this convention, cargo inspection process is required upon arrival at China.
This convention is also effective in terms of reduction in transit cost and tariff between CIS
and Russia, who are members of this convention.
(3) FITA (The Federation of International Trade Associations)
In order to export to countries in Central Asia from non-Central Asian countries, several
numbers of procedures are required and authentication by FITA is required. In order to passthrough countries non-FITA member countries, change of the carrier is required on all suchoccasions.
(4) SPECA (UN Special Programme for the Economies of Central Asia)
SPECA is one of the regional cooperation framework led by ESCAP and intended for
integration of Central Asia into the global economy and the strengthening of regional
economic cooperation in Central Asia. SPECA member is currently seven countries such as
Afghanistan, Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan andUzbekistan.
(5) Union of Common Customs Clearance
Union of Common Customs Clearance was jointly come into the force by three countries,
Kazakhstan, Russia, and Belarus on January 1, 2010. It will finally become effective inJune 2011. Among these unions, freight movement within the member countries is allowed
one customs clearance thanks to common declaration form. Tajikistan and Kyrgyzstan iscurrently an observer, and are expected to be future members.
(6) Other country-base agreements
China and Kazakhstan:In case exporting from Kazakhstan to China, it is prohibited to enter China by Kazakh
trucks in principle. However, Kazakh truck can transport up to Urumqi and rail cargo canbe transported up to Khorgos thanks to bilateral agreement of two countries. The border
point is jointly managed as border cooperation center (neutral place).
Kazakhstan, Uzbekistan, and Turkmenistan (Treaty of three countries):
In case a foreign-flag truck (Kazakhstan and Turkmenistan) operated in Uzbekistan, it isnecessary to declare transport route when entering to Uzbekistan. Subsequently, map withdesignated route will be distributed to truck drivers. The drivers are prohibited to drive nondesignated route. There are similar treaties among Uzbekistan, Kyrgyzstan, and China.

31

(7) Other regional cooperation framework
According to JICA (2007), there are several regional frameworks in Central Asia andsurrounding regions and countries as shown in Table 3.1. These frameworks are expected
to enhance and facilitate efficient freight transport in the region.

Table 3.1 Regional Framework Surrounding CIS Countries Related to Freight Transport
Kazakhstan

EEC

SCO

CAF

CAREC

ECO

CACO

CIS

TRACECA

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Uzbekistan
Kyrgyzstan

yes

yes

yes

yes

yes

yes

yes

yes

Tajikistan

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Turkmenistan
Russia

yes
yes

China

yes

yes

yes

Iran

yes

Azerbaijan

yesGeorgia

yes

yes

yes

yes

EU
Othersyes
Belarus

Pakistan,
Turkey,Afghanistan

Belarus,
Moldova

Armenia

Source: JICA (2007)
EEC: Eurasian Economic Community
SCO: The Shanghai Cooperation Organization
CAF: Central Asia Forum
CAREC: Central Asia Regional Economic Cooperation
ECO: Economic Cooperation Organization
CACO: Central Asia Cooperation Organization
CIS: Commonwealth of Independent States
TRACECA: Transport Europe Caucasus Central Asia

3.1.6 Summary of Freight Transport Problems
From the interview survey, typical transport risks which will contribute on additional costwhich may be considered for their route choice are examined. Risks associated with freighttransport in Kazakhstan and Uzbekistan (1) trivial problems, (2) serious problem, forviolating stable transport supply are summarized. In the survey, potential freight transportrisks, which are listed up prior to the interview survey, were asked. After that, othertransport risks were answered by open-ended question method.
32

(1) Trivial problems- damage on goods transported by railway container freight transport is few, in the caseof cargo damage occurs, insurance can be applied- cargo damage caused by vibration is few as far as following haulage regulation- The regulation change in rail freight transport is managed by Central Soviet Union
Railway Company located in the Moscow office. The information will be noticed to thecountry. Therefore, the problems caused by a sudden rule change are few.
- During the winter season, special container is used for transporting cargo in order toprevent freeze.
- No damage on the cargo due to rain, snow, and water leakage- Lack of the container in Uzbekistan is only during the peak season, and can be copedwith the problems by renting containers from Russia and Kazakhstan.
- Pilferage of cargo is problematic, however, the loss due to the pilferage can be coveredby insurance. In case it happens in rail transport, railway company takes responsibility
on it.
- The issue of terminal capacity at the terminal because of lack of space is notproblematic.
- Negative impact due to language differences at the border is not also problematic.

(2) Serious problem- Failure of the rail vehicle is problematic although no failure on track line. Among total
domestic freight by rail in Uzbekistan from the beginning of 2009 till September 2009,the vehicle broke down about 20 times during the period. The total capacity of rail
freight transport is approximately 240 million tons.
- There is waiting time caused by congestion in rail transport. Approximately 5% of total
train does not comply with the timetable.
- Lack of equipment for scanning cargo at the border is causing the long delay- Lack of resting place for drivers- In Kazakhstan, the problem is engine failure due to entering the desert sands.
- Because of delays at the border affecting the fare increase, it should be improved. That
delay occurs randomly, thus it is tough to predict the delay.
- On the haulage to Mersin in southern Turkey facing the Mediterranean Sea fromUzbekistan, the driver’s visa is sometimes expired due to the waiting time for the ferry.
- Unofficial Payment may be illegally collected by the customs officials and border
guards. It happens mainly at the border.
- Lack of tracing function (JICA, 2007)33

3.1.7 Findings
From the field survey in Central Asia, several problems were found. They can be divided
into two types of problems which are trivial and serious problems. As an overall trend, it
can be observed that trivial problems are normally risks which are not regularly occurs,
despite its impact. Also, they are normally covered by insurance. On the other hand, trivial
problems are related to delay due to the low reliability of the route. For example, someanswered that expiring driver’s visa is problematic. In fact, this happens due to theshipment time variability and drivers are suffered from unexpected waiting time. Shipment
time variability regularly occur, but it is difficult to predict the scale of it. Therefore, it canbe concluded that one of the most serious concern for considering additional cost of inland
route choice problem is shipment time variability.

3.2 Field Survey in Lao PDR
3.2.1 Overview of the Survey
The field survey was conducted between the period of September 15th and 21st, 2010 in Lao
PDR, January 10th and 14th, 2011 in Bangkok, and January 17th and 20th, 2011 in Hanoi.
Interview surveys were conducted with 51 freight forwarders in total. Since Lao PDR andsurrounding coastal countries are target area for model development in chapter 4, 5, and 6,
these three countries were selected as second field survey of this dissertation.

3.2.2 Basic Data of Lao PDR
The population of Lao PDR is 6,477,211, which ranks 103rd in the world, as of 2011 (CIA,2011). As for economic condition, it shows similar trend to GMS surrounding countries,
such as Thailand and Vietnam as shown in Figure 3.9. However, the scale of growth rate ismuch lower than those surrounding countries. GDP growth rate marked positive except theperiod of Asian Finance Crisis in 1997. Posterior to extinction of aftereffect of financial
crisis, Lao PDR continuously achieves positive economic growth. In 2010, it reachednearly 1,000 USD per capita. However, again, the GDP growth rate is substantially lower
than surrounding countries.

34

1,2000.4
GDP Growth Rate
GDP per Capita

0.3
0.2

800

0.1
0

600

-0.1

400

-0.2

200

GDP Growth Rate (%)

GDP per Capita (USD)

1,000

-0.3
-0.4

1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008 est.
2009 est.
2010 est.

0

Figure 3.9 Change in GDP Per Capita and Economic Growth Rate in Lao PDR
Data Source: IMF (2009)

Trade partners of Lao PDR for both exporting and importing case are shown in Figure 3.10
and 3.11, respectively. Thailand is the most important country in terms of both cases ofimport and export (29% and 66% of total trade). Total exporting value in 2009 is 1.10
Billion USD and import value in 2009 is 2.03 Billion USD (CIA, 2010). Types of goods
exporting in 2009 are wood products, coffee, electricity, tin, copper and gold whereas
importing goods are machinery and equipment, vehicles, fuel, consumer goods.

Thailand

China
VietnamUK
Others

0

5

10

15

20

25

30

35

Figure 3.10 Trade Partners of Lao PDR (Export)
Source: CIA (2009)

35

40
(%)

Thailand
China
Vietnam
Others
0

20

40

60

80
(%)

Figure 3.11 Trade Partners of Lao PDR (Import)
Source: CIA (2009)

3.2.3 Cross-Border Transport
The Thanaleng/Nong Khai border (Friendship Bridge, near Vientiane capital) is the busiestof all border-crossing points in Lao PDR, according to the interview survey. This isreasonable because Laem Chabang seaport deals predominantly with Lao cargo, andVientiane is the largest city in terms of economic scale. The Savannakhet/Mukdahan border(Friendship Bridge II) is the second busiest border. Savannakhet is the second largest city
in Lao PDR.
An exporter of Lao PDR is required to submit several documents, such as a bonded
application form, an invoice, a packing list, and so on, to the Ministry of Industry andCommerce of Lao PDR. Subsequently, cargo must be inspected by the economic police(Department of Inspection, Ministry of Industry and Commerce), and a sticker from theCustoms Department must be put on the container. These formalities must be completed on
paper but not through electric devices. Thus, cumbersome processes by paper violate theshipment time reliability. Poor institutional aspects also contribute to difficulties predicting
expected shipment times. According to the interview survey, early closings and different
opening times of the border make cross-border haulage conditions worse. For example, theLao side of the Thanaleng/Nong Khai border closes at 10 p.m., whereas the Thai side isopen 24 hours. Two borders with Vietnam, Nam Phao/Cau Treo (route to Hai Phongseaport) and Dansavan/Lao Bao (route to Da Nang seaport), limit the times they are open totrucks (see Figure 3.12). The Lao side is open from 8 a.m. until 4 p.m., whereas theVietnamese side is open from 8 a.m. until 11 a.m. and from 1 p.m. until 4 p.m. The
Vietnamese side also restricts border crossings during lunchtime. In addition, open times
sometime change without any prior notice.

36

Figure 3.12 Road Map from Vientiane and Savannakhet to Seaports

ADB (2006) documented that freight modal share in Lao PDR is mostly truck, which
accounts for 65.6% of the ton-km base. The rest (34.4%) is coastal shipment using the
Mekong River. However, coastal shipments from Lao PDR cannot access the sea becauseof the Khone Phapheng Falls in the Champassak Province of Laos near the border with
Cambodia.

3.2.4 Seaport Choice
Currently, overseas cargo from Vientiane and Savannakhet is bound primarily for the Thai
seaport. According to the interview survey, the Laem Chabang seaport accounts forapproximately 90% of the cargo volume of the Lao international maritime trade. The
remaining 10% is bound for Vietnamese seaports, such as Hai Phong and Da Nang. 51
logistics companies (7 in Vientiane, 32 in Bangkok, and 12 in Hanoi) were asked by openended question style about the reasons for choosing a Thai seaport. The results are
summarized in Table 3.2.
37

Table 3.2 Reasons for Choosing a Thai SeaportNo. (%) of answers

Reason33 (64.7%)

Interdependency with shipping lines

33 (64.7%)

Low shipment cost

27 (52.9%)

Short shipment time

19 (37.3%)

Better infrastructure

13 (25.5%)

Severe geographic conditions on the Vietnamese side

11 (21.6%)

Low shipment time variability (including high punctuality)

10 (19.6%)

Ease of customs formalities

7 (13.7%)

Empty haulage problem

5 (9.8%)

Language

4 (7.8%)Proximity to borderInterdependency with shipping lines and low shipment cost were the most important factors
in choosing a Thai seaport. Several mother vessels are available in the Laem Chabangseaport. The cost of haulage between Vientiane and the Laem Chabang seaport isapproximately 1,600 USD/TEU or 1,700 USD/FEU, whereas 1,800 USD/TEU or 2,000
USD/FEU is charged for access to the Hai Phong seaport, the gateway seaport of Hanoi. In
contrast, 1,500 USD/TEU is charged for access to the Da Nang seaport, which is the lowest
among the seaport choices. However, Da Nang seaport cannot currently compete with
Laem Chabang and Hai Phong seaport because of the low level of its facilities. For example,
Da Nang seaport has three gantry cranes, whereas Hai Phong seaport has six (Japan
External Trade Organization, 2008). However, it should be noticed that Da Nang seaport iscurrently upgraded rapidly as designated seaport to be invested intensively by thegovernment of Vietnam. Thus, the seaport would be the competitor of other two seaports inthe near future. Shipment cost here is estimated on the basis of the interview survey andincludes several fees, such as those for delivery ordering, terminal handling (freight station),transit customs formalities, loading/unloading, border clearance, and return haulage forcontainers.
Shipment time was the third most important reason for choosing a Thai seaport. Shipment
time between Vientiane and Laem Chabang is approximately 13 hours, whereas thatbetween Vientiane and Hai Phong is 19 hours. In addition to shipment time itself, shipment
time variability was also important (11 companies mentioned this). In general, theVietnamese route is more variable than the Thai route. Some companies pointed out that
because logistics companies are sensitive to punctuality, there should be a tendency to
avoid highly variable routes. 13 companies mentioned the severe geographical conditions
on the Vietnamese side. The Vietnamese side contains the Annamite mountain range,
38

which means more difficult conditions for smooth haulage compared to the Thai side. On
the other hand, Thai side is almost all flat in terms of terrain.

3.3 Summary and Findings
From the interview survey in Lao PDR, several problems related to LLDCs’ freighttransport are identified. In any route of cross-border transport from/to Uzbekistan andKazakhstan, shipment time is variable. Many of them are caused at the border. As
identified in the field survey in Lao PDR, 21.6% of surveyed companies are recognized thatshipment time variability is important factor in Lao PDR as well. In that sense, it ispostulated that there should be adverse impact due to shipment time variability, andconsequently, additional cost due to shipment time variability.

39

CHAPTER 4
IMPACT OF SHIPMENT TIME VARIABILITY ON
ESTIMATION OF EXPECTED SHIPMENT TIME

40

4.0 Introduction
As can be seen till previous chapter, shipment time between LLDCs and seaports inneighboring coastal countries are highly variable because of delays at two bottlenecks: theborder and the seaport. The logistics related organizations are recognized such problem liedat the seaport accessing is as serious problem to be solved. In general, information on
delays at the border and the seaport is not provided and is difficult to predict prior to thedeparture. In such case, it can be considered that estimation of next shipment time is very
likely to be difficult. This, it can be postulated that shipment time variability adversely
affect on the estimation of expected shipment time for logistics related decision makers (inthis study, freight forwarder). In order to clarify the impact of shipment time variability on
estimation of expected shipment time for freight forwarder, one hypothesis is set up for
estimating expected shipment time to examine the hypothesis. The objectives of thischapter are (i) to observe which type of shipment time significantly affects the estimation of
expected shipment time and (ii) to compare parameter across the five different routes which
involve different bottlenecks in terms of the level of shipment time variability.
Subsequently, verification of hypothesis is examined on the basis of results obtained. Inorder to achieve these objectives, a case study on cross-border routes from Lao PDR to
Thailand/Vietnam is presented. The result of this chapter is not methodologically related tochapter 5 and 6, which present inland cargo flow model development. The result of thischapter would be one of the motivations for developing inland cargo flow modeling infollowing chapters.

4.1 Literature Review for Methodology
Transport behavior models generally assume an individual traveler’s evaluation function
for expected travel time of mean, maximum, minimum, weighted average value, and so onof factors expressing traveler’s behavior mechanism (Morichi et al., 1995). Nevertheless,traveler’s behavior in the real world varies according to place, scene, situation, etc. Thus,
Morichi et al. (1995) attempted to develop a model for estimating shipment time in highlyvariable routes. In this context, the generalized mean concept can be applied to determine
which experiences significantly affect the estimation of expected shipment time. The
generalized mean incorporates several evaluation functions of an individual by changing
parameters in the model. An operation that aggregates multiple values to one value can be
expressed as following Equation (4.1):
h : [ x1 , x2 , x3 , !, xn ] . x

41

(4.1)

Among the aggregation operations, an operation that satisfies relationship of Equation (4.2)
is called as a mean operation, which ranges between minimum and maximum values. Using
Equation (4.3), the generalized mean can express various means by changing parameter α(α ≠ 0) as shown in Table 4.1.
min(x1 , x2 ,! xn ) / h( x1 , x2 ,!, xn ) / max( x1 , x2 ,!, xn )

(4.2)

1

5 x+ , x2+ , ! , xn+ 2 +0
x -3 1
3
0
n
4
1

(4.3)

Table 4.1 Mean Operation by Equation (4.3)
Condition

Equationa) Minimum value ( + . 67 )x 67 - min(x1 , x2 ,!, xn )x 61 -b) Harmonic mean ( + . 61 )

1
x1

,

1
x2

n
,!,

1
xn

1
)n

c) Geometric mean ( + . 0 )x 0 - ( x1 8 x 2 8 ! 8 x nd) Arithmetic mean ( + . ,1 )x ,1 -e) Maximum value ( + . ,7 )x ,7 - max( x1 , x2 ,!, xn )x1 , x 2 , ! , x nnTo enable observation of the importance of each explanatory variable, weighted parameter
wi is introduced into Equation (4.3). Because the sum of the weighted parameter is equal to1, the weighted generalized mean is formulated as following Equation (4.4):

9

:

1

x - w1 x1+ , w2 x 2+ , ! , wn x n+ +n;wi - 1

(4.4)

i -1

4.2 Hypothesis
As clarified in literature review (chapter 2) and field survey (chapter 3), shipment time
reliability is hugely violated by several uncertain problems at the border and seaport. The
shipment time variability is not predictable in general. In addition, information of delay is
not provided in any developing countries. Thus, in the highly variable route, the decision42

maker of logistics company might estimate the expected shipment time based on their past
shipment experiences and exogenously given information such as timetable. In thisdissertation, one hypothesis to be examined has been established, that is, the decision
makers estimate expected shipment time based on several type of shipment time such as;(i) experiences of recent shipment,(ii) general shipment time,(iii) and perception shipment time.
The detail explanation and definition of each type of shipment time above will be addressedin section 4.4. In case shipper’s surrounded situation is very unstable and confidence on
their perception to shipment time is rather low, shipper might rely on several information
sources to increase their confidence level for the estimation. This hypothesis can be more
imaginably explained considering the situation when people make trip from origins todestinations in developed country. In this case, people may refer only few information or
one information sources since the shipment time is pretty stable. In this case, little
information source is enough to predict expected travel time for next journey being going to
be conducted. Oppositely, in case of trips in developing countries, people may need muchinformation sources rather than those of developed countries since expected shipment time
is difficult to be estimated due to the variable conditions. In this dissertation, the model isapplied to five different routes so that the parameters of each variable are compared. If the
hypothesis is correct, more reliable routes would receive the result that one specificparameter is dominant in terms of weighted factor since little information source is neededin theory. At this time, the parameters of more variable route would be more equallyweighted comparing to those of reliable routes. If this hypothesis is verified as true, morevariable routes impose additional cost on logistics companies due to the variable conditions.
In this case, it would be rational that the shipment time variability is considered as one of
the cost for route choice problem. Here, the model is developed based on freight forwarders
behavior. On the basis of interview surveys in Lao PDR, it is clarified as common trendthat shippers are heavily dependent upon freight forwarders in terms of decision making onroute and departure time choice. Thus, data collection is done with freight forwarders inthis dissertation.

4.3 Study Area
The model is developed for export case within the responsibility of land transport,
following the rule of Free on Board (FOB). According to FOB, the seller (exporter) mustbear any costs and risks of loss of or damage to the goods until the goods have been loaded
onto the vessel from the departure points such as warehouse of logistics companies. On the43other hand, the buyer (importer) takes responsibility for all costs incurred after the cargo
has been on board. Therefore, in this dissertation, a shipment is considered starting at the
shipper’s warehouse or truck terminal in Lao PDR and continuing across one border till theloading of the goods onto a vessel at a Thai or Vietnamese seaport, as shown in Figure 4.1(see Figure 3.12 in chapter 3 for the map of study area).
Laem Chabang seaport,
Hai Phong seaport,
Da Nang seaport(Bottleneck)

Border(Bottleneck)

Vientiane,
Savannakhet

Thailand/Vietnam

Lao PDR

Figure 4.1 Study Range of Chapter 4

4.4 Model Formulation
As shown in Equation (4.5), expected shipment time, E(st), is a set of four types ofshipment time. Shipment times are not interviewed separately because interviewees are not
likely to remember or record each time. For example, shipment time between border anddestination is not obtainable. Therefore, interviewees were asked to answer the shipment
time between the origin and the destination in the study range (Figure 4.1). The transportmode is assumed to be truck based on real condition (JICA, 2010).
4

E ( st ) - ; xi

<

i -1

lc

cc

cc

=

(4.5)

x - E (lt ), E (bt ), E (lt ), E ( pt )whereE(st):
E(ltlc):E(bt):
E(ltcc):E(ptcc):Expected shipment time (hr)Expected shipment time on the road in the LLDC (hr)Expected shipment time at the border (hr)Expected shipment time on the road in the coastal country (hr)Expected shipment time at the seaport in the coastal country (hr)In general, it can be postulated that the most recent and second most recent shipment
experiences as well as the average and maximum perception shipment times are not likely
to be of the same importance for inferring expected shipment time. Thus, the weighted
generalized mean formula, shown in Equation (4.4), is applied so that the differences inimportance of recent experiences can be examined. The shipment time estimation model
44

based on past shipment experiences is formulated as a nonlinear regression model asfollowing Equation (4.6):n1

E ( st ) - A 8 (; wi sti , wgen st gen , wmin stmin , wave stave , wmax stmax ) , C
+

+

+

i -1

n

+

+

+

(4.6)

;w -1
i -1

where
sti:
stgen:
stmin:
stave:
stmax:
α:
A:
C:
wi:

i

Recent shipment time i times before (hr)General shipment time (hr)Minimum perception shipment time (hr)Average perception shipment time (hr)Maximum perception shipment time (hr)Parameter
Scale parameter
Constant
Weighted coefficient

As parameter α increases, expected shipment time comes close to the maximum value.
Conversely, expected shipment time comes close to the minimum value as α decreases.
Thus, in addition to weighted coefficient, parameter α can be an indicator of whichshipment experiences (relatively long or short shipment time) are important for estimating
expected shipment time. Furthermore, attitude toward risk of delay can also be inferred.
When α is small, the decision maker is relatively risk loving, because a shorter shipment
time is more important for expected shipment time; however, when α is large, the decision
maker may be risk averse, because a longer shipment time is more important.
With regard to the most recent and second most recent shipment experiences, a higher
coefficient is expected to be associated with the most recent one. In order to analyze thistrend, one needs to include actual recent shipment time taken in reality (sti) Here, i = 1, 2,
and 3 denote the most recent, 2nd most recent, and 3rd most recent actual shipment time,
respectively. In reality, the shippers/freight forwarders interviewed did not remember orrecord actual shipment times occurring a long time ago (e.g., 10 or 20 shipments past).
Furthermore, the impact of long-ago experiences is very likely to be low. Therefore, in thisdissertation, shipment times for the three most recent shipments were collected in order tosecure the reliability of the collected data.
General shipment time (stgen) is also included in the model. This is the shipment time that isindicated on the time schedule of each company. Companies are generally operating45

schedule cargo, and thus they normally have a time schedule. Unless the companies have atime schedule, they simply refer to the book. General shipment time is usually used toestimate generalized shipment time on haulage for the purposes of calculating cargo flow ininternational logistics. It can be postulated that logistics decision makers might first refer tothe general shipment time in order to make a decision on how long a shipment takes ingeneral. For example, one logistics decision maker in Bangkok estimated the shipment timefrom Savannakhet to Dansavanh in Lao PDR as 4 hour and 30 minutes as provided by
JETRO (Japan External Trade Organization, 2008). This possibly affects the time
estimation process.
Perception shipment time (stmin) (stave) (stmax), which is individually perceived shipment
time based on any sources, such as past shipment experiences, information in books or on
the internet, rumors, and so on, is also included in the model as a variable. Shippers might
have their own perception shipment time based on all of their experiences. For example,
people who have experienced a terrible delay are going to have a relatively longer
perception shipment time.

4.5 Data Collection
Data collection on each shipment time was conducted simultaneously with the interview
survey in Lao PDR, Bangkok, and Hanoi (section 3.2 of chapter 3). The following eighttypes of shipment time were asked in following order:
1.
2.
3.
4.
5.
6.
7.
8.

Average perception shipment time (stave)
Maximum perception shipment time (stmax)
Minimum perception shipment time (stmin)
General shipment time (stgen)
Most recent shipment time (st1)Second most recent shipment time (st2)Third most recent shipment time (st3)Expected shipment time

Perception time was asked first. If perception time is asked after actual time, thenperception time is likely to be biased because of the aftereffects of answering about theactual shipment time. Subsequently, the most recent, second most recent, and third mostrecent shipment times were asked based on their historical records. The companies
interviewed took place the shipment to/from seaports weekly-monthly; thus, the timing of
the shipment experience differed across the companies. The companies dealt with daily
goods such as beer, tobacco, shoes, shirts, and so on; however, the type of commodity was46

not considered separately. All cargo was transported in 20- or 40-feet containers without
exception because maritime transport was used hereinafter. Thus, no significant difference
among the types of commodities in terms of shipment time would be observed.
Five cross-border routes were selected: Vientiane to Laem Chabang (VTE-LCB),
Savannakhet to Laem Chabang (SAV-LCB), Savannakhet to Hai Phong (SAV-HAI),
Vientiane to Hai Phong (VTE-HAI), and Vientiane to Da Nang (VTE-DAN). The profilesof each route are shown in Table 4.2. The routes differed in terms of the level of variability
in shipment time as well as frequency of shipments. Variability is leveled according to thestandard deviation of distribution of shipment time variability. The derivation method ofstandard deviation of each bottleneck will be mentioned in chapter 6. Shipment frequency
is also asked for each route.

Table 4.2 Routes Profile
Distance
(km)

Route

Standard deviation (hour)
BorderSeaportConvoluted

Frequency(shipments/month)

VTE-LCBa

700

0.479

1.186

1.275

5.43

b

700

0.599

1.186

1.320

3.72

c

1,150

0.625

1.519

1.630

2.23

d

880

0.674

1.519

1.657

1.21

VTE-DANe

780

0.625

1.612

1.742

1.40

SAV-LCBSAV-HAI
VTE-HAIaVientiane to Laem Chabang.
Savannakhet to Laem Chabang.
c
Savannakhet to Hai Phong.
dVientiane to Hai Phong.
eVientiane to Da Nang.
b

4.6 Correlation Analysis
In order to select variables including in the model, Pearson’s correlation analysis was
conducted. Results are shown in Table 4.3.
First of all, since frequency is varied in terms of route and company, three recent shipment
times are impossible to discuss season specific effect. For instance, some companies
conducted last shipment one week before the interview survey; others did one month before
the interview survey. As for three recent shipment experiences, the variables are highly
correlated one another (0.493, 0.483, and 0.456). High correlation can also be foundbetween minimum perception shipment time and general shipment time (0.541). Here,47

general shipment time tends to be close to free-flow shipment time since delay due tocongestion is tended to be normally excluded from the shipment time. Therefore, it can beconsidered that high correlation between these variables was observed. The correlation
related to maximum perception shipment time is low. One of the reasons to this is that the
maximum shipment time answered by respondents was highly fluctuated. Some answered
more than 2 days, and some answered very close shipment time to average perception
shipment time. The fluctuations of their perception on maximum time possible affect on
this result.

N=141
st1
st2
st3

Most
recent ST
(st1)
1

nd

Table 4.3 Result of Correlation Analysis

2 mostrecentST (st2)

3rd most
recentST (st3)

GeneralST(stgen)Ave.
perception
ST (stave)Max.
perception
ST (stmax)

Min.
perception
ST (stmin)

0.493

0.483

0.354

0.292

0.118

0.447

1

0.456

0.344

0.524

0.033

0.555

1

0.315

0.584

0.301

0.430

1

0.254

0.048

0.541

1

0.028

0.617

1

-0.048

stgen
stave
stmax
stmin

1

*Bold-faced values receive more than 0.4

As a result of correlation analysis, minimum perception, second most recent, and third mostrecent shipment times were excluded from the model. Minimum shipment time has asignificant correlation with general shipment time. Moreover, the average values of generaland minimum perception shipment time are relatively close. The three recent shipment
experiences (most recent, second most recent, and third most recent) were highly correlatedwith one another, and thus only the most recent one remains in the model. Most recent,general, average perception, and maximum perception shipment times are selected as
variables in the model. Thus, the model in Equation (4.6) has been re-written as Equation(4.7).
+

+

+

+

1

E ( st) - A 8 ( w1st1 , wgen st gen , wave stave , wmax stmax ) , C
n

;w -1
i -1

i

48

+

(4.7)

4.7 Model Specification and Discussion
After testing correlations among the explanatory variables in order to avoid problems like
multicollinearity among the variable sets, and after fitting several models, one model of thefive routes in Table 4.4 is presented. Convergence calculation was conducted using SPSS
18.0 to estimate the parameters of the nonlinear regression model presented in Equation(4.7).

Table 4.4 Expected Shipment Time Model
VTE-LCBa

SAV-LCBb

SAV-HAIc

VTE-HAId

VTE-DANe

Parameter (α)

0.432 (2.46*)

0.331 (2.37*)

2.698 (4.35**)

6.741 (2.67**)18.011 (2.43*)

Scale parameter (A)0.332 (1.68)

0.471 (2.23*)

0.573 (2.89**)

1.388 (2.54*)

2.969 (2.34*)

Constant (C)4.439 (3.67*)

11.253 (3.71**)

10.023 (5.12*)

10.023 (4.34**)-0.885 (3.12**)

Most recent ST f (w1)0.014 (2.12*)

0.035 (1.64)

0.046 (2.24*)

0.196 (1.91)

0.248 (2.11*)

General ST (wgen)

0.005 (1.88)

0.054 (1.79)

0.136 (2.34*)

0.190 (2.43*)

0.200 (2.87**)

0.978 (3.87**)

0.838 (2.23*)

0.699 (2.56*)

0.433 (2.18*)

0.329 (2.25)*

0.003 (1.78)

0.073 (2.10*)

0.119 (2.19*)

0.181 (2.21*)

0.223 (1.81)

R2

0.711

0.583

0.767

0.673

0.591

Sample number

37

36

22

20

20

Average perception
ST (wave)
Maximum
perception ST (wmax)

Coefficient (t-value)aVientiane to Laem Chabangb
Savannakhet to Laem Chabangc
Savannakhet to Hai PhongdVientiane to Hai Phonge
Vientiane to Da NangfShipment time*
Significant at the 5% level**
Significant at the 1% level

Several significant trends are found among the five routes (Table 4.4) for verifying
proposed hypothesis addressed in section 4.2. The coefficient for average perception
shipment time in any route receives the highest value among all of the coefficients. From
this result, it can be inferred that average perception shipment time is the most importantfactor in estimating expected shipment time at any level of variability. As shipment time
variability increases, the coefficient of average perception shipment time decreases, as
49

graphically shown in Figure 4.2. This implies that on highly variable routes, one
information source, average perception shipment time, is not sufficient for estimating
expected shipment time. Instead, other shipment times become relatively more importantfor inferring the expected shipment time on highly variable routes. From the set up
hypothesis point of view, we can also understand that average perception time is primaryused as inferring expected shipment time. In comparatively high reliable route, one
information source, which is average perception shipment time, is enough, however, Other
shipment time gradually increase its importance for estimating next shipment time. Here,
we should interpret that in highly variable route, one information is not enough to explain
shipment time estimation since several types of shipment time are gradually increasing its
weight. Thus, hypothesis is verified as true. In other words, as shipment time gets higher
variability, shippers need more information sources since the confidence level for shipment
time estimation is low.
The trends of two coefficients, the most recent and maximum perception shipment times,
are as expected. The most recent shipment time is obviously less important in less variable
routes than more variable routes. In less variable routes, shipment time is stable. In this case,the most recent experience is not always necessary. The high coefficient for maximum
shipment time in more variable routes is also the proper result. Because logistics companies
hate to be late to their destination, they tend to put more importance on maximum
perception shipment time than companies on less variable routes.
Regarding general shipment time, which is highly correlated with minimum perception
shipment time, we can postulate that general shipment time would become a somewhat
more important factor as shipment time variability decreases. This is because shipment timeon less variable routes is closer to free-flow shipment time, which is the minimum shipmenttime. This means that general shipment time would be expected to decrease as variability
increases. However, Figure 4.2 shows the opposite result. Among the four shipment times
in the model, general shipment time is the only shipment time that is not based on pastexperience. It can be inferred that logistics companies rely on the time schedule rather thanexperience because estimating shipment time is difficult for highly variable routes.
In addition to shipment time variability, the frequency of shipment might also affect theestimation of shipment time. For example, we obtained unexpected results for general
shipment time: dependency on general shipment time increases as variability increases.
From the viewpoint of frequency, because general shipment time is indicated in thetimetable, dependency on general time increases as frequency decreases. In lower
frequency routes, average perception shipment time is not relied on for estimating shipment
time because of insufficient shipment experiences. However, in high-frequency routes
perception time might be reliable because of the large number of previous experiences.
50

Thus, shippers rely on their own perception time rather than the time schedule, most recent
experiences, and maximum perception shipment time, particularly in the highly variable
routes. Parameter α increases as variability increases. This trend shows that a relatively
long shipment time is weighted in the highly variable routes. Thus, it can be postulated thatdecision makers are somewhat risk averse when it comes to estimating expected shipment
time toward risk of delay. The constant value is somewhat high in three routes (SAV-LCB,
SAV-HAI, and VTE-HAI). The constant terms in this non-linear regression analysis isregarded as the set of unobserved factors in the model. Therefore, relatively high constantterms that are more than 10, imply that there might be high interpretability of factors which
are not included in the model. In order to develop robust model in this case, one needs tomodify the model, for example, adding or excluding variables, introducing interaction
terms, etc. However, the model in this chapter is developed for the objective of comparing
weighted factors across the routes. Thus, we admit that there is further potential
improvement of the model robustness and finally specify the model presented Table 4.4.
The coefficient of determination (R2) of the model is high enough to explain expected
shipment time. The R2 of the Savannakhet to Laem Chabang model is somewhat smaller
than those of the other models. Nevertheless, it is not too low to reject the robustness of themodel.
1.00

Weighted Coefficient (wi)

0.80

0.60

Latest ST

0.40

General ST
Average ST
Maximum ST

0.20

0.00
VTE-LCB

SAV-LCBSAV-HAI

VTE-HAI

VTE-DAN

Route
Low

VariabilityHighST, shipment time
VTE-LCB, Vientiane to Laem Chabang
SAV-LCB, Savannakhet to Laem Chabang
VTE-HAI, Vientiane to Hai Phong
SAV-HAI, Savannakhet to Hai Phong;
VTE-DAN, Vientiane to Da Nang

Figure 4.2 Result of Estimated Weighted Coefficient51

In theory of generalized mean concept, as α increased, the weighted coefficient of
maximum value (in this model, maximum shipment time) in the model would get to behigh. However, as a result of the parameter estimation, the highest weighted coefficient is
on average perception shipment time, not maximum perception shipment time. Here,maximum perception shipment time is meant the shipment time occurring due to the event
which rarely occurs by unusual event such as an accident causing very long shipment time.
The values of α in the two highest variability routes (VTE-HAI and VTE-DAN) arecomparatively larger than those of other routes. However, maximum perception shipment
time receives relatively small weight. We interpret such trend as that maximum shipment
time does not regarded as very important due to the its rareness of the occurrence in any
five routes. Instead, we interpret as average shipment time which is second largest value isregarded as important. Focusing only on the relationship between maximum shipment timeand parameter α, as variability increases, both parameters also increase, which is consistent
with the theory. Considering all above factors, we conclude that maximum perception
shipment time is the shipment time occurred by sudden unexpected accident and it does notregarded as very important. Even though Maximum perception shipment time is rather
higher than other three shipment times, which are relatively similar magnitude, maximum
perception shipment time receives relatively large weight. This is interpreting due to thehigh value of α. In case simple multiple regression model, which will be presented
following and excluding α, simple regression model receives higher weight in the highest
route.

Table 4.5 Expected Shipment Time Model by Multiple Regression Model
VTE-LCB

SAV-LCBSAV-HAI

VTE-HAI

VTE-DAN

Most recent ST (w1)0.010 (2.43*)

0.037 (2.10)

0.045 (2.54*)

0.185 (2.21)

0.234 (2.51*)

General ST (wgen)

0.007 (2.15)

0.057 (2.03)

0.142 (2.87*)

0.183 (2.76*)

0.198 (2.99**)

0.966 (4.64**)0.823 (2.57*)

0.663 (3.15*)

0.411 (2.49*)

0.297 (2.24)*

0.017 (2.35)

0.083 (2.54*)

0.150 (2.41*)

0.221 (2.32*)

0.271 (2.91)

4.612 (3.54*)

12.091 (3.34**)

10.321 (5.00*)

10.531 (4.21**)-2.182 (3.67**)

R2

0.710

0.591

0.756

0.664

0.592

Sample number

37

36

22

20

20

Average perception
ST (wave)
Maximum
perception ST (wmax)
Constant (C)In order to certify the trend of weighted coefficient, multiple regression analysis is
implemented using following model (4.8) and confirms that the trend obtained from52

multiple regression models is almost identical to generalized mean model. The results areshown Table 4.5.
E ( st ) - w1 st1 , wgen st gen , wave stave , wmax stmax , C
n

;w -1
i -1

(4.8)

i

4.8 Chapter Conclusion
In all of the routes, average perception shipment time received the highest weight among
the four types of shipment time. Because average perception shipment time is based on past
shipping experiences, past shipment experiences clearly influence the estimation of
expected shipment time. Nevertheless, recent shipment experiences do not significantly
affect the estimation of expected shipment time, particularly for less variable and higher
frequency routes. In particular, recent experience is not an important factor in estimating
expected shipment time in low-variable routes. It can be postulated that shippers basicallymake their decisions about shipment time on their rich shipment experiences. In this study,
data were collected from interviewees who experienced a high frequency of shipments(1.40-5.43 shipments/month). The frequency of shipments might also affect the weightingof each shipment time in estimating the expected shipment time. As frequency decreases,
the importance of general shipment time and recent shipment experiences increases.
The results of this study also imply that general shipment time might not be suitable for
estimating the generalized cost of cross-border haulage in LLDCs. Generalized cost is used
for route choice models that are often used for cargo flow simulation. General shipment
time is normally shorter than average perception time. Thus, there is a possibility ofunderestimating the generalized cost of LLDC cross-border haulage.
We can also understand that average perception time is primary used as inferring expected
shipment time. In comparatively high reliable route, one information source, which isaverage perception shipment time, is enough, however, other shipment time gradually
increase its importance for estimating next shipment time. Here, we should interpret that in
highly variable route, one information is not enough to explain shipment time estimation
since several types of shipment time are gradually increasing its weight. Thus, hypothesis isverified as true. In other words, as shipment time gets higher variability, shippers needmore information sources since the confidence level for shipment time estimation is low.

53

CHAPTER 5
VALUATION OF SHIPMENT TIME VARIABILITY

54

5.0 Introduction
This chapter lays out the process of the valuation of shipment time variability of the crossborder transport activities of GMS region. It is organized into 6 sections. The first section
addresses the literature review related to the method for valuation of shipment time
variability. In this research field, most of those are related to people’s transport activity, andapplication to freight transport field is limited. Thus, literature review has been done withmostly non-freight transport issues. However, fundamental idea to tackle with this issue isbasically same. The second section discusses the formulation for utility function which
estimated marginal utility, which shall be the parameter of several values specification
taking marginal rate of substitution. The third section presents the design of Stated
Preference (SP) questionnaire survey in Vientiane, Bangkok, and Hanoi with freight
forwarders. As mentioned in chapter 4 section 4.2, data collection is done with freight
forwarders. This questionnaire survey was taken place simultaneously with interview
survey in section 3.3 of chapter 3. The fourth and fifth sections illustrate model
specification and discussion on the basis of estimated outcomes. The chapter concludes
with the explanation of the significant findings. The work flow diagram of this chapter isshown in Figure 5.1. As can be understood from Figure 5.1, this chapter is a part of inland
cargo flow model which fulfills objective 3 and 4.

55

Inland Cargo Flow Model
Work Flow of Chapter 5 (Valuation of Shipment Time Variability)-

Questionnaire Design
SP questionnaire
Derivation of distribution of shipment
time variability
Orthogonal design-

Questionnaire Survey
Vientiane, Bangkok, Hanoi
Freight forwarder
Face-to-face interview-

Discrete Choice Model
Binomial logit model-

Estimation of Each Value
Value of time for freight forwarder
Value of schedule delay early
Value of schedule delay latePenalty of being late-

Input Data:
Min. and max. shipment time
between Vientiane and Laem
Chabang seaport

Discussion
Chapter 6
Cost of schedule delay and Scenario Analysis

Figure 5.1 Work Flow Diagram of Chapter 5 in Inland Cargo Flow Model

5.1 Literature Review
5.1.1 Valuation on Travel Time Reliability
Within the field of transport economics, the impact of unreliability is often in regard totravel time. Of course, there are other aspects of the transport phenomena which may be thecause of unreliability. For example, according to Bates et al. (2001), whether or not it ispossible to gain a seat on a crowded train, whether or not a buffet service will be provided
and adequately stocked, etc. Nevertheless, the most usual reference of reliability is topossible late arrival, spending longer in certain activities than expected, as well as the stressassociated with uncertainty itself. The cause of travel time unreliability is mostly due to the56congestion. In this dissertation, cause of unreliability is to be defined as two bottlenecks
such as border and seaport. The concept of shipment time variability refers to the freight
forwarders inability to forecast how long their shipment will last. Following Bates (2001),
shipment time is defined as variable when random factors may have an impact on the
duration of the shipment in such a way that actual arrival time does not coincide with thedesired one. In general, one needs to estimate marginal utilities that are base parameter for
valuation of shipment time variability so that schedule delay early and late due to shipment
time variability converts to cost. In this dissertation, such cost is called as schedulevariability cost, being marginal cost of freight forwarders against extent of shipment time
distribution.

5.1.2 Mean-Variance Approach
For the valuation of shipment time variability, there are three approaches used in general.
First approach is called mean-variance approach. This approach does not differentiate howlong he/she arrives at destination earlier or later to their Preferred Arrival Time (PAT).
According to several literatures (Bates et al., 2001; Lam and Small, 2001; Small et al.,2005), this model simply depicts the “inconvenience” of the shipper due to the shipment
time variability. Shipper’s utility is determined by several variables such as shipment cost,
average shipment time, and shipment time variability, as the simplest structure. The
indicator of shipment time variability is often expressed by several values, such as standard
deviation and percentile values. In general, standard deviation is more widely used. In thismodel, value of variability can be estimated taking marginal rate of substitution of theparameters with respect to cost and standard deviation parameter. In practice application,
particularly big project, mean-variance approach has inevitably widely been applied due toits easiness and clearness of the analysis method. In Netherlands, the benefit brought byincrease in travel time reliability is expected to be incorporated into the guideline of theCost Benefit Analysis (CBA). Nevertheless, the rationale of micro economics for use ofstatistical measure such as mean and standard deviation of the shipment time distribution
for the parameter of utility function is not clearly clarified. In short, mean-varianceapproach is the method on the basis of the view of supply side since how long he/she
arrives at destination earlier/later is not accounted. The utility of shipper can be formulated
as Equation (5.1) as basic formula to mean-variance approach. Shipper’s utility isdependent on shipment cost C, average shipment time ET, shipment time variability σT.
U - *C , +ET , "> T

57

(5.1)

where δ, α, and ρ are marginal utility of shipment cost, time, and its variability, respectively.
Value for travel time variability can be estimated taking the marginal rate of substitution
obtained from Equation (5.1) as following Equation (5.2) and (5.3), respectively.
Value of Travel Time Variability Value of Travel Time -

+
*

"
*

(5.2)
(5.3)

The mean-variance approach is not suitable to apply to this dissertation purpose. As
mentioned, mean-variance approach does not distinguish between late arrival and earlyarrival. However, particularly in the field of transport logistics, the impact of late arrivaland early arrival is totally different. In general, late arrival receives more adverse impact on
the disutility of individuals. Therefore, it is required that the model which can express theasymmetry of their values.

5.1.3 Scheduling Approach
The second approach for valuation of shipment time variability is scheduling approach.
Scheduling approach is formulated under the assumption of that shipper who has PAT(shipper with time constraint) make a decision on departure time so as to minimize theirdisutility considering shipment time variability (Bates et al., 2001; Noland and Polak, 2002;
Noland and Small, 1995). Shipper’s utility is determined by shipment cost, shipment time,schedule delay late, and schedule delay early. Regarding to the last two terms, schedule
delay late and early will be explained in detail. These affects on departure time choice. In
general, piecewise linear function is used to visualize schedule delay as shown in Figure 5.2.
The magnitude of schedule delay late is normally larger than that of early in several
experimental results (Bates et al., 2001; Noland and Polak, 2002; Noland and Small, 1995).
The typical model for scheduling approach is formulated in Equation (5.4). SDE denotes
schedule delay early, defined as Max(0, PAT-[th+T(th)]), whereas SDL denotes theschedule delay late defined as Max(0, [th+T(th)]-PAT) where th is departure time and T(th) is
shipment time between th and PAT. Ignoring the pure disutility due to shipment time T, theremaining contributes to utility, which we may refer to as “schedule disutility”. At theearliest departure times considered, the traveler arrives too early, and suffers disutility as aresult of non-zero values of SDE. As th increases, this disutility declines up to point where
the traveler arrives exactly at his PAT, thus at this point th=PAT-T(th). Immediately
thereafter, the traveler will arrive late, and the disutility jumps up by the value of θ (Figure5.2). As the value of th increases, SDL contributes increasingly to disutility which is to be
expected that the coefficient γ is greater in absolute value than β since people will normallyprefer to arrive earlier than late.
58

Schedule Disutility

Slope = γ

Slope = βθtrArrival Time

Figure 5.2 Typical Schedule Disutility

Noland and Small (1995) extend the model to departure time choice under travel time
variability. In this framework, value of schedule delay early and late are obtained by
dividing each marginal utility with respect to cost. This approach is consistent with axiomsof micro economic theory because the outcome of individual’s behavior, such as earlyarrival, late arrival, and travel time, is included as parameter in utility function (Fukuda
2010). In general, the utility of the shipper is determined based on the factors of shipment
cost (C), shipment time (T), schedule delay early (SDE), and schedule delay late (SDL) as
the simplest form. In case of shipment time can be regarded as random variable, schedulingapproach can be treated as maximization problem of expectation utility of the shipper. The
utility function can be expressed as Equation (5.4).
EU - *C , +ET , AE(SDE ) , @E(SDL) , ?PL(5.4)

PL is denoted as penalty of being late, which is incorporated into the model in order toexpand the difference between the value of schedule delay early and late. As indicated in
the section of mean-variance approach, value of each shipment time variability can be
estimated taking marginal rate of substitution as following equation (5.5), (5.6), (5.7) and(5.8).
+*(5.5)

VT (SDE) -(5.6)

VT (ST ) -A*@
VT (SDL) *?
VT (PEN ) *

59

(5.7)
(5.8)

The penalty of being late is not marginal value since it is imposed no matter the scale oflate arrival. In this way, two values related to shipment time variability can be specified. In
the mean-variance approach, values between marginal value of schedule early and late are
not distinguished because indicator to express shipment time variability is simply standard
deviation of distribution of travel time variability, which is based on statisticalmeasurement. Because this dissertation considers each individual’s behavior, thedistinguishing between early and late arrival is necessary in order to estimate more accurateschedule variability cost. In this reason, scheduling approach is preferable comparing tomean-variance approach.

5.1.4 Integrated Approach
In addition to mean-variance and scheduling approach, another approach is recently
developed by Fosgerau and Karlstrom (2007) and Fosgerau and Fukuda (2008). Thisapproach is called integrated approach. The background behind to build integrated
approach is mainly two reasons. First one is the lack of rigorous theoretical background on
mean-variance approach although practical application is relatively easier due to its
simplicity. The mean-variance approach includes the statistical measurement of travel time
variability using standard deviation into the utility function. Another reason is difficulty indata collection and a doubt on the assumption on distribution of travel time variability inregard to scheduling approach. However, the utility on scheduling approach is determinedby early arrival or late arrival. In that scene, integrated approach can be regarded ascomplemented model of each approach.
Integrated approach is expansion of Noland and Small (1995) and prove that shipper’s
maximum expectation utility under any type of distribution of travel time is to be resulted
in form of mean-variance approach. Initial utility function is formulated by schedulingapproach. At this moment, traveler’s PAT is not explicitly stipulated. Besides, because
formulation is simple mean-variance type, practical application is relatively easier. The
traveler’s PAT is standardized to be “0”. The utility is determined by actual travel time (T)and departure time (-d). Based on actual travel time and the magnitude of early arrival andlate arrival, the utility function is specified as Equation (5.9).

U (d , T ) - BD , #T , ( (T 6 D),(5.9)

According to Fukuda (2010), the interpretation of each term of Equation (5.9) are first term:
disutility due to early departure anticipating shipment time variability, second term:
disutility due to shipment time itself, and third term: disutility due to late arrival to PAT.
(T-d)+ is equivalent to time of late arrival, returning value of late arrival if positive value,60otherwise, 0. The η, λ, and ω are parameters expressing marginal utility of each variable.
This model is reformulation of Equation (5.4). In this case, the relationships of η=β, ω=α-β,λ=β+γ are established (Fosgreau et al., 2008; Fosgreau and Karlstrom, 2010).

5.2 Model Formulation
In case treating the problem on travel time variability, several terminologies, such as“variability”, “reliability”, “regularity”, “punctuality” are frequently used alternatively(Takahashi, 2010). Particularly in case of economic evaluation, the expression of “travel
time reliability” is frequently appeared in several papers because primary unit is often
expressed as “Value of Reliability”. The “punctuality” is mainly used for evaluation of
public transport service such as bus and railway. In this dissertation, the target is notpassenger transport, but freight transport. In addition, the primary problem of thedissertation is additional cost induced by the variability of shipment time. Thus, “shipment
time variability” is used for expressing primary unit for economic evaluation in thisdissertation. Besides, “shipment time reliability” is used as an antonym of shipment time
variability. However, one needs to pay attention to its treaty. The high shipment time
variability is equivalent meaning to low shipment time reliability. Similarly, the low
shipment time variability is indicating the high shipment time reliability.
This chapter covers a part of inland cargo flow model development. In several simulation
model of international cargo flow (i.e. Shibasaki and Watanabe, 2008), shipper is treated to
be a main player of international freight transport market, whose behavior is focused on formodeling. This dissertation focuses on shipper’s behavior, however, it can be often
observed that haulage-related decision making such as route choice and departure time
choice are dependent upon freight forwarders. This type of phenomena can be observed
particularly in developing countries like Lao PDR. Therefore, decision maker related tocross-border haulage in GMS region is set as freight forwarder in this dissertation. Routechoice decision process is specified with a logit model. Under random utility theory
assumptions, the deterministic component of the utility function for forwarder i whenchoosing alternative j is based on schedule delay function and can be expressed as Equation(5.10)Vij (d ) - V (Cij , STij , SDEij , SDLij , PLij )- *C , +E ( STij ) , AE ( SDEij ) , @E ( SDLij ) , (PLijMonetary cost of the alternative j for freight forwarder i
Cij:
STij: Average shipment time of the alternative j for freight forwarder i
SDEij: Schedule delay early of the alternative j for freight forwarder i61(5.10)

SDLij: Schedule delay late of the alternative j for freight forwarder i
PLij: Penalty of being late of the alternative j for freight forwarder i
Monetary cost is the variable which is all expenses for alternative j. Shipment time is theaverage time required for alternative j. Schedule delay early and schedule delay late arevariable which is explained in section 5.1.4. Penalty of being late is the variable which is
introduced in order to expand the asymmetry of disutility between schedule delay early andlate. Penalty of being late captures additional impact on utility, which is independent fromthe magnitude of the delay. The model is structured as simple as possible following axiomof Occam’s razor which implies us that in order to explain matters being interested in, one
does not need to assume more than needed. Thus, variables only needed for estimatingschedule variability cost are included in the model. The expectation operator E(㺃) is applied
to the shipment time, schedule delay early, and schedule delay late because shipment time
can be regarded as random variable in such route in this study area. As Arvis (2007)
mentioned, shipment time of the cross-border transport in developing country is
characterized by lognormal distribution. In this context, scheduling approach can be treatedas expected utility maximization problem. Binomial logit model is applied to estimate theparameters. All explanatory variables are expressed as the difference between their values
in route A and B, which are both hypothetical routes in SP context. As mentioned before,taking marginal rate of substitution, each value for shipment time variability can be
estimated as Equations (5.11), (5.12), (5.13), and (5.14). PL is expressed as 0-1 without unit.
Since λ is disutility of late arrival, marginal utility of λ and shipment cost C is defined aswillingness to pay for avoiding disutility of being late. The unit of PEN is USD/TEU since
the unit of shipment cost C and λ are USD/TEU and nothing, respectively.
+*A
VT (SDE) *(5.11)VT (SDL) -(5.13)VT (ST ) -

PEN -

(
*

@
*

(5.12)(5.14)In the concept of scheduling approach, it is assumed that among the available departuretime, it is chosen so as to maximize the utility of Equation (5.10). In case departure time ischosen earlier time as usual, disutility due to decrease in time for non-shipment task.
However, this disutility is ignored in scheduling approach (Small et al., 2001). This study
follows the idea of scheduling approach for specifying utility function. Here, it is assumed
that the distribution of arrival time is independent from departure time. Thus, thedistribution of arrival time is identical in regard to choosing any departure time.
62

5.3 SP Questionnaire Survey
5.3.1 Estimation of Shipment Time Distribution
In the questionnaire, two routes are presented to respondents. Respondents are asked tochoose one of them, which maximize their satisfaction. Each route (alternative) consists offive scenarios with respect to arrival time. Five scenarios are obtained from 1st, 3rd, 5th, 7th,and 9th deciles of the distribution of shipment time variability. Let each ith deciles denote ti(i=1, 3, 5, 7, 9). As mentioned in previous section 5.2, shipment time from LLDC to seaportin coastal country can be regarded as random variable following lognormal distribution.
Thus, ti should be extracted from the distribution of shipment time variability which ischaracterized by lognormal distribution. The characteristics values, mean (1st moment) andvariance (2nd moment) of lognormal distribution of shipment time variability is specified bysteps shown in Figure 5.3.
1. Obtain ave, max, and min time of t ~ LN(m, σ2)Using existing data of shipment time
(Vientiane – Laem Chabang)
Average shipment time: Tave
Minimum shipment time: Tmin
Lognormal Dist.
Maximum shipment time: Tmax
(Arvis, 2007)
2. Obtain t ~ N(λ, ζ2)
Average ST of Normal Distribution: ln(Tave)Max ST of Normal Distribution:ln(Tmax)
Min ST of Normal Distribution:ln(Tmin)Estimate characteristics values of normal distribution:( - ln(Tave )E-

1
8 C<(ln(Tmax ) 6 ln(Tmin )=/ z D
2>> get N(λ, ζ2)

3. Obtain t ~ LN(m, σ2)Random sampling using N(λ, ζ2):
n1, n2, n3, …, n20000>> exp(n1), exp(n2), exp(n3), …, exp(n20000)
Non-parametric estimation for m, σ2>> get t ~ LN(m, σ2): f(t)Figure 5.3 Steps to Obtain 1st and 2nd Moments of Lognormal Distribution

63Firstly, using the results of survey on shipment time between Vientiane and Laem Chabangseaport for the best and worst cases (Banomyong, 2000), average, minimum, and maximum
shipment time required for this route are obtained. These extracted shipment time withnatural logarithm are assumed to be corresponded to minimum and maximum time within arange of 99.7% of standard normal distribution shown in Figure 5.4. In this case, standard
deviation of normal distribution can be obtained by Equation (5.15) (Ang and Tang, 1975).
1 K ln(STmac ) 6 ln(STmin ) H
>- J
G
2I
z
F

(5.15)

99.7%

ln(Tmin)σln(Tmax)
σ

σ

σ

σ

σ

z*σ
Figure 5.4 Concept of Minimum and Maximum Time in Standard Normal Distribution

The value of z equals to 3 because it is assumed that extracted maximum and minimum
values are corresponded to maximum and minimum within 99.7% range. 1st and 2nd
moments of the normal distribution can be estimated using relationship showing Equation(5.16) or (5.17)
ln(STmin ) - L 6 >ln(STmax ) - L , >(5.16)(5.17)

Secondly, 20,000 samples are randomly extracted from the obtained standardized normal
distribution. The generated samples are returned into an antilogarithm taking andexponential function. Because these returned samples follow lognormal distribution intheory, 1st and 2nd moments can be estimated non-parametrically. This method would be64

useful for the specification of shipment time variability in LLDCs where few data and little
information are available. In this method, interview survey is only required to obtain input
values. This implies that this method is applicable enough in other LLDCs.

(i) Text format (Asensio and Matas, 2008)(ii) Clock panel format (Bates et al., 2001)(iii) Bar graph format (Hollader, 2006)
Figure 5.5 Presentation of SP Questionnaire
65Regarding the type of graphical design of questionnaire form for illustrating shipment time
variability, several ways have been attempted. For example, clock-format and bar-chart
designs are used as shown in Figure 5.5. As a result of several attempts, simple text format
would generate the least errors and mistakes by respondents (Bates et al., 2001). Therefore,
text format is applied to present the choice sets of the questionnaire survey in thisdissertation.

5.3.2 SP Questionnaire Design
This section provides details about the design of the stated preference survey used to obtain
the data needed for model calibration. The survey was carried out September 2010 in
Vientiane and January 2011 in Bangkok and Hanoi. Freight forwarders were contacted intheir offices where face-to-face interview survey was conducted. Freight forwarders wereasked to complete the survey at that time. The survey on route choice questionnaire isconsisted of a set of 18 choice questions, characterized by monetary cost, average shipment
time, departure time, and shipment time variability. For each variable, six possible variables
are considered referring survey result of Banomyong (2000). In this study, because inlandtransport which premises the maritime transport posterior or prior to inland transport,
twenty feet container (TEU) is used for freight transport. Thus, unit is used as USD/TEU
for shipment cost. Monetary cost and average shipment time variables are also related to
the fact that in every choice scenario presented in the questionnaire, for example, the fastest
alternative is also the most expensive. In this way, choices are developed as similar
situation as possible to usual shipment of freight forwarders.
The level of values for average shipment time and cost were set six for each, referring tosurvey results of Banomyong (2001) for shipment time and cost in GMS region. For the
early departure time, one value is that which would result in punctual arrival if there was novariability of travel times, while other five imply departing earlier. In this study, values
themselves have six levels but three values for difference between scenario A and B. In thiscase, the full factorial of a stated preference experiment designed in this was has 33
scenarios which can be reduced to a fractional factorial of 18 scenarios (Louviere et al.,2001). Table 5.1 shows the level of all variables used in the choices for each group. The
differences between alternatives in terms of average shipment times, monetary costs, andearly departure time only take three possible values, were used in the presentation of thecharacteristics of each alternative to avoid complexity.

66

Table 5.1 Scenarios for Questionnaire
Scenario 1

A
B

Monetary
cost
1700
1400

Scenario 2

A
B

1900
1300

16
24

Scenario 3

A
B

1700
1400

Scenario 4A
B

Scenario 5

Route

Averageshipment time
14
19

Early
departure52

Distribution of possible shipment time
12
16

13
17

14
19

16
21

18
24

2
7

14
20

15
22

16
24

18
28

20
32

16
24

5
5

14
20

15
22

16
24

18
28

20
32

1700
1400

12
23

2
7

10
19

11
21

12
23

13
26

15
30

A
B

1900
1300

12
23

0
3

10
19

11
21

12
23

13
26

15
30

Scenario 6

A
B

1900
1300

14
19

2
2

11
16

12
17

14
19

16
21

18
23

Scenario 7

A
B

2100
1200

12
23

0
3

10
19

11
21

12
23

13
26

15
30

Scenario 8

A
B

2100
1200

14
19

3
0

11
16

12
17

14
19

16
21

18
23

Scenario 9

A
B

2100
1200

16
24

0
3

14
20

15
22

16
24

18
28

20
32

Scenario 10

A
B

2100
1200

12
23

4
4

10
19

11
21

12
23

13
26

15
30

Scenario 11

A
B

1900
1300

14
19

2
2

11
16

12
17

14
19

16
21

18
23

Scenario 12

A
B

1700
1400

14
19

0
0

11
16

12
17

14
19

16
21

18
23

Scenario 13

A
B

1700
1400

16
24

0
3

14
20

15
22

16
24

18
28

20
32

Scenario 14

A
B

1900
1300

16
24

5
0

14
20

15
22

16
24

18
28

20
32

Scenario 15

A
B

1700
1400

12
23

2
2

10
19

11
21

12
23

13
26

15
30

Scenario 16

A
B

2100
1200

16
24

7
2

14
20

15
22

16
24

18
28

20
32

Scenario 17

A
B

1900
1300

12
23

5
0

10
19

11
21

12
23

13
26

15
30

Scenario 18

A
B

2100
1200

14
19

2
7

11
16

12
17

14
19

16
21

18
23

Average shipment time is specified from distribution of shipment time variability. In the
study area, shipment time is heavily variable. Therefore, freight forwarder possibly departs
earlier in order to arrive at destination before arrival restriction time. Early departure time isset and formulating schedule delay (SDi) which indicates arrival time variability from target
arrival time, shown in equation (5.18).
67

SDi - (d , t i ) 6 t e 6 (d , st )(5.18)- t i 6 t e 6 stdd

st: expected shipment time
ti: shipment time of ith deciles of f(t)

d
te

SDi

Figure 5.6 Relationship among SD, ti, and st

ti denotes shipment time including schedule delay extracted from shipment time variability
following lognormal distribution. st denotes average shipment time. In case letting
departure time be d, the point of d+st would be target arrival time, which is corresponding
to ta. If SDi is positive value if the shipment is late arrival, otherwise, early arrival. In thisway, SDi is estimated five values for scenario setting. Referring to Small et al. (1999),
model imputation values of SDE, SDL, and PL are formulated as equation (5.19) – (5.21).
Ki is dummy variable returning to 1 if shipment is late arrival, otherwise, 0.
5

SDE -

1
SDi 8 ( K i 6 1)
5 i -1SDL -1
SDi 8 K i
5 i -1

PL -

;
5

;

(5.20)

51Ki
5 i -1;K1 SDi M 0
Ki - J
I0 SDi / 0(5.19)(5.21)(i - 1,"5)(5.22)In this study, the utility of Equation (5.10) is assumed to be changed as departure time ischanged. In addition, since disutility due to early departure time is ignored in this study, thechoice of departure time is not stipulated in questionnaire form as shown in Figure 5.7.
Instead, the utility of E(SDE), E(SDE), PL is considered for total utility of Equation (5.10)
as changes of departure time. In this way, the consistency of the idea of schedulingapproach and SP survey of this study is maintained.

68ROUTE A

ROUTE B

Average shipment time: 23 hours
The shipment cost is 1,300 USD/TEU

Average shipment time: 17 hours
The shipment cost is 1,500USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 8 hours early̽ 6 hours early̽ 4 hours early̽ 2 hours early̽ 5 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 4 hours early̽ 2 hours early̽ on time̽ 5 hours late̽ 8 hours lateA

B

Figure 5.7 Presentation of Choice Set(*see all choice sets in Appendix A)

5.3.3 Questionnaire Survey
Using SP questionnaire form developed employing the method showed till previous section,questionnaire survey has been conducted with freight forwarder including Third Party
Logistics (3PL) by face-to-face interview survey, 5 companies in Vientiane, 31 companies
in Bangkok, and 12 companies in Hanoi. The survey periods are 16th-22nd September, 2010
for Vientiane and 10th-14th January, 2011 for Bangkok, and 17th-20th January, 2011 for
Hanoi. Valid answers are 860 and 4 invalid answers.

5.4 Specification of the Model
Using BIOGEME1.8 (Bierlaire, 2003), parameter estimation was conducted. In the analysis,correlation between variables was low. Thus, model can be regarded as robust in terms ofvariable correlations.
The results of parameter estimation and statistical test are shown in Table 5.2. The sign of
the all parameters except constant value is negative, which is expected and reasonable
result. The t-value is also high enough to satisfy statistical significance at 99% confidence
level. Considering all above results, the reliability of specified model is high enough toobtain necessary values such as each value for shipment time variability.

69

Table 5.2 Result of Parameter Estimation
Estimate

t-value

Monetary cost (δ)-0.00311-8.68

Shipment time (α)-0.235-7.08

Schedule delay early (β)-0.806-5.80

Schedule delay late (γ)

-1.34

-2.99

Penalty of being late (λ)

-3.67

-2.71

Constant

0.366

1.28

Null log likelihood-596.107

Final log likelihood-491.608

Likelihood ratio test

208.997

Adjusted rho-square

0.165

Sample number

860

5.5 Results and Discussion
As mentioned before, each value of shipment time variability can be estimated by using thetheory of marginal rate of substitution of the model. Estimated each value is shown in Table
5.3. The relationship of VT(SDE)<VT(SDL) is expected result and matching with previously
demonstrated results in several literatures (i.e. Small et al., 1999). Nevertheless, thedifference of them is extremely large, which marginal value of late arrival is 5.7 times
larger than normal value of shipment time. This implies that reduction in 1 hour late arrival
is equivalent to reduction in 5.7 hours of shipment time in case the condition of late arrival
is conserved. Because average shipment time between Vientiane and Laem Chabang isapproximately 18 hours (Banomyong, 2001), 5.7 hours occupies approximately 30% of
total shipment time. From the interview survey, it was implied that late arrival is totally
unacceptable for freight forwarders, thus, huge difference between VT(SDE) and VT(SDL)
is understandable result. Similarly, huge value of penalty of being late can be appropriate
result.
Regarding to early arrival, this is a time which is not expected before the departure. Due tothis reason, according to the interview survey, freight forwarders recognize that earlyarrival has more adverse contribution to shipment cost. Therefore, the relationship of is
VT(ST)<VT(SDE) also appropriate.

70

Table 5.3 Results of Each Value of Shipment Time
Value of Shipment Time

Estimated value (USD/TEU-hr)

Value of shipment time: VT(ST)

75.56

Value of schedule delay early: VT(SDE)

278.14

Value of schedule delay late: VT(SDL)

430.87

Penalty of being late: PEN

1,180.06㻌 (USD/TEU)

In order to examine the reliability of parameters estimated based on SP data, it is preferable
to calculate hit ratio using RP data. In this dissertation, SP questionnaire survey isconducted in Vientiane, Bangkok, and Hanoi. The freight forwarders in Bangkok andHanoi chooses Laem Chabang and Hai Phong seaport, respectively and they are not in thesituation to choose the route. Therefore, only seven forwarders in Vientiane are in the
choice situation for seaport and RP data can be used. Out of seven forwarders, only four RP
data were obtained. Thus, hit ratio is not calculated due to the low number of data.
In order to increase the reliability of parameters based on SP data, one needs to set thevalue of each attribute as appropriate value. For fulfilling this, they were set based on realdata, and inspected by an expert of GMS logistics and confirmed the appropriateness of thevalues set. In addition, since the survey was conducted by face-to-face method, unclear
point of the questionnaire form was properly explained to interviewee. Considering allabove factors, the reliability of the estimated parameter might be relatively high.

5.6 Chapter Conclusion
In this chapter, the effect of shipment time variability on estimation of expected shipment
time is examined by applying the model separately for five routes, which own different
levels of variability. The applied model for this is on the basis of generalized mean concept
and multiple regression model as a supplemental analysis. From the results, significant
difference in trend for estimating expected shipment time variability is observed
quantitatively.
The results imply that as variability of shipment time increases, the more information
sources are needed for estimation of shipment time. In this case, it might be possible toconclude that logistics companies are suffered from variability because in general, it can be
postulated that if the situation is very uncertain, people need more number of information
sources. Vice versa, little information is sufficient to be relieved in case situation is stable.

71

CHAPTER 6SCHEDULE VARIABILITY COST AND SCENARIO ANALYSIS

72

6.0 Introduction
This chapter explains the freight forwarders’ behavior on cross border transport under the
shipment time variability. The behavior is depicted on the basis of problems found up tochapter 3, several literature reviews and field surveys. The main objective in thisdissertation is to develop inland cargo flow model considering shipment time variability forcross border freight transport and estimating impact of improvement in shipment time
reliability, which is corresponding to chapter 5 to 7. Up to chapter 4, it had been showedthat cross-border transport in developing countries is suffered from shipment time
variability due to two bottlenecks; border and seaport. In chapter 4, it is implied thatshipper’s burden by shipment time variability is getting higher as shipment variability
increases. Thus, such additional cost should be incorporated into the generalized cost of
cross border transport. However, there has been no model so far proposed for route choicemodel.
As seen in literature reviews (chapter 2), seaport is also a cause of shipment time variability.
For instance, dwelling time at Mombasa seaport in Kenya, being gateway seaport of
Uganda, is much fluctuated. It sometimes requires more than 30 days (Arvis, 2007).
Comparing the difference between maximum and minimum time required on the route
between Vientiane and Laem Chabang seaport, it comes out that approximately 10 hours
and 15 hours difference at the border and seaport, respectively (Banomyong, 2000). As
seen in chapter 4, trucks can be operated at almost free-flow speed on the inter-city and national road due to the low traffic volume. The fact that no big difference exists between
the best and worst cases of the shipment time survey (Banomyong, 2000) can support thisassumption is rational. Thus, shipment time would also be reduced if shipment time
variability at the border and seaport is decreased. In this way, on cross-border route
accessing to seaport, two bottlenecks hugely contribute on shipment time variability. In
order to solve or alleviate problems related to shipment time variability in GMS, several
facilitation plans have been launching such as single stop, common use of trucks, etc. Here,one need to develop inland cargo flow model considering characteristics mentioned above
in order to quantitatively understand the impact of impact of several policies. In this field,
MICS developed by Shibasaki and Watanabe (2009) is the representative model. The
notable characteristic of this model is that interdependency between shipping lines andshippers is incorporated for seaport choice. Shipping lines are assumed to be in theoligopoly market, thus, calculation process is somewhat small. The cost of border crossing,
they divide into two; such as “border resistance in narrow sense” and “border resistance inbroad sense”. In narrow sense, it is defined as resistance encountered in real border crossing
point, on the other hand, in broad scene, it considers cost incurred at the stage ofpreparation of the shipment since large number of the documents are required particularlyin the developing world. Border resistance is set by five grades which are determined based73

on the literature review and field survey. Subsequently, the estimated cost is included into
the generalized cost of the haulage. However, this method does not explicitly include theshipment time variability which affects the route choice under heavily variable route. In thisdissertation, inland cargo flow model is developed considering cost of shipment time
variability which is estimated on the basis of objective input values such as shipment time.
Using developed model, we will discuss how facilitation plans for cross-border transport
have impact on reduction in shipment cost and cargo flow in the route between Lao PDR
and seaports in Thailand/Vietnam.

6.1 Overview of the Model
6.1.1 Study Area and Target
In several international cargo flow simulation model (i.e. Shibasaki and Watanabe, 2009),
model are developed focusing on shippers which is as one of the main players ininternational freight transport market. This study also focuses on the behavior of shippers asprevious study done, however, decision making related to route choice and departure time
choice is often seemed to be dependent upon freight forwarders particularly in developing
countries like Lao PDR. Therefore, the term freight forwarder is denoted as decision makerof freight transport in this study.
The study range is within between LLDCs and large international market for example, US,
Europe, etc. However, in this dissertation, the range of land transport which is prior orposterior to maritime transport from/to seaport under the condition of FOB as terms of trade
is considered into the cost function. Maritime transport is very long travelling and thus,
three designated seaport in this dissertation would not significantly affect on costdifferences. Thus, they are treated as homogenous and consequently, this model is virtuallyregarded as seaport choice problem. In case of FOB, the seller (exporter) must bear the all
cost and risks such as pilferage, damage on transported goods between the range of theirdeparture point (i.e. warehouse, logistics center, …) until loading goods onto the vessel atseaport. Hereafter, buyer (importer) bears the all cost and risks of the shipment. Hence, thisstudy focus on cross-border routes between departing warehouse or truck terminal of
freight forwarders in Lao PDR until loading goods at the seaport in Thailand or Vietnam
through one border as shown in Figure 6.1. The origins are set as four; Vientiane (VTE),
Savannakhet (SAV), Luang Phabang (LPB), and Champassak (CPS) whereas thedestinations are Laem Chabang seaport (LCB) in Thailand, Hai Phong (HAI), and Da Nang
(DAN) seaport in Vietnam (see Figure 6.2). Here, in the case of import by Lao PDR, theorigins and destinations are set as opposite.

74

Vientiane,
Savannakhet,
Luang Phabang
Champassak

Laem Chabang,
Hai Phong,
Da Nang(Bottleneck)

Border(Bottleneck)
Lao PDR

Thailand/Vietnam

International market(i.e. US, Europe, Japan)

Maritime transport(Ignored in model)

Figure 6.1 Study Range for Inland Cargo Flow Model

Figure 6.2 Road Network of Study Area

In real field of seaport accessing in developing world, normally only one route is possible
choice for one seaport. Thus, route and seaport are chosen simultaneously. Consequently,
this study is route choice problem on the basis of route choice, not link base choice problem.
The inland cargo flow model will be developed between Lao PDR and seaport in Thailand75

or Vietnam as cargo volume of export and import using maritime transport prior orposterior to inland transport is given.
In the route between LLDCs and seaport in coastal countries, the common characteristic interms of bottlenecks is basically shipment time variability exposed at border and seaport.
The prominent point of this model is considering border and seaport as bottlenecks, which
is basically applicable for other LLDCs. As for application for landlocked developed
countries such as Switzerland, Austria, etc. mainly in European countries, since variability
at border is very small or zero which implies that border resistance can be ignored, one
needs to exclude the border variability term in the model. On the other hand, in LLDC
having terrible political risk, like Central African Republic, shipment time variability is
expected to be hugely apart from existing probabilistic distribution. In addition, according
to Collier (2007), road condition from Central African Republic is extremely poor. For
example, paved road is not almost available on inter-city and -national road. Thus, road is
impassable during the rainy season. In such situation, shipment time variability would be
generated on the link due to the poor road condition, and therefore, this model is no longer
applicable in such case even the country is classified as LLDCs.
In this case study, all roads at all sections in Lao PDR, Thailand and Vietnam are the part ofAsian Highway (AH). Therefore, huge delay due to the reason of insufficient roadcondition is not highly expected. In the field survey in Lao PDR, trial run is conducted on
the part of AH 12. Some of the holes had been found in the road of AH 12. However, roads
are paved and uncongested. Thus, the delay which significantly affects on shipment time
variability can be assumed to occur. As for shipment time variability at bottleneck,
although no statistical data exists, 45 interviewed companies (88.2%) out of 51 companies
answered that border variability is the problematic based on survey in Chapter 3. Regarding
the variability at seaport, number of companies pointed out is decreased up to 38 companies(74.5%). Considering all above factors, it can be concluded that the model is applicable to
Lao PDR. Based on the interview survey, the border crossing from/to Lao PDR is recently
not very tough comparing to other LLDCs. This can be also understood comparing toshipment time at the border in East Europe, South America, and Africa. These data are
based on Arvis et al. (2007). This relatively easiness border crossing might be due to thecontribution of recent effort such as Cross Border Trade Agreement and infrastructuredevelopment.
Besides, this model is applicable to countries which have no accessible seaport within the
countries. In such countries like East Timor, Haiti, semi-LLCs one needs to across theborder for accessing to the seaport in neighboring countries.

766.1.2 Freight Forwarder’s Behavior
The freight forwarders chose a route for export or import so that expected generalized isminimized. Although the interdependency with shipping lines is important factor to choosea seaport, the shipping lines are treated as homogenous in this study because of the reasonsthat the impact of the infrastructure development is to be difficult to be observed and it
would be expected that as Vietnamese seaport increases their LOS, behavior of shippinglines would be also changed. In general, it is very tough to capture all effect. The
forwarders involving freight transport in LLDCs might make a decision on the routeconsidering time cost of arrival time variability generated due to the low reliability ofshipment time in the route. In general, reliability of the shipment time is expressed as “low”
in case shipment time variability is high, and vice versa (Bates et al., 2001).
It is assumed that the behavior of freight forwarder is followed by stochastic distribution
model. Here, there is a stochastic variability in generalized cost perceived by freight
forwarders. In other words, perceived generalized cost C~ k is composed of measureable
generalized cost GCik and stochastic error term εik as shown in Equation (6.1).

~
C ik - GC ik , ! ik

(6.1)

Assuming Gumbel distribution of scale parameter θ on error term εik of utility function,
distributed cargo volume qik for route k can be expressed as equation (6.2) as is logit typemodel.
q ik - Q i

C

exp 6 ? 8 GC ik

D; exp C6 ? 8 GC D
ik

(6.2)

kON

where
Cargo volume of route k from/to city i (TEU)qik:
Qi:
Cargo demand from/to city i (TEU)θ:
Scale parameterik
GC : Shipment cost of route k from/to city i (USD/TEU)
Scale parameter is always positive unknown parameter which cannot be observed by the
analyzer, and is estimated to reproduce current situation as much as possible. The
generalized cost (GCik) is denoted as all the cost of inland freight transport of route k
from/to city i, which is estimated by Equation (6.3).

77

GC ik - E(st ik ) 8 VT (ST ) , E (SVC ik ) , USC 8 D ik , BC ik , TC ik

whereE(stik):VT(ST):
E(SVCik):
USC:
Dik:
BCik:
TCik:

(6.3)

Expected shipment time of route k from/to city i (hr)Value of time of freight forwarder (USD/TEU-hr)
Expected schedule variability cost of route k from/to city i (USD/TEU)
Unit shipment cost (USD/TEU-km)
Shipment distance of route k from/to city i (km)Border crossing cost of route k from/to city i (USD/TEU)
Terminal cost at seaport of route k from/to city i (USD/TEU)

The notable characteristics of this model is the second term of the Equation (6.2), which
considers the cost due to variability of shipment time caused at the border and seaport. In
this study, it is assumed that variability is found at the destination, which generates two
types of delay, such as schedule delay early (SDE) and schedule delay late (SDL). Thisadditional cost generated due to SDE and SDL is defined as schedule variability cost (SVC).
Equation 5.10 and 6.3 are equal relationship even though the expression is different.
“USC*D㸩BC㸩TC” of Equation 6.3 is to be monetary cost other than time consuming cost.
This is corresponding to δC in Equation 5.10. αE(st) and “βSDE 㸩 γSDL 㸩 λP” arecorresponding to expected time consuming cost st*VT(ST) and schedule variability cost
E(SVC), respectively. Since V=-GC, the right-hand-side of Equation 6.3 and 5.10 is equal
relationship.

6.1.3 Formulation of Schedule Variability Cost
Schedule variability cost is defined as weighted cost of early and late arrival by
probabilistic distribution of arrival time variability. Following this definition, schedulevariability cost can be formulated as following Equation (6.4).
E ( SVC ) -

ta

tr

7

P u(t ) 8 f (t )dt ,P s(t ) 8 f (t )dt , P g (t ) 8 f (t )dt
67

k

ta

k

tr

k

where
u(t): Function of schedule early arrival cost (t < ta)s(t): Function of value of time of freight forwarder (ta < t < tr)g(t): Function of schedule late arrival cost (tr < t)Target arrival timeta :tr:
Arrival restriction time
fk(t): Function of arrival time probabilistic distribution [t ~ LN(mck, σck2)]
78

(6.4)

The cost function of u(t)㸪s(t)㸪g(t) can be formulated as Equation (6.5) to (6.7).
u(t ) - VT (SDE) 8 (6t , t a ) , (VT (ST ) 8 Tb )(6.5)

s(t ) - VT (ST ) 8 (6t , t r )(6.6)

g (t ) - VT (SDL) 8 (t 6 t r ) , PEN(6.7)

t, ta, Tb, and tr indicates time, target arrival time, arrival buffer time, and arrival restriction
time, respectively.
Using Figure 6.3, the detail of the concept of Equation (6.4) is explained. Target arrival
time ta of the freight forwarders is defined as the mode of distribution of arrival time fk(t)for route k. In the route where shipment time has strong variability, freight forwarders are
not likely to choose departure time d to arrive at the destination at precisely same as arrival
restriction time because the late arrival would contribute on their disutility. In this case,freight forwarder seems to choose departure time incorporating arrival buffer time Tb inorder to avoid late arrival. In this way, freight forwarders might attempt to arrive at thedestination Tb before arrival restriction time. The target arrival time is located at the modeof shipment time distribution. In case freight forwarders are set their arrival time ta, it isvery likely to arrive at the time of ta with the highest probability comparing to other time.
Thus, target arrival time is assumed to be corresponded to the mode of arrival time
distribution. Here, the target arrival is defined as limit time of the availability of the use ofscheduled maritime transport in exporting case. In case target time is exceeded, freight
forwarders miss the opportunity of use of maritime transport. In case of importing case, thisis also defined as the limit time to avoid stopping production line or stock expiration.
Arrival buffer time is defined as the time difference between arrival restriction time tr andtarget arrival time ta. In the next section, the detail explanation of how to determine thescale of Tb will be addressed. Tb is previously expected time before the departure. Thus, forestimating the cost, normal value of time [VT(ST)] is corresponded to this time. In casearriving at the destination before target arrival time (ta), the cost due to time consumption isestimated using value of early arrival [VT(SDE)]. The slope of buffer time and early arrival
is different. This is because of the difference of expected or unexpected time before thedeparture. Obviously, unexpected time has more impact on the cost, thus earlier arrival thantarget arrival time has steeper slope comparing to buffer time. Similarly, value of latearrival [VT(SDL)] is applied for cost estimation due to time consumption of late arrival. In
regard to relationship of impact of early and late arrival on scheduling problem, several
researches have been demonstrated the results of that larger impact of late arrival
comparing to early arrival (Bates et al., 2001; Noland and Small, 1995). In the field oflogistics, this type of asymmetry between the differences of is very likely to be higher
impact rather than trip f people. By introducing the penalty of being late (λ), the asymmetry
can be expended.
79

In general, the cost of penalty of being late and late arrival is fairly high in case freight
forwarders encountered late arrival in reality. However, this study is route choice problem
prior to the departure. In this case, the decision maker might be use expected value which isweighted by the probability that they encounter the events. To do this, expected schedulevariability cost that is the product of cost function and probability would be comparatively
lower than pure cost. Similarly, probability corresponding to late arrival is only few percent
of upper shipment time of long tailed probabilistic distribution; thus, the values obtained
would be small enough. In summary, although the cost of late arrival itself is enormously
large, the expected cost of late arrival is estimated small enough value due to setting arrival
buffer time. The fk(t) is the probabilistic distribution function in terms of shipment time
following lognormal distribution which is convoluted distribution of two lognormal
distribution such as shipment time at the border [t ~ LN(mbk, σbk2)] and seaport [t ~ LN(msk,
σsk2)]. Hereafter following section, specification method for each unknown parameters are
addressed.

Expected SVC
E(SVC)

Cost function of late arrival
Cost function of early arrival
u(t)

g(t)

Normal value of times(t)Departure
time dta

Arrival buffer time Tb

λtr

Arrival time (t)T0.96
Arrival restriction timeTarget arrival timefk(t)Variability of arrival time

Figure 6.3 Expected Cost Function for Early and Late Arrival

In terminal, there are several processing required, such as customs clearance and cargoinspection. Transshipment is also sometimes required at the border. At this time,
incomplete documents, trouble in machinery, congestion, and laziness of customs officials.
Here, there are more reasons to make shipment time lengthy rather than that of making
shipment time shorter. In case for the shortest shipment time, due to the physical80

constraints, lower limit exists. Therefore, it is understandable that the distribution of righttail would be longer.
In regard to the assumption that distribution is following lognormal density, total 22
empirical results are presented in East Europe (Bulgaria, Albania, Bosnia, and Croatia),
South America (Argentina and Brazil), and Africa (Kenya). The lognormal distribution isdetected using Kernel density estimation and Arvis et al. (2007) mention that this is thestylized fact. However, these data are not based on the data from Lao border and seaport ofThailand and Vietnam. Thus, in order to examine application of above results to Laopractice, steps at the border and seaport are compared between above data and Lao context.
In above obtained data, discharge, customs clearance (paper-based), transshipment of truck
and besides, waiting time is included. On the other hand, processing at seaport includes
devanning of cargo from vessel, declaration of transit at customs office, and cargoinspection and besides, waiting time due to congestion. Comparing to Lao border, theprocessing is almost same except weight inspection in Lao PDR, which is not consideredobtained data. Regarding to seaport process, the prominent differences are not found.
In this dissertation, shipment time at border and seaport were not able to be obtained. Thus,
Kolmogorov-Smirnov test which examines goodness-of-fit for distribution shape andKarnel density estimation which estimate the probability density function from the sample
data were not conducted. However, it is needed to examine and confirm that shipment timeat border and seaport is following lognormal distribution from the actual data.
In many border, opening time is fixed for passing the border. In principle, in case failing topass across the border before closing it, border crossing would be after tomorrow. In thiscase, there are some possibilities to occur “second” distribution for shipment time
probabilistic distribution. However, all freight forwarders interviewed well understood thisproblem, thus, it can be postulated that freight forwarders are choosing departure time not
to encounter such stuck. Considering all above, “second” distribution would be quite small
and ignorable for estimating generalized cost.

6.2 Estimation of Schedule Variability Cost
6.2.1 Specification of Distribution of Arrival Time
In order to specify the characteristics values for the lognormal distribution which is
convoluted, each distribution of border and seaport should firstly be identified. Using
maximum and minimum time required at the border and seaport obtained from interview
survey, distribution characteristic values of lognormal distribution are estimated following
81the method showing in chapter 5. Estimated results are shown in Table 6.1 with interviewed
minimum and maximum times (see Figure 6.2 for the location of each border and seaport).

Table 6.1 Distribution Characteristics Value for Each Bottleneck (unit: hour)
Values based on interview survey

Distribution characteristics
value (Estimated)

Max (SD)Min (SD)

Mean (SD)

Mean

SD

Thanaleng/Nong Khai
(T/N) [N=40]

5.4 (0.98)

2.4 (0.71)

3.5 (0.88)

3.5

0.479

Savannakhet/Mukdahan(S/M) [N=38]

6.8 (1.13)

2.5 (1.45)

3.5 (1.21)

3.5

0.599

9.4 (2.71)

1.4 (1.56)

4.4 (1.42)

4.6

1.500

7.8 (1.67)

3.1 (1.14)

4.3 (1.27)

4.3

0.674

4.3 (1.74)

3.3 (1.11)

4.1 (1.41)

4.0

0.625

12.9 (2.23)

5.3 (1.94)

7.9 (2.09)

8.0

1.186

15.1 (3.42)

6.9 (3.21)

8.5 (2.82)

8.8

1.519

17.1 (3.74)

6.8 (3.54)

10.3 (3.10)

10.4

1.612

Nameo/Nam Xoi(N/N) [N=30]
Nam Phao/Cau Treo
(N/C) [N=33]
Dansavan/Lao Bao(D/L) [N=35]
Laem Chabang seaport(LCB) [N=40]
Hai Phong seaport(HAI) [N=35]
Da Nang seaport(DAN)[N=31]

Next, convolution of two independent lognormal distributions at the border and seaport for
shipment time variability is attempted. The convoluted distribution of two distributions atborder and seaport is to be the distribution of arrival time variability since both shipment
time distribution is generated independently in the route k. According to Crow and Shimizu(1988), convoluting dependent M number of lognormal distribution (Lk) would beapproximated to normal distribution by the Central Limit Theorem in case M=∞. However,in case M is small enough like in this study which is M=2, Λ can be approximated tolognormal distribution. Therefore, convoluted distribution is assumed to be lognormal
distribution considering equation (6.8).
Q-

M

;
k -1

Lk -

M

;e - e
k -1

82

Yk

Z

(6.8)

For the method of convolution two lognormal distributions approximating lognormal
distribution, one needs to obtain 1st and 2nd moment (mean and variance) of Λ. The method
to get these values has been actively developed in the field of wireless communication
technology (i.e. Schwartz and Yeh, 1982; Beanlieu and Xie, 2004; Lam and Le-Ngoc,
2006). For example, Wilkinton’ moment-matching method (Schwartz and Yeh, 1982),
Fenton-Wilkinton approximation (Fenton, 1960), Schwartz-Yeh’s recursive method(Schwartz and Yeh, 1982), Farley method (Schwartz and Yeh, 1982), mini-max
approximation method (Beaulieu and Xie, 2003), least-square approximation method (Zhaoand Ding, 2007) are widely recognized. For all approximation method here, it is assumed
that more than two lognormal distributions are approximated into lognormal distribution.
Arvis et al. (2007) concluded that total shipment time in a route of passing through twobottlenecks such as border and seaport, which is following lognormal distribution can beexperimentally approximated into lognormal distribution. However, existing approximation
method is all the results in other research field. Thus, it is not clear whether existing method
can be applicable to transport field. In addition, According to Nadarajah (2008), theconvoluted distribution of lognormal distributions can be generally approximated tolognormal distribution, which is the most widely used assumption. In this assumption,lognormal distribution that will be convoluted should be independent each other.
Consequently, the required condition to assume lognormal distribution is approximated tolognormal distributions is that (i) the distributions should be independent each other and (ii)number of distributions are small enough, otherwise, convoluted distribution would benormal distribution by central limit theorem. However, there has been no establishedmethod developed in the field of transport studies. Therefore, this study take place asimulation of actual situation encountered in the real field to obtain Λ. Figure 6.4
graphically shows the overview of the simulation. Freight forwarders are firstly exposed toshipment time variability at the border crossing point in the cross-border route. At this time,
one sample can be extracted from the distribution of shipment time variability at the border.
Let this sample denote nbk. Secondly, another sample is extracted from the distribution at
the seaport where is the second bottleneck in case of exporting. Let this sample denote nsk.
In route k, since bottleneck which violating the stability of shipment time is only two points,
border and seaport, schedule variability affected by freight forwarder is nbk+nsk. 20,000
samples are generated randomly by using the method addressed above. Finally,
nonparametric estimation for obtaining 1st and 2nd moments was done. The distribution
characteristics values for convoluted lognormal distribution of each bottleneck are shown in
Table 6.3 and one graphical example of convolution is shown in Figure 6.5.

83

2nd Step: Non-parametric estimation for 1st and 2nd moments of convoluted distribution
nbk1+nsk1, nbk2+nsk2, nbk3+nsk3, … , nbk20000+nsk20000=nck1, nck2, nck3, … , nck20000>> Estimate 1st and 2nd moments of [t ~ LN(mck, σ2ck)]
1st Step: Random sampling from each distribution
nsk1, nsk2, nsk3, … , nsk20000
nbk1, nbk2, nbk3, … , nbk20000

t~LN(mbk, σ2bk)r
Lao citiest~LN(msk, σ2sk)nBorder(Bottleneck)s
Seaport(Bottleneck)

Figure 6.4 Steps to Obtain 1st and 2nd Moments of Convoluted Lognormal Distribution

Table 6.2 Distribution Characteristics Value for Convoluted Distribution (unit: hour)
Border and seaport

Mean

Standard Deviation

Thanaleng/Nong Khai – Laem Chabang (T/N-LCB)

11.5

1.275

Nam Phao/Cau Treo – Hai Phong (N/C-HAI)

13.2

1.657

Dansavan/Lao Bao - Da Nang (D/L-DAN)

14.5

1.742

Savanakhet/Mukdahan - Laem Chabang (S/M-LCB)

11.5

1.320

Nameo/Nam Xoi - Hai Phong (N/N-HAI)

13.4

2.141

Nam Phao/Cau Treo – Da Nang (N/C-DAN)

14.8

1.750

Dansavan/Lao Bao – Hai Phong (D/L-HAI)

12.8

1.630

84

0.14
0.12

Nameo/Nam Xoi (Border)t ~ LN(4.6, 1.5002 )0.1

Hai Phong (Seaport)

Probability

N/N-HAI (Convoluted)
0.08
0.06
t ~ LN(8.8, 1.5192 )

0.04
0.02

t ~ LN(13.4, 2.1412 )

0
0

10

20

30

40

50

Time Required (hr/TEU)

Figure 6.5 Graphical Example for Convolution of Lognormal Distribution
Using maximum likelihood method, the parameter of lognormal distribution can beformulated as following equation (6.9) and (6.10);

L̂ >ˆ 2 -; ln xkkn;k (ln xk 6 Lˆ ) 2
n(6.9)(6.10)

Using the 20,000 data obtained from above method, the parameter of lognormal distribution
is estimated for validation. As a result, the estimated parameters using above two equations
are very similar, which means the error is less than 1%. Thus, the method to estimate 1 stand 2nd moments of lognormal distributions applying in this dissertation is acceptable.
In case other distribution is assumed, the probability of occurrence of schedule early andlate arrival would be increased or decreased. Thus, comparing to assuming lognormal
distribution, generalized cost would be changed. The specific value is dependent on the
shape of distribution that is assumed. In case exponential distribution is assumed, theoccurrence probability of early arrival would be increased due to its characteristics of the
shape. In this way, the magnitude of generalized cost would be changed according to85

assumed distribution. However, magnitude relation of generalized cost is conserved among
the routes, thus, the trend of the result of scenario analysis is not significantly violated.

6.2.2 Determination of Arrival Buffer TimeIn this section, the detail of how to determine arrival buffer time Tb is addressed. Arrival
buffer time should be values to be determined endogenously since it might be variables
dependent on scale of variability. It is determined on the basis of values obtained frominterview survey. For the route between Vientiane and Laem Chabang seaport, average
shipment time is clarified as 21.0 hours in average from the interview survey (see Table 6.4
for values appeared in this section). This is approximately 3 hours shorter than existing
study (Banomyong, 2000). In this dissertation, shipment time for exporting case includes
time until loading freights onto a vessel, on the other hand, Banomyong (2000) accounts
shipment time until arriving at seaport. This seems to be a reason of the difference between
both shipment times. Regarding to departure time, it is obtained that freight forwarders aredeparting 24.5 hours before the arrival restriction time, which is final available time fortaking maritime transport. From this result, arrival buffer time is implicitly calculated as 3.5hours. At the same time, the mode till 95% upper point of convoluted lognormal
distribution of shipment time in the route between Vientiane and Laem Chabang seaport is
3.84 hours. This time is pretty close to arrival buffer time clarified by the interview survey.
Regarding the route between Vientiane and Da Nang seaport, average shipment time is 24.3
hours and departure time is 30.0 hours before the arrival restriction time. At this time,
arrival buffer time can be calculated as 5.7 hours. In this route, shipment time between themode and 96% point of the convoluted distribution of arrival time variability is 6.07 hours.
Here, Root Mean Square (RMS) error of arrival buffer time between actual value (TbA)
which is based on interview survey and time between the mode and 99% point of arrival
time distribution (TbE) as shown in Equation (6.11) is calculated.

RMS Error -1NN; (T 6 T )
i -1

E
b

A 2
b

(6.11)

where N denotes number of cases. As a result of error calculation, minimum RMS error ofthree routes has been obtained at the point of 96% upper point. Consequently, arrival buffer
time is defined as arrival time between the mode and 96% upper point of convoluted
distribution of shipment time variability in the model. A beneficial point of this is that
arrival buffer time can be determined on the basis of the scale of the variability (standard
deviation) of border and seaport. In this way, as bottlenecks variability getting larger,
departure time is set as earlier. We can also understand that in this model, determination of
the arrival buffer time is regarded as dynamic problem. Supposed reduction in shipment
86

time variability due to some transport related policy, the scale of shipment time variability
would be decreased and consequently, both risks of early and late arrival would also be
decreased. In this case, arrival buffer time is estimated smaller since shipment time
reliability gets improvement. In this way, this model can account for impact of reduction in
shipment time variability for each bottleneck.

Table 6.3 Difference between Arrival Restriction Time and Departure Time, and Average
Shipment Time (unit: hour)
VTE-LCB[N=40]

VTE-HAI[N=32]

VTE-DAN[N=28]

Difference between Arrival Restriction Time anddeparture time

24.5 (2.17)*

36.2 (3.13)*

30.0 (3.15)*

Average Shipment Time

21.0 (0.86)*

30.2 (1.11)*

24.3 (1.29)*

Arrival Buffer Time

3.5

6.0

5.7

Time between Mode and 96% point of fk(t)

3.84

5.64

6.07

*(Standard Deviation)

6.3 Specification of Scale Parameter θ
6.3.1 Setting Input Values
In order to estimate cargo volume of each route using this model, one needs to prepare
several input variables both exogenously and endogenously for Equation (6.2). Amongthem, values given endogenously are VT(ST) and E(SVC). The method for estimation
process has already been addressed up to previous chapter. Other input values are all
exogenously given. In this section, data preparation is illustrated.
E(st), USC, and BC are estimated based on interview survey with 51 companies. E(st)denotes average expected shipment time for each route. As shown in Figure 6.5, the time
between D and ta is corresponded to E(st). USC is the cost per one kilometer in the route
except the cost required at the border and seaport. Regarding to BC, there is unfortunately
no reliable data, thus, average value of interview results are used. However, slightvariability of the obtained data was observed among the interviewed companies. The reason
might be the difference of license owned by companies, document preparation prior toarriving at border, type of goods transported, etc. These differences would affect on theprocedure at the border. In summary of interview survey, necessary processes at the border
for all freight forwarders are submission of necessary documentations, payment for the toll
and tariff, immigration of driver and truck itself, inspection of truck weight by weight87

bridge, cargo inspection. Submission of the documentation includes invoice, cargo list,
purchase order, affidavit, and passport are generally required. Certificate of origin, which iscalled Form D, and HS code in ASEAN are sometimes required to submit as needed. Thus,
the required process is varied border by border, and unfortunately, requirement ischangeable by person in change. Considering all above situations, regarding to BC, 100
USD/TEU and 80 USD/TEU are set for all Thai border and all Vietnamese borders,
respectively.
TC and D are determined referring JETRO (2008). Although various processes are included
in TC, charge in loading and unloading, document preparation, customs declaration, andcargo inspection are almost common price among the domestic seaport in Thailand andVietnam, respectively. Among the domestic seaport, terminal handling charge only
generate difference in the price. Considering all above factors, 1,400 USD/TEU for Laem
Chabang seaport, 800 USD/TEU for Hai Phong seaport, and 850 USD/TEU for Da Nangseaport are set as input value of TC. All input values determined here is assumed ascommon amount for import and export case. In Figure 6.5, exogenously determined input
values are shown.

Table 6.4 Input Values Exogenously Given
Variable

Explanation

MethodE(strsk)

Expected
shipment time ofroute k between
OD pair rs

Average shipment timefor each route including
expected time at borderand seaport

USC

Unit shipment
cost

Average unit shipment
time obtained frominterview survey

2.3 USD/TEU-km

Drsk

Distance of routek between OD
pair rs

JETRO22)

BCrsk

TCrsk

Values
LPB-LCB: 20.2 hr

LPB-HAI: 19.7 hr

LPB-DAN: 29.1hr

VTE-LCB: 21.0 hr

VTE-HAI: 30.2 hr

VTE-DAN: 24.3 hr

SAV-LCB: 20.8 hr

SAV-HAI: 34.5 hr

SAV-DAN: 19.3 hr

CPS-LCB: 23.7 hr

CPS-HAI: 26.6 hr

CPS-DAN: 24.4hr

LPB-LCB: 1,000 km

LPB-HAI: 690 km

LPB-DAN: 1,150km

VTE-LCB: 700 km

VTE-HAI: 880km

VTE-DAN: 780 km

SAV-LCB: 700 km

SAV-HAI: 1,150 km

SAV-DAN: 430 km

CPS-LCB: 950km

CPS-HAI: 1,300 km

CPS-DAN: 880 km

Borderprocessing costof route k
between OD pairrs

Average borderprocessing cost obtained
from interview survey

All border with Thailand: 100 USD/TEU

Terminal cost ofroute k between
OD pair rs

JETRO22)

Laem Chabang seaport: 1,400 USD/TEU

All border with Vietnam: 80 USD/TEU

Hai Phong seaport: 800 USD/TEU
Da Nang seaport: 850 USD/TEU

88

6.3.2 Estimation of Cargo Generation and Attraction
One of the conditions to confirm the reliability of developed model would be reproductionof current condition with high accuracy. The data of cargo volume between each Lao cityand seaport is required in order to reproduce the current cargo flow. However, there isunfortunately no such data. In this study, therefore, the required data for reproduction isestimated.
The target type of goods is cargoes transported by containers, such as daily goods. As far as
cargoes are transported by containers, there would be no difference for rushing. Thus, theprominent differences in value of time across a type of goods are not expected. The
container cargo volume (TEU/year) from/to Lao PDR to/from all over the world in 2003
estimated by Shibasaki and Watanabe (2008) is used to estimate required data. From thisdata, cargo volume of from/to Lao PDR to/from Thailand, Vietnam, Cambodia, andSouthern China (Yunnan, Guangxi, Guangdong, Guizhou, and Funan) which might betransported by land transport are excluded and regarded as cargo volume of Lao PDRseaport in Thailand or Vietnam. In Shibasaki and Watanabe (2008), zoning of Lao PDR isthree zones, such as Northern, Middle, and Southern part of Lao PDR. In this study, it isassumed that most of cargo in Northern and Southern part are generated and attracted in
Luang Phabang and Champassak, respectively. Northern cargo volume and Southern cargovolume are respectively allocated to Luang Phabang and Champassak where are denoted as
centroids. Thus, cargo volume of Luan Phabang and Champassak are estimated as many as
those of Vientiane and Savannakhet. Regarding the cargo volume from/to middle part,
Vientiane and Savannakhet occupies large portion. Using cargo generation data (JICA,
2010), which is based on ton/year for each city of Lao PDR, cargo volume generated in
middle part is proportionally distributed to Vientiane and Savannakhet. The cargo volumedata of Shibasaki and Watanabe (2008) is converted as 10 tons = 1 truck = 1TEU. Besides,
air cargo and non-containerized cargo of maritime transport are excluded from theestimation of cargo flow generation. Using this method, cargo volume of each centroid canbe estimated as Table 6.5.

Table 6.5 Result of Cargo Generation Estimation (TEU/year, 2003)
Export (Qiek)

Import (Qiik)

Luang Phabang (k=1)

5,968

7,603

Vientiane (k=2)

4,736

10,805

Savannakhet (k=3)

8,525

4,698

Champassak (k=4)

3,856

4,978

Centroid89

Excluding Savannakhet, the results are well reflected the current state that is import surplus
Lao PDR (JICA, 2010). Savannakhet has a large amount of copper exports that are
produced in Sepon mine, thus, export is surplus (JICA, 2010). Since copper exports from
Laos are dominant in Savannakhet, export surplus in Savannakhet is a reasonable result.
Next, the cargo volume from each city to each seaport is estimated. However, since there isno such data, we estimate the cargo volume to each seaport using ratio of choosing seaportin Thailand and Vietnam which were obtained from interviews. The trend of seaport choiceobtained in the interviews is found almost no variation among companies. For example,
cargo volume from/to Vientiane is occupied by Thai seaport by 85%. Among the seaports
in Thailand, because Laem Chabang seaport deals almost exclusively from Lao PDR.
Laem Chabang seaport cargo volume from from/to Lao PDR is to be 0.850Qi2. Among
Vietnamese seaports, Hai Phong and Da Nang seaport are heavily used, and the use of ratio
was obtained as 7 : 3. Therefore, the volume of cargo heading to the Vietnam’s seaport ofHai Phong and Da Nang are set as 0.105Qi2 and 0.045Qi2, respectively.
Rerouting in the city might often be conducted according to the traffic condition, however,it may have only minor impact on seaport choice in approximately 1,000 km distance crossborder route. Therefore, assumption of setting one route for one destination is likely to beconsidered reasonable. Should shipment time at the border is to be very long due to somereason, freight forwarder change a seaport choice itself rather than route change. Since the
network covered in this study is cross-border long-haul transport, route change under fixing
seaport choice is accompanied by a very large additional shipment cost.

6.3.3 Specification of Scale Parameter θParameter θ is specified to reproduce the current states as high accuracy as possible. To put
it concretely, RMS errors of estimated (QikE) and actual cargo volume (QikA) is minimized
under the assumption that one destination is assigned one route. The probability ofchoosing the route k has already been estimated. Here, since the network is assumed as
flow independent states, it is not expected that any delays due to for example, traffic
congestion, occurred on the link connecting each nodes in the study area. Hence, routechange posterior to the departure due to the congestion is not considered. Even thoughroute change has possibly been done in the city due to the traffic congestion, the impact of
this type of route change is very likely to be too low to consider in the model in very longdistance shipment which lies nearly 1,000km for each route. Therefore, this study treatsflow independent cargo assignment problem, not flow dependent which considers the effectof traffic congestion. Queuing or waiting time can be easily observed at the border andseaport, however, such cost due to the queuing is considered as a part of the generalized90

cost. Freight forwarders assumed to determine the route before departure considering its
congestion cost.

RMS Error "

1
N

N

! (Q

rakE

# Q rakA ) 2(6.12)i "1Among the total cargo volume treated at seaport in coastal countries, the occupancy rate ofcargo volume from/to Lao PDR is very low. Hence, increase in cargo volume due to somereason such as improvement of LOS would not generate congestion. For example,
outbound cargo volume at Laem Chabang seaport in 2003 is 1,573,176TEU (Bangkok
Shippers and Agents Association, 2003), whereas actual cargo volume estimated in thisstudy is 11,934.90TEU, which accounts only 0.008% of total cargo volume treated.
Therefore, change of cargo volume from/to Lao PDR would not affect on the waiting timeand congestion at the seaports in coastal countries.
Using Equation (6.1), non-linear simultaneous equation is formulated for each centroids,
which include Luang Phabang, Vientiane, Savanakhet, and Champassak and subsequently,
scale parameter θ is deduced to minimize square error of estimated and actual values. The
reason to estimate scale parameter θ for each centroid is because each centroid can be
regarded as independent. Actual cargo volume for centroid i route k (Qiek) has already been
estimated, thus generalized cost of route k (E(GCik)) can be estimated using the proposedmodel.
As a result, scale parameter θ is estimated as 0.0026-0.0282. The transport network of thisstudy is approximately 1,000km that is long-distance network. In such case, generalized
cost of the route is estimated fairly high. As the scale of generalized cost increases, factors
contributing the variability of generalized cost are also increased. Thus, each freight
forwarder’s perception on generalized cost would be differed. In this case, the gap ofgeneralized cost of the routes would not be accurately perceived. Consequently, scaleparameter is estimated as small value since the sensitivity on the route choice is decreased.
Using estimated scale parameters for reproducing cargo flow, the result were obtained asshown in Table 6.6 and Figure 6.6. The accuracy seems to be pretty good.

91

Table 6.6 Comparison of Actual and Estimated Cargo Volume (TEU/year)
Laem Chabang seaport

Hai Phong seaport

Da Nang seaport

from/to Luang Phabang
Estimate

5,361.8

359.0

247.1

Actual

5,371.2

477.4

119.4

Error

-0.2%

-24.8%

107.0%

from/to Vientiane
Estimate

4,044.8

278.8

412.4

Actual

4,025.6

528.3

142.1

Error

0.5%

-50.9%

190.2%

from/to Savannakhet
Estimate

6,905.3

1.32*10^-18

1,619.8

Actual

6,820

170.5

1,534.5

Error

1.2%

-

5.6%

from/to Champassak
Estimate

3,122.7

0.6

732.7

Actual3084.8

77.12

694.1

Error

1.2%

-99.2%

5.6%

8,000
Estimate

7,000

ActualTEU/year

6,000
5,000
4,000
3,000
2,000
1,000
0

LCB HAI DAN LCB HAI DAN LCB HAI DAN LCB HAI DANLuang Phabang

Vientiane

Savannakhet

Champasak

Figure 6.6 Actual and Estimated Cargo Volume (TEU/year)92

6.4 Scenario Analysis
In this chapter, inland cargo flow model for Lao PDR is developed, which fulfilled
objective 3. The forth objective will be fulfilled in this section, which is several scenario
analysis using a model developed from chapter 5 to previous section. In the scenario
analysis, standard deviation of border or seaport shipment time variability distribution isdecreased. In other words, reliability of border and seaport are increased. Besides, the meanof arrival distribution is reduced at border and seaport for comparison purpose.

6.4.1 Improvement in Reliability of Border
As a first scenario analysis, reliability of border crossing time is increased. At this time,change in schedule variability cost and cargo volume of each seaport is analyzed. The
scenarios are made up 10% reduction till 90% reduction by 20% interval for the standard
deviation of shipment time variability of the border. Figure 6.7 shows change in schedulevariability cost as a result of increasing border reliability. As an overall trend, the reductionin schedule variability cost can be observed. Particularly, reduction rate of Nameo/Nam Xoi– Hai Phong seaport is the highest. This is because original value of standard deviation is
the highest by far among the all distributions. At the 90% reduction of standard deviation,
the variability of each border becomes almost similar resistance. Therefore, schedulevariability cost is heavily dependent on seaport variability in case 90% decrease in borderresistance. In 90% decrease case, the order of schedule variability cost is in same order ofscale of seaport variability.
At present status, average schedule variability cost is 1,811 USD/TEU, and at 90%
reliability increase, 1,219 USD/TEU in average. This teaches us that increase in 90%
border reliability benefits 32.7 cost reduction in schedule variability cost. Schedulevariability cost at the present status occupies 29.3% of total generalized cost. It is going todecrease up to 20.6% of schedule cost and 9% of total cost. Average schedule variability
cost is 1,441 USD/TEU for 90% shipment time reduction case which still occupies 23.2%
of total generalized cost, which contributes only 6.1% total cost reduction. The results ofshipment time reduction of bottlenecks are shown in Appendix C.

93

Schedule Variability Cost (USD/TEU)

4,000
3,500
Tanaleng/Nong Khai - Leam Chabang

3,000

Nam Phao/Cau Treo - Hai Phong

2,500

Dansavan/Lao Bao - Da Nang
Savannkahet/Mukdahan - Leam Chabang

2,000

Nameo/Nam Xoi - Hai Phong

1,500

Nam Phao/Cau Treo - Da Nang
Dansavan/Lao Bao - Hai Phong

1,000
500
Current

10%
down

30%
down

50%
down

70%
down

90%
down

SD of Shipment Time at BorderFigure 6.7 Change in Schedule Variability Cost due to Decrease in Border Variability

Figure 6.8-6.11 show change in seaport choice. In any cities, as the gap between eachborder variability decreasing due to decreasing shipment time variability, cargo volume ofLaem Chabang seaport is turned to seaport in Vietnam. In Figure 6.8 for Luang Phabang –Hai phone seaport, Nameo/Nam Xoi border is originally to be recognized as serious
bottleneck for accessing Haiphong seaport since its border resistance is very strong. As
such bottleneck is dissolved, cargo volume of Hai Phong seaport where advantage point isshorter distance is dramatically increased. In cargo flow from/to Vientiane shown in Figure
6.9, although slight increase in cargo volume bound for Vietnamese direction can beobserved, however, cargo volume from/to Laem Chabang seaport where the reliability anddistance has advantage is still the highest. As for cargo flow from/to Savannakhet shown in
Figure 6.10, cargo volume of Da Nang seaport where distance advantage owns isdramatically increased as border resistance decreases. For cargo volume from/toChampassak shown in Figure 6.11, it is structured that cargo always pass throughSavannakhet on the transport network. Comparing to Savannakhet, total shipment distance
between Champassak and seaport is longer and distance advantage is diminished. Thus,
cargo volume incensement in Da Nang seaport is as much as that of cargo volume ofSavannakhet. In the view of total cargo volume from/to Lao PDR, cargo volume sharing at
each seaport at 90% variability reduction is almost equal amount as shown in Figure 6.12.

94

Cargo Volume from/to Luang Phabang(TEU/year)

14,00012,000Current

10,00010% down

8,000

30% down
50% down

6,000

70% down

4,000

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure 6.8 Change in Seaport Choice from/to Luang Phabang due to Decrease in Border
Variability

Cargo Volume from/to Vientiane(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure 6.9 Change in Seaport Choice from/to Vientiane due to Decrease in Border
Variability

95

Cargo Volume from/to Savannakhet(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down
90% down

2,000
0
LCB

HAI

DAN

SeaportFigure 6.10 Change in Seaport Choice from/to Savannakhet due to Decrease in Border
Variability

Cargo Volume from/to Champasak(TEU/year)

8,000
7,000
6,000
5,000

Current

4,000

10% down
30% down

3,000

50% down

2,000

70% down
90% down

1,000
0
LCB

HAI
SeaportDAN

Figure 6.11 Change in Seaport Choice from/to Champassak due to Decrease in Border
Reliability

96

Cargo Volume from/to Laos (TEU/year)

50,00045,00040,000Current

35,00010% down

30,00030% down

25,00050% down

20,00070% down

15,00090% down

10,000
5,000
0

LCB

HAI

DAN

SeaportFigure 6.12 Change in Seaport Choice from/to Lao PDR due to Decrease in Border
Variability

6.4.2 Improvement in Reliability of Seaport
As a second scenario analysis, change in schedule variability cost and cargo volume of eachseaport is analyzed by reducing shipment time variability at seaport. The scenario is set
similarly to border reliability incensement, which is changing 10% reduction till 90%
reduction by 20% interval of standard deviation of shipment time variability at seaport.
The rate of schedule variability cost reduction due to increase in seaport reliability is higher
than that of border reliability. This is due to the reason that shipment time variability ofseaport is originally higher than that of border. In case of current situation, averageschedule variability cost is 1,811 USD/TEU which occupies 29.3% of average total cost,whereas in case 90% reduction, schedule variability cost is down up to 394 USD/TEU,
being 7.6% of average total shipment cost, which means 21.7% of total cost can be reduced
by diminishing seaport variability. For comparison, 1,288 USD/TEU is estimated asschedule variability cost when 90% shipment time is decreased but variability is fixed. In
this case, it occupies 20.8% of total shipment cost, which is much less impact on total cost
reduction comparing to shipment time variability reduction. As shown in Figure 6.13,
schedule variability cost of Nameo/Nam Xoi – Hai Phong seaport is high. This is due to thedominance of border variability cost in schedule variability cost. In case of increase inseaport reliability, it can be observed that two Vietnamese seaports increase their cargovolume whereas cargo volume at Laem Chabang seaport is decreasing.

97

Schedule Variability Cost (USD/TEU)

4,000
3,500
3,000

Tanaleng/Nong Khai - Leam Chabang

2,500

Nam Phao/Cau Treo - Hai Phong
Dansavan/Lao Bao - Da Nang

2,000

Savannkahet/Mukdahan - Leam Chabang

1,500

Nameo/Nam Xoi - Hai Phong

1,000

Nam Phao/Cau Treo - Da Nang

500

Dansavan/Lao Bao - Hai Phong0
Current

10%
down

30%
down

50%
down

70%
down

90%
down

SD of Shipment Time at Seaport

Figure 6.13 Change in Schedule Variability Cost due to Decrease in Seaport Variability

Figure 6.14 – 6.17 shows change in cargo volume at seaport from/to each city. Cargovolume in regard to from/to Luang Phabang shown in Figure 6.14 is increasing its volume
at Hai Phong seaport where distance advantage owns. Nevertheless, the increase rate isduller comparing to the case of border reliability increases since Nameo/Nam Xoi bordervariability is still very high. Regarding to cargo volume from/to Vientiane shown in Figure
6.15, it becomes almost same volume at Laem Chabang and Da Nang seaport at more than
50% reduction in seaport variability since the resistance of both routes are to be almost
same and distance is also almost no difference between two routes. As for cargo volume
from/to Savannakhet and Champassak as shown in Figure 6.16 and 6.17, dominance at Da
Nang seaport can be observed similarly to border reliability increase. However, seaportreliability increase case is higher rate of increase in cargo volume. The reason for this trend
is that because Dansavan/Lao Bao border is originally relatively low variability, decreasing
Da Nang seaport variability itself, Da Nang seaport where distance advantage owns would
be dramatically increased in terms of cargo volume. In regard to cargo volume from/to Lao
PDR as shown in Figure 6.18, cargo volume increase at Da Nang seaport can be observed.
In the scenario analysis for border variability decreasing, three seaports were gotten almost
same amount of cargo volume at 90% reduction case. However, seaport reliability
increasing case shows duller increase at Hai Phong seaport. One of the reasons to explain tothis is that cargo volume at Hai Phong seaport is not increased as much as border scenario
analysis case from/to Luang Phabang since Nameo/Nam Xoi border is still very highvariability.
98Cargo Volume from/to Luang Phabang(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down

90% down

2,000
0

LCB

HAI
SeaportDAN

Figure 6.14 Change in Seaport Choice from/to Luang Phabang due to Decrease in Seaport
Variability

Cargo Volume from/to Vientiane(TEU/year)

14,00012,00010,000Current

8,000

10% down

30% down

6,000

50% down

4,000

70% down

90% down

2,000
0
LCB

HAI
SeaportDAN

Figure 6.15 Change in Seaport Choice from/to Vientiane due to Decrease in Seaport
Variability99

Cargo Volume from/to Savannakhet(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down
90% down

2,000
0
LCB

HAI

DAN

SeaportCargo Volume from/to Champasak(TEU/year)

Figure 6.16 Change in Seaport Choice from/to Savannakhet due to Decrease in Seaport
Variability

10,000
9,000

8,000
7,000
Current

6,000

10% down

5,000

30% down

4,000

50% down

3,000

70% down

2,000

90% down

1,000
0
LCB

HAI

DAN

SeaportFigure 6.17 Change in Seaport Choice from/to Champassak due to Decrease in Seaport
Variability

100Cargo Volume from/to Laos (TEU/year)

50,00045,00040,00035,000Current

30,00010% down

25,00030% down

20,00050% down

15,00070% down

10,00090% down

5,000
0
LCB

HAI

DAN

SeaportFigure 6.18 Change in Seaport Choice from/to Lao PDR due to Decrease in Seaport
Variability

6.4.3 Results and Discussion
The scenario analysis has shown several insights. The most notable finding is that total cost
reduction in such variable cross-border transport is higher when shipment time variability isdecreased comparing to shipment time itself even though the decrease scale of shipment
time itself is longer than that of shipment time variability. As seen in Figure 6.19,
improving the reliability for the highest bottlenecks of each is very effective to reduce thetotal costs, which overwhelm the reliability improvement of all borders.

Average Schedule Variability Cost䠄USD/TEU䠅

2,000
1,800
1,600

Decrease in Border Time

1,400
1,200

Decrease in Seaport Time

1,000

Decrease in Border Variability

800

Decrease in Variability at Nameo/Nam
Xoi and Da Nang seaport
Decrease in Seaport Variability

600
400
200

0
Current 10% 30% 50% 70% 90%
down down down down down
Decrease Rate of Bottleneck Resistance

Figure 6.19 Change in Average Schedule Variability Cost for Each Scenario101

On the basis of the result of scenario analysis, it is possible to propose several measures inorder to contribute to economic development of Lao PDR. In the scenario analysis, it was
revealed that the improvement of reliability of each bottleneck is more efficient to decrease
total shipment cost rather than decrease in normal shipment time. The decrease in shipment
cost would lead to decrease in price of goods. Therefore, the investment to facilitate theprocessing at the border and seaport is needed. Specifically, introducing Information andCommunication Technology (ICT) would significantly contribute on smooth processing.
Currently, almost all processing is done by paper-based document. In case of cargoinspection, X-ray scanner can significantly contribute on not only reliability improvement
but also shipment time itself. This is pointed out by a lot of interviewee in all field survey
location, Uzbekistan, Kazakhstan, and Lao PDR as mentioned in chapter 3. Besides, if
other country’s truck can enter and drive freely in other countries, transshipment at theborder is omitted. However, this model cannot capture how each specific countermeasure
reduces the variability even though this is important to know. For this, one needs to develop
new model which explanatory variable is standard deviation of the bottleneck variability.

6.5 Chapter Conclusion
In this chapter, schedule variability cost of freight forwarder’s behavior is formulated, andsubsequently, scenario analysis had been conducted using developed model by changing
several indicators related to shipment time at two bottlenecks, border and seaport. As forthe formulation of schedule variability cost, the notable function is setting arrival buffer
time is set as dynamic problem dependent on the scale of the standard deviation of thebottlenecks. To do this, the expected generalized cost is relatively estimated lower in caseshipment time reliability increased. The scenario analysis brought several insights. One ofthem is that the impact of increase in bottleneck reliability is much more effective tofacilitate the haulage or reduce total shipment cost. Thus, the result implies to policy makerin Lao PDR that shipment time should be stable in order to facilitate inland cargo flow.

102

CHAPTER 7
CONCLUSION

103

7.0 Introduction
In this dissertation, several analysis and survey have been conducted in order to fulfill four
objectives in regard to the issues on freight transport of LLDCs. In chapter 3, the problems
lied on this issues were clarified through the field survey in Central Asia and Lao PDR. As
a result, the shipment time variability at border and seaport are to be serious problems for
LLCDs. In chapter 4, we set up and examine one hypothesis, which as route is getting morevariable, people needs more number of information sources for estimating expected
shipment time. In this case, it can be concluded that people suffer from additional cost due
to the shipment time variability. On the basis of problem finding in field survey explainedin chapter 3 and some literature review in chapter 2 and suggestions from chapter 4, inland
cargo flow model had been developed and analyzed several hypothetical situations so thatthe impact on cargo volume treated at seaport and change in the schedule variability cost isobserved. Inland cargo flow model can be divided into mainly two parts, which are thevaluation of shipment time variability (chapter 5) and formulation of schedule variability
cost (chapter 6).
Shipment time variability has been discussed several decades mainly in internationalorganization as serious problem for facilitation on freight transport in landlocked countries,
and generates additional cost. However, there was no qualitative model to evaluate theimpact of shipment time variability cost. Thus, developing inland cargo flow model which
can quantitatively analyzes the impact of shipment time variability is significant
contribution to this research field. Using developed model, several scenarios were analyzed.
As a result of several scenario analysis, important outcome for policy making of Lao PDR
were obtained, which is the reduction rate of total shipment cost is higher when shipment
time variability is decreased rather than the reduction in shipment time itself. Besides, itwould be possible to reverse the sharing of cargo volume at the seaport of Thailand andVietnam depending on the reduction amount of the variability at two bottlenecks. Sincemain contributor to increase schedule variability cost is differed depending on the route, it
is implied that the countermeasures are needed for each seaport to make shipment timestable. For example, in order to increase in cargo volume at Hai Phong seaport from/to
Luang Phabang, it is better choice to increase the reliability of the border of Nameo/Nam
Xoi comparing to increasing at Hai Phong seaport. In case of Da Nang seaport, seaportvariability itself should be down in order to increase the share of cargo volume treated atseaport since Dansavan/Lao Bao border where is the node of the route to Da Nang seaport,
variability is originally relatively low. As for valuation of shipment time variability, eventhough it is a part of inland cargo flow model, there was no existing practice to estimate
value of shipment time variability in the region of GMS so far, thus it is valuable result for
further application to other research.

1047.1 Objective Based Conclusions
7.1.1 Objective 1
The first objective set for this dissertation has been to clarify the problems on freighttransport of LLDCs through field surveys and literature reviews. The problems here mean
not only physical issues but also institutional issues adversely contributing on LLDCs’
freight transport. From the survey results, several problems were identified, for example,
waiting time due to the congestion in rail transport and lack of scanning equipment that cancontributes on shortening time fluctuation. These problems may ultimately result inshipment time variability. In the interview surveys in Lao PDR, 21.6% of surveyed
companies are recognized that shipment time variability is important factor for seaportchoice problem. In that sense, additional cost due to shipment time variability is expected.

7.1.2 Objective 2
The second objective is to identify the impact of shipment time variability on estimating
expected shipment time. In the highly variable routes, it can be postulated that estimationfor expected shipment time would be quite tough task. If situation is quite unstable, people
needs more numbers of information sources to be confident on the prediction. On the basisof this hypothesis, one model using generalized mean concept is employed as explanatory
variable is set as expected shipment time. The result is match with hypothesis set up. In allof the routes, average perception shipment time received the highest weight among the fourtypes of shipment time. Because average perception shipment time is based on past
shipping experiences, past shipment experiences clearly influence the estimation of
expected shipment time. Nevertheless, recent shipment experiences do not significantly
affect the estimation of expected shipment time, particularly for less variable and higher
frequency routes. In particular, recent experience is not an important factor in estimating
expected shipment time in low-variable routes. As shipment time becomes more variable,
more number of information sources is needed. This implies that shipment time variability
additionally contributes on shipment cost. This type of effect might be able to be appeared
as early departure or change in route choice.

7.1.3 Objective 3
The third objective has been to develop inland cargo flow for Lao seaport choice which canbe applied for other LLDCs. It is even applicable to doubly LLCs, like Uzbekistan. In thiscase, the convolution of lognormal distribution would be at least three, which is only one
105point to be modified. In Shibasaki and Watanabe (2008), shipment time variability atborder is roughly considered but rigorous theoretical background was somewhat in doubt.
Besides, seaport is not treated as a bottleneck in his model. The model proposed in thisdissertation solved these problems. The core parts of modeling for objective 3 are;valuation of shipment time variability and formulation of schedule variability cost.

7.1.4 Objective 4
After developing inland cargo flow model which considers the cost of shipment time
variability for schedule constrained freight forwarders, scenario analysis had been
conducted using developed model by changing several indicators related to shipment timeat two bottlenecks, border and seaport. As for the formulation of schedule variability cost,
the notable function is setting arrival buffer time is set as dynamic problem dependent on
the scale of the standard deviation of the bottlenecks. To do this, the expected generalized
cost is relatively estimated lower in case shipment time reliability increased. The result of
this dissertation implies that the stability of shipment time is more effective to reduce total
shipment cost. However, one needs to remind that this model cannot capture how eachspecific countermeasure reduces the variability even though this is important to know. For
this, one needs to develop new model which explanatory variable is standard deviation of
the bottleneck variability.

7.2 Future Scope
7.2.1 Improving the Model Considering Other Factors
The inland cargo flow model has several further potential improvement points. In inland
cargo flow model, transit cargo and potential demand generated due to improving LOS are
not considered. For transit cargo, it is expected to be increased as border resistancediminished. Here transit cargo is mainly mentioned for between Thailand and Vietnam
where cargo volume in the region is relatively high. In this case, cargo diversion frommaritime transport since currently, relatively large number of cargo volume is transported
by maritime transport between two capitals, Bangkok and Hanoi. As mentioned in chapter
6, the cargo share from/to Lao PDR in seaport of Thailand is extremely small, such as
0.008% of total handling volume. This trend might be similar in other LLDCs. For example
in 2003 Mongolian case, even though all maritime cargo generated in Mongolia is assumed
to use Tianjin seaport in China, it accounts only 1.4% of total cargo handling in Tianjin
seaport (American Association of Port Authorities, 2006). However, there are somepossibilities to change LOS due to the cargo from LLDCs.
106

The survey results in section 3.3 (more specifically, Table 3.2) implies that geographic
condition might be a factor to be considered for route choice problem. In the interview
survey, some of them answered the reason why Vietnamese seaport is not chosen is due tosevere geographical condition of Vietnamese side. In LLDCs, it is often found suchproblem. Thus, the model incorporating geographical condition would be interesting.

7.2.2 Improving to an Analytical Method
Another future scope to the model proposed in this dissertation can raise the reduction in
calculation process since the model includes integral calculus. Since this model would bevery useful for practical problems in terms of freight transport in LLDCs, it is highly
expected to reform to closed-form which includes no integral calculus for estimating
expected schedule variability cost. Hence, one of the weakness points of this model is thatthe amount of calculation process is to be large. Calculation processes can be reduced if theintegral calculus is excluded from the model.

7.2.3 Examining Lognormal Distribution in Lao Context
In this dissertation, shipment time at border and seaport are assumed as lognormal
distribution based on the facts of other countries context. Actual data of shipment time at
the border and seaport were not able to be obtained. Thus, Kolmogorov-Smirnov test which
examines goodness-of-fit for distribution shape and Karnel density estimation which
estimate the probability density function from the sample data were not conducted.
However, it is needed to examine and confirm that shipment time at border and seaport isfollowing lognormal distribution from the actual data.

107

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113

APPENDIX AQUESTIONNAIRE FORM FOR VALUATION OF SHIPMENT
TIME VARIABILITY

114

Choice of Shipment Route to Seaport
Now, two routes are available for same origin and destination for inland freight transport.
Please choose one route which maximizes your satisfaction. Two routes are differed interms of shipment cost, average shipment time, variability of shipment time.
Please assume a situation below;
Please assume a situation below;
1. Youto transport
youryour
goods
fromfrom
Vientiane
to to
Seaport
1. areYouplanningare planningto transport
goods
Vientiane
Seaport(until loading
your
goods
onto
vessel)2. Transported goods to seaport will be transported to Europe after the arrival at
2. Transported
seaportgoods to seaport will be transported to Europe after the arrival at seaport
3. Your
responsibility
is transporting
goodsgoods
until loading
onto vessel
3. Your responsibility
is transporting
until loading
onto vessel
4. Tworoutes(ROUTE
A
and
ROUTE
B)
are
available
toseaport
4. Two routes (ROUTE A and ROUTE B) are available to seaport
5. Thereare possibilities
to arrive
early earlyor lateordue
thetounexpectedwaitingtime at
5. Thereare possibilities
to arrive
latetodue
the unexpectedwaitingtimeborderator seaportborder
6. Transportmode is truckor seaport
7. Pleasechoose yourpreferred
6. Transportmode
is truckROUTE.
7. Please choose your preferred ROUTE.
ROUTE A
Origin(Vientiane)

Destination(Seaport)

Border crossing
ROUTE B

Europe, US,
Japan...

Please consider within this range
Please choose ROUTE A or ROUTE B
ROUTE A

ROUTE B

Average shipment time: 12 hours
The shipment cost is 1,500USD/TEU

Average shipment time: 15 hours
The shipment cost is 1,200USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 15 hour early̽ 13 hour early̽ 10 hour early̽ 5 hour early̽ 5 hour late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ on time̽ 15 hour early̽ 13 hour early̽ 10 hour early̽ 5 hour early

ExampleA

B
115

Case 1: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 14 hours
The shipment cost is 1,700 USD/TEU

Average shipment time: 19 hours
The shipment cost is 1,400 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 6 hours early̽ 5 hours early̽ 3 hours early̽ 1 hours early

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 5 hours early̽ 4 hours early̽ 2 hours early̽ on time̽ 3 hours late

A

B

Case 2: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 16 hours
The shipment cost is 1,900 USD/TEU

Average shipment time: 24 hours
The shipment cost is 1,300USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 4 hours early̽ 3 hours early̽ 2 hours early̽ on time̽ 2 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 11 hours early̽ 9 hours early̽ 7 hours early̽ 3 hours early̽ 1 hours late

A

B

Case 3: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 16 hours
The shipment cost is 1,700 USD/TEU

Average shipment time: 24 hours
The shipment cost is 1,400 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 6 hours early̽ 5 hours early̽ 3 hours early̽ 1 hours early

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 9 hours early̽ 7 hours early̽ 5 hours early̽ 1 hours early̽ 3 hours late

A

B

116

Case 4: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 12 hours
The shipment cost is 1,700 USD/TEU

Average shipment time: 23 hours
The shipment cost is 1,400 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 4 hours early̽ 3 hours early̽ 2 hours early̽ 1 hours early̽ 1 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 11 hours early̽ 9 hours early̽ 7 hours early̽ 4 hours early̽ on timeA

B

Case 5: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 12 hours
The shipment cost is 1,900 USD/TEU

Average shipment time: 23 hours
The shipment cost is 1,300 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 2 hours early̽ 1 hours early̽ on time̽ 1 hours late̽ 3 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 5 hours early̽ 3 hours early̽ on time̽ 4 hours late

A

B

Case 6: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 14 hours
The shipment cost is 1,900 USD/TEU

Average shipment time: 19 hours
The shipment cost is 1,300 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 5 hours early̽ 4 hours early̽ 2 hours early̽ on time̽ 2 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 5 hours early̽ 4 hours early̽ 2 hours early̽ on time̽ 2 hours late

A

B
117

Case 7: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 12 hours
The shipment cost is 2,100 USD/TEU

Average shipment time: 23 hours
The shipment cost is 1,200 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 2 hours early̽ 1 hours early̽ on time̽ 1 hours late̽ 3 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 5 hours early̽ 3 hours early̽ on time̽ 4 hours late

A

B

Case 8: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 14 hours
The shipment cost is 2,100 USD/TEU

Average shipment time: 19 hours
The shipment cost is 1,200 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 6 hours early̽ 5 hours early̽ 3 hours early̽ 1 hours early̽ 1 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 3 hours early̽ 2 hours early̽ on time̽ 2 hours late̽ 4 hours late

A

B

Case 9: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 16 hours
The shipment cost is 2,100 USD/TEU

Average shipment time: 24 hours
The shipment cost is 1,200 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 2 hours early̽ 1 hours early̽ on time̽ 1 hours late̽ 4 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 5 hours early̽ 3 hours early̽ 1 hours late̽ 5 hours late

A

B
118

Case 10: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 12 hours
The shipment cost is 2,100 USD/TEU

Average shipment time: 23 hours
The shipment cost is 1,200 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 6 hours early̽ 5 hours early̽ 4 hours early̽ 3 hours early̽ 1 hours early

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 8 hours early̽ 6 hours early̽ 4 hours early̽ 1 hours early̽ 3 hours late

A

B

Case 11: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 14 hours
The shipment cost is 1,900 USD/TEU

Average shipment time: 19 hours
The shipment cost is 1,300 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 5 hours early̽ 4 hours early̽ 2 hours early̽ on time̽ 2 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 5 hours early̽ 4 hours early̽ 2 hours early̽ on time̽ 2 hours late

A

B

Case 12: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 14 hours
The shipment cost is 1,700 USD/TEU

Average shipment time: 19 hours
The shipment cost is 1,400 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 3 hours early̽ 2 hours early̽ on time̽ 2 hours early̽ 4 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 3 hours early̽ 2 hours early̽ on time̽ 2 hours early̽ 4 hours late

A

B

119

Case 13: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 16 hours
The shipment cost is 1,700 USD/TEU

Average shipment time: 24 hours
The shipment cost is 1,400 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 2 hours early̽ 1 hours early̽ on time̽ 2 hours late̽ 4 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 5 hours early̽ 3 hours early̽ 1 hours late̽ 5 hours late

A

B

Case 14: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 16 hours
The shipment cost is 1,900 USD/TEU

Average shipment time: 24 hours
The shipment cost is 1,300 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 6 hours early̽ 5 hours early̽ 3 hours early̽ 1 hours early

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 4 hours early̽ 2 hours early̽ on time̽ 4 hours late̽ 8 hours late

A

B

Case 15: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 12 hours
The shipment cost is 1,700 USD/TEU

Average shipment time: 23 hours
The shipment cost is 1,400 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 4 hours early̽ 3 hours early̽ 2 hours early̽ 1 hours early̽ 1 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 6 hours early̽ 4 hours early̽ 2 hours early̽ 1 hours late̽ 5 hours late

A

B

120

Case 16: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 16 hours
The shipment cost is 2,100 USD/TEU

Average shipment time: 24 hours
The shipment cost is 1,200 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 9 hours early̽ 8 hours early̽ 7 hours early̽ 5 hours early̽ 3 hours early

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 6 hours early̽ 4 hours early̽ 2 hours early̽ 2 hours late̽ 6 hours late

A

B

Case 17: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 12 hours
The shipment cost is 1,900 USD/TEU

Average shipment time: 23 hours
The shipment cost is 1,300USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 7 hours early̽ 6 hours early̽ 5 hours early̽ 4 hours early̽ 2 hours early

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 4 hours early̽ 2 hours early̽ on time̽ 3 hours late̽ 7 hours late

A

B

Case 18: Please choose ROUTE A or ROUTE B
ROUTE A
ROUTE B
Average shipment time: 14 hours
The shipment cost is 2,100 USD/TEU

Average shipment time: 19 hours
The shipment cost is 1,200 USD/TEU

The shipment has an equal chance of arriving at
seaport at any of the following times:̽ 5 hours early̽ 4 hours early̽ 2 hours early̽ on time̽ 2 hours late

The shipment has an equal chance of arriving at
seaport at any of the following times:
̽ 10 hours early̽ 9 hours early̽ 7 hours early̽ 5 hours early̽ 3 hours early

A

B

121

APPENDIX BSCENARIO ANALYSIS(DECREASE IN SHIPMENT TIME AT BORDER)

122

Schedule Variability Cost (USD/TEU)

4,000
3,500
Tanaleng/Nong Khai - Leam Chabang

3,000

Nam Phao/Cau Treo - Hai Phong

2,500

Dansavan/Lao Bao - Da Nang
Savannkahet/Mukdahan - Leam Chabang

2,000

Nameo/Nam Xoi - Hai Phong

1,500

Nam Phao/Cau Treo - Da Nang
Dansavan/Lao Bao - Hai Phong

1,000
500
Current

10%
down

30%
down

50%
down

70%
down

90%
down

Mean of Shipment Time at BorderFigure B.1 Change in Schedule Variability Cost due to Decrease in Shipment Time at
BorderCargo Volume from/to Luang Phabang(TEU/year)

14,00012,000Current

10,00010% down

8,000

30% down
50% down

6,000

70% down

4,000

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure B.2 Change in Seaport Choice from/to Luang Phabang due to Decrease in Shipment
Time at Border123

Cargo Volume from/to Vientiane(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure B.3 Change in Seaport Choice from/to Vientiane due to Decrease in Shipment Timeat Border

Cargo Volume from/to Savannakhet(TEU/year)

12,00010,0008,000

Current

6,000

10% down

4,000

50% down

30% down
70% down

2,000

90% down

0
LCB

HAI

DAN

SeaportFigure B.4 Change in Seaport Choice from/to Savannakhet due to Decrease in Shipment
Time at Border124

Cargo Volume from/to Champasak(TEU/year)

8,000
7,000
6,000
5,000

Current

4,000

10% down
30% down

3,000

50% down

2,000

70% down
90% down

1,000
0
LCB

HAI
DAN
SeaportFigure B.5 Change in Seaport Choice from/to Champassak due to Decrease in Shipment
Time at Border

Cargo Volume from/to Laos (TEU/year)

50,00045,00040,000Current

35,00010% down

30,00030% down

25,00050% down

20,00070% down

15,00090% down

10,000
5,000
0

LCB

HAI
SeaportDAN

Figure B.6 Change in Seaport Choice from/to Lao PDR due to Decrease in Shipment Timeat Border

125

APPENDIX CSCENARIO ANALYSIS(DECREASE IN SHIPMENT TIME AT SEAPORT)

126

Schedule Variability Cost (USD/TEU)

4,000
3,500
Tanaleng/Nong Khai - Leam Chabang

3,000

Nam Phao/Cau Treo - Hai Phong

2,500

Dansavan/Lao Bao - Da Nang
Savannkahet/Mukdahan - Leam Chabang

2,000

Nameo/Nam Xoi - Hai Phong

1,500

Nam Phao/Cau Treo - Da Nang
Dansavan/Lao Bao - Hai Phong

1,000
500
Current

10%
down

30%
down

50%
down

70%
down

90%
down

Mean of Shipment Time at Seaport

Figure C.1 Change in Schedule Variability Cost due to Decrease in Shipment Time at
SeaportCargo Volume from/to Luang Phabang(TEU/year)

14,00012,000Current

10,00010% down

8,000

30% down
50% down

6,000

70% down

4,000

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure C.2 Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment
Time at Seaport127

Cargo Volume from/to Vientiane(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure C.3 Change in Seaport Choice from/to Vientiane due to Decrease in Shipment Timeat Seaport

Cargo Volume from/to Savannakhet(TEU/year)

12,00010,0008,000

Current

6,000

10% down

4,000

50% down

30% down
70% down

2,000

90% down

0
LCB

HAI

DAN

SeaportFigure C.4 Change in Seaport Choice from/to Savannakahet due to Decrease in Shipment
Time at Seaport

128

Cargo Volume from/to Champasak(TEU/year)

8,000
7,000
6,000
5,000

Current

4,000

10% down
30% down

3,000

50% down

2,000

70% down
90% down

1,000
0
LCB

HAI
SeaportDAN

Figure C.5 Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment
Time at Seaport

Cargo Volume from/to Laos (TEU/year)

50,00045,00040,000Current

35,00010% down

30,00030% down

25,00050% down

20,00070% down

15,00090% down

10,000
5,000
0

LCB

HAI

DAN

SeaportFigure C.6 Change in Seaport Choice from/to Lao PDR due to Decrease in Shipment Timeat Seaport

129

APPENDIX DSCENARIO ANALYSIS(DECREASE IN SHIPMENT TIME VARIABILITY AT
NAMEO/NAM XOI AND DA NANG SEAPORT)

130

Schedule Variability Cost (USD/TEU)

4,000
3,500
Tanaleng/Nong Khai - Leam Chabang

3,000

Nam Phao/Cau Treo - Hai Phong

2,500

Dansavan/Lao Bao - Da Nang

2,000

Savannkahet/Mukdahan - Leam Chabang
Nameo/Nam Xoi - Hai Phong

1,500

Nam Phao/Cau Treo - Da Nang

1,000

Dansavan/Lao Bao - Hai Phong500
0Current

10%
down

30%
down

50%
down

70%
down

90%
down

Decrease Rate of SD of Shipment Time at Nameo/Nam Xoi and Da Nang seaport

Figure D.1 Change in Schedule Variability Cost due to Decrease in Shipment Time
Variability at Nameo/Nam Xoi and Da Nang Seaport

Cargo Volume from/to Luang Phabang(TEU/year)

14,00012,000Current

10,00010% down

8,000

30% down
50% down

6,000

70% down

4,000

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure D.2 Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment
Time Variability at Nameo/Nam Xoi and Da Nang Seaport131

Cargo Volume from/to Vientiane(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down

90% down

2,000
0
LCB

HAI

DAN

SeaportFigure D.3 Change in Seaport Choice from/to Vientiane due to Decrease in Shipment Time
Variability at Nameo/Nam Xoi and Da Nang Seaport

Cargo Volume from/to Savannakhet(TEU/year)

14,00012,00010,000Current

8,000

10% down
30% down

6,000

50% down

4,000

70% down
90% down

2,000
0
LCB

HAI

DAN

SeaportFigure D.4 Change in Seaport Choice from/to Savannakahet due to Decrease in Shipment
Variability at Nameo/Nam Xoi and Da Nang Seaport132

Cargo Volume from/to Champasak(TEU/year)

10,000
9,000

8,000
7,000
Current

6,000

10% down

5,000

30% down

4,000

50% down

3,000

70% down

2,000

90% down

1,000

0
LCB

HAI
SeaportDAN

Figure D.5 Change in Seaport Choice from/to Luan Phabang due to Decrease in Shipment
Variability at Nameo/Nam Xoi and Da Nang Seaport

Cargo Volume from/to Laos (TEU/year)

50,00045,00040,000Current

35,00010% down

30,00030% down

25,00050% down

20,00070% down

15,00090% down

10,000
5,000
0

LCB

HAI

DAN

SeaportFigure D.6 Change in Seaport Choice from/to Lao PDR due to Decrease in Shipment
Variability at Nameo/Nam Xoi and Da Nang Seaport133