FREIGHT TRANSPORT MANAGEMENT IN THE
CENTRAL BUSINESS DISTRICT:An Empirical Analysis of the Traffic andEnvironmental Impacts of the Cooperative Delivery
SystembyNashreen G. Sinarimbo

Submitted to the Department of Logistics and Information Engineering in
Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF ENGINEERING

at theTokyo University of Marine Science and Technology
September 2005

© Tokyo University of Marine Science and Technology 2005
All Rights Reserved

Abstract
Movement of goods in the city contributes to the steady increase of traffic congestionand serious deterioration of urban environment. This problem can be mitigated either
by supplying necessary infrastructures or by applying policy measures to curve thevolume of traffic. These two methods are mostly known as, 1) infrastructure-based
measure and 2) TDM-based measure. The first type of measure is normally achieved
for a long period of time (e.g. construction of freight terminal may take years) andrequires huge financial investment while the second type of measure is adaptable for ashort period of time (e.g. reservation of parking space for trucks) and less expensive.
This study concerns to the second type of measure. In particular, it investigated theapplication of cooperative delivery system (CDS) which is one of most powerful
TDM measures for freight transport to different types of central business district(CBD).
The CDS can be designed to meet the following objectives, 1) reduce traffic
congestion, 2) improve urban environment, 3) used as a tool for urban planning andcity protection, 4) reduce transportation cost and 5) enhance companies’ social
reputation. There are several models of CDS. The one adopted in this study is the“multiple carriers” and “multiple shippers” concept where different carriers andshippers agreed to cooperate and coordinate their operation. Moreover, there are three
types of CDS designs namely 1) Horizontal CDS, 2) Vertical CDS, and 3)
combination of Horizontal and Vertical CDS. Under the first design, trucks ofdifferent sizes and loads have to bring their cargoes to the stock point (SP) or depot
for consolidation. Small trucks, normally non-polluting vehicles, will complete thedelivery going to the customers. At the (off) loading area of the building, the driverwould unload goods and deliver them to the recipients. The second design of CDS on
the other hand entails to assigning of staff at high rise buildings with high number ofgoods attracted. When the delivery trucks arrive, the staff would unload the goods anddelivery them to the recipients. The third design of CDS which is the combination of
Horizontal and Vertical CDS combined the functions of two systems resulting tocomprehensive delivery system that has strong impact both on traffic andenvironment.
Different designs of CDS under the “multiple carriers” and “multiple shippers”
concept are applied to different types of central business district (CBD) to assess theirsuitability and applicability. From the perspective of application of CDS, CBD can beclassified into four categories: 1) low density CBD in developing countries, 2) highdensity CBD in developing countries, 3) low density CBD in developed countries, and4) high density CBD in developed countries. Microsimulation analysis is selected as
method to evaluate traffic and environmental impact of the CDS due to its highreliability and friendliness when many cases are required to be analyzed. Data of CBD
needed for analysis is obtained through survey (i.e. survey in Tokyo was conducted in
February 2002 while March 2005 in Metro Manila).

iTraffic and environmental impact of CDS to low density CBD which is mostly
located in developing countries appears to be limited due to the very high number ofprivate cars (around 50%) and saturated transportation network. Share of trucks on theother hand is considerably low at 1.2% which suggests that even removing all thetrucks on the road, the network is still saturated. In the future however, the proportionof vehicle modes might change as the development of the central business district
intensifies which would transform the CBD into high density. Share of trucks can be
expected to increase that would further aggravate traffic congestion and pollutant
emissions. When CDS is applied under this condition, the CDS produced notable
positive effect to the road network as well as to the urban environment. In essence,cooperative delivery system becomes more important as the development of the CBD
intensify. The limited capacity of infrastructure necessities a management based
measure that would limit the number of trucks while serving the same amount ofgoods.
When the CDS is applied to a low density CBD of developed country (Marunouchi
district of Tokyo), the traffic impact of CDS is observed to be high at 12% of the total
traffic. Reduction to both NOx and SPM is also substantial at 89% of all those comingfrom trucks. Aside from the estimation of traffic and environmental impact, the study
evaluates the ratio of truck trips to be covered by the CDS in order to avoid queues oftrucks at the (un)loading area of each building. It was found out that at least 70% of
the truck trips should be covered to meet this objective. Modifications to the CDS
however should be made when the proportion of retail space (i.e. shops andrestaurants) reached 23% due to tremendous increase of truck trips (presently, shareof retail space is 2.7%). These changes can be done in the form of increasing thenumber of CDS staff and/or increasing the number of freight facilities (i.e. parking
space and freight elevator). Regarding the working time (i.e. labor time and deliverytime), a 50.4% reduction (from 63,314 min to 31,403 min) can be achieved by the
CDS.
It was necessary to identify which of the CDS designs is applied depending on
building density and building use in the CBD to realize its full potential. Analysis
shows that Horizontal CDS has the lowest distribution time in a low density CBD (15story) while the two other CDS designs brought increase to the distribution time.
Distribution time refers to the time derived from delivery (vehicle’s running time),
unloading, waiting time for parking, labor time at stock point (SP), and verticaldelivery. However, when the building density increases and each building reaches 20story, all 3 CDS designs brought enormous benefits in the form of time saved(distribution time). The same trend is observed when the buildings reached 30-story
and 40-story. These findings would be useful guidelines in selecting which CDS
design is adopted and when it is adopted based on the characteristics of the CBD.
It appears that the general trend is that as the building density of CBD increases, thegreater it needs of the CDS. It is because the number of trucks followed the same
trend and as its numbers increases the more problems it generated. This problem canbe in the form of traffic congestion which can cause delay to the goods and othervehicles, truck’s queue at the (un) loading area of the buildings, and environmental
pollutions.

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Acknowledgments
During the course of my studies, I received assistance which was essential to the completionof this dissertation from many talented and knowledgeable people. I am forever indebted tothem and I would like to take this opportunity to acknowledge their contribution.
I am blessed to have Prof. Yoji Takahashi as my advisor. Prof. Takahashi who provided
considerable insight into the role of cooperative delivery system in managing freight transport
in the central business district, who had the difficult task of encouraging me to work and think
independently, and introduced me to so many helpful colleagues, has been a major influence
to my academic career. His broad range of interests and knowledge makes him and excellent
scholar to consult on any research issues. He motivated me to use my imagination in
overcoming research difficulty, something I shall appreciate forever. Looking back, I realized
how truly fortunate I was to have such an excellent mentor and in the future, I won’t see
myself complaining if I receive continued advice from him for I always see myself as his
student.
It was also privileged to have Prof. Hirohito Kuse and Dr. Tetsuro Hyodo as thesis committee
members. They had provided very important contribution in shaping the direction of thisdissertation. Prof. Kuse, who kept asking me to the academic contribution of my work andwho would benefit from it had been steady and supportive in critiquing papers, helpful inclarifying ideas, and willing to entertain questions. Dr. Hyodo who had offered manysuggestions for the technical content and arrangement of this dissertation, had always been
open for consultation for any research issues.
Another scholar who was kind enough to share his knowledge and wisdom with me is Dr.
Hussein S. Lidasan of the University of the Philippines. Dr. Lidasan always mesmerizes mewith his ability to simplify research issues and he never failed to remind me to concentrate inproducing concept or model of the issue which was far more important than the calculation I
was spending most of my time on. I will treasure the advice he gave me and I hope tocontinue working with him even after I graduate. Dr. Jun T. Castro of Hiroshima University
contributes significantly to the order of the paper’s contents. He had been a great friend andhelpful in the discussion of research issues.
Life as doctoral student had been great and enjoyable partly because of friendly and lively
discussions with other doctoral students. I was fortunate for having Tatsumi Tokunaga andTakehiko Tsuboi as laboratory-mates who have complete dedication to their work. Tokunaga
who works for international consultancy firm had been kind to offer his experience which
contributes greatly to the paper while Tsuboi who works for Toshiba logistics had always
been ready for any logistics-related discussion.
As often said, one way of truly enjoying student life in a foreign land is by speaking thelanguage. I was fortunate to have some dedicated Japanese language teachers who were readyto help me in the best way they can: Maiko Funakoshi, Hiroko, and Kimiya sensei. I am
ashamed of Kimiya sensei for not having mastered the language despite his best effort.
Support provided by my tutors was also crucial to successful dealings in Japan. I would liketo thank them for their help that made my stay more comfortable: Dr. Jun Castro (2002),
Masamishi Nishiwaki (2002-2003), Yoshitaka Komatsu (2003-2004), Ayumu Yashiro (20042005), and Nobuyoki Matsuhashi (2005). Yoshitaka had been more than a tutor; he has been aiii

wonderful friend and I was really glad to know him. Those coffee sessions with him at Kanda
discussing anything that comes to mind in a hope to liberate self from the cage of researchand personal problems were better than any therapy. I bet I am going to have more adventureswith him for a long long time. Matsuhashi had to translate to Japanese every presentation I am
presenting to Japanese audience. It is hard to describe how grateful I was for their assistance.
Through out my life in the university, I am grateful of having a chance to met and worked
with wonderful students. Their dedication and friendship help created a good working
environment in our Laboratory; (2002) Keisuke Tanaka, Yasuku Yasuda; (2003) Masamaki
Nishiwaki, Namiko Takemoto, Mami Sakuta, and Kiyoaki Takamuri; (2004) Yoshitaka
Komatsu, Syuya Sasai, Takahiro Shimizu, Ryota Koike; (2005) Koota Maeda, Ayumu
Yashiro, Genki Tuchida, Megumi Ohwada; Sideney Schiener, Jr., Akio Ono, Kinoshita,
Kayoko Ito and Sakiko Suzuki; (2006) Jun Kamiya, Nobuyuki Matsuhashi, Tsukuba, Araki,
Nakagawa, Yoshimiya, Yuri Sato, Yukiko Furusawa. Jun Kamiya had to accompany me tohospitals, banks and other establishments which I was really grateful for. Kota Maeda was mytroubleshooter when I missed to renew my student VISA due to negligence. I thank all ofthem for the good memories!
My first encounter of VISSIM simulation was through Yuko Otsunomiya. She taught me thebasic operation of the software and for that, I am very grateful. Later on, Sideney Schiener, Jr.
demonstrated the proper use of the software which further enhances my understanding. I was
grateful to the assistance I received from both of them.
During my research regarding the goods flow of Marunouchi district in Tokyo, I worked
together with Ryota Koike and Namiko Takemoto and benefited from these collaborations.
Koike had been kind to share his knowledge of Simul8 software while Takemoto hadprovided some graphs of the survey result. My survey in Ortigas in Metro Manila wasfacilitated by Dr. Lidasan and Jim Madrigal. I sincerely appreciate the dedication of Jim toour survey which resulted in good data. Later, he helped me organizing the table of contentsof the dissertation and its final arrangement.
If the dissertation’s contents are consistent, readable and understandable, it is partly becauseof the help provided by my friends. Michelle Parumog, a doctoral student from Komamoto
University and a good friend of mine absorbed the burden of reading almost all the text of thepaper while Sahid Kamid, another good friend, edited and provided some very criticalsuggestions regarding the content of chapter 6 and Chapter 2. I thank both of them for theirassistance.
Finally, I would like to thank my family for their enduring support that kept those darkest
winters warm. Special thanks go to Kaka Nimbo and Kaka Datu for being wonderful brothers.
Kaka Nimbo has been the gauge for academic excellence while Kaka Datu has been like a
locomotive of the family that drives us through for the past years. I remember while I wasyoung, Papawa in his very own way made me aware of the importance of being ambitious andreadiness to bear difficulties to achieve bigger goal while Ina has always been a class on her
own way and made me deeply aware of the value of courage, determination and wonder.
Those memories always bring joy to me wherever I am.

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Contents
Abstracti

Acknowledgments

iii

Contents
List of Figures
List of Tables

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CHAPTER1
Introduction

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1.1 Background and problem description
1.2 Objectives of the study
1.3 Significance of the study
1.4 Scope of the study
1.5 Structure of the study
CHAPTER2
Traffic and environmental problems associated to freight transport in theCentral Business District (CBD)
2.1 Introduction
2.2 Trends and developments shaping urban freight transport
2.2.1 Changing society and its impact on freight transport
2.2.2 Information technology advancement
2.2.3 Shifting of vehicle types and changing structures of commercial
transport
2.2.4 Changing form of freight management
2.3 Problems associated to urban freight transport
2.3.1 Increasing volume of road freight
2.3.2 Deteriorating travel speed
2.3.3 Environmental problem
2.3.4 Low load factor
2.3.5 Rising of transportation cost
2.4 The Need for Responsive Policy Intervention for Freight Transport
Management in the CBD
2.5 Chapter summary
CHAPTER3
Policies and Initiatives on Urban Freight Transport around the world
3.1 Introduction
3.2 Urban freight policy trends
3.2.1 Interest groups
3.3 European transport policy and other related studies
3.4 Experiences in Europe

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FranceDemonstration project in Paris (Delivery depot ‘Magasin de
Quartier’)3.4.2 Germany
Demonstration project in Freiburg and Kassel (City Logistik)
3.4.3 Italy
Demonstration project in Genoa (Urban freight distribution called
M.E.R.Ci)
3.4.4 Netherlands
Demonstration project in Amsterdam (Logistics chain and modal
shift)
3.4.5 The UK
Demonstration project in York (Cyclone Courier)
3.5 Experiences in the USA
3.6 Experiences in Japan and some Asian countries
3.6.1 Japan
Demonstration projects in Japani)
Tenjin Joint Delivery Systemii)
Shinjuku Matenro Cooperative Delivery
iii)
Cooperative Delivery in Horidome
iv)
Textile Cooperative Delivery in Chojamachiv)
Akihabara TDM pilot projectvi)
Poppo Machida Freight Distribution Managementvii)
Kashiwa’s TDM experimentviii)
Sapporo city distribution experimentix)
Nihonbashi track time planx)
Saitama freight distribution to new core urban areaxi)Motomachi’s freight distribution strategy
xii)
Kichijouhi’s freight distribution scheme
3.6.2 Philippinesi)
Truck ban
3.6.3 Freight transport policies elsewhere in Asiai)
Truck ban in Indonesia, Korea and Thailand
ii)
Freight terminals
3.7 Comparative review of policies
3.8 Chapter summary
CHAPTER4
Cooperative Delivery System and other Transportation Demand Management(TDM) measures
4.1 Introduction
4.2 Freight transport policy goals
4.3 Diversity of key drivers for implementation of urban freight transport
policy4.4 Freight movement management
4.5 Policy options
4.6 Concept of Transportation Demand Management
4.6.1 Assessment to the suitability of each TDM measure to the
problems at the CBD
4.6.2 Review of the of the most common TDM
4.6.3 CDS and its potential to respond to the problem in the CBD
4.7 Concepts and Components of cooperative delivery system (CDS)
4.7.1 Cooperative delivery system (CDS) as transport instrumenti)
Objectives of CDS

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Different models of cooperative delivery system and its
applicationiii)
Different types of design of cooperative delivery system
4.7.2 Freight centers and its role to CDSi.)Freight centers classificationsii.)
Operational models of freight centersiii.)
Issues and concerns regarding freight centers
4.8 Chapter summary

CHAPTER5
Trends and Recent Developments of Central Business Districts in the world
5.1 Introduction
5.2 Central Business District
5.2.1 Early theories
5.2.2 Defining CBD
5.2.3 CBD and the role of cooperative delivery system
5.3 CBD and freight transport
5.3.1 CBD characteristicsi)
CBD’s common forms and functionsii)
Employment densityiii)
Building use and trip pattern
5.3.2 Freight transport characteristicsi)
Parking supply
ii)
Share of freight vehicles
5.4 Characterizing central business district
5.4.1 Transition from low density of high density
5.4.2 Framework for analysis
5.4.3 Some CBD profilesi.)
Low Density CBDs
ii.)
High density CBDs
5.5 Chapter summary
CHAPTER6
Application of Cooperative Delivery System to Low Density Central BusinessDistrict: Case of Developing Country
6.1 Introduction
6.2 Ortigas Center
6.2.1 Transportation system and freight transport
6.2.2 Transportation problems associated to shopping centers in the CBD
6.3 Data Collection
6.3.1 Brief background of two shopping centers in the CBD
6.3.2 Survey
6.3.2.1 Survey design
6.3.2.2 Survey implementation
6.3.2.3 Findings from the survey
6.3.2.4 Data analysis and output
6.4 Simulation Analysis for traffic improvement
6.4.1 VISSIM simulation software
6.4.2 Alternatives considered for traffic improvement
6.4.3 Route for the cooperative delivery
6.4.4 Validation of simulation result
6.5 Results of the simulation analysis

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6.5.2

Traffic impact
Environmental impact
6.5.2.1 Emission Factors
6.5.2.2 Amount of emission from each mode (simulated)
6.5.2.3 Impact of the Cooperative Delivery System
6.5.3 Travel speed improvement
6.6 Role of CDS to the transformation of the CBD from low density to highdensity
6.7 Chapter summary
CHAPTER7
Application of Cooperative Delivery System to Low Density Central BusinessDistrict: Case of Developed Country
7.1 Introduction
7.2 The social experiment7.2.1 Location of Marunouchi district
7.2.2 Background of the social experiment7.2.3 Outline of the social experiment7.2.4 Properties of the buildings under the experiment
7.3 Result of the social experiment7.3.1 Observed behavior of cooperative delivery system
7.3.2 Number of trips and freight attracted to the four (4) buildings
7.3.3 Comparison of delivery style before and during the experiment7.3.4 Analysis on horizontal cooperative delivery and parking
management
7.3.5 Setting up the attraction units of the four buildings
7.3.6 Estimating freight vehicles’ trips to the district
7.3.7 Distribution of trip attraction to the district
7.3.8 Distances between buildings and from stock point
7.3.9 Situation at the (off) loading area with and without cooperative
delivery system
7.3.10 Comparison of the number of trips and freight with and without
CDS
7.4 Simulation modeling7.4.1 About SIMUL8
7.4.2 Properties of the simulation model
7.4.3 Setting-up the arrival distribution of freight vehicles
7.4.4 Results from the simulation analysis
7.4.5 Introduction of cooperative delivery system
7.4.5.1 Impact of CDS on the number of waiting trucks and waitingtime depending on the number of trips covered by CDS
7.4.5.2 Impact of CDS on the number of waiting trucks and waitingtime when the share of retail space increases
7.4.6 Relationship of buildings characteristics and the simulation result
7.4.7 Distribution of number of waiting trucks and their waiting time
among the buildings
7.4.8 Reduction of trips
7.4.9 Effect of the reduced trips to traffic volume in the area
7.4.10 Reduction of environmental emissions
7.4.11 Reduction of working time
7.5 Comparison of CDS impact between CBD of Metro Manila and CBD ofTokyo
7.6 Chapter summary

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CHAPTER8
Application of Cooperative Delivery System to High Density Central BusinessDistrict: Case of Developed Country
8.1 Introduction
8.2 Urban goods (off) loading process
8.3 Common problems related to (off) loading activities
8.4 Improvement measures for urban goods
8.5 Application of the measures to improve goods delivery
8.6 Method of Data Collection
8.6.1 Data of freight vehicles’ arrival
8.6.2 Description of the surveillance system
8.6.3 How does it work to trace the delivery floor of goods
8.6.4 Results of PEAMON monitoring
8.6.5 Survey summary
8.7 Simulation modeling8.7.1 Process of building simulation model
8.7.2 Description of the ProModel
8.7.3 Data fitting
8.7.4 Computation of working time
8.7.5 Properties of the simulation model
8.7.6 Properties of the building
8.7.7 Results
8.7.8 Model validation
8.8 Discussion of results
8.9 Comparison of time saved from Vertical CDS, Horizontal CDS and the
combination of Horizontal and Vertical CDSChapter summary
8.9.1 Data requirements
8.9.2 Simulation results
8.9.3 Selecting which of CDS design is adopted based on CBD’s building
density and building use
8.10 Chapter summary

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CHAPTER9
Conclusion and Future Research204

9.1 Introduction
9.2 Summary
9.3 Conclusions
9.4 Recommendations
9.5 Future research direction

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Appendices
Appendix 1
Appendix 2
Appendix 3
Appendix 4
Appendix 5a
Appendix 5b

Robinson’s Galleria survey communication letter(Manila Case)
SM Megamall survey communication letter (Manila Case)
Transit survey form (Manila Case)
Vehicular volume survey form (Manila Case)
Goods Delivery survey form A (Manila Case)
Goods Delivery survey form B (Manila Case)

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List of Figures
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Title
Structure of the dissertation
Process of the study
ICT and logistics system
Road freight growth in the US and EU-15
Performance by mode in transport of goods in Japan (ton-kilometer)
Growth in freight transport and GDP in EU-15
Peak-hour traffic speed in Japan
Average traffic speeds in Greater London
CO2 share of each transportation mode
Load factor in Japan by weightTransportation cost in Japan
Impact of urban freight transport
Freight transport policy trends
Bus lane delivery area under Mobilien program in Paris
Protection to the city centre leads to the introduction of depot where
goods are consolidated and delivered by non-polluting vehicles
Stakeholders and topics for discussion in the FQP
Cyclone courier
Map of Bristol city
Delivery vehicle and key persons of the scheme
Plan of distribution business centers for Tokyo Metropolitan Area
Number of goods handled by Matenro cooperative from 1994-2000
Provision of (off) loading area near Kashiwa station
The thoroughfares where the improved delivery of goods scheme are
implemented
Photo of the district before and after the scheme
Location of vendor campus which serves as distribution center
Change of number of cars parked in the street
Change of parking time
Truck ban routes of Metro Manila
Truck registration after the truck ban implementation
Concept of TDM
Congestion level at the CBD
Freight distribution in the CBD with and without CDS
Freight distribution system and location of the depot
UDC and depot
Different models of cooperative system
Design of Horizontal CDS
Design of Vertical CDS
Design of combination of Horizontal and Vertical CDS
Types of Urban Distribution Centers
Central business district’s model of Burgess
Sector and Nuclei Models
Graphic representation of Horwood and Boyce’s CBD core-frame
differentiation with selected functional centers and principle goods
flow

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Title
Illustration of CBD functions and depot of CDS
The Core CBD of Paris appears flat while the Manhattan of New
York is populated with skyscrapers
Job density inside and outside the CBD
CBD parking spaces per 1000 jobs, 1990
Share of Freight Vehicles to the total Registered Vehicles
Transition of CBD from low density to high density
Framework for analysis
Map of the Ortigas center showing its transportation network
Average Land values of Makati CBD and Ortigas Center
Development plan of Pudong in Shanghai
The Lujiazui Finance and Trade Zone in New Pudong Area
Hong Kong’s CBD contains high rise buildings
The master plan of Canary Wharf business district in London
Map of Ortigas CBD showing major industries and shopping centers
Typical traffic congestion scenario at the shopping center’s accessroad and immediate environment
Distribution of space usage
Robinson’s Galleria showing delivery/receiving areas
SM Megamall showing the delivery/receiving areas
SM Megamall, claimed to be one of the biggest shopping centers inAsia located along EDSA
Locations of surveyors
Illustration of typical van used for goods’ distribution
Types of goods delivery in the shopping center
Quick facts to surveyed shopping centers and other shopping centers
in Metro Manila
Screenshot of the simulation network with map background
Time window
Truck routes with and without CDS
Comparison of survey count and simulation count
Reduction result of CDS to total traffic
Amount of pollutant emissions by mode (present situation)
Emission share by transport mode
Reduction of pollutant emissions derived from cooperative delivery
systemPollutant emissions appear to increase under the Time window
scheme as the share of trucks reaches 10%
Travel speed
Travel times of vehicles
Map of Tokyo showing the location of Marunouchi district
Supply Volume of Large-scale Buildings by Area in Tokyo
Tokyo’s 3 central wards and 23 wards ratio of functions to Japan
Map of Marunouchi District (Building 6, 7, 8, and 9 were covered by
the experiment)
Horizontal Cooperative Delivery System
Delivery pattern before and after the experiment
Change of the number of truck parking
Simulation cases
Screenshot of the simulation model
Distribution pattern
Average waiting trucks and average waiting time of trucks in the
district depending on the trips covered by the CDS

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7-13
7-14
8-1
8-2
8-3
8-4
8-5
8-6
8-7
8-8
8-9
8-10
8-11
8-12
8-13
8-14
8-15
8-16
8-17
8-18

Title
Number of trucks did not avail parking space inside the building after
5PM
Average number of waiting trucks with and without cooperative
delivery system as the share of retail space increases
Average waiting time of each truck in the district with and without
cooperative delivery system as the share of retail space increases
Delivery and unloading activities
Time-sharing of elevators
Time-sharing of parking spaces
Parking Accumulation by Land Use by Time of the Day
Coordinated delivery inside the building
PEAMON model and sequential installations of power antenna
2/19 PEAMON monitoring result
2/20 PEAMON monitoring result
2/21 PEAMON monitoring result
Process of building simulation model
Fitted arrival distribution
Diagram of different time calculated to attain working time
Case of a 15-story building
Case of a 20-story building
Case of a 30-story building
Case of a 40-story building
Average waiting time of trucks based on which CDS is adopted
Time saved from three types of CDS for an office-oriented CBD

xii

Page
158
168
159
169
172
172
173
174
177
178
180
182
185
187
188
190
191
192
193
196
201

List of Tables
Table No.
3-1
3-2
3-3
3-4
3-5
3-6
4-1
4-2
4-3
4-4
4-5
4-6
5-1
5-2
5-3
6-1
6-2
6-3
6-4
6-5
6-6
6-7
6-8
6-9
6-10
6-11
6-12
6-13
6-14
7-1
7-2
7-3
7-4
7-5
7-6
7-7
7-8
7-9
7-10
7-11
7-12
7-13

Title
Main interest group in regard of urban goods transport
Effect of city logistics
Time zone for each type of vehicle
Distribution of truck fleet
Differences in polices among countries
Urban Freight Transport Project Research authorized by European
Commission
Key-factors in the implementation of measures regarding urban freighttransport
Problems and developments in the freight transport sector
TDM measures for freight transport and their objectives
Existing public distribution facilities in Tokyo Metropolitan Region
Classification of freight platforms (BESTUFS, 2002)
Urban distribution center models
CBD and transport variables
Trip attraction of space use in a building per 1000 sq meter
Characteristics of CBD
Number of parking space
Data recorded by surveyors at SM Megamall
Types of delivered goods
Parking time of trucks
Traffic composition
Public Utility Vehicles (PUVs) in Metro Manila
Data input
Comparison of number of trucks with and without CDS
Difference between survey count and simulation count per vehicle
Emission factors (g/km)
Emission factors for utility vehicles and motorcycles (g/km)
Proportion of gasoline and diesel vehicle registration in Metro Manila(1981- 2004)
Proportion of gasoline and diesel vehicles used in the computation ofemissionsComparison of environmental impacts of CDS and Time Window
Policy Instruments of the Experiment
Properties of Buildings Covered by the Experiment
Number of Trips and Freight into the Buildings under the ExperimentDistribution of trips within a day
Measures of observation
Share of participants in the experiment
The number of Trucks that Transshipped Freights at SP
Attraction unit of trips and freight
Distribution of trips and freight’s to the buildings in the district
Distribution of trips and freight to the buildings in a day
Distance of the buildings from the stock point
Distances between the buildings in the district
Total trips and freight in the Marunouchi bldg without CDS

xiii

Page
21
25
43
50
55
56
65
67
71
81
81
84
95
97
100
117
118
119
119
120
123
125
126
128
129
130
130
131
134
144
144
145
145
147
147
147
148
149
149
150
150
152

Table No.
7-14
7-15
7-16
7-17
7-18
7-19
7-20
7-21
7-22
7-23
7-24
8-1
8-2
8-3
8-4
8-5
8-6
8-7
8-8
8-9
8-10
8-11
8-12
8-13
8-14
8-15
8-16
8-17
8-18
8-19
8-20
8-21
8-22
8-23
8-24
8-25

Title

Page

Number of trips and freight when the CDS covers 20% of trips to the
Marunouchi bldg
Number of trips and freight when all trips to Marunouchi bldg iscovered by the CDS
Properties of the simulation model
Properties of the buildings in the district
Comparison of average number of trucks in queue, their waiting time,
and total number of trucks in the district with and without CDS
Waiting time of truck vehicles to each building without CDS
Comparison of groups of buildings based on available freight elevator
Distribution of the number of waiting trucks and their waiting time
among the buildings
Trips total with and without CDS
Environmental emissions
Changes derived from CDS
Problems and countermeasures of (off) loading activity
Improvement policy for urban goods movement
Properties of the four buildings
No. of truck trips and goods delivered
Example of station ID and signal strength
Staff1’s route
Staff2’s route
Staff1’s route
Staff2’s route
Staff1’s route
Staff2’s route
Averages observed from the survey
Distributions Fit
Input data of the simulation
Variations of number of goods and time interval between trips
Comparison of surveyed data and Simulation countComparison of surveyed time and simulation-generated time
Perceived suitable number of goods per truck based on the capability ofthe two staffs
Properties of the buildings
Number of trips without CDS and under Vertical CDS
Number of trips and goods under Horizontal CDS and combination of
Horizontal and Vertical CDS
Comparison of Without CDS scheme and combination of Horizontal
and Vertical CDS scheme
Comparison of Without CDS scheme and Vertical CDS scheme
Comparison of Without CDS scheme and Horizontal CDS scheme
Total delivery time from each scheme

152

xiv

152
154
155
156
160
161
162
163
164
165
170
171
175
176
177
179
179
180
181
182
183
184
186
189
189
193
194
194
195
195
196
197
198
199
200

CHAPTER

1Introduction
1.1

Background and problem description

There is a strong pressure on the policy-makers to address the mobility needs of urban
freight in ways which are economically, socially, and environmentally acceptable.
Efficient freight transportation has been the goal of every urban center in view ofsustaining the economic competitiveness of the area and maintaining or improving thequality of life it offers. Formulating a policy measure that would embrace theeconomic, social and environmental elements requires fundamental understanding on
the complex nature of urban freight transport. This appears to be a difficult goal andyet, the only option in view of sustainable development.
The substantial efforts poured on managing freight transport at the urban centers suchas construction of distribution centers and application of Intelligent Transportation
System (ITS) and Information Communication and Technology (ICT), have yielded
notable result but still far from desirable. Travel speed of vehicles continues to decline
and so do environmental desirability. Moreover, there is a general acknowledgementthat moving goods in urban areas is increasingly becoming difficult. Access of trucks
to customers in the inner part of the city is seriously hampered by congestion. Trucks
delivering goods are often having problems on locating (un) loading areas. Lack ofrelated freight transport facilities such as freight elevators are often limited if not
missing, which reduces the efficiency of delivery chain. The ability to expand
transport capacity is seriously hampered by the lack of resources. Authorities on theother hand are responding by imposing restrictions that limit the access of trucks outof belief that trucks are main source of the transport problems.
Evidently, there is a need for policy intervention to reduce the negative impacts oftrucks without harming its financial viability and service performance. This study isabout exploring the potential of cooperative delivery system (CDS) in meeting
efficient freight transport.

1.2

Objectives of the study

The goal of the study is twofold, 1) to evaluate the applicability of CDS to different
types of CBD and 2) to evaluate quantitatively traffic and environmental impacts of
CDS in the CBD using empirical data. For the specific objectives of the study, these
are listed below:To review the current development and practices of cooperative delivery
system and the factors for the implementation of such measure.

1

1.3



To clarify transportation conditions in urban areas and review the urban
freight transport policies in selected developed and developing countries.



To provide guidelines of which, CDS design is suitable for applications basedon the CBD’s characteristics and right timing of measure application.

To assess the roles of CDS as the CBD transform from low density to highdensity structure.

To evaluate the impacts of cooperative delivery system to the traffic flow in
CBD road networks and also to the improvement of urban environment.

Significance of the study

Foremost, the study would advance our understanding to the role of CDS in managing
freight transport in the CBD. CDS has been known to have potential to respond to theemerging problem regarding freight distribution in the CBD; however, there is lack ofenough evidence if its result can be both socially and economically acceptable.
Moreover, there are many types of CDS design, i.e. Horizontal CDS, Vertical CDS
and the combination of Horizontal and Vertical CDS, and yet there are few available
materials which provide hint on when to apply each design to maximize its potential.
Selecting suitable measure to a given problem is also very important in view that
unsuitable measures might deteriorate the problem when applied. There might besome typical characteristics of CBD which could signal to the right circumstances toapply CDS where benefits in terms of economic and social perspective are maximized.
In essence, this study aims to be a guideline for the application of CDS in the CBD.

1.4

Scope of the study

The scope of this study is mainly limited to CDS and its application to office-oriented
CBD. It has not been tested to other areas such as residential zone where presence ofhigh number of trucks is not desirable. CDS can be an influential tool in achieving
this goal and therefore it would be of interest to evaluate its applicability and impact
as well.
Moreover, the case studies are limited to the CBD of Tokyo and Metro Manila. The
aim is to make the CBD of Metro Manila representative of low density CBD in thedeveloping regions which are characterized by few share of trucks and saturated
transportation network while the CBD of Tokyo as representative of developing
regions with are characterized by high share of trucks and efficient public
transportation network.

2

1.5

Structure of the study

Position of cooperative
delivery system among
TDM measures

Chapter 2

Chapter 3

Chapter 4

Review of CBDdevelopment
Chapter 5

CDS in low density CBD
in developing country

CDS in low density CBD
in developed country

Chapter 6

Chapter 7

CDS in high density
CBD in developed
country
Chapter 8

Enumerated the findingsof the study
Chapter 9

Figure 1-1. Structure of the dissertation

3

Part II

Government freighttransport policies and
TDM examples

Part III

Major development in
Logistics andtransportation problems

Part IV

Chapter 1

Part VThe framework of theresearch is definedPart IThe study has four parts. Part I sets the framework of the study, i.e. objectives wereset, method of analysis is defined and output of the research is partly revealed (Figure1). Part II reviews the recent developments in urban freight transport and the impactof these changes to both the way freight is manage and to the urban environment.
Also given lengthy discussion in this section is the policies taken by different cities as
response to the growing challenges of managing city distribution. Policy measuresthat produces significant benefits such as cooperative delivery system, road pricing,
ITS and ICT implementation were also reviewed.

The Part III of the study characterizes central business district in selected cities whichlead to identify distinguished features of those CBD in developed countries and those
CBD in developing countries. The study area is also defined in this part. The
evaluation of the impact of CDS to mitigate the growing problems in the urban center,in particular in the CBD, composes Part IV of the study. CDS’s impact was firstassessed in a CBD of a developing country. This assessment also takes into accountfurther development in the district which could lead to high number of trucks. After
quantifying the impact of CDS to the transport environment of CBD in developing
country, the application of this measure is shifted to a CBD in developed country.
Comparison of CDS results between CBD of Manila and Tokyo is also presented
which highlights the notable traffic and environmental reduction in Tokyo while very
limited for the case in Manila. Moreover, the guidelines in selecting which CDS
design is adopted and when it is adopted based on the characteristics of the CBD arepresented here. Part V of the study contains significant findings of the study.
The flow of the study on the other hand begins at reviewing the problems of urban
freight transport (Figure 1-2). After the review, the study visited the policy focus ofgovernments around the world as a response to the growing problems identified
before. This review is coupled with some applications of TDM measures to local
cities.
Reviews of available measures normally applied to mitigate the problems of urban
freight transport are then discussed. This review both discussed hard (infrastructurebased measures) and soft measures (TDM-based measures). The study then identified
cooperative delivery system as promising measure to confront the problems of freighttransport in the CBD. Different characteristics of CBD which the CDS will be applied
are then discussed. Four groups of CBD emerged from this grouping which are: 1)
low density CBD in developing countries, 2) high density CBD in developing
countries, 3) low density CBD in developed countries, and 4) high density CBD indeveloped countries. After this grouping, the application of cooperative delivery
system of the different groups of CBD is conducted and some comparison of results is
made.

4

Identification of freight transport
problems in the CBD

Government policies andexamples of TDM application

Available measures to confront
the problems
Cooperative delivery system as
powerful tool to respond to the
problems

Grouping of central business
district and role of CDS
Simulation analysis
Application of cooperative
delivery system to different
CBDs

Comparison of the impacts ofcooperative delivery system todifferent CBDs

Figure 1-2. Process of the study5CHAPTER

2

Traffic and Environmental
Problems Associated to FreightTransport in the CBD
2.1

Introduction

The quality of urban environment is largely influenced by freight transportation invarious ways and means. These factors may be felt in terms of traffic and vehicle
emissions and degradation of renewable and non-renewable capital resources. In
general, traffic, emission and capital resources are three main actors in shaping thebuilt environment towards sustainable urban system.
The availability of freight transport related facilities such as (off) loading areas,
exclusive parking space for trucks, freight elevators coupled with favorable policies
toward trucks and its governing environment (i.e. exclusive lanes for trucks andprotection of reserved spaces for delivery vehicles) are potential elements to ensure
the realization of efficient freight transportation.
This chapter describes current trends and developments that shape the freighttransport industry. It argues that the current changes to customers’ behavior along the
expansion of the market and advancement in the field of technology are major factorsthat overhauled the management of urban freight transport. The contents of thischapter are mainly sourced from existing materials and to some extent from Internet.
There are five (5) sub-sections comprising this chapter. Section 2.2, which has four (4)sub-sections, traces the trends and developments affecting urban freight transportation.
Under Section 2.2, Section 2.2.1 articulated the changing customer behaviors that leadto the change of management style of freight transport service providers. Section 2.2.2
explored the advancement in the field of information technology that simplified theconnection between suppliers and the customers. Section 2.2.3 discussed the vehicle
type shift by the freight distributors to transport goods in urban areas while Section
2.2.4 briefly tackled the current freight transport management principles andapproaches. The problems associated to urban freight transport are enumerated in
Section 2.3. This section is subdivided into five (5) sub-sections. The continuous
growth of road freight transport share is highlighted in Section 2.3.1 while Section
2.3.2 illustrates the speed characteristics of vehicles in the urban areas, as affected byfreight transportation. Section 2.3.3 provides critical discussion on the environmental
problems, particularly on the different hazardous gases, as well as particulate matters
due to transport related activities, where freight transportation has significant share.

6

This section also discusses transport modal share in terms of emission. Section 2.3.4
traces the continued decline of load factor of trucks while Section 2.3.5 shows theincreasing cost in delivering freight transport goods to urban areas. Section 2.4
emphasized the necessity to adopt freight transport management as countermeasure to
the pressing problems associated to freight transportation. Finally, Section 2.5
concludes the chapter.

2.2

Trends and Developments shaping urban freight transport

2.2.1

Dynamic society and its impact on freight transport

The massive development of Information and Communication Technology (ICT)industry connects customers to variety of choices in the market. This customers’
behavior known as e-shopping (electronic shopping through the internet) has lead tomore goods delivery to village homes - attracting substantial amount of truck traffic.
Added to this is the dramatic drop of ICT service cost, hence private firms and thegeneral consumers exploited the situation, thereby influenced the urban logistics
system (Nemoto et el, 2001).
An A.T. Kearney/ELA ‘Insight to Impact’ (1999) study quoted by City Freight (2002)
study identified customer sophistication as a major driver of development in logistics
and supply chain systems. The report highlights that customers are demanding greater
product variety/assortment, more and tailored value-added services, custom/tailoredproducts, shorter response times and greater accuracy and support throughout the
product’s life cycle. This service-oriented demand is a potential factor to affect thelevel of use of freight systems, thereby more stress on the transportation network.
In Germany, for example, the amount of food consumed has not grown so much in thelast three decades, but food transport (in tone/km per capita) almost doubled. The
reasons include customer preferences for food from other countries, transport policies,
and production patterns of the food industry and the policies and location of retailers(FAW, 2000). This trend in one way or another shapes the flow of services across theexisting freight transport systems, the built transport network and more importantly,
the environment.
2.2.2

Advancement of information technology

The advancement in ICT in both fronts – hardware and software – put firms into
position to developed system architectures that have enormous capacity. Real-time
positioning of trucks can now be done with the aid of Global Position Systems (GPS)that could enhance urban freight networks and freight operation. Firms are using
Internet as a medium to reach more customers and showcase their services. In
addition, this technology allows different firms to share real-time data, thereby
opening the possibility for partnership through fleet sharing, monitoring support
systems (e.g. GPS stations and the like) and infrastructure improvements, among
others.
Internet on the other hand gives people variety of choices beyond their geographicalborders. There is a growing trend wherein customers demand for quick delivery and7high quality of service, which pushes the freight transport sector to improve theirtraditional freight systems. These phenomena translated into more trips in order tomeet the quick time requirement of customers at the expense of low load factor andbackhauling. Consequently, reactive service due to real-time demands increased the
distance traveled by delivery vehicles.
E-commerce (transaction), e-logistics (cargo), and e-fleet management (vehicle anddriver) have taken freight transport and logistics management into another level(Figure 2-1). With the aid of ITS, trucks and cargoes can be traced in real time.
Apparent benefits from this system can be manifested in trucks receiving real-timeadvises to avoid certain congested roads.

B2B, B2C
transaction(e-commerce)Internet
S2L, L2L
transaction(e-logistics)

Note
B – businessS – shippers
L – logistics service
providers
ITS – Intelligent
transportation
systemITS
Logistics operation(e-fleet management)

Figure 2-1. ICT and logistics system
Source: Logistics development supported by ICT & ITS in the Asia-Pacific Region,
2003
2.2.3

Shifting of vehicle types and changing structures of commercial transport

Parallel to the above developments in the freight transport industry is the rush on the
fleet and cargo management to cater strong customer demands. One of the quick
responses being adopted is the use of small trucks in city freight distribution –delivering goods in small quantities or volumes. The rate of proliferation of small
trucks in the goods delivery level is caused by several factors, which includes a)changes in the modes of conduct of business and b) government policies.
There is a management philosophy called “Just In Time” (JIT), where in terms oflogistics and production, a certain amount of product is shipped in total time precision,
which basically requires the type of vehicle with high maneuvering capability incongested urban networks. The system hopes to serve customers with demands for
Quick-Response (QR) system to receive product orders. But unfortunately, the same
customers are usually found to have lack of parking areas for use by the delivery
firms. To address this situation, the freight forwarders/forwarders opted to use small

8

delivery trucks due to its maneuvering capability and less requirement for parking
space. This strategy is somehow favored by the present condition of urban centers,
which are usually served by small-scale transport networks.
Similarly, government policies also contribute to this scenario. For instance, a truck
control measures like truck ban along a congested road would bring changes on how
the freight operators would conduct their business. Trucking companies, for instance,
in Metro Manila chose to shift from large to small trucks (less than 4 tons) after the
implementation of the truck ban in its main thoroughfare (Castro, 2004).
Also, in cities where historic places are treasured, both the engine and weight of the
distribution truck has to meet the standards set by the local government as has been
seen in some cities in Italy (see freight transport policy report for Italy in Chapter 4
for detail).
2.2.4

Changing form of freight transport management

There is also a big shift from old form of management among the key players infreight transport. New management principles and approaches like JIT, QR, Lead
Time Management, Time Compression, Lean Logistics, and Efficient Consumer
Response have surfaced in the freight transportation industry, which helps firms
maximize their logistics operation.
1 800

USA

1000 milliom tonne/km

1 600
1 400
1 200

EU-15

1 000
800
600
400
200
0
1970

1980

1990

1991

1995

1997

1998

1999

2000

2001

Year

Figure 2-2. Road freight growth in the US and EU-15
Source: EU Statistical Pocketbook, 2000
(http://europa.eu.int/comm/dgs/energy_transport/figures/pocketbook/2004_en.htm)

2.3 Problems associated to urban freight transport
2.3.1

Increasing volume of road freight

The trends and developments shaping freight transport industry have great impact tothe flow of freight traffic in urban areas. One trend that can be commonly observed
among the developed countries is the continuous rise of road freight that puts
9

tremendous pressure to the urban network (Figure 2-2 and Figure 2-3). Developing
countries are believed to be on the same path of freight situation, although the lack ofdata would make it hard to estimate the pace of the trend over the past years.
Japan faces a situation where road freight’s share is increasing, while the share ofother two modes’ (shipping and rail) is declining (Figure 2-3). Clearly, there is a needto transfer some share of freight transport from road to other modes of transport to
reduce reliance on the road-based network, which usually induces further congestion.
The Japanese government is trying to rectify this mode ratio by launching modal shiftprogram in 2002 where incentives are given to firms that are considering a plan toutilize other modes except trucks. Also in the interest of this program is to curve
greenhouse gases sourced from trucks in line with the government’s commitment to
the Kyoto protocol.
Ideally, a modal migration from truck transport in shipping is preferable due to factors
such as energy efficiency and reduced carbon-dioxide emission per unit of transport.
But the capacity of the railroad network for freights in Japan is extremely limited
because most of it has been utilized by passengers and there are only very little
opportunity for time windows for freights (Takahashi, 2005).
350

Road
307

300

313

313

242

244

22

22

295
274

250

245

Shipping
238

229

Volume (billion)

222
206

200
184

179

150
130
100

50

47

Rail

37
22

27

25

23

0
1975

1980

1985

1990

1995

1999

2000

2001

Y ear

Figure 2-3. Performance by mode in transport of goods in Japan (ton-kilometer)
Source: MLIT data available at www.mlit.go.jp with the filename y1204000.xls
In Europe, the growth of freight transport overtook the growth of EU’s GDP (Figure2-4). This growth is largely due to the creation of single European market supported
by globalization and advancement of information and communication technology.
The recent expansion of EU to eastern part of Europe would further increase thefreight transport. It must be emphasized that amidst this freight growth is the potential
increase of road freight share in the overall network traffic.
10

Passengers, Goods, GDP
1990-2002
135
130
125

1990=100

120
115
110
105
100
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Passengers (1) (pkm)
Goods (2) (tkm)
GDP (at constant 1995 prices)

Notes:(1): passenger cars, buses & coaches, tram+metro, railways, air(2): road, rail, inland waterways, pipelines, sea (intra-EU + domestic)
Annual Growth Rates EU-15 (% change per year)
1980-1990

1991- 2001

2001- 2002

GDP (real growth)

2.3

2.1

1.0

Industrial production

1.8

1.5

-0.8

Passenger transport

3.0

1.8

1.3

1.6

2.5

0.8

pkm ( 5 modes )Freight transport tkm ( 5modes )

Figure 2-4. Growth in freight transport and GDP in EU-15
Source: EU Statistical Pocketbook, 2000
(http://europa.eu.int/comm/dgs/energy_transport/figures/pocketbook/2004_en.htm)

Like in Japan, policy makers in the European community are also trying to address
the same form of problem regarding the unbalanced ratio of different modes in freighttransportation. The modal shift initiative known as Marco Polo, which aims tomaintain the traffic share between the various transport modes for the year 2010 at its
1998 level was launched in July 2003. The key idea of this policy is to eradicate long11

distance freight transportation by trucks to other less harmful modes of transportation
to environment.
2.3.2

Deteriorating travel speed

Serious traffic congestion brings inefficiency to the road network as well as negative
impacts to the movement of goods. In Tokyo, like any other large urban areas,indication of worsening traffic condition is manifested by the continuing decline oftravel speed (Figure 2-5). The average travel speed in inner Tokyo was 22.5 km/hr in1980 and declined by 17% in 1997 (see Chapter 5 for the average speed of otherurban areas). It is believed that the share of trucks to the total vehicles on the road ofTokyo is quite high at 45% (Takahashi, 2001, Cox, 2001). This indicates that trucks
have substantial contribution to the serious traffic congestion.
For the purpose of presenting comparative data regarding congestion problem, we
may take a look the average travel of speed in London as shown in Figure 2-6. The
trend is identical to that of Tokyo where the travel speed continues to drop down.
London’s Department for Transport tries to mitigate the serious problem of traffic
congestion by implementing congestion charging where vehicles entering thecharging zone (inner part of London) are charged for certain price. The idea of thescheme is to reduce the number of vehicles by imposing road-use prices.

45
40

1980

39.2

1997

1999

35.2 35
35

32.7

km/hr

30

26.1

25

25
22.5

21
18.5

20

21

17.5

15
10
5
0
National average

Tokyo metropolitan
area

Inner-Tokyo (23w ards)Three metropolitan
areas

Target at the
beginning of 21st
century

Figure 2-5. Peak-hour traffic speed in Japan
Source: Logistics Developments Supported by ICT & ITS in the Asia-Pacific Region,
2003

12

30.0

25.0

Daytim e off-peak
24.6

Traffic speed (km/hr)

21.9
20.0

21.4

22.7
Morning peak
20.0

22.0

20.9

20.0

19.5

19.6

15.0

18.3

19.3
17.9

17.7

17.4

Evening peak period
10.0

5.0

0.0
1977-1982

1983-1990

1990-1997

1997-2000

2000-2003

Year

Figure 2-6 Average traffic speeds in Greater London
Source: TfL (http://www.tfl.gov.uk/tfl/ltr2003/road-related-trends-2.shtml)

2.3.3

Environmental problem

Among various urban environmental issues, addressing air pollution is a major
challenge for many cities in East Asia. Public concerns over air quality increases withthe rising standard of living, as it exhibits direct risks to human health. The major
sources of air pollution in large cities are automobiles, although there are otherindustrial-based sources such as factories. Air pollution caused by traffic is mostnotable and serious in mega-cities in which the number of vehicles is increasing fasterthan the pace of population growth (Dhakal, 2003).
As mentioned above, although environmental problem comes from many sources
such as factories, commercial establishments, transport sector is believed to have thegreat share on the said problem. In Japan, for instance, 21% of CO 2 comes fromtransportation. Furthermore, of all the carbon dioxide coming from transportation,
18% are from trucks (Figure 2-7). In the UK, it was estimated that road traffic is
responsible for at least 90%, 20% and 49% of the overall levels of CO, CO2, andNOx gas respectively (Sumalee, 2004).
Dhakal (2003) stated that CO 2 emissions in Tokyo has increased more than two times
in the last three decades with 2.5 % annual average growth rate (1970-1998). During
the last 20 years, the ratio of diesel vehicles to all cargo vehicles in Tokyo has
increased from 22% to 61% wherein it is believed that 70% of CO 2 that came fromtransportation is sourced from diesel vehicles. In addition, almost all SPM fromtransportation is believed to come from diesel vehicles. This chemical, SPM, isknown to have harmful effects on human health, damages respiratory organs andcontributes to lung cancer.

13

Production
process
4%

generation
7%

Air Rail
4% 3%

disposal
2%business
12%

Coastal ship
6% Bus
2% Taxi
2%
Business truck
16%

transportation
21%
households
13%

Private truck
12%

Passenger car
55%

industry
41%

Figure 2-7. CO 2 share of each transportation mode
Source: Takahashi, 2005

2.3.4

Low load factor

As mentioned, the changes in the management of freight transport, as response to thechanging customer behavior is remarkable. Freight transport service providers have todeliver small quantity of goods to meet the customers’ demand (Figure 2-8).
Customers are becoming more demanding to have their goods delivered in shortperiod of time, translating into many delivery vehicles on the road.
40
35

33.7

30
Percentage (%)

26.2
25

23.1

22

20

18
15.2

14.9

14.8

1995

1996

1997

15
10
5
0
1970

1975

1980

1985

1990

Figure 2-8. Load factor in Japan by weightSource: Tokyo Metropolitan Government (http://www.kankyo.metro.tokyo.jp/)

14

2.3.5

Rising of transport cost

The escalating congestion in the road network contributes greatly to the rise oftransportation cost. For instance, it was reported that Federal Express claims that it
costs 40% more to deliver in New York City (NYC) than in other comparable
locations because of the serious traffic congestion. Similarly, United Parcel Service
states that it costs them $0.40 more to deliver a package in NYC than in Atlanta,
Georgia resulting in an extra $50 per day per driver in NYC (Paaswell and Petretta,
1993).
The rising use of multiple (small-sized) vehicles by the freight companies not only
contributes to the increase of goods transportation cost but also traffic congestion,
more so on the built environment.

100%
90%
80%

32

21

16

20

62

59

1995

1998

46

70%
60%
Share

23

18

50%
40%

20

30%
20%

50
35

10%
0%
1975

1985
Transportation cost

Storage cost

OthersFigure 2-9. Transportation cost in Japan
Source: Tokyo Metropolitan Government (http://www.kankyo.metro.tokyo.jp/)

2.4 The Need for Responsive Policy Intervention for Freight Transport
Management in the CBD
It becomes apparent that there is a need to form schemes to manage the distribution of
freight in view of several arising problems. Volume of road freight is expected tocontinue due to the lack of competitiveness of other modes and proactive
development in the ICT area to further expand the market for customers. Along thisdevelopment, traffic congestion and environmental problem will reach new criticallevel, adversely affecting the lives of the general public. On the other end, transport
service providers would face further serious financial challenge as transportation cost
continues to rise partly brought by traffic congestion and small delivery activities.

15

Supplying freight and transport infrastructure, for instance road expansion andprovision of freight terminal, is one of the hard measures that can be employed but it
always bear the issue on financial investment and term effectiveness. Equallyimportant measure to improve traffic flow and environmental condition is by adopting
Transportation Demand Management (TDM) measures that require lesser financial
investment. One potential measure is the Cooperative delivery system (CDS), where
different carriers coordinate their operations to reduce the number of trucks requiredin delivering goods, thereby limiting the negative impact of trucks.
Although the above-described problem is rampant in almost every part of urban areas,
the peak of this problem can be normally witnessed at the core part of the city, whichis the central business district (CBD). CBD attracts large number of freight traffic
comprised by goods delivery to commercial establishments and large number ofpeople coming either for work or for shopping as well. The concentration of theseestablishments in small areas where normally, their location is arranged by functions(i.e. restaurants are located in a single row and retail shops to another row) offers
great opportunity to consolidate goods deliveries to the area.
Any attempt to improve the quality of environment and traffic flow will be hard tp
realize without policy intervention as has been learned from the current freighttransport situations in developed and developing cities. Sustainable freight transport is
becoming the new primary objective in the overall development appraisal, as
environmental issues have been continually dominating across planning andengineering disciplines. In particular, life in urban areas is becoming very affected byurban accessibility factors like heavy congested roads and the like, so there is anenormous call for innovative transport solutions and concepts to properly handle city
distribution of goods.

Social impact

highSustainable urban freight transport
low
past

present

futureTime
Figure 2-10. Impact of urban freight transport

16

Figure 2-10, adopted from OECD report (2002) with some modifications, illustrates
the direction of freight transport’s impact, which rose out of the sustainable urban
freight transport area during the past years. The figure shows that without posing
policy intervention, the social impact is likely to continue increasing in the future. A
policy or a group of measures therefore is essential to counter this growth with theprimary aim of pulling back the line to sustainable area.

2.5 Chapter summary
Important changes in technology, expansion of markets, and new concept of freighttransport management have led to major changes in the freight transport industry.
Through ICT, in particular the Internet, the customers’ reach expanded remarkably
and within few minutes, they can locate and browse over the menu of their desired
products through websites. Customers’ appreciation to shorter response time puts
great pressure to the freight transport service providers to deliver products in the
shortest possible time. This scene greatly contributes to the increase of the number ofvehicles in the road network, which often results to exponential level of traffic
congestion.
The chapter also highlighted the increase of road freight phenomena common in the
US, EU, and Japan. This development has already been recognized by most
governments by extending efforts to rectify the unbalance share of freight transport
among different modes that are currently in place. Both Europe and Japan for instancehave launched modal shift program aims to transfer some freight transport from trucks
to other modes such as rail and maritime shipping. Such programs however have
limited impact to city distribution of freight since its target is long distance freight. It
must be noted that at the core of the city, congestion is extremely serious, which
necessitates employment of scheme that aims to enhance the freight distribution
efficiency with limited social impact. Under this premise, the cooperative delivery
system has enormous potential to meet the said objective through reduced number oftrucks while serving the same volume of freight. This scheme is thoroughly discussed
in Chapter 4.
In the next chapter, the policies adopted by policy makers for urban centers aroundthe world are reviewed. These policies are thought to be their response to the growing
problems occurring in cities with great impacts on the community. Aside from policy
reviews, application of TDM measures which basically aims to reduce the social
impact of trucks are also tackled.

17

REFERENCES
Bringing Tokyo back to life- For a Cleaner and Greener Tokyo - A Tokyo
Metropolitan Government’s policy paper. (2004).
http://www.kankyo.metro.tokyo.jp/kouhou/english2004/honpen/main_1.html
City Freight (2002). Comparative survey on urban freight, logistics and land use
planning systems in Europe. www.cityfreight.org
Dhakal, S. (2003). Comparative Analysis of CO2 Emission in Beijing, Seoul, Tokyoand Shanghai. Proceedings of the IGES/APN Mega-City Project Workshop 2003.
http://www.iges.or.jp/en/ue/activity/mega-city/article/article.htm
EU Statistical Pocketbook, (2000).
(http://europa.eu.int/comm/dgs/energy_transport/figures/pocketbook/2004_en.htm)
FAW, 2000: Lifestyles, Future Technologies and Sustainable Development. Schauer,
T. FAW -Research Institute for Applied Knowledge Processing. Ulm, Germany.
Nemoto, T. and Kawashima, H. (2003). Logistics development supported by ICT &ITS in the Asia-Pacific Region. Asian Task Force, Institute of Highway Economics,
Japan.
Newman, P. and Kenworthy, J. (1989) Cities and automobile dependence: Aninternational sourcebook, Gower, Aldershot, England.
OECD. 2003. Sustainable transport in the Asian Region. Report on the International
Conference on Environmentally Sustainable Transport in the Asian Region. March
23-25, 2003. Nagoya, Japan.
Paaswell, R. and Petretta, D. (1993). Goods Movement Characteristics in the New
York. City Region. University Transportation Research Center, Region II, City
University of New York.
Sharma, C. (2003). Energy and Emissions in South Asian Mega-cities: Study on
Kolkata, Delhi and Manila. Proceedings of the IGES/APN Mega-City ProjectWorkshop 2003. http://www.iges.or.jp/en/ue/activity/mega-city/article/article.htm
Sumalee, Agachai (2004). Optimal Road Pricing Scheme Design. Unpublished
Doctoral Dissertation. The University of Leeds, UK.
Takahashi, Y. (2005). An Evaluation Study on the Social Experiment of Modal Shift
to Reduce Carbon Dioxide Emission. Paper to be presented to 6th EASTS Conferencein Bangkok, Thailand. September 21-24, 2005.
Tokyo Metropolitan Government (http://www.kankyo.metro.tokyo.jp/)
Transport for London (2005). http://www.tfl.gov.uk/tfl/ltr2003/road-related-trends2.shtml

18

CHAPTER

3

Policy Initiatives on Urban
Freight Transport
3.1 Introduction
The purpose of this chapter is to review the policies of countries which has been in theforefront of developing innovative strategies dealing with urban freight transport
problems. Depending on the availability of source materials in each country,
discussions of national policies are presented along with more specific urban center’s
policy. The review is followed by describing some of city’s freight distribution
practices initiated by either national or local authorities. In the latter part of thischapter, lessons drawn from public policies presented are summarized.
In the beginning, efforts to improve the quality of environment in the city resulted tothe introduction of different transportation demand management (TDM) schemes.
Dablanc (1998) after investigating about twenty projects related to freight transport
efficiency concluded that - “The unanimous goal is the reduction of environmental
impacts in inner cities (energy consumption, noise, air pollution) and the means toachieve that is based on the reduction of trucks movements through consolidation of
goods and trips.”
In section 3.2, the evolution of policies related to urban freight transport is revisited.
This policy recollection is accompanied by clarifying the roles of different groups thathave interest to freight transport. Consequent section 3.3 present policies and studies
conducted by the European Union (EU) to facilitate uniformity of goal in their urban
centers. The goals and policies of some countries within EU are then reviewed alongwith illustrations of demonstration projects in section 3.4. Further in this chapter, thecourse of the review led to the discussion of the policies adopted by the USA
concerning urban freight transport in Section 3.5. Discussion of freight transport
policies of some Asian countries including Japan is presented in Section. 3.6. Japanand Philippines were tackled in detail for the reason that the former has advance andinnovative strategies dealing with freight transport while the latter has the well-known
truck ban which is typical in some developing country. A comprehensive discussion
on the policies of Japan is available in Section 3.6 where several TDM measures bysome cities in this country are discussed extensively. A brief review of freight policies
in other Asian countries is also available in Section 3.6.3. Comparative analysis
regarding the policies and strategies of the countries is presented in Section 3.7. A
summary consisting of grouped policies and their merits as well as demerits is finallypresented in Section 3.8.

19

3.2 Urban Freight Policy trends
Policies are made when problems arise and objectives have to be set to avoid thereappearance of the same problems. Early freight transport policy efforts weredirected to improve the circulation of traffic. These measures include construction of
more roads, provision of (off) loading zones, freight terminal, and improvement ofroads to the ports. This can be viewed widely as efforts to equalize supply of
infrastructure to meet the growing demand.
However, demand is hardly met and chaos eventually occurs necessitating
management. Moreover, intensive use of infrastructure brought negative result such ascongestion, air pollutions, and safety.

Complex

Figure 3-1 shows that the formulation of policy is getting complicated as time
progressed due to complex interest in consideration. Among these interests are private
sector’s profitability, meeting consumer’s demand, minimization of negative social
impacts.
SUSTAINABILITY and
SAFETYOBJECTIVE:
Maximize social benefits

TIME

DEMAND MANAGEMENTOBJECTIVE:
Optimize use of existing
infrastructure

SimpleINFRASTRUCTURE SUPPLYOBJECTIVE:
Improvement of accessibility
and circulation

Figure 3-1 Freight transport policy trends

3.2.1

Interest groups

The conflicting interest of stakeholders in freight transport has been observed
throughout history. Often, cause of conflict arises from a particular sector pushing its
interest into limit leaving few options to the other sector. For instance, customers(shoppers) are demanding quick delivery of service which a transport company has tomeet at all cost.

20

Table 3-1. Main interest group in regard of urban goods transport
Actor

Main interest in regard of urban goods transport

Shipper

Delivery and pick-up at lowest cost while meeting the needs of their customers

Transport company

Low cost but a high quality of transport operation, satisfaction of the interests of theshipper and receiver (shop)

Receiver/ shop owner

Products on time delivered at a short lead-time

Inhabitant

Minimum hindrance caused by goods transport

Visitor / shopping public

Minimum hindrance caused by goods transport and a high variety of the latest
products in the shops

Local government

Attractive city for inhabitants and visitors: minimum hindrance but having aneffective and efficient transport operation

National government

Minimum external effects by transport, maximum overall economic situation

Source: Handbook of Logistics and Supply-chain Management, 2001
3.3 European freight transport policy and other related studies
The European Commission (EU), a politically independent institution that represents
and upholds the interests of the EU as a whole, has authorized and funded several
studies tackling the problems related to movement of goods within the Europe. The
overall goal of these studies is to provide a ground to harmonize all policies across
EU countries related to freight transport. A brief review of these papers would provide
useful indication to the differences of policies among the EU countries and to which
direction each country’s policy would head as they try to hit a common objective as acommunity (Table 3-6).

3.4 Experiences in Europe
Innovative strategies tackling problems related to the delivery of goods in urban
centers have been witnessed in some of the countries in Europe. Such strategies were
prompted by wide array of circumstances (e.g. preservation of historic places,
decongesting urban centers, etc.) that necessitates careful planning of goods delivery.
In the following section, public freight transport policies of a handful number ofcountries within the EU community were reviewed to get practical knowledge on howeach country react to a problem posed by freight transport. A case study that enhances
the efficiency of urban goods delivery to each country is also provided.
3.4.1

Freight Transport Policies in France

The development of policies concerning freight transport in France can be traced backfrom three major laws: 1) The Law of Orientation on Urban Transport (LOTI 1982), 2)
The Law of the Air and the Rational Use of the Energy (LAURE 1996) and, 3) The
Law of Solidarity and Urban Renewal (SRU 2000). The LOTI has a particular strong
endorsement in planning movement of goods to equate in terms of importance as toplanning of passengers’ movement. Under SRU’s Urban Development Plan (PDU),
the importance given to goods movement is further clarified. PDU has been instituted
compulsory for metropolitan areas with more than 100,000 inhabitants. Government
bodies such as the Ministry of Transport and the Agency for the Environment got
involved in Urban Goods Movement program as early as 1993. These bodies assist in21

legal and environmental concerns of projects that enhance the efficiency of city
distribution. A major national research program on freight transport was launched in
the 1993, the year where the government organizations got involved, which focuses
on data collection, modeling and development of pilot projects. In the same year, theregional council of Paris completed an extensive survey on goods' movements in themost urban part of the Paris region.
Practical solutions to urban freight transport problems were also tackled in different
ways in the policies. Urban distribution centers (UDC) for instance, which serves as
public transshipment depots where goods are dispatched in a coordinated/consolidated
way, are being tested in the cities of la Rochelle, Nancy, Besancon, and Monaco. The
Plate-forme ELCIDIS of la Rochelle hosts 400 parcels delivery daily to the historic
center using electric vans. In total, more than 20 UDC projects were mentioned in the
French Urban Mobility Master Plan but most of them were abandoned after thoroughfeasibility study. One of the reasons of the failure of these projects is financial
difficulties where government subsidy is far too high from collections generated fromthe customers. Despite this large subsidy from the local government, transportcompanies still find the cost expensive and were reluctant to use the facilities for fearof unequal competition. Aside from UDC, application of IT to aid the delivery of
goods was also tested in Toulouse and Rouen. Moreover, in Paris, there is a plan toprovide a lane for bus so it can be isolated from general traffic. This plan, which isunder the Mobilien project, can also be utilized by other modes such as taxies,
bicycles, police and emergency vehicles and trucks (Figure 3-2). Trucks however will
be only allowed for a certain time; 9:00-16:30 and 19:30-7:30.
Figure 3-2. Bus lane delivery area under Mobilien program in Paris

Source: BESTFUS
Recently, policy development relating to delivery of goods to the center of Paris isdeveloped. This is a result of several factors (i.e. poor utilization of freight facilities,
complexity of guidelines relating to freight transport, etc.) and aims to improve theefficiency of goods movement and reducing its impact on the environment. The newplan guidelines for freight transport are listed below.
Improvement of the delivery bay 1
The total number of delivery bays is 9,780, which represents 15% of total space
allocated for parking. Evaluations show that there has been a low rotation to the use ofbays which might be a result of mismatch between the needs of freight operator and

1

The plan guidelines for Paris is received from Mr. Hervé Levifve of Agence de la Mobilité of Paris

22

characteristics of the facility. With this regard, a term of reference, which could serve
as guide to locate a delivery bay based on one’s demand, will be published.
30 minutes rotation on delivery bays
Currently, there is no time limitation to the use of delivery bay and no restriction on
the type of vehicles. This lack of guidance made it difficult to distinguish if a freightvehicle is making a delivery or just using the bay as parking area. Starting 2006, a
parking disk which would facilitate identification of truck’s arrival would be installed
and will be manned by police officers. The use of parking bays will also be limited to
30 minutes.
Professionalisation of some delivery bays
Some delivery bays (located in areas with high number of shops) will be reserved toprofessional carriers or shippers identified by a badge. In effect, this would improve
drivers working conditions and facilitate shopping deliveries.
Simplification of regulation
The current regulation is based on the space used, the time of day, and the type ofstreet, which are proven difficult to understand among the concern parties (i.e. drivers
and police officers). A simpler regulation is prepared by the City of Paris with thecooperation of the chamber of commerce of Paris, the police department and thefederation of carriers.
Improvement of the plan of control
This entails training of police officers on enforcing the guidelines in delivery bays.
Upgrading their skills would result to efficient regulation and consequent efficient useof the bays.
Charter of goods uses
The idea of this charter is to solicit cooperation from all stakeholders such as freightshippers, freight carriers, and police department to the City of Paris. The commitmentof the professionals from freight industry (i.e. freight shippers and freight carriers) is
to adhere to the guidelines which seen as beneficial for the city and citizens of Paris.
On the other side, the City of Paris and police department commit on actions points,
which facilitate the day to day work of drivers. This Charter is to be singed in 2006.
Introduction of the environmental concept in the regulation
Introduction of this scheme is in recognition to the negative impact of freighttransport to the environment. Access restriction based on environmental criteria will
be applied to freight vehicles. This policy is another way of encouraging freightshippers and carriers to utilized non-polluting vehicles.
Demonstration Projects
Delivery depots for both shopkeepers and private end-consumer
In Paris, an experiment called ‘Magasin de Quartier’ serves both private
consumers(end) as well as shopkeepers(retail). The project’s objective is to reduce
the number of deliveries in dense commercial and residential areas using delivery
depots. The depot serves as drop-off zones for transport operators delivering parcels
received by mail order, telephone, fax, and internet and has the strong support of23

transport companies, their organizations, and Paris CCI. In fact, it has been proposedin the regional transport plan of 2000. Two to three staffs operate each depot and they
inform clients once their product is available. Either they come to pick up theirproduct or the staff will deliver them for extra charge. The size of the depot is no
more than a few hundreds square meters.
3.4.2

Germany

Germany is facing the problem of continuing freight traffic growth as a result of theSingle European Market and the enlargement of the EU to the east and globalization.
Freight traffic is forecasted to grow by 64% before 2015. The German government
aims to tackle the problem by creating and efficient transport infrastructure toaccommodate the growth in traffic demand, improve the rail freight network, andcreate fair competition between different modes. Thus, after a long discussion thegovernment in April 2002 approved the introduction of distance-related charges forthe use of the motorway system by heavy goods vehicles (HGVs). The act allows theintroduction of distance based charging on the motorway networked and some parts ofthe federal highways and the toll revenues can be used for infrastructure project(Sumalee, 2004).
In parallel with the above policy, some key program to the realization of sustainable
freight transport are in place (i.e. Flexible Transport Chain) to reduce road transport
by 100 mill truck km/year through the creation of a more efficient transport sector andthrough the use of intelligent transport systems and technologies. The program hascurrently 16 large demonstration projects.
The government in particular the Ministry of Transport and the Ministry of Research
are financing also some programs that are in line with the national policy. Thisnational policy is a broad framework for transport research based on the interests of
the industry. Some of these projects are terminal infrastructure which serve as a hub
for intermodal network.
Example of such intermodal terminal is like Bremen Guterverkehrszentren (GVZs)
which is one of the leading intermodal freight transfer or distribution centers in
Europe, in terms of size and number of companies involved. In many respects, theBremen facility is a prototype for other GVZs planned for Germany within the next
few years. They serve as transfer points where short- and long-distance surface/freighttraffic meet. The aim is to promote co-operation between the industries and to create
logistic synergy. In essence, these GVZs are industrial areas where various freighttransport enterprises are located, all of which remain independent, retaining their legal
and economic autonomy.
Demonstration Projects
City Logistics
In Germany, the concept of City-logistik (city logistics), which serves as a platform to
conduct cooperative pick up and delivery of goods in urban areas, has been formed in
1990s. Two main objectives of city logistics are: (1) reduction of costs in distributing
goods, and (2) reducing the number of Heavy Goods Vehicles (HGV) in the city
(Kohler, 2001).

24

This concept, City-logistics, reduces lorry numbers and improves the urban
environment. It is currently in operation in Berlin, Bremen, Ulm, Kassel and Freiburg.
The Freiburg example has several pointers to the future shape of freight transport inurban areas. There are currently 12 partners in the scheme. Three of the partners leavecity centre deliveries at the premises of a fourth. The latter then delivers all the goods
involved in the city centre area. A second group of five partners delivers all its goods
to one depot located near the city centre. An independent contractor (City Logistik)delivers them to city centre customers. A third group, this time with only two service
providers, specializes in refrigerated fresh products. These partners form an unbroken
relay chain, one partner collecting the goods from the source and the other fordelivery to the city centre. Effect of such partnership to the movement of trucks isshows in Table 3-2.
The Kassel scheme showed a reduction of vehicle kilometers traveled by 70 % andthe number of delivering trucks by 11 %. This has reduced the costs of all thecompanies involved and increased the amount of work that can be done by eachvehicle/driver combination. (Thematic Network on Freight Transfer Points andTerminals , http://www.eutp.org/download/Deliverables4/)
Table 3-2. Effect of city logistics
Variables

BeforeAfter

Difference

Total journey time per month

566 hrs

168 hrs

29%

Montly number of trucks

440 hrs

295 hrs

67%

Time spent by the lorries in
the city per month

612 hrs

317 hrs51%

Source: City Logistics II
3.4.3

Italy

In Italy, issues related to transportation are tackled in the four different administrative
levels – the Central government, the Regions, the Provinces, and the Municipalities.
Every 5 to10 years, the Central government produces a national transport plan called‘Piano Generale dei Trasporti’ (PGT). In it are sets of broad transport policy
objectives and principles to be followed and met by the regions when planning theirtransport infrastructure. In essence, the regions are the one setting up tangible policies
within the guiding principles of the PGT.
This devolution of authority to the local levels allows each region to set-up policies
based on the characteristics of the cities. Most Italian cities require careful planningwith regard to freight transport operation due to their historic centers. This situation iscompounded by the presence of narrow streets, which usually populated by touriststhat further limits the capacity of the roads. Authorities are approaching these
problems by adopting traffic restriction policies like the time windows in the city ofVicenza where delivery vehicle of small size is allowed to enter the city center from7:00-9:00 in the morning and 14:00-16:00 in the afternoon. Also on the card is thepossibility of implementing pricing policies.
Freight transport service providers are responding to this structural change byadopting two strategies – fleet innovation and systems innovation. Specification of25

freight vehicle - small in dimension and has low environmental impact - is beingordered from the automobile manufacturers. On the same manner, the advantage
provided by the Information, Technology and Communication (ITC) is being
maximized to rationalize freight vehicle’s trip.
Urban freight transport in Italy is mostly done by road and freight is carried by lessthan 4-ton vehicle. This size of vehicle used to transport freight has something to dowith the characteristics of the cities being old and therefore not suitable for heavy
vehicles. Another driving force to the proliferation of this small vehicle owes to thepractice of ‘Just-in-time’ (JIT) where small amount of goods is shipped throughout
the day.
One of the structural changes to urban freight transport caused by the JIT concept is
the relocation of logistics platform from the urban centres to areas where the logisticalactivities is concentrated. These areas are port, airports, intermodal terminals. This
relocation of infrastructure however cause profit decline to some carriers which arequick enough to respond by pulling together their resources proved logistic service
providers through consolidation of loads.
Demonstration projectIntermodal centre
A project initiated by Mobility and Parking Directorate, a government organ, has been
experimented in the city of Genoa in Italy to deliver goods to city centres using nonpolluting vehicles. This type of vehicle is also responsible for collection of goods
from the historic city centre going to the exchange center (hub). The hub is where thetransfers of freight from one mode (normal truck) to another (electric or natural gas
vehicle) occur (Figure 3-3). The project is also evaluating the possibilities to combine
the collection of recyclable materials with the distribution of goods.
Non-polluting truck
TRUCKSDEPOTCITY CENTERTRUCKS

Figure 3-3. Protection to the city centre leads to the introduction of depot where
goods are consolidated and delivered by non-polluting vehicles

26

3.4.4

Netherlands

In 2001, over six million cars, more than half million vans and more than a hundred
thousand trucks were registered in the Netherlands. Problem of congestion isparticularly heavy in the Randstad, an area that is boarded by the cities of Rotterdam,
The Hague, Amsterdam and Utrecht. Because of its strong economic activity,
cooperation among neiboring cities on how best to manage the existing infrastructure
to support the freight is conceived and called G-4.
Goods vehicle share in the road network stands between 6-15% and this increased to
20-22% at the city centers during peak hours. Delivering goods in some cities madedifficult not only by the traffic congestion but also by the presence of old
infrastructure that are not suited to accommodate vans or trucks. With regard to NOxemission, the government’s target of 25 million kilograms in 2010 is too optimistic
when in fact, it is expected that NOx reached between 85-110 k/tones.
The Dutch government is strongly concern to balance efficient flow of traffic andquality of life of its residents. This is manifested in its National Traffic and Transport
Plan (NVVP) that has an objective of ‘to offer an effective, safe and sustainable
functioning traffic and transport system, of which the quality for individual users is inbalance with the quality of society as a whole’. This national policy, which is the
successor of the Second Transport Structure Plan of 1989, is framed from 2002 to2020 with a perspective on 2030.
Critics pointed out that although this is an important policy initiative, lessappreciation to freight transport or in particular urban goods distribution is evident.
General freight issues were merely described and urban goods distribution was
highlighted as among the topic of concerns. Four main goal of NVVP-freight policy
are: 1) accessibility, 2) efficient freight transport, 3) safe freight transport, 4)
sustainable freight transport.
The accessibility has something to do with the effective use of the existing
infrastructure and giving good network connection to the ports. Some of the spelled
policies are: i) pricing policy, and ii) modal shift and traffic management. Efficientfreight transport aims at maintaining the current level of transport kilometre while
speeding up the economic development. This policy is running against the current
trend where both economic and ton-kilometer of trucks are moving up in parallel. The
third goal of NVVP - addressing freight policy related issue such as safe freighttransport - stresses out the responsibility of both shippers and carriers in maintaining
the existing safety level of infrastructure. The last freight related policy is sustainable
freight transport, which can be done by maintaining the present quality of life with
continued economic growth.
Forum on Urban Goods Distribution
The seriousness of problems brought by freight transport got the attention of thenational government who initiate the establishment of Forum on Urban Goods
Distribution (PDS). This forum, which composed of the Ministry of Transport,
companies and the Association of Dutch City Councils, serves as a platform to thesegroups to discuss and initiate projects beneficial to city distribution. The Forum27

enjoys credibility among Dutch cities as it is considered a serious partner in finding
solutions to urban freight problems.
Many innovative and successful city freight distribution initiatives have also beentested in the Netherlands. In Amsterdam for instance, distribution centres were
established where shippers can deliver their freight and from where shipments areconsolidated and transported to the shops and retail facilities. This concept ofconsolidation is also practices by some food centres to facilitate their goods supply.
DHL has tried the use of water in transporting their packages in spite of the alreadyintensive used of canals by round-trip-boats. Another innovative initiative is called‘Packstation’ where customers can send and receive packages around the clock using
publicly accessible deposit box systems. The central idea of this initiative is tofacilitate the gap between carriers and consumers. Oftentimes, parcel recipients are
not home during the delivery hours that force the carrier to make a return trip. This
project was very successful in some cities of Germany like Maizn, Dortmunt andFrankfurt. Moreover, a possibility of mixing passengers and freight in metro system
during the night was studied.
Project Demonstration
Logistic chain and modal shift
Improving efficiency within the entire logistical chain to help reduce CO 2 and NOx isof prime importance to the government. With this view, a lot of project has been
supported through subsidies or by organizing the involved parties. One of theseprojects in line with the principle of reducing transport externalities is called
Transactie Modal Shift (TMS). TMS was established in 1999 through the initiative of
the Dutch Ministry of Transport, Public Works and Water Management with theparticipation of some transport organizations. The central theme of the project is toimprove road transport efficiency, reduce the number of kilometers, saving fuelconsumption, and transferring goods transport to other mode from road-based.
As a result, companies which received subsidy up to 2002 yielded a total reduction of
145 million kilometres. The government is hoping that the acquired knowledge by
these companies through participation would be useful to them even without the
government’s financial aid. With this aim, the government produces a manual in CDROM which interprets the project for immediate use.

3.4.5

United Kingdom

Recently, the UK central government is showing strong interest to improve freighttransport through creation of units attached to government bodies and by publishingdocuments that outlined sustainable distribution of freight especially in urban areas.
Agencies such as Freight Distribution and Logistics Unit under the Department ofEnvironment, Transport and the Regions (DETR) have published important documentin the freight transport like the 1998 Transport White Paper entitled “Sustainable
Distribution”. This paper turns out to be the first public policy document of thegovernment demonstrating efforts of understand modern logistics and distribution
system. On the same manner, the Department for Transport (DfT) launched programs
that aim to engage freight transport stakeholders to work together in addressing

28

problems associated with freight transport. The government is also a signatory of the
Kyoto protocol which targets to reduce greenhouse emissions. Under the agreement,
the UK government has to reduce 12.5% below 1990 levels by the period 2008 to2012 which is equivalent to 27 million tones of CO 2. The UK also has a domestic aimof reducing CO2 emissions to 20% below 1990 levels by 2010 and is committed toputting itself on the path to cut CO 2 emissions by 60% by about 2050. This legal
obligation is believed to further energize the government’s effort to find ways to makethe transport of freight more efficient.
As a general trend, although the UK adopted the JIT distribution concept, thegovernment is skeptical of the idea of Urban Distribution Centre or similar concept asa solution to the growing problem of goods distribution in the city. Despite indications
that city logistics schemes are both cost-efficient and environmentally friendly has
potential of reducing the number of lorries on the road, little progress towards
implementing such plans have been made in the UK.
The 1998 Transport White Paper and its daughter document, “Sustainable
Distribution: A Strategy”, published in March 1999, affirmed the importance of thedevelopment of an integrated, sustainable freight transport system, that supports
economic growth, whilst simultaneously reducing adverse impacts on society and theenvironment. The Report confirms that “there may also be scope for reducing thenumber of lorry and van movements by promoting greater consolidation of loads anddrawing on the experience of 'City Logistics' systems where goods destined for city
centres are diverted into common transhipment facilities with local distribution being
carried out using specialised vehicles which may be smaller, quieter and lesspolluting. We will learn from the experiences gained in Europe from operating such
systems”.
In line with this effort of sustainable city freight distribution, the Department forTransport has subsequently published guidelines for initiation and consolidation ofprojects similar to the German city logistics. The Department for Transport has named
such schemes Freight Quality Partnerships (FQP) and published a best practice guideand a case studies guide on the benefits of FQPs and how to establish them. And inorder to further accelerate this effort, the Department of Transport, Local Government
and the Regions in its Annual Report 2003 restated its commitment to sustainabledistribution and the development of Freight Quality Partnerships (FQP).
FQPs aim to tackle issues affecting the supply chain at a local level. They allow more
efficient deliveries to town centres, while reducing their effect on the environment.
They have produced agreements on routing, load sharing and town centre access,helping to reduce congestion, emissions and the number of vehicles in urban centres.
There are currently 31 FQPs in Great Britain, involving local authorities, the freightindustry, business communities, residents and environmental groups. The reportsurveys examples of such initiatives in the UK and describe the successes of The
Newton Abbot area, Reading, Hampshire County Council and Winchester, Devon
County Council, Merseyside and Halton, North West Freight Advisory Group, andDerbyshire County and Derby City Councils.

29

Government, shippers, carriers, residence(Freight Quality Partnership)

Subjects for discussions: efficient deliveries to town centres,
reducing their effect on the environment
Solution: agreements on routing, load sharing and town centre
accessFigure 3-4. Stakeholders and topics for discussion in the FQP

As for the definition of FQP, the Department for Transport’s Good Practice Guide
335 defines FQPs as "a means for local government, businesses, freight operators,
environmental groups, the local community and other interested stakeholders to work
together to address specific freight transport problems (Figure 3-4). They provide aforum to achieve best practice in environmentally sensitive, economic, safe andefficient freight transport. We need to achieve more sustainable distribution that holds
the needs of the economy, the environment and society in balance. Freight Quality
Partnerships can help to achieve these sustainable distribution objectives throughdeveloping constructive solutions that reconcile the access to goods and services withlocal environmental and social concerns".
Access restriction is a common approach in UK like in the city of Preston and London.
London is charging every vehicle of all form (5 GBP) that enters the city centre. The
purpose of this approach is two fold – one is to decongest the urban centre and thesecond one is to upgrade the transportation infrastructure by using the generated
money from the scheme.
Demonstration Project
Cyclone Courier
Cyclone courier is a private company with contracts with circa 40 parcel carriers(Figure 3-5). Cyclone couriers fills a commercial gap as York is a congested medieval
city with a vehicle ban from 11 a.m. to 4 a.m. Cyclone couriers operate a next day
delivery service on behalf of their contracted parcel carrier customers.
They transship from carrier’s vehicles or collect. They consolidate and deliver the
next day. This is only possible for a cycle due to the vehicle bands and even before
the ban the cycles are faster, use bike lanes and are cheaper. The cycles are similar tocycle rickshaws but built for freight. The tricycles feature 42 speed transmission,30

hydraulic disc brakes and aircraft grade steel and accommodate loads up to 250 kg.
Cargo boxes and passenger seats can be swapped in minutes.
Cyclone couriers also run collect and deliver courier services both within York andfrom carriers to York. With this, they use normal bikes, thus avoiding
loading/unloading restrictions as well as using bikes lanes, being faster in congested
traffic and cheaper.
Cyclone couriers is financed by the clients (parcel carriers and others) paying for theservice. And since a bike and rider costs 60 GBP a day as opposed to 250 a day for a
van and driver, it is cheaper to use bikes to achieve multiple delivery slots thanequivalent number of vans.

Figure 3-5. Cyclone courier
Source: BESTUF
Freight Consolidation Scheme in Broadmead, Bristol 2
BackgroundAs part of the VIVALDI European Commission supported project(www.vivaldiproject.org) Bristol City Council have established a freightconsolidation centre serving city centre retailers (Figure 3-6 and Figure 3-7). This
scheme aims to combine individual loads destined for retailers in the Broadmead
shopping centre for onwards consolidated delivery. The target area for the
consolidation centre is part of Bristol’s Local Air Quality Management Area wherelevels of pollution are forecast to exceed standards. The scheme also forms a key
component of the Council’s Clear Zone Strategy, aimed at promoting moresustainable access to the city centre, which was submitted to UK Government in July
2003.

2

Note: This section is mainly taken from e-mail received from Mr. Pete Davis of Bristol City Council

31

Innovative Aspects
The Broadmead consolidation scheme is the first serving an urban centre in the UK.
Being so innovative in the UK context, of necessity the specification and developmentof the scheme initially evolved, and has been subsequently refined, throughdiscussions with the key stakeholders. These include the manager of the Galleries
shopping centre, the Broadmead Board who represent the interests of retailers,members of the Freight Quality Partnership, Business West (formerly the Bristol
Chamber of Commerce) and UK Department for Transport.
Clearly the views of the retailers themselves were also important and an extensive
survey in summer 2003 gauged their interest in the scheme, ascertained current
delivery patterns and therefore suitability for consolidation, and identified delivery
problems which the scheme could potentially overcome. This survey illustrated thescope and impact of the scheme if consolidated deliveries were rolled out across theentire Broadmead area - with potential savings of over 59,000 freight delivery trips
per year based upon pilot phase levels of consolidation. The planned expansion of the
Broadmead shopping centre, increasing retail space by 40% over the next 5 years, hasalso heightened awareness of the importance of managing urban freight movements.

The target area
has approximately
325 retail unites

Figure 3-6. Map of Bristol city32

Developing the Scheme
The freight consolidation scheme seeks to reduce the number of delivery vehicles
operating in the target area with resultant transport and air quality benefits, and also toprovide an improved delivery service supporting city centre economic vitality.
To ensure the technical and operational robustness of this novel scheme the Council,
through public procurement, engaged supply chain experts Exel to run the
consolidation centre. Exel were able to draw on their experience of operating the twoother UK retail consolidation schemes serving Heathrow Airport and an out-of-town
shopping centre. In addition they also had existing relationships with a number ofretailers participating in these schemes some of whom became the all important early
adopters in Bristol. The scheme operates from a warehouse of some 5,000 ft2 on thenorthern edge of the city close to the strategic road network approximately 7 miles
from the city centre target area.
To encourage retailers to participate the service had to be free of charge during thepilot phase – with the value of the service being the reason for their involvement
rather than coercion imposed through access restrictions or landlords.

Figure 3-7. Delivery vehicle and key personnel of the scheme
Progress to Date
The 6-month pilot consolidation scheme commenced operation in May 2004 with an
initial target of involving 20 retailers. This has subsequently been extended and todate 51 retailers have joined the scheme ranging from major high street brands (suchas Tie Rack and Monsoon) to small independent stores (such as Kathies Comics),
with a reduction of 67% in the number of delivery trips amongst the retailers involved.
Through consolidation the centre has to date saved over 29,300 lorry kilometres
otherwise made into and around the city centre removing over three and a half tonnesof Carbon Dioxide.
In addition a survey of retailers in November 2004 has illustrated the positive benefits
that the scheme is bringing in terms of improved reliability of delivery times, timesaving when receiving deliveries and a consequent increase in time spent withcustomers.
The Future
The Council and operator Exel are currently working to develop a sustainablebusiness model for the scheme including the allocation of costs across beneficiaries.
An important element of this process will be developing value added services such as33

remote stock rooms, peak/seasonal storage, pre-retailing services and waste/packing
collection and recycling.

3.5 Freight Transport Policies in the US
US policy focuses on improving transport efficiency and reducing the transport shareof GDP. However, there is no overarching freight logistics policy. The policies are
separate by mode: surface, air, sea. There is still a significant infrastructure approach.
The main exception to this is an emphasis on ITS (Comparative Public Policy on
Freight Logistics, 2001).
Although it appears that the US policy is still on infrastructure approach, there is an
emerging sign to integrated approach to the different modes. This has its root in 1991
with the passage of the Intermodal Surface Transportation Efficiency Act (ISTEA-91).
For the first time, urban freight was specifically mentioned as one of the 15 planning
factors that required attention in the planning process (Czerniak et el, 200). Thislegislation made Intermodal facilities eligible for funding as part of the National
Highway System from the government. In essence, it gives Intermodal project equal
opportunity to secure a fund from the government just like any other project of the
National Highway System. This policy has this pronouncement: “It is the policy of theUnited States to develop a National Intermodal Transportation System that is
economically efficient and environmentally sound, provides the foundation for the
Nation to compete in the global economy, and will move people and goods in anenergy efficient manner” (Intermodal Surface Transportation Efficiency Act of 1991,
Section 2 found on http://ntl.bts.gov/DOCS/istea.html).
The ISTEA-91 stressed out the necessity to provide access to the freight systems andthe use of intelligent vehicles and other technologies to improve cost effectiveness.
The fund of US$155 billion over 6 years between 1992 and 1997 is largely spend to
infrastructure projects, intermodal loading units, reimbursement of operating deficits
for certain non-profit routes and transport studies. State/local authorities in
conjunction with private parties selected projects.
The second policy appeared after the ISTEA was the Transportation Equity Act forthe 21st Century (TEA-21) which has an authorized funding of US217.5 billion
between 1998 to 2003. The fund will be linked to monitoring freight flows at regional
and international levels, establishing public-private partnerships and re-organizing
government functions, financing missing links in transport infrastructure networks.

3.6 Experiences in Japan and some Asian countries
3.6.1

Japan

The evolution of Japanese logistics policy has its origin in 1950’s when the economy
start showing a healthy growth. This growth has reshaped the balance of population
distribution between urban and rural areas as thousands of people sought to findeconomic related-activities in major urban centers like Tokyo, Osaka and Nagoya.
From the late 1950’s to the early 1970’s, 400 to 600 thousand people migrated yearly34

to these three metropolitan areas which constituted only 10.4% of the total land areaof Japan (Takahashi, 2003). This phenomenon unlocked two parallel streams ofdevelopment; first, the concentrated labor force further propelled the economy andsecond, the centralization of population brought serious problems like traffic
congestions, housing shortage and environmental degradation.
While trying to improve the situation in these major urban centers by constructing
infrastructures, the government also launches series of program to encourage people
to return to rural areas. The idea is to have parallel growth between these two regions
to attain balance growth of Japan.
The report entitled “Basic policies on the re-development plan of Tokyo and Osaka”
which was the result of the Forum on the Problems of Large Cities in 1963 lied down
the foundation to the idea of relocating factories from urban centers to suburb and theformation of a multi-core urban structure against urban sprawl. The major policies put
forward to achieve these objectives were the formation of sub-centers, and theformation of distribution centers (Castro, 2002).
In 1966, a law that spelled-out the realization of distribution centre was endorsed. The
central idea of the distribution business centers is to facilitate the merging in operationof small companies which lack funds to upgrade their logistics facilities and relocate
them outside the city. In 1969, the master plan that identified location of distribution
business centers for Tokyo Metropolitan Areas was released. Four areas located 30
km away from the center of Tokyo was identified as ideal location which would also
serve as sub-city cores. These areas are Omiya, Chiba, Tachikawa and Yokohama. A
highway network would connect these four areas and four to five distribution centerswould be established to re-organize the flow of freight in the Tokyo Metropolitan area
(Figure 3-8)(Takahashi et el, 1995).

Tachikawa
50 km

Omiya

40 km
Saitama
Region30 km

Saitama
Region

Tokyo

Chiba
RegionKanagawa
RegionYokohama
Chiba

Figure 3-8. Plan of distribution business centers for Tokyo Metropolitan Area

35

The Comprehensive Program of Logistics Policies (CLPL)
The government in 1997 introduced the Comprehensive Program of Logistics Policies(CLPL). The threefold general objectives of CLPL are:To provide one of the most convenient and attractive logistics services in the
Asia-Pacific region.
To provide logistics services at a reasonable cost by strengthening
international competitiveness of Japanese businesses.
To address negative externalities related to logistics such as environmental,
energy and congestion and safety issues.

Among the specific aim of CLPL are:

In urban logistics, increase load factor of trucks to 50% and increase urban
truck travel speed to 25 kilometres per hours.

In regional logistics, encourage multi-modal transport in order to cover 90%of the population in a half day.



In international logistics, by 2001, reduce waiting time of ships form thepresent 4-5 days to 2 days through container shipping and reduce landtransport cost by 30%.

Standardization by year 2001, increase palletization rate of existing
palletizable cargo from 70% to 90%.

The program is being by implemented by the Ministry of Land, Infrastructure andTransport (MLIT) and the Ministry of International Trade and Industry (MITI) withsupport by other ministerial offices and agencies.
The 1997 Annual Report of Transport Economy charts the areas where necessary
action is needed to construct an efficient logistics system. These are:

Improvement of the social infrastructure,
Promotion of deregulation, andDevelopment of a more sophisticated logistics system.

In 2001, a new version of the CLPL was approved and released by the government inJuly of the same year. The new objectives are:
 To establish logistics market that can compete internationally in terms of cost.
 To establish logistics systems that reduces environmental road and social
problems.
The announced strategies to achieve the above objectives are:
 By appropriate partnership between actors, such as public-private setors, andcentral-local governments.
 By establishing fair and competitive logistics service market.
 By efficient development of logistics infrastructure and effective use ofexisting infrastructure.

36

The policy measures indicated by the new CLPL are:
 Establishment of efficient logistics system (i.e. simplified procedures, uniloading, intermodal transport, utilization of EDI and ITS, and TDM measures,etc.).
 Establishment of logistics system responsive to social problems (i.e. intensify
emission control in line with the Kyoto Protocol, fuel efficient vehicles,
reverse logistics, improve safety through utilization of ITS).
 Establishment of logistics system to support people’s life (i.e. deregulation on
tariff system, logistics infrastructure during emergencies).
Although the CLPL has a tone of social oriented logistics, the government has also
recognized the importance of providing sufficient infrastructure to facilitate efficientlogistics services. For instances, it was announced that the government would
promote the redevelopment of international ports and airports coupled with theconstruction of linkages among roads and railways, ports and harbours, airports andlogistics centers to facilitate the fluidity of goods.
In line with the Kyoto Protocol, modal shift was also promoted by the government
through subsidizing projects deemed to help reduce greenhouse gases. Along with theidea of encouraging enterprise to shift their freight operation from truck-base to railbased or ship-based, the projects want also to rectify the imbalance of cargo shareamong the different modes.
It appears that the government policy is tacking both the social and physicalinfrastructure of logistics to provide what they termed as ‘most convenient andattractive’ logistics services in the region.
Demonstration projectsi)Tenjin Joint Delivery System

Perhaps the oldest cooperative system of freight delivery in Japan was started inTenjin district of Fukuoka city in 1978. The district has an area of 370,000 m 2 and atthat time experiencing serious traffic congestion which resulted to the deterioration of
the environment. Particular problem of the district is the limitation of (off) loading
spaces on and off street which generates illegal parking and compounded thecongestion.
At the beginning, twenty-nine (29) freight carriers joined the joint delivery systems
supervised by the Regional Transport Office of Ministry of Transport. In 1994, thirtysix (36) companies have established the Tenjin District Joint Distribution Company
Ltd. for promoting the systems.
Freight carriers bring their goods to Hakozaki distribution centre of the Joint
Distribution Company that is located in the suburb of Fukuoka city, close tointerchange of urban expressways. Then the Joint Distribution Company will deliver
goods from member carriers to each receiver at Tenjin district after sorting goods toeach building. The Joint Distribution Company also collects goods from costumers inTenjin district and unloads them at the distribution centre of the Joint Distribution
Company where the freight carriers take them over individually. The Joint

37

Distribution Company delivers about 90,000 parcels and collects about 10,000 parcels
per month at Tenjin district.
Each freight carrier pays 160 yen per parcel below 50 kg. No subsidies are provided
by the public agencies. The Regional Transport supports the joint distribution systems
in Tenjin district in institutional ways. The Regional Transport Office provides aplatform for discussing related things and coordinating many stakeholders including
shippers, freight carriers, residents and administration who are involved in thesystems.
This system benefited freight carriers, shippers, road users and residents alike. Ieda etal (1992) based on modeling estimated the following as benefits of the system:





ii)

decrease of number of trucks in the served area by 65%
decrease of total distance traveled (km/day) by 28%
decrease of total distance traveled within Tenjin district (km/day) by 87%
decrease of total frequency of parking by 72%
decrease of total parking time (h/day) by 17%
Shinjuku Matenro Cooperative

The Materno Cooperative system is designed primarily to conduct join delivery andpick-up of parcels to and from high-rise buildings in Shinjuku, Tokyo. It has been in
operation since April 1992 until now. A staff is waiting for the arrival of truck at a
docking area of the building, unloads the goods put in a small container from the truck
and release the truck. The goods are then brought to the freight elevator anddistributed by the staff from the top to the down floors. Each parcel is limited up to
20 kg in weight, and 120 cm in height, width, and depth. The delivery truck runs on a
fixed route three times a day on a regular schedule – 10:00 and 11:30 in the morningand 1:50 in the afternoon.
35,00031,00030,00026,000
Number of goods

25,00022,00020,00017,00026,00021,00016,00015,00011,00010,000
5,000
July 1994 July 1995 July 1996 July 1997 July 1998 July 1999 July 2000 Dec 2000
Ye ar

Figure 3-9. Number of goods handled by Matenro cooperative from 1994-2000

38

The cooperation involved 21-high rise buildings and 3-underground shopping malls.
As shown in Figure 3-9, the volume of parcels delivered to these buildings increases
to 6,000 after one year in operation. Without the system, accommodating such huge
shipment is difficult given the limited number of parking slots in the area. The
timeline of the project is found below:
 April 1992, the Shinjuku Ground Transportation Cooperative launcheddelivery services for skyscrapers as “Matenro Staff”.
 1993, “The Committee for Promotion of Joint Distribution Systems for
Skyscrapers” was established to consider how best to promote the services.
 March 1996, “The Matenro Center” is established as distribution base facility.
iii)

Cooperative Delivery in Horidome, Nihonbashi, Tokyo

Horidome is home to 600 companies engaged in textile business that are sourced fromall over the country. These wholesalers are concentrated to a small area with radius of
500m. They are thought to be the distributors of textile fabrics of the entire Higashi
Nihon, which covers Chiba, Tokyo, Ibaraki, Saitama, Gunma, Tochigi, Kanagawa,
Yamanashi, Nagano and Niigata. Congestion is felt in Haridome due to large volumeof truck trips. This incident prompted the shop owners to realize efficient form oftransporting their product that would require small number of trucks.
In March 1972, “The Groups of 10 Textile Wholesalers” was inaugurated andlaunched integrated cargo collection and distribution services in July. This group wasthen renamed in 1982 to “Tokyo Textile Collection and Distribution Organization” tohighlight the cooperation among the shop members in collection and distribution of
their products. Twenty-one year (21) later and in April 1993, the organization was
renamed to “Japan Joint Collection and Distribution” and distributors were grouped
into two to encourage competition.
The length of years the organization has been in operation is an indication of its
success in addressing several problems that are common to the delivery of goods into
urban centers. It has been reported that the efficiency of trucks has improved
remarkably that locating an (un)loading area becomes less concern. In a congested
business district, approaching an available area to park temporarily the truck is one of
the hardest parts of delivering goods. Since both collection and distribution have been
jointly operated among the textiles shops, significant reduction of the number oftrucks was also noted. This reduction to the number of trucks handling their products
has also positive effect to the transport cost by observing cost reduction.
Although several benefits were derived from the system, concern to the loss oftransport company providers is also reported. In addition, the loss of each shop’s
liberty to handle its own operation was seen as another setback of the system.
iv)

Textile Wholesalers Street, Chojamachi, Nagoya

In 1993, a survey was conducted for working out guidelines to facilitate distribution
in the region where commercial businesses concentrated.

39

In June 1994, “G-Net Cooperative” was established for promotion of information
networks for textile wholesalers.
In November 1994, implementation of online system for shipping and collection.
v)

Akihabara TDM pilot project

Akihabara, located in Chou-ku which is at the heart of Tokyo, is famous to its
electronic products and known to be the place to visit for latest product in the market.
The area commands large number of customers being the largest area displaying
electronic products.
The face of famous old Tokyo is very much visible in Akihabara where the streets are
narrow and stores are rowed and have very limited space. Number of stores in the area
could reach hundreds and shop managers are making up to the limited space bydisplaying their products outside the store. This practice often consume portion of
space intended for pedestrians thus obstruct the fluidity of pedestrians.
The limited number of off-street parking space by these shops generates traffic
congestion and compounded by the fact that customers are observed to come by car
since this gives them convenience to carry their purchased product home. Trucks
distributing the shops product are so huge that it was observed that there was anaverage of 15.5 vehicles per 100 meter. Travel speed in the area is at low of 14.3 km/
hour.
From October 10 to November 11 2004, a social experiment designed to createsmooth traffic flows by reducing the number of trucks on the road was conducted in
Akihabara. This was realized by establishing consortium which consists of local
authorities, retailers, manufacturers, carriers, academe and the Ministry of Land,
Infrastructure and Transport (MLIT). The central theme of the experiment is theutilization of Radio frequency identification (RFID) to manage the transport of goods.
Wikipedia, an online encyclopedia, define RFID as a method of remotely storing andretrieving data using devices called RFID tags/transponders. It is a small object, suchas an adhesive sticker, that can be attached to or incorporated into a product. RFID
tags contain antennas to enable them to receive and respond to radio-frequency
queries from an RFID transceiver.
To utilize RFID, two distribution centers were temporarily built that would house theelectronic products for Akihabara. One is located in Chiyoda-ku, which is close toChou-ku, and the other center is in Urayasu city in Chiba prefecture, beside Tokyo.
The center in Chiyoda-ku used for storing products such as PC, DVD, printer while
the center in Urayasu stored item such as TV, Refrigerator and other house appliances.
A maximum of 5-minutes on-street parking was allowed while vehicles wishing topark longer were directed to reserve parking spaces. A shuttle bus was also provided
that would take customers to Marunouchi area near the Tokyo station. The idea is tohave the customers’ cars park in Marunouchi and the bus will service them from there.

40

Aside from the previously mentioned groups, 8 unions of shopping stores and 15
companies were involved in this project.
vi)

Poppo Machida

Machida City is located in the southwestern part of Tokyo and about 30 km from thecenter of the metropolis. Above the underground Machida train station is scores of
shopping stores dotted to the streets of town. Train station in Japan is normallysurrounded by high-density urban development.
Trucks distributing goods to these shops are mixing up with customer’s cars making
the area heavily congested. To improve the flow of the traffic, vacant spaces near the
shops were designated as parking space for delivery trucks. Instead of using portion ofroad as parking space, these trucks would instead park their trucks to the reserved
parking space, unload their goods and carry them going to the recipients. An 82% of
the number of trucks that are passing the area was observed.
vii)

Kashiwa city

Kashiwa City is located in Chiba-ken and it is 30 kilometers away from Tokyo. The
city has been experiencing rapid growth and becoming ideal residential suburb ofpeople working in Tokyo due to its proximity. The city is accessible both by train andby car for one hour or so.
Two stages of urban development was conducted in the city to facilitate smooth
movement of traffic around the city’s main train station. The capacity of these roads isvery limited which prompts concern authorities to adopt TDM measures in 2000.
Some of the measures undertaken are summarized below.
1. Improvement of physical layout in front of the station- Extension of sidewalk- Organization of bicycle parking
2. Provision of parking information- Reduction of the waiting for trucks at the parking lot.
- Reduction of the number of vehicles and their parking time.
- Reduction of the environmental impact by reduction of obstruction andidling stop.
3. Cooperative delivery of goods- (Off) loading space was installed.
4. Park and ride area was prepared
Owing much to the success of the first adopted TDM measures, in 2003, further
improvement were made such as marking of the designated (off) loading area to avoid
confusion on which side of the street to park trucks (Figure 3-10). This measure
improved fluidity of the traffic and is believed to reduce traffic impact on theenvironment.

41

Figure 3-10. Provision of (off) loading area near Kashiwa station
viii)

Sapporo City distribution experiment

Sapporo is located in Hokkaido, the northernmost island of the Japanese archipelago.
The city is not only the prefectural capitol but also the center of administration,
economy and culture in northern Japan. It is the fifth biggest city of Japan.
What looks to be a very simple experiment taken in Sapporo city in 2000 yelleddrastic improvement to the traffic flow in its major thoroughfare (Figure 3-11). The
strategy was to provide the right lane as (off) loading area for delivery vehicles.
Freight vehicles , however, are not allowed to utilize the right lane of the road. Time
which frieght vehicles allowed to deliver are 9:30 to 11:30 in the morning and 14:30
to 16:30 in the afternoon.
(Off) loading is
prohibited(Off) loading lanes(Blue)

Figure 3-11. The thoroughfares where the improved delivery of goods schem are
implemented

42

Initial findings suggest there was an improvement of the bus’s travel speed. Dwelling
time of freight vehicles menwhile was shortened. It appears that the key to the
expirement’s outcome was the removal of of time spend by freight drivers locating anavailable space to park.
ix)

Nihonbashi track time plan

Nihonbashi, literally meaning “Bridge of Japan” is a city district of Tokyo, locatedjust north of Ginza. The bridge, in the middle of which, is the marker from which
mileage throughout Japan is measured. Since the district’s development came aheadof modern’s planning time,
In 1995, a plan to divide the hours for different types of vehicles was enforced todecongest the small street. The early morning provides access to delivery vehicles
who are rushing to have their goods to the shops. Shoppers and alike who visit the
district by car could park their car free of charge from 10:00 to 16:00. In the afternoon,
freight vehicles are given access again for delivery and pick-up activity (Table 3-3).
This arrangement gives higher utilization rate to the parking area and all sectors wereserved (Figure 3-12).
Table 3-3. Time zone for each type of vehicle
7:00 – 10:00 (parking timefor freight vehicles)(10:00 – 16:00) parking time
for private cars

(16:00 – 19:00) parking timefor freight vehicles

Freight vehicles can use thespace without parking meter
which correspond to someamount of money. Other
type of vehicles uses theparking meter at this timespan.

Freight vehicle still can use
the space however has to be
charged by the parking
meter.

Freight vehicles can use thespace without parking meter
which correspond to someamount of money. Other
type of vehicles uses theparking meter at this timespan.

Before segregating the parking time
(1993)

After segregating the parking time (1995)

Figure 3-12. Photo of the district before and after the schemex)

Saitama new urban core area

Saitama Prefecture is part of the Tokyo Metropolitan area and currently promoting a
new urban development project called the Saitama New Urban Centre. The project43

aims to create a bustling, integrated city with easy access and mobility for people,
goods and information alike.
In 2000, a vendor campus (like distribution center) was installed in the suburban
section to serve as consolidation of goods (Figure 3-13). Non-polluting vehicles
transport goods from the consolidation center to the city. Collection and delivery aredone on fix schedule. Shops who received customer orders can deliver the goods tothe center and call the center for pick up. The idea of the scheme is to limit the impact
of delivery vehicles to the environment by reducing its number.
Reported benefits of the system are the following: 1) improved delivery service, 2)
reduction of number of delivery vehicles, 3) reduction of environmental impact, and 4)
reduction of transport cost.

VendorcampusSaitama new urban
core area

Figure 3-13. Location of vendor campus which serves as distribution
centerxi)

Motomachi shopping street in Yokohama

The Motomachi district attracts big crowds everyday being home to shopping shops in
Yokohama. Yokohama is located on the western coast of Tokyo Bay directly south ofTokyo, and is the second largest city in Japan.

44

A social experiment of cooperative delivery system was carried out between October
20 to November 2 in 2000. This was in line to the desire of all parties concern to the
problems of congestion particularly blames from delivery truck. An access restriction
was enforced that would not allow delivery trucks to deliver between 12:00 to 16:00
otherwise parking meter would be applied.
Some of the TDM measures enforced in the area are: 1) cooperative delivery, 2)
promotion of utilization of public transport for staffs, 3) regulation of delivery time, 4)
control of illegal parking, and 5) control of idling stop. The cooperative delivery
system able to control the number of trucks operating in the district from 100 to 29.
The 29 trucks serve as common delivery vehicles among the 11 transport operators.
Regarding the utilization of public transport, out of 17 shopping centers, five
responded by facilitation means for the staff to utilize public transport. After theimposition of time restriction from 12:00 to 16:00, the rate of vehicles delivering thisperiod was reduced to 33% of total trucks from 37%. The social experiment has also
tremendous impact to the parking behavior of vehicles. Before the experiment, andaverage of 959 vehicle a day was recorded which was reduced to 708 during theexperiment and even lower down to 628 after the experiment.
xii)

Kichijouji in Tokyo

The kind of measure employed in Kichijouji district is unique in a way that parking
lots of temples and shrines were utilized along with the area of some banks. Kichijouji
is located outside of Tokyo's 23 wards and attracting significant visitors during theweekend for people wanting to get a break from Tokyo’s crowded area.
45

3500

40

3000

30

2144
2000

M inu te s

N um ber of v hecle (v he)

35
2500

1801
1500

23.2
20.7

25
20
15

1000

10
500

19.5

16.2

1183
762

5

0

0

Before an experiment
Private car

Under an experiment

Before an experiment

Truck
Track

Private car

Figure 3-14. Change of number ofcars parked in the street

Under an experiment
Truck
Track

Figure 3-15. Change of parking timeIts main station is suffering from traffic congestion which caused by several factor
such as shortage of parking facilities, unorganized delivery of goods, and highproportion of pedestrians. It was thought that if the delivery trucks could be organized45

by proving facilities where they can perform their operation, perhaps the traffic
situation would improve. This idea came to realization after considering the numberof spaces available to some temples and shrines in the area. The method employed
reduces the number of trucks parked on the street to 16% while its reduction rate to
private cars is 35.6% (Figure 3-14). Regarding the parking time, the truck’s parking
time was shortened by 2.5 minutes and 3.3 minutes for private cars (Figure 3-15).

Policies has always been perceived leaning towards the side of passenger transport.
This can be best attested to the enforcement of the truck ban along the major road ofMetro Manila. While policies concerning trucks tended to focus on controlling andrestricting their activities rather than addressing issues about how to assist them andmake them more efficient, the trend regarding passenger transport is more of a way ofaccommodating them.
3.6.2

Philippines

Philippines has adopted truck ban as early as 1978 to the major thoroughfares ofMetro Manila as a means to decongest the serious traffic congestion brought bylimited road space and high number of vehicles. This ordinance was issued by thenMetro Manila Authority (MMA). The obvious parties receiving the benefits of thispolicy are commuters (improved travel time), buses (less competition for space), andprivate cars (more freedom to drive). On one hand, this policy brought some negative
impacts to the business of shippers and truck operators who are affected by the route
restriction. Freight terminal was once an issue among the freight actors however,realization of this scheme never comes to reality due to problems related to landacquisition, lack of funding, and to some extent opposition from the trucking industry.
The recent Medium-Term Philippine Development Plan (1999-2004 MTPDP) has
four broad objectives regarding the transport policies of the country. These are : 1)
provision of more intermodal transport system to create efficient flow of traffic
particularly at major urban terminals and transshipment points, 2) establishment of a
Geographic Information System (GIS)-based urban transport infrastructure network, 3)
application of non-engineering measures such as stricter traffic enforcement, demand
management, prioritisation of high-occupancy vehicles, and land use regulations, and
4) improvement of rail, water and air transport facilities.
It appears that provision of major infrastructure is of prime importance to the MTPD.
In line with this, the 2002 JICA report identifies major projects which include: Davao
International Airport Development Project, Laguindingan Airport Development
Project, MIAA – NAIA Terminal 3, MIAA – New Manila General Aviation Airport,
NAIA International Cargo Terminal, New Iloilo Airport Development Project,
Selected airports trunkline development project (Bacolod and Tacloban), and Third
airports development project. In addition, JICA identifies nationally funded
development projects at Dumaguete, Kalibo, Laoag Interational, Legaspi, Roxas, San
Jose, Surigao, Tagbilaran, Tuguegarao and Zamboanga International airports (JICA,
Master Plan Study on the Strategic Policy of the Air Transport Sector in Indonesia.
Draft Final Report. 2002.).

46

In the following sections, the truck ban ordinance is presented while its effect toshippers and carriers is discussed.
3.6.2.1 Truck Ban
The present truck ban is covered by Ordinance No. 5, series of 1994. This ordinanceis categorized into two types, the peak hour and the “all-day” truck ban. The “all-day”
truck ban covers the lone stretch of EDSA from Bonifacio Monumento to Taft
Avenue.
The peak hour truck ban on the other hand prohibits trucks from using 10 major
thoroughfares from 6 AM to 9 AM and from 5 PM to 9 PM except Saturdays,
Sundays and holidays (Figure 3-16) . The 10 major thoroughfares covered by thetruck ban are the following:

▲

R8

CH05

R9

▲

R7

▲EX01

CH04
C5
C4 (EDSA)
C3

▲CH08
R6

C2

Port
Area

▲

C1

Legend:CH09

6AM – 9PM

R5

6–9 AM, 5–9 PM
Alternate truck routes▲ Cordon stations▲ CH12
R1

EX02 ▲

R3

Figure 3-16. Truck ban routes of Metro Manila
Source: Castro, 2005

47

1. Espana Street through Quezon Avenue from Echague Street to the intersectionof West and South Avenues.
2. Claro M. Recto Avenue from Legarda Street to Del Pan Street.
3. President Querino Avenue from South Superhighway to Roxas Boulevard.
4. Padre Burgos Street through Ayala Boulevard, Legarda Street, Magsaysay
Boulevard from Roxas Boulevard to Boston Street.
5. E. Rodriguez Avenue throught Bonifacio Avenue from Welcome Rotunda to
Shoe Avenue.
6. Sooth Superhighway through Nagtahan Bridge, and Alfonso Mendoza Street
from EDSA to Dimasalang Street
7. Taft Avenue through Quirino Avenue from Redemptorist Street to Plaza
Lawton, to include McArthur, Quezon and Jones Bridges.
8. Bonifacio Drive through Roxas Boulevard and MIA Road from Aduana Street
to the Manila Intenatoional Airport.
9. Rizal Avenue Extension form Carriedo Street to the Bonifacion Monument.
10. Reina Regente Street through Abad Santos Street from Reina Regente Bridgeto Rizal Avenue Extension.
The ordinance also provided alternate routes to be used by the trucks while the ban isin effect. The said routes radiating to and from the port of Manila are the north bound,south bound and east bound.
Northern Truck Route
From Port Area to North Diversion Road
From Port Area, traveling northward along R-10, turns right to Capulong
towards Tayuman, straight ahead towards Gov. Forbes, left Maria Clara, turns
left to Dimasalang, right Blementritt, left A. Bonifcaio.
From North Diversion Road to Port Area
From the north traveling southward along A. Bonifacio Avenue, turns right toBlumentritt, turns left to Aurora Boulevard, turn right to Dimasalang St., turnright to Aragon St., turns right to Gov. Forbes towards Tayuman St. andCapulong St. and finally, turn left to R-10 to the Pier Zone.
Southern Truck Route
From Port Area to South Superhighway
Bonifacio Drive (Roxas Boulevard) and then diverts to P. Burgos Street
towards Ayala Boulevard, turns right to San Marcelino St. and turns left at
Pres. Quirino Avenue and turns right to South Superhighway.
From South Superhighway to Port Area
From the South Superhighway, turns right to President Quirino Avenue, turns
left to United Nations Avenue, turns right to Romualdez Street, turns left to
Ayala Boulevard towards P. Burgos St., turns right to Bonifacio Drive towards
the Port Area.

48

Eastern Truck Route
From Port Area to Marcos Highway
The eastern truck route passes along R-10 (Marcos Road), turns right toCapulong towards Tayuman, straight ahead to Gov. Forbes, turns left to Maria
Clara, cross Dimasalang, straight ahead towards Constancia, turns left at
Laonglaan, straight ahead towards D. Tuazon, turn left at Quezon Boulevard
Extension, straight ahead Elliptical Road to Don Mariano Marcos Avenue(Commonwealth Avenue), turns left to Aurora Boulevard, straight ahead
towards Marcos Highway.
From Marcos Highway to Port Area
From Marcos Highway, straight towards Aurora Boulevard, turns right at
Katipunan, straight towards Tandang Sora, turns left to Don Mariano Marcos
Avenue, straight towards Elliptical Road, take Quezon Boulevard Extension,
straight towards West Avenue, turns right West Avenue, turn left Del Monte
Avenue, straight towards Bonifacio, turns left to Bonifacio Drive, turn right toBlementritt, turn left to Aurora Boulevard, turns right to Dimasalang, turns
right to Gov. Forbes towards Capulong and Tayuman, turns left to the Pier
Zone.
Shift to small trucks
The implementation of truck ban along EDSA resulted to the proliferation of small
trucks that are not covered by the ban (Figure 3-17). Small trucks having gross weightof less than 4,000 kg do the pick-up and delivery. Increase of this type of vehicles isremarkable few years after the implementation of the truck ban in 1978. For the last
decade, an increase of 14% in the registration of small freight vehicles has been
observed in Metro Manila.

Num ber of Registrations

200000T otal Trucks
U tility Vehicles ( < 4.5 T)
Private Trucks ( > 4.5 T)
F or H ir e Tr ucks ( > 4.5 T)

150000100000

50000

0
1975

1976

1977

1978

1979

1980

1981

1982

Y ear

Figure 3-17. Truck registration after the truck ban implementation
Source: Castro, 200249

A survey conducted in 2000 to 29 medium sized freight forwarders with average
employment of about 52 workers and 12 large-sized freight shipping companies withaverage employment of about 1,100 workers in Metro Manila reveals that around onethird (32 to 36.6 percent) of the total truck fleet used for distribution belongs to fourwheel trucks (Table 3-4). This type of trucks is not covered by the ban having grossweight of less than 4.5 tons.
It appears that companies into freight transport put their priority on acquiring trucks
that are ban-exempt so they can continue their operation without changing their routeor operation time due to the ban. In essence, the truck ban distort the size distributionof the fleet towards small trucks and the it also serves as driver that accelerate thesudden surge of the number of small trucks.
Table 3-4. Distribution of truck fleet
Truck type4-wheel truck (<4.5T)
2-axle 6-wheel truck (8-16T)
3-axle 10-wheel truck (25T)
3-axle semi-trailer truck (20T)
4-axle semi-trailer truck (20T)
5-axle semi-trailer truck (27T)
Truck-trailer (34T)
Total

3.6.3

Freight forwarders (%)

Freight shippers (%)

36.6
14.0
8.0
19.1
2.0
16.8
3.5
100

32.0
24.0
24.0
3.0
3.0
6.0
9.0
100

Freight Transport Policies elsewhere in Asia

Most of the countries in Asia incorporate their policy concerning freight transport totheir national transport plan. There is no such plan that crosses among the transportmodes which could serve like a funnel to the flow of goods. Instead, there is aseparate policy to each mode of transport (air, rail, road, and water) that is updated
every few years to reflect the actual situation on the ground. Thus, in the following
sections, two freight transport measures that are active in the region are discussed.
3.6.3.1 Truck Ban in Indonesia, Korea and Thailand
Truck ban is enforced where traffic congestion is serious. The central idea is to pushout trucks from major thoroughfares during the peak hours. Trucks are believed to be
slow moving and consumes large portion of the road thus its contribution to traffic
congestion is believed to be higher. Authority in major city of Indonesia, Korea,
Philippines and Thailand used truck ban measure to counter deteriorating travel speedof vehicles caused by lack of road space and other related factors (i.e. high number ofvehicles, poor maintenance and utilization of road, etc.). The characteristics of truck
ban to each city is described below:


In Indonesia, trucks whose gross vehicle weight is heavier than 3.5 tons are
not allowed to enter the downtown area of Jakarta between 7-9 AM andbetween 3-5 PM on weekdays, and between 7-9 AM and between 1-3 PM on
Saturdays.

50






In Korea, all trucks over 2.5 tons are banned from circulating within central
Seoul during working hours to help relieve congestion and to push truck
arrivals and departures into the night.
In Thailand, four and six-wheeled trucks are restricted in the Greater Bangkok
Area during peak hours (6-9 AM and 4-8 PM) while ten wheelers and larger
trucks are restricted in the morning between 6-10 AM and in the afternoon
between 3-9 PM everyday except official holidays.
In Philippines, as mentioned, the ban prohibits movement of cargo trucks
over 4.5 tons along 12 specific routes during the peak periods from 6 to 9 AM
and from 5 to 9 PM during the weekdays to the major thoroughfares of Metro
Manila.

Both Bangkok and Manila registered high annual growth of registration of small
freight vehicles exempted from the ban. Bangkok has 10% while Manila, as
mentioned, has 14% increases in the registration of small freight vehicles during thelast decade. In 1997, vans and pick-up trucks in Bangkok already correspond to 20
percent of all registered vehicles, which is more than four times that of trucks. This issignificant as the share of freight vehicles in 1990 was only about 16 percent. In
Manila, a sudden increase in utility vehicle registration was observed after theimposition of the truck ban in 1978. Thus, it may be that the effect of the truck ban is
to worsen congestion in peak hours.
Apart from developing countries in Asia, imposing truck ban and other restrictions onaccess are also popular in European cities. For instance, in Vicenza, Italy, only
vehicles with a length of less than 2.5 meter and a capacity of less than 7.5 ton are
allowed access without restrictions to the historical old town during the permitted
hours. In Amsterdam, the Netherlands, access to the city were classified based on theweight of freight vehicles. Those freight vehicles with weight between 3.5 to 7 tons
have wider access as compared to those trucks with weight of higher than 7 tons. In
particular, freight vehicles whose weight is over 7.5 tons may only use the maintransport route in the city centre. This resulted to 43% increase in the number ofdeliveries by vehicles lighter than 7.5 tons and 9% decrease of vehicles heavier than
7.5 tons from 1997 to 1999 after its adoption (BESTUFT, 2002).
Imposition of access restriction appears to be done in different objectives between
mentioned cities in Asia and cities in Europe. While it is obvious that the drivers
behind the adoption of truck ban to Asian cities are factors such as congestion andlack of road space, European cities appears to be more on protection to theenvironment as well as preservation to the old towns. Their effects to the size
distribution of trucks however are the same which heads to small trucks.
3.6.3.2 Freight terminals
Freight terminal has potential to sort out the problems of traffic congestion caused bytrucks in major urban centers. The facility could serve as storage for different truck
operators and could serve as an area where they can organize their routes and cargoes
before bringing them into the city. This measure has been widely used in Europe andJapan to solve physical distribution problems.

51

In developing countries, however, although past studies on the development of such
facilities were done in Jakarta, and Manila, their implementation were never realized
due to problems in funding, land acquisition, and some opposition from the trucking
industry. In Manila, truckers opposed the idea of developing public freight terminals
as they wanted to keep full control over all aspects of their operations. They are morefavorable to the further expansion of roads, provision of (off) loading areas, and strict
enforcement of traffic laws.
In Bangkok however, three public freight terminals has been serving the freightindustry since 1999. These facilities are located in the north, west, and east of the
Bangkok Metropolitan Region. Its long distance location from the city center however
has raised issues on its suitability.

3.7 Comparative Review of Policies
There seems to be general recognition to the importance of producing efficientlogistics system along with a policy that responds to the environment (Table 3-5).
Except Germany and the US, all countries have explicit public policy that aims totackle negative effect on the environment imposed by trucks. The drivers of theseobjectives are however quite different like the case of Japan where economic
objective is highly preferential. Countries that have old town centers like Italy andFrance were obviously prepared a policy that would safeguard their historic towns.
Controlling the access of trucks is present in all countries though its form may differ
from one country to another. In addition, within a country, form of implementation
may vary from one urban center to another. For instance, London is charging all
vehicles type that enter the city center while other cities in UK like Preston is
implementing route restriction to its city center. Some cities adopted access restriction
based on vehicle characteristics like in New York for instance; freight vehicle longer
than 33 feet cannot enter the Financial District and Midtown core of Manhattan. With
the exception of London in the UK, restricting truck’s access in these countries
appears to have link to urban management than say decongesting the area. Also notedin the table is the eco-zoning measure where only vehicles with low emission are
given access permit to certain area. In essence, access restriction can be categorized to1) time of delivery, 2) delivery efficiently, and 3) vehicles characteristics.
Although the UK has shown great interest to freight platform, there has no facilities
constructed and prefer instead the approach of providing a forum where different
stakeholders could exchange knowledge on how to best solve the problems
confronting the sector. Since rail in the US has substantial share to freight transportand yet has limited access to the urban centers, intermodal facilities are highly
important that could facilitate smooth transfer of goods from rail to truck. Other
countries in Europe including Japan and Thailand have been utilizing freightterminals as a means to organize their freight traffic.
Although freight routes exist to some countries, its primary objective has not been to
make the flow of freight more efficient but rather to avoid some sensitive places totruck or to decongest some routes by deviating freight vehicles.

52

Most city administrators are becoming more concern to the damage to environment byfreight transport thus; access restriction is becoming a common urban management
tool. This reaction from the authorities’ pushes shippers, transport carriers, and shop
owners to explore cooperation schemes that reduce the number of vehicles operating.
City logistics where stakeholders across the freight transport are cooperating havebeen proven successful in Germany and Japan.
All the countries have shown their commitment to reduce the emission level byexperimenting freight vehicles that are compatible to the environment. The Japanese
government has been long involved to activity and even supplying the technology to
the firms that have shown their concern to produce a logistics system that are lessharmful to the environment and to the society in general. In the US, the 32,000 fleetof the Department of Interior (DOI) includes more than 300 alternative fuel vehicles(AFVs) of which 15% use either natural gas or propane. Some trash packers inWashington D.C. are CNG trucks that have been in operation since 1997.

3.8 Chapter summary
There has been transition in urban freight transport policy objectives overtime. In the
beginning, provision of infrastructure through road expansion, construction of freightterminals and parking facilities is of high importance. These physical infrastructures
were designed to relieve traffic congestion in major urban centers which attract great
number of people and goods due to centralized economic activities. After having theinfrastructure, the policy objective is shifted to institutional measures for proper
utilization of the facilities. Access restriction of trucks by different means (e.g. nightdeliveries, total truck ban, time window, etc.) is imposed and utilization of IT is
promoted. The policy objective is then bended toward sustainability and safety whichtries to achieve environmental and social benefits. Access restriction measure fordifferent purpose aside from eliminating congestion such as preservation of historicsites is gaining fame while partnership between public and private sector and among
companies is endorsed.
Most countries in Europe and Japan have been experimenting schemes that aim to
strike a balance between economic and social benefits. The key issue is on how todeliver the same amount of goods in less number of freight vehicles. In addition, thevehicles used for operation met detailed specifications so as not to disturb the physicalenvironment. Issues such as consumption of urban space, emission and noise
pollution, and physical hindrance to people are something that is unlikely to become
prime movers for policy direction in the early years of policy evolution. However,recent urban management approach changed remarkably as has been seen to thedemonstration projects, which tends to put higher weight to these concerns.
Moving the policy review from developed countries to developing countries provides
an opportunity to get a wider look to the current freight transport system. The gap
between these two groups ranges from infrastructure to institutional measures.
Majority of the developed countries attain sufficient infrastructure, which support the
movement of freight. Moreover, these countries have dedicated government organ
supervising the plan and implementation of logistics facilities. The US for instancehas two government bodies, i.e. Office of Intermodalism and Office of Freight53

Management under its Department of Transport, that looks for freight issues andfreight productivity. The absent among developing countries of these two vitalelements of freight transport, i.e. freight facilities and freight institutions, is anindication to the lack of concerted effort to fully understand the underlying freight
issues. In addition, this could also be interpreted as a sign that freight issues are notamong the immediate issues in transportation. In the next chapter, the focus of
discussions will be cooperative delivery system along with other TDM measures thatare potential candidate for freight transport management.

54

Table 3-5. Differences in polices among countries
FranceGermany
Italy
The Netherlands
United Kingdom
Japan
USA
Efficiency
Reduction of local Reduction of freight Efficiency
Efficiency
Improving transportEfficiency
improvement
emissionstraffic and shopping
improvement
improvement
improvement byefficiency
Reduction of
Accessibility
trips
Reduction of
Reduction ofengaging stakeholders Reducing the transport
hindrance
improvement
Reduction of local
energy consumption
environmental impact
Reduction ofshare of GDP
emissionsand emissions
environmental impact
Transport
High transport cost 
Underlying problems
Urban structuralProtection of historic Congestion
Congestion
Environmental
inefficiency
Congestion
enforcement
problems
centersEnvironmental

Rising of freight traffic
Heavy duty trucks Congestion
Accessibility
Transport inefficiency 
problems
in urban areasEnvironmental
problems
dominated by roadproblems
transport
Implementation of Implementation of 
Licensing andImplementation of 
Implementation of time 
Access control to
Access control to
Implementation of time windows andweight restrictions
regulations
time windows, weighttime windows, weightwindows and weightsome urban centerssome urban centersweight restrictions
Implementation of and volume
and size restrictions 
restrictions
Road pricing
Truck lane restrictions
Experiment withpermits to enter
restrictions
Experiment with

Experiment with lowVehicle length
low emission zones
shopping malls
Experimenting withpermits (green
emission zones
restriction
temporary closing
sticker)when emission limits
are exceededExperiments with Implementation of 
Freight centersImplementation of 
Implementation of 
Implementation of
FQP calls for dialogue 
Implementation of
freight village
consolidation
among stakeholders
different types ofintermodal facilities
freight villages
freight centers (GHZ)
terminals
freight terminals
Freight routes
No special routes Experiment withNo special routes

Attempt to use bus Some routes specified 
Handful of US cities
Truck ban in outer 
freight routes
routesfor HGV
lanes of some routes
with truck routesIntercity freight
Experiments withat night
trainsfreight routes nearindustrial areas
City logistics

No city logistics

Implementation of 
Small freight depots in 
Skeptical to the idea 
A few cases ofNo reported case
Attempt but failed experience
cooperation in city
implementationsome cities

No experiments
logistics but ending
Government
promotion
Experiments with Subsidizing of
Low-emission vehicles 
Experiments with Experiments with Experiments with non- Experimented to some Experimented with
pollution trucks
electric/hybrid trucks
citieselectric vehicles
CNG trucks to someelectric trucks
electric and CNG
truckscitiesConsultation
Local consultation
Local consultation
Local consultation
Local consultation platform National consultation
Local consultation platform
National consultation
platforms
platforms
platform
platforms
platform
Policy level
National
National
National
National
National
National
National
Source: Visser, et al, 1999
Note: Italy, UK and USA are work of the author

Two main policy
objectives

55

Table 3-6. Urban Freight Transport Project Research authorized by European
Commission
Name of project

Background and Objectives

LEAN - Integrationof LEANLOGISTICS in
urban multimodaltransport
management to
reduce spacedemand andoptimise use oftransport mode

The project aims at developing and demonstrating new concepts todistribute and collect goods in urban areas. The scope include fivedifferent levels: 1. Logistics concepts to improve the productivity intransport organisation; 2. City-Terminal operation to improveforwarding processes in view of the whole logistics chain, even withadditional goods transfer points and handling costs; 3. Telematics
applications, to improve control of goods distribution process; 4.
Tools for administrations to influence transport without radical
disruption of economic activities; 5. Alternative transport modes
recommendations to support significant modal shift to rail.
After a review of the actual and current European city-logistic
schemes, the feasibility of new concepts will be analysed for thefollowing: 1. The logistic operational level will address the problem ofbuilding an efficient organisation of transport chains with the supportof new software applications, also to manage the highly automatedtransport information chain on a 'real-time' basis and provide required
information availability to the respective staff. 2. The City-Terminal
operation concept will organise the distribution and recollection of
goods on a sustainable basis for traffic and environment at an
acceptable price level. It will make use of telematics application tooffer new concepts for highly automated data interchange between
different types of data, especially considering the mobile data
communication requirements and their costs. 3. The City
Administration level will implement new ways of access control andregulation procedures, and space ressource management for urban
goods transport without major risk of low acceptance, neither by the
economy nor by the inhabitants. In particular, innovative dynamic
loading management systems will be tested.

BESTUFS –Thematic Network"Harmonisation ofstrategies andhighlighting
best practice todetermine
optimum URBANFREIGHTSOLUTIONS"IDIOMA –BESTUFS is a co-ordination activity to encourage the co-operation
between domain experts, re-search institutions, urban transport
operators and city administrations in order to identify and todisseminate innovative urban freight transport solutions which areconsidered as best practices within Europe.
BESTUFS is establishing and maintaining an open European
network between urban freight transport experts, usergroups/associations, ongoing projects, interested cities, the relevant
European Commission Directorates and representatives of nationaltransport administrations in order to identify, describe anddisseminate best practices, success criteria and bottlenecks withrespect to the movement of goods in urban areas. The network iscollecting, comparing and summarising available experiences andresults of projects and initiatives mainly in Europe, is organisingthematic workshops, clustering meetings and conferences and isfurther disseminating its results via the Internet, via newsletters andvia other dissemination channels. BESTUFS is contributing to theintegration of urban collection and delivery services into door-to-doortransport and logistics chains and to the improvement of the quality oflife in urban areas
The IDIOMA project aims to demonstrate the possibilities to improve56

Innovative
distribution withintermodal freight
operation in
metropolitan areas

the distribution of goods within metropolitan areas and between
intermodal transport terminals/ freight centres and metropolitan
areas. The IDIOMA project consists of five Demonstration Projects
which, during the project, will be prepared, implemented, validated
and evaluated. Before that, an over-view of all relevant European
projects related to intermodal transport and city logistics has been
provided.

COST 321 –Urban goods
transport

COST 321 is a research project studying the design and operation of
innovative measures to improve the environmental performance offreight transport in urban areas. Analysing how the air pollution, noiseand energy consumption are reduced by optimising the use of trucks
in city traffic through the application of modern logistical devices andappropriate administrative measures.

SOFTICE –Survey on FreightTransport
including a costcomparison for
EuropeThe project consortium is composed of 7 partners coming fromdifferent countries within EU and Switzerland.
The aim of SOFTICE is to demonstrate the interactions between
production costs, transport costs and transport demand for freight,
and to demonstrate the benefits of harmonized freight transport
costs.
The project will also provide a framework for discussions at theEuropean level and facilitate the dissemination of national results andfindings. It will assist the development of comprehensive policies on
freight transport at the Community level, as well as informing nationaland regional governments.

FV-2000 – qualityof freight villages
structure andoperations

Freight Village is structured by its objectives:
Objective 1 : analysis and evaluation of the impacts of FV lay-outs
and operations on the improvement of intermodal transport market
share, i.e., determination whether the proximity of different transportand logistic activities is a key factor for improving the use ofintermodal transport (integrated vs. non-integrated FVs);
Objective 2 : establishment of the merits and limits of thedevelopment of Freight Villages for the enhancement of intermodaltransport competitiveness; this will be based on benchmarking andanalysis of the best practices and case studies;
Objective 3 : establishment of the environmental impact of FVs andintermodal terminals;
Objective 4 : definition of guidelines and management tools for the
improvement of risk and environment management, working
conditions and safety in FVs;
Objective 5 : increasing the awareness of FV operators (FVpersonnel, private transport companies personnel, etc.) with regard
to the environment, work safety, quality, risk prevention andassessment.
The project will consolidate extensive knowledge based on the actual
structure and operations of FVs, taking into account the transport
environment in which they operate. For this purpose, specific data57

will be collected at the test sites, as well as a validation andverification of the management tools with reference to theparticipating freight villages.
The project activities will take place in 7 countries in the European
Union, with the participation of one or more freight villages.
The countries are: · Denmark, Finland, France, Germany, Italy, Spainand Sweden.
FREYA – Towards
the networking ofEuropean freightvillages

The FREIA Project has these three main gols:


INFREDAT –Methodology forcollecting
intermodal freighttransport data

The INFREDAT project aims at developing a consistent methodology
for collecting intermodal freight transport data. The proposed
INFREDAT methodology will be achieved by the following major
operational objectives:--

REFORM –Research on

To improve the accessibility of European intermodal freightvillages and their related transport networks for small andmedium sized freight operators
To stimulate the commercial options of transport SME's andthereby improving the market share of intermodal transport.
To develop models and concepts for a virtual freight village
information system through the use of Internet or VAN-networks.

to identify and to monitor the actual transport (door-to-door) flows
of goods in complete chains of different transport modes;
to study the composition of intermodal transport chainsconcurrently with the development of different logistic services bymaking the distinction of the so-called logistic families;
to identify market segments for intermodal freight transportconsidering all different transport modes;
to clearly define the intermodal freight transport data to becollected as well as the methodologies for data collection andprocessing;to investigate existing information sources and methodologies
and to derive approaches for best practice;
to elaborate a data model for data collection and data processing
including rules to avoid double counting;methodologies for the forecasting/estimation of missing data;- modal split estimations;to identify appropriate sources of information and new
methodologies considering automatic data collection in a mostefficient and profitable way to both data providers and data
users;
to set-up a mechanism for gathering in a consistent way thebasic data on transport chains, with prominence to maritime
and/or air stage;
to demonstrate the feasibility of the proposed methodology andto evaluate the results under the three pilot cases;
to evaluate the cost-benefit aspects of the intermodal data basein general and of the data collection methodology proposed;
to design a detailed implementation plan for the realisation of the
project methodology on a European level considering aspects ofcommercial exploitation and further research needs.
%

The REFORM project has two fold objectives:58

freight platforms
and freightorganization

Primary
Analyse and evaluate the effects of freight platforms regarding theurban traffic. 2. Provide guidelines and criteria for designing, locating
and organising freight platforms in urban areas with the view of
optimising the benefits of these platforms and to reduce their
negative effects.
Secondary
1.
2.
3.

4.

City Freight

Empirical investigation as a basic requirement for modellingand simulating the effects of freight platforms.
Develop a methodology to calculate or estimate the different
effects of freight platforms.
Practical application and evaluation of freight platforms by adetailed analysis of test sites in Berlin, Brussels, Madrid andRome. The focus of the practical application is set
on: · coordination of big interports (long distance traffic) withcity terminals (urban and regional traffic); · organisationaland operational requirements for the development of
successful freight platforms; · multimodality of freightplatforms (road, rail, waterborne traffic); · operational
improvements to be expected from co-operation schemes.
Derivate recommendations and guidelines.

City Freight is a research project on inter- and intra-urban freight
distribution networks. It will carry out an analysis of selected freighttransport systems already functioning in Europe and evaluate theirsocio-economic and environmental impacts in an urban context, witha common assessment methodology. CITY FREIGHT will focus on
innovative and promising logistic schemes in the seven countries
represented in the project consortium. The objective is to provide
guidance to interested stakeholders (government, regional or local
authorities, network operators, shippers and consignees) on theadvantages and drawbacks of some recent innovations in the field ofinter- and intra-urban freight distribution systems.
In the City Freight project seven innovative city logistic projects
and/or policy plans in seven different countries, will be analysed,
evaluated and compared. Afterwards, a Best Practices Handbook will
be compiled. In this handbook best practices for the different kinds ofactors (logistic services providers, shippers and retail companies,
public authorities) will be de-scribed categorised by the effects they
have on f.i. environment, economy, land use etc. For the various
cities involved also recommendations and an implementation plan
will be derived.

59

REFERENCES
1. Allen Consulting Group (2001).Comparative Public Policy on Freight Logistics.
A report prepared to the Department of Transport and Regional Services.
2. BESTUFS - Best Urban Freight Solutions (2004). A project funded by theEuropean Community under the “Competitive and Sustainable Growth Program.
www.bestufs.net
3. Brewer, A. et al (2001). Handbook of Logistics and Supply-chain Management.
Elsevier Science Ltd. Oxford, UK.
4. Castro, J. et al (1999). Estimating the Suitable Number, Location and Scale ofUrban Physical Distribution Facilities. Journal of Eastern Asia Society forTransportation Studies.
5. Castro, J. et al (1999). Study on location planning of distribution facilities. City
Logistics I. Institute of Systems Science Research, Japan.
6. City Freight (2002), Comparative survey on urban freight, logistics and land use
planning systems in Europe. www.cityfreight.org
7. Department for Transport, 2003, Good Practice Case Study 410, “Freight Quality
Partnerships” (http://www.freight.dft.gov.uk/gpcs410/pdf/gpcs410-final.pdf).
8. Department for Transport, 2003, Good Practice Guide 335, “A guide on how to
set up and run Freight Quality Partnerships”( http://www.freight.dft.gov.uk/gpg355/pdf/gpg335-final.pdf).
9. Department for Transport, 1999, “Sustainable Distribution: A Strategy”( http://www.dft.gov.uk/itwp/susdist/ ).
10. European Commission, 2000, “A Sourcebook: Good Practice in FreightTransport” (http://europa.eu.int/comm/environment/trans/freight/booklet_en.pdf).
11. European Commission, 2001, “European Transport Policy for 2010: Time to
Decide”,(http://europa.eu.int/comm/energy_transport/library/lb_texte_complet_en.pdf)
12. Forum for City-logistik (via: http://www2.city-logistik.dk).
13. Freight Logistics and Transport System in Europe. European Council of Applied
Sciences, Technologies and Engineering (2000). Paris.
http://www.euro-case.org/Activities/Freight_ExecutiveSummary.pdf
14. Good Practice Case Study 410 (2003).Freight Quality Partnership
http://www.dft.gov.uk/stellent/groups/dft_freight/documents/page/dft_freight_507
561-01.hcsp

60

15. He, K. et al. (2003). A Comparative Study on Urban Transport system andRelated Environmental Impact in Asian Mega-cities: Beijing, Shanghai and Tokyo.
Proceedings of International Workshops on Policy Integration towards Sustainable
Urban Energy Use for Cities in Asia. Institute for Global Environmental Strategies.
16. Hesse, M. and Rodrigue, J. (2003). The transport geography of logistics andfreight distribution, Journal of Transport Geography. The transport geography oflogistics and freight distribution, Journal of Transport Geography
17. Holgui_Veras, J. et al (2005) Off-peak Freight Deliveries: Challenges andStakeholders Perceptions. TRB 2005 Annual Meeting CD-ROM.
18. Ieda, H. et al (2002). Comparison of traffic and environmental load of freighttransport – Bangkok and Tokyo. City Logistics II. Institute of Systems Science
Research, Japan.
19. Intermodal Surface Transportation Efficiency Act of 1991. U.S. Federal Highway(http://ntl.bts.gov/DOCS/istea.html)
20. JICA, Master Plan Study on the Strategic Policy of the Air Transport Sector inIndonesia. Draft Final Report. 2002.
21. Kohler, U. (1999). City logistics in Kassel. City Logistics I. Institute of Systems
Science Research, Japan.
22. Kubota, H. et. al (). Kamakura’s Composite Experimental Scheme of TDM.
EASTS
23. Kuse, H. et al. (2002). Conceptual study on urban freight transport policy planningfrom the viewpoint of logistics. City Logistics II. Institute of Systems Science
Research, Japan.
24. MMUTIS, 1999. Traffic environmental study, air and noise pollution in Metro
Manila, Metro Manila Urban Transport Integration Study Technical Report 10,
Department of Transport and Communications, Metro Manila.
25. Newman, P. and Kenworthy, J. (1989) Cities and automobile dependence: Aninternational sourcebook, Gower, Aldershot, England.
26. OECD. 2003. Sustainable transport in the Asian Region. Report on theInternational Conference on Environmentally Sustainable Transport in the AsianRegion. March 23-25, 2003. Nagoya, Japan.
27. OECD/EMT (1996) Urban travel and sustainable development. OECD, Paris.
28. Ogden, K. W. (1992) Urban Goods Movement. Ashgate, Hants, England
29. Ohmae, K The Borderless World, Fontana, London (1990).

61

30. Pivo, G. and Carlson D. (1997). Learning from Truckers: Moving goods incompact, livable urban areas. Research report for Washington State
Transportation Commission in cooperation with U.S. Department of Transport.
Research Project T9903, Task
69.( http://depts.washington.edu/trac/bulkdisk/pdf/431.1.pdf)
31. Presentation on the redevelopment projects in Marunouchi business district (2004).
13th Annual Workshop of the Workplace Network.
(http://twn2004.jp/final_report/Report/May_10_site_visit_to_Marunouchi.pdf)
32. Serageldin, I. and Barrett, R. (1993) Environmentally sustainable urban transport:
Defining a global policy. World Bank, Washington D.C.
33. Takahashi Y. et al. (1999). A Simulation Study on the Effect of PhysicalDistribution Facilities in the Tokyo Metropolitan Region. City Logistics I.
Institute of Systems Science Research, Japan.
34. Takahashi et al (2004). A Study on Traffic and Environmental Effects of
Cooperative Delivery Systme in CBD. Case Study in Marunouchi Area. WCTR .
35. TRILOG (Trilateral Logistics), 2000. Asia-Pacific Task Force, Logistics
Integration in the Asia-Pacific Region, Institute of Highway Economics, Tokyo.
36. Transportation Policy: Evolution of Federal Freight Transportation Policy(http://www.ops.fhwa.dot.gov/freight/theme_papers/theme_paper_index.htm)
37. US-Federal Highway Administration (2000). Freight Systems: From System
Construction to System Optimization. A working paper.
http://ops.fhwa.dot.gov/freight/theme_papers/final_thm6_v4.htm

62

CHAPTER

4

Cooperative Delivery Systemand other Transportation
Demand Management measures
4.1

Introduction

In the previous chapter, it was shown that cooperative system has been widely used inmany cities in different parts of the world as an instrument to reduce the number oftrucks on the road. Some are adopting the system to counter the decline of profits
because of the structural re-organization of the sector as it has been seen in Italy whileothers opt to adopt the system to fit-in to the requirements of the city which are notsuitable for large number of trucks. Many literatures pointed out that the reduced
number of trucks because of the enforcement of cooperative system is benefiting theenvironment in great deal.
Obviously, there is a need to produce a package of measures to minimize if noteliminate the problems induced by freight transport to the environment and to thesociety as a whole. These problems can be seen in the form of congestion,
environmental pollution, accidents, and consumption of urban space. The cause of
these problems is very dynamic in nature although there are two clear trends that are
not positive in urban freight sector: 1) unbalance share of modes in freight transportand 2) increasing ton-km of trucks.
This chapter rationalizes cooperative system as one of the most powerfultransportation demand management measures (TDM) to confront the growing
problems associated with trucks in the urban environment. Section 4.2 charts somegoals and general objectives of freight transport policies. As policies are driven by avariety of factors, Section 4.3 identified the key drivers of policy implementation andnoted the trend of policy direction between developed and developed countries. The
following section presents a summary of strategies for managing freight movement
and all kinds of policy usually adopted by both private and public sector to restrain
problems born out of urban freight distribution. Section 4.5 then illustrates how they
can be classified into infrastructure-based and transportation demand-based strategies.
The concept of TDM and its examples, including CDS, are shortly discussed in
Section 4.6. Section 4.7 thoroughly discussed CDS and the concept of freight centers,
which has been in used to many countries in Europe as well as in Japan. This section
clarifies their differences in terms of function, organization and objective. The chapter
concluded in Section 4.8 where the issues related to urban freight distribution issummarized.

63

4.2

Freight transport policy goals

Any policy is driven by a certain goal. And goals can be categorized to short and longterm. Generally, short term goal involve regulatory measure; for instance, reserving
portion of the road as (off) loading area for trucks to allow the fluidity of the traffic.
On the other hand, long term goals are deemed to be achieved in a long period of timesince drastic changes are usually involved. An example is the new measure in thedraft city master plan of Paris (expected to come into effect on 2006) requiring newcommercial and industrial buildings (to be constructed) above 500 m2 and within the“Zone urbaine générale” (General urban zone) to provide delivery and accessfacilities in the city.
Ogden (1992) provides a clear direction in which policies can be aimed by classifying
objectives into economic, efficiency, environmental, safety, infrastructure, and urban
structure. This classification shows just how broad the objectives are and how drafting
solution to serve one or two of these objectives can be challenging. The broadness of
this policy objective could also be a source of conflict when applying measures. For
instance, measure like encouraging night delivery of goods to reduce congestion
during the day suits economic and efficiency objectives but might not fit with safety(previous study shows that driver’s awareness is decreasing and becomes prone toaccident [Castro, 2003]) environmental objectives (noise emission is the biggest
problem with night deliveries that affects the quality of life of inhabitants). The
complicated issues of producing a measure or bundles of measures that would serve
every end of the policy objectives will be discussed as the chapter progresses.
The importance and controversy of deciding measures for urban freight transport
assured to generate attention among the concerned parties. Another influential study
conducted concluded that policy actions generally centre on three main goals which
are listed below (City Freight, 2002).
Improvement of accessibility and circulation: These policies are aimed at achieving
infrastructure that complies with certain quality demands. Measures can consist ofbuilding infrastructure, using infrastructure more efficiently and/or imposing
regulation on infrastructure use. What is positive for one traffic sector, however, canwork out negatively for other sectors within or outside the traffic and transport world.
Improvement of environment and safety: Reduction of emissions and enhancing
the safety of the traffic systems are the goals of these policies. Sustainability
definition issues, the trade-offs between optimal and maximal safety, and the impactsof environmental questions on the transport and logistics sector will be among thetask expected to be discussed.
Strengthening of competitive position: This entails creation of right conditions tomaximize competition of the sector as a whole, and enhancement of thecompetitiveness of transport companies and logistics service providers. Employment
in the logistics sector and its contribution to the GDP and tax returns are dependent on
its competitive position. Further, an efficient logistic sector helps to increase theproductivity and competitiveness of the whole economy.

64

4.3

Diversity of key drivers for implementing urban transport policy

Congestion is often the key driver for issuing a new set of policies relating to freighttransport as the case of truck ban in most developing countries (see Chapter 3 fortruck ban discussion). For a developed country, this might be just one of the forcesthat pull the policy formulation as many other issues such noise, vibration havesignificant influence to the birth of the new policy. A European comparative study(Lewis, 1997) concluded that congestion, air pollution, noise, safety and intrusion are
considered as the most important negative impact of freight traffic within urban areas
and are seen as key factor in implementing policy measures (Table 4-1). It appears
that developed region has broader basis for policy formulation while developing
region is singling out congestion as sole springboard for policy intervention.

Bologna






















3
3
1
1
2
2
0
1
3
3
1



2










Total scoreBarcelona

ZurichChester/
LondonWinchester

Congestion
Environment
Noise
SafetyIntrusion
Political considerations
Cost
Lack of loading facilities
High percentage of in-house transport
Poor utilization of vehicles
High proportion of commercial traffic
Retail balance between retail and transport
practices

KasselKey Factors in implementation

MonacoTable 4-1. Key-factors in the implementation of measures regarding urban
freight transport

Source: Lewis, 1997
The above discussions on key factors in implementing policy measures between
developing and developed countries exposed just how much approach to freighttransport issues differ. Apparently, the difference in approach has a probable link tofactors such as economic performance, digital infrastructure (ITS and ICT) and otherfactors. For instance, e-shopping (shopping through internet) is quite popular in
European cities and other cities in developed countries which would aggravate thealready poor state of vehicles utilization (i.e. low load factor). If this practice becomes
common among freight carriers, this would deteriorate the traffic flow and might
become a source for implementing a measure such as access restriction based on loadfactor. E-commerce is only successful when digital infrastructure is efficient which ishardly found in developing country.

65

4.4

Freight Movement Management

There are number of strategies that can be pursued to advance freight transportation
management. Meyer (1997) list down some of the measures that when taken bringenormous improvement to the movement of goods.
Traffic Management – Transportation System Management (TMS) measures toimprove the flow of freight include such things as truck-only lanes or roads, providing
region-wide traffic information to freight movers, and preferential moments at toll oraccess points.
Improvements at shipping/receiving points – On-street loading and unloading can
be facilitated by designing additional curb space for loading zones and enforcing time
restrictions. In addition, intermodal transfer facilities can be developed or enhanced tofacilitate truck-rail cargo transfer.
Reducing operational and physical constrains – Traffic signal timing can bechanged, or demand-actuated signals can be used at intersections with large volumesof trucks to compensate for the acceleration, deceleration and turning characteristicsof trucks. Intersections can be widened and horizontal and vertical obstacles (e.g.
islands, lamps and utility poles) can be removed or relocated.
Changes in business operating practices – Business-operating practices to reduce
the time required for pick-up and delivery reduce the transport costs of shippers andreceivers, and improve the performance of the transportation system. In addition,changes in time of delivery, e.g., at off-peak hours, will provide positive benefits topeak hour travels.
Changes in public policy – The commonly considered technique for relief of truckinduced congestion is the separation of trucks from other traffic. Land use planning,zoning and industrial location policies and building regulations requiring off-street
loading and unloading facilitates can be used to separate freight-oriented. Anotherpolicy action is to charge tolls during peak hours to encourage truck movements toshift to no-peak hours, less congested routes, etc.
Investment in rail – Improvements in rail transport could alleviate heavy truck use of
highways leading to ports and freight terminals.
Goods movement management during peak period traffic can effectively reduceoverall congestion. The development of the off-peak hour system for urban goods
movement through various incentives (tax and otherwise) assist in the congestion.
A more detailed measure that is being applied to improve freight transportation isavailable in Table 4-2. Although the contents were arrived through analyzing freight
distribution problems in European cities, nonetheless, most of the measures are quite
general in form and therefore can be adopted by any city. Problems like traffic
congestion and negative impact of freight transport to the environment are two mostcommon problems associated to the industry and measures to curve these problems
are also quite the same – for instance, provision of (off) loading facilities, time
windows, etc.

66

Table 4-2. Problems and developments in the freight transport sector
Problems and developments

Initiatives (implemented, planned or suggested)

Congestion in town centerConsolidation of deliveries, Urban distribution
centers
Off peak and night deliveries
Access and parking restrictions (based on thetime of the day, vehicle weight, length, capacity
usage, destination)
Dedicated routes for freight traffic in the cities
Usage of bus lanes for freight traffic
Dedicated lanes for freight traffic on highways
Development of bypass roads and ring roads
Increased capacity in traffic bottlenecks
Underground delivery
Usage of alternative transport modes (rive boats,trains, trams, metro, pipelines, bicycles)
Reduction of passenger traffic (public transports,home deliveries and deliveries to collect points)
Intelligent traffic management system

Scarcity of space in town centers(especially in historic ones)

Usage of vans or bicycles in deliveries
Underground distribution

Reduced accessibility to towns or
premises

Flexible and widened time windows
Harmonization of regulations (atcity/region/country/int’l level)
Long term stability of regulations in order to allow
time for developing new technologies to copewith the regulations
Integration of regulations with information policies
and investments














Environmental impacts
Emission








Noise



Awareness programs and codes of behavior
Restricting access of codes vehicles abovecertain tonnage, age or emission level
Reverse logistics
Usage of environmentally friendly fuels
Promotion of alternatives transport modes (rail,waterway, bicycles)
Promotion of intermodal transport (facilities,
documentations)

Restricting night deliveries and access toresidential areas
Rerouting of freight traffic
Relocation of logistics and industrial activities
Underground deliveries

Safety risks

Routing of dangerous goods traffic
Development of vehicle designs
Collision warning system

Lack of on-street or off-street
unloading places for delivery
vehicles

Designated unloading places for delivery vehicles
Taking delivery requirements into account intown planning and building permit processes
Guidance for studying and planning unloading
space needsIntelligent Parking Management System
Van and truck design allowing fastunloading/loading (lateral doors, low floor)
Unloading and loading equipment






67

Problems and developments

Initiatives (implemented, planned or suggested)

Lack of parking places for goodvehicles

Designated parking areas
Intelligent Parking Management System

Violation of parking and access
restrictions

Simplification and (city/regional/country level)
harmonization of regulations
Support of restrictions by positive initiatives suchas information and investment
Checking the consistency of regulations withother relevant factors influencing urban traffic(other public measures) and market driven
development
Entry poles, access cards and certificates
Improved law enforcement and surveillance(cameras)
Adequate and clear signaling and guidance







Fragmented and frequentdeliveries







Consolidation of shipments
Urban distribution centers
Nominated day and time deliveries
Restricted access of vehicles with low loading
percentage
Usage of ICT applications in logistics planningand operations

Poor unloading conditions



Freight traffic needs to be taken into account inplanning and granting building permits

Urban freight traffic neglected intown and traffic planningUrban freight policies and strategies
Planning guideline manuals
Research on the topics

Lack of cooperation and exchange of information between public and 
private actors
Distribution of e-trade purchases

Cooperative forums
Urban transport policies and programs
Urban transport studies
Collection points and boxes (both for private andpublic customers)
Allowing deliveries in the evening

Variety in traffic regulations

Harmonization in regulations (access restrictions
based on time and vehicle characteristics,
standards)

Lack of information concerning
urban freight trafficStudies, surveys, workshops
Development of databases and statistics
Best practice dissemination

Market demands (customer
tailoring, low price level, speed)

ICT applications in production and logistics
planning and operations (warehousing,transportation)
Standardization of loading units
Cooperation of companies
Infrastructure development
Company subsidies to improve transport
efficiency
Van and truck design (lowered floors)







Source: City freight, 2002

68

4.5

Policy options

Policies measures can be grouped into two, infrastructure-based measures and TDMbased measures. Measures under the first category are those measures that involve
transportation facilities and infrastructure development (e.g. freight terminals
development, road network improvement, truck parking and loading facilities) which
are characterized by huge amount of capital investment. On the other hand, measures
under the second category are those measures that involve policy and regulatory
which do not require large capital investment.
Although it was emphasized earlier that the focus of the thesis is on the TDM
measures rather that the infrastructure-based measures, it is rather appropriate tosupply information regarding freight centers due to its familiarity as freight transportmeasures both to European and Asian cities (e.g. UDC of Tokyo and publicdistribution centers of Thailand). Freight centers serve as central facilities where it ispossible to process and store goods before bringing in to the city center. Thesefacilities also provide the platform where different carriers could perform cooperative
delivery taking advantage to the common location where their logistic activities are.

4.6

Concept of Transportation Demand Management (TDM)

Transportation Demand Management (TDM) refers to a wide range of actions andmeasures taken to improve the traffic flow and enhance the transport environment
without substantial capital investment. The key to this strategy is the efficient use oftransport resources. TDM operates through legislative, traffic engineering, andoperational measures to attain the goal of better traffic environment.
TDM becomes increasingly important alternative measure as significant improvementof road transport becomes difficult in urban areas. Since this measure does not require
substantial capital investment to implement, it becomes the focus of attention of many
policy makers both in developed and in developing countries. The objectives of TDM
are very broad that it may pose enormous impact on the social, economical, andenvironmental condition of the society. These objectives are:To enhance the efficiency of the transportation system by reducing demand
while increasing supply;
To promote balanced modal split among different modes of transport;
To improve environmental conditions by reducing transport congestion; andTo promote sound and balanced development of a society through efficienttransportation system.

These objectives can be better understood by looking at Figure 4-1. The figure
illustrates the striking difference in method of addressing the growth of traffic
between traditional method and TDM method. In most cases, there is an imbalance
between traffic volume and the capacity of transportation infrastructure. Traditional
method opts to meet the volume of traffic by expanding the capacity of thetransportation infrastructures. Traffic volume however is hardly meet thus sooner oflater, traffic congestion and other related problems occurs. Further, allowing thetraffic to increase at its natural pace would be hard to meet through upgrading the69

infrastructures without harming the environment. There are certain limits where theinfrastructure could be expanded without posing damage to the environment. On thecontrary, the TDM approach intervenes the growth of traffic to ensure that suchgrowth is within the capacity of the infrastructure. Construction of additional
infrastructures to meet future demand under the TDM measure is also done with great
consideration to the capacity limit of the environment.

Present situation

Traffic Demand

Capacity

Tradional Plan

TDM

Environmetal Capacity
Traffic Demand Construction

Traffic Control Construction

Figure 4-1.Concept of TDM
Note: This TDM interpretation is of Prof. Yoji Takahashi. There are lots of TDM interpretation such as
this one available from the website of Tokyo Metropolitan Government( http://www2.kankyo.metro.tokyo.jp/jidousya/roadpricing/1siryo4.htm)

4.6.1

Objectives of some TDM measures for freight transport improvement

The objectives of TDM measures that are mainly aim to improve freight distribution
are presented in Table 4-3. In the preceding chapters and in the early part of thischapter, there were lots of discussions regarding the importance of formulating abundle of measures that is capable of meeting social and economic benefits as
precondition to its success. Castro et. al. (2002) states that most efficient measures arelikely those that meet social and economic benefits simultaneously.
By looking at the same table, it can be realized that CDS is ahead of other TDM
measures in meeting all possible objectives for implementing policy measure. Further,present urban freight transport condition favor cooperative delivery system as many70

urban centers banning trucks to minimize its social impact (Best Practice Handbook,2001). There is also the difficulty of supplying freight and transportation
infrastructures in view of difficulty of finding financial resources.
Table 4-3. TDM measures for freight transport and their objectives
Objectives
TDM list

Traffic

Cost

Environment

SafetyFreight terminal
Cooperative delivery system
Road link improvement
Truck parking and loading facilities
Guidance and information system for goods
Freight regulation
Road pricing and parking charges

4.6.2

Review of the application of most common TDM measures

Although CDS appears to be the most powerful measure to improve freight
distribution in the city, it is still of interest to review the application of other TDM
measures to note their strength and limitation. Further, this review covers the mostcommon TDM measures that both impacting freight and passenger transport. These
measures, i.e. road pricing, modal shift, cooperative delivery, access restriction,utilization of ITS and ICT, and land use zoning, are defined below. The list appears tobe quite large and each measure’s ultimate goal may vary from one to another. The
different goals of these measures will be discussed to have an idea on which among
the measures has the strongest impact to improve city freight distribution.
Road pricing
A handful of countries have been benefiting from the development of road pricing.
Singapore, Norway, and the UK have enjoyed financial revenues derived from thisscheme which they intelligently invest to the improvement of transportation
infrastructure. Critics argue however that there are still many issues to be resolved inthis scheme which includes the public and political impact (politicians might have asecond opinion to the scheme due to the fear of public opposition), equity impact (i.e.
fairness of the right of access to transport infrastructure for different groups of people
and the distribution of benefits associated with reduced congestion), and economic
impact (those businesses lost customers due to the scheme). Moreover, road pricing isperceived as a measure more to do with passenger transport although it does have an
impact to freight transport.
Modal shift
Modal shift strategy can help balance the overall modal share of transport and also
plays significant role to reduce the green house gases sources from the transportation.
This measure calls for the modal shift from road freight transport to rail, waterway
and, short sea shipping. The idea is take away long distance freight transportationfrom trucks to other less harmful modes of transportation to environment. Europe has

71launched on July 2003 the Marco Polo which aims to maintain the traffic sharebetween the various transport modes for the year 2010 at its 1998 level. The Japanese
government in line with its commitment to Kyoto Protocol also adopted modal shift
strategy to reduce environmental problem sources from road freight transportation. It
appears that modal shift is a strategy aimed to balance the distribution of freighttraffic among the transport modes which when done is expected to contribute to theimprovement of environment through reduced traffic congestion. However, thisstrategy’s impact to city distribution where the congestion is serious is very limited
due to the limited access of other modes in the city centers. At the end of the
distribution chains, trucks would still be called to service the freight coming fromother transport modes.
Cooperative delivery
The idea of cooperative delivery system which is to improve the freight distribution
thorough consolidation of loads is very compelling. Success stories of this scheme canbe witnessed in some cities of Germany and Japan with several discussions regarding
the idea taking place across Europe. The TDM examples mentioned in Chapter 3
reveals that as early as 1970s, there already exist cooperative delivery systems inJapan (in Nihonbashi, Tokyo in 1972 and in Tenjin, Fokouka in 1978). The key issues
to this scheme centered on the financial difficulty of carriers and their reluctant toparticipate for various reasons. Although the existence of successful operation of thisscheme might serve as evidence to remove cloud of doubts from private sector, thesetwo concerns deserves further research that would give them total confidence to idea.
Access restriction
Access restriction is wide-spread in Europe and appears to be sourced from two
reasons: protection of historic sites and protection of the environment. Current applied
regulations can be formed into five groups which are: 1) regulations related to thetype of transport means especially to vehicles emissions, weights, and sizes; 2)regulations related to the access time in predetermined areas; 3) regulations related topreferred truck routes; 4) regulations related to loading and unloading zones; 5)
regulations based on licenses. Among the regulations on the first group, weight
restrictions are the most common regulations and they tended in the past to be more
and more restrictive in urban areas, which has enhance the use and number of smalldelivery vehicles. On the second group, night delivery scheme appears to be the mostcontroversial like the case in cities in France where some cities consider the idea as agood strategy to decrease the number of trucks in the city during the day. Other cities
however, argue that truck and delivery noise impacts are too high and night deliveries
should be banned. The third group of regulations has something to do with assigning aparticular route for trucks to avoid congested thoroughfares like the case of truck
routes alternatives in Metro Manila. Providing dedicated loading zones falls under the
fourth group of access restriction. In Copenhagen, the access to freight handlingfacilities is based on a license. In Paris, utilization of their loading bays is good for 30
minutes for every truck which they modeled from the experience of Barcelona.

72

Intelligent Transport Systems and Information Communication andTelecommunication
Intelligent Transport Systems (ITS) and Information Communication andTelecommunication (ICT) can enhance the delivery of freight in several ways. New
and automated enforcement technique formed from ITS and ICT make it easier forpolice to implement access restriction measures. One sector that is benefiting from theapplication of ICT is the police forces who is tasked for instance to enforce access
restriction of trucks to city center. Enforcement is traditionally a labor intensive task
and is therefore costly. However new applications of ICT may improve the“enforcement efficiency” and enlarge the scope of enforcement. This type of
application has been applies in Barcelona, Spain where the monitoring of vehicles
entrance to (off) loading bays is monitored by camera that fed the data to control
center. The system wants to make sure that freight vehicles were following the 30
minute maximum stay to the bay and if they are delivering within the allowable timeframe. On the part of transport operators, these technologies could facilitate them tointegrate their operation or even could facilitate them for cross-company managementof loads and fleets. The result would be improved scheduling and routing which
would reduce freight vehicle mileage and increase load factors (e.g., avoiding empty
backhauls). This can be accomplished through increased computerization andcoordination among distributors. However, application of ITS and ICT relies heavily
to digital infrastructure (e.g. internet connection) which could be a reason for limited
application in developing countries which do not possess abundantly of suchtechnology.
Land use zoning
Land use zoning has enormous impact to the settlement and thereby the flow of traffic.
For instance, sitting manufacturing and assembly processes closer to their destination
markets reduces the amount of travel required for goods distribution. Requiring new
establishments to provide logistics facilities (i.e. parking area, loading zone) would
have positive influence to the traffic. Freight vehicles would no longer use the road
space while (off) loading their load should these facilities are available and with goodaccess location.
As a case of example, Paris is divided into three zones in which each establishment
has to provide logistics facilities depending on its location. The “Zone de grands
services urbains” requires establishment to offer 25 m2 dedicated for logistics
facilities. The “Zone urbaine générale” directed commercial and industrial buildings
larger than 2500 m2 and office buildings larger than 500 to provide delivery andaccess facilities. And lastly, the “Zone urbaine verte” ordered the preservation ofparks and areas of architectural interest. Under this zone, logistic activity is allowed
only on a time-sharing basis and using non-polluting delivery vehicles. Obviously,
this zoning ordinance has effect to the flow of freight traffic as many transport
operators would be pushed and encouraged to form a partnership with othercompanies in order to deliver their goods to the customers located the third zone (zoneurbaine verte).

73

4.6.3

CDS and its potential to respond to the problem in the CBD

The issue at hand now is how to improve the current city distribution of freight withreference to sustainable urban freight transport. Throughout the discussion of thedevelopment of measures, it appears that the city administrators are aggressively
pursuing access restriction measure to safeguard the city’s physical environment andto minimize the contact between freight transport activities and the city’s citizens.

Congestion

Obviously, the congestion level is extremely high at the inner part of the city (orcentral business district) and it decreases remarkably as the distance is getting bigger
away from the city center (Figure 4-2). Congestion is borne out of the idea that too
many vehicles are operating in too small area. This problem, congestion, thenbecomes the source of several problems in urban areas such as environmental
pollution, safety risk among others. It appears therefore that reduction of the volume
of vehicles granted access to the city is the most promising solution.

CBD

Outer area

Urbanized area

Figure 4-2. Congestion level at the CBD

4.7

Concept and Components of Cooperative Delivery System (CDS)

The idea behind conceptualization of CDS is to improve the freight distribution
thorough consolidation of loads. This means that the aim is to reduce the negative
externalities of trucks (air pollution, vibration, noise pollution, etc) to the residents
and physical environment of the city. Goods destined for city centres are diverted into
common transshipment facilities (depot) with local distribution being carried out
using specialized vehicles which may be smaller, quieter and less polluting (Figure 43). This delivery method could result to the reduction of vehicle kilometers, reductionof delivery time, reduction of the number of trucks, reduction of environmental
emissions, and reduction of transportation cost.

74Normal form of freight distribution in the CBD
1

B

2

C

3

Shippers

Carriers

A

CDS form of freight distribution in the CBD

Carriers

B

Depot

2

Shippers

1

A

3

C

Figure 4-3. Freight distribution in the CBD with and without CDS
As mentioned, the current trend in urban management highlights the significance ofcooperative delivery system as a measure that could serve the goods to the urban
centers with less harm both to the physical structure of the city and environment. The
key element of this trend is the desire of city authorities to reduce number of freighttraffic to the city especially in areas with large number of pedestrians and/or withhistorical significance.
The fact that bulk of freight that enter urban areas are usually concentrated in small
highly dense places offers great opportunity to organize the goods traffic throughcooperative scheme. City Freight (2002) confirms that the distribution of urban goods
is not organized efficiently and that there is considerable scope for reducing urban
goods traffic (vehicle-km) through coordination and consolidation of transport. This isbecause restaurants, shops, offices and other commercial functions are normallylocated in one small area that are served by different carriers.
The effect of cooperative scheme is the consolidation of goods to be distributed
and/or collected by different transport operators or supplies. Consolidation reduces
the number of vehicles needed for delivering the same amount of goods and thus
improves cost-efficiency of transport companies and reduces environmental impact.
The consolidation of goods may be organized in a number of ways. Among these aretransport companies reach a mutual agreement on the consolidation arrangements, or75

a third party operator carries out collection of goods from different suppliers and later
takes care of consolidation in its own distribution centre for distribution.
Another appeal of cooperative delivery system aside from those mentioned above isthat it can be virtually applied to every city. Although sophisticated ICT could
enhance the management of the operation, it is not a pre-requisite to its
implementation. This element of cooperative system allows cities with limited ICT
hardware (e.g. cities in developing countries) to adopt the system.
i)

Urban distribution center (UDC) and depot as consolidation center

UDC and depot as mentioned are essential component of a CDS since it is at thisfacility where the consolidation of goods is made. Figure 4-4 illustrates the freight
distribution system and the location of UDC for city goods. Normally, the distribution
center is located outside the city (see distribution center of Tokyo in Chapter 3) inview of limiting the number of trucks in the city. This location of the facility however
should have good access road leading to the main road to the city. Goods areorganized at the distribution center where packages for the same destination arebundled together.
Regional Freight Transport

Urban Freight Transport

Distribution center

Distribution system between
citiesCity distribution system

Distribution system between
districts

Figure 4-4. Freight distribution system and location of the depot
Regarding the goods for district
distribution, the depot should be locatedat the border of the district (Figure 4-5).
The objective is still to limit the number
and movement of trucks in the district tominimize its negative impact. A single
depot might serve part of the CBD andanother depot to other parts of the CBD.
Suitable location and number of depot
per CBD depends on many factors suchas the size of the CBD, its building
density and building use.

Arterial road

Urban Distribution Center

Depot 1

Depot 2

Depot 3Bldg 1

Bldg 2Bldg 3Figure 4-5. UDC and depot
764.7.1

Cooperative delivery system (CDS) as transport instrumentii)

Objectives of CDS

At the beginning of the development of cooperative system, public sector appears tobe the initiator for such undertaking as a means to improve city’s traffic flow andprotect the quality of life of residents. For instances, the Tenjin Cooperative System inFukouka was originally promoted by the Local Office of the Japanese Ministry ofTransport to rationalize movement of trucks and reduce the traffic and environmental
impacts in the city (Ieda et al, 2001). Recent trends, however, suggest that companies
are becoming more sensitive to the protection of environment as market advantage(also called “social reputation”). They try to enhance their standing as responsible
companies regarded as mature and reliable. The following objectives (i.e. reduce
traffic congestion, environmental improvement, urban planning and city protection,
cost reduction, and improved social reputation) thus might serve as key driver for the
implementation of cooperative system.
Reduce traffic congestion
Cooperative system is capable of satisfying both the interest of private and public
sector as shown by the study conducted by Taniguchi et. al. (1995). The study uses aquestionnaire survey which identified that the main benefits of co-operative systems
were the cost reduction for shippers and freight carriers. Many companies haveenjoyed both cost reduction and the provision of better services to customers after
implementing co-operative systems. In addition, a reduced number of trucks will be
used and the total travel time of trucks will also be reduced in co-operative systems.
Environmental improvement
Both traffic congestion and environmental impacts are strongly influenced by travel
times and speed, which depend on traffic flow (Taniguchi et al, 1999). Taniguchi et al.
(1999) analyzed the effect of co-operative systems on CO 2 emissions within smallroad network using dynamic traffic simulation. They found that co-operative systems
substantially contributed toward ensuring that CO 2 emissions remained at the samelevel as the base case for pick up/ delivery trucks when the demand for goods doubles.
Urban planning and city protection
Although originally, the target of setting up cooperative systems is to reduce thenumber of freight vehicle operating in the area as a means to decrease traffic
congestion, recently there has been a growing recognition of the benefit ofcooperative systems as urban management tool. Reduction of the consumption ofurban space for transport infrastructures and delivery points can be achieved throughthis system. This system’s effect would increase the accessibility of the city making it
attractive for business.
For some cases, cooperative system can play a role to preserve a historic part of thecity by deliberately avoiding the sensitive place during the planning process. This
would enhance the image of the companies involved among the residents as
responsible and sensitive to the negative externalities caused by freight transport.

77

Cost reduction
Taniguchi et al (1995) using survey questionnaire survey identified that the main
benefits of cooperative systems were the cost reduction for shippers and freight
carriers. Most companies enjoyed both cost reduction and provision of better services
to customers after implementing cooperative systems.
Enhance social reputation
Reductions in environmental impacts generate double dividend. Companies benefit
from reduce costs and an improved competitive position. They will be more attractive
to customers eager to buy “green” products and services (Good practice in freighttransport, 2000).
iii)

Different models of cooperative delivery system and its application

There are many terminologies used to describe cooperative system; among them arecooperative freight transport system (CFTS) and voluntary cooperation. Yamada et al(1999) gives a clear definition of CFTS as systems in which a number of shippers or
carriers jointly operate freight vehicles to reduce their costs for collecting anddelivering goods and provide higher levels of service to their customers. Castro (2002)
on one hand stated that cooperative delivery is concerned with the promotion ofchange in the form of urban delivery, from independent private transport toconsolidated transport using public carriers.
In essence, cooperative system is attained when two or more companies (either
shippers or carriers) pool their resources and start coordinating their operation. Kuse
(1999) provided what appears to be extensive list of cooperation delivery systemmodels among the stakeholders in freight transport (Figure 4-6).
Type

Before Application After Application

Remarks
Mutual cooperation amongshippers resulting to consolidated
transport

Consolidated transport
between shippers(Multiple shippers)

Mutual cooperation among
carriers resulting to consolidat ed
transport

Consolidated transport
between carriers(Multiple carriers)

Shipments are consolidated at a
distribution center, then deliver ed
cooperatively to several outlets byrouting

Cooperative delivery(Multiple shippers,
multiple carrier)

Individual shipments are delivered
to a designated carrier, whichthen performs consolidateddelivery

Cooperative delivery(Single shippers,
multiple carrier)

Carrier performs cooperative
delivery to several outlets byrouting

Cooperative delivery(Multiple shippers,
single carrier)

Shippers
Carrier

FIGURE 4-6. Different models of cooperative system

78

iv)

Different types of design of cooperative delivery system

There are three ways to design a cooperative delivery system namely; horizontal
cooperative delivery system, vertical cooperative system, and combination of verticaland cooperative delivery system. Under the first design, trucks of different sizes andloads have to bring their cargoes to the stock point (SP) or depot for consolidation(Figure 4-7). Small trucks, normally non-polluting vehicles, will complete thedelivery going to the customers. At the (off) loading area of the building, the driverwould unload goods and deliver them to the recipients. Usual practices of cooperative
delivery system follow certain time interval for delivery and assigned regular route.
For instance, the Shinjuku Matenro cooperative has two delivery time in the morning(10:00 and 11:30) and one (13:50) in the afternoon and the route has been prepared toeasily trace the delivery vehicles.
Vertical cooperative delivery system on the hand entails to assigning of staff at highrise buildings or establishment with high number of goods attracted. When thedelivery trucks arrived, the staff would unload the goods and delivery them to therecipients. Under this system, trucks arrived without fix frequency and the goods are
not consolidate due to the absent of Stock point where the consolidation can takeplace (Figure 4-8).

15F
3F
2F
1F
Depot

Bldg
Horizontal Cooperative

Figure 4-7. Design of Horizontal CDS
The third design of cooperative delivery system which is the combination ofhorizontal and vertical cooperative system combined the functions of two systems
resulting to comprehensive delivery system that has strong impact both on traffic andenvironment. Under this system, staff are usually stationed at each building coveredby the joint delivery (Figure 4-9). The task of the staff is to free the driver from thedifficulty of delivering goods going to the recipients. Instead, the staff would replace
this role from the driver. After the truck docked-in at the (off) loading area of thebuilding, the staff would unload the goods and the driver could move immediately tothe next delivery destination. This procedure allows limited parking time of the truck
and the delivery is faster. The familiarity of the staff to the layout of the building
contributes to speedy delivery. The cooperative delivery system that was adopted in
Marunouchi district falls under the second category of design of cooperative.
79

3F
2F
1F

Vertical Cooperative System

15F

Bldg

Figure 4-8. Design of Vertical CDS

3F
2F
1F
Depot

Vertical Cooperative

15F

Bldg
Horizontal Cooperative

Figure 4-9. Design of combination of Horizontal and Vertical CDS
4.7.2

Freight centers and its role in the CDS

The idea behind its development is twofold: 1) to facilitate logistic activities withlocation, space (e.g. storage) and transport facilities (transshipment), and 2) toconsolidate goods flows by developing certain transport services (Visser et a, 1999).
The first idea deals with the facilitation of logistic activities aims at realizing
synergies by concentrating business activities, whether from single company or
between several companies (BESTFUST, 200). The second idea is to consolidate
goods flow aims at increasing the efficiency of the collection or distribution process,thereby reducing the environmental impact of urban delivery activities.
The first freight centers in Europe were established in Paris during the mid 1960s in
response to urban congestion. Urban distribution centers (a type of consolidation
center) were developed in the UK and later in the Netherlands and Monaco (City
Freight, 2002). Handful of countries included the development of freight centers intheir national policies. Italy was the first in 1990, followed by Germany in 1992 andFrance in 1993. Japan as mentioned in Chapter 3 has produced a master plan in 196980

that identify the location of urban distribution centers for Tokyo Metropolitan Areaand currently, five facilities exist (Table 4-4).
Table 4-4. Existing public distribution facilities in Tokyo Metropolitan Region
Distribution center

Land Area (ha)Handling capacity (tons/day)

Adachi, Tokyo

33.3

8,335

Itabashi, Tokyo
Keihin, Tokyo
Kasai, Tokyo
Koshigaya, Saitama

31.4
62.9
49.2
73.2

7,262
10,1507,964-

Source: Castro et al, 1999i)

Freight centers classifications

The concept of freight centers is very different among countries and has given manydifferent names. Some of most frequently use terms are clarified: Freight platform,
Urban Distribution Center (UDC), Freight Village and City logistics.
Freight platform
BESTUFS quoted REFORM (1999) that freight platform can be defined as areas inwhich different transport related companies such as forwarders, logistic service
providers etc, are established. It is a transshipment area where, ideally, at least twotransport modes are connected. Usually these transport modes consist of road and rail,
but waterborne and air transport can also be integrated. The concentration of transport
related companies inside a freight platform promotes synergies and primary effects, if
the process of integration is planned and enforced in co-operation with all thecompanies and with involved local authorities.
Table 4-5. Classification of freight platforms (BESTUFS, 2002)
Terminal development

Area development
Industrial +Transport +
some
other
transport

Intra-company
synergies

Inter-company
synergies

TransportorientedSingle-company
Urban
Distribution
CenterMulti-company
Urban
Distribution
CenterFreightvillage

Industrial andlogistic park

TransportmodeRoad-roadRoad-rail

Road-roadRoad-rail

Road-roadRoad-rail(bridge)

Road-roadRoad-rail

Road-road

Main aims

Optimization
logistic
operations

Optimization
logistic
operations,urban traffic
reduction

Modal shift,urban traffic
reduction

Regional
economic
growth, modal
shift

Revitalizing
small andmediumsized firms

Regional
economic
growthCompany
structure

One single hugeforwarder,
retailer ortransport
company

Co-operatingtransport
companies

Mostlysmall
companies

Wholesalers
and transportcompanies

International
orientalcompanies

Land use

Small areas inurban areas or in
the outskirts

Small areas inurban areas orin the outskirts

Largeareas in theoutskirts

Large areasin theoutskirts

Nearairports andharbors

Spatialorientation

urban

urban

Urban andregional

Urban andregional

international

81

Large
industrial
companies
and transportcompanies
Large areas in
the outskirts
or at the oldindustrial
areas
Regional andinternational

Businessgrouping
development

Port related

Speciallogistic area
Roadsea/air
Road-railsea/air

In short, a freight platform integrates on concentrated area modes, carriers and service
related to logistics and freight transport (Table 4-5).
Urban distribution centre
According COST 321 (1998), an Urban Distribution Center (UDC) is a place oftransshipment from long distance traffic to short distance (urban) traffic where theconsignments can be sorted and bundled. Its main purpose is to achieve a high degreeof collection in the goods flows in order to supply efficient transport from the UDC tothe city centre and vice versa. (Van Duin 1995). It is this focus on distribution
efficiency and its city orientation that separates the UDC from other freight platforms.
Therefore, UDCs are sometimes also referred to as city terminals or urban
consolidation centers. They can be stand-alone platforms of a singles forwarded or anelement in the logistic chain of huge companies. More common however is theintegration into logistic urban network. Connected to freight villages in the outskirts,
they are used as central urban, multi-company consolidation centers. (REFORM 1999)
City logistics
Eisele et al. (2000) describes City Logistics as: “all coordinated measures comprisinglogistic collection and delivery activities of logistic service providers in urban areasthat aims at the reduction or prevention of commercial traffic and its negative external
effects”. However, the notion of City Logistics differs a lot between countries. Thisquite narrow notion of City Logistics is rather common in Germany whereas
particularly in the English language areas city logistics is a rather generic term that
includes much more than bundling of deliveries or a UDC.
Freight village
Freight village generally focus on bi- or multimodal transport. The distinguishingelement of these platforms is the transshipment terminal. Service providers are
established on site, as well as large number of forwarders and transport companies.
(REFORM 199) Actually freight villages are areas with several terminals (Visser et al.
1999) which can even include and Urban Distribution Center when located close tocity borders, interfacing with long distance transport and city distribution services. On
the other hand, some freight villages are used as transshipment points on a European
scale. In Germany and Italy, freight villages are known as Guterverkehrszentren(GVZ) and Interporti respectively. (REFORM 1999)
ii)

Operational models of freight centers

Freight center operations are also different among the countries. Dablanc (1998)
classified three general models of freight centers: 1) Monaco model wherein thegovernment owns the facility and contracts the operation of freight distribution to asingle transport company, 2) Dutch model wherein licenses are given below byauthorities to operators who meet certain criteria to deliver goods in the city, and 3)
German model which is a private carrier initiative to consolidate freight and distributeit cooperatively (Figure 4-10). The BESTUFS (2002) study quoting the report of
Ministere de l’Equipment (2002) clarify the different models.

82

Monaco Model

Dutch Model

Owner: Government

Owner: Government

UDC

Operator: Single operator

German Model
Owner: Private company

UDC

UDC

Operator: Many operators

Operator: Many operators(Delivery lisence is issued)

Co. A

delivery

Co. A

Co. B

Co. C

delivery

Co. A

Co. B

Co. C

deliveryFigure 4-10. Types of Urban Distribution Centers
In Monaco, the UDC is owned and operated by the government. In 1989, thegovernment contracted out the operation of freight distribution to a single carrier (aregional transport company). This sub-contractor was given a monopoly over all themunicipal depot. Added to this was a partial monopoly on the delivery of goods. All
trucks over a GVWR of 9 tons (this limit should be lowered to 3.5) are banned fromthe city of Monte Carlo. If they are to deliver goods to client there, they have to go to
local freight platform and unload first. The municipal service then takes the final
distribution in charge, with specific vehicles. The costs of the service are shared
between the municipality, which give financial aid and free warehouse space to thevehicles, and finally the retailers who supposedly pay for the amount of goods theyreceive through the service (Ministere de l’Equipment, 2002).
Following a national program of energy reduction in cities, the Dutch cities setup asystem for urban freight distribution licenses. Strict operating regulations are imposedon operating licensees in exchange for an extended usage of street space and longer
delivery hours. Applicant carriers must respect a list of criteria such as good level oftruck loading, minimum number of shipments and the use of electric vehicles. Thiskind of municipal organization can lead to quasi-monopoly of distribution where avery limited number of registered carriers dominate the market of urban distribution.
(Ministere de l’Equipment, 2002).
In Germany, at carriers’ own initiative, a private service of goods distribution is set up
with the help of the locality. Different carriers join together to consolidate freight anddistribute it cooperatively. The system provides addition kinds of services, such ashome deliveries, collect and recycle service or short time storage. Government
support can take the form of the distribution of an official “City Logistik label” ontrucks and warehouses. Governments can also participate in the financing of thesystem (Ministere de l’Equipment, 2002).
After having reviewed different models of UDC, it appears that they can be classified
into four categories. These categories are 1) pubic sector funded UDC for single user,

832) public sector funded UDC for multiple users, 3) private sector funded UDC for
single user and 4) private sector funded UDC for multiple users (Table 4-6).
Table 4-6. Urban distribution center models
Provider of infrastructure
Private sector

iii)

Single1) Monaco modelMultiple

User

Public sector1) Dutch model2) UDC of Metropolitan Tokyo
3) Bangkok freight terminals

1) German model
2) Tenjin CDS in Fukouka

Issues and concerns regarding freight centers

Although the idea of freight centers is very interesting in particular to the concept of
improving the efficiency of freight traffic through consolidation, there were some
reported setbacks to this scheme. A handful number of freight center in the
Netherlands, France, and Germany has been terminated for commercial reason and tosome extent, lack of sufficient support by public policy. Financial difficulty is one of
the most reported cases to the limited success of distribution centers (Ieda et al 2001,
COST 321 1999). The following summary of issues regarding freight centers arecompiled by BESTUFS which were taken from the results of several researches.








Lack of economic interest (interruption of the transport chain at the
distribution center causes additional costs that are not offset by acorresponding gain in efficiency).
Lack of willingness to cooperate because of fierce competition (fear of
disclosing competitive information about order quantities, products, customers,
know-how, etc., fear of losing customers to their competitors).
Reluctant to relinquish control over merchandise and transport chain,
particularly the responsibility for the goods transported.
Loss of direct contact between the receiver and the delivering company (theact of delivering offers and opportunity for the transport company to promoteitself and to establish a customer relationship – it is the company’s “businesscars”).
Many companies give much higher priority to customer service andcompetitive advantage than to reduced transport cost.
Reduced need for multi-company consolidation because of the general
concentration process in the transport business (for large retail companies with
their own distribution network the benefit of multi-company consolidation israther small).

84

4.8

Chapter summary

There are many policy goals that can be pursued to design freight transport measures.
Objectives such as economic and efficiency calls for sufficient freight infrastructurethat could facilitate the smooth movement of goods especially in the city centers.
Environment and safety objectives, on the other hand, dominate policy formulation
process nowadays as the deterioration of urban environment is largely blamed to
freight transport. Infrastructure and urban structure objectives are also starting toappear and influence measures concerning freight operation. Italy having blessed for anumber of old cities are pulling every measure available such as access restriction topreserve these heritage. More so, Paris recently found out that their freight
infrastructure (loading bays) are not being taken care by the users thus new measures
were taken to safeguard these bays. Clearly, the objectives are very diverse and one
measure could hardly meet one or two objectives. In the ideal settings, mixed policy
measure has the strongest effect and best potential to serve great number of freighttransport objectives.
Congestion and negative impact of freight transport to the environment still continue
to be the key drivers of policy interventions as shown by the study done by Lewis(1997). Interestingly, there is a wide array of available measures to improve thecurrent status of freight distribution ranging from infrastructure-based measures to
TMD-bases measures. Freight center is one these measures that have enormous
impact to the flow of traffic in the city. This facility is very important as it can serveas platform to conduct cooperative delivery among the different carriers. It’s scaleguarantee enough space where goods could be stored and organized. The existing
freight centers discussed above are operated is quite different ways. The differences
can be traced to what role the government played in each of them. For instance,
facilities in Monaco and the Netherlands (the project has been terminated) areprovided by the government while freight centers in Germany are sole initiatives ofprivate sector.
The concept of freight centers is fascinating but is not always successful. For instance,
many local governments in France are having difficulty in operating the center for thereason that it is not financially viable due to small number of companies using it asstaging point before distributing the freight to the city. This rather lack of interest
from the private sector is wide-scope in nature that involves financial, operational,
customer relation issues. According to some companies, the idea that they have tobring their goods to the freight center creates additional operating cost while somecompanies claims that this system will limit their in-person contact with theircustomers. This issue is both complicated and motivating that further research isneeded.
As the chapter advances, the idea that cooperative delivery system is perhaps the mostpowerful TDM measure to reduce substantially the negative impact of freight
distribution is rationalized. The nucleus of the problem is located at the heart of thecity where too many vehicles are operating in a very small area. Reducing the numberof freight vehicles without reducing the number of freight suppose to arrive would
bring enormous improvement to the urban environment. This idea can best fulfill byadopting cooperative delivery system where different logistics operators are85surrendering part of management of their goods so it can be consolidate with othercargoes before bringing in into the city center.
The issue at hand appears to be sustainable urban freight transport which aims to
reduce social and environmental impact of freight distribution. Again, cooperative
delivery system can contribute significantly to the realization of this long term goal.
Sustainable urban freight transportation is an ultimate goal that involves lot ofstrategies to be achieved. For instance, it calls for the reduction of impact of eachmode through cleaner engines, cleaner fuels, efficient driving, shift of regular trucks
to non-polluting trucks, reduction of number of vehicles in the city, and cooperative
operation between shippers and carriers.

86

REFERENCES

1. Ashida, N. et al (2002). A study on the improvement of home delivery systems
and the effect of introudincg a new measure in a concentrated high-rise residential
area. City Logistics II. Institute of Systems Science Research, Japan.
2. Ieda, H. et al (2002). Comparison of traffic and environmental load of freighttransport – Bangkok and Tokyo. City Logistics II. Institute of Systems Science
Research, Japan.
3. Ieda, H. et al (2002). Why don’t multi-carrier joint delivery services in urban areas
become popular? A gaming simulation of carriers’ behavior. City Logistics II.
Institute of Systems Science Research, Japan.
4. Iwao, E. and Kuse, H. (2001) Traffic and Environmental Impact Analysis of
Large-scale Facilities in the Commercial Business District. Journal of Eastern
Asia Society for Transportation Studies, Vol. 4, No.1, 523-535.
5. Kohler, U. (1999). City logistics in Kassel. City Logistics I. Institute of Systems
Science Research, Japan
6. Kohler, U. (2002). City-logistics in Germany. City Logistics II. Institute of
Systems Science Research, Japan.
7. Kuse, H. et al. (2002). Conceptual study on urban freight transport policy planningfrom the viewpoint of logistics. City Logistics II. Institute of Systems Science
Research, Japan.
8. Li, Maying (2001). Urban Goods (Off) Loading Chain. Paper presented at the
NECTAR Conference no 6 EUROPEAN STRATEGIES IN THE
GLOBALISING MARKETS, Helsinki, Finland.
9. Meye, M. (1997). A toolbox for alleviating traffic congestion and enhancing
mobility. Institute of Transport Engineers.
(http://www.itsdocs.fhwa.dot.gov/jpodocs/repts_te/5dz01!.pdf)
10. Morris, A. and Kornhhi auser, A (2001). Relationship of Freight Facilities inCentral Business District Office Buildings to Truck Traffic. Transportation
Research Record 1707, Paper No. 00-0197.TRB, National Research Council,
Washington, D.C.
11. OECD. 2002. Policy Instruments for Achieving Environmentally Sustainable
Transport
12. Odani, M. et al (2002). An experiment to demonstrate the effectiveness of onstreet parking facilities for delivery vehicles. City Logistics II. Institute of
Systems Science Research, Japan.

8713. Okamoto, A., Asakura, Y., Suzuki, A., and Lee Y. (2001). Monitoring Personal
Travel Behavior using a Cellular Phone System with Power Antennas and CSID
Analysis (2001). 8th ITS World Congress in Sydney. CD-ROM.
14. Park, S. et al (1998). A Simulation Analysis on Loading/Unloading and PickUp/Delivery Activities for Efficient Goods Movement at the CBD in Japan, No.
199, 8th World Conference on Transport Research, Antwerp, Belgium.
15. ProModel User’s Guide (1997). ProModel Corporation. USA.
16. ProModel Reference Guide (1997). ProModel Corporation. USA.
17. Tanaka, H., Sekimoto, Y., and Shibaki R. (2001). Reconstruction of Spatiotemporal Distribution of Event Visitors by Fusing Multi-source Data. Paper
presented at the 22nd Asian Conference on Remote Sensing, Singapore.
18. Taniguchi E. and Thompson, R. (1999) City Logistics I. Thompson, Institute of
Systems Science Research, Japan.
19. Visser, J. et al (1999). Urban freight transport policy and planning. City Logistics
I. Institute of Systems Science Research, Japan.
20. Yamada, T. et al (1999). Attitudes of companies towards introducing cooperative
freight transport system. City Logistics I. Institute of Systems Science Research,
Japan.

88

CHAPTER

5

Trends and Recent
Developments of Central
Business Districts in the world
5.1

Introduction

The previous three chapters (2, 3, and 4) provided significant information to the
problems and current trends that shape urban freight distribution. The focus area of
this study which the CBD and the logistic issues it faces have been well rationed to becaused by management of trucks. Policy responses of the governments to the said
problems were also drawn. The potential of CDS to respond to this problem was also
given weight in Chapter 4.
The main objective of this chapter is to characterize the CBD in developing anddeveloped countries and define the areas within the CBD where the CDS may be
adopted. Section 5.2 provides the definition of CBD and its typical characteristics.
Further discussions on the characteristics of CBD are available in Section 5.3. These
discussions compare the characteristics of CBDs in developing and developed Asian
cities and some CBDs in Europe and US. The following Section 5.4 presents
classification of CBDs as well as some examples for each classification. The chapter
concluded in Section 5.5 where all the highlights of the chapter are noted.

5.2

Central Business District

5.2.1

Early theories

Although this research is not intended to analyze CBD itself, this section is importantas it provide contextual background on the target area of CDS. Various names are use
to refer CBD which includes “central commercial district”, “retail business section”,“downtown business district” , “central business district” and several others. Even
“urban core” or “core” are sometimes used to pertain to CBD or the central area of thecity. Murphy (1971) provided a compact background on grasping the fundamental
functions of CBD as well as on how to delimit its area.

89

ily
Se
Dw
co
ell
nd
in
Se I
gs
m
ttl m
em ig
en ra
Li
t nt
Si ttlecilyChicago, 1920s

Si
ng

le
Fa
m

Model

Ghetto LOOP
a
Ap
rtm
en

s
se

Black BeltoutH

Two Plan
Area

Residential District
Bungalow
SectionI - Loop (downtown)

IV - Working class zone

II - Factory zone

V - Residential zone

III - Zone of transition

VI - Commuter zone

Figure 5-1. Central business district’s model of Burgess
Source: http://people.hofstra.edu/geotrans/eng/media.html

Early efforts regarding CBD focus on defining CBD and its delimitation. Burgess(1929), as quoted by Murphy (1977), forwarded the theory that CBD can be one of
the six concentric zones of American city (Figure 5-1). He organize the city structureas 1) CBD, 2) factory zone, 3) zone in transition, 4) zone of workingmen’s homes, 5)
residential zone, and 6) commuters’ zone. Burgess has this to say on CBD:“The Central Business District – At the center of the city as the focus of its
commercial, social, and civic life is situated the Central Business District. The heartof this district is the downtown retail district with its department stores, its smart
shops, its office buildings, its clubs, its banks, its hotels, its theaters, its museums, andits headquarters of economic, social and civic , and political life”.
In the end, the effect of land pricing or rent and the influence of major transportationroutes on the pattern of urban growth are reflected in the work of Homer (1939). He
argued that as a city grew, competition for land at the center of the city resulted inland values so high that only business could afford the land and the result is the CBD.
This growth of land values or rent gradually spread from the center outward of thecity. But instead of the rise taking place along a broad circular front, it occurs along
main transportation routes.
Another theory explaining the role of multiple nuclei in city growth instead of growtharound a single CBD core as in the concentric zone and sector theories (Figure 5-2).
This concept is reported by R.D. McKenzie but known especially through thearticulation works of C.D. Harris and E.L. Ullman. Contrary to the two theories,
multiple nuclei suggest that an urban area may have more than one center and that a
new centers of activities or sub-nuclei may be develop within existing nuclei.

90Sector

Nuclei

3

2

3
4
3

3

1

2

5

4

3

3

5

3
3

1

2

3

7

6

4

9

1 CBD
2 Wholesale and light manufacturing
3 Low-class residential
4 Middle-class residential
5 High-class residential

8

6 Heavy manufacturing
7 Sub business district
8 Residential suburb
9 Industrial suburb

Figure 5-2. Sector and Nuclei Models
Source: http://people.hofstra.edu/geotrans/eng/media.html

Still, another group of researchers tries to tackle CBD in the framework of centralplace theory. The idea in this concept is that CBD is only one of the central places thatcan be recognized within an urban center. Proudfoot (1937) suggested a hierarchicalarrangement of retail areas within the city. His work arrived to the followingclassification: 1) CBD, 2) outlying business center, 3) principal business thoroughfare,4) neighborhood business street, and 5) isolated store cluster. This classificationsuggests a hierarchical arrangement within the city. Later, Hans Carol as quoted by
Murphy applied the central-place hierarchy idea to the city of Zurich, Switzerland.
His classification are: 1) local business district (lowest order), 2) neighborhoodbusiness district (low order), 3) regional business district (middle order), and 4) CBD(high order).
Each theory mentioned offers perspective of the CBD. In the beginning, the centralityof the CBD was emphasized before other elements such as the impact of land value(rent) and transportation to the settlement were appreciated. The multi-nuclei theory
made a dramatic shift from single-concentric view given to a CBD by recognizing thepossibility of having many centers within a city. These theories provided basic
foundation to advance understanding of CBD to early researchers. Murphy (1977) has
this to say on CBD:91

Intercity

Light
manufacturing

Intercity

Transportation
terminals

Multi-family
residence
CBD core
CBD frame

Intracity
Intercity
Intracity

Wholesaling
withstocksSpecialservice e.g.
(medical)Auto sales
and service
Intracity

Intercity

Primary goods
flow

Secondarygoods flow
includingwarehouse

Intracity

Figure 5-3. Graphic representation of Horwood and Boyce’s CBD core-frame
differentiation with selected functional centers and principle goods flow
Source: The Central Business District, Murphy (1977)“The CBD being in the highest order functions at the highest level among thesebusiness areas. It is able to offer goods and services superior to those of any businessarea of lesser order. And, as the city’s top-ranking central place, the CBD occupies
the location of prime accessibility while other business centers are relegated to lessadvantageous locations. The CBD is characterized by a concentration of thoseestablishments that need maximum accessibility to the entire city and its tributary
areas. Thus, it attracts specialized retailing, large-scale banking, advanced medical
services and other functions so unusual that people come from all parts of the urban
area and even from smaller, neighboring cities to take advantage of them. In total, the
CBD offers a much greater number and variety of goods and service than any other ofthe city’s business areas.”
The Core-Frame Concept of Horwood and Boyce (1959) add another element thatfurther isolates the area and services at CBD (Figure 5-3). In their concept, the “core”
and “frame” constitute the CBD. At the “core” is where the greatest concentration of
economic and social activities while at the”frame” is where different social services
might occur. There would be movement of people and goods, though limited, among
the services that would be within the “frame”. The authors also pointed out that thegrowth of the area is up-ward and its growth horizontally would be very limited.
Reading the Core-Frame Concept brings the images of CBDs that grow horizontallysuch as Hong Kong, Singapore and Manhattan of New York.
Aside from the work of Murphy, another notable work regarding CBD is produced by
Kuse (1980). His work has comprehensive discussion to the effort of past researchersand came up with his own parameters that would be vital in understating CBD. These
are: 1) Accessibility, 2) Rent value, 3) City activity, and 4) Land use. And lately, there
are scores of CBD-related researches. Some of the new researches with some92

important contribution are those of Kenworhty and Laube (1999) which drew
comprehensive list of CBDs around the world and their basic characteristics, Curtis(1993) which provides illustration of land use patterns in the CBD, Hartshorn (1992)
which gives some methods in delimiting the CBD, and to least extent Lewis (1992)
which highlights issues in small downtowns.
5.2.2

Defining CBD

The above discussion highlights the difficulty of producing single description of CBD
in view of its different forms and functions around the world. One of the common
features of each theory, though, is the appreciation to the CBD as the premiercommercial region of the city. It is the area which normally has the greater
concentration of the tall buildings than any other region of the city. The following
characteristics can often be found in any CBD.














5.2.3

It has a distinct land use pattern that can be delimited from the rest of thesettlement.
It is the geographical centre of the settlement.
It contains the settlement's main public buildings.
It contains the major retail outlets
Similar activities within it are concentrated in certain areas (functional zoning).
It features vertical zoning.
It has the greatest concentration and number of pedestrians and traffic ingeneral.
It is a focal point for transport.
It contains the greatest proportion of the settlement's offices.
It has the tallest buildings in the region to maximimise land use.
It has the highest land values of the region.
It attracts people from outside its sphere of influence to work and spendmoney inside.
It is advancing into new areas (assimilation) and/or losing old commercial
functions (discard).

CBD and the role of CDS

CBD holds many institutions, financial and commercial. Figure 5-4 illustrates typicaltype of CDS in CBD. The depot which serves as consolidation center of goods is justoutside at the core area of the CBD. The idea is to locate the depot closer to thebuilding destinations of goods to minimize travel distance of trucks thus its negative
impact , i.e. emissions, vibrations, etc., is limited.
In a large CBD, it is ideal to have several depots to ensure the closeness of thesefacilities to the buildings where the goods will be delivered. A single large depot for
CDS might be difficult to realize because it is rare to find vacant space large enoughfor the facility. Also, as mentioned, this would result for a long route for every truck
which would result to accumulation of substantial travel distance and travel time oftrucks in the CBD.

93

Financial

Commercial

DEPOT

TrucksFigure 5-4. Illustration of CBD functions and depot of CDS

5.3

CBD and freight transport

The CBD is normally just very small part of the city but has the highest building andjob density (Figure 5-1). For instance, the CBD of New York occupies just 0.30% of
the city’s total land area but hosts around 22% jobs of the city. One observation thatcan be deduced in the same table is that those CDBs in developing countries havebigger land area ratio as compared to those in developed countries. This issue alongwith other important parameters of the CBD is discussed below.
5.3.1

CBD characteristicsi)

CBD’s common forms and functions

The pattern of CBD is obviously different from one to another which was the result of
its long history, economic development, and policies pursued by local authorities. For
instance, the core CBD of London and Paris is famous for its 3 to 4-story buildings
projecting a unified height when viewed above. At the beginning, legislation in thesecities tried to control building height for safety purposes from overcrowded city. In
contrast, Manhattan of New York, Shinjuku of Tokyo, CBD of Singapore, and Hong
Kong have adopted a policy which promotes the construction of high rise buildings.
These two different patterns of growth is often referred as horizontal (the land area
covered by the CBD keeps on expanding) and vertical growth (growth of the CBD is
upward like Hong Kong and Singapore). Figure 5-5 shows these contrasting figures ofcore CBD of Paris and Manhattan of New York.

94

Table 5-1. CBD and transport variables
Developing Asian cities
Jakarta
Kuala Lumpur

Bangkok
GRP/capita (US$)
Population

ManilaDeveloped Asian cities
Hong Kong Singapore

Tokyo

European and American cities
LondonParis
New York

3,826

1,508

4,066

1,099

14,101

12,93936,95322,21533,60928,703

6,356,685

8,222,515

3,024,750

7,948,392

5,522,281

2,705,115

31,796,702

6,679,699

10,661,937

16,044,012

Urban area (ha)42,58048,12953,24240,135

18,38031,160448,000

157,829

231,085

768,310Urban density (persons/ha)

149.29170.8456.81

198.04300.45

86.81

70.97

42.32

46.14

208.31

2,657,132
2,056

2,831,000
4,313

1,190,000
1,625

2,718,000
3,600

2,573,165
113

1,537,011
725

8,296,547
4,208

6,000,000
2,697

5,109,107
2,333

10,530,000
2,331

Total jobs
Area of the CBD (ha)Employment at CBD

400,140

877,610290,000

815,400

193,520

280,000

2,300,738

1,260,500

1,025,000

2,305,545

Population of the CBD
Job density inside CBD (ha)Job density outside CBD (ha)Motor vehicles
Private cars (%)
Freight vehicle (%)
Buses (%)
Other (%)
Total road (km)667,579
195
56
2,045,814
43.90
16.40
1.00
38.60
3,803

1,014,137
203
45
1,649,252
37.20
11.50
0.20
51.10
4,448

200,000
178
17
1,424,473
42.20
7.80
0.60
49.40
4,746

1,601,234
227
52
684,778
76.10
7.90
0.80
15.20
4,941

12,8561,713
130433,769
54.60
31.00
1.90
12.60
1,529

60,000386
41452,352
50.60
21.30
1.70
26.40
2,882

266,012547
144,429,268
60.20
28.00
0.04
11.40
122,489

170,000467
31
2,781,000
83.50
9.13
2.62
13,305419,214439
184,547,990
84.37
15.63
9,977

528,300
989
11
10,254,034
86.80
11.48
0.16
84,335

Average speed of traffic (km/h)

13.10

23.60

29.40

25.50

25.70

32.50

24.40

30.20

25.70

25.80

Road supply (m/person)

0.60

0.50

1.50

0.60

0.30

1.10

3.90

1.90

0.93

0.53

Parking supply in the CBD

50,848No data

86,03022,0006,376

45,87098,755

138,843

172,000

138,148

Parking space per 1000 jobs

127

No data

297

27

33

164

43

121

168

60

PublicPrivate

30
60

36
41

26
58

54
28

74
9

56
22

59
16

40
46

36
49

54
35

Walking/cycling

10

22

17

18

17

22

25

14

15

11

Mode split: journey to work (%)

Source: Kenworthy and Laube, 199995

From the perspective of freight transport, both patterns, i.e. horizontal growth andvertical growth, presented different challenges. Old CBDs normally lack appropriate
freight transport facilities such as freight elevator and (un) loading zones which makes
it difficult to process the delivery of goods. On the other hand, delivering goods tohigh rise buildings requires parking facilities for trucks since it takes longer time todeliver the goods. Utilization of parking space therefore is low (number of trucks
serve is few) while the demand is high. This particular scenario often results for thedrivers to park their trucks illegally which cause disruption to the flow of vehicles andcontributing to traffic congestion.
In terms of function, as mentioned, some CBDs have integrated function such as
residential, commercial and finance all together in a well planed area. London andParis have all these functions concentrated. Other CBDs on the other hand are planned
for specific function to hold. Marunouchi for instance is more of a financial center
than commercial district. Ortigas Center by all indication is planned for commercial
and financial functions as shown by the presence of headquarters of major industries
and well as major shopping centers (see Chapter 6 for detail).

Paris

New York Manhattan

Figure 5-5. The Core CBD of Paris appears flat while the Manhattan of New
York is populated with skyscrapers
Source: spaceimaging.com

ii)

Employment density

There are many observations that can be deduced from Figure 5-6. First, there is highconcentration of jobs to CBDs with high rise buildings like Hong Kong, New Yorkand Tokyo. Second, there is a big gap between the concentration of jobs between the
CBDs of develop countries and developing countries. Bangkok, Jakarta and Manila
has an average of 185 jobs per hectare while CBDs in developed Asian cities reached
881 jobs per hectare. The difference appears to lie in the fact that the three wealthy
Asian cities have developed extensive rail systems able to bring huge number ofcommuters into tightly confined areas of job concentration with very little space96

demand. By contrast, the poorer Asian cities have major problems with traffic
congestion and poor bus systems which means that the high numbers of central city
jobs have to spread out to maintain adequate accessibility (Barter and Kenworty,1997).

2,000
Job density inside CBD
1,800

1,713

Job density ouside CBD

1,600

Number of jobs per ha

1,400
1,200
989
1,000
800
547

600

467

439

386
400
227

178

Jakarta

Kuala Lumpur

Manila

41

14

31

18

11
New York

52

Paris

17

London45

Bangkok

130

56

Tokyo

203

195
200

Singapore

Hong Kong-

City nam e

Figure 5-6. Job density inside and outside the CBD

iii)

Building use and trip attraction

Land use has tremendous effect to the movements of trucks. Table 5-2 shows thatspace used for commercial purposes, i.e. either host shop or restaurant, has large
number of attracted trips. Moreover, it appears that pulling strength of CBD in termsof attracted trips does not seem to be determined by country’s level of economic
development. Trip attractions of space for commercial use in Manila are even stronger
than the trip attractions in Ginza although smaller than that of Marunouchi district.
Table 5-2. Trip attraction of space use in a building per 1000 sq meter
CBD name

GLA (m2)

Number of
trips

OfficeShops and
restaurants

Tokyo (Marunouchi district)

153801

442

2.6

14.1

Tokyo (Ginza district)

218 shops

-

2.9

10.64

Manila4711583858-

12.03*

Note: *data is taken from shopping centers97

5.3.2

Freight transport informationi)

Parking supply

Parking facilities in the CBD is very important in view of ensuring that freightvehicles have a place to stay while delivering the goods. Lack of it would compel thedrivers to park illegally which would disrupt the fluidity of traffic. Figure 5-5 has mix
presentation regarding the supply of parking space inside the CBD. Developing Asian
countries except Manila has shown high number of parking space indicating that they
are beginning to ready the infrastructure to accommodate the growing number ofvehicles. These cities together with Metro Manila are experiencing rapid increase ofregistered vehicles in the past decades as a result of strong economic growth.
Normally, when the supply of parking space is low, commuters would have difficultyusing their cars which means that other modes of transport should be available forthem.
Kuala Lumpur has responded well to the strong use of private vehicles while going towork as shown by the high number of parking supply. Tokyo and Hong Kong where
space is premium have low number of parking supply but they have efficient railways
which could serve the commuters. Moreover, these cities have adopted policies which
try to restrain the growth of car ownership to limit the severe negative effect of traffic
congestion. London and Paris despite the high share of public transport (40% and
36% respectively) to trip-to-work still have relatively high supply of parking space(Table 5-7).
350
297

250
200
150

168

164
127

121

100
27

33

Hong Kong

60
50

ManilaParking per 1000 jobs

300

43

New York

Paris

LondonTokyo

Singapore

Kuala Lumpur

JakartaBangkok0City nam e

Figure 5-7. CBD parking spaces per 1000 jobs, 1990
Note: London data is1981
ii)

Share of Freight vehicles

The share of freight vehicles to the total number of registered vehicles is remarkably
low to cities in developing countries (Figure 5-8). Developed Asian cities on the other
hand has very high share of freight vehicles almost doubling the share of freightvehicles found in developing countries (average share is 10.90 for developing98countries while 26.77 for developed countries). London and New York too are quite
similar to that number in developing countries.
Although the share of freight vehicles to the total number of registered vehicles might
be different to the actual share of freight vehicle in the road network, there is highprobability that where number of register vehicles is high so as the share of freightvehicles in the road network. Tokyo for instance has 45% share of freight vehicles in
the road network (Takahashi, 2001). Tokyo together with Hong Kong and Singapore
are considered as industry-oriented city, i.e. major industries are located inside thecity, which could be the reason to the notable high number of trucks as compared toother cities in developed country such as New York and London. Both Singapore andHong Kong possessed large ports which perhaps needs services of large number oftrucks.
On the other hand, it is worth to note that except for Kuala Lumpur, all the mentioned
cities in developing countries have active access restriction measure (truck ban).
Presence of this measure can be one of the reasons of the low increase of trucks sincecompanies are becoming less dependence to the services of truck. For instance, inMetro Manila, it was reported that Jeepneys, a type of public utility vehicle, are being
used for goods delivery since this vehicle is not covered of the truck ban.

31.00

30.00

28.00
25.00
21.30

20.00
16.40

15.00

11.48
9.13

11.50

10.00

7.90
7.80

5.00

New York

LondonTokyo

Singapore

Hong Kong

ManilaKuala Lumpur

JakartaBangkok

Share of trucks

Share of freight vehicles to registered vehicles

35.00

City nam e
City nameFigure 5-8. Share of Freight Vehicles to the total Registered Vehicles
5.4

Characterizing CBD

5.4.1

Transformation from low density to high density

Obviously, there is large difference between those CBDs in developed countries andthose in developing countries. For instance, share of freight traffic in developed
countries is quite large while those in developing countries are relatively small. Table
5-3 shows that low density CBDs are mostly located in developing countries whilehigh density CDBs are found in developed countries. This suggests that development99

in the CBD is strongly link to the economic performance of the country. It can betheorized therefore that continued growth in this low economically region would
transform the CBD from low density to high density (Figure 5-9). In such event,increase number of trucks can be expected which would further deteriorate the travel
speed in the network.

Low Density

High Density

Table 5-3. Characteristics of CBD
CBD Name

Description

CBD type

Area (sq. km)

employment

Density

NY Manhattan CBDSouth of 59 st.

High-rise

23.31

1,967,000

84,384

Hong Kong core CBDCentral and western

High-rise

1.13

193,520

71,257

Tokyo CBD core

3 wards

High rise

42.22

2,434,163

57,654

LondonCentral London

Low-rise29.01

1,260,500

43,451

SingaporeCore CBD

High-rise

7.25

280,000

38,621

Paris

Arrondissements i-x

Low-rise

29.01

1,025,000

35,333ManilaCity of Manila

High-rise

36.00

815,400

22,650

JakartaCore CBD

High-rise

43.13

877,610

20,348

Kuala Lumpur

Core CBD

High-rise

16.25

290,000

17,846

BangkokCore CBD

High-rise

20.46

271,944

13,291

Shanghai CBD

Pudong devt area

High-rise

28.00

-

-

Sources: Kenworthy and Laube, 1999 and Demographia(http://www.demographia.com/db-intlcbddens.htm)

Low

High density
Future Ortigas
Future CBD of Bangkok
Future CBD of Jakarta
Future CBD of Shanghai

Ortigas
CBD of Bangkok
CBD of Jakarta
CBD of Shanghai

Marunouchi

Transition

Future Marunouchi
CBD of Hong Kong
CBD of Singapore

High

Freight traffic

Low density

Figure 5-9. Transition of CBD from low density to high density

5.4.2

Framework for Analysis

Important elements in order to evaluate the traffic and environmental impacts of the
CDS in the CBD are presented in Figure 5-10. The four main variables are land use,freight transport characteristics, freight transport facilities, and transportation
characteristics.

100Data of these variables are obtained through survey (see Chapter 6 and Chapter 7 fordetail of the surveys) to have a strong basis for analysis. Microsimulation analysis isthen utilized to quantify the effect of the CDS policy. The application of the CDS isdone to different development levels of the CBD. This means that the present
condition of the CBD is first analyzed before proceeding to analyze the future of the
CBD.

Land use
Variables

Freight transport
operations

Freight transportfacilities

Transportation
characteristics

Variables for analysis
1) Number of buildings
2) Number of floors3) Share of office space andretail space

1) Number of trips2) Number of goods3) Number of staff
4) Parking time of trucks

1) Number of
elevators
2) Number ofparking spaces

1) Road networks
2) Share of passenger
and freight vehicles

Application ofsimulation Analysis

Cooperative System andother TDM measures

Developed country

Developing country

1.
2.

1.
2.

Low density CBDHigh density CBD

Low density CBDHigh density CBD

Traffic impact
Environmental impact

Figure 5-10. Framework for analysis

5.4.3

Some CBD profilesi)

Low Density CBDs

Ortigas Center of Metro Manila
Ortigas Center is the most important commercial and business district after Makati in
Metro Manila, Philippines. The district is bounded by the Epifanio Delos Santos
Avenue in the northwest, Ortigas Avenue in the northeast, Meralco Avenue in thesoutheast, and Shaw Boulevard in the southwest and has grown to about 1.08 square
kilometer of land area as of 1998 (Figure 5-11. About 8.7 percent of this area is under101

the jurisdiction of Quezon City while the remaining is divided between Mandaluyong
City (44.5 percent) and Pasig City (46.8 percent).

Figure 5-11. Map of the Ortigas center showing its transportation network

The district is home to the main headquarters of the Asian Development Bank and theheadquarters of San Miguel Corporation, the country's biggest corporation. SM
Megamall, one of the biggest shopping malls in Asia and the premiere mall of thelargest chain of shopping malls in the country, can be found here, as well as the
Shangri-la Plaza Mall and Star Mall. Shopping malls are one of the biggest trip
attractor establishments (both vehicle and person trip) pulling tremendous number ofpeople and vehicles.

102

For the purpose of comparison, Ortigas is only second in terms of revenue generation
to Makati and land value is also much expensive in Makati CBD (Figure 5-12)

Figure 5-12 Average Land values of Makati CBD and Ortigas Center
Source: Philippine Property Market Overview.Colliers International Quarterly
Research Reports: Philippines January 2004.

Pudong District of Shanghai
Pudong New Area in Shanghai has a land area of 533.44 square kilometers of which
28 square kilometer is to be developed as center of finance, business, and trade area
(Figure 5-13). In the past, due to the lack of bridges and tunnels across the river,
Pudong lagged far behind the old downtown of Shanghai in term of economic growth.
But after its consideration as one of new CBDs in Shanghai, rapid development has
taken place which offers new lease of life to the town.
The central government has played a key role to this achievement. The area ofPudong was designated by the Chinese government as a high priority project whichallows it to receive great attention both from public and private sector. The
investments of international firms further accelerate this development and just after 12
years, 621 distinctive buildings have already been erected in the area (Figure 5-14).
Perhaps the centerpiece of this district is the plan construction of Shanghai World
Financial Center which would have 101 above-ground floors soaring 492 meters high.
This skyscraper will not only host office floors but also a hotel, shops, an observation
deck, conference facilities, and a private club.
The entire area has sub-zones to three major districts which would enhance andcomplement the value and function of each other. These three sub-zones are Lujiazui
financial district, Zhuyuan commercial and trade center, and Longyan residence area.

103

Figure 5-13. Development plan of Pudong in Shanghai
Source: mori.co.jp
Lujiazui Central Financial District
This district would house dozens of super high-rise buildings jostling one another.
Some are already erected such as the 468-meter-high Oriental Pearl TV Tower
rocketing to the sky, the 94-storey World Financial Center and the 88-storey Jin Mao
Mansion.
Zhuyuan Commercial and Trade Zone
This district is to be developed into a commercial and trade center that would bothserve domestic and international firms. Transportation infrastructures are currently
being developed that would allow it to be readily accessible. Office buildings wheresome of the most established firms will locate their headquarters are expected to fill
the area.
Longyang Residential Area
This district will be developed into a modern commercial and residential center that
supports the Financial District and the Zhuyuan Commercial and Trade Center. Rail
networks will be extend to this area that would cater people from the two district aftet
office hour. The supporting facilities located in this area include banking, insurance,
commerce, postal service, and security service. Luxurious residential buildings,
shopping centers and various cultural and recreational centers will also be built tofurther enhance both value and attractiveness of the district. For instance, the Longyu
City, a high-grade 200,000 sq. meter residential project invested by the Korean
Daewoo Corporation has already started construction since 1997.

104

Figure 5-14. The Lujiazui Finance and Trade Zone in New Pudong Area

ii)

High Density CBDs

Core CBD of Hong Kong
Central and Western District, the CBD of Hong Kong, is located in the northern partof Hong Kong Island. The CBD is one of the 18 districts of Hong Kong which has a
261,884 population in 2001. Central was the site of Victoria City, the first British
urban settlement in Hong Kong that they expanded until to its present area. The
modernization process of the CBD has its beginning in 1970s as the economy of Hong
Kong experienced rapid growth. This effort to develop the CBD into modern andfunctional district that serves both domestic and international demands was rewarded
as the area became the center of financial, insurance and property and businessservice. Today, scores of skyscrapers that host headquarters of multinational financial
services corporations populated the area forming one of the most highly dense
business districts in the world (Figure 5-15).
Among the CBDs in the world, the CBD of Hong Kong has the highest job densitywith 1713 jobs per hectare which is almost twice higher to the CBD of New York
(Figure 5-6.

105

Figure 5-15. Hong Kong’s CBD contains high rise buildings
Source: spaceimaging.com

Manhattan of New YorkManhattan is the cultural and commercial center of the city, and its skyline
symbolizes New York City around the world. It has an area of 57 square kilometerand population of 1,537,195 in 2000. The Core CBD of Manhattan has an area of
23.37 square kilometer and its parking supply is 60 per 1000 jobs (Figure 5-4).
The important role of the CBD (or to some extent Manhattan) in the metropolitan
economy is apparent. Job density (number of jobs per hectare) is as high as 989 jobs
in the heart of the CBD while only 11 in the other part of the city. As far as the roleof the whole Manhattan is concern to the economy, the Regional Plan Association, aCommittee on A Regional Plan of New York and its Environs, has declared:
Although Manhattan has never exceeded the total of 2.8 million jobs that it had in
1969, the total never slipped below 2.3 million and nearly attained the 1969 peak in
2000. Although its share of employment has been gradually declining for years,
nearly one out of every four jobs in the region is still located in Manhattan. (Regional
Plan Association, http://www.rpa.org/pdf/Spotlight46.pdf).
Delivering goods in the CBD is quite a real task owing to difficulty of locating
parking space. Further, it is expected that parking time of freight vehicles is long sincebuildings where the delivery is to be made are often high-rise thus the number ofvehicles can be severed by the parking space is limited. This is confirmed by a study
conducted in 2001 which found out that the average time before a freight vehicles
leave a parking space is around 33 minutes (Morris et al., 2001).

106

i)

Core CBD of London

The core CBD of London has an area of 29.01 with an available job of 1.26 million.
London as a city adopted building control policy as early as 1191. The control of new
structures was borne out of several reasons such as 'prevention of view' of existingbuildings and fire precaution measures. In the Financial City, many houses are oldwith many of them are three or four storey buildings. It is rare to find 20 or 30
storey building with the exception of the No.42 Edifice which has 42-floors and theonly skyscraper in the city. Job density inside the CBD is 467 per hectare while 31
outside the CBD (Figure 5-16).

Figure 5-16. The master plan of Canary Wharf business district in London
Source: http://www.woodwharf.com/masterplan/index.html

Lately, there’s a renewed interest in high buildings in London. This case is reflected
in a technical report commissioned by Greater London Authority entitled “London’sSkyline, Views and High Buildings” conducted in 2002. The report centered its
arguments on the following:The role of high building in any city relates to the context. London is different
from Paris, Frankfurt, or Berlin, and required a contextually specific approach.
High buildings do offer the potential to positively contribute to the city. This isdefined by their particular role and location, and requires strategic management of
their impacts.
London has a need for tall commercial buildings in key locations that have multimodal means of access, a large catchment area, and high market demand. This
need is derived of a locationally specific demand for large volumes of space, inhigh value areas, where land availability is limited.

107Another notable development concerning CBD in London is the revitalization of
Canary Wharf at the East of London that started in 1981 but was only emerge as
leading business district in early 1990s (Figure 5-16). The area has 21 square
kilometer and already rivaling London’s traditional financial centre, The Square Mile.

5.5

Chapter summary

The key parameters that shaped CBD around the world are very diverse at times andunified at times. For instance, there was an opposite direction taken by old CBDs ofEuropean cities and CBDs of Asian cities. Although most of the businesses districts ofcities in developing countries Asia haven’t reach the development maturity of CBDs
in European cities or wealthy Asian cities, there is a strong indication that they areheading the direction of large and high density CBDs where skyscrapers pronounce
the location of CBD. On the other hand, of all the cities considered in the review, thepull attraction of CBD in employment is apparently strong creating huge gap between
the job density inside and outside the CBD.
Pressures from old centers to expand the area of CBD vertically to accommodate
growing needs for space are resulting to creation of new centers as observed in
London. The traditional financial district of London is facing a stiff competition fromthis new CBD that although cannot match the prestige of old center but has better
facilities for business to offer. In Paris, the city was saved from sprout of skyscrapers
and large shopping malls by creating the La Defence CBD outside the old city butvery accessible by its railway. Clearly, this strategy demonstrated just how
determined the local authorities to keep the old identity of the city in the face of
mounting pressure for structure reform.
It appears that CBD of cities in developing countries is in the direction of large andhigh density CBD. This development would pose a major challenge to cater travel
demand. Although there is a sign that these cities are investing to the development of
rapid mass rail transport system to meet this demand, there seems to be lack ofstrategies on how to respond to freight transport demand. Concentration of different
economic activities in a confined area would surely result to some transportation
problems.

108

REFERENCES
1. Barter, P. and Kenworty, J. (1997). Implications of a global review of urban
transport and land use patterns The challenges and opportunities of high density inbooming Eastern Asian cities. Working Paper, Asia Research Center, Murdoch
University, Murdoch, Perth, Western Australia.
2. C. Knowles and P. Pitt, The History of Building Regulation in London 1189-1972.
(Architectural Press: London, 1972), 5.
3. Curtis, James R. (1993). Central Business Districts of the Two Laredos.
Geographical Review 83:54-65. (Does a good job showing land use patterns in
the CBD)
4. Getis, A., Getis, J. and Fellmann, J. (2006). Introduction to Geography, 10th ed.
New York: McGraw-Hill.
5. Hartshorn, Turman A. (1992). Interpreting the City: An Urban Geography(selected pages). New York: J. Wiley & Sons. (Provides some ways / ideas indelimiting the CBD)
6. Herbert, David. (1973). Urban Geography: A Social Perspective. New York:
Praeger Publishers. (Provides classic examples of land use and patterns within acity)
7. Hibbert C. and Weinreb B., ed., The London Encyclopedia (2d ed., 1993); S.
Inwood, A History of London (1999); P. Ackroyd, London: The Biography (2001).
8. Kenworthy, J. et al (1999). An international sourcebook of automobile
dependence in cities 19960-1990. University Press of Colorado, Niwot, Colorado.
9. Kuse, H. (1979). A Study on the Central Business Districts in Local Cities inJapan. Unpublished PhD dissertation. Waseda University, Tokyo, Japan (inJapanese)
10. Lewis, Peirce F. (1992). Down on Main Street. Boston Globe. (Discusses some of
the issues related to small town downtowns)
11. London’s Skyline, Views and High Buildings, (2002). A report prepared forTransport of London. Regional Plan Association,
http://www.rpa.org/pdf/Spotlight46.pdf
12. Morris, A. and Kornhhi auser, A (2001). Relationship of Freight Facilities in CBD
Office Buildings to Truck Traffic. Transportation Research Record 1707, Paper
No. 00-0197.TRB, National Research Council, Washington, D.C.
13. Murphy, Raymond E. and J. E. Vance, Jr. (1954). Delimiting the CBD. Economic
Geography 30(3):189-222.

109

14. Shanghai Lujiazui Finance and Trade Center.
http://www.shld.com/develop/ly/liste.htm
15. The United States Census Bureau's 2000 census data
http://www.census.gov

110

CHAPTER

6

Application of Cooperative
Delivery System to a Low
Density Central BusinessDistrict: Case of Developing
Country
6.1

Introduction

This chapter evaluates the traffic and environmental impacts of cooperative delivery
system to Ortigas CBD in Metro Manila. It was assumed that there exists anagreement among shippers and carriers to coordinate their operation that would result
to the consolidation of loads using lesser number of delivery trucks. The futurechanges in the context of supply phenomena of the CBD in the form of increased
number of freight vehicles as a result of further development were also considered in
the analysis. This process aims to answer if CDS is a suitable policy measure to thepresent general condition of the CBD or if the CDS is better adopted in high density
CBDs.
In the succeeding Section 6.2, brief information of the CBD under consideration isprovided. This section highlights some locational characteristics of Ortigas CBD.
Also, the overall transportation system affecting it is also discussed, tackling the
CBD’s accessibility by public and private transport vehicles, as well as mass transit
system. The common problems associated to vehicle and human traffic encountered in
the shopping centers of the CBD was also underlined. Section 6.3 tackles the methodof obtaining necessary data to evaluate the impact of cooperative delivery systemmeasure. This section covers the result of the survey, as well as brief analysis of thedata obtained. Section 6.4 explains the simulation analysis procedure, and also themethod to validate simulation result. The result of the simulation analysis isthoroughly discussed in Section 6.5 which stresses importance to the benefits gained
from cooperative delivery system in the form of improved travel speed, reduced
traffic volume, and improved environmental condition. The role of CDS in freighttransportation management as the development of CBD intensifies is given in Section
6.6. The chapter is concluded in Section 6.7 where some significant points wererevisited.

111

6.2

Ortigas CBD of Metro Manila

The Ortigas Center located at the center of Metro Manila and is the most importantbusiness district of the country next to Makati. Bounded by the Epifanio Delos Santos
Avenue in the northwest, Ortigas Avenue in the northeast, Meralco Avenue in thesoutheast, and Shaw Boulevard in the southwest, the district has grown to about
1,082,891 square meter of land area as of 1998 (Figure 6-1). About 8.7 percent of thisarea is under the jurisdiction ofIndustry headquarter
Quezon City. The remaining
Shopping center
Robinsonsarea is divided between
Study area
Mandaluyong
City
(44.5
percent) and Pasig City (46.8
percent).
RBC
ADB

The district is regarded as oneof the country’s leadingbusiness district. Headquartersof Asian Development Bankand some prominent companies
such
as
San
MiguelCorporation, Robinsons Land
Corporation,
andJollibee
Foods Corporation can befound in Ortigas Center.
Famous malls like SM Mega
Mall, Robinson’s Galleria,
Podium and Rustans arelocated in the CBD. In
addition, Ortigas also havenumber of high-rise buildings
mostly used for office space,commercial activities, andhotels.

PodiumSM
San Miguel

Rustans
Jollibee

Figure 6-1. Map of Ortigas CBD showing majorindustries and shopping centers

Ortigas Center is highly
accessible to public transportpassenger and private vehicles. EDSA and major roads such as Meralco Avenue,
Ortigas Ave, Doña Julia Vargas Avenue, and ADB Avenue and Shaw Boulevard
provides access to Ortigas Center. Public transport alternatives include Metro RailTransit (MRT), bus via EDSA, FX, and taxi.
6.2.1

Transportation system and freight transport

The CBD is well connected to the rest of Metro Manila either by bus or by railway.
The main avenue of the capital, which is EDSA, provides an easy access to the CBD
either by bus or by other public transportation mode such as FX and Jeepney. The Bus
services along EDSA corridor ferries large amount of passengers to and from almost
every corner of the Metropolis, while the MRT transports passengers either to thenorthern or southern side of the capital.

112

Concerning freight transport, approximately 85% of Philippine foreign trade passes
through the Port of Manila; 90% of imports enter this port for distribution to otherprincipal cities via trucks and inter-island vessels, where some are providing Roll-OnRoll-Off (RO-RO) services. Most of the Philippine's national importers anddistributors are also located in Manila.
Trucks do the distribution of goods from port to destination and vice versa. While thetruck ban prohibits trucks from passing EDSA from 6:00 am to 9:00 pm, there
happens a shift in the time of delivery of trucks from daytime to nighttime, whichsometimes, causing delays in container deliveries. With such time of delivery
adjustment, residential districts suffer from noise and vibration caused by the trucks.
The implementation of truck ban along EDSA resulted to the proliferation of small
trucks that are not covered by the ban. Small trucks having gross weight of less than
4,000 kg, instead of the conventional large vehicles, do the pick-up and delivery.
Loading and unloading of goods are done inside the commercial buildings with builtin parking spaces and/or loading-unloading bays.
6.2.2

Problems associated to shopping centers in the CBDCentral business districts in developing countries is home to many shopping centerswanting to take advantage of the potential commerce brought by the large volume ofpeople from different zones in and around the Metropolitan. Generally, shopping
centers pull tremendous number of people that are usually coming either by car or by
public transport, given their various trip patterns and characteristics. This process oftrip attraction or generation obviously leads to several problems such as traffic
congestion due to roads that are often exceeding their volume capacity (Figure 6-2),

Congestion of main road

Vehicular conflictat entrance/exit
gate

Shopping Centers
Shopping Center

Mixing of cars, trucks
and customers

bus
bus

Congestion of access roadbus

Accumulation ofpassengers getting
in/out of bus

Figure 6-2. Typical traffic congestion scenario at the shopping center’saccess road and immediate environment
113

increased vehicle to vehicle and vehicle to pedestrian conflicts, and also queuing ofloading and unloading buses at unauthorized areas. Moreover, at this instance, buses
competing for passengers tend to outmaneuver each other, resulting to buses stopping
beyond the bus bay. Therefore, the capacity of road is reduced and cars behind haveto wait until the buses move. Add to this chaotic situation are the Taxi service
vehicles loading and unloading almost everywhere.
During mid-day or rainy day, shoppers tend to deviate from the pathways dedicatedfor pedestrians as they try to find the shortest path that would bring them to the
shopping mall or certain bus stop. This situation where cars and people are mixing
together brings high probability of accident occurrence aside from disruption on theflow of traffic. Among the factors making the scene worse are poorly designed accessfacilities, inefficient personnel of the shopping centers being tasked on traffic flow
management inside the jurisdiction of the malls and also, uncooperative pedestrians
and drivers.
6.3

Data collection

Understanding the characteristics of freight transportation, particularly the CBD’s
associated traffic flow and environment requires set of data for use as input parameterin the simulation process. This includes information concerning modal share, total
volume of vehicles, road networks, volume of goods, parking time, and delivery time,
among others. Freight transport data for Metro Manila is often difficult to find due tolimited numbers of studies conducted about it. In most cases, a primary data survey isneeded to obtain information required in a study.
50%

47%

Robinson's
45%

SM Megamall

40%
35%
31.25%
30%
25%

22.00%

20%
14.50%

15%

16.92%
15.50%

11.08%
10%
5%
5%

7.00%
6%
4.33%

9%
6.42%

3%
1.00%
0%
OthersBookstore

Amusement

Clothing

Movie House

Dept Store

Groceries

Dining0%

Figure 6-3. Distribution of space usage
Note: Distribution of floor usage to SM Megamall is estimated using data of Palmiano (1999).

114

Ortigas entrance

Bus stop

EDSA shrine entrance

To Car ParkEDSA

Supermarket RDU

Shopping Center A
Delivery Receiving Area
Mall Entrance
Mall Entrance (also used for deliveries)
Delivery truck parking

Figure 6-4. Robinson’s Galleria showing delivery/receiving areas

Originally, the study intended to obtain the same set of data or information on freight
and traffic flow for the two shopping centers, i.e. Robinson’s Galleria and SM
Megamall, in the CBD in order to have a comparative look (Figure 6-1). In such away, their influence to the total freight traffic in the CBD may be identified andanalyzed. However, the failure to obtain permission from SM Megamall to avail of its
freight data hampered this effort. As a result, freight delivery and traffic volume data
were availed and produced for Robinson’s Gelleria, while SM Megamall only has
traffic volume data for input to simulation (Figure 6-4 and Figure 6-5).
The SM Megamall has a big role to the analysis of CDS’s impact on traffic andenvironment because it is located at the center of the CBD where vehicular traffic
flow is extremely high. If only that the freight delivery data was obtained for SM
Megamall, comparative analysis could have been performed for more interestingfindings, solutions and perhaps, policy recommendations.

115

Bldg

Bldg
Julia

PARKINGPARKINGEDSA

Delivery Receiving Area
Mall Entrance
Mall Entrance (also used for
d li i truck)
Deliveryparking

Basement deliveries

Shopping Center B

Figure 6-5. SM Megamall showing the delivery/receiving areas

6.3.1

Brief introduction of the two shopping centers

The two shopping centers are located along EDSA corridor. This road being the
backbone of Metro Manila’s transport network system stretches some 54 km,
connecting northern and southern parts of the Metropolis. The all-day truck ban isenforced in this road in the effort to improve traffic flow and give preference topassenger cars. The two shopping centers are separated by 300 meters or so and bothcan be reached by train, bus, and car and FX using EDSA. Robinson’s Galleria has agross leasable area (GLA) of 99,888 square meters while the estimated GLA of SM
Megamall is 209,848 square meters.
Shopping centers in the Philippines normally allot considerable space for department
stores, movie houses, groceries (supermarket), which are managed by the center’sowner (Figure 6-3). The composition of the remaining floor area herein referred to as“others” includes novelty shops, public areas for resting, multi-purpose stage, nonmerchandizing uses such as banks, telecommunication and computer products andservices, health clubs; and recreational facility sites such as skating rinks, bowlingalleys, and indoor parks (Palmiano, 1999).
Parking space for private cars is normally well provided in the large shopping centersof Metro Manila. Aside from in-building parking spaces, open space parking facility
is also provided, equipped with necessary markings and signage for maneuvering andsafe parking (Table 6-1). Docking area for delivery vehicles are also prepared which
are curved from the establishment itself (Figure 6-4 and Figure 6-5). Unlike parking
spaces for cars, freight delivery areas are properly located near the establishment tominimize delivery time. Aside from the driver, additional staffs working on thehandling of the goods can also be observed, although it was not inquired if those staffs
work for the goods of certain shops or for the whole shops in the shopping center.

116

Table 6-1. Number of parking space
Robinson’s
Total number of parking space 1625
Parking space for cars
1574
Parking space for trucks
51*Estimated using the ratio of GLA and total parking space

6.3.2

SM Megamall
4078*

Survey6.3.2.1 Survey design
Permission before the implementation of the survey was sought to the administrator of
Robinson’s Galleria and SM Megamall. The idea is to get their assistance to avoid
misunderstanding between the surveyors and the guards manning the shopping centers
during the survey. Permission to conduct a survey was granted by the Robinson’s
Galleria while approval from the administrator of SM Megamall was not obtained
prior to the survey proper. But interestingly, the administrator of SM Megamall
showed considerable interest and willingness to cooperate with the survey, however
due to the limited time where the survey must be conducted, the permission was notsecured.
Secondary data for Robinson’s Galleria such as size of the shopping center, number ofparking spaces and other information associated to vehicular traffic was obtained
from its administrator. In the case of SM Megamall where permission was not
obtained, Internet and the paper of Palmiano (1999) were used to estimate thebreakdown of space usage (Figure 6-3).

Figure 6-6. SM Megamall, one of the biggest shopping centers in Asia
located along EDSA
Source: http://www.smprime.com.ph117

6.3.2.2 Survey implementation
The failure to obtain permission from SM Megamall hampered to position thesurveyors inside the area of the establishment (Figure 6-6). The original plan is tohave surveyors in every delivery zone of both shopping centers to record the numberof incoming vehicles and their parking time. Also in the interest is the overall delivery
time of the goods to its particular recipient center.
Table 6-2. Data recorded by surveyors at SM Megamall
Location

NumberTotal

Data Collected

1, 2, 3

2

10

Truck arrival, number of trucks in queue, parking time,number of goods delivered

4

2

4

Truck arrival, number of trucks in queue, delivery time,parking time, number of goods delivered

5, 6, 72

4

Number of stopping bus, FX or taxi dwelling time

2, 6

1

5

Entry and exit volume (classified volume count)

2, 4

2

2

Classified volume count (passing only)

The administrator of Robinson’s Galleria gave approval to conduct the survey inside
the area of establishment (not inside the building) in order to record the freighttransport related activities. However, for SM Megamall, surveyors were positioned
outside the area of the shopping center. This came to be a difficult task and required
20 surveyors due to the numerous entrance/exit points of the establishment (Figure 67).
Aside from recording the truck’s arrival and documenting the transport of goods
movement, the vehicle traffic passing along the thoroughfare bounding the shopping
center was also counted (Table 6-2). This data is essential input to the simulation
modeling which may illustrate the traffic flow characteristics in the area, thereby
identifying potential bottlenecks.

SM Mega Mall Building A

2

3

J. VARGAS

1

4SM Mega Mall Building B

7

6

EDSA

5

Figure 6-7. Locations of surveyors

118

6.3.2.3 Findings from the survey
Robinson’s Galleria
Initially, the data on goods was recorded by interviewing the driver of the trucks
arriving at the shopping center. Surveyors deployed around the shopping centerswould immediately approach the driver upon the arrival. This approach went well, as
all of the drivers were familiar with the quantity and description of his truck’s load.
Apart from the surveyors conducting interview of the drivers, another surveyor
recorded the parking time of the trucks.
Table 6-3. Types of delivered goods
Types of delivery

Description

Boxes and bags (pcs)

For shops (e.g. clothes)
For restaurants (e.g.
bread)For Supermarket (e.g.
meat)
For shops (e.g. radio)

Trays and crates (pcs)
Perishable goods (kgs)
Appliances (pcs)

Number of
goods
696

Number oftrucks
47

652

28

4111

13

795

10

The goods arrived in the shopping center were classified into four groups: 1) boxes
and bags, 2) trays and crates, 3) appliances and other hard products, and 3) perishable
goods (Table 6-3). Regarding the parking time of trucks, it was observed that trucks
delivering the supermarket goods have the longest time spent (Table 6-4).
Table 6-4. Parking time of trucks
Unloading area

Description of the area

Goods mostly delivered to
shops and restaurants
Goods delivered toSupermarket RDU
supermarket
Goods for shops and
EDSA Shrine Entrance
restaurants
See Figure 6-2 for the location of each unloading area.
Ortigas Entrance

Average parking
time (min)Share oftrucks

35

70%

37

12%

28

18%

Vehicles used for delivery
Several types of vehicles were being used to deliver goods into the shopping centersin the CBD such as small trucks, vans, and even jeepneys. During the survey, one
jeepney was observed loaded with supermarket goods. Actually, this practice has
already been mentioned in the paper of Castro (2003) where some companies are
utilizing jeepneys to transport their goods from the port of Manila. The reason for
utilizing such vehicles, which appears not suitable for cargos, is that they are notcovered by the truck ban and therefore, not restricted to move in any part of themetropolitan at any time.
As a general observation, most of the vehicles used for delivery are small having agross weight of not more than 4 tons to avoid the restrictions of the truck ban (Figure6-8). Most of these vehicles are delivery vans.

119Typical Delivery Van

L

Gross Wt:
Net Capacity:
L x W x H:

3,000kg
1,650kg
8x6x6 ft

Typical Delivery TruckH

Gross Wt:
Net Capacity:
L x W x H:

4,000kg
2,000kg
10x6x8 ft

Figure 6-8. Illustration of typical van used for goods’ distribution

SM Megamall
Since permission was not granted, activities related to delivery of goods from thedocking area going inside the shops were not recorded. Traffic volumes however wererecorded. This shopping center lies at the heart of the district and therefore, it is ofinterest to have its traffic flow recorded as this will be vital for the simulation phaseand analysis. Table 6-5 provides the number of vehicles recorded for SM Megamall,as well as information for other shopping centers. The idea is to have a comparison of
the traffic characteristics with other shopping centers and to know if the volume ofvehicles during the survey day can be considered as normal.
6.3.2.4 Data analysis and output
There were 53,684 vehicles recorded at the vicinity of SM Megamall during theduration of the survey however only 79% of the vehicles passed the gate of theestablishment (Table 6-5 and Figure 6-10). Obviously, private cars have the highest
share, which accounted for 52% while delivery vehicles (combined number ofdelivery vans and trucks) have a modest share of 1.02%. Out of 837 delivery vehicles
recorded, only 85% were observed to deliver goods at the shopping center.
Table 6-5. Traffic composition
Shopping
Center
*EGO
*EGA
*SM
Alabang*SM
Fairview
SM
Megamall

CAR

TAXI

FX

MCYCLEPUJ

DELVAN

TRUCK

TRI

60%
52%

24%
6%

4%
6%

4%
7%

1%
10%

4%
3%

0%
0%

4%
17%

TOTAL
VEHICLES
10,997
6,933

51%

10%

12%

3%

2%

4%

1%

17%

23,551

62.4%

26.3%

4.5%

2.1%

1.6%

2.6%

0.3%

0.2%

19,327

50%

30%

9.1%

3.6%

0%

1%

.02%

0.0%

53,684*Data taken from Palmiano (1999)

120

Boxes and Bags Delivery

Tra ys and Crate s Delive ry

200

250

180
160

200

140
120

150

100
80

100

60
40

50

20
0

0

1

3

5

7

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

1

3

5

7

9

Truck Num be r

11

13

15

17

19

21

23

25

27

Truck Num be r

Perishable goods(kilograms)

Appliances and other hard products
(pcs)
400

1600

350

1400

300

1200

250

1000

200

800

150

600

100

400

50

200
0

0
1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

6

7

8

Truck Num be r

Truck Num be r

Figure 6-9. Types of goods delivery in the shopping center

121

9

10

11

12

13

SM North Edsa

SM Fairview

GLA: 174,254 m

TLA: 20 ha
2
GFA: 130,600 m
2
GLA: 82,629 m
Total vehicles: 19,327
Share of trucks: 2.8%
2Vehicle trips per 1000 m : 234
2
Truck trips per 1000 m :6.62

SM Sta. Mesa
GLA: 84,354 m

2

SM Manila
GLA: 105,376 m

2

Ever Gotesco
Commonwealth2

TLA: 50,000 m
2
GFA: 80,974 m
2
GLA: 57,951 m
Total vehicles: 10,997
Share of trucks: 4.3%
2
Vehicle trips per 1000 m : 190
2
Truck trips per 1000 m : 7.02

Green Hills
Shopping Center
GLA: 65,570 m

2Robinson’s Galleria
2

TLA: 41,045 m
2
GFA: 212,090 m
2
GLA: 99,888 m
Total trucks: 106

Ever Gotesco Ortigas
2

TLA: 60,000 m
2
GFA: 63,129 m
2
GLA: 43,767 m
Total vehicles: 6,933
Share of trucks: 2.8%
2Vehicle trips per 1000 m :158
2
Truck trips per 1000 m :4.41

SM Megamall
TLA: 10.5 ha
2
GFA: 331679 m
2
GLA: 209,848 m
Total vehicles: 42,425
Share of trucks: 1%
2
Vehicle trips per 1000 m : 202
2
Truck trips per 1000 m : 2.56

Shangri-la Plaza
GLA: 78,300 m

2

Harrison Plaza
GLA: 56,488 m

2

SM Bicutan
GLA: 71,327 m

SM Sucat
GLA: 41,852 m

2

2

SM South Mall
TLA: 20 ha
2
GFA: 205,120 m
GLA: 159,592
Total vehicles: 23,551
Share of trucks: 5.2%
2
Vehicle trips per 1000 m : 148
2
Truck trips per 1000 m : 7.61

Uniwide Coastal Mall
GLA: 102,268 m

2

Figure 6-10. Quick facts to surveyed shopping centers and other shopping
centers in Metro Manila

122

Although the focus of the study is on freight transportation management in the CBD,it was perceived to establish more meaningful findings by comparing the freight andtransportation data of shopping centers in the Ortigas CBD with those in other CBDs.
This would provide a comparative picture and idea on how these shopping centersfare with others in terms of traffic attraction and size.
Raw data of other shopping centers except Robinson’s Galleria and SM Megamall
were taken from the study of Palmiano (1999). Figure 6-10 reveals that SM Megamall
B has the biggest GLA with the highest volume of vehicles generated (42,425). In SM
Alabang, the share of freight vehicles is observed to be high compared to SM
Megamall. One possible reason for this low share of freight vehicles is due to theproximity of this shopping center to EDSA where there is an active restriction to large
delivery trucks brought by the truck ban scheme. It is then possible that some of thedeliveries were made during the times outside of the truck ban coverage. The traffic
survey at the surroundings of the shopping center started at 9:00AM.

6.4

Simulation Analysis for traffic and environmental improvement

6.4.1

VISSIM simulation software

The VISSIM 3.5 was used as the tool for simulation and analysis. It has an excellent
modeling capability and graphic presentation. It is a microscopic, time step andbehavior based simulation model developed to analyze the full range of functionality
classified roadways and public transport operations.
The first step in modeling using VISSIM is to import aerial photo or schematic
drawing of the study area into simulator (Figure 6-11). The next step is building of the
networks and applying its attributes (i.e., lane widths, speed zones, priority rules, etc).
Links are generally straight or follow the curvature of the road. Connectors, which areused to connect links, are typically used to model turning areas and lane expansion
and contractions.
Table 6-6 shows the typical types of vehicles using the road network of Metro Manila.
These modes along with trucks and motorcycles constitute the vehicle traffic
component of the simulation model.
Table 6-6. Public Utility Vehicles (PUVs) in Metro Manila
PT ModeDescription

Jeepney

An indigenous Filipino PUV; evolved from the wartime
U.S. Jeeps, 2 passengers sit with the driver in front
while the rest of the passengers sit in rows face to faceon either side; fixed PT route
FX
Toyota Tamaraw car model and evolved into a PUV;
functions like an air-conditioned jeepney; flexible route
but with route endpoints defined
Taxi
Common taxi cab; maybe air-conditioned or not;flexible routeBus
Fixed, long distance routes; not always available(depends on location of establishment)
Source: Palmiano, 1999

123

Passenger
Capacity
16 to 2010

460

Figure 6-11. Screenshot of the simulation network with map background
6.4.2

Alternatives considered for traffic improvement

There are several alternatives that can be pursued to improve traffic flow in the
district under study. Measures such as road pricing, provision of truck parking andloading facilities, cooperative delivery system, and time window are proven toenhance the fluidity of the road traffic. However, for this study, only cooperative
delivery system and time window are adopted for they are simple and realistic toimplement. Applying road pricing requires comprehensive data that in order todetermine the traffic analysis zones (TAZ) cordon line. Moreover, this measure mightbring negative impacts to road users since they will be paying to use the roads that are
traditionally used free of charge. Truck parking and loading facilities are usually
located in the shopping centers. These facilities are hard measures that are essential tocater the amount of truck traffic generated by the shopping centers. The descriptionsof the two adopted measures are given below.
Time window: This measure prohibits the delivery of trucks into the district from 9:00
to 10:30 in the morning and from 2:30 to 6:30 in the afternoon. The volume ofvehicles is observed to be high during these periods (Figure 6-12).
Cooperative Delivery System: This measure assumes that a cooperative delivery
system exists among the carriers and shippers in the district, which would reduce thenumber of trucks, needed to deliver the same amount of goods.

124

4000
Car

Bus

Del Veh

MCYCLEFX

Jeepney

3500

Number of vehicles

3000
2500
2000
1500
Afternoon
deliveryMorning delivery

1000
500

20:30 - 21:00

20:00 - 20:30

19:30 - 20:00

19:00 - 19:30

18:30 - 19:00

18:00 - 18:30

17:30 - 18:00

17:00 - 17:30

16:30 - 17:00

16:00 - 16:30

15:30 - 16:00

15:00 - 15:30

14:30 - 15:00

14:00 - 14:30

13:30 - 14:00

13:00 - 13:30

12:30 - 13:00

12:00 - 12:30

11:30 - 12:00

11:00 - 11:30

10:30 - 11:00

10:00 - 10:30

9:00 - 9:30

9:30 - 10:00

0

Time

Figure 6-12. Time window
Each of the two alternatives is expanded into five cases (Table 6-7). This means thatcooperative delivery system will be applied in which the road network is assumed tocontain the volume of trucks share as 5%, 10%, 15%, and 20% of the total traffic. The
same cases are also applied to time window.
In all cases, the volume of other vehicles is kept constant while the share of trucks isincreased (Table 6-7). The idea of increasing the volume of trucks is based on threeassumptions: 1) number of freight vehicles will increase when the development of
CDB intensifies, 2) number of freight vehicles will increase when the truck ban islifted, and 3) passenger cars will not substantially increase due to the completion of
the railway networks.
Table 6-7. Data input
Alternatives

Del Veh

Car

Bus

MC

FX

Jeepney

Total

Present volume (trucks)

837

42924

3156

1919

4827

21

53,684

Share of freight vehicles is
5%

3844

42924

3156

1919

4827

21

56,104

Share of freight vehicles is
10%

5332

42924

3156

1919

4827

21

57,592

Share of freight vehicles is
15%

7997

42924

3156

1919

4827

21

60,258

Share of freight vehicles is
20%

10663

42924

3156

1919

4827

21

62,924

Trucks servicing perishable goods such as meat, frozen goods are normally excluded
in a cooperative delivery system. The reason is that these types of goods require
special handling facilities, which make it difficult to include especially if theconsolidation center is not sophisticated enough. Table 6-8 shows that “trays andcrates” delivery and “perishable goods” were not affected by cooperative delivery
system as far as the number of trucks servicing them is concern.
125

The ratio of freight vehicles to each type of delivery is derived from the survey
conducted in the Robinson’s Galleria (Figure 6-9 and Table 6-3). With the present
volume, the required number of trucks delivering goods in the district could be
reduced from 713 trucks to 137 when a cooperative delivery system is adopted.
Table 6-8. Comparison of number of trucks with and without CDS
Types of delivery
Ratio
No. of goods per truck without
cooperative
No. of goods per truck withcooperative
Present volume (trucks)
CDS (trucks)
Share of freight vehicles is 5%
CDS (trucks)
Share of freight vehicles is 10%
CDS (trucks)
Share of freight vehicles is 15%
CDS (trucks)
Share of freight vehicles is 20%
CDS (trucks)

6.4.3

0.48

Trays andcrates
(pcs)
0.29

15

14

1500

17

188
342
27
1843
147
2557
204
3835
306
5114
408

14
204
204
1098
1098
1523
1523
2285
2285
3047
3047

1500
95
95
510
510
707
707
1061
1061
1414
1414

399
73
4
392
20
544
27
816
41
1088
55

Boxes andbags (pcs)

Perishable
goods (kgs)

Appliances and otherhard products (pcs)

Total

0.13

0.10

1

713
329
3844
1775
5332
2462
7997
3693
10663
4924

Route of cooperative delivery system

Delivery vehicles follow the normal routes in the district without the cooperative
delivery system (Figure 6-13a). On the other hand, when the CDS is applied, delivery
vehicles have particular routes to follow (Figure 6-13b). There are no changes to theSP--- Route 1--- Route 2a) Without CDSb) With CDS

Figure 6-13. Truck routes with and without CDS
126

routes of other vehicles such as cars, buses, and jeepneys. The 124 delivery vehicles
that were not observed to make delivery at the shopping center (Megamall) will retain
their routes also. Following the cooperative delivery system’s model discussed in
Chapter 3, the depot should be located outside the CBD in order to reduce the numberof trucks in the district. The depot in this study is assumed to be located in the borderof Shaw Boulevard at the end of the truck routes shown in Figure 6-11b. The Survey
result showed that large volume of delivery vehicles (44%) came from this direction.
Furthermore, there is difficulty of locating the depot to the other sides of the CBD due
to prevalent presence of one-way traffic scheme.
6.4.4

Validation of simulation result

The aim of the validation process is to ensure that the behavior of vehicles in the
network is close enough to the behavior of vehicles in the actual system. This can bedone by comparing the input data recorded by survey and the data recorded by thesimulation. The vehicle count from survey data is 53684 while the simulation count is51622, which brings the difference of merely 4% (Figure 6-14).
2000
Survey count

1800

Simulation count1600
Number of vehicles

1400
1200
1000
800
600
400
200

20:30 - 20:45

20:00 - 20:15

19:30 - 19:45

19:00 - 19:15

18:30 - 18:45

18:00 - 18:15

17:30 - 17:45

17:00 - 17:15

16:30 - 16:45

16:00 - 16:15

15:30 - 15:45

15:00 - 15:15

14:30 - 14:45

14:00 - 14:15

13:30 - 13:45

13:00 - 13:15

12:30 - 12:45

12:00 - 12:15

11:30 - 11:45

11:00 - 11:15

10:30 - 10:45

10:00 - 10:15

9:30 - 9:45

9:00 - 9:15

0

Tim e

Figure 6-14. Comparison of survey count and simulation count
There are three (3) generation points in the simulation process where vehicles in thesimulation model originate (Figure 6-21). Comparing modal share in each generation
point using survey data and simulation data would provide a picture on how close is
the simulation in replicating real number of vehicles per type. Table 6-9 shows thatthe simulation closely replicated the real distribution of vehicles with the difference of
not more than 10% except for Jeepney. This high difference is due to the small
number of Jeepney in the network, which is only 7 while the simulation has 6 (Table
6-9).

127

Table 6-9. Difference between survey count and simulation count per vehicle
mode
Generation point 1
Type of vehicles(To EDSA right)
Total vehicles
5,951
Car
6%
Bus
3%
Del Veh1%
MCYCLE
4%
FX
5%Jeepney8%
Total
5%
Note: % difference from survey data

6.5

Results of the Simulation Analysis

6.5.1

Traffic impact

Generation point 2(To EDSA)
7,497
2%
0%
8%
0%
1%

Generation point 3(EDSA)
40,236
0%
0%
5%
2%
14%
1%

The total traffic reduction achieved by adopting cooperative delivery system under thepresent traffic condition is just 0.72% (Figure 6-15). This small traffic reduction canbe attributed to the small share of trucks in the road network of the CBD, which
stands to only 1.02%. However, despite this limited traffic reduction achieved throughcooperative delivery system, it can be assumed as significant impact on the traffic
flow in the form of improved travel speed. Truck’s size is considerably larger thanordinary vehicles thus, any reduction of its number would equal to some amount of
freed space that can be utilized by other vehicles. Furthermore, trucks have slow
acceleration, which adds further delay in a congested network.
10
9.12

Reduction of traffic due to CDS (%)

9
8
7.14
7
6
4.98
5
3.69

4
3
2
1

0.72

0
Present situation

5%

10%
Cases

Figure 6-15. Reduction result of CDS to total traffic

128

15%

20%

In the same figure, as the share of trucks in the total traffic increases, the traffic
impact of cooperative delivery system also increases. This implies that as thedevelopment of CBD intensifies, the role of CDS is becoming more important in viewof reducing the number of trucks delivering goods to the CBD.

6.5.2

Environmental impact

6.5.2.1 Emission factors
The environmental impact of CDS is determined by utilizing the travel speed andtravel distance for each link derived from the simulation model. Environmental
emissions are then calculated by multiplying traffic volume per mode to its respective
emission factor for each environmental pollutant (i.e. CO, NOx, SOx, SPM). Pollutant
emissions are assumed to be dependent on travel distance, travel speed, and emission
factors for each vehicle type.
Emission factors depend on variety of factors including speed, age of vehicle, loadfactor, weight of truck, frequency of stops and acceleration, altitude, temperature,operating conditions, and others (Campbell, 1995, Minato, K. et al 2003, Noda, A. et
al, 2003). Satisfying these conditions is very difficult so the simplified emission factor
on the study “Metro Manila Urban Transport and Integration Study” (MMUTIS),
which is also available in the paper of Castro (2003) was used (Table 6-10). Emission
factors of some vehicles (i.e. utility vehicles and motorcycles) are not available in theformer reference so another source was used (Table 6-11). Emissions can be
estimated as:
Pollutant (i) = travel distance × emission factor at average travel speed(veh-km)(g/veh-km)
Table 6-10. Emission factors (g/km)
Pollutant
Type
CO

NOx

Sox

SPM

VehicleType

Average Speed
10 - 20 km/h

> 20 km/h

Gasoline

Car
Small truck

27.57
47.58

23.5
52.2

18.7
41.14

DieselCar
Small truck
Large truck

7.85
8.02
8.12

6.54
6.8
7.11

5.94
6.2
6.5

Gasoline

Car
Small truck

2.75
4.7

2.76
3.59

2.78
3.53

DieselCar
Small truck
Large truck

5.65
8.95
11.24

4.28
7.66
10.59

3.89
7.01
9.22

Gasoline

Car
Small truck

0.013
0.015

0.011
0.011

0.011
0.01

DieselCar
Small truck
Large truck

0.14
0.18
0.2

0.08
0.121
0.15

0.07
0.11
0.1

Gasoline

Car
Small truck

0.07
0.07

0.05
0.06

0.05
0.05

DieselCar
Small truck
Large truck

1.2
1.8
2.3

0.07
0.9
1.5

0.07
0.81
0.80

Source: Castro et al, 2003

< 10 km/h

Note: Bus is treated as large truck129Based on Table 6-10, there is an increasing amount of pollutant emissions as thevehicles speed is reduced. The table further shows that carbon monoxide (CO) ishigher in gasoline-fueled trucks while NOx is higher in diesel-fueled trucks. CO andNOx emissions from cars also follow the same pattern. For utility vehicles, gasoline
powered has higher emission of CO and NOx (Table 6-11). SPM is considerably low
in gasoline-fueled cars and small trucks and higher in diesel-fueled cars, small andlarge trucks.
Table 6-11. Emission factors for utility
vehicles and motorcycles (g/km)
McycleUtility vehicle
Gasoline Diesel
CO
60
2.5
26
NOx
3
1.4
0.2
SOx
0.014
0.115
0.004
SPM
0.12
0.9
2
Source: Vergel, K. (2002)
Note: FX and Jeepney were treated as utility vehicles
Pollutants

Identifying the fuel use of each vehicle covered by the survey is very difficult andwould require lot of efforts. One way to address this difficulty is by adopting thehistorical data of registered vehicles by fuel type, which is normally available in thegovernment agency tasked with vehicle registration. In the Philippines, the Land
Transportation Office (LTO) is responsible for such a task. Historical data of vehicle
registration by fuel type from 1998 to 2004 is availed and presented in Table 6-12.
Using such data, the share of gas-fueled and diesel-fueled vehicles for thecomputation of emission pollutant is produced in Table 6-13.

Table 6-12. Proportion of gasoline and diesel vehicle registration in Metro
Manila (1981- 2004)
Year

Total vehicles

Share

Car gas8,198,88696%

Car diesel

384,543

4%

UV gas3,935,424

46%

UV diesel

4,658,303

54%

Truck gas

138,780

11%

Truck diesel

1,076,873

89%

Bus gas

14,0898%

Bus diesel

168,004

92%

Motorcycle gas

2,518,915

100%

Motorcycle diesel

6,517

0%

Trailer
256,014
Source: Land Transportation Office (LTO) - www.lto.gov.ph

For the span of more or less 23 years, the dependence of cars to gas fuels is very
apparent (96%), while utility vehicles have relatively fair usage of gas and diesel
fuels. Trucks and buses dependence to diesel fuels were at 89% and 92% respectively.
For obvious reason, motorcycles have 100% usage of gas fuels.

130

Table 6-13. Proportion of gasoline and diesel vehicles used in the computation ofemissionsType of fuel used (%)
Gasoline
Diesel
96
4
11
89
8
92
46
54
100
0

Type of vehicles
Car
Truck
Bus
FX/Jeepney
Mcycle6.5.2.2 Amount of emission from each mode (simulated)
Figure 6-16 shows the amount of pollutant emissions by mode in a typical day
between 9AM to 9PM. Cars have the highest share of carbon monoxide emitted,
mainly due to its high dependence on gas fuels and high emission factor. The amountof CO emission caused by FX and Jeepney were almost as high as those of the buses,
which is almost 40% of the CO emission by cars (Figure 6-16a). In terms of SOx and
SPM (Figure 6-16b), cars recorded an amount of 1.0 and 2.2kg respectively, whilebuses, FX and jeepneys are almost close to doubling (4.22 and 4.20kg) the amount ofemission produced by cars except SOx. Buses, FX and jeepneys are known for
dependence on diesel fuels; hence the high amount of emitted SPM. The emission ofNOx pollutant (Figure 6-16a) is noted high for cars only.
Carbon monoxide (CO), nitrogen oxide (NOx) as well as suspended particulate matter(SPM) are identified as components of ground-level ozone, which poses serious andharmful threat to human health and the environment. Such can occur throughinefficient burning of gas by vehicles, where CO emission becomes hazardous anddeadly.

700.0
624.1

CO

4.5

Nox

4.0

4.22

4.2

Sox
SPM

3.5

500.0
Amount of emission (kg)Amount of emission (kg)

600.0

400.0
300.0

248.18

248.2

200.0
121.0
100.0
20.3
3.3

18.19

18.2

3.0
2.5

2.2

2.0
1.5
1.0
0.4

0.5

0.1

Truck

Bus

FX/Jeepney

0.53

0.5
0.0

0.0

Car

0.9

1.0

12.2

-

Mcycle

Car

(a)

Truck

Bus

FX/Jeepney

Mcycle(b)

Figure 6-16. Amount of pollutant emissions by mode (present situation)
Looking at the emission share of each mode in Figure 6-17, car has the highest recordin terms of gaseous pollutants such as CO, SOx, and NOx. The share of trucks onsuch emission was at about 2% for CO, SOx, NOx and 3% of SPM. On the other131

hand, in terms of CO, SOx and SPM, the Buses, FX and jeepneys emission ranged
between 20% and 40%.
The above distribution provides useful indicator to properly address the issue ofpollutant emissions contributed by the transport sector. For instance, a substantial
shift of car users to other modes such as rail or public transport modes withappropriate travel demand measures would translate to significant reduction of CO,
SOx, and NOx while continuous increase and patronage of cars using thetransportation network would yield for opposite impact. Reduction of the SPM
emission can likewise be addressed through strict implementation of policy measures
towards diesel-intensive vehicles, like non issuance of operation permit due to poorly
maintained engines or perhaps some form of regulation in the chemical content offuels that are widely used by vehicles.
As to what should be compromised in the process of mitigation and intervention
among the forms of pollutants mentioned above, there can be no clear declaration
about it since all emissions are undesirable to human health and the built environment.
One way to look at it could be on the basis of morbidity and cost of negative impacts
to the environment and also, close monitoring if emission occurrences conform tospecified standards.
Mcycle1%

FX/Jeepney

Mcycle0%

FX/Jeepney

22%

25%

Car
48%
Car
53%

Bus
22%
Truck
2%

Truck
2%

Bus
25%

CO

FX/Jeepney

SOx

Mcycle8%Mcycle
0%

Car
18%

11%
Bus
11%

Truck
3%
FX/Jeepney

Truck
2%

35%

Bus
36%

Car
76%

SPM

NOx
Figure 6-17. Emission share by transport mode
132

6.5.2.3 Impact of Cooperative Delivery System
There is so much potential in the reduction of pollutant emission through CDS (Figure
6-18 and Table 6-14). As can be observed, the highest reduction achieved is with theCO (1.93%) followed by SPM (1.72%), NOx (1.65%) while the smallest reduction iswith SOx (1.36%). As the share of trucks increases, the emission reduction rate due to
CDS also increases, indicating significance of CDS a policy measure.
On the other hand, it appears that there are no benefits gained from time window interms of environmental variables (Figure 6-19 and Table 6-14). This scheme is rather
increasing the volume of emissions as the share of freight vehicles is reaching 10%.
This indicates that the travel speed of vehicles in some of the links in the network
deteriorates, thus emission pollutants increases.
The total amount of emission from different schemes adopted is available in Table 614. In this table, vehicle kilometer (total distance traveled by vehicles) is alsopresented. Under the base case volume of vehicles, a reduction of 0.52% (from 51,211
to 50,944) of the total vehicle kilometer is achieved which increased to 6.8% when theshare of delivery vehicles reached 20% of total traffic. Other notable benefit of the
CDS is the reduction of delivery time in the district. Delivery time of trucks can be
reduced to 54% (1,099 min to 509 min) under the base case volume of vehicles.
25.00

Pollutant emission reduction due to CDS?(%)

22.18 CO
20.00

19.20 NOx
17.78 SPM
17.75
15.31
14.55

15.00
12.68
10.00

9.52

12.15

10.90
10.66
8.76

8.16
8.13
6.62
5.00

15.05 SOx

1.93
1.72
1.65
1.36

Present
situation

5%

10%

15%

20%

Case sFigure 6-18. Reduction of pollutant emissions derived from cooperative delivery
system133

CasesPres ents ituation
0.50

Reduction of time window (%)

-0.50

5%

-

10%

-

15%

-0.00
-0.22

-

-0.00

-0.01

-0.36
-0.68

-0.64

-1.00

20%

-0.94
-1.45

-1.50
-2.00

-2.01

-2.08

-2.50
-3.00
-3.50
-4.00

CO

NOx

Sox

-4.23

SPM

-4.50

Figure 6-19. Pollutant emissions appear to increase under the Time window
scheme as the share of trucks reaches 10%
Table 6-14. Comparison of environmental impacts of CDS and Time Window
Cases
Present volume (base case)Distance(km)

Sub-cases

Share of freight vehicles is 10%

Share of freight vehicles is 20%

SOx

SPM

51,211

932.16168.18

1.93

11.23

With CDS

50,944914.13166.41

1.90

11.03

Time window

51,211

932.16168.18

1.93

11.23

CDS Reduction (%)

0.52

1.93

1.05

1.36

1.72

-

-

-

-

Without CDS

53,380-1,021.41

14,923.16

2.15

12.80

With CDS

51,946924.1914,913.66

2.00

11.76

Time window

53,3801,021.41

14,923.16

2.15

12.80

CDS Reduction (%)

2.69

9.52

0.06

6.62

8.13

TW Reduction (%)

-

-

-

-

-

Without CDS

54,411

1,063.83

20,640.84

2.25

13.55

With CDS

52,422928.97

20,627.66

2.05

12.11

Time window

54,411

1,085.18

20,641.40

2.26

13.58

CDS Reduction (%)

3.65

12.68

0.06

8.76

10.66

TW Reduction (%)
Share of freight vehicles is 15%

NOx

Without CDS

TW Reduction (%)
Share of freight vehicles is 5%

CO

-

+2.01

+0.00

+0.64

+0.22

Without CDS

56,2581,139.80

30,881.16

2.43

14.89

With CDS

53,275937.5430,861.40

2.13

12.73

Time window

56,2581,187.98

30,882.76

2.47

15.03

CDS Reduction (%)

5.30

17.75

0.06

12.15

14.55

TW Reduction (%)

-

+4.23

+0.01

+1.45

+0.94

Without CDS

58,1051,215.79

41,125.33

2.61

16.23

With CDS

54,132946.1441,099.00

2.22

13.35

Time window

58,105

1,241.11

41,126.10

2.63

16.29

CDS Reduction (%)

6.84

22.18

0.06

15.05

7.78

TW Reduction (%)

-

+2.08

+0.00

+0.68

+0.36

134

6.5.3

Travel speed improvement

The enforcement of time window appears to generate benefits in terms of travel time
reduction although not substantial. Even the congested road of EDSA improved its
travel speed from 34kph to 35kph. Under CDS, the travel speed on EDSA increased
to 38kph for all vehicle types (Figure 6-20 and Figure 6-21).
Vehicles turning to the CBD (Ortigas Center) from EDSA registered 7%
improvement of travel time (47kph to 50kph) while CDS has the highest gained
benefits through increased speed of 52kph. However, vehicles leaving the CBD andmerging to the major thoroughfare that is EDSA experienced further delay while time
window scheme is in place. This can be attributed to the large number of trucks
exiting after having delivered goods to the shopping centers in the CBD. Under thesame scheme, the travel speed on the same road declined from 30kph to 28kph while
it improved to 32kph under the CDS. Another road (C to EDSA in Figure 6-21),
which serves access to EDSA, also benefited from both time window and CDS with42kph and 46kph improvement from the original travel speed of 40kph.
From EDSA

EDSA
20%

9%

13%

Difference

7%

3%

0%
-1%
-1%

-5%

CDS

8%

11%

11%
7%

7%

6%

3%

-2%

-3%

14%

14%

13%

14%
12%

10%
DifferenceTime w indow

CDS

15%

5%

18%

17%
Time w indow

-1%

-2%

-6%
-10%

-7%

Present
volume

5%

10%

15%

20%

Present
volume

5%

10%

Tim e

To EDSA
60%

30%

22%

22%

25%

27%

28%

50%

53%
Time w indow

CDS

0%
-8%
-13%

-20%

-14%

5%

30%
15%

20%
10%

8%

5%

-5%

-10%

CDS

-33%

-40%
Present
volume

30%

0%

-19%
Time w indow

10%

15%

45%
37%

40%

10%

Difference

Difference

20%

-30%

20%

C to EDSA

40%

-10%

15%

Tim e

-8%

-5%

-20%

20%

Present
volume

Tim e

5%

10%

15%

20%

Tim e

Figure 6-20. Travel speed
Cooperative delivery system shows consistent travel time improvement as the share of
freight vehicles increases, while time window appears to show the other way around.
As the share of trucks increases, the travel speed of vehicles under the time window
scheme deteriorates which could be attributed to the reduced capacity of the network
to accommodate the large volume of trucks at the moment the time windowcommences at 10:30 in the morning. It can therefore be inferred that time window
hardly work as a measure to improve traffic flow in a congested network.

135

Present

40

40

PM De live ry

0

10

Tim e

Figure 6-21. Travel times of vehicles
Tim e

136
Time w indow

AM De live ry

20:00 - 20:15

50

20:00 - 20:15

19:00 - 19:15

18:00 - 18:15

17:00 - 17:15

16:00 - 16:15

15:00 - 15:15

14:00 - 14:15

13:00 - 13:15

12:00 - 12:15

Time w indow

19:00 - 19:15

60

18:00 - 18:15

60

Present

17:00 - 17:15

80

16:00 - 16:15

20
40

15:00 - 15:15

EDSA

14:00 - 14:15

120
5

13:00 - 13:15

AM De live ry

11:00 - 11:15

100

10:00 - 10:15

Cooperative

12:00 - 12:15

20

9:00 - 9:15

120

Time (sec)140

11:00 - 11:15

40

J. VARGASTo EDSA

10:00 - 10:15

SM Mega
Mall Building A
60
80

Time (sec)

AM De live r y

20:00 - 20:15

Time w indow

19:00 - 19:15

18:00 - 18:15

17:00 - 17:15

16:00 - 16:15

C to EDSA

9:00 - 9:15

AM De live ry

20:00 - 20:15

Time w indow

19:00 - 19:15

15:00 - 15:15

14:00 - 14:15

13:00 - 13:15

12:00 - 12:15

Time (sec)Present

18:00 - 18:15

Present

17:00 - 17:15

16:00 - 16:15

15:00 - 15:15

11:00 - 11:15

10:00 - 10:15

9:00 - 9:15

0

14:00 - 14:15

13:00 - 13:15

12:00 - 12:15

11:00 - 11:15

10:00 - 10:15

9:00 - 9:15

Time (sec)

C to EDSA
45

To EDSA
Cooperative

35

30

25

20

15

10

0

AM De live ry

SM Mega Mall Building
B
AM De live ry

Tim e

Tim e

From EDSA

Cooperative

EDSA

100

From EDSA
Cooperative

30

20

0
PM De live ry6.6

Role of CDS to the transformation of the CBD from low density to highdensity

Catering the road-based demands of vehicles in cities of most developing countries
can be of great concern among authorities. While hard measures are typical form ofintervention to urban related transport demands, mere improvement of the roadinfrastructures through expanding its capacity is most of the time restricted by thelack of funds. Thus, the growing number of vehicles, cars in particular, cannot be
ended nor controlled easily due to the absence of appropriate counter-mechanisms.
This tends to put pressure on the existing infrastructures to absorb additional demand,
which is already beyond its capacity.
Transportation Demand Management (TDM) measures play significant roles in theintervention and control of the abovementioned dilemma. Among such, as far as
freight transportation is concerned is the consideration of CDS for adoption in
addressing logistics and transport network related issues such as optimum system use,
efficient service delivery, as well as economically and environmentally soundtechniques in freight movement and management. CDS has shown bright potentials toachieve the goals such as economic efficiency, social benefits and environmental
sustainability through series of empirical analyses.
Further development of the CBD from low density to high density can translate into
increased freight traffic, thereby imposing critical look into CBD’s travel demandmeasures and system management tools. As there can be strong pressure on thelimited road space, the issue of rising number of car owners and users will inevitably
surface. Therefore, it should be included in exploring best solutions and approaches tofreight transport issues, since freight vehicles and private cars as well as other modesof transportation share common access network and directly or indirectly affect eachother’s mobility over the road network. Issues on high car usage should then be
analyzed in the context of modal shift from private to mass transit system.
On the other side, freight movements to and from a CBD cannot be shifted to othermodes of transportation so, strategic management of truck operation is necessary
given the difficulty of providing new infrastructures in view of limited ability togenerate fund.
The absence of CDS means increased demand for trucks, which also means, increased
trips of trucks with some unutilized extra cargo spaces. This is not a healthy practiceboth in economic and social point of view. Experience of European cities (see Chapter3) shows that normally, when problems arise concerning freight vehicles, authorities
usually respond by imposing access restriction, which limits the freedom of trucks tooperate. This policy forces truck carriers to organize themselves.
In essence, cooperative delivery system can be more appreciated as the developmentof the CBD intensify. The limited capacity of infrastructure necessitates managementbased measures that would limit the number of trucks while serving the same amountof goods. Moreover, the failure of time window scheme to provide substantial
improvement to travel speed and reduction of emission pollutants underlines theimportance of selecting suitable freight policy. In this regard, social experiment137

(policy is implemented in a limited area for certain time for trials) can be a tool toassess effectiveness of a policy before implemented in the whole area permanently.
6.7

Chapter summary

Cooperative delivery system has potential to deliver economic and environmental
benefits even in developing countries where road infrastructure is largely congested.
The more the area is congested, the more it requires appropriate policy measure thatwould bring significant improvements and benefits. For the case of CDS, although the
reduction to the total traffic appears to be limited, its impact to travel times is very
notable. Such improvement to travel times coupled with reduced number of trucks
brings benefits to the environment and the society as a whole. Although the monetary
cost that can be saved from CDS was not discussed explicitly, the reduced vehicle
kilometer (VHK) can be interpreted as benefits related to distribution cost.
Central business districts in Metro Manila and other capital of developing countries,
for instance Kuala Lumpur and Bangkok, normally houses large shopping centers
attracting large volume of both people and vehicles. Although these establishments
normally provide ample parking spaces, the access roads are suffering from serious
traffic congestion especially during peak hours. This problem is more serious during
weekends when most people crowd the center as a way to spend the day off.
The survey result confirms the dominance of cars in the network of Metro Manila,
which accounted for 50%. This high share of private cars speaks the importance ofcompletion of mass railway system as alternative mode, coupled with formulation of
measures that would discourage the use of cars. This is one way to refrain the rapid
increase of travel demand that often exceed the capacity of infrastructure.

138

REFERENCES
1. Campbell, J.F. (1995) Peak period large truck restrictions and a shift to off-peak
operations: Impact on truck emissions and performance. Journal of Business
Logistics, Vol. 16, No.2, pp. 227-247.
2. Castro, J. et al, (2003), A study on the impact and effectiveness of the truck ban
scheme in Metro Manila, Eastern Asia Society of Transportation Studies. (CDROM)
3. Ing Hsu, T. et al, (2003). A comparison study on motorcycle development in some
Asian countries – case of Malaysia, Taiwan and Vietnam. A Final Report underInternational Cooperative Research Activity of EASTS.
(http://www.easts.info/activities/icra/2001/ICRAComparisonStudyMotorcycleDev
elopment.pdf).
4. Minato, K. and Hirota, K. (2003) A Study on Fuel Regulation of Large-size Car.
Automobile Studies. Vol 5, Institute of Automobiles of Japan, 2003.
5. MMUTIS, 1999. Traffic environmental study, air and noise pollution in Metro
Manila, Metro Manila Urban Transport Integration Study Technical Report 10,
Department of Transport and Communications, Metro Manila.
6. Noda, A. and et al (2003) A Study on the Effect Forecasting Method ofConsumption Restraint Policies based on Analysis of Factors affecting
Automobile Fuel Consumption. A Paper for the Institute of Transport Safety andEnvironment.
7. Palmiano, Sean (2001). Traffic Impact Analysis and High Public Transport Modal
Share: A Case Study of Metro Manila Shopping Centers. Unpublished masteralthesis. Tokyo institute of technology.
8. Vergel, Karl (1999), Analysis of Road Traffic Flow and Traffic Environment inMetro Manila. Unpublished Doctoral Dissertation. Tokyo Institute of Technology.
9. Yashima, H. et al (1997) Evaluation of Energy Consumption and CO 2 Emission
resulted from International Freight Transport. Environmental System Studies. Vol.
25, pp 303-309, 1997.10,

139

CHAPTER

7

Application of Cooperative
Delivery System to a Low
Density Central BusinessDistrict: Case of Developed
Country
7.1

Introduction

In the preceding chapter, the impact of CDS to traffic flow and environment in the
CBD of a developing country was evaluated. It was learned that the role of CDS is
becoming crucial as the development of CBD gears up and attracts large number of
freight vehicles. CDS can help decongest the road network by limiting the number oftrucks that distribute the same volume of freight. Although some benefits are gainedsuch as improved travel speed and less pollutant emissions, the situation in the urban
environment of developing country is still far from desirable as much can still bedone to tackle the rampant use of cars which easily create serious traffic congestion to
the limited road network capacity.
In this chapter, aside from estimating the traffic and environmental impact of the CDS
in the CBD, the ratio of truck trips needs to be covered by the CDS in order to avoid
queues of trucks and eliminate their waiting time is also estimated. The setting is also
shifted from CDB of a developing country to CBD of developed country seeing thatdifference of transport environment between these two CBDs requires separate
analysis.
Section 7.2 focuses on the conceptualization of the social experiment and provides
necessary background of the district the Marunouchi district. The result of theexperiment as well analyses of the data gathered is presented in Section 7.3.
Comprehensive analysis to the merit of CDS using simulation modeling in the districtis available in Section 7.4. This analysis includes the evaluation to the ability of CDS
to absorb increasing truck trips when the district changes from office-oriented CBD tocommercial type of CBD. Comparison of CDS’s traffic and environmental impact
between Manila and Tokyo is presented in Section 7.5. The chapter is finallyconcluded in Section 7.6.

140

7.2

The Social Experiment

7.2.1

Location of Marunouchi District

The Tokyo’s core central business districts are Chiyoda-ku, Chuo-ku, and Minato-ku
with a total area of 42.13 km2 and offering jobs to 14.6%. Marunouchi district islocated in Chiyoda-ku and hosts over 4,000 company offices which employs morethan 240,000 people (Figure 7-1). Historically, the concentration of working force inthis district started as early as 19th century after the Meiji revolution. By that time, thedistrict is the biggest CBD, which holds true to present time. The presence of theImperial palace, the official residence of the Imperial family, prompted the limitationof building height to 31-meter which has been modified lately into 260-meters.

Marunouchi

Figure 7-1. Map of Tokyo showing the location of Marunouchi district
Of similar importance is a 2002 survey of a real estate developer which observed thatthe supply of large-scale office buildings (building with floor space of over 10,000 m2)
is concentrated in three central wards of Tokyo, namely, Chiyoda-ku, Minato-ku, andChuo-ku (Figure 7-2). Mori Building Company Ltd. further noted that this trend willcontinue and fierce competition among these wards would persist. Figure 7-2, which
was taken from the same report, shows the amount of supplied office spaces in Tokyo.
Aside from the three mentioned wards, two additional wards composing the five
central wards are Shinjuku-ku and Shibuya-ku.
7.2.2

Background of the Social Experiment

Most of the important functions of the country had been absorbed by Tokyo,
especially by the three central wards, namely Chiyoda, Chuo and Minato. Figure 7-3
provides a comparison numbers between the three central wards, the rest of the Tokyo
wards, and Japan.
Marunouchi District, located between the Tokyo Railway Station and the Emperor’s
Palace, is the oldest and the biggest central business district in Japan since late 19th
century, after the Meiji Revolution, up to this day. Similarly, most large company
head offices are concentrated in the Marunouchi District in pursuit of better
communication and business chance. The relaxation of the 31-meter building height
restriction in this district has paved the way to urban revitalization activities.

141

million m2
2.50

2.17

2.00

2.01
1.83

1.83

Toky o's 23 wards

1.50
1.25

1.18

1.00

0.83

0.55

0.68

0.72

0.60

0.69

3 central wards

0.36

0.52

0.53 0.58

0.57

0.52

0.44
0.50

0.85

0.72

0.74

0.56

0.55

0.54
0.27
0.50

1.01

0.99
0.71

0.41

0.56

0.50

0.91 0.87

1.19
0.92

5 central wards

0.64

1.17

1.14

1.08 1.04

1.00

0.81

0.66

0.81

0.74 0.70

0.77

0.58 0.53 0.65
0.58

0.47

0.36

0.29

0.21 0.31

0.17

0.75

0.57 0.53

0.44

0.31

0.32

0.23

0.00
86

87

88

89

90

91

92

93

94

95 96
Year

97

98

99

00

01

02

03

04

05

06

07

Figure 7-2. Supply Volume of Large-scale Buildings by Area in Tokyo

Sales of Publication and
Printing Industries
No of University students

13,345 billion yen
2,400 thousand
846 thousand

No of Employment(Information/Service)

163 offices

No of Foreign Bank

1,596 companies

No of Foreign Companies

1,845 trillion yen

Amount of Discount1,573 companies

No of Headquarters with
Capital more than 5 billion
Annual Sale (Commerce)

479 billon yen
313,068 billion yen

Industrial Output

490,715 billion yen

Gross Production

125,570 thousand

Population

Tokyo 23 wards

Tokyo three central wards

Figure 7-3. Tokyo’s 3 central wards and 23 wards ratio of functions to JapanThe government of Tokyo made a redevelopment plan for Marunouchi District. It
espouses change in function from a business or office-oriented district into a multi-use
complex which includes business, culture and commercial functions. In September1422002, Marunouchi Building was rebuilt as the first redevelopment project in this
district and will be followed by other many projects. These redevelopment projects,
however, would cause some city problems that necessitated appropriate responses,
which has been strongly recognized by public and private sectors. Among these is theincrease in the number of delivery vehicles plying the district that would disrupt thephysical environment. It is also expected that some of these trucks would load/unload
their freight on-road and thus disrupt the traffic flow. In addition, as building heightincreases, the freight deliveries would require longer staying period of trucks at thedocking area. Normally, the increase in delivery time inside the building produces along queue of vehicles outside.
In recognition of these problems, all concerned parties such as the building owners,
tenants, carriers, the Metropolitan Police Agency and Ministry of Land andTransportation, has organized a committee called “An Executive Committee for
Improving the Efficiency of Freight Delivery in Marunouchi District” or better known
as Executive Committee. The purpose of this committee is to discuss the possibility of
introducing a new freight delivery system which could improve the efficiency of
freight delivery, limit air pollution, and improve parking control. In February 2002,
the Executive Committee conducted a social experiment of freight transportation in
Marunouchi District. The experiment area, which has a size of 630 m by 240 m, isshown in the map in Figure 7-4. Table 7-1 provides detail information of the buildings
involved in the project.
To Imperial Palace
Marunouchi Blk 2

Marunouchi Blk 3

15

13
14

Tokyo International Forum

2

1

5

4

3

6

7

8

9

Tokyo Mitsubishi BankTokyo Building
Tokyo Central Post Office10

GYOUKO ST.

NAKA ST.

BABASAKI ST.

1211

1. Meiji Seimei
2. Kishimoto
3. Mitsubishi Denki
4. Syoji
5. Yusen
6. Furukawa
7. Mitsubishi Juko
8. Mitsubishi Syoji
9. Mitsubishi
10. Marunouchi
11. Kokusai
12. Fuji
13. Shin Kokusai
14. Shin Nisseki
15. Shin Tokyo

Tokyo Station
STOCK
POINT

Figure 7-4. Map of Marunouchi District (Building 6, 7, 8, and 9 were covered by
the experiment)

7.2.3

Outline of the Social Experiment

Two major components of transportation management, cooperative delivery andparking management, were included in the social experiment from February 5 until143February 28, 2002. Five (5) major carriers and 13 small-scale carriers had taken partin the experiment. Freight had to be carried first into the SP by the carrier participants,
then, loaded unto a natural gas truck altogether and then delivered to each building.
7.2.4

Properties of the Buildings under the Experiment

The district is has just recently been known as shopping district and has been
primarily home only to designer clothes shops. The figures in Table 7-2 show that it ismore of an office-oriented area where big portion of the space is allocated to officefirms.
Table 7-1. Policy Instruments of the Experiment
Experiment Area

Five buildings in Marunouchi, Tokyo

Term of Experiment

February 1 to February 28 (19 weekdays), 8 am to 5 pm
Cooperative delivery by 5 major carriers and other participants

Policies

Coop Delivery

Delivery through SP to each building
Horizontal
Coop Delivery

Natural gas trucks
Fee from SP to offices of the buildings is 50 yen per freight

Vertical Coop
Delivery

Mail, papers, packages except cold storage and frozen foods, etc.
Staff deliver freights from underground of the buildings to offices inside thebuilding
Drivers drive through buildings without delivering freights
Guards control illegal on-road parking

Parking Management

Ban on bringing in freights to the front entrance of the buildings
To use freights elevators for delivering
30 min free parking at underground parking space of buildings

Table 7-2. Properties of Buildings Covered by the ExperimentNo
6

Building name

No. offloor

Gross floor
2(m )Leasable2
space (m )Leasable%

Office space2(m )Furukawa

9 (4)51637.00

30029.47

58%27749.94

Shops and
restaurants
2(m )2279.53

Yaesu

9 (1)

18379.00

13598.4474%13330.54

267.9

7

Mitsubishi

15 (4)

62906.38

40483.66

64%

40248

235.66

8

Mitsubishi Juko

10 (4)

45985.00

31861.06

69%30447.23

1413.83

9

Mitsubishi Syoji

15 (4)

55259.00

37829.04

68%37829.04

058 (17)46833.28

153801.6767%149604.75

4196.92

Total

Note: ( ) number of basement floor

7.3 Result of the Social Experiment
7.3.1

Observed Behavior of Cooperative Delivery System

The district has at least 15 high-rise buildings but not all were covered by theexperiment due to perceived difficulty of managing wide-area for experiment and, tosome extent, unwillingness of other companies to participate in the project.

144

MITSUBISHI
BUILDING

8 min 30 sec.65 kmSP

JUKO
BUILDING

2 min
7 freight/trip

5 freight/trip
1 min 30 sec
unloading time

3 min unloading time

6 min 30 sec.54 kmFURUKAWA& YAESU3 min 20 sec.36 kmSYOJI
BUILDING

3 min 10 sec

6 freight/trip
2 min unloading time

5 freight/trip
1 min 20 sec unloading time

Figure 7-5. Horizontal Cooperative Delivery System
The unloading time for freight in the four (4) buildings has an average of 15 minutes
before the experiment. The combined effect of two CDSs reduced this time to merely
2.35 minutes (Figure 7-5). Average unloading time for freight at each building isshown in Figure 3 as well as the average number of freight delivered. Further, thedistance of each building from one another is also reflected and the average travel
time of the CNG truck.
7.3.2

Number of Trips and Freight Attracted to the Four (4) Buildings

Table 7-3. Number of Trips and Freight into the Buildings under the ExperimentNo
6

Furukawa

Total
no. of
trips
89

Yaesu

13

Building name74

Trips toshops &
restaurants
15

10

3

Trips tooffices

Total no.
of freight

Freight toofficeFreight to shops& restaurants

198

133

65

14

11

3

7

Mitsubishi

116

93

23

456

271

185

8

Mitsubishi Juko

92

74

18

284

184

100

Mitsubishi Syoji

132

132

0

772

772

0

Total

442

361

81

1724

1199

525

9

Note: Furukawa and Yaesu buildings are sharing some logistics infrastructure and treated as one

Table 7-4. Distribution of trips within a day
No

Building name

No of freight vehicle's trip per day
8:00 9:30 12:00 9:30
12:00
17:00
Total

8:00 9:30

No of freight per day
9:30 12:00 12:00
17:00
Total

6

Furukawa & Yaesu

19

37

46

102

73

65

74

212

7

Mitsubishi Juko

24

29

39

92

112

84

88

284

8

Mitsubishi Syoji

29

46

57

132

116

228

428

772

9

Mitsubishi

19

42

55

116

118

218

120

456

Total

91

154

197

442

419

595

710

1724

Percentage

21

35

45

100

24

35

41

100

A one-day survey conducted in four buildings in the district recorded both the total
number of trips (442) to each building and the amount of goods (1724) delivered. As
noted in Table 7-3, most of the freight was delivered to the offices which accounted
around 70 percent. The number of freight and number of trips are not proportional as
more than 81 percent of the trips were intended for the offices. Taking into account145

the total number of freight and the total number of trips, each trip has an average offour (4) freight loads per trip.
Aside from identifying the destination of freight and trips, either to the office or to theshops and restaurants as shown in Table 7-3, the survey also recorded the time arrivalof each trip which is reflected in Table 7-4. For the span of 90 minutes, there were 91
trips made in the morning, which suggest that the interval between trips is quite closeor less than one minute. The same can be said to the 9:30 – 12:00 time span where
154 trips were recorded. The afternoon schedule suggest longer time interval between
trips compared to the two other schedules in the morning with an average of 5.45
minutes.
The recorded number of freight shown in the same table could come up with thepresumed delivery trip attribute that the early morning schedule has an average of lessthan five (5) freight loads in every trip and the mid-morning and afternoon schedules
have an average of less than four (4) freight loads per trip.
7.3.3

Comparison of delivery style before and during the experiment

The experiment changed the pattern of delivery from individual delivery truck
separately delivering their freights to a consolidated shipment originated from the SP.
Freight loaded by the natural gas trucks are handled by the two staff members
assigned to each building. This process allows the truck drivers to move out of thebuilding immediately. The guards stationed at the surrounding roads controlled theillegal on-road parking and guide cars into underground parking spaces. In addition, it
was also restricted for all truck drivers to bring in their freights into the front entranceof the building. The changing pattern of delivery is illustrated in Figure 7-6 while
Table 7-5 shows the detailed information regarding the policy of the experiment.

MARUNOUCHINORMAL DELIVERY SYSTEMSPMARUNOUCHICOOPERATIVE DELIVERY SYSTEM

Figure 7-6. Delivery pattern before and after the experiment146

Table 7-5. Measures of observation

Questionnaireby drivers
at stock pointby staffs at underground and loading space
on-road parking and underground parking lot
traffic volume
utilization of freight elevators
stock point
for participants, non participants, tenants, building owners, office staffs, shoppers, and staffs
of experiment

Othersby car navigation systems and digital pedometers

Daily Reports

Surveys

Out of 232 total numbers of carriers operating in the area, 7.8 percent joined theexperiment including five major and 13 minor carriers. The delivery trucks thatbelonged to the carriers who did not join the experiment continued to deliver theirfreights directly to the building establishments. They are not, however, allowed toutilize the unloading spaces reserved for the experiment. Drivers of these trucks are
expected to look for parking spaces on their own.
As shown in Table 7-6, although the number of carriers participated accounted for
only 7 percent, 22 percent of freights were covered.
Table 7-6. Share of participants in the experiment
No of carriers
No of trucks
No of freights

7.3.4

Total in the area
232
442
1724

Participants
18
32
383

Share of participants
7.80%
7.20%
22.20%

Analysis on Horizontal Cooperative Delivery and Parking Management

Table 7-7. The number of Trucks that Transshipped Freights at SP
Total of 19 days
Average per day

No of trucks
arrived at SP
186
9.8

No of trucks
started from SP
125
6.6

Reduction rate of thenumber of trucks
33%
33%

Only 30 percent of the freight by the carriers who joined the experiment was delivered
to the SP for consolidation. Seventy percent of freights had been delivered directlyfrom their own depots near the experiment area to each building without stopping at
the SP. Table 5 shows the number of trucks originating from the SP. The presence of
the SP resulted in the reduction of 33 percent of the total number of vehicles supposed
to deliver in the district (Table 7-7). On the other hand, the policies such as preparing
underground unloading spaces, 30 minutes free parking, ban on bringing in the freight
through main entrance of buildings, and restriction of on-road parking resulted to 50
percent reduction of on-road parking and 35 percent increase of underground parking
space utilization rate as shown in Figure 7-7.

147

Figure 7-7. Change of the number of truck parking
7.3.5

Setting up the attraction units of the four buildings

The attraction unit as explained in Chapter 4 is useful to estimate the number of trips
and goods that are expected to be shipped to the buildings based on the size of theirleasable space. Obviously, attraction unit of a space occupied by shops and restaurant
is higher than a space which housed offices. This is the reason for computing
separately the attraction unit of shops and restaurants and office space as shown in
Table 7-8.
Table 7-8. Attraction unit of trips and freight
Trips/100 (m2)
Freight/100 (m2)
Shops &
Shops &
OfficeOfficerestaurants
restaurants
Furukawa
0.27
0.66
0.48
2.85
Yaesu
0.08
1.12
0.08
1.12
Mitsubishi
0.23
9.76
0.67
78.50
Mitsubishi Juko
0.24
1.27
0.60
7.07
Mitsubishi Syoji
0.35
2.04
Total
0.26
1.41
0.92
8.41
Note: - there was no observed trips and freight to shops and restaurants
Building name

7.3.6

Estimating Freight Vehicles’ Trips To The District

The estimated number of trips and freight to each building in the district is shown in
Table 7-9, which was obtained by utilizing the attraction unit of trips and freight inTable 7-8. Obtaining this data is crucial to estimate the impact of the cooperative
delivery in the district in the assumption that all trips are covered by CDS.
A look into the same table would reveal that of the 741,741 square meters total
leasable space in the district, shops and restaurants occupied only 2.72 percent.
Although the share of shops and restaurants is small, their share of trips and numberof freight are remarkably high with 13 percent and 20.4 percent respectively.

148

Table 7-9. Distribution of trips and freight’s to the buildings in the district
Building

Leasable
space
(m2)

Officespace(m2)

Shops &
Restaurants (m2)

1
2
3
4
5

Meiji Seimei
Kishimoto
Mitsubishi Denki
Shouji
Yusen

96673
22082
29966
35918
33569

94036
21479
29149
34938
32653

6

Furukawa &
Yaesu

43628

7

Mitsubishi
JyukouMitsubishi Shouji
Mitsubishi
Marunouchi
KokusaiFuji
Shin Kokusai
Shin Nisseki
Shin Tokyo
Total

No

8
9
10
11
12
13
14
15

Trips per day

No of freight per day

OfficeShops &
Restaurants

Total

OfficeShops &
Restaurants

Total

2637
602
818
980
916

244
56
76
91
85

37
8
12
14
13

282
64
87
105
98

865
198
268
321
300

222
51
69
82
77

1087
248
337
404
377

41080

2547

84

18

102

144

68

212

31861

30477

1414

74

18

92

184

100

284

37829
40484
104000
75975
53220
50365
17268
68903
741741

37829
40248
101163
73902
51768
48991
16797
67023
721534

0
236
2837
2073
1452
1374
471
1880
20238

132
93
263
192
135
127
44
174
1870

0
23
40
29
20
19
7
27
285

132
116
303
221
155
147
50
201
2155

772
271
931
680
476
451
155
617
6632

0
185
239
174
122
116
40
158
1702

772
456
1169
854
598
566
194
775
8335

* Shaded rows contained actual data obtained during the experiment

7.3.7

Distribution of trip attraction to the district

The figures obtained in Table 7-9 show the total trip and freight received by eachbuilding in a day. These figures are then distributed according to the observed trips
and freight distribution recorded during the experiment. The trips are distributed inthis pattern: 21 percent for the 8:00 to 9:30 time schedule; 35 percent for 9:30 to
12:00 time schedule and 45 percent for the afternoon schedule.
By following the said distribution of trips arrival and its corresponding amount offreight, the pattern of trip arrival in the district is shown in Table 7-10.
Table 7-10. Distribution of trips and freight to the buildings in a day
No

Building name

No of freight vehicle's trip per day
8:00 9:30 12:00 Total
9:30
12:00
17:00

8:00 9:30

No of freight per day
9:30 12:00 12:00
17:00

Total

1

Meiji Seimei

58

98

126

282

264

375

448

1087

2

Kishimoto
Mitsubishi
Denki

13

22

29

64

60

86

102

248

18

30

39

87

82

116

139

337

4

Syoji

22

36

47

105

98

139

166

404

5

20

34

44

98

92

130

155

377

19

37

46

102

73

65

74

212

7

Yusen
Furukawa &Yaesu
Mitsubishi Juko

24

29

39

92

112

84

88

284

8

Mitsubishi Syoji

29

46

57

132

116

228

428

772

9

Mitsubishi

19

42

55

116

118

218

120

456

10

Marunouchi

62

106

135

303

284

404

482

1169

11

Kokusai

46

77

99

221

208

295

352

854

12

Fuji

32

54

69

155

145

207

246

598

13

Shin Kokusai

30

51

65

147

138

195

233

566

14

Shin Nisseki

10

18

22

50

47

67

80

194

15

Shin Tokyo

41

70

89

201

188

267

319

775

3

6

149

7.3.8Distances between buildings and from stock point

The size of the district where these 15-buildings stands is around 870 m2. The stock
point’s distance to each building has an average of .68 km which means a delivery
truck can reach even the farthest building in few minutes. On the other hand, theaverage distance that truck would travel from one building to another is 422 meters(see Table 7-11).
Table 7-11. Distance of the buildings from the stock point
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Building name

Km/trip

Meiji Seimei
Kishimoto
Mitsubishi Denki
Syoji
Yusen
Furukawa & Yaesu
Mitsubishi Juko
Mitsubishi Syoji
Mitsubishi
Marunouchi
KokusaiFuji
Shin Kokusai
Shin Nisseki
Shin Tokyo

0.78
0.77
0.77
0.77
0.77
0.77
0.77
0.65
0.65
0.65
0.65
0.65
0.54
0.54
0.54
0.68

Average

The distances of each building from the stock point and other buildings in the district
are important to estimate the average traveled distance of each truck entering the
district. Under the CDS therefore, the average traveled distance of a truck is 1.78
(Table 7-12). This was obtained by combining the distances traveled by the truck:
outgoing trip including first stop in a building to deliver goods is .68 km, second stop
to deliver the remaining goods is .422 km, and the return trip’s distance is .68 km
again.
Table 7-12. Distances between the buildings in the district
OD
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

1
.36
.36
.36
.36
.24
.48
.24
.48
.48
.51
.51
.63
.63
.63

2
.36
0
.12
.12
.48
.24
.48
.24
.24
.63
.63
.75
.75
.75

3
.36
0
.12
.12
.48
.24
.48
.24
.24
.63
.63
.75
.75
.75

4
.36
.12
.12
0
.48
.24
.48
.24
.24
.63
.63
.75
.75
.75

5
.36
.12
.12
0
.48
.24
.48
.24
.24
.63
.63
.75
.75
.75

6
.24
.48
.48
.48
.48
.36
0
.36
.36
.63
.63
.51
.51
.51

7
.48
.24
.24
.24
.24
.36
.36
0
.12
.75
.75
.63
.63
.63

8
.24
.48
.48
.48
.48
0
.36
.36
.36
.63
.63
.51
.51
.51

9
.48
.24
.24
.24
.24
.36
0
.36
.12
.75
.75
.63
.63
.63

150

10
.48
.24
.24
.24
.24
.36
.12
.36
.12
.75
.75
.63
.63
.63

11
.51
.63
.63
.63
.63
.63
.75
.63
.75
.75
.12
.24
.24
.24

12
.51
.63
.63
.63
.63
.63
.75
.63
.75
.75
.12
.24
.24
.24

13
.63
.75
.75
.75
.75
.51
.63
.51
.63
.63
.24
.24
0
.12

14 15
.63 .63.75 .75.75 .75.75 .75.75 .75.51 .51.63 .63.51 .51.63 .63.63 .63.24 .24.24 .240.12.12.12 Average

Average
.418
.386
.386
.386
.386
.402
.378
.402
.378
.386
.492
.492
.476
.476
.484
.422

7.3.9Situation At The (Off) Loading Area With And Without Cooperative
Delivery System

Generally, three different scenarios are intended to be analyzed – regular form ofdelivery (without CDS), when some of the trips were covered by the CDS and when
all the trips into the district were covered by the CDS (Figure 7-8). This means that allof the firms and carriers agreed to have taken part the CDS.

10F

10F

･
･
･
･

･
･
･
･

1F

1F

SP
EV

EV

Simulation analysis

Simulation analysis

Without CDS

With some trips covered by the CDS

10F

･
･
･
･
1F

SP

EV

Simulation analysis

With full participation of firms into CDS

Figure 7-8. Simulation cases
With the regular form of delivery, each driver of freight vehicle will deliver their loadto the recipient across the building floors after parking the vehicle at the loading /unloading spaces. This form of delivery would allow the freight vehicle to stay longer
at the loading / unloading area thus reducing its utilization rate. Queues of vehicles
can be also expected to the buildings where firms with high demand for trips andfreight are situated. When some of the trips were covered by the CDS, there would be
staff members waiting at the loading / unloading area. Freight vehicles participatedthe CDS would receive support from these staff once reaching the reserved parking
spaces. This arrangement, however, is not available to the drivers who did notparticipate in the CDS and they are expected to locate their own parking space.

151

A more organized form of freight transport is expected to realized, however, when all
incoming trips of freight vehicles are covered by the CDS. This form of freightdelivery would not only reduce the number of trips but as a result, would also improve
the state of environment due to the less number of vehicles emission.

Comparison of the Number of Trips and Freight With and Without CDS
The Marunouchi building (building 10) is taken as a case study in this section of thepaper because it is the one having the highest attraction of trips. Therefore, the trips
difference between with and without cooperative system can be easily spotted. The
estimated trips and freight attracted to the building is shown in Table 7-13. Without
the CDS, a short interval between trips with few number of freight can be expected.
Table 7-13. Total trips and freight in the Marunouchi bldg without CDS
Time

Total time(m)Interval bet
trips (m)8:00-9:30

90

62

1.45

284

4.58

9:30-12:00

150

106

1.41

404

3.81

12:00-17:00

300

135

2.22

482

3.57

No of trips

No of freight

Ave freight per trip

Table 7-14. Number of trips and freight when the CDS covers 20% of trips to the
Marunouchi bldg
Time

Total no of
trips

Total no of
freight

No of trips
with CDS

Interval bet
trips (m)No offreight

No of trips
without CDS

Interval bet
trips (m)No offreight

8:00-9:30

62

284

12

15

57

50

1.24

227

9:30-12:00

106

404

21

30

81

85

1.24

323

12:00-17:00

135

482

27

60

96

108

2.25

386

Table 7-15. Number of trips and freight when all trips to Marunouchi bldg iscovered by the CDS
Time

No of trips

Interval bet trips (m)Total no of freight

Ave freight per trip

8:00-9:30

6

15

283

47

9:30-12:00

5

30

402

80

12:00-17:00

5

60

480

96

Table 7-14 shows the difference in terms of number of freight carried by delivery
vehicles of CDS and those delivery vehicles performing normal distribution of freight.
The numbers of trips under the CDS were obtained by multiplying the total trip in atime schedule (62) to the rate of trips covered by CDS (20 percent). The committee
responsible for the social experiment set the time intervals 15, 30, and 60 minutes andbeing followed for the simulation analysis. The same process was performed in orderto obtain the number of freight expected to be received by the building in a certain
time interval.
The expected trips to Marunouchi building can be reduced to 3.6 percent if all the
incoming trips are covered by the CDS (Table 7-15). This system would also improvesignificantly the load factor of delivery vehicle and the long time intervals between
trips would allow the parking spaces to accommodate all incoming trips.

152

The procedures discussed show the estimation of trips depending on the rate coveredby the CDS. This procedure was applied to all the buildings in the district which wasused as an input data of the simulation model.
7.4 Simulation Modeling
Estimating the impact of the CDS to the district is not possible without the generated
data discussed earlier because simulation analysis required detailed input data.
Although these data were not obtained through a survey, they have a strong basis tobe accurate since the foundation of obtaining them comes from the surveyed data of
the four-building.
As discussed in Section 5.3 in Chapter 4, simulation approach suits in a problem
where multiple alternatives are being consider – i.e. what is the impact of the CDS tologistical activities if 10 percent of the trips is covered (this incriminations is done
until 100 percent is covered). Further, this powerful analytic tool is often used to
identify opportunities and evaluate solutions to a complex problem that cannot beeasily brought to light by other models and analysis.
7.4.1

About SIMUL8

The simulation analysis of the study was entirely designed in the commerciallyavailable manufacturing simulation software SIMUL8. This software combines thegraphical interface and spatial simulation. The works on the principle of drawing
fromdifferent
spatialcomponents
of
study
area
composedoforganization/establishment to be analyzed. Data specifying the dimension of theorganization/establishment or that would define the behaviors of the workers orvehicles can be done by filling in numerical information ready available.
The drawing of simulation model is done by clicking buttons and dragging the objects
to their location. In the study’s model, first created were those fixed objects such as
Stock point, loading dock, floors of the building. Routes between these objects weremade by clicking the arrow and entering appropriate figure say speed, distance andother dimensions that characterize the behaviors of elements of the model. On theother hand, controlling the arrival of freight vehicle is made by selecting appropriate
distribution built-in the software.
The software further enhance its analytical power by allowing the user to control
virtually all the objects inside the simulation by permitting external sources likespreadsheets or Visual Basic. Working environment of the software and thescreenshot of the study’s model is shown in Figure 7-9.
7.4.2

Properties of the Simulation model

The properties of simulation model are shown in Table 7-16. These figures wereobtained during the social experiment. The average unloading time per freight fromthe vehicle up to the elevator is 19.8 seconds while it took 27 seconds to deliver eachgood from the elevator up to the receiver. It can be observed that the delivery time is153

longer as compared with the unloading process since the delivery staff sometimes has
to wait for the receiver.

Figure 7-9. Screenshot of the simulation model
Table 7-16. Properties of the simulation model
Without CDS

With CDS

Unloading time

19.8 sec

19.8 sec

Delivery time

27 sec27 secElevator time per floor

5 sec

5 sec

Elevator’s maximum
capacity

20

20

No of workers

0

Based on the number of elevator plus 1 staff

During the experiment, it was recognized that the two staff assigned at each building
were not able to hold the incoming freight to the buildings where attraction of trips
are high. Further, the 15-minute interval between trips is too close for the two staff
members to deliver the first trip and clear the (off) loading area before another trip
arrives. In this paper, the number of staff stationed at a building was based on thenumber of elevator. One additional staff is provided, aside from the mentioned
number of staff, designed to be permanently station at the loading / unloading area tohelp unload the freight from the vehicle with the driver and sign the document
confirming that the freight were delivered. This process would allow the driver tomove immediately making the loading / unloading space available for another income
trips.
The shown number of parking spaces in Table 7-17 excluded the spaces assigned for
private cars.

154

Table 7-17. Properties of the buildings in the district
No

Building name

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Meiji Seimei
Kishimoto
Mitsubishi Denki
Syoji
Yusen
Furukawa & Yaesu
Mitsubishi Juko
Mitsubishi Syoji
Mitsubishi
Marunouchi
KokusaiFuji
Shin Kokusai
Shin Nisseki
Shin Tokyo

Gross floor(m2)
14727.73
33972.00
46102.00
55259.00
51645.00
70016.00
45985.00
55259.00
62906.38
160000.00
116885.00
81877.00
7748.00
26566.00
106005.00

Leasable
space (m2)96673.02
22081.80
29966.30
35918.35
33569.25
43627.91
31861.06
37829.0440483.66
104000.00
75975.25
53220.0550364.60
17267.90
68903.25

No. offloors30 (4)
11 (2)
10 (4)
15 (4)
15 (3)
9 (4)
10 (4)
15 (4)
15 (4)
37 (4)9 (6)
10 (4)
9 (4)
11(2)
9 (4)

No. of parking slot for freightvehicles
8
4
4
4
4
4
4
4
4
8
4
4
4
4
4

No. of elevator
5
2
1
1
2
1
1
1
1
5
1
1
1
1
2

* Number in the parenthesis is underground floors

7.4.3

Setting-up the Arrival Distribution of Freight Vehicles

The arrival of freight vehicles for the case of CDS follows the format set by theCommittee designed the social experiment – 15 minutes interval from 8:00 – 9:30; 30
minutes interval from 9:30 – 12:00 and; 60 minutes interval from 12:00 – 17:00.
For the case of normal form of delivery, the distribution pattern of the recorded arrival
time of the one-day survey is followed (Table 7-3). The arrival of freight vehicle
follows exponential curve as reflected in Figure 7-10. Close to 35 percent of trips has
equal or less than 1-minute interval while close to 40 percent has 1 to 2 minutes
interval. The said distribution is used as input data that determines the arriving patternof freight vehicle in the simulation. For the case of CDS, however, a fixed interval
between trips is used.
45%
40%

Percentage share

35%
30%
25%
20%
15%
10%
5%
0%
0 :0

0: 0

0
0 :0

1: 0

0
0 :0

2: 0

0
0 :0

3: 0

0
0 :0

4: 0

0
0 :0

5: 0

0

A rrival time

Figure 7-10. Distribution pattern

155

0 :0

6: 0

0
0 :0

7: 0

0
0 :0

8: 0

0

7.4.4

Results from the Simulation Analysis

As explained, pooling together all concerned parties of urban goods movement is very
difficult. Lies ahead are a long process of negotiation that has to consider interest ofeach stakeholder which are hard to meet. With that in mind, the simulation model
tried to observe the number of waiting trucks, their average waiting time before aspace is available for their use. Those trucks who are not able to secure a space before
five o’clock (delivery time assumed to be 8:00am to 5:00pm) were also noted.
Observed number of delivery trucks that were not able to deliver due to the shortageof parking space reached 124 to the whole district (Figure 7-3). This is a clear
example where demand (2155 trips) is hardly met by supply (68 parking lots inside
the 15 building).
7.4.5

Introduction of Cooperative Delivery System

7.4.5.1 Impact of CDS on the number of waiting trucks and waiting timedepending on the number of trips covered by CDS
The present form of distribution of goods in the district would meet a lot of problems
if trucks are only allowed to use the parking facilities of each building. This meansthat trucks are not allowed for on-street parking to avoid disrupting the flow of traffic.
With that policy adopted in the district, each building in the district would experiencelong queue of trucks (10.1) with an average waiting time of 27.1 minutes (Figure 7-11
and Table 7-18).
When at least 10% of trips in the district is covered by the CDS, the average numberof trucks in queue and their average waiting time could be reduced to 9 and 23
respectively. This result is still far from efficient way of distributing goods and it canbe assumed that trucks in queue could hamper the fluidity of traffic since most of thebuildings are right beside the streets. CDS is having great influence when at least 70%
of all truck trips is under the CDS. Trucks will have an immediate access to theparking lots of each building and the staff of CDS waiting at each building would
handle the goods upon the arrival of each truck.
Another freight distribution problem that would arise without CDS is shown in Figure7-12. A total of 124 trucks could not make their way to the parking are of eachbuilding after 5PM. As mentioned, trucks are not allowed to park anywhere except the
parking space of each building. All the trucks could only have access to the parking
spaces and subsequently delivery their load when at least 50% of trips is under the
CDS.
Table 7-18. Comparison of average number of trucks in queue, their waiting
time, and total number of trucks in the district with and without CDS
Variables
Avg. number oftrucks in queue
Avg. waiting time
Total number oftrucks in the CBD

Without CDS

60% of trips is underCDS

All trips is under
CDS

10

2

0

27

1.1

0

1078

495

120

156

12.0

30.0
Avg. no. of waiting trucks
27.1

Avg. waiting tim e (m inutes )

10.0

25.0
10.1

23.0
9.0

8.0

20.0

6.7
6.0

15.0

14.7

4.6

4.0

Minutes

No of trucks

18.5

10.0
8.1
2.7

2.0

5.0

4.3
1.0

1.1
0.2

0.0
0%

10%

20%

30%

40%

50%

0.0
60%

70%

0.0

80%

0.0

0.0 0.0

90% 100%

Truck trips covered by the CDS

Figure 7-11. Average waiting trucks and average waiting time of trucks in the
district depending on the trips covered by the CDS

Adopting a policy is sourced to a particular objective. The simulation result showsthat the optimal point where elimination of truck’s queue is achieved is at 70%. This
result implies that the 68 total parking slots in the district able to accommodate allincoming trips coming both from the 30% trucks who have not taken part to thecooperative delivery and the 70% trucks who have participated the system. If theobjective is to eliminate truck’s queue and fit the trips to the available spaces, this canbe done without complete participations of the carriers. However, if the primaryobjective of the cooperative system is being pursued, which is to reduce the negative
impact of trucks by reducing its number, the system has to cover all the trips.
7.4.5.2 Impact of CDS on the number of waiting trucks and waiting time when
the share of retail space increases
As mentioned, the share of retail space (shops and restaurants) in the district is just
2.7% of the total leasable space. In the future, there can be a change of space usage infavor of retail space as the district tries to head towards commercial district. Thischange to space usage normally have implication to the freight transportation because
space which host shops and restaurants has strong trip attraction. It is of interest
therefore to find out how the present CDS (the CDS assumed in the simulation model)
would absorb such increase of trips.

157

140
124

120

112
100
No of trucks

88
80

60

40

36

20
13
0
0%

10%

20%

30%

40%

0

0

0

0

0

0

50%

60%

70%

80%

90%

100%

Truck trips covered by CDS

Figure 7-12. Number of trucks did not avail parking space inside the building
after 5PM
80.0

70.0

69.3

60.0

60.1
53.2

Number of trucks

50.0
46.7

Without CDS line
40.4

40.0
33.7
30.0

28.9
20.4

20.0

22.7

13.9
10.0

10.1

0.0

0

CDS line
0.03

3%

0
5%

0
8%

0

0

0

0

0

0

11%

13%

16%

18%

20%

23%

25%

0.03

28%

Share of retail space

Figure 7-13. Average number of waiting trucks with and without cooperative
delivery system as the share of retail space increases

158

Figure 7-13 illustrates the rise of the number of trucks in queue as the share of retailspace increases. The average number of trucks in queue at the present make-up of the
district (the 2.7% retail space is rounded off to 3% in the figure) reached 46.7 when
the share of retail space totaled to 20%. The limit of the current composition of the
CDS is reached when at least 25% of the space in the district would be consumed byshops and restaurants (Figure 7-13). This number translates to 170,000 sq. m. being
used for commercial purposes. Under this circumstance, there would be time wheresome trucks have to wait for a little time before having access to the parking space.
The average waiting time of trucks is shown in Figure 7-14. Unless the share of retailspace reached 25% of the total space in the area (GLA), trucks would not have delay
before getting a space inside the buildings. It is also of interest to note that at least 2
trucks could not longer get an access to the parking space of the building after 5PM
under the 25% of the space is consumed by shops and restaurants. This number
increases to 8 when the share of retail space reaches 28%.
120.0

102.7

100.0
95.6

Average waiting time (min)

87.0
81.0

80.0

Without CDS line

72.3

65.1
60.0

56.5
49.4
44.4

40.0
34.4
27.1
20.0

0.0

0
3%

0
5%

0
8%

0

0

11%

13%

CDS line
0
0
16%

18%

0.47
0

0

20%

23%

25%

0.53

28%

Share of retail space

Figure 7-14. Average waiting time of each truck in the district with and without
cooperative delivery system as the share of retail space increases

Amendment to the cooperative delivery system in order to meet the future demand in
the district can come in many forms such as increasing the number of staff involved in
the cooperative work. Increasing the number of parking space for freight vehicles bytaking some slot reserved for private cars would also yield positive result to thefreight delivery. This can be done in two ways, 1) permanent addition of a parking
slot for freight delivery and 2) time sharing of parking slots where the freight vehicles
could use the area reserved for private cars during off-peak hours and vice-versa.
Allowing the delivery staff to utilize the elevators for passengers during off-peak hour
159

would also increase the delivery pace in view of the shortage of freight elevator.
Adding the number of freight elevator by erecting one or two, though difficult due tothe current layout of the building, is another option that can also be taken.
7.4.6

Relationship of buildings characteristics and the simulation result

It is imperative to assess what makes some buildings free from queue of freightvehicles or to have shorter queue than other buildings to understand the elements thatfacilitates the efficiency of freight delivery. Table 7-19 shows that only eightbuildings (i.e. buildings 2, 3, 4, 5, 6, 7, 14, 15) were equipped with enough freightfacilities (i.e. parking spaces and freight elevators) resulting to having all the trips
accommodated without delay. Queues of freight vehicles in varying numbers areobserved to the remaining seven buildings which warrant further examination.
Grouping the buildings based on their number of elevator produces some hint that
could explain on why some buildings were able to hold all incoming trips. Table 7-20
shows that three groups of building is formed, one is a group of buildings with one
freight elevator, two is a group of buildings with two freight elevators, and the last is agroup of buildings with five freight elevators. Buildings with one freight elevator andbuildings with two freight elevators have almost the same number of floors, numberof parking spaces, number of incoming trips, and number of incoming freight. The
similarities however ends there as the buildings with one freight elevator were clearlyin difficult position to hold incoming trips. On the contrary, those buildings with two
freight elevator were had efficient flow of freight movement as indicated by theabsent of waiting trucks. This analysis shows that buildings with a floor of 11 or sowith one freight elevator is likely to experience queues of vehicles as the demand ofdelivery staff for the elevator is high.
Table 7-19. Waiting time of truck vehicles to each building without CDS

8

No of
trips per
day
278

No of
freight per
day
1083

No of
waiting
trucks48

Waitingtime per trip
(min)
96

45

4

63

247

0

0

0

1

4

86

336

0

0

0

15

1

4

103

403

0

0

0

15

2

4

96

376

0

0

0

9

1

4

102

212

0

0

0

10

1

4

92

284

0

0

0

15

1

4

132

772

16

59

19

15

1

4

116

456

6

26

0

37

5

8

299

1166

43

93

41

Kokusai

9

1

4

218

851

27

83

19

Fuji

10

1

4

153

596

9

36

0

Shin Kokusai

9

1

4

145

564

3

13

0

14

Shin Nisseki

11

1

4

50

194

0

0

0

15

Shin Tokyo

9

2

4

198

772

0

0

0

Total

215

26

68

2131

8312

152

406

124

Bldg
code

Building
name

No of
floor

No of
elevator

Parkingspace

1

Meiji Seimei

30

5

2

Kishimoto
Mitsubishi
Denki
Syoji

11

2

10

9

Yusen
Furukawa &Yaesu
Mitsubishi
Juko
Mitsubishi
Syoji
Mitsubishi

10

Marunouchi

11
12
13

3
4
5
6
7
8

160

Trips did
admittedTable 7-20. Comparison of groups of buildings based on available freight
elevator
Variablesbuildings with 1 elevatorbuildings with 2 elevatorsbuildings with 5 elevators

Avg. no. of floors

11.3

11.7

33.5

Ave. parking spaces

4.0

4.0

8.0

Avg. no. of trips in a day

119.7

119.0

288.5

Avg. no. of freight

466.8

465.0

1124.5

Avg. no. of waiting trucks

6.1

0.0

45.5

Avg. waiting time (min)

21.7

0.0

94.5

Avg. trips did not admitted

3.8

0.0

43.0

7.4.7

Distribution of number of waiting trucks and their waiting time among
the buildings

It appears that the degree of problem concerning freight transport vary from one
building to another as reflected in Table 7-21. Obviously, the extent of problem seen
to the different buildings have close relationship to the building’s height, parking
space, freight elevator, number of incoming trips and freight. Building 1 and building
10 have many in common – 5 freight elevators and 8 parking slots for freight vehicles– and the number of trips covered by the CDS before they are freed from vehicle’s
queue are also the same at 60%.
Taking into account all the buildings, a complete elimination of freight vehicle’s
queue can be completely eliminated when the CDS covered at least 80 percent of trips
into each building. Further, number of trips covered by the CDS needs not to be thesame – for instance, there are buildings that can be freed from vehicles queue at evenlower rate of trips covered by the CDS.

7.4.8

Reduction of trips

Shown Table 7-22 are the number of trips each building is expected to receive withand without cooperative system. Prior survey in the district reported that for every
truck, it has an average load of 8 goods to be delivered to two buildings. The same
pattern is followed in the simulation where the trucks coming from the stock point
have to make a two stop before it return.
The total number of trucks that would enter the district without the CDS is 1078 while
120 for the case of CDS. The difference therefore is 958 trucks which is substantiallya high reduction to the total traffic in the area (discussed in the later part). Further, theaverage load of a CDS truck would be 70 goods and every stop would unload 35
goods.

161

Table 7-21. Distribution of the number of waiting trucks and their waiting time
among the buildings
Building code | Variables

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Percentage of trips covered by the CDS
0%

No. of waiting trucks

48

10%
45

20%
42

30%
27

40%
2

50%
0

60%
0

70%
0

80%
0

90%
0

100%
0
0

Avg. waiting time (m)

96

92

92

69

6

2

0

0

0

0

Trips not admitted

45

42

40

14

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

16

11

4

0

0

0

0

0

0

0

0
0

Avg. waiting time (m)

59

44

22

12

1

0

0

0

0

0

Trips not admitted

19

10

4

0

0

0

0

0

0

0

0

No. of waiting trucks

6

4

3

2

0

0

0

0

0

0

0
0

Avg. waiting time (m)

26

18

14

10

1

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

43

42

28

19

18

8

0

0

0

0

0

Avg. waiting time (m)

93

87

70

55

51

28

0

0

0

0

0

Trips not admitted

41

41

26

7

0

0

0

0

0

0

0

No. of waiting trucks

27

27

23

21

20

7

3

0

0

0

0

Avg. waiting time (m)

83

82

78

74

63

35

16

0

0

0

0

Trips not admitted

19

19

18

15

13

0

0

0

0

0

0

No. of waiting trucks

9

4

0

0

0

0

0

0

0

0

0
0

Avg. waiting time (m)

36

15

1

1

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

3

2

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

13

7

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

No. of waiting trucks

0

0

0

0

0

0

0

0

0

0

0

Avg. waiting time (m)

0

0

0

0

0

0

0

0

0

0

0

Trips not admitted

0

0

0

0

0

0

0

0

0

0

0

Total no. of waiting trucks

152

135

100

69

40

15

3

0

0

0

0

Total waiting time (min)Total Trips not admitted

406

345

277

221

122

65

16

0

0

0

0

124

112

88

36

13

0

0

0

0

0

0

162

Table 7-22. Trips total with and without CDS
No

Building nameWithout CDS

1Meiji Seimei

282

16

2

Kishimoto

64

16

3

Mitsubishi Denki

87

16

4

Syoji

105

16

5

98

16

102

16

7

Yusen
Furukawa &Yaesu
Mitsubishi Juko

92

16

8

Mitsubishi Syoji

132

16

9

Mitsubishi

116

16

10

Marunouchi

303

16

11

Kokusai

221

16

12

Fuji

155

16

13

Shin Kokusai

147

16

14

Shin Nisseki

50

16

15

Shin Tokyo

201

16

Total

2131

240

6

7.4.9

With CDS

Effect of the reduced trips to traffic volume in the area

The result of the simulation shows that at least 120 trucks are necessary in order toship the freights going into the district. Comparing the 120 trucks needed to deliver
the goods for the case of CDS is far less than 1078 trucks normally delivering goods
in Marunouchi. In short, the horizontal cooperative delivery system could reduce thenumber of delivery trucks into 12%.
According to a traffic survey in the area, the share of freight vehicles to all traffic isapproximately 32 percent. This value was obtained through equation 1:

where V is the total vehicle volume, Vp is the volume of passenger car, and Vt is thevolume of freight vehicle. The CDS deals only with the delivery of goods and it istherefore important to isolate the number of vehicles collecting goods. In order toquantify the effect of the cooperative delivery to the total traffic, it was assumed thatpercent of the truck traffic is delivering freights while the remaining half is collecting.
Equation 2 would isolate the number of freight vehicle delivering goods fromcollecting:where V’ is the number of freight vehicle delivering freights, Vt/2 is half the numberof total freight vehicle, and a is the reduction rate of freight vehicles due to theimplementation of cooperative delivery which is .11. With this assumption, thereduction rate of CDS on the total traffic in the area can be computed by following
equation 3:163

where V is the total number of freight vehicle reduction induced by cooperative
delivery system. The final equation that would give the value of the ratio of vehicle
reduction as an effect of the cooperative system is given below:

Using the equations above, a 12.48 percent reduction in all total vehicle volume wasarrived. This is a significant number that would result in traffic flow improvement
since freight vehicles have often been blamed to cause traffic jams due to theirunpredictable nature of loading and unloading freights at any given space.
7.4.10 Reduction of environmental emissions
The average length of a trip with two stops is 1.78 km as mentioned in Section 6.3.
The district therefore is admitting 216 grams of NOx and 13 grams of PM fromdelivery trucks alone. Obvious means to decrease these numbers are by ensuring thatfew numbers of trucks are entering the district. By adopting cooperative delivery,
88% of both NOx and PM can be offset in case of using the same type of trucks while
the reduction can be further increase by utilizing CNG trucks that would give a 99%
reduction for NOx and eliminating the PM (Table 7-23).

Table 7-23. Environmental emissions
Existing delivery

NOx

PM

1078 (trucks) x 1.78(km) x .118 (g/km) =226 (g)1078 (truck) x 1.78(km) x 0.007 (g/km) =13 (g)

Cooperative delivery by existing
trucksCooperative delivery by CNG trucks

120 (truck) x 1.78 (km) x .118
(g/km) = 25 (g)120 (truck) x 1.78 (km) x .0118 (g/km)= 2.52 (g)120 (truck) x 1.78 (km) x 0.007(g/km) = 1.49 (g)120 (truck) x 1.70 (km) x 0 (g/km) =0 (g)Note: Emission factors for NOx and PM were based on Technical Methods of Road Environment
Assessment

7.4.11 Reduction of working time
Logistics cost is composed of fuel, depreciation, and personal expenses among others.
In this study, however, the focus is on personal expenses and working time of bothtruck drivers and staffs for cooperative delivery. One of the prime gains of the CDS is
the reduction of time (85 hours) for delivery vehicles (see Table 7-24). This reduction
can be translated to improved quality of the urban environment where trucks run at 20km/hr.
The recorded average time for trucks is 20 km per hour and the average distance ofbuilding from the stock point is .68 km. Similarly, the average distance of building
from each other is .42 km. Any delivery route therefore would have to travel anaverage of 1.78 km for the speed of 20 km/h. This distance has an average travel timeof 5.34 minutes which was used to evaluate the running time of trucks in the district.

164

Table 7-24. Changes derived from CDS
Variables
Delivery time in the area
Unloading time in the building

Existing Delivery (min) Cooperative Delivery(min)1078 trucks x 5.34 min
120 trucks x 5.34 min = 640.80= 5756.52
8333 freight x .33 min =8333 freight x .33 min = 2,750
2,750Time Saved (min)- 5115.75-0Waiting Time for parking

46475

0

- 46475

Labor time at SP

08333 freight x 1 min =8,333
63,314.52

8333
41 staff x 480 min (staff labortime) = 19,680
31403.8+ 8333

Vertical Cooperative Delivery
Total+ 11441- 31910.72

7.5 Comparison of CDS impact between CBD of Metro Manila and CBD ofTokyo
The traffic and environmental impact of CDS in the Ortigas CBD is rather limited
while considerably high in the Marunouchi CBD (Table 7-25). These contrasting
results appear to lie to the characteristics of the transportation system where thenumber of trucks is notably high in Tokyo while incredibly low in Metro Manila.
Although the impact of CDS is greater when applied to high density CBD whichmostly located in developed countries, this does not indicate that it is not an
intelligent policy solution to the growing problems in a low density CBD mostly
located in developing countries. These cities have very limited road infrastructure thus
any policy that yield positive effect to the transportation networks should be pursued.
Impact of one measure might be limited but if bundled together with other measures
might produce a significant result towards realization of efficient urban traffic andhealthy environment. Further, in the future, CDS would become important part of any
policy set aims to improve freight transportation as the CBD transformed from low tohigh density. Number of trucks would be expected to increase and load factor would
still be low as JIT concept dominates the freight transport management. Normally,
when the problems of freight transport escalate, city authorities are becoming
notorious by imposing access restriction to protect urban traffic and environment.
This scenario can be avoided if CDS is adopted. Aside from pleasing these authorities,
adopting cooperative delivery system could also be a boost in the market image oftrucking industry as responsible sector which have concern to the well being of theenvironment.

Table 7-25. Comparison of CDS impact to Manila CBD and Tokyo CBD
Variables

ManilaTokyo

Traffic impact

0.78%

12%

Environmental impact
NOx
SMP

54% (3274 g to 1517 g)
54% (359 g to 193 g)

89% (226g to 25 g)
89% (13 g to 1.49 g)

Delivery time in the district

54% ( 1,099 min to 507 min)

89% (5,756 min to 640 min)Number of trucks

54% (713 to 329)

89% (1078 to 120)165

7.6 Chapter summary
Providing secured (off) loading places for freight vehicles is considerably difficult
and required the cooperation of authorities and truck drivers. Among the tasks of theauthorities in the district is to ensure that the place is not illegally occupied by private
cars which has been often reported in literature – e.g. Best Practice Handbook.
Similarly, truck drivers have to make every effort to follow the regulation in placesuch as observing the allowed duration for parking. This complex issue calls forfurther research.
The chapter demonstrates that CDS is a very powerful policy tool that could confront
several problems of urban freight transport. The substantial number of trucks reduced
from the district (12% to the total traffic) is a gain of the environment in the form of
improved travel speed and less source of emissions. In addition, few trucks requirefew parking spaces which have been a real issue to every major city in the world.
The first analysis in Section 7.4.5 reveals that if the interest is to hold the incoming
trips in order to avoid the queues of trucks, except the eight buildings (2, 3, 4, 5, 6, 7,
14 and 15) the rest have to adopt cooperative delivery. Level of participation to the
CDS however varies from one building to another. This uneven participation rate is a
result of several factors such as volume of trips and goods, number of parking spaces,
and number of freight elevator.
As the analysis goes deeper, it was learned that although full participation to the CDS
could make an 89 percent reduction to the current number of trucks, its effect to thetotal traffic volume of the district is registered only to 12 percent. This is because
truck’s share is less than half of the total traffic (32%).
Effect of freight-related-facilities (parking space for trucks and elevator for freight)
was also confirmed to be highly important to the fluidity of the delivery chain.
Buildings which two freight elevators were having smooth goods delivery process ascompared with those buildings having one as confirmed previously.
The highest reduction rate to chemical emissions (NOx and PM) is achieved when
CDS is fully adopted and the trucks use to deliver goods are non-polluting vehicles.
With these two policies adopted, 99 percent of NOx is reduced while a CNG truck
does not emit PM. Assuming the regular trucks would continue to deliver goods under
the CDS, 88% of both chemicals could still be reduced.
The reduced number of trucks when converted to time is 5115 minutes while the total
time saves from the CDS is 31910 minutes. Cooperative delivery therefore thus notnecessarily increased the operation cost of the companies as shown by the study.
Regarding the comparison of the traffic impact of CDS between Ortigas CBD of
Metro Manila and Marunouchi district of Tokyo, it was found out that the CDS’s
traffic impact to the former is rather limited while notably high to the latter. These
contrasting results appear to lie in the difference of transportation characteristics
where share of trucks in Ortigas is substantially low (1.7%). Moreover, thetransportation network appears to be saturated that even if the number of trucks is
withdrawn, the network is still holding large number of vehicles.

166

REFERENCES

1. Mori Building Company Ltd., Homepage. Market Trend Survey of Large Scale
Office Buildings in Tokyo’s 23 Wards.
(http://www.mori.co.jp/companyInfo/press/pdf/2003reportEnglish.pdf. Accessed
February 24, 2004)
2. Mitsubishi Estate Company Homepage. Creating a stage where individuals shine,
Marunouchi shows the way to the next generation.
http://www.mec.co.jp/report/marubiru/growing_e.html. Accessed July 29, 2004
3. Presentation on the redevelopment projects in Marunouchi business district (2004).
13th Annual Workshop of the Workplace Network.
(http://twn2004.jp/final_report/Report/May_10_site_visit_to_Marunouchi.pdf)
4. Sinarimbo, N. et al (2004), Freight Transport Management: A Simulation
Analysis of Cooperative Delivery System in the Marunouchi. CD-ROM. 2004
WCTR, Istanbul, Turkey, 2004, Paper No. 116.
5. The Advisory Committee on Otemachi- Marunouchi- Yurakucho Area
Development Homepage. (http://www.aurora.dti.ne.jp/~ppp/01_english).
Accessed July 29, 2004
6. Takahashi, Y. et al. (2004), A Study of Traffic and Environmental Effects of
Cooperative Delivery System in CBD: Case Study in Marunouchi Area. CD-ROM.
2004 WCTR, Istanbul, Turkey, 2004, Paper No. 421.
7. Tokyo Convention and Visitors Bureau Homepage. Yes Tokyo.
(http://www.tcvb.or.jp/en/hot/mustsee/mustsee_top.html). Accessed July 29, 2004.

167

CHAPTER

8Application of Cooperative
Delivery System to a HighDensity Central BusinessDistrict: Case of Developed
Country
8.1

Introduction

Intra-city goods movement is profoundly affected by the facilities and services
available for pickups and deliveries (Morris et el, 2001). Insufficient numbers ofparking space and lack of (off) loading places are some of the common problems thatdelivery trucks have to deal with. Increasing the utilization rate of the limited parking
facilities by ensuring limited parking time for each truck is an important measure inview of the shortage in parking space in the CBD. Attaining this brief parking time,
however, requires a shift from the traditional way of moving goods to policy-driven
methods. Traditional way presents an image where a driver has to leave his truck andmake his way to the floor of the building to make freight delivery. Although thismethod remains to be popular due to its simplicity, there exist methods that whencarefully enforce would present economic and financial benefits to the urban goods
movement in general. One of these policies is by assigning staff members inside abuilding to facilitate the (un) loading activities to free the driver immediately. This
chapter discusses implementation of CDS in high-density CBD characterized by highrise buildings. Further, it tries to produce guidelines regarding what CDS design issuitable depending on the characteristics of the CBD. Moreover, it provides a hint
when to adopt any of the CDS design which would useful for planners around theworld.
In the analysis, the height of the buildings in the district is extended to anticipate
future development. Any redevelopment of buildings in the area would haveenormous impact to the distribution of goods in the area. The flow of discussions
begins by introducing the unloading process once the truck entered the building.
Typical problems that hampered the efficiency of delivery are made known before
available measures are enumerated. The main discussion of the chapter, which is theapplication of vertical cooperative delivery system and the simulation analysis, arepresented and summarized in the end of the chapter.

168

This Chapter begins by reviewing related papers in management of the delivery
system inside a building. Section 8.2, Section 8.3, Section 8.4 are devoted to thereview of significant papers in the delivery of goods. Along the review process,common problems in urban centers of moving goods were noted such as the difficultyof driver locating available parking space. Some important data from Chapter 7 werere-used and presented in Section 8.5. The method to obtain the data is presented indetail in Section 8.6. These data are important information that be used for simulation
modeling in the later part of the chapter. Simulation modeling is discussed in detail in
Section 8.7. This discussion includes the process of selecting distribution arrival that
could replicate the actual arrival of trucks as recorded in the survey. Section 8.9 tried
to produce a guidelines when and what CDS design is adopted based on thecharacteristics of the CBD. The chapter is concluded in Section 8.10 whichhighlighted the issues given a thorough assessment.
As mentioned, the main premise of making the delivery process inside the building
efficient is that delivery vehicles that cannot access the docking area normally use any
available space near the delivery establishment to (un)load their freight. Accelerating
the turnaround of the delivery vehicles at the docking area would also reduce thenumber of vehicles in queue that sometimes long enough to occupy portion of thestreet hence obstruct the fluidity of the traffic.

8.2

Urban Goods (Off) Loading Process

Iwao et al (2001) produces the most notable illustration of delivery process inside(unloading activity) the building (see Figure 8-1). The chain of this process iscomposed of six steps which start as soon as the delivery truck arrives. In the figure,the truck is parked in an on street (off) loading area which has the longest time ofloading/unloading activity as compared to truck parked in a parking space locatedinside the building or beside the building (off-street) (Park et al, 1998). The obvious
reason is its distance from the freight elevator located inside the building.

building④
Logistics
center③①

Activities:①delivery②unloading③conveyance inside
the building④receiving⑤return from⑤ receiving to parking
space⑥return trip

ParkingSpacesSidewalk②

roadway⑥

Figure 8-1. Delivery and unloading activities (source: Iwao et al, 2001)

169

8.3

Common problems related to unloading activities

Problems of (off) loading activity range from lack of sufficient number of parking
space to lack of freight elevator. In New York for instance, inadequate freighthandling facilities in the CBD were identified as the major barriers to freight mobility.
Inadequate docks or receiving areas and insufficient freight elevators did not support
the increasing number of freight deliveries, resulting in a significant amount of offloading on the streets (Morris et el, 2001).
Further, it is more difficult to deliver goods in the buildings in ancient cities where
freight elevator usually does not exist. In this condition, the goods carriers have toutilize the stair which naturally resulted to longer delivery time.
Parking spaces and freight elevators are two important elements that could determine
the smoothness of the delivery activity. As the number of parking space increases, theprobability that the waiting time of vehicle is short also increases. Parking time ofvehicle on the other can be influenced by the availability of enough number of freight
elevators. Truck drivers that do the delivery would find it fast to return to their truck if
elevator is right waiting near the parking area.
Table 8-1. Problems and countermeasures of (off) loading activity

Trucks trip

Parking of goods
vehiclesProblems

Countermeasures

Delayed delivery

Cooperative delivery, regulation ofdelivery time

Reduction of travel speed

Provision of priority lanes for goods
vehicleMixing of passenger cars and freightvehiclesTraffic regulation, traffic imposition

Parking congestion of passenger
cars and freight vehicles at on-street
parking facility

Time and spatial separation ofvehicles at on-street parking facility

Increase of illegal parking

Conduction of off-street parking
facility (on-street parking facility
regulation, parking charge discount)Competing people and goods
Loading/unloading

Lack of conveyance paths
Lack of elevator for exclusive use ofgoods inside the building

The reinforcement of the criterion on
the planning of inside parking facility
for goods vehicle
Spatial separation of people andgoods
Design standards for conveyancepath
Design standards of elevators for
goods

Source: Park et al, 1998
Park et al (1998) has summarized the frequent encountered problems during thedelivery of goods and its countermeasures (see Table 8-1). Congestion reduced the
speed of trucks thus delay the delivery (see Chapter 2 for in-dept discussion). This
problem is compounded by the increasing number of vans used for delivery which
propelled by the Just-in-time (JIT). It is argued that using vans instead of trucks will
increase total traffic thus congestion.

170

The lack of parking spaces and (off) loading spaces for goods vehicles resulted to therampant practice of occupying the spaces not intended for goods delivery. Althoughsometimes there are reserved spaces for vehicles delivering goods, it has beenreported that in most cases, these are occupied by private cars and hence double
parking occur. Protection of reserved places for goods vehicles therefore is necessary
to ensure that right type of vehicles occupied the places.
Exclusive elevator for delivery is often inexistence or short in number affecting theentire chain of delivery – this means truck has to spend longer time in the (off)loading area.

8.4

Improvement measures for urban goods

There are measures that can be applied to realize efficient delivery of goods in viewof the difficulty faced by the sector (see Table 8-2). Although most of the measureslisted are rather familiar, there are quite unusual measures that have not been widelyreported in literature such as time-sharing of both parking spaces and elevators, andconsolidated delivery inside the building. Time-sharing is referring to allow the use of
facilities intended for private cars (parking space) and passengers (elevator) to goods
delivery during the determined off-peak periods in a day. This policy would increase
the capacity of freight-handling facilities hence increase the fluidity of goods.
Similarly, consolidating the goods intended for the same receiver would decrease theutilization of elevator. This scheme however might require some staff members
permanently station at the (off) loading area to perform the consolidation.

Hard

Establishment of depot in the inner city
Increase of lane capacity
Signal control improvement
Provision of (off) loading and parking places
Automatic conveyance material system inside the buildingSoft

Type of Measures

Table 8-2. Improvement policy for urban goods movement

Consolidated delivery
Efficient checking and sorting inside the building
Cooperative delivery
Time-sharing of parking spaces
Time sharing of elevators
Coordinated delivery inside the building

Source: Iwao et al, 2001
Measures in Table 8-2 that are not very familiar are discussed further to make theChapter more accessible. As mentioned, protection to the city center of old towns byrestraining access of delivery trucks is one of common reasons to erect a depot. Cities
that were built even before the start of motorization were simply not suited toaccommodate heavy vehicles and therefore were in need of other method to deliver
the goods. This case has been witnessed in some European cities as described in
Chapter 3. The depot then serves as consolidation area for goods that will be
distributed to the final receivers by a truck that suits the environment of old town. In
some cases, a staff in the depot would inform the recipient that a package has arrived
and it can be delivered or be pick-up by the recipient.

171

Building

Building

1F

2F
Freight elevator

2F

Passenger elevator

After

Passenger elevator

Before1F

Freight elevator

Signal control improvement has also been proven as an effective measure to improve
the flow of traffic. This measure can be done by optimizing traffic signal timing plans,
coordinating traffic signal control, and implementing adaptive signal control.
Normally, results that can be attained for improving the signal control includes
improvement of travel time, reduction of delays and stops of vehicles in the network.

B1

B1

Each elevator is dedicated to aparticular customer

Both elevators can be used forgoods delivery during off-peakperiods of the day

Figure 8-2. Time-sharing of elevators

Before

After

15F

15F

1F

1F
LIFT

LIFT

Limited number of parking space isallotted to delivery vehicles while therest spaces is reserved for private cars

Delivery vehicles can use the parking
spaces including those reserved for
private cars during off-peak periods ofthe day

Figure 8-3. Time-sharing of parking spaces

172

It appears obvious that there is a need to distinguish the difference between
consolidated delivery and cooperative delivery. Literature tends to suggest that
consolidation takes place when a cooperative delivery among companies is formed(Castro, 2002, Visser et al, 1999). Cooperative delivery change the form of urbandelivery from independent private transport to consolidated transport that resulted tohigher load factor and reduction of the number of trucks. It is worth to note thoughtthat consolidated delivery can also be done by a single transport company having a
multiple customers in an area.
Allowing freight carriers to use the passenger elevator during off-peak periods would
increase their mobility (Figure 8-2). In Japan, there was a reported case where 70% oftrucks in a day arrived to a store before it opens (Iwao et al, 2001).
Parking management is a powerful too to control the flow of traffic. This measureincludes provision, control, regulation or restriction of parking space. Parking
management can consist of actions that fall into six categories: on-street parking, offstreet parking, fringe and corridor parking, pricing, enforcement and adjudication, andmarketing.
100
90

Percent of Peak Time

80
70
60
50
40
Residential
OfficeRetailRestaurant
Cinema

30
20
10

10PM

9PM

8PM

7PM

6PM

5PM

4PM

3PM

2PM

1PM

12AM

11AM

10AM

9AM

8AM

7AM

6AM

0

Time

Figure 8-4. Parking Accumulation by Land Use by Time of the Day
Source: Meyer, M.D. (1997). A Toolbox for Alleviating Traffic Congestion and Enhancing Mobility,
Institute of Transportation Engineers.

Another parking management strategy that is being considers in many communities isthe concept of shared parking. Shared parking in essence means that two or more land
uses controlled by one or may owners can share the same parking space over thecourse of a day, week, or month. The basic premise for this strategy is shown in
Figure 8-4 which indicates that different activities generate different temporaldemands for parking which provide good opportunities for more efficient parking
provisions.

173

BeforeAfter

Building

Building

2F

Receivingarea

2F

Receivingarea

1F

Receivingarea

1F

Receivingarea

Parking area

Consolidationarea

Parking area

Consolidationarea

Figure 8-5. Coordinated delivery inside the building
Organizing the goods after it was unloaded from the trucks would also bring somebenefits such as utilization to the freight elevator would become more rationalize.
This would remove unnecessary waiting time for staff delivering the goods since theelevator is hardly use.

8.5

Application of measures to improve goods delivery

Four policies from Table 8-2 were seen adopted in the social experiment in the
Marunouchi, Tokyo – establishment of depot, provision of (off) loading and parking
places, cooperative delivery, and consolidated delivery inside the building. The social
experiment is discussed in length in Chapter 4 while the analyses for the effect of the
adopted policies are available from this Chapter onwards. This Chapter in particularanalyzes the (off) loading activity inside the building.
Freight elevator supports all the building covered by the experiment and designatedparking spaces for delivery vehicles are also available (see Table 8-3). The number oftrips and goods found in the table is recorded prior to experiment. This was done toobserve the natural process of performing delivery activity and to determine theseverity of truck’s traffic.
At the (un)loading area inside the four (4) buildings joined the social experiment, two
staff members are permanently station to assist the drivers of delivery trucks. Afterunloading the goods, the truck would leave and move to the next building and thesame process is perform there. This arrangement, however, is not available to those
who did not participate to the social experiment (see Chapter 4 for more discussion).
The effect of this scheme reduced the parking time of each truck from 15 minutes to
2.35 minutes. This reduction to the parking time of trucks was mainly credited to thepresence of two staff which handles the goods once the truck entered the docking area.

174

The usual responsibility of the driver, that is to deliver the goods to the final recipients,
has been passed to the staff. This arrangement allows the driver to bring out the truck
to the next delivery destinations.

Table 8-3. Properties of the four buildings
Building
No of floor
No of freight elevator
No of staff member at unloading area
No of parking space
Gross floor (m2)2
Leasable space (LS) (m )Percentage of LS
Office space (OS) (m2)Percentage (OS)Shops & Restaurants (SR) (m2)Percentage (SR)OfficeTrips per day
Shops & Restaurants
Total
OfficeNo of freightper day
Shops & Restaurants
Total

Furukawa &
Yaesu
9
1
2
4
70016
43628
62%
41080
94%
2547
6%
84
18
102
144
68
212

8.6

Method of Data Collection

8.6.1

Data of freight vehicles’ arrival

Mitsubishi
Jyukou
10
1
2
4
45985
31861
69%
30477
96%
1414
4%
74
18
92
184
100
284

Mitsubishi
Shouji
15
1
2
4
55259
37829
68%
37829
100%
0
0%
132
0
132
772
0
772

Mitsubishi
15
1
2
4
62906
40484
64%
40248
99%
236
1%
93
23
116
271
185
456

Although data of the four buildings are available, presentation and in-dept discussion
were only performed to the Mitsubishi building since it has the most complete set ofdata. The data obtained from trucks delivering goods during the three (3) day
experiment to the Mitsubishi building are available in Table 8-4 (data of trucks who
did not participate the experiment were not included). Observations suggest that timeinterval from each trip in the early morning delivery is very short - for instance is theinterval between the first trip and second trip on the fist day of the experiment. The
first delivery truck arrived at 8:48 carrying 14 goods and followed shortly by anothervehicle transporting 16 goods at 8:52 resulting to 4 minutes time interval between thetrips. It is believed that in this part of the day, most business establishments andoffices are storing goods supplies that could at least serve the whole day. This might
be the primary reason that could explain why there is a big amount of goods to eachtrip having very short time interval.
The relatively small number of carriers participated is visible to the few number oftrips observed - seven (7) trips on February 19, eleven (11) trips on Feb 21 and seven(7) trips on Feb 21 (18 carriers participated out of 232 operating in the area). Numberof goods was also considerable few as compared to the actual number of goods
observed in Table 8-3. In the table, 456 goods were reported while recorded numberof goods in the experiment is only 108 on the first day, 106 on the second day and 108
again on the last day.

175

Table 8-4. No. of truck trips and goods delivered

8.6.2

Date

Arrival time

2/19
2/19
2/19
2/19
2/19
2/19
2/19
2/20
2/20
2/20
2/20
2/20
2/20
2/20
2/20
2/20
2/20
2/20
2/21
2/21
2/21
2/21
2/21
2/21
2/21

8:48
8:52
8:53
9:04
9:22
9:39
14:56
8:37
8:46
8:47
9:07
9:10
9:24
10:08
10:48
11:11
13:09
14:04
8:36
8:46
8:46
9:09
9:17
9:47
13:09

No of
goods
14
16
32
39
4
1
2
13
30
2
6
36
12
1
2
2
1
1
11
31
2
9
35
8
12

Description of the surveillance system

All the staff members assigned at the (off) loading places of the buildings was
equipped with a surveillance device called PEAMON (Personal Activity Monitor).
This device records the location positioning data through its built in PHS (Personal
Handy phone System) function. The location of the staffs in a particular time islocated through these data.
Asakura et al (2001) describe it as a pocket-size travel data collection equipped with
PHS (Personal Handy phone System) receiver, three-dimensional acceleration sensor,
CPU, compact flash memory card and powered by lithium ion battery (Figure 8-6).
The unit’s size is 120 mm in height, 12 mm in thickness, and has a weight of 125
gram. The location positioning data (longitude, latitude, and time) is generated every
15 seconds via its built-in PHS function. This travel-monitoring unit uses the signal
strength and the ID number of 3 to 7 base stations of PHS communication systemcapable of monitoring activities for 48 hours. Although PEAMON could collect dataon the staffs’ detailed activities, a weak and disturb signal could lead to a missing data.
In that case, this is substituted by the daily reports recorded by the staffs.

176

PA 5
Floor 5
PA 4
PHS function

Floor 4
PA 3
Floor 3

Acceleration sensor

PA 2
Floor 2
PA 1
Floor 1Figure 8-6. PEAMON model and sequential installations of power antenna

8.6.3

How does it work to trace the delivery floor of goods

PEAMON’s signal is relying to the power of base station and therefore cannot record
with the absence of signal. In order to mitigate this difficulty, a Power Antenna (PA)with range of 300 meters was installed to each floor of the building totaling to 19.
These PAs, which solely developed to support PEAMON function, were installed
sequentially in order to have a sequential base station ID. PEAMON will record the
ID of each station and its corresponding signal strength to distinguish it from one
floor to another.
The installation of PAs is illustrated in Figure 8-6 while example of base station ID isshown in Table 8-5. As can be seen from the table, each base station has a unique
identification number making it simple to trace which floor the unit holder located.
Interpreting the table would state that the unit holder was in Floor 1 from 9:10:00
until 9:10:15 and moves to Floor 5 at 9:10:30 and arrived to Floor 4 at 9:11.

Table 8-5. Example of station ID and signal strengthTime

PA1 (ID, SS)

9:10:00
9:10:15
9:10:30
9:10:45
9:11:00

1, 12
1, 20

PA2 (ID, SS)

PA3 (ID, SS)

PA4 (ID, SS)

PA5 (ID, SS)

5, 40
5, 25
4, 16

177

The monitoring of the activities of the two staffs inside the building assigned totransport the goods work in this way: they will write to the logbook their destination
floors, number of offices intended for delivery and the number of goods going todeliver every time they leave the docking area located at the basement (B3) floor of
the building. The PEAMON cannot record data while the holder is inside the elevator
due to the absence of signal. However, PEAMON monitoring will resume as soon as
the elevator opened up to the destination floor. Data will be recorded continuously
every 15-seconds until they return again to the elevator.

8.6.4

Results of PEAMON monitoring

The result of PEAMON monitoring is presented in two ways. The first one is in theform of graph showing the routes of both staffs starting from their first delivery in themorning until their last delivery at the end of the day. The second one is in the formof tables which clarify the individual route of the staffs shown in the graph. Every
single graph is composed of two supplemental tables.

Floor Number

The monitoring result of PEAMON on the first day (Feb 19) of experiment is shownin Figure 8-7. The delivery activity started by Staff1 at 9 o’clock in the morning andfollowed shortly by Staff2 at 9:15. The first delivery stop of Staff1 was on Floor 1 andhis last delivery was as early as 10:40 - brings three (3) pieces of goods to Floor 2. On
the other hand, Staff2’s first stop was on Floor 5 and ends his delivery after sending
goods to Floors 4 and 1 at 2:55. The detail of the routes of Staff1 and Staff 2 is shownin Table 8-6 and 4-14 respectively.

15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
8:00

Staf f 1

9:00

10:00

11:00

12:00

13:00

14:00

Staff 2

15:00

Time

Figure 8-7. 2/19 PEAMON monitoring result

178

16:00

Table 8-6. Staff1’s route
Time (Feb 19)
9:00

9:25

9:50

10:15

10:40

Route
B3⇒F1⇒B3

Floor Location No of office No of delivery items Destination
1Floor

1

10

B1

1

19

B3⇒B1
B1
B1⇒F1

0:00:08
0:01:25
0:00:05

1Floor

2

5

F1
F1⇒B3

0:00:210:00:09

7Floor2

94Floor1

2

3Floor

1

8

B3⇒F7
F7
F7⇒F4
F4
F4⇒F3
F3
F3⇒B3

0:02:43
0:03:51
0:00:09
0:01:55
0:00:05
0:00:25
0:07:50

12Floor1

1

10Floor1

2

B3⇒F12F12F12⇒F10F10F10⇒B3

0:00:25
0:02:52
0:00:08
0:02:30
0:13:15

2Floor

1

3

B3⇒F2
F2
F2⇒B3

0:00:11
0:02:19
0:00:11

B3⇒B1⇒F1⇒B3

B3⇒F7⇒F4⇒F3⇒B3

B3⇒F12⇒F10⇒B3

B3⇒F2⇒B3

Time

B3⇒F1
F1
F1⇒B3

0:00:09
0:01:51
0:00:09

Table 8-7. Staff2’s route
Time (Feb 19)

Route

Floor Location No of office No of delivery items Destination

Time

9:15

B3⇒F5⇒B3

5Floor

1

12

B3⇒F5
F5
F5⇒B3

0:01:13
0:03:45
0:04:15

9:35

B3⇒F2⇒B3

2Floor

1

7

B3⇒F2
F2
F2⇒B3

0:00:11
0:04:49
0:00:1115Floor1

1

B3⇒F15F15

0:01:57
0:00:23

10Floor

1

3

7Floor

1

4

B1

1

2

F15⇒F10F10F10⇒F7
F7
F7⇒B1
B1
B1⇒B3

0:00:37
0:06:210:00:09
0:04:53
0:04:37
0:03:38
0:00:07

2Floor

2

6

B2

1

1

B3⇒F2
F2
F2⇒B2
B2
b2⇒B3

0:00:11
0:01:53
0:03:07
0:00:55
0:00:05

B1

1

11

B3⇒B1
B1
B1⇒B3

0:00:07
0:01:53
0:00:07

4Floor1

1

1Floor

1

1

B3⇒F4
F4
F4⇒F1
F1
F1⇒B3

0:00:49
0:00:51
0:00:09
0:00:51
0:00:09

9:50

10:20

10:40

14:55

B3⇒F15⇒F10⇒F7⇒B1⇒B3

B3⇒F2⇒B2⇒B3

B3⇒B1⇒B3

B3⇒F4⇒F1⇒B3

179

Floor Number

The second day of PEAMON monitoring result is depicted in Figure 8-8. The route
network of Staff1 started at 9:35 delivering goods to Basements 1, 2, and ends as
early as around 11:54 after completing to deliver the goods for Basement 4. For thecase of Staff2, his route network started at 9:45 and terminated at around 2:07 in theafternoon after having delivered the last goods to Floor 7. Although there were several
rising of both networks of the two (2) staffs as shown in the graph, it does not suggestthat all the trips were made to deliver goods. The staffs made several trips to otherfloors which did not match when verified with the logbook.

15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
8:00

Staf f 1

9:00

10:00

11:00

12:00

13:00

14:00

Staf f 2

15:00

16:00

Time

Figure 8-8. 2/20 PEAMON Monitoring result
Figure 8-7. 2/20 PEAMON monitoring result

Table 8-8. Staff1’s route
Time (Feb 20)

Route

9:35

B3⇒B1⇒B2⇒B3

9:55

B3⇒F1⇒B3

10:20

B3⇒F2⇒F1⇒B3

10:50

B3⇒B1⇒B3

11:10

B3⇒B4⇒B3

Floor Location No of office No of delivery items Destination
B3⇒B1
B1
1
24
B1
B1⇒B2
B2
1
1
B2
B2⇒B3
B3⇒F1
1Floor
2
18
F1
F1⇒B3
B3⇒F2
2Floor12
F2
F2⇒F1
1Floor
1
5
F1
F1⇒B3
B3⇒B1
B1
1
7
B1
B1⇒B3
B3⇒B4
B4
1
2
B4
B4⇒B3

180

Time
0:00:07
0:00:56
0:00:05
0:00:55
0:00:05
0:00:09
0:01:210:00:09
0:00:11
0:10:37
0:01:53
0:02:06
0:00:09
0:00:08
0:02:55
0:20:20
0:00:05
0:44:25
0:00:05

Table 8-9. Staff2’s route
Time (Feb 20)

Route

9:45

B3⇒F10⇒F8⇒F6⇒F4⇒B3

10:15

B3⇒F7⇒B3

10:30

B3⇒F4⇒B3

13:10

B3⇒B1⇒B3

14:05

B3⇒F7⇒B3

Floor Location No of office No of delivery items Destination
B3⇒F1010Floor1
1F10F10⇒F88Floor
14F8F8⇒F66Floor1
1F6F6⇒F44Floor
2
5
F4
F4⇒B3
B3⇒F7
7Floor
4
14
F7
F7⇒B3
B3⇒F4
4Floor
1
1
F4
F4⇒B3
B3⇒B1
B1
1
1
B1
B1⇒B3
B3⇒F7
7Floor
1
1
F7
F7⇒B3

Time
0:00:49
0:06:23
0:00:07
0:02:55
0:03:35
0:00:23
0:00:07
0:02:47
0:00:13
0:00:19
0:02:25
0:07:35
0:00:41
0:01:22
0:00:38
0:00:08
0:01:52
0:00:08
0:01:13
0:01:12
0:00:18

The two tables complementing the routes shown in Figure 8-8 are Table 8-8 andTable 8-9. Table 8-8 explains the route network of Staff1 on the second day of thesurvey while Table 8-9 details the route of Staff2. As can be observed from Table 8-8
there are three (3) recorded time appear to be long enough to warrant further
clarification. First is the 10 minute and 37 second time spent in delivering goods at the
10:20 trip. A look at PEAMON data, however, shows that staff spends this wholetime in Floor 2 without moving to other floors. This gives the impression that the staff
was made to wait for some time before the receiver was available. The second caseneeds to be clarify because of the unusual longer time spend in delivering goods is the
10:50 trip. Data recorded by PEAMON shows that the staff after having delivered thegoods for Basement 1, went to Floor 1 without carrying goods before finally returning
to docking area. The third case, which raises concern due to spending more time thanthought to be required, is the 11:10 trip. As shown in the PEAMON data, this staff
spend the whole 44 minutes and 25 second in Basement 4. There must be unusualinterruption which cause him to spend more time in facilitating this particular delivery.
Staff2’s first trip chain as reflected in Table 8-9 revealed that he delivered goods
continuously to Floor 10, Floor 8, Floor 6, and Floor 4. The longest delivery time wasobserved in the 10th floor, where one (1) piece of goods delivering took 6 min and 32
sec.

181

Floor Number

15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
-1
-2
-3
-4
8 :0 0

Sta f f 1

9 :0 01 0 :0 0

1 1 :0 0

1 2 :0 0

1 3 :0 0

1 4 :0 0

S ta f f 2

1 5 :0 0

1 6 :0 0Time

Figure 8-9. 2/21 PEAMON monitoring result
On the third day of experiment, PEAMON recorded data is illustrated in Figure 8-9.
Staff1 made his first delivery to Floor 5 at 9:15 while Staff2 had delivered nine (9)pieces of goods to Floor 1 ten (10) minutes earlier. Both staffs have their lastdeparture from the docking area at 1:10 sharing the freight elevator. Although thesame figure suggested that Staff2 still made one more departure at around 3 o’clock in
the afternoon, records in the logbook shows that this trip was not related intransporting goods.
Table 8-10. Staff1’s route
Time (Feb 21)

Route

9:15

B3⇒F5⇒B3

10:00

B3⇒F2⇒F1⇒F2⇒F1⇒B3

10:40

B3⇒B1⇒B3

13:10

B3⇒F3⇒B3

Floor Location No of office No of delivery items Destination Time
0:00:30B3⇒F55Floor1
10
F5
0:03:40F5⇒B3
0:00:50
0:00:11B3⇒F22Floor
1
4
F2
0:00:25
0:00:05F2⇒F1
1Floor1
2
F1
0:02:10F1⇒F2
0:00:05
2Floor
1
4
F2
0:03:55
0:00:05F2⇒F1
1Floor1
6
F1
0:05:06
F1⇒B3
0:00:09
0:00:08
F3⇒B1
B1
1
2
B1
0:01:22B1⇒B3
0:00:08
0:00:12B3⇒F33Floor1
1
F3
0:00:48F3⇒B3
0:00:12The details of each trip chain by the two staffs graphed in Figure 8-9 are shown in
Table 8-10 and Table 8-11. Staff1 actually visited and made delivery to seven (7)floors of which he spent the longest stay at Floor 6. On the other hand, Staff2 madedelivery to eleven (11) floors visited 15 offices scattered to the floors building.

182

Table 8-11. Staff2’s route
Time (Feb 21)

Route

9:10

B3⇒F1⇒B3

9:45

B3⇒F7⇒F6⇒F4⇒B3

10:15

B3⇒F15⇒F10⇒F8⇒F7⇒F6
⇒B3

13:10

B3⇒B1⇒B3

8.6.5

Floor Location No of office No of delivery items Destination
B3⇒F1
1Floor
1
9
F1
F1⇒B3
B3⇒F７
7Floor
4
17
F7
F7⇒F6
6Floor1
2F6F6⇒F44Floor
2
6
F4
F4⇒B3
1
1
B3⇒F15
15Floor1
1
F15F15⇒F10
10Floor1
3F10F10⇒F88Floor1
1F8F8⇒F77Floor1
1F7F7⇒F66Floor
1
1
F6
F6⇒B3
B3⇒B1
B1
1
11
B1
B1⇒B3

Time
0:00:09
0:00:51
0:00:09
0:00:18
0:00:25
0:00:05
0:00:25
0:11:05
0:00:25
0:08:05
0:00:55
0:00:52
0:00:38
0:09:22
0:00:08
0:01:25
0:00:05
0:01:55
0:05:35
0:00:43
0:00:17
0:00:08
0:02:25
0:00:35

Survey summary

The survey reveals several insights that can be useful in understanding the complexityof goods offloading chain. The arrived data averages and properties of elevator are
shown in Table 8-12. Opening and closing time of elevator is 3.5 sec and 4.9 sec
respectively. Furthermore, the elevator time for movement from one floor to next
immediate floor (either up or down) is 5 sec. However, when the desired location ishigher than that – say from first floor going to 5th floor – the elevator time from firstfloor to second floor is 5 seconds, while from second floor to 3rd floor is 2 seconds(Table 8-12).
The processing time for 13 goods (average per trip) in the docking are (B3) is 8
minutes. This time refers to the moment the two staffs work on the goods from thevehicle, sort out, divide the amount and identify each other’s route up to theirdeparture by the elevator. For instance, if the delivery is to be made at Floor 15, thecomputed time will look like this: 8 minutes (processing time) plus 30 sec (elevatortime) plus 8.4 sec (opening and closing time of elevator) plus 37 sec (average delivery
time on that floor). This case assumes that the elevator is uninterrupted to the otherfloor and the first stop is the 15 floor.

183

Table 8-12. Averages observed from the survey
Floor
F15
F14
F13
F12
F11
F10
F9
F8
F7
F6
F5
F4
F3
F2
F1
B1
B2
B3
B4

Elevator time
30.0
28.5
27.0
25.5
24.0
22.5
21.0
19.5
18.0
16.5
15.0
13.5
12.0
10.5
9.0
7.0
5.0
0.0
5.0

Ave time
0:00:37

Ave no of office
1.0

Ave no of goods
1.0

No of trip
2

0:02:52

1.0

1.0

1

0:06:09

1.0

2.3

4

0:02:10
0:02:27
0:00:30
0:03:43
0:01:28
0:00:36
0:04:00
0:01:50
0:02:03
0:00:55
DOCKING AREA
0:44:25

1.0
2.2
1.0
1.0
1.4
1.0
1.2
1.3
1.0
1.0

2.5
7.7
1.3
11.0
3.0
5.0
4.3
7.0
9.6
1.0

2
6
3
2
5
2
6
8
8
2

1.0

2.0

1

The lack of available data concerning the sizes and weights of goods and the distanceof offices from elevator make it difficult to explain the real cause on why somedelivery time takes longer than the others do. Although logic might state that theamount of goods delivered and number of offices visited is directly proportional to theamount of time spent for delivery, it is also important to consider the distance ofoffices from the elevator. For instance, it would be difficult to explain why theaverage delivery time to Floor 5 is shorter than say Floor 10 given that Floor 5 hasmore number of goods to be delivered. One possible explanation is perhaps the officevisited in Floor 5 is closer to elevator than that of Floor 10.
The data of Basement 4 is considered irregular and was taken out during thecomputation for average delivery time to each floor which used as input data forsimulation. There was a strong indication that there were interferences during thedelivery to B4 and the time recorded did not reflect the actual delivery time.

8.7

Simulation modeling

Simulation approach is becoming more popular than any other analytical tools due toits increasing reliability and user-friendliness. One advantage of this approach is its
ability in illustrating in graphical form the condition under observation as well as its
effectiveness for quantifying benefits and limitations of the different possible
alternatives. This presentation in graphical form enables to identify elements of thesystem that requires attention due to the likely occurrence of instabilities – forinstance is inability of staff members to hold incoming goods.
The identified instabilities can then be treated by manipulating the input data or
increasing the capacity of some elements of the simulation – e.g. parking space,elevator.

184

8.7.1

Process of building simulation model

Shown in Figure 8-10 is the process of building a simulation model in ProModel. This
simulation software has five important elements to describe either existing or planned
facility – location, entity, routes, processing rule, and arrival. Locations represent
places where raw materials arrived or process. Raw material is anything that moves –for this study, the entity is goods. After having the two elements, the next step is todevelop the routes used by entity at the same time connects one location to another.
Processing rule is set to define what the modelers want the entity to do while at alocation. Processing time and other ready commands can be issued in processing rule.
The last element is the setting of arrival time and interarrival of raw materials. Several
arrival distributions are available in the software. If the arrival or interarrival time
does not follow a pattern, StatFit statistical software included in the ProModel can beused to identify appropriate distribution.
8.7.2

Description of ProModel

The simulation models built were done using ProModel Version 4.1, a commerciallyavailable discrete event simulation package used for simulating logistical activities.
The entire process involved in transporting the goods is modeled as a manufacturing
network where each floor of the building, (off) loading area, stock point is considered
as location (Figure 8-10). The goods coming in to the building is assigned as entity
and the two staff members and the trucks considered as resources. Elevator was not
represented through resources due to perceived difficulty of handling it in the
EXISTING / PLANNED FACILITY

BUILDING THE MODEL
Define locations, raw materials
Develop routing, processing rule
Identify parts and materials arrival
RUN SIMULATIONREVIEW OUTPUT

WHAT IF . .

•We add locations and resources•We reduce batch size
•We change arrival of raw material•We change the selection routines
IMPLEMENT CHANGESRESULTS•Reduced WIP (work-in-process)•Increase throughput•Reduced operating expenses
Figure 8-10. Process of building simulation model

185

software since a resource (elevator for instance) cannot transport another resource(staff members). However, the network on which the staff travels models the samefunction as the elevator.
For modeling convenience, certain assumptions were set:



8.7.3

No distinction is made between types, sizes, and weights of cargo.
From the floor where the delivery is to be made, it took 2 minutes and 24
seconds for staffs to return to the elevator disregarding the number of offices
and goods (this is obtained by getting the average of all floors’ averages
monitored by PEAMON).
Data fitting

Since the surveyed data do not follow any particular pattern, for instance every 15
minutes, it is not possible to input a fix interarrival. To mitigate this difficulty, theperceived appropriate way to determine the best distribution that could represent theinterarrival is the use of StatFit. The collected data for arrival times of trucks in threedays were worked in Excel spreadsheet and plugged into the statistical software. Afterrunning the software, however, only the second day-data generated distributions fit
and the two other arrival data were rejected due to insufficient of data point.
The suggested distributions to represent the interarrival times are shown in Table 8-13.
Several distributions fitted the data which could be applied to interarrival of trucks
trips. Normally, chi-square test is applied to see if the data came from a specificdistribution. However, lack of adequate number of data points prevented fromperforming this test. Two other useful tests are Kolmogorov-Smirnov test (K-S test)
and Anderson-Darling test (A-D test). Like the chi-square test, these tests are used todecide if the sample comes from a specific distribution. The A-D test is a
modification of K-S test and gives more weight to the tails than does the K-S test.
Table 8-13. Distributions Fit
Distribution
Inverse Gaussian (-3.35, 23.6, 40.1)
Lognormal (-0.483, 2.93, 1.3)
Pearson 5 (-7.7, 1.75, 39.4)
Weibull (1, 0.682, 28.8)
Exponential (1, 35.7)
Erlang (1, 1, 35.7)
Gamma (1, 1, 35.7)
Pearson 6 (1, 102, 1.27, 4.23)
Log-Logistic (1, 1.51, 23.4)
Beta (1, 4.95e+6, 0.984, 1.23e+05)
Pareto (1, 0.35)
Triangular (0.951, 177, 0.986)
Uniform (1, 158)

Rank
100
99.9
99.4
98
96.2
96.2
96.2
91.9
87
73.4
7.37
0.898
0.0027

AcceptanceacceptacceptacceptacceptacceptacceptacceptacceptacceptacceptacceptacceptrejectK-S test
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not rejectNo fitDo not reject
rejectA-D test
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not reject
Do not rejectNo fitrejectrejectResult of K-S test, at .05 significance level, shows that only the Uniform distribution
was rejected. On the other hand, the A-D test aside from rejecting the Uniform
distribution also rejected the Triangular distribution. Both tests did not give value tothe Pareto distribution. The lack of adequate data is seen as the main reason for thevague results of the tests.
186

Figure 8-11. Fitted arrival distribution
Statistically, any of the remaining ten distributions could be applied for time intervals
between trips. However, it is important to determine which distribution could provide
an output that would reflect the real behavior of the system. Aside from applying all
the accepted distribution to the simulation, another method is by plotting the input
data against the selected distribution as reflected in Figure 8-11. As shown in the
figure, the shape of the data is the same as the plot of exponential distribution.
8.7.4

Computation of working time

Equation 1 shows the computation process of staff’s working time (utilization
percentage of the staff). This is defined as the percentage of time the resource spent
traveling or to be used, transporting or processing an entity, or servicing a location orother resource. Utilization percentage shows the total time of the staff members
involve in processing the goods. This value is the reflection of real time the staff
members involved in activities related to the delivery of goods excluding idle timeand rest. The three (3) variables composing the equation are defined below:

Working time =Total Travel to Use Time + Total Time In Usage x 100
Total Scheduled Time(1)

Total Travel to Use Time (TTUT) is the time where the resource (staff members)
spent traveling to a location (floors, docking are, etc) to transport or process an entity(goods), or to service a location or other resource. It does not include any pick up time,
but does include any blocked time.
Total Time in Usage (TTU) is the total time the resource spent transporting orprocessing and entity (goods), or servicing a location or other resource.
Total Schedule Time is the total number of hours scheduled or made available for the
resources (staff members).
The 100 multiplier in the equation is a decimal conversion multiplier to make thepercentage figure a whole number rather than a pure decimal value.
187

Delivering goods to
the recipient

TTU 2

TTUT 3

TTUT 1
Unloading/sorting

TTU 1

TTUT 2
Waiting time forthe elevator

Figure 8-12. Diagram of different time calculated to attain working time
As explained, working time of the staff comes from their different activities related totransport the goods across the building. As can be seen in Figure 8-12, TTUT is thesum of time spent by the staff in unloading and sorting the goods, while waiting forthe elevator, and delivery time to the recipient. On the other hand, TTU can be
described as combined travel time of the staff from the docking area to the elevatorand from the origin of the elevator to the final floor where the delivery is to be made(Figure 8-12).
8.7.5

Properties of the simulation model

The properties of the 15-story building were replicated for the simulation to analyze
the impact of frequency of trips and number of goods to the capacity of the two staffs.
Ocular inspections to the building show that the total time for the elevator to close andopen is 8.4 seconds while its speed is 2.5 seconds from one floor to another (Table 814).
The total time in a day of the staff in delivering all the incoming goods were
processed to arrive 36.92 seconds which is the average time a staff spent to unload apiece of goods from the truck up to the elevator. Obviously, the configuration of thedata is worked to fit it to the simulation. With regard to the amount of time spend by
the staff to deliver goods to each floor, the average time arrived is 2.24 minutes foreach piece of goods. If the staff were delivering two parcels, the delivery time to thefloor alone where the shop is located would be 4.48 minutes.

188

Table 8-14. Input data of the simulation
Buildings

No ofelevator

Speed from one
floor to next

Opening andclosing

Unloading / goods
up to elevator

Goods perfloor trip

15-story
20-story
30-story
40-story

222
2

2.5 sec
2.5 sec
2.5 sec
2.5 sec

8.4 sec
8.4 sec
8.4 sec
8.4 sec

36.92 sec
36.92 sec
36.92 sec
36.92 sec

2.24 min2.24 min2.24 min2.24 minThe simulation tried to assess the impact of two variables to the capacity of the staff –first variable considered was the frequency of trucks and the second variable was thenumber of goods loaded on each arriving truck. The obvious reason is to find abalance between the variables and the staff’s capacity to realize efficient delivery of
goods.
The survey found that every arriving truck has an average load of 13 goods and theaverage trips to the building is eight. This data was used as the base case for thesimulation for the following reasons:
1) To validate the accuracy of the simulation in replicating the real operation of
goods delivery2) To distinguish the working time and rest time of the staff given the amount ofgoods arriving
3) To estimate the number of goods that the staff able to deliver in a day4) To determine the ‘right circumstances’ to increase the number of staff
There are three time interval and three differrent number of goods per trip as shown in
Table 8-15. The 13 goods which is the base case was taken from the average numberof goods carried by delivery vehicles from the survey. The number of arriving goods
was then doubled and tripled since it is difficult to predict the exact number of goods
that would arise in the future. In this case, a series of choices are available which
could be used as reference.
Table 8-15. Variations of number of goods and time interval between trips
No of goods per
arrival

13

26

39

Time between
arrivals
exponential
15 min30 min60 minexponential
15 min30 min60 minexponential
15 min30 min60 minTotal no of
goods arriving

Scheduled time
(hr)

32
16
8

416
208
104

8
8
8

32
16
8

862
416
208

8
8
8

32
16
8

1248
624
312

8
8
8

No of trips

Regardless of which interval and goods number is used as input data, the simulation isscheduled to run for 8 hours. This system assumes that all the staff members startworking at 8 in the morning and end at 5 in the afternoon for 8 hours service. Taking

189into account the variations in number of arriving goods and time between arrivals,
there are twenty seven (27) simulation cases.
8.7.6

Properties of the buildings

As discussed in Chapter 4, the district is currently experiencing renewal of its
buildings to create a CBD that caters business, cultural and commercial functions.
Data obtained suggest that at present, the share of shops and restaurant is merely 3
percent while 97 percent is devoted to office space. Effort to raise the share ofcommercial establishments would also increase the number of goods and truck’s trips
into the district.
In order to make the area of simulation analysis broad and general, it is important toproduce different models that depict different buildings with different heights topredict to what extent the buildings in the district will be extended in height. Further,
the output is aim to serve as decision support not only for Marunouchi district but also
to other central business districts in Japan and other countries.
With that aim, the first simulation studied a 15-story building which was taken as abase case. Since this is the building which has real data recorded through survey, thesimulation’s result can be compared for validation. After the validation, the building’s
floor is increased to 20, then 30, and 40.
8.7.7

Results(i)

Case of the 15-story building

The number inside the boxes in Figure 8-13 reflects the amount of goods left at thedocking area after the eight-hour run of the simulation. These numbers imply theinability of the two staffs to handle the quantity of goods delivered in the building in a
day.
100
648

90
0

80
Working time (%)

233
39

70
0

60

0

50

0
0

40
0
30
15 min20

30 min60 min

10
0
0

5

10

15

20

25

Num ber of goods

Figure 8-13. Case of a 15-story building

190

30

35

40

45

A careful look at the same figure would reveal that with every arriving truck has aload of 13 goods the two staff managed to deliver all the goods to the recipients
within the building. In addition, the staffs’ working time are placed well below the
100% mark which could be interpreted that they are getting a break in between trips.
A 100% working time means that the staffs have spent the 480-minute working hour.
The highest working time number of the staffs is 74% while the lowest is 29%. The
74% working time is equal to 354-minute. This suggests that the staffs have almost afour-minute break between trips (126 minutes divided by 32 trips is equal to 3.9
minutes). With regard to the 29% working time, this implies that the two staff is underutilized with a rest between trips of 43 minutes.
As the load of arriving trucks increases to 26, the 15-minute interval is too short thatthe staffs were not able to deliver 233 goods. Increasing further the amount of goods
to each arriving truck would further limit the ability of the two staffs as shown by theincreasing number left at the docking area after the 8-hour run of the simulation.
(ii) Case of the 20-story building
The one-hour interval between trips is enough time for the two staffs to deliver all the
goods as shown in Figure 8-14. With every arriving truck’s load of 39, the average
working time of staff stands at 78% implying that the staffs could afford to have restof 13 minutes between trips (106 minutes divided by 8 trips is equal to 13.25 minutes).
This large amount of time suggests that the volume of goods could still be increased
from it present number of 39.
110
08

90

60
0

80
Working time (%)

683

242

100

0

70
0

60

0

50
0

40

15 min30

30 min60 min

20
10
0
0

5

10

15

20

25

30

35

40

45

Number of goods

Figure 8-14. Case of a 20-story building
When the interval between trips was set to 30-minutes, the two staffs can still manage
to deliver goods as much as 26 but have shown limitation at 39. Reading the working
time rate of the staff at 26 goods per arrival stick to 89 % that has an equivalent ofworking for 429 minutes out of 480-minutes scheduled working hours. This ratio
would allow the staffs to have rest of roughly 3 minutes between trips (51 minutes
divided by 16 trips is equal to 3.18 minutes). On the other hand, it is notable in the191same figure that under the 15-minute time interval between trips, the staffs could notdeliver all the goods carried by trucks.
(iv) Case of 30-story building
As the floor of the building reaches 30, the two staffs have hard time to deliver the
coming goods especially when the interval between trips is 15-minutes (Figure 8-15).
At 15-minute interval, the staffs working hour is notably high that almost reach the
maximum working time which is 100%. Increasing the length of time interval
between trips from 15-minutes to 30-minutes ensures that the staffs were able to sendout all the goods to the recipients provided that the load of every coming truck islesser than 26. As shown in the same figure, at 26 goods per arriving truck, there wereat least 47 goods left at the docking area after the 8-hour run of the simulation. While
the staffs have difficulty of shipping out all the goods under the 15-minute and 30minute interval between trips, this is not the case when the length of trips intervalreached 60-minutes. Even if the load of every incoming truck reached 39, all the
goods are shipped out before the end of the day’s working hour. Under this condition,
the staffs working time stands at 84% implying that they have spend 403 minutes outof 480-minutes working time. It can be said that they have an almost 10-minute rest
between trips assuming that their delivery time of every trip is equal (77 minutes
divided by 8 trips is equal to 9.6 minutes).

110
11

100
90

454

883

47

210

Working time (%)

80

0

70

0

60

0

50
0

40

15 min30

30 min60 min

20
10
0
0

5

10

15

20

25

30

35

40

45

Number of goods

Figure 8-15. Case of a 30-story building(v)Case of 40-story building
The working time of two staffs reached 100% on several occasions as shown in
Figure 8-16 indicating that their whole attention is dedicated to the delivery of goods.
The 60-minute interval between trips is sufficient time for the staffs to fulfill thedelivery with 91% being the highest working time rate which was attained when
every arriving truck has a load of 39 goods.
192

110

557

64

100

335
115

90

0

80
Working time (%)

973

0

0

70
0

60
50
40

15 min30

30 min60 min

20
10
0
0

5

10

15

20

25

30

35

40

45

Number of goods

Figure 8-16. Case of a 40-story building
8.7.8

Model validation

In order to check if the simulation presented before was able to replicate the real
operation inside the building, a simulation count to a 15-story building and therecorded data from the survey is compared as shown in Table 8-16. Trips refers to thenumber of times the staffs visit a floor to deliver freight. The number of goods
delivered by the staffs, which generates 52 trips to both counts, is 103.
Table 8-16. Comparison of surveyed data and Simulation
count
Floor
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
B1
B2
B3
B4
Total

No of trips
Surveyed data
Simulation count
2
2
0
4
0
3
1
2
0
2
4
4
0
3
2
3
6
1
3
3
2
3
5
4
2
4
6
1
8
2
8
4
2
2
0
3
1
2
52
52

193

Another useful test to validate the accuracy of the simulation model is by comparing
the real time recorded during the survey and the simulation-generated time. As
reflected in Table 8-17, the recorded time to deliver 103 goods across the 15-floor
building is 16.13 minutes while the simulation time is 17.41. The difference therefore
is merely 6%.
Table 8-17. Comparison of surveyed time and simulation-generated time
Variables

Delivery time for 1 truck trip

Time (8 hours)

No of goods

Surveyed data
Simulation result

16.12 min17.41 min128.96 min
139.28 min

103
103

8.8

Discussion of results

Adopting cooperative delivery system requires advance knowledge not only to thenumber of companies intended to join, number of goods covered but also to the small
components of the system such as correct delivery schedule and right number of staff.
Employing excessive number of staff is detrimental to all parties concern such as theshippers and carriers since this will increase the cost involved. On one hand, assigningstaff that could not fulfill the delivery the way the system was designed would weaken
its merit and may miss its objectives.
Table 8-18. Perceived suitable number of goods per truck based on the capabilityof the two staffs
Building

No. of goods per trip
(1- hour interval)

No. of goods per trip
(30-min interval)

No. of goods per trip
(15-min interval)

15-story

39 - 45

26 - 30

13 -2020-story

39 - 45

26 - 30

Less than 13

30-story
40-story

26 - 35
39

Less than 26
Less than 26

Less than 13
Too short interval

The series of simulation models built could support in resolving the above dilemma(Table 8-18). The capability of the two staff with regard to the delivery of goods wasdetermined in relation to the amount of goods, time interval between trips and theheight of the building. The results of the two tests affirmed the reliability of themodels8.9Comparison of time saved from Vertical CDS, Horizontal CDS and the
combination of Horizontal and Vertical CDS

In order to evaluate which CDS design is to be applied as a management measure for
freight transport in the CBD, a new simulation analysis is conducted to the
Marunouchi district. At first, all the 15-buildings were assumed to have the same
number of story which is 15. The number of story is then increased to 20, 30, and 40.
The simulation and analysis process is the same of that in Chapter 7 where average
waiting time of trucks is compared between with and without CDS schemes. Further,
the same simulation software, Simul8, is used. The idea here is to determine which

194

CDS design is most suitable depending on the average height of the buildings in the
CBD.
In this section of the study, four cases is compared; 1) without CDS, 2) Vertical CDS,
3) Horizontal CD, 3) combination of Vertical and Horizontal CDS. Definition of each
CDS is available in Chapter 4.
8.9.1

Data requirements

The Marunouchi building (Building number 10 in Chapter 7) was taken as model toestimate the likely GLA of the building area when it reaches 40-story. At present, nobuilding exceeded 40-story. Marunouchi building being the tallest has 37-story. With
that consideration, the likely configuration of the building in the area is shown in
Table 8-19. As mentioned, the area is more of an office-oriented CBD where around
97% of building space is dedicated for office use.
Table 8-19. Properties of the buildings
Building story

GLA(sq. m.)Parking
space

Freight
elevator

15 story

42,162

420 story

56,21630 story
40 story

Space usage

Trip attraction per 1,000 sq. m.
Shops and
OfficeRestaurants

OfficeShops andRestaurants

2

97

3

2.6

14.1

5

3

97

3

2.6

14.1

84,324

6

4

97

3

2.6

14.1

112,432

8

5

97

3

2.6

14.1

Note: Trip attraction is based on the survey in Marunouchi district.

The estimated number of trips based on the trip attraction in Table 8-19 is available in
Table 8-20. The number shown is total delivery trips but since each truck would maketwo deliveries, the total number of trucks entering the district is therefore half of thetotal trips. For instance, the total number of trucks that would enter the CBD when all
the buildings are 15-story is 900 trucks.
Without CDS scheme and Vertical CDS scheme have the same pattern of truck trips.
As mentioned in Chapter 4, the only difference between them is that under the
Vertical CDS, there are staff inside the building that would help the driver unloads the
goods. In essence, there is no depot and goods consolidation under the Vertical CDS.
Table 8-20. Number of trips without CDS and under Vertical CDS
Number of trips
Building story
15 story
20 story
30 story
40 story

8:00 - 9:30
29
38
58
77

9:30 - 12:00
42
56
84
112

12:00 - 17:00
49
65
98
131

Total
120
160
240
320

Total trips
to 15-bldgs
1800
2400
3600
4800

Note: Average goods per delivery to one building is 4. Survey results show that every truck had madetwo stops for each delivery, the total number of goods per truck therefore is 8.

Horizontal CDS scheme and the combination of Horizontal and Vertical CDS scheme
have the same pattern of truck trips (Table 8-21). The only component that separates
them is the availability of staff inside the building for the combination of Horizontal
and Vertical CDS. Horizontal CDS relies to the ability of the driver to unload all the195consolidated goods. Further, there is a depot where goods are consolidated underthese two schemes as discussed in Chapter 4 and Chapter 7.
Table 8-21. Number of trips and goods under Horizontal CDS and combinationof Horizontal and Vertical CDS
8:00 9:30

Number of trips
9:30 12:00 12:00
17:00

15 story

6 (19)

5 (34)

5 (39)

20 story

6 (26)

5 (45)

30 story

6 (38)

40 story

6 (51)

Building
story

Total

8:00 9:30

Number of goods
9:30 12:00 12:00
17:00

Total

16

115

168

196

479

5 (52)

16

153

224

262

639

5 (67)

5 (79)

16

230

336

393

959

5 (90)

6 (87)

18

307

448

524

1279

Note: Figures in the parenthesis are number of goods per delivery to one building. Since CDS truck
would make two stops for each delivery, the total number of goods per truck is therefore double of that
number.

8.9.1

Simulation Results

The average waiting time of trucks is considerably low when the building has 15story to all the schemes. Even without any form of CDS, the average waiting time ismerely 2.27 minutes. There was also a small waiting time under the Vertical CDS at
0.7 minute. This can be attributed to the high number of trucks in the morning andlimited number of space where trucks could park while waiting for the assistance ofthe staff inside the building.
100
96.62
90

Without CDS

H + V CDS

V ertical CDS

Horizontal CDS

Average waiting time per truck (min)

80
70
60
50
40
32.53

30
16.46

20

15.3
10
0

2.27
0.7
0
0.0
15-story

6.8
5.6
2.2
0.0
0
0
20-story
30-story
Building he ight

7.5
040-story

Figure 8-17. Average waiting time of trucks based on which CDS is adopted

196

It can be observed that there is no waiting time of trucks even after the building
reaches 40-story under the combination of Horizontal and Vertical CDS while there isa steady increase of waiting time under the Horizontal CDS scheme. This result
underscores the necessity to assign CDS staff inside the high rise buildings. The
performance of Vertical CDS based on truck’s waiting time is getting better ascompared with the Horizontal CDS as the building’s height increases. The reason isthat as the building gets tall, it takes some time for the driver to deliver the
consolidated goods and therefore the truck has to occupy the parking space longer.
There were events that other CDS trucks have to wait until the parking space is
emptied by another CDS which causes delay for the delivery.
Table 8-22. Comparison of Without CDS scheme and combination of Horizontal
and Vertical CDS schemea. 15-story
Variables

Computation process

Without
CDS

Delivery time

898 trucks x 5.34 min

4,796

Unloading time7185 goods x 0.33 min

2,371

Waiting time for parking

1796 trucks X 2.27 min

4,077

Labor time at SP

No SP

Vertical Delivery time7185 goods x 1 min

Total

7,185

Computation
process

H+V
CDS

Difference

120 trucks x 5.34 min

641

4155

7185 goods x 0.33

2,371

0

240 trucks X 0 min
1 min/goods X 7192
goods
45 staff x 421 min

-

4077

7,192

-7192

18,945-1176029,148-1071918,430Note: 5.34 minutes of delivery time are derived from Chapter 7 while 0.33 minute and 1 minute (forlabor time at SP and vertical delivery) are derived from survey data that was discussed in the same
chapter. The number of staff is equal to total number of freight elevator plus one additional staff while
waiting time of trucks and staff’s working time (vertical delivery) are derived from simulation result.

b. 20-story
Variables

Computation process

Delivery time
Unloading time
Waiting time for parking

1198 trucks x 5.34 min
9585 goods x 0.33
2396 trucks X 16.46 min

Labor time at SP

No SP

Vertical Delivery timeTotal9585 goods x 1 min

Without
CDS
6,398
3,163
39,442

9,585
58,588Computation process

120 trucks x 5.34 min
9585 goods x 0.33
240 trucks X 0 min
1 min/goods X 9589
goods
60 staff x 437 min

H+V
CDS
641
3,163
-

Difference

5757
0
39442

9,589

-9589

26,22039,613-1663518976

c. 30-story
1798 trucks x 5.34 min

Without
CDS
9,602

120 trucks x 5.34 min

H+V
CDS
641

14385 goods x 0.33

4,747

14385 goods x 0.33

4,747

0

116,986

240 trucks X 0 min
1 min/goods X 14383
goods
75 staff x 437 min-

116986

14,383-1438332,775-1839052,546

93174

Variables

Computation process

Delivery time
Unloading time
Waiting time for parking

3596 trucks X 32.53 min

Labor time at SP

No SP

Vertical Delivery time

14385 goods x 1 min

Total

14,385
145,720

197

Computation process

Difference8961

d. 40-story
Variables

Computation process

Delivery time
Unloading time
Waiting time for parking

2398 trucks x 5.34 min
19185 goods x 0.33
4796 trucks X 96.62 min

Labor time at SP

No SP

Vertical Delivery timeTotal

19185 goods x 1 min

Without
CDS
12,806
6,331
463,414

19,185
501,736

Computation process

135 trucks x 5.34 min
19185 goods x 0.33
270 trucks X 0 min
1 min/goods X 9589
goods
90 staff x 443 min

H+V
CDS
721
6,331
-

Difference

12085
0463414

19,178-1917839,87066,100-20685435636

Table 8-23. Comparison of Without CDS scheme and Vertical CDS schemea. 15-story
Variables

Computation process

Delivery time

898 trucks x 5.34 min

Without
CDS
4,796

Unloading time7185 goods x 0.33 min

2,371

Waiting time for parking
Labor time at SP
Vertical Delivery time
Total

1796 trucks X 2.27 min
No SP
7185 goods x 1 min

4,077
7,185
18,430Computation
process
898 trucks x 5.34 min
7185 goods x 0.33
min1796 trucks X 0.7 min
No SP
45 staff x 432 min

Vertical
CDS
4,796

Difference

2,371

0

1,257.38
017,28025,7042820
0-10095-7,275Computation
process
1198 trucks x 5.34
min9585 goods x 0.33
2396 trucks X 2.2
min
No SP
60 staff x 475 min

Vertical
CDS

Difference

6,397

1

3,163

0

5,271.75

34171

028,50043,3320-18915
15,256

Computation
process
1798 trucks x 5.34
min14385 goods x 0.33
1798 trucks X 5.6
min
No SP
75 staff x 470 min

Vertical
CDS

Difference

Computation
process
2398 trucks x 5.34 min
19185 goods x 0.33
2398 trucks X 7.5 min
No SP
90 staff x 470 min

VerticalCDS
26,4086,527.80
35,971.88
042,300111,208b. 20-story
Variables

Computation process

Without
CDS

Delivery time

1198 trucks x 5.34 min

6,398

Unloading time9585 goods x 0.33

3,163

Waiting time for parking

2396 trucks X 16.46 min

39,442

Labor time at SP
Vertical Delivery time
Total

No SP
9585 goods x 1 min

9,585
58,588c. 30-story
Variables

Computation process

Without
CDS

Delivery time1798 trucks x 5.34 min

9,602

Unloading time

14385 goods x 0.33

4,747

Waiting time for parking

3596 trucks X 32.53 min

116,986

Labor time at SP
Vertical Delivery time
Total

No SP
14385 goods x 1 min

14,385
145,720

9,602

0

4,747

0

20,139.00

10642

035,25069,7380075,982d. 40-story
Variables

Computation process

Delivery time
Unloading time
Waiting time for parking
Labor time at SP
Vertical Delivery timeTotal

2398 trucks x 5.34 min
19185 goods x 0.33
4796 trucks X 21 min
No SP
19185 goods x 1 min

Without
CDS
12,806
6,331
463,414
19,185
501,736

198

Difference

0
0
10642
0
0
390,528

Table 8-24. Comparison of Without CDS scheme and Horizontal CDS schemea. 15-story
Variables

Computation process

Delivery time
Unloading time
Waiting time for parking

898 trucks x 5.34 min
7185 goods x 0.33 min
1796 trucks X 2.27 min

Labor time at SP

No SP

Vertical Delivery timeTotal7185 goods x 1 min

Without
CDS
4,796
2,371
4,077

Computation
process
120 trucks x 5.34 min
7185 goods x 0.33
240 trucks X 0 min
1 min/goods X 7192
goods
7185 goods x 1 min

7,185
18,430

Horizontal
CDS
641
2,371
-

Difference

4155
0
4077

7,192

-7192

7,185
17,38801041

b. 20-story
Variables

Computation process

Delivery time

1198 trucks x 5.34 min

Unloading time9585 goods x 0.33

Waiting time for parking

2396 trucks X 16.46
min

Labor time at SP

No SP

Vertical Delivery timeTotal9585 goods x 1 min

Without
CDS

6,398
3,163

Computation
process
120 trucks x 5.34
min

Horizontal
CDS

Difference

641

5757

9585 goods x 0.33

3,163

0

-

39442

9,589

-9589

9,585
22,978035611

Horizontal
CDS

Difference

641

8961

4,747

0

1,632

115354

14,383-14383

14,385035,788109932

Horizontal
CDS

Difference

721

12085

6,331

0

4,131

459283

19,178-19178240 trucks X 6.8 min

39,442

1 min/goods X 9589
goods
9585 goods x 1 min

9,585
58,588c. 30-story
Without
CDS

Variables

Computation process

Delivery time1798 trucks x 5.34 min

9,602

Unloading time

14385 goods x 0.33

4,747

Waiting time for parking

3596 trucks X 32.53 min

Labor time at SP

No SP

Vertical Delivery time

14385 goods x 1 min

Total

116,986

14,385Computation
process
120 trucks x 5.34
min14385 goods x
0.33
240 trucks X 6.8min
1 min/goods X
14383g oods
14385 goods x 1
min145,720d. 40-story
Variables

Computation process

Without
CDS

Delivery time2398 trucks x 5.34 min

12,806

Unloading time

19185 goods x 0.33

6,331

Waiting time for parking

4796 trucks X 96.62 min

Labor time at SP

No SP

Vertical Delivery time

19185 goods x 1 min

Total

463,414
19,185
501,736

Computation
process
135 trucks x 5.34
min
19185 goods x
0.33
270 trucks X 15.3min
1 min/goods X
9589 goods
19185 goods x 1
min19,185049,546452190In this section of the study, the term “distribution time” will be used to describe time
derived from delivery, unloading, waiting time for parking, labor time at stock point(SP), and vertical delivery (Table 8-23). The summary of distribution time from Table
8-22, Table 8-23, and Table 8-24 is shown in Table 8-25. For a district which has anaverage height of 15-story buildings, Horizontal CDS has the lowest delivery time199

followed by without CDS scheme. The reason that the combination of Horizontal andVertical CDS have high distribution time is because of the substantial time by staff
inside the building. This is a large time that can not be easily offset through reductionof delivery time and waiting time.
For a CBD with an average of 20-story buildings, Horizontal CDS still has the lowest
delivery time. This is followed by the combination of Horizontal and Vertical CDS
and Vertical CDS comes third. One notable observation is that all the CDS design
have lower distribution time as compared to the present scheme (without CDS).
For a high density CBD with an average building height of 30-story, still the
Horizontal CDS produces the smallest distribution time. Although it has the lowest
distribution, it should also be noted that it has the highest waiting time of trucks
among the 3 CDS schemes at 6.8 minutes (Figure 8-17). The order of distribution
time remain the same even after the height of the buildings in the CBD is increased to
40-story – Horizontal CDS being the scheme having the lowest distribution time.
Waiting time of this CDS design however increases to 15.3 while Vertical CDS
increases to 7.5 minutes (Figure 8-17). The combination of Horizontal and Vertical
CDS did not recorded any waiting time from different CBD densities.
Table 8-25. Total delivery time from each scheme
Building
story

Without CDSTOTAL TIME (min)H + V CDS
Horizontal CDS

Vertical CDS

15-story(2) 18,430(4) 29,148(1) 17,388(3) 25,704

20-story(4) 58,588(2) 39,613(1) 22,978(3) 43,332

30-story(4) 145,720(2) 52,546(1) 35,788(3) 69,738
40-story(4) 501,736(2) 66,100(1) 49,546(3) 111,208
Note: Figure in the parenthesis is rank of each scheme, 1 being the scheme having the lowest
distribution time

8.9.2

Selecting which of CDS design is adopted based on CBD’s building
density and building use

From the social point of view, i.e. reduction of environmental emissions and othernegative impact of trucks, any CDS scheme is desirable. Reduced number of trucks
would mean few sources of emissions, vibration, accident and other negative
externalities born out of truck’s presence. There is no change to the number of trucks
under Vertical CDS since there is no consolidation of goods however there is benefits
in the form of reduced waiting time.
However, when other components of distribution system are considered such as labortime at depot and unloading time, each CDS has its own strength. For instance, whena CBD is low density, i.e.15-story buildings, Horizontal CDS is the most suitableowing to the 6% time saved (Figure 8-18). Adopting any of the other two CDS would
increase distribution time which can be interpreted as increase to distribution cost as
shown by the negative position of the curve.
The positive effect of all the CDS begins when the building height reaches 20-story.
This means that the building administration has more options of which CDS to adopt
knowing that this would not necessarily increase the distribution cost. The time saves
200

from the combination of Horizontal and Vertical CDS is notable since under the CBDwith 15-story buildings, it has the largest loss.
It must be noted that the analysis is performed to an office-oriented CBD andtherefore there might be changes to the pattern of time saves from the different CBD
designs. Based on this result however, it can be theorized that significant changes ofbuilding use from office to commercial (i.e. shops and restaurants) would put the
combination of Horizontal and Vertical CDS into strong position as the most effective
measure. This theory have two grounds; 1) there would be substantial increase to thenumber of trucks and gained from delivery time alone would be tremendous and 2)
there would be a long waiting time of trucks which could be removed when
Horizontal and Vertical CDS is adopted. Of course adopting the Horizontal CDS
would have also bring tremendous reduction of truck trips however the without theassistant of staff inside the building, it would be difficult for the driver alone todeliver the consolidated goods. This situation would significantly increase delays toother CDS trucks.
100%
90%
87%
78%

75%

80%
61%

Time savings from 3 CDS

Vertical CDS

52%

32%

40%
20%

64%

Horizontal CDS

60%

6%

26%

0%
15-story

20-story

30-story

40-story

-20%

-40%
-39%
-60%
-58%
-80%
Building he ight

Figure 8-18. Time saved from three types of CDS for an office-oriented CBD

8.10

Chapter summary

Ensuring efficient delivery of goods – i.e. safe and on time – lies to many segments ofthe delivery chain. Congestion may delay the truck, lack of parking space for truck
may prolong the delay, and lack of sufficient number of freight elevator may further
extend the delay. Facilities related to delivery of goods inside the building therefore
require serious examination in the same manner as that of traffic congestion outside
the building.
201

This chapter at the beginning brought to light activities that are often receiving lessattention from both transportation and urban planning. The complexity of delivery
process is discussed thoroughly by integrating past studies that has notable discussion
to the sector in the early part. The papers of Park et al (1998) and Iwao et al (2001) inparticular were able to demonstrate what is happening inside the building after thedelivery trucks docked.
The highlight of the chapter is the identification of suitable CDS design depending on
the building density of the CBD. It was found out that Horizontal CDS is the only
design suitable for low density CBD in view of distribution time. Adopting any of thetwo other CDS designs, i.e. the combination of Horizontal and Vertical CDS and
Vertical CDS, would bring increase to the distribution time and therefore would
increase the distribution cost. However, when the building density increases and eachbuilding reaches 20-story, all the 3 CDS designs bring enormous benefits in the formof time saved. These findings would be useful guidelines in selecting which CDS
design is adopted when it is adopted based on the characteristics of the CBD.
It appears that the general trend is that as the building density increases, the greater itneeds of the CDS. It is because the number of trucks followed the same trend and as
its numbers increases the more problems it generates. This problem can be in the formof traffic congestion which can cause delay to the goods and other vehicles, truck’squeue at the (un) loading area of the buildings, and tremendous environmental
pollutions.

202

REFERENCES
1. Asakura, Y., Tanabe, J., Lee, Y. (2000). Characteristics of Positioning Data for
Monitoring Travel Bahaviour. 7th ITS World Congress in Torino. CD-ROM.
2. Ashida, N. et al (2002). A study on the improvement of home delivery systems
and the effect of introudincg a new measure in a concentrated high-rise residential
area. City Logistics II. Institute of Systems Science Research, Japan.
3. Elbeyli, S. and Krishnan P. (2000). In-Patient Flow Analysis Using ProModel
Simulation Package. Unpublished FREC staff paper. Department of Food andResource Economics. University of Delaware. Newark, Delaware.
4. Harutoshi Y (1994). Demand forecast and financial feasibility of new freighttransport systems. OECD Seminar TT3 on Advanced Road Transport
Technologies, OECD, Paris
5. Iwao, E. and Kuse, H. (2001) Traffic and Environmental Impact Analysis of
Large-scale Facilities in the Commercial Business District. Journal of Eastern
Asia Society for Transportation Studies, Vol. 4, No.1, 523-535.
6. Li, Maying (2001). Urban Goods (Off) Loading Chain. Paper presented at the
NECTAR Conference no 6 EUROPEAN STRATEGIES IN THE
GLOBALISING MARKETS, Helsinki, Finland.
7. Meyer, M.D. (1997). A Toolbox for Alleviating Traffic Congestion andEnhancing Mobility, Institute of Transportation Engineers.
8. Morris, A. and Kornhhi Auser, A (2001). Relationship of Freight Facilities inCentral Business District Office Buildings to Truck Traffic. Transportation
Research Record 1707, Paper No. 00-0197.TRB, National Research Council,
Washington, D.C.
9. Okamoto, A., Asakura, Y., Suzuki, A., and Lee Y. (2001). Monitoring Personal
Travel Behavior using a Cellular Phone System with Power Antennas and CSID
Analysis (2001). 8th ITS World Congress in Sydney. CD-ROM.
10. Park, S. et al (1998). A Simulation Analysis on Loading/Unloading and PickUp/Delivery Activities for Efficient Goods Movement at the CBD in Japan, No.
199, 8th World Conference on Transport Research, Antwerp, Belgium.
11. ProModel User’s Guide (1997). ProModel Corporation. USA.
12. ProModel Reference Guide (1997). ProModel Corporation. USA.
13. Tanaka, H., Sekimoto, Y., and Shibaki R. (2001). Reconstruction of Spatiotemporal Distribution of Event Visitors by Fusing Multi-source Data. Paper
presented at the 22nd Asian Conference on Remote Sensing, Singapore.

203

CHAPTER

9Conclusions, Recommendations
and Future Research Directions
9.1

Introduction

The two parallel goals of the study defined earlier in Section 1.2 of Chapter 1 hadbeen achieved. These goals, which were elaborated in five objectives, are 1) toevaluate the applicability of CDS to different types of central business district, and 2)
to evaluate quantitatively traffic and environmental impacts of CDS in the CBD
using empirical data. The application of CDS to different types of central business
districts was carried out to evaluate its traffic and environmental impact. The effect of
CDS from social view point tends to be large in a high density CBD where thenumber of trucks is high. On the other hand, the effect of CDS to a low density CBD
which is mostly found in developing country appears to be limited due to the very low
share of trucks and saturated transportation network. Despite this minimal impact,
CDS however should be given serious consideration as a policy option in view of
rapid economic development in these countries that would eventually increase thenumber of trucks in the road network.
After conducting series of analysis, this chapter concludes the study. After Section 1.1,
further four sections follow. The highlights of each chapter are presented in Section
9.2 while Section 9.3 enumerates important findings of the study. Section 9.4 provides
short recommendations for urban freight transport stakeholders (i.e. shippers, carriers,
residents, and authorities) and other interest groups based on the result of analyses
while Section 9.5 sets the direction for future studies to strengthen knowledge on therole of CDS in managing freight transport.

9.2

Summary

Chapter 1 presented the overview, goals and objectives, problem definition,
significance, scope and the structure of the dissertation. The five objectives of thestudy are: to review the current development and practices of cooperative delivery
system and the factors for the implementation of such measure; 1)to review thecurrent development and practices of cooperative delivery system and the factors forthe implementation of such measure; 2) to clarify transportation conditions in urban
areas and review the urban freight transport policies in selected developed anddeveloping countries; 3) to provide guidelines of which, CDS design is suitable forapplications based on the CBD’s characteristics and right timing of measureapplication; 4) to assess the roles of CDS as the CBD transform from low density tohigh-density structure; and 5) to evaluate the impacts of cooperative delivery system204

to the traffic flow in CBD road networks and also to the improvement of urban
environment.
Chapter 2 enumerates the problems currently faced by urban freight transport anddrivers due to the emergence of logistical challenges. Highlights of this chapter
include the imbalance growth of freight transport (vkm) among the different modes
where road freight is continuing to rise while the others are declining. This trend
which is common to almost every country drew policy reaction in the form of
promotion of modal shift by the government as has been seen in EU and Japan. The
expansion of market through IT which resulted to the sophistication of customers was
also reviewed. This customer attitude is believed to greatly contribute to the increase
number of trucks in the road with few load. After the review to these current
developments impacting urban freight transport, the main problems confronting theindustry were presented. These are increasing traffic congestion and air pollution,
increased cost of goods movement, and the slide down of truck’s load factor.
Chapter 3 reviews the fundamental policy of the governments towards the realizationof efficient freight transport in the urban areas. This review exposed the disparity inpolicy formulation between countries in developed and developing countries. Asidefrom the preparedness of freight facilities, most countries in developed regions foundto have a dedicated government institution which manages policies and programs
relating to freight transport. Developing countries, on the other hand, are still focusedon passenger transport. Concerning the TDM examples, it appears that CDS is a
product of several access restrictions. For instance, access restriction of trucks bydifferent means (e.g. ban of night deliveries, total truck ban, time window, etc.) is
imposed in some cities in Europe to preserve historic sites and decrease negative
impact of truck on the environment. It was also noted, however, that there were casesthat due to efforts of companies to cope-up with financial loss, CDS is formed.
Chapter 4 presented policy goals that a measure in freight transport in urban areas canbe aimed. Means to achieve these goals can be in the form of infrastructure-based
measure (hard) and TDM-based measure (soft). Hard measures such as further
development of road and construction of freight terminal require huge financial
resources and they are achieved in a long period of time. On the other hand, soft
measures require less financial investment and can be adopted for a short period oftime. The highlight of this chapter is the rationalization of CDS as one of the mostpowerful measures to address the problems brought by trucks in the CBD. CDS meetsthe objective of reducing traffic congestion, reducing transportation cost, reducing
negative impact of trucks into the environment, and improving safety. The idea of
CDS is that different logistics operators are surrendering part of management of theirgoods so it can be consolidated with other cargoes before bringing in into the city
center. In essence, less number of trucks is bringing the same amount of goods to thecity. Expected benefits that can be derived from CDS are: 1) reduction of vehiclekilometers, 2) reduction of delivery time, 3) reduction of the number of trucks, 3)
reduction of environmental emissions, and 4) reduction of transport cost.
Chapter 5 isolated the study area of the study and came up with groups of central
business district. It was observed that most CDBs in developing countries are low
density while those in developed countries are high density. Share of trucks to thetotal vehicles is considerably low also to the CBDs in developing country. One

205

plausible explanation to the low number of vehicles is that people are very muchdependent on car as the public transportation is unreliable which resulted to a very
high traffic volume. In Metro Manila, apart from the survey conducted for this study,
other surveys also found out that more half of the vehicles in the road network arecars. The CBDs from the perspective of freight transport planning can be grouped into
four: 1) low density CBD in developing country, 2) high density CBD in developing
country, 3) low density CBD in developed country, and 4) high density CBD indeveloped CBD.
Chapter 6 presented the first application of CDS in a low density CBD in developing
country. Ortigas CBD in Metro Manila is taken as case study. Impact of CDS totraffic was observed to be limited at 0.76% due to the high number of cars. Meaning,
even if the number of trucks is withdrawn, there are still substantial number ofvehicles left in the network. With regard to environmental impact of the CDS, NOx
coming from trucks can be reduced by 54% (just 1.6% if emission from other vehicles
is included) while SPM to 54% (2.15% if emission from other vehicles is included).
In the future, the proportion of vehicles might change as the development of thecentral business district intensifies and turns the CBD into high density. Share oftrucks can be expected to increase that would further aggravate traffic congestion andpollutant emissions. When CDS is applied in this situation, i.e. share of truck is to thetotal traffic is 20%, its impact is far larger at around 9.12%. This reduced number oftrucks resulted to improved travel speed. With regard to environmental impact, NOx
can be reduced by 19.20% and 20.63 for NOx. In essence, CDS becomes more
important as the development of the CBD intensify. The limited capacity of
infrastructure necessitates a management based measure that would limit the numberof trucks while serving the same amount of goods.
Chapter 7 evaluated the impact of CDS to a low density CBD in a developed country.
Impact of CDS to traffic is observed to be high at 12% of the total traffic. Reductionof both NOx and SPM emissions is as high as 89%. This chapter also tried to estimate
the necessary number of trucks that must be cover by the CDS in order to avoid
queues of trucks at the (un)loading area. It was found that at least 70% of the truck
trips should be cover to meet this objective. Modifications to the CBD however
should be made when the proportion of retail space (i.e. shops and restaurants)
reached 23% due to tremendous increase of truck trips (presently, share of retail spaceis 2.7%). These changes can be done in the form of increasing the number of CDS
staff and/or increasing the number of freight facilities (i.e. parking space and freight
elevator). Regarding the working time (i.e. labor, delivery time), a 50.4% reduction(from 63,314 min to 31,403 min) can be achieved by the CDS. Comparison of CDS
results between Manila and Tokyo is also presented which highlights the notable hightraffic and environmental reduction in Tokyo while very limited for the case in
Manila.
Chapter 8 focuses on the logistical activities inside the building when the district turns
to high density CBD. Height of the building is increased from 15 floors to 20, 30 and
40 story. The newly renovated Marunouchi building which is the highest building in
the district has a total floor of 37. Thus, it appears reasonable to limit the simulation
analysis to 40-story. After determining this building height limit, the study assessed
the relationship of the number of staff working in the CDS inside the building, heightof the building, frequency of trips, and amount of goods for each delivery. For

206

instance, the maximum number of goods per trip under 15-minutes trip interval is 28
goods for a 15-story building. This maximum number of goods could be increased to
37 when the interval of trips is 30-minutes and can be as high as 50 goods per trip if
the interval of trips is 60-minutes. These results can be a guide to determine thenumber of staff necessary depending on the height of the building and attracted
number of goods. The highlight however of the chapter is the identification to thesuitable CDS design depending on the building density of the CBD. It was found outthat Horizontal CDS is the only design suitable for low density CBD in view of
distribution time. Adopting any of the two other CDS designs, i.e. the 1) combinationof Horizontal and Vertical CDS, and 2) Vertical CDS, would bring increase to the
distribution time and therefore would increase the distribution cost. However, when
the building density increases and each building reaches 20-story, all the 3 CDS
designs bring enormous benefits in the form of time saved. These findings would be
useful guidelines in selecting which CDS design is adopted and when it is adopted
based on the characteristics of the CBD.

9.3

Conclusions

Based on the review of many policies in the world concerning urban freight transportand simulation analysis of different cases, the following can be concluded:a) CDS is adopted mostly in developed countries like Japan and some countries in
Europe and there are several reasons for its implementation such as difficulty ofdelivering goods into the city centre due to access restriction, effort of thecompanies to cope-up with loss and also enhance their social standing as matured
and responsible companies (Chapter 3).
b) Access restriction is a common measure among the cities however; motivations
for implementation are quite different. For instance, developed countries applied
access restriction not only to mitigate serious traffic congestion but also for great
concern to protect the urban environment and to some cases to protect historic
centre. For the case of developing countries, access restriction is often adopted topush out trucks from public transport’s key avenues (e.g. EDSA of Metro Manila)
to limit the competition of buses and other public transport modes to the limited
road space. Cities in these countries rely heavily on road-based public transport
due to the lack of an efficient rail-based public transport system (Chapter 3).
c) The study confirmed that CDS is among the most powerful measures to improveurban traffic and environment and meets every objective of TDM measures for
freight in the CBD (Chapter 4).
d) From the perspective of application of CDS, central business district can beclassified into four categories: 1) low density CBD in developing countries, 2)high density CBD in developing countries, 3) low density CBD in developed
countries, and 4) high density CBD in developed countries (Chapter 5).
e) Traffic and environmental impact of CDS appears to be limited on low density
CBD in developing countries because of the small share of trucks to the total

207f) In the future, if the share of trucks increases due to loosen regulation anddevelopment of the CBD, CDS can be a more effective measure to improve thetraffic flow and the environment as general. In essence, the impact of CDS
increases as the share of trucks in the road network also increases (Chapter 6).
g) Traffic and environmental problems in the CBD of developing countries can not
be mitigated by simply adopting CDS. This is because much of the problems are
sourced from private and public transport vehicles. For instance, 76% of NOx
comes from cars and only 2% from trucks. Thus, addressing traffic andenvironmental problems requires forming a bundle of policy that involves bothtrucks and other vehicles (Chapter 6).
h) CDS has high impact in terms of traffic and environment even in low density
central business district in developed countries due to the high share of trucks inthe network (Chapter 7).
i) Aside from reduction of traffic and pollutant emissions, CDS can also be a tool toaddress the problem of shortage of parking space in the CBD. If the objective is toeliminate waiting time of trucks to parking, this can be achieved even without
total participation of carriers to the CDS (Chapter 7).
j) Horizontal CDS is the only CDS design that brought reduction to the distribution
time in a low density office-oriented CBD (15-story) and therefore the mostsuitable measure. Vertical CDS and the combination of Horizontal and Vertical
CDS increases the distribution time to a low density CBD and therefore would
increase the distribution cost. Distribution time refers to time derived fromdelivery (vehicle’s running time), unloading, waiting time for parking, labor timeat stock point (SP), and vertical delivery (Chapter 8).
k) When the height of each building in the CBD reaches 20-story, all of the CDS
produces remarkable reduction of distribution time. Horizontal CDS has thelowest distribution time followed by the combination of Horizontal and Vertical
CDS and followed by Vertical CDS. The trend is the same when the buildings in
the CBD are 30-story and 40-story (Chapter 8).

9.4

Recommendationsi)Applicability of CDS to different types of CBD

It is difficult to assert whether CDS is a suitable measure for freight transportmanagement to a low density CBD in developing countries in view of limited benefits
achieved from the scheme. On one hand, it is interesting to argue that any policy thatyields positive effect to the transportation network should be pursued in view oflimited infrastructure. Impact of one measure might be limited but if bundled together
with other measures might produce significant result towards the realization ofefficient urban traffic and healthy environment. On the other hand, there would be208difficulty of persuading the companies to abandon their current operation toparticipate in a scheme where social benefits are limited and economic rewards are
uncertain. It should be noted that willingness of companies to join the CDS is to someextent because of their consciousness to project a matured and responsible company
which could enhance their image in the market.
In essence, social benefits of CDS are low in a congested and car-dominated CBD
which could describe those CBDs in developing countries and notably high in the
CBDs of industry-oriented cities (i.e. CBD with high share of trucks in the road
network). Despite this limited benefits, CDS should not be left out as freight
management tool. Recent trend suggests that there is a need for transport providers toorganize themselves as the city authorities are becoming notorious in imposing access
restriction to protect urban traffic and environment. Trucks have always been an easytarget when these two issues are addressed out of belief that they are the main sourceof such problems.
ii)

Selecting which CDS design is adopted based on the building density of
the CBD

Low density CBD both in developed and developing countries would gain benefits
from Horizontal CDS both from economic and social point of view. The reduced
number of trucks would improve the environment even in a small way while the freed
road space could be utilized by other transport modes. Developing countries are indifficult position of finding source of finance for its transport infrastructure and would
greatly benefits when few numbers of trucks could be used to deliver the same
volume of goods. The analysis performed shows that adopting CDS in a low density
CBD would not necessary increase the distribution time which can be basis for
distribution cost. Perhaps this CDS can be best applied in the CBD of London, Paris,
Metro Manila and other CBDs of Asian cities where building height is not very high.
In a commercially oriented high density CBD, the combination of Horizontal andVertical CDS would be effective. This is followed by Vertical CDS. CBD of New
York, Hong Kong, Singapore, and some high density CBDs of Tokyo like Shinjuku
would greatly benefits from this measure.
Although residential areas were not covered by the analysis, there are some residential
areas which do not welcome the presence of trucks as has been seen in some quartersof Paris (see Chapter 3 for the discussion of some TDM in Paris). It is believed that
Horizontal CDS could also serve this area without financial loss (without increasing
the distribution time) and greatly benefits the residents from the reduced number oftrucks. This would bring calm and increase the livability of the area.
iii)

Combination of policy measures for freight and passenger transport

Efforts of the government in the developing countries should be directed on how toenhance the services of public transportation and to propose mechanism that would
discourage use of cars and encourage use of public transportation. The road capacityof cities from these countries is extremely limited and there is a difficulty to expand
this capacity through provision of infrastructure in view of limited financial resources.

209

Demand therefore could hardly be met through construction of roads and demand
management measures should be initiated.
Concerning trucks, as much as possible, they should not be denied access but instead
there should be an initiative on how to assess them. Providing platform for discussion
like the FQP model of UK (see Chapter 3 for FQP discussion) is a good example on
how to engage trucking industry into public forum in search of sustainable freighttransport.

9.5

Future Research Direction

There are many issues identified in the course of this study that require further
research to strengthen implementation mechanisms for CDS. These issues are listed
below.
i)

Integration of pick-up of goods into the CDS

Ideally, all goods going in and coming out should be covered by CDS. However, it isdifficult to compel participation in the CDS due to unwillingness of companies tosurrender part of operation of their business. The adopted CDS to both central
business districts did not cover this part of freight transport and it would be interesting
to see how effective CDS in reducing traffic and improving urban environment shouldboth pick-up and delivery is covered.
ii)

Simultaneous application of CDS and other TDM measures for passenger
cars and public transportation

CDS is not an all-around solution to the complex problems of freight transportation in
the central business district. It would of interest to measure the degree of
improvement in the urban traffic and environment if CDS is pair with other TDM
measures.
iii)

Identifying suitable number of stock point

A stock point is crucial elements of CDS as it serves as platform to consolidate thegoods carried by different carriers before sending it to the central business district.
The study just consider one stock point to every CDS and it would be of great interest
to see a study which could produce a guidelines regarding correct number of stock
point based on its influence area.
iv)

Method to avail land to host the stock point

Aside from difficulty of persuading companies to form CDS, to minimize the negative
impacts of trucks distributing freight, it is also difficult to locate suitable area to hostthe depot for cargo consolidation. It is of interest therefore to have a kind of study that
could identify successful practices in locating stock point.

210

REFERENCES
1. City Freight (2002). Comparative survey on urban freight, logistics and land use
planning systems in Europe. www.cityfreight.org
2. Nemoto, T. and Kawashima, H. (2003). Logistics development supported by ICT& ITS in the Asia-Pacific Region. Asian Task Force, Institute of Highway
Economics, Japan.
3. OECD. 2003. Sustainable transport in the Asian Region. Report on theInternational Conference on Environmentally Sustainable Transport in the AsianRegion. March 23-25, 2003. Nagoya, Japan.
4. Paaswell, R. and Petretta, D. (1993). Goods Movement Characteristics in the New
York. City Region. University Transportation Research Center, Region II, City
University of New York.

211

Appendices
Appendix 1
Appendix 2
Appendix 3
Appendix 4
Appendix 5a
Appendix 5b

Title

Page

Robinson’s Galleria survey communication letter(Manila Case)
SM Megamall survey communication letter (Manila Case)
Transit survey form (Manila Case)
Vehicular volume survey form (Manila Case)
Goods Delivery survey form A (manila case)
Goods Delivery survey form B (manila case)

A-2

A- 1

A-3
A-4
A-5
A-6
A-7

SCHOOL OF URBAN AND REGIONAL PLANNINGU N I V E R S I T Y
E.

JacintoO F

S t r e e t,

T H E

D i l i m a n,

P H I L I P P I N E S

QuezonCity

1101

March 14, 2005
MR. JOSE RAYOS III
Complex Administration Manager
Robinsons Galleria
Edsa, Ortigas, Mandaluyong City
Dear Mr. Rayos:
The Tokyo University of Marine Science and Technology (TUMST) is currently
undertaking a comparative study of freight delivery operations and practices in malls
at urban centers in Asia. The study aims to look at the possibility of organizing freight
distribution through cooperative delivery between freight forwarders and assess theefficiency of the use of available resources and facilities. Your establishment is
considered as one of the malls to be part of the comparative study and the only mallone identified for the Philippines.
The TUMST study team has requested our assistance in making the necessary
arrangement and coordination with your establishment in carrying out the datacollection and surveys for their study. Among the vital information that we would liketo obtain includes the following:Total lot area, total building area, total floor area, gross leasable space andtotal number of floors.
Tenancy – percentage of office space, shops, restaurants and others.
Total parking space (slots) for private cars and trucks, total number ofpassenger elevators and freight elevators.

These information, we believe are readily available from building plans andspecifications and will also be verified and validated by manual inventory. In addition,
we would like to collect information on physical distribution and daily delivery
operation through the conduct of a one day survey. This survey aims to determine
the frequency of delivery arrivals, type and number of goods delivered, averagedelivery time, parking time of truck while unloading, number delivery service staff,
and delivery truck details. We would also like to count the number of vehicles
entering and exiting or just passing by your establishment. Please find the attacheddocument for further information regarding the survey. Should it be fine with yourcompany, we would like to conduct the survey on March 18 or 19, 2005.
Mr. Jim Joel Madrigal will be the person that would directly coordinate with you in thearrangement of the data collection and survey in your establishment. You cancontact him at Tel. no. 929-0495 loc 217, Telefax: 928-8305.

Rest assured that the mall’s regular operation would not be hampered in anyway.
The data and other pertinent information acquired will be handled with strict
confidentiality and that only summaries will be reported. Likewise, the study teamwould be more than grateful to furnish your management a copy of data andpertinent report arising from the study.
Thank you very much and we sincerely hope for your favorable response.

Very sincerely yours,

HUSSEIN S. LIDASAN, Ph.D.
Associate Professor

Appendix 1. Robinson’s Galleria survey communication letter

A- 2

SCHOOL OF URBAN AND REGIONAL PLANNINGU N I V E R S I T Y
E.

JacintoO F

S t r e e t,

T H E

D i l i m a n,

P H I L I P P I N E S

QuezonCity

1101

February 17, 2005
MS. LIZA SILERIO
Regional Operations Manager
SM Megamall
Ortigas, Mandaluyong City
Dear Ms. Silerio:
The Tokyo University of Marine Science and Technology (TUMST) is currently
undertaking a comparative study of freight delivery operations and practices in malls
at urban centers in Asia. The study aims to look at the possibility of organizing freight
distribution through cooperative delivery between freight forwarders and assess theefficiency of the use of available resources and facilities. Your establishment is
considered as one of the malls to be part of the comparative study and the only mallone identified for the Philippines.
The TUMST study team has requested our assistance in making the necessary
arrangement and coordination with your establishment in carrying out the datacollection and surveys for their study. Among the vital information that we would liketo obtain includes the following:Total lot area, total building area, total floor area, gross leasable space andtotal number of floors.
Tenancy – percentage of office space, shops, restaurants and others.
Total parking space (slots) for private cars and trucks, total number ofpassenger elevators and freight elevators.

These information, we believe are readily available from building plans andspecifications and will also be verified and validated by manual inventory. In addition,
we would like to collect information on physical distribution and daily delivery
operation through the conduct of a one day survey. This survey aims to determine
the frequency of delivery arrivals, type and number of goods delivered, averagedelivery time, parking time of truck while unloading, number delivery service staff,
and delivery truck details. Should it be fine with your company, we would like toconduct the survey at the end of March, after the Holy Week.
Mr. Jim Joel Madrigal will be the person that would directly coordinate with you in thearrangement of the data collection and survey in your establishment.
Rest assured that the mall’s regular operation would not be hampered in anyway.
The data and other pertinent information acquired will be handled with strict
confidentiality and that only summaries will be reported. Likewise, the study teamwould be more than grateful to furnish your management a copy of data andpertinent report arising from the study.
Thank you very much and we sincerely hope for your favorable response.

Very sincerely yours,

HUSSEIN S. LIDASAN, Ph.D.
Associate Professor

Appendix 2. SM Megamall survey communication letter

A- 3

SCHOOL OF URBAN AND REGIONAL PLANNINGTOKYO UNIVERSITY OF MARINESCIENCE AND TECHNOLOGYU N I V E R S I T Y
E.

JacintoO F

S t r e e t,

T H E

D i l i m a n,

P H I L I P P I N E S

QuezonCity

1101

Survey on goods movement in the Shopping Centers
in Metro Manila
Transit Survey
LocationDate
W eather
RecorderTime Started
Time Ended
Code
Sheet

TIME (15 min Vehicle
Interval)No.

VEHICLE TYPE:
B - BusC - Private Car/Van

VehicleType

Arrival TimeM - MegaTaxi / FX
T - Taxi

Departure TimeJ - JeepV - Delivery Van

Appendix 3. Transit survey form

A- 4

of
Queue
LengthO - Others (Specify)

TOKYO UNIVERSITY OF MARINE SCIENCE AND TECHNOLOGY
andSurvey on goods movement in the Shopping
Centers in Metro Manila

S C H O O L O F U R B A N A N D R E G IO N A L P LA N N IN G
U N I V E R S I T Y
E.

J a c i n t o

O F

S t r e e t,

T H E

D i l i m a n,

P H I L I P P I N E S

Q u e z o n

C i t y

1101

Code:

VOLUME COUNT

Date:

Location

Weather:

From: (Aproach)Direction
Veh
Type
Time

PrivateCars

Surveyor:

Sketch of the area

Motorcycle

Taxi

FX

Delivery
Vans

TrucksJeepneyBus

Appendix 4. Classified traffic volume survey form

A-5

PrivateCars

Motorcycle

Taxi

FX

Delivery
Vans

TrucksJeepneyBusTOKYO UNIVERSITY OF MARINE SCIENCE AND TECHNOLOGYandSC H O O L O F U R B A N A N D R EG IO N A L P LA N N IN G
U N I V E R S I T Y
E.

J a c in to

O F

S t r e e t,

T H E

D i l i m a n,

P H I L I P P I N E S

Q u e z o n

C ity

Survey on goods movement in the Shopping
Centers in Metro Manila

1101

C ode:
Sheet:G O O D S D E LIV E R Y S U R V E Y (F orm A )Location:
D ate:
R ecorder:
T im e(1 5 m in
In te r v a l)of:

W eather:T ime S tarted:
T ime E nded:D e liv e r yNo.

P la te N o .

T ru c k in g /F o rw a rd in g C o m p a n y

REGIONAL PLANNING LABORATORYTokyo University of Marine Science and Technology
Etchujima, Tokyo, Japan

Appendix 5a. Goods delivery survey form (A)A- 6

A r r iv a l
T im eD o c k in g
T im eD e p a r tu re
T im eM a x im u m
Q u e u e (p e r 1 5
m in s o n ly )

TOKYO UNIVERSITY OF MARINE SCIENCE AND TECHNOLOGYandSC H O O L O F U R B A N A N D R EG IO N A L P LA N N IN G
U N I V E R S I T Y
E.

J a c in to

O F

S t r e e t,

T H E

D i l i m a n,

P H I L I P P I N E S

Q u e z o n

C ity

Survey on goods movement in the Shopping
Centers in Metro Manila

1101

Code:
Sheet:GOODS DELIVERY SURVEY (Form B)
Location:
Date:
Recorder:DeliveryNo.

of:

Weather:
Time Started:
Time Ended:
Goods Delivered

Plate No.
Description

Quantity

REGIONAL PLANNING LABORATORYTokyo University of Marine Science and Technology
Etchujima, Tokyo, Japan

Appendix 5b. Goods delivery survey form (B)

A- 7

Unit

Destination

Other Delivery
Destinations