2nd Joint Student Seminaron Civil Infrastructures
6 July 2009
Bangkok, Thailand
Co-Organized by
School of Engineering and Technology,
Asian Institute of Technology (AIT), Thailand
International Center for Urban Safety Engineering (ICUS)
Institute of Industrial Science
The University of Tokyo, Japan
BK-21 Global Leaders in Construction Engineering,
Korea University, Korea
Myongji University, Korea
King Monkut’s Universityof Technology North Bangkok, Thailand
National Chung-Hsing University, Taiwan
Khulna University of Engineering and Technology, Bangladesh
Chulalongkorn University, Thailand
andResearch Team for Maintenance Technology of Infrastructure,
Konkuk University, Korea
Edited by
Dr. Shinji Tanaka, Dr. Hyunmyung Kim, Dr. Kyung-Ho Park,
Prof. Muhammed Alamgir and Prof. Keh-Jian Shou

Organization Teams
Dr. Kyung-Ho Park (AIT, Thailand)
Dr. Pison Udomworrat (KMUT-NB, Thailand)
Dr. Hyunmyung Kim (AIT, Thailand)
Dr. Anat Ruangrassamee (Chula Univ., Thailand)
Dr. Jaroon Rungamornrat (Chula Univ., Thailand)
Dr. Kunnawee Kanitpong (AIT, Thailand)
Mr. Zaw Zaw Aye (SEAFCO, Thailand)
Dr. Adisorn Owatsiriwong (ALPS, Thailand)
Dr. Shinji Tanaka (UT, Japan)
Dr. Wataru Takeuchi (UT, Japan)
Dr. Kawin Worakanchana (UT, Japan)
Dr. Shinya Hanaoka (TIT, Japan)
Dr. Sano Kazushi (Nagaoka Univ. Of Tech, Japan)
Prof. Atsushi Koizumi (WU, Japan)
Dr. Koichiro Mizuno (EJRC, Japan)
Prof. Young-Uk Kim (MJU, Korea)
Dr. Jung-Sik Kong (KU, Korea)
Dr. Seok-Won Lee (KKU, Korea)
Prof. Ikki Kim (HYU, Korea)
Prof. Young-Ihn Lee (SNU, Korea)
Dr. Gye-Chun Cho (KAIST, Korea)
Dr. Gun-Ha Kim (HNU, Korea)
Dr. Yong-Taek Lim (CNU, Korea)
Dr. Chang-Yong Kim (KICT, Korea)
Dr. Dong-Hoon Shin (K-water, Korea)
Dr. Ik-Soo Ha (K-water, Korea)
Prof. Keh-Jian Shou (NCHU, Taiwan)
Prof. Muhammed Alamgir (KUET, Bangladesh)Dr. Borinara Park (Illinois State Univ., USA)
Dr. Jung-Seok Oh (Wastern Michigan Univ., USA)
Dr. Eckhard Kraft (BU Weimar, Germany)
Dr. Semih Kuchkarslan (Istanbul Technical Univ., Turkey)

2nd JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURES
July 2009
PREFACEAs our activities expand beyond the border, international collaboration
becomes more and more important. And, to build up a good relationshipwith other communities during young school days will be of advantage inthe future. Considering this background, we held the 1st seminar in July
2008, and following this success, “the 2nd joint student seminar on civil
infrastructures” was held on 6th July 2009.
The objectives of this seminar are,1) to experience the organization of international seminar,
2) to improve the presentation skill,3) to share the research information and friendships.
The number of participants was 34, consisting of 16 faculties and 18
students from Korea University, Chonnam National University, Seoul
National University, Suranaree University of Technology, Chulalongkorn
University, Asian Institute of Technology, Khulna University ofEngineering & Technology and The University of Tokyo. In this seminar,
there were 4 faculties’ lectures and 18 students’ presentations. The topics
were varied from all areas of civil engineering and every student did theirbest in his/her presentation as well as in the discussion. At the end of theseminar, excellent presentation awards were given to the following 3
students.
1.

Ms. Hoang Thuy Linh from Japan2.

Mr. Jung-Yub Lee from Korea

3.

Mr. Md. Reaz Akter Mullick from Thai

The student participants played the major role of this seminar,discussing on their research topics and communicating with students fromother countries. The seminar was quite successful and fruitful in that thisseminar gave not only knowledge and information but also a lot of otherstimuli to the students. We hope to continue to hold this kind of interchange
activities in the coming years.
Finally, we would like to express our sincere gratitude for those who
kindly supported and contributed to the success of this seminar.
SHINJI TANAKA, HYUNMYUNG KIM, KYUNG-HO PARK,MUHAMMED ALAMGIR AND KEH-JIAN SHOU

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Table of Contents
Opening Session
SECTION 1
Invited Lectures

SECTION 2

SECTION 3

Page

Indigenous Approach for the Construction of a Pilot Scale Sanitary Landfill in
Bangladesh
Muhammed Alamgir

1

A Target-oriented Network Design Model for Transportation Mode Choice
ProblemYoungtaek Lim

19

Current Remote Sensing for Land Environment and Disaster
Haruo Sawada

29

Public Transportation in Bangkok
Thirayoot Limanond

49

Invited Papers
Methods for Determining Physical, Particle-related Characteristics Relevant to
Fluid Flow Phenomina in Biowaste
Eckhard Kraft

59

Analysis of Floor Acceleration Demands in Multistory Buildings
Semih Kucukarslan

71

Impacts of Base Flow Discharge on Nitrate in Surface Water Quality
Woo Sangdeok

83

Student Presentations
Suggestion of PSC Box Bridge Performance Profile According to Corrosion of
TendonHee-Joo Kim

91

Environmental Performance Indicators for Concrete Containing High Volumeof Recycled Materials
German A. Pardo

99

Seismic Hazard Map of Thailand
Jitti Palasri

109

Effect of Oxygen and Water on Macro-cell Corrosion
Vu Viet Hung

119

Construction Step Analysis and Collision Simulation of CFTA
Sun-Ae Yi

129

Experimental Study on the Evaluation of a Cavity and Loosening in Soil
Mari Sato139

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SECTION 4

Foundation System Adopted to Construct the Civil Infrastructure in Khulna
University of Bangladesh
Md. Abdur Razzaque

149

A Representation of Motorbike Dominated Traffic using Cellular Automata
Hoang Thuy Linh

161

Development of Pedestrian Simulator for Transfer Center Operation Analysis
Dae-Sik Nam

171

Exploring Impacts of Countdown Timers on through Movement at a Signalized
Intersection in Bangkok
Pramuk Prabjabok

185

A Mathematical Model for Transit Network Assignment
Jun-Seok Jang

199

Hierarchical Shortest Path Algorithm with Probabilistic Travel Time
Theonette Ruba Maribojoc

205

Development of Multi-class Pedestrian Assignment Algorithm
Jung-Yub Lee

227

The Application of Stochastic Aassignment Technique to Network Design
Problems
Tawin Tiratanapakhom

239

Estimation of Land Surface Water Coverage (LSWC) with AMSR-E andMODIS
Shumon Mori

265

Comparative Analysis of Planned and Actual Disaster Management Operations
Performed During and Just After the 2007 Cyclone Sidr in Bangladesh
Taiki Kou

275

Environmental Flow in Water Resources Management: Status in South Asia
Md. Reaz Akter Mullick

287

Experimental Study on Shaking Table Test of a Concrete Gravity Dam
Bupavech Phansri

297

Tunneling-Induced Ground Movement in Twin-Tunnels of Bangkok MRT
Subway Project
Tran Viet Dung

305

Student Report
Reprot from Student Participations on The 2nd Joint Student Seminar on Civil
Infrastructures
German A. Pardo R., Vu Viet Hung, Mari Sato, Hoang Thuy Linh, Taiki Kou andShumon Mori

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Booth at seminar

Opening ceremony (Dr. Joydeep Dutta)

Prof. Muhammed AlamgirProf. Youngtaek Lim

Prof. Haruo Sawada

Dr. Thirayoot Limanond

Ms. Hee-Joo Kim

Mr. German A. Pardo R.

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Ms. Bupavech Phansri

Mr. Vu Viet Hung

Ms. Sun-Ae Yi

Ms. Mari Sato

Mr. Md. Abdur Razzaque

Ms. Hoang Thuy Linh

Mr. Dee-Sik Nam

Mr. Pramuk Prabjabok
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Mr. Jun-Seok Jang

Ms. Theonette Ruba Maribojoc

Mr. Jung-Yub Lee

Mr. Tawin Tiratanapakhom

Mr. Shumon Mori

Mr. Taiki Kou

Mr. Md. Reaz Akter Mullick

Excellent Presentation from Japan

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Excellent Presentation from Korea

Excellent Presentation from Thai

Group Photo

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INDIGENOUS APPROACH
FOR THE CONSTRUCTION OF A PILOTSCALE SANITARY LANDFILL
IN BANGLADESH
MUHAMMED ALAMGIR and MD. REZAUL ISLAM
Department of Civil Engineering
Khulna University of Engineering & Technology, Bangladesh
alamgir63dr@yahoo.com
ABSTRACTThis paper describes the process employed for the construction of a
Pilot Scale Sanitary Landfill (PSSL) at the ultimate disposal site of
Municipal Solid Waste (MSW) at Rajbandh, Khulna, Bangladesh. A very
simple but technically compatible design is considered here to use local
building materials and to avoid any imported materials such as any kind ofgeosynthetics. A local contractor having no field experience in theconstruction of sanitary landfill is entrusted for the construction. The
available indigenous approach mostly manual labour intensive was
employed to complete the construction of landfill base and the daily
operation during waste deposition period. This field experience described in
the paper will help in establishing the sanitary landfill construction
standard for Bangladesh based on the local conditions, socio-economic
settings and technological capabilities.
1. INTRODUCTION
Insufficient and ineffective government policy and response, lack ofpolitical will, lack of required economic and human resources, weak local
institutions and in absence of appropriate & integrated system result in poor
waste management in Bangladesh, especially in the big cities. These cities
therefore face major problems relating to public health and environmental
pollution (Alamgir et. al 2005a). Among the various problems exist in the
Municipal Solid Waste (MSW) management tiers such as poor collection
and unpleasant scenario at the secondary disposal sites, uncontrolled open
dumping remains as one of the major striking environmental issues in
Bangladesh. Like other Least Developed Asian Countries (LDACs),
ultimate disposal sites of MSW are situated in and around the city areas at
low-lying open spaces, unclaimed land, riverbanks and roadsides(WasteSafe 2005). Uncontrolled dumping of solid waste around the world
becomes one of the major striking social and environmental issues. There
are no controlled/engineered/sanitary landfills in Bangladesh; however,recently Dhaka City Corporation has taken an initiative to convert ‘MatuailOpen Dumping Site’ into the Engineered Landfill (Ahmed 2008). Due tosevere financial constraints and the priorities to other sectors such as food,shelter, health and education, central and local governments are not able to11

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address this social and environmental issue despite the realization that the
only affordable disposal solution in Bangladesh for the foreseeable future –is to establish engineered landfills (WasteSafe 2005, Alamgir et al. 2008).
By considering this, the Pilot Scale Sanitary Landfill has constructed
and being operated to establish landfill construction technology in
Bangladesh. Locally available construction techniques, equipments andbuilding materials were used for the excavation of earth, construction ofvarious components of the landfill such as approach road, site office, baseliner and leachate collection system, leachate holding tank, leachate
treatment tank etc. In every phase of PSSL construction such as material
processing, maintaining slope, placement, remolding and compaction work
manual labors are used where female participant was viewed a focus
because 70% of labors were female. The deposition of waste has beenmonitoring and other necessary aspects have been controlling to ensure thequality management of daily operation. In spite of a pilot scale sanitary
landfill, this is the second experience of the construction of sanitary landfillin Bangladesh. It is observed that using locally available construction
materials and methods using manual labor intensively, the sanitary landfill
can be constructed successfully with necessary components such ascompacted clay liner, leachate detection and collection system. This small
scale but real experience using indigenous method will provide confidence
to the city authority and the concerned stakeholders about landfill
technology in the contrast of presently practicing crude open dumping.
2. PILOT SCALE SANITARY LANDFILL IN BANGLADESH
To establish appropriate construction technology for Bangladesh conditions
using local building materials, technical capabilities and the available
technology, a pilot scale sanitary landfill is designed and hence constructedduring the first-half of 2008 and currently is being operated. In the designand construction, very simple approach relevant to the condition of LDACs
is considered, the details are discussed in the following sections.
2.1 BackgroundTo address one of the most striking environmental and social issues in
the urban areas of LDACs i.e. MSW management, a 12 months feasibility
study project entitled as “Integrated management and safe disposal ofmunicipal solid waste in LDACs - WasteSafe”, was conducted by the
Department of Civil Engineering, Khulna University of Engineering &Technology (KUET), Bangladesh during the period of 2004 to 2005, cofinanced by Asia Pro Eco Programme of the European Commission. The
project proposed a system named as ‘WasteSafe Approach’ with somespecific guidelines to address the MSW issues in an integrated andsustainable way. An appropriate method of MSW management can beestablished for any specific location/region of LDACs considering local
conditions with the analysis and evaluation of practical application of thisapproach. To develop a safe and sustainable management of MSW in22

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Bangladesh through the practical application of WasteSafe Approach withrequired reality check and evaluation of the implemented parts, a three years(2007 to 2009) partnership project entitled as “Safe and Sustainable
Management of MSW in Bangladesh through the Practical Application ofWasteSafe Proposal – WasteSafe II” has been conducting since January 01,
2007 co-financed through a grant received from EU-Asia Pro Eco II
Programme of the European Commission. One of the key activities of thisresearch project is to establish the landfill construction technologies suitablefor Bangladesh conditions as realized from field level experience through apilot scale sanitary landfill (WasteSafe II 2007). To this endeavour, thelandfill cell of the dimension of 50x50x6m, which is 3m below and 3m
above the existing ground surface, has been constructed with the necessarycomponents, at Rajbandh, Khulna, the ultimate disposal site of MSW of
Khulna City Corporation (KCC).
Pilot Scale
Sanitary
Landfill

Open
dump
Open
dump

RajbandhKhulnaCity Map

Khulna-Satkhira
Road

Figure 1: Location and layout of pilot scale sanitary landfill (PSSL) at
Rajbandh as shown with respect to Khulna city map
2.2 Location and sub-soil conditions of the landfill sites
The site located at Rajbandh, Khulna with an area of 5 acres, is 8km far
from the city centre i.e. Royal & Castle Salam Square of Khulna city andsituated along the North-side of Khulna-Satkhira highway as shown in
Figure 1. Actually, it is known as New Rajbandh, the second ultimate open
disposal site of MSW of KCC, just 700m away from the older one, know as
Old Rajbandh of 20 acres land. The New Rajbandh consists of 5 cells where
paddy plantation and fish cultivation were continued till the waste
deposition started. Despite the partial filling of MSW in the older one, KCC
started to dump MSW in the New Rajbandh since January 2007 and firsttwo cell cells along the Khulna-Satkhira Highway were started to fill. The
site of the PSSL is located at the north-west cell as shown in the Figure 1.
The performance of Geoenvironmental structures such as landfill
liners, covers, impoundments of vertical barriers depends mainly on thebasic characteristics of the soils. The geotechnical characteristics of the sub-Indigenous
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soils were determined in the laboratory using conventional test methods
after collecting the soil samples through a sub-soil exploration by washboring method up to a depth of 17m. The existing ground surface exists at a
depth of 1m from the road level, while the ground water table isencountered at a depth of 2m. The fine-grained soils predominated in thesite till the executed depth with the presence of significant portion oforganics in the depth of 2 to 3.5m. The laboratory result shows that in thedepth of 0-2m, the average value of liquid limit, plastic limit and theplasticity index are 53%, 33% and 20, while in the subsequent layers, thesevalues vary from 36-71%, 22-37% and 13 to 41, respectively.
Hydraulic conductivity is a main indicator to judge the suitability ofclay to prepare Compacted Clay Liner (CCL) for the construction of landfill.
Liner soil should have at least 30% fines and 15% clay to achieve hydraulicconductivity in the range of 1x10-7cm/s (Benson et al.1994). In this context,
it is revealed that the clay available in the site can be used for natural barrier
to achieve a hydraulic conductivity in the range of 1x10-7 cm/s, as it
possesses suitable amount of clay and fine fractions. But the soil must not
be placed at too high water content as it results low shear strength and may
be great risk of desiccation cracks forming if the soil dries, and ruts may
form when construction vehicles pass over the liner.
The hydraulic conductivity varies as the water content changes and it
is measured that as the molding water content increases from 12 to 28%, thehydraulic conductivity reduces from 25x10-7 to 15x10-5cm/s. The watercontent of CCL material at the time of compaction is the most importantvariable that controls the engineering properties of the compacted material.
For this soils, it is observed that soils compacted at water content less than
optimum tend to have a relatively high hydraulic conductivity: soils
compacted at water contents greater than optimum tend to have a low
hydraulic conductivity.
2.3 Waste streams
In Bangladesh the solid wastes mainly consists of food and vegetables
waste (Alamgir et al. 2005b). Other items are presents in negligible
percentages. For a basic understanding of the nature of the wastes that are
generally encountered, the type distribution of particle sizes must be known. The
particle size distribution of waste was determined by sieve analysis of the sieveof openings are 400, 300, 200, 100, 75, 38.2, 19.1, 9.52, 4.76 and 2.38mm. The
result shows that the average percent finer in Bangladesh are 100% in 200mm
sieve openings whereas 83% in 100mm, 72% in 76.2mm, 53% in 38.2mm and
33% in 19.1mm and gradually decreases for smaller sieve openings. Physicalproperties of wastes are determined before the deposition in the pilot scale
sanitary landfill. The average moisture content of the waste deposited in thesanitary landfill is 64.48%. The typical composition is shown in Figure 2,
which represents the percentage of solid waste as food & vegetable 93%,
plastics 3%, demolition 2% and others 2%.

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Figure 2: Composition of MSW in Khulna
2.4 Design aspects and various components of PSSL
Despite a pilot scale sanitary landfill cell, the WasteSafe II Teamdecided to consider all the relevant aspects of a standard sanitary landfill
while designing the cell and the components. Emphasis is also given for thebest use of locally available building materials and construction techniques.
However, scientific and technical considerations, guided by field
experiences, are given while fixing up the dimensions and materials
specification of the various components of the landfill. The PSSL consistsof the main components of a standard landfill such as (i) Waste deposition
cell, (ii) Compacted clay liner on a geological barrier with a drainage layer
on top (iii) Top Cover with compacted clay liner, drainage layer, top soil asvegetation cover, surface run-off and percolated water collection system,(iv) Gas measurement and management facility, (v) Leachate detection andcollection system with leachate holding tank, (vi) Leachate pond withleachate treatment facility, (vii) Vehicle inspection and washing facility,

Figure 3: Schematic diagram of the pilot scale sanitary landfill

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July 2009, Thailand(viii) Access Road and Site office, (ix) On-going and post closure
monitoring facilities.
Analysis and Design of the PSSL was completed by WasteSafe Team
members by December 2007 guided by field experiences, local conditionand project provision while fixing up the dimensions and materials
specification of the various components of the landfill. The MSW collectedfrom Khulna city will be deposited in shortest possible time with moderatecompaction efforts. It is decided to follow the standard landfill operation
system with local perspectives will be followed during the construction,
waste deposition and operation, and monitoring phases. Post closure
monitoring will be conducted till the end of the project, which will be
continued by KUET till the active period of the landfill.
The size of the waste containment is 50x50x6m, which is 3m below
and 3m above the ground surface with a side slope of 26o the schematic
diagram is presented in Figure 3. The base liner, the most importantcomponent, includes a leak detection sump system, compacted clay liner,
leachate collection pipe system with a leachate collection layer. It is
designed considering hydrological data of the site, the size of landfill,suitability of construction and locally available of material as shown in
Figure 4. The base liner has a 400mm thick of CCL just above thegeological barrier of 15m clay deposits, over which 200mm thick sand layer
as drainage layer. Leachate collection pipe is placed in the drainage layer,
while the leachate detection pipe is placed just below the CCL. The
generated leachate will be stored in leachate holding tank of 2x2x4m andlater transfer to the leachate treatment pond of 10x20x3.5m. The system isdesigned in such a way so that the leachate can be collected and thus stored
in the tank through gravity flow. The leachate detection pipe is also
designed and connected in the leachate holding tank by ensuring gravity
flow. From tank the leachate will be transferred to the pond using pump.

Figure 4: Schematic diagram of base liner66

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The final cover of PSSL as shown in Figure 5, consists of top soils,
percolation water collection layer, compacted clay liner and gas collection
pipe system with gas collection layer. The inclusion of biofilter for methane
oxidation is kept as possible inclusion in the top cover. The top has gas
collection layer at the top of 200mm thick just over the waste, then 300mm
CCL, 150mm fine sand and 150mm sand plus brick aggregates as
percolation and drainage layer which is followed by 600mm top soil. The
combination of fine sand layer and then sand and brick aggregates is given
to ensure capillary rise of water for the keeping CCL wet as much as
possible to prevent possible desiccation and cracking. Top soil layer of
600mm thick will help to support and maintain the growth of vegetation byretaining moisture and providing nutrients. There is a Leachate
Recirculation System that will maintain moisture and enhance degradationof waste. To control possible soil erosion, mild slope is maintained at the
top cover, which is 15o at the edge to middle and then 7o from middle to top.

Figure 5: Schematic diagram of the cap liner

3. CONSTRUCTION OF LANDFILLThis PSSL is the first of this kind of construction in Bangladesh. The
construction works have been conducted based on the design ensuring closemonitoring by the project engineer. Another important aspects is that the
locally available construction techniques, equipments and building materials
were used for the earth excavation, construction of various components of
the landfill such as approach road, inspection point, site office, base liner,
leachate collection and detection systems, leachate holding tank, leachate
pond and the small scale leachate treatment option.

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3.1 People’s participation
In a waste management system, social issue plays very important roleand sometimes overrule the technical requirements and aspects. Moreover,as there is no sanitary landfill in Khulna, it is necessary to inform the people
about this pilot project, which might play very important role for taking
decision about actual one. From this view point, after completion ofnecessary official requirements, an inauguration ceremony was arranged on
19th March 2008 at the site, as shown in the Figure 6 to demonstrate thesalient features of the project, positive impacts on the environment and its
future on waste management. The Hon’ble Mayor of KCC, Ward
Councilors, KCC officials, local people, civic societies, academicians,
researchers, NGOs/CBOs, journalists and other relevant stakeholders were
present. In the site, project coordinator graphically presented all the relevant
elements and the related aspects of the sanitary landfill. This demonstration
played a very positive role in the next course of actions as realized during
construction and operation phases.

Figure 6: Demonstration on PSSL at site
3.2 Appointment of the contractor
An open tender for the construction of the PSSL was published on
January 03, 2008 by KCC in the major local and national both the English
and Bengali newspapers. It also launches in the website: www.wastesafe.org.
The tender documents kept available both in KCC and KUET for theinterested bidder. The closing date for the submission of document was 27
Jan., while the opening date was 28 Jan. 2008. Meeting of Technical
Evaluation Committee was held at KCC Bhaban, Khulna on 22nd February
2008. Among the four bidders for the construction of pilot scale sanitary
landfill, considering all the relevant aspects, it was decided to select thelowest bidder for the construction works. None of the firm has theexperience of the construction of sanitary landfill. However, acknowledging
the reality and the volume of works, selection was made. The work order
was given to the selected construction company on March 12, 2008 and theconstruction works started on March 19, 2008.
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3.3 Layout and site preparation
The site consists of five shallow ponds having earth embankments inall sides; those have been using for traditional agriculture and fish
cultivation before the starting of waste dumping from the beginning ofJanuary 2007. For the PSSL construction, the pond located in the north-west
corner is considered. As per the design and location of various components,
layout was completed using traditional civil engineering survey equipments
and the site was cleaned by removing grass, existing paddy plants and others
unplanned trees as shown in the Figure 7.

Figure 7: Layout and preparation of site to start construction
3.4 Earth excavation
The main volume of works is the excavation of earth for theconstruction of landfill cell till the required depth. The ground surface wasexisted at the depth of 1m from the road level. Firstly, it was decided thatthe excavation will be conducted mechanically using the excavator,
however, the non-availability of the machine, the alternate option came intomind. Moreover, due to the existence of very soft soil and the size of theland, it was decided to conduct the excavation works using manual labour
and traditional digging tools as shown in the Figure 8. Daily about 50 to 100
people works at the site and nearly 70% of the work force were female. The
excavation of earth was completed successfully maintaining proper slope ofthe cell as per the design and specification. Very close monitoring was
conducted by the project engineer for the proper execution of the works.
During the execution of the earth excavation, the soil from the top 2m,
which seems to be suitable for the preparation of CCL, were collected withcare and deposited in the middle of cell as well as on the bank, those wereused later. During excavation, soils were also placed properly in the northside and south-west corner for the construction of the earth embankment
surrounding the cell. Initially, it was thought that manual work might not besuitable for such sophisticated works, however, finally it was proved that,
this kind of work at small scale can be done properly within reasonable
timeframe if proper level of monitoring can be ensured. However, suchworks will not be suitable in the monsoon season.

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Figure 8: Manual excavation of earth using traditional digging tools
3.5 Preparation of compacted clay liner
Liner on the bottom and sides of landfills that contain solid wastes has
been considered necessary in many countries since the late 1970s. The main
requirements of liners are the minimization of pollutant migration, highadsorption capacity and retardation of pollutants, resistance to chemicals
and low swelling and shrinkage potential (Brandl 1992). Compacted ClayLiner (CCL) is the most important element of a sanitary landfill to prevent
migration of leachate from landfill (Daniel & Keorner 2007). Clay and clay
minerals play an important role in increasing containment removal capacity
as well as in reducing hydraulic conductivity of soil because of their large
specific surface area and high Cation Exchange Capacity (CEC) (Oweis &Khera 1998; Czurda & Cranston 1991).
The low hydraulic conductivity of clay minerals makes them potential
materials to use as CCLs in sanitary landfill for environmental protection.
The attenuation positively charged chemical species in leachate through aclay liner is a function of CEC of the liner material. Higher CEC of a clay
liner material will result in greater amount of cationic containments being
removed from the leachate (Kayabali 1997); Rowe et. al. (1995)
recommended that soils with a minimum CEC of about 10 meq/100g of soil
might be specified for clay liner. Soils classified as inorganic clay with highplasticity (CH) is considered as the suitable material for landfill liner (Oweis& Khera 1998). The clay liner is typically designed to have a hydraulicconductivity ≤1x10-7cm/s. The origin of this design criterion is unclear;
1x10-7cm/s was evidently selected on the assumption that this was anachievable value that would result in negligibly small seepage through theliner. In the PSSL, the clayey soils collected from the depth of 0 to 2m ofthe site, were used for the construction of CCL as the test results revealed
the suitability of the clay. The stock pile of clay used to construct CCL andlater its spreading over the bed for compaction are shown in Figure 9.

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Figure 9: Stock pile of appropriate and its spreading to prepare CCL
The compaction of soil was done manually in three layers by using
locally manufactured hammer made of cast iron connected with timber
handle. The clay were placed uniformly over the bed and then compacted byadding required amount of water as shown in Figure 10. To ensure
uniformity of compaction, a locally practiced technique is maintained while
applying the hammer drop by a group of female worker. The thickness of
the layer was maintained in such a way that the resultant thickness of the
CCL reached as 400mm. The degree of compaction of the CCL waschecked in the field by sand cone test method as shown in Figure 11, usuallyused as field compaction test. Initially, it was planned to prepare the CCL by
using Sheep foot or smooth wheeled roller, however, due to the soften
nature of soils; later compaction was done using manual intensive practiceand equipments. However, finally a very impressive CCL was constructedwith locally available technology and equipments without heavymachineries and skilled people as shown in Figure 12.

Figure 10: Manual compaction of Figure 11: Field compaction test ofclay to prepare CCL of the PSSL
the prepared CCL of the PSSL

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Figure 12: Partial view of completed CCL
3.6 Leachate detection pipe System
To depict the applicability of simple liner system and the functionalityof CCL, leachate was collected through a leachate detection pipe placed justbelow the CCL. A long trench of 0.75mx0.75m starting from the center
point of the cell till the leachate storage tank was dug in the bed of naturalsoil barrier of the landfill site to accommodate the pipe. A No. 10 perforated
PVC filter pipe of 38mm in diameter used as leachate detection pipe system
was placed in the trench which was surrounded by 150mm thick layer of
granular soil to ensure free drainage of detected leachate. To ensure thegravity flow of the collected leachate, 1% slope towards the sump wasmaintained as shown in Figure 13. A vertical pipe having larger diameter isconnected with the outlet end of the pipe, which is just at the inner surfaceof the tank, so that the leachate can be collected and the flow can bemeasured by removing leachate from the top. In all these works, local
materials and traditional techniques were used and the performance so far isfound quiet satisfactory.

Figure 13: Placement ofleachate detection pipe

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3.7 Leachate collection system

Figure 14: Jointing of leachatecollection pipe in the field

Figure 15: Preparation of bed
to place leachate collection pipe

Leachate Collection System (LCS) is considered as one of the mostimportant components of sanitary landfill. The system commonly comprises
perforated pipe at regular spacing in a continuous blanket of granularmaterial to collect leachate. The primary function of leachate collection
system is to control the leachate head acting on the liner system. Controllingof leachate head minimizes the advective transport of contaminants and also
controls side slope leachate breakouts. Lowering height of leachatemounding, leachate seeps can be minimized. Leachate pressure head onliner gets reduced, hence gradient through liner gets reduced resulting flow
reduction through liner. Finally, removing contaminants from the landfill
reduces the available amount contaminants for transport.
To collect the leachate through gravity the bed was constructedmaintaining a 3o slope and the leachate collection pipe is placed at the
middle of the cell. The leachate collection layer of 200mm thick was
construction to accommodate a perforated leachate collection pipe with
100mm dia. and surrounded by washed brick aggregates as shown in the
Figures 14-17. The collection was laid maintaining a slope of 3o towards theleachate holding tank to ensure the easy movement of leachate throughgravity flow as mounded on the leachate collection pipe.

Figure 16: Placement ofleachate collection pipe at bed

Figure 17: Leachate collection pipewith brick aggregates as filter media

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A Leachate holding tank of 2x2x4m size was constructed with brickmasonry and properly connected with both the leachate collection anddetection pipes as shown in Figure 18. From the tank, the leachate can betransferred to the treatment tank before discharge to natural streams asshown in Figure 18. Leachate pond has the size of 10x20x3.5m which was
excavated, lined with locally available polythene sheet with double layer
and compacted clay liner, using local technique as adopted from theconstruction of cell.

Figure 18: Leachate holdingtank & treatment pond

Figure 19: Washing platform for
waste carrying vehicle

A vehicle washing platform of 4x7m size was constructed just in frontof site office to wash the outgoing vehicle to prevent any possible litteringof waste while running out in the street as shown in Figure 19. For theconstruction of this platform very strong reinforced cement concrete wasused. The access road was also constructed by the compacted brick bats andsands for the easy movement of vehicles.
The site office as shown in Figure 20 was constructed using local
material such as golpata and bamboo for roofing, wooden pile (bullah) andterrazzo for column support and fencing around the periphery and brickmasonry and concrete work for foundation and floor. The entire area is
protected by constructing a boundary using bamboo pillar and barbet wire.
The site office has been using to control and to inspect incoming vehicles
and the wastes quality and quantity, office recording, demonstration oflandfill construction and operation to the visitors and to conduct otherrelevant works.

Figure 20: Site office & control room

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4. WASTE DEPOSITION AND DAILY OPERATIONMSW generated in KCC areas has been deposited in the PSSL. The
incoming waste carrying vehicles are being counted, volume of waste
measured roughly, weighted indirectly, inspected and hence recorded
properly. Initially, deposited wastes were spreaded manually, later KCC’s
vehicle such as Back-wheeled Compactor cum Excavator and Chain-dozer,
were employed for the spreading of wastes and hence compacted by the
self-weight of vehicle and repeated passes. In some instances, forconvenience, the manual labor and the compactor worked together for waste
plantation and compaction as shown in Figure 21. At the beginning
polythene sheet was used for temporary daily cover and presently sand isused irregularly as daily cover. Heavy rainfall during monsoon createdspecial problem to operate the landfill properly, such as damage of approach
road, movement of vehicle on the wastes, difficulties of waste plantationand compaction and generation of huge amount of leachate. During
monsoon, to relieve the pressure of contaminants in the landfill, leachate
was transferred to the nearest pond temporality; those were evaporated
during dry season. One of the most difficult tasks is to keep the landfill freefrom birds, flies and dogs. A traditional system is being used to keep thelandfill free from birds. However, in spite of limitation of resources and due
to inexperience of landfill operation in local as well as Bangladesh condition,
the waste deposition and daily operation have been running satisfactorily.

Figure 21: Manual spreading waste
5. CONCLUSION
The existing practice of crude open dumping in Bangladesh leads toirrecoverable environmental hazards throughout the country. The country
needs to convert the existing ultimate disposal sites of MSW through thedevelopment of sustainable landfill based on its prevailing socio-economic
settings, technological capabilities and the local conditions. The successhistory of the studied PSSL achieved so far, based on local building
materials, available techniques, locally available traditional equipments,
manual labor intensive works and simple design, will bring a positivechange in the attitude of the local authority to switch from existing practiceof crude opening dumping to sanitary landfill.
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ACKNOWLEDGEMENTS
The financial supports provided by European Commission through the
project of EU-Asia Pro Eco II Programme (Grant Contact No.
ASIE/2006/122-432) are gratefully acknowledged.
REFERENCES
Ahmed, M.F. 2008. Improvement of solid waste facilities at Matuail in
Dhaka City. Proc. of the National Seminar on Solid Waste
Management – WasteSafe 2008, 9-10 February 2008, Khulna, pp.1-12.
Alamgir, M. Ahsan, A., McDonald, C.P., Upreti, B.N. and Islam, R. 2005a.
Present Status of Municipal Solid Wastes Management in Bangladesh.
Proc. International Conference on Waste-The Social Context, May
11-14, Edmonton, Alberta, Canada. Pp 11-20.
Alamgir, M., Chowdhaury, K.H., Roehl. K.E., Stentiford, E.I. and Bari, Q.H.
2005b. Basic Characteristics of Municipal Solid Wastes ofBangladesh. Proc. International Conference on Waste-The Social
Context, May 11-14, Edmonton, Alberta, Canada. Pp 21-26.
Alamgir, M., Bidlingmaier, W., Hossain, Q.S., Mohiuddin, K.M. and Islam,
M.R. 2008. Future Prospects of CDM Framework to Develop a
Sustainable Landfill System in Bangladesh to Replace Open Dumping.
Proc. of 6th Intl. ORBIT Conf. on ‘Moving Organic Waste Recycling
Towards Resource Management and Biobased Economy’, October 1315, 2008 at Wageningen, the Netherlands, pp. 246/1-10.
Bensen, C.H., Zhai, H. and Wang, X. 1994. Estimating hydraulicconductivity of clay liners. Journal of Geotechnical Engineering,
ASCE, Vol.120, No.2, pp.366-387.
Brandl, H. 1992. Mineral liner for hazardous waste containment,
Geotechnique, Vol 42, No. 1, pp. 57-65.
Czurda, P. and Cranston, M. 1991. Observation on the performance ofcomposite clay liners and covers”, Proceedings of Geosynthetic
Design and Performance. Vancouver Geothechnical Society,
Vancourver, British Columbia.
Daniel, D.E. and Koerner, R.M. 2007. Waste Containment Facilities, 2nd
Edition, American Society of Civil Engineers, 978-0-7844-0859-9,
Reston, Virginia.
Kayabali, K. 1997. Engineering aspects of a novel landfill liner material:
bentonite amended natural zeolite”, Engineering Geology, Vol. 46, pp.
105-114.
Oweis, I.S. and Khera R.P. 1998. “Geotechnology of waste management”,
2nd Edition, PWS Publishing Company, USA.
Rowe, R.K., Quigley, R.M. and Booker, J.K. 1995. Clayey barrier system
for waste disposal facilities”, E& FN Spon, London.
WasteSafe 2005. Integrated Management and Safe Disposal of Municipal
Solid Waste in Least Developed Asian Countries: a feasibility study.

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M. Alamgir, C. McDonald, K.E. Roehl and A. Ahsan Edited
WasteSafe Report, KUET, Bangladesh.
WasteSafe II 2007. Annual Report 2007 of WasteSafe II – Safe andSustainable Management of Municipal solid waste in Bangladesh
through the Practical Application of WasteSafe Proposal. A project of
EU-Asia Pro Eco II Programme of EC, KUET, Khulna, Bangladesh.

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A TARGET-ORIENTED NETWORK DESIGN MODEL
FOR TRANSPORTATION MODE CHOICE PROBLEMYONGTAEK LIM
Dept. of Transportation & Logistics
Chonnam National University, Korea
limyt@chonnam.ac.kr
ABSTRACTThe network design problem (NDP) is to determine a set of design
parameters that leads the transportation system to an optimal state after
allowing for travellers’ responses. This paper presents a modified NDP fortransport mode choice problem, called target-oriented NDP, which contains
a target that we try to arrive in real world. Unlike general NDP which seeks
an optimal value to minimize or to maximize objective function, in targetoriented NDP traffic manager or operator can set a target level and then try
to find an optimal design variable to attain this target. An example forsimple transportation mode choice problem is given to test the model.
1. INTRODUCTION
In order to alleviate traffic congestion, the network design problem (NDP)
has been used because it is to determine a set of design parameters that leads
the road network to an optimal state. The scope of the NDP includes traffic
signal control, traffic information provision, congestion charge and newtransportation modes as well as road expansion. In general, the NDP usuallyformulated a bi-level problem, which has an upper level part that represents
system design and a lower level one that represents travellers’ responses, try
to find an optimal value to minimize or to maximize objective functions
such as total travel time or net social benefit.
We have a long list of network design problems, which can beclassified into the two classes of discrete network design problem (DNDP)
and continuous network design problem (CNDP) according to the nature of
the design parameter. For solving such CNDP, the sensitivity analysis ofuser equilibrium was introduced by Tobin and Friesz (1988) and has beenused for the static network design problem by Yang (1995; 1997) and in the
dynamic case by Heydecker (2002). Various sensitivity analysis-basedheuristic algorithms are also proposed for the CNDP and relevant problems(Friesz et al, 1990; Yang and Yagar, 1994;Yang et al, 1994, Lim et al, 2005).
Recently Maher et al (2001) proposed a bi-level problem for trip matrix
estimation and traffic control problem with stochastic user equilibrium(SUE), and their solution algorithms in which SUE assignment map was

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approximated as a linear relationship. More detail and wider literature
reviews and their algorithms are described in the paper of Yang et al (1998).
This paper presents a modified NDP for transport mode choiceproblem, called a target-oriented NDP, which contains a target that we try to
arrive in real world. Unlike general NDP described above, the targetoriented model sets a target level in advance and then try to find an
optimum under this constraint, which represents a sub-optimum, comparedto general NDP. To reach the target, two methods have been developed suchas direct sensitivity design model and cross sensitivity design model that use
derivative information. This paper has been organized as follows. In next
section, the basic idea of target NDP is given, and its formulation andsolution algorithm are also described. An equilibrium condition fortransportation mode choice problem is defined in section 3. In section 4, anexample for a simple mode choice problem is given to test the model.
Finally, conclusions are drawn in section 5.
2. TARGET-ORIENTED NDP FOR TRANSPORTATION MODE
CHOICE PROBLEM
2.1 Basic idea of target network design model
The main difference between general NDP and the target NDP is thatin target NDP traffic managers set a prior specific level, a target, which theywish to attain for the transportation system. On the contrary, in general NDP
they try to optimize the system without setting a target. For instance,
congestion pricing is introduced to maximize net social benefit oftransportation system and search optimal design parameter (pricing rate) ingeneral NDP. But in target NDP traffic managers set a target level of service(LOS) in advance, like LOS C, and then try to search an optimal design
parameter able to reach the target. Hence the target NDP may be a variant of
the general NDP. This kind of target NDP can be expressed as follows.
min

L( p( X )) = L( F − F * )subject(1)

F = p( X )Where, X is design parameter and F * is target level. F is a functionof the design parameter, which represents traffic assignment or mode choice
mapping in transportation problem. Equation (1) is similar to traffic control
model for attaining desired goal, for example ramp metering on expressway
system, in that case the parameter X can be interpreted as a control variable
and F as a state variable.
2.2. Model formulation for transportation mode choice problem
In this section we present a target NDP for mode choice problem. If
we set a target value as p r* , target split of mode r , then the design20
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parameter can be determined by following mathematical minimization
program given equation (2), which minimizes the difference between target
modal split and estimated modal split derived from logit mode choice modelin constraint.
1
(2)
min
L( p(t (d ))) = ( p r − p r* ) 22e −θtrSubject to p r (t ) =∑ e −θtuu

Where t i = ∑ vij + d i , vij is the j attribute (travel time or travel cost,thjetc.) of mode i , and d i is design parameter of mode i . θ is a scaleparameter of the logit model. This minimization program can be solved by
two types. The first is direct sensitivity design method, which use thesensitivity of change of probability with respect to its own attributes toachieve its target. The other is cross sensitivity design method, which use
indirect sensitivity of other transportation mode.
Consider the first method. By using the direct sensitivity, equation (2) canbe linearly expanded at d r0 into equation (3).
L( p (t (d r ))) ≈ L( p(t (d r0 ))) +∂L ∂p r ∂t r
(d r − d r0 )∂p r ∂t r ∂d r d = d 0
r

(3)

r

Here notes that this linear equation should be equal to zero at
minimum, so we get following formulation.
L( p(t (d r0 ))) +∂L ∂p r ∂t r
(d r − d r0 ) = 0∂p r ∂t r ∂d r d = d 0
r

r

−1

⎤
⎡ ∂L ∂p ∂trr⎥ L( p(t (d r0 )))dr = d − ⎢⎢⎣ ∂p r ∂t r ∂d r d r = d r0 ⎥⎦
0
r

(4)

*

Finally we get optimal design parameter of d r for reaching the target
modal split through equation (4), which is a recursive formula computed∂p (t )∂L
= ( p r (t ) − p r* ) and rwith ease. Where,
can be derived from logit
∂t r
∂p r
model as,∂pr (t )=∂t r− θe −θtr (∑ e −θtu ) − e −θtr (−θ )e −θtru(∑ e −θt u ) 2= −θpr (t ) + θ ( pr (t )) 2u

= −θpr (t )(1 − pr (t ))A Target-Oriented
Transportation
Mode
ChoiceProblemTarget-OrientedNetwork
NetworkDesign
DesignModel
ModelforFor
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Mode
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Following the same way, we get cross sensitivity design method fortransportation mode r with regard to design parameter d a , (a ≠ r ) of othermode a , as given in equation (5).
−1⎤⎡ ∂L ∂p ∂tar⎥ L( p(t (d a0 )))da = d − ⎢⎢⎣ ∂p r ∂t a ∂d a d a =d a0 ⎥⎦∂p (t )∂L
= ( p r (t ) − p r* ) , and r
Where,= θp r (t ) p a (t ) .
∂p r
∂t a
0
a

(5)

Based on the equation (4) and (5), solution procedures for direct andcross sensitivity methods can be listed as follows.
[step 0] initialization
set iteration number n = 0
set initial design parameter d in , (i = r , a )and target value of mode r as p r*
[step 1] n = n + 1[step 2] calculate p r (t (d in −1 )) by logit model with d in −1[step 3] update(3.1) direct sensitivity design method
−1

n
r

d =d

n −1
r

⎡ ∂L ∂p ∂t⎤rr⎥ L( p (t (d rn −1 )))−⎢⎢⎣ ∂p r ∂t r ∂d r d r = d rn −1 ⎥⎦(3.2) cross sensitivity design method ( a ≠ r )−1⎡ ∂L ∂p ∂t⎤ar⎥ L( p (t (d an −1 )))d an = d an −1 − ⎢⎢⎣ ∂p r ∂t a ∂d a d a =d an −1 ⎥⎦[step 4] convergence test
If d in − d in −1 < κ , then stop; otherwise, proceeds [step1]
Where κ is a predetermined small value for convergence criteria.
3. TRANSPORTATION MODE CHOICE EQUILIBRIUM IN LOGIT
MODELConsider a transportation network with one origin-destination (OD) pair w
connecting two transport modes of auto and transit, then the flows ofauto, f a , can be calculated by following logit model.
f a = qwe −θta∑ e −θtuu

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Where q w is travel demand for OD pair w . Following the same way,for rail f r is computed.
f r = qwe −θtr∑ e −θtuu

If we divide f a by f r , thenf a e −θta=
f r e −θtr
Take logarithm on both sides.

⎛ f ⎞ln ⎜⎜ a ⎟⎟ = −θ t a + θ t r⎝ fr ⎠
And then we finally get following equation.

ta +

1

θ

ln( f a ) = t r +1θln( f r )(6)

Equation (6) represents a transportation mode choice equilibrium
condition in logit model. Let the condition define equivalent travel time formode i ,Eci = t i +1θln( f i ),∀ mod e i(7)

in which Eci is constant for each mode at equilibrium mode choice
condition.
4. NUMERICAL CALCULATIONSIn order to illustrate use of the model and the solution algorithm suggested
in the paper, a simple example for mode choice problem is used. As shownin figure 1, this example has two transportation modes of auto and rail
serving travellers between one origin-destination pair. We assume that thetravel time of auto is a function of flows on the roads, but that of rail isconstant (fixed value) irrespective of passengers on board. Travel demand
for the OD pair is 5 units.
Travel time of auto : t a = 2 + f a
Travel time of rail : t r = 5
Travel demand : q w = f a + f r = 5

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July 2009, Thailand
July 2009, Thailand( f a , ta , d a )( f r , tr , d r )Figure 1: Example for target mode choice problem
In initial stage with no design parameter, the modal splits for eachmode are divided by p a = 0.52 , p r = 0.48 with f a = 2.6 , f r = 2.4 andt a = 4.6 , t r = 5 . Now consider target problem. Let target modal split of rail
set 0.6, or p r* = 0.6 , and the parameter of logit model θ = 0.2 . If we
introduce the design parameter to the travel time of auto or rail, then thefunction can be rewritten as follows.
t a = (2 + f a ) + d a
tr = 5 + d r

4.1 Direct sensitivity design parameter
Figure 1 depicts the change of modal split of rail ( p r ), design
parameter ( d r ), and travel time of rail ( t r ). As iteration number increases,p r converges to the target modal split ( p r* = 0.6 ) and design parameterdecreases from zero to the value of -2.4274, which leads the travel time ofrail to the value of 2.5726 from 5. These findings show that the model
proposed in this paper produces correct solution and converges to steadystate. Table 1 shows some major findings from the model with varying
target modal split of rail from 10% to 70%. The optimal design parameter*( d r ) decreases according to increasing the target mode split of rail as we
expect. Among figures in the table, the partial derivative of rail split with∂pregard to travel time of rail, r , has negative sign, which represents that∂t rmodel split of rail decrease as increase of travel time of rail. Note here that
Eca and Ecr last two rows in the table have the same value, which
represents transportation mode choice equilibrium described in section 3.
Figure 3 illustrates some sensitivities of objective function withrespect to design parameter for varying θ in logit model. All sensitivities
are going down to zero point, which represents that no more increments ofobjective with regard to increment of travel time exist.

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proportation of pr & design parameter (dr), tr(target pr=0.6, theta=0.2)
0.7

6
5

0.6

4
3

0.4

2
t_pr=0.6

0.3

pr

0

dr

0.2

-1

tr

0.1

1

time

proportation

0.5

-2

0
1

2

3

4

5

6

7

8

-3
9 10 11 12 13 14 15 16 17 18 19 20
iteration

Figure 2: Change of modal split of rail ( p r ) and design parameter ( d r ) as
iteration number increases : direct design
Table 1: Some key finding with varying target split of rail ( p r* ): direct design
target split of rail, p r*
0.1

0.2

0.3

0.4

0.5

0.6

0.7

d r*

10.5861

6.5314

3.8364

1.6273-0.4000 -2.4274 -4.6365

fa

4.32233.79513.34012.92222.52002.11701.6963fr

0.67771.20491.65992.07782.48002.88303.3037ta

6.32235.79515.3401

4.92224.52004.11703.6963tr

15.5861 11.5314

8.8364

6.62734.60002.5726

0.3635

pa

0.9

0.8

0.7

0.6

0.5

0.4

0.3

pr

0.1

0.2

0.3

0.4

0.5

0.6

0.7

∂pr
∂t r

-0.018-0.032-0.042-0.048-0.05-0.048-0.042Eca (auto) 13.6411 12.4636 11.3702 10.2839

9.14137.86696.3386Ecr (rail ) 13.6411 12.4636 11.3702 10.2839

9.14137.86696.3386

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Transportation
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ChoiceProblemTarget-OrientedNetwork
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sensitivities with varying theta (t_pr =0.6)

0.008

sensitivity

0.007
0.006

theta=0.01

0.005

theta=0.1
theta=0.2

0.004
0.003
0.002
0.001

iteration

0
1

Figure 3: Sensitivities of

3

5

7

9

11

13

15

⎤
∂L ⎡ ∂L ∂p r ∂t r
⎥ for varying θ in with=⎢∂d r ⎢ ∂p r ∂t r ∂d r d = d n −1 ⎥
r
r
⎦
⎣
*
p r = 0.6

4.2 Cross sensitivity design parameter
Figure 4 shows the change of modal split of rail ( p r ), design
parameter of auto ( d a ), and travel time of auto ( t a ). The rail split also
converges to the target split ( p r* = 0.6 ) as iteration number increases. Afterseveral iterations, we get steady stable values of d a and t a shown in the
figure. Compared to the figure 2 of direct sensitivity design parameter, thedesign parameter of auto ( d a ) gradually increases, which leads to incrementof travel time of auto, and finally to increase of split of rail in system.
Table 2 explains key findings derived from the cross sensitivity design
method, which includes optimal cross design parameter ( d a* ), travel times,∂p r). This table∂t aalso shows that Eca and Ecr has the same value, which represents theexistence of mode choice equilibrium. Note here also that in case ofp r* = 0.1 and p r* = 0.2 , travel times of auto ( t a ) has negative values, which
does not exist in real world. This result comes from the fact that under fixedrail travel time ( t r = 5 ), auto travel time should be decreased below zero for
reducing the split of rail to 0.1 or 0.2. So this phenomenon implies that we
can not achieve such target splits just by reducing travel time of auto.
modal splits for each mode, and derivative of rail split (26
26

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37
proportation & design parameter (da), ta(target pr=0.6, theta=0.2)
0.7

10
9
8

0.5

7

0.4

6

0.3

4

5

time

proportation

0.6

3

0.2

2

0.1

1
0

0
1

2

3

t_pr=0.6

4

5

6

pr

7

8

ta

9

10

11

12
iterationdaFigure 4: Change of modal split of rail ( p r ) and design parameter ( d a ) as
iteration number increases : cross design
Table 2. Some key finding with varying target split of rail ( p r* ): cross design
target split of rail, p r*
0.1

0.2

0.3

0.4

0.5

0.6

0.7

d a*

-9.9861 -5.9315 -3.2365 -1.0273

1.00003.0273

5.2365fa
fr

2.00003.00002.00003.00002.00003.00002.00003.00002.00003.00002.00003.00002.00003.0000ta
tr-5.9861 -1.9315
5.0000 5.0000

0.76355.00002.97275.00005.00005.00007.02735.00009.23655.0000

pa
pr

0.9
0.1

0.8
0.2

0.7
0.3

0.6
0.4

0.5
0.5

0.4
0.6

0.3
0.7

∂pr
∂t a

0.018

0.032

0.042

0.048

0.05

0.048

0.042

Ec a (auto) 1.5343
Ec r (rail ) 1.5343

5.00005.00007.02737.02738.46578.46579.58159.581510.4931 11.2638
10.4931 11.2638

A Target-Oriented
Transportation
Mode
ChoiceProblemTarget-OrientedNetwork
NetworkDesign
DesignModel
ModelforFor
Transportation
Mode
ChoiceProblem27
27

July 2009, Thailand
July 2009, Thailand

5. CONCLUSION
In this paper, we propose a target-oriented network design model fortransportation mode choice problem, which is a variant of general network
design problem. To reach the target, two methods have been developed suchas direct sensitivity design and cross sensitivity design model. Due to theexistence of explicit function between travel demands of each mode anddesign parameter in logit model, we can easily derive the derivative andintroduce it to the solution procedures. From the numerical examples, we
calculate optimal design parameters, sensitivity and some other key findings
as well as equivalent travel time ( Eci ), which represents the equilibrium
condition among transportation modes. These results show that the model
converges to steady state and its algorithm is feasible and advantageous interms of real world applications.
REFERENCES
Friesz, T.L.,Tobin, R.L.,Cho, H.-J. and Mehta, .J. 1990. Sensitivity analysis
based heuristic algorithms for mathematical programs with variational
inequality constraints. Mathematical Programming, 48, 265-284.
Heydecker, BG 2002. Dynamic equilibrium network design. In:
Transportation and Traffic Theory in the 21st Century (ed MAP
Taylor). Oxford: Pergamon, 349-70.
Lim, Y., BG. Heydecker, S. Lee 2005. A continuous network design modelin stochastic user equilibrium based on sensitivity analysis, Journal ofAdvanced Transportation, Vol 39 No. 1, 63-79, WinterMaher, M.J., Zhang, X, and Van Vliet, D. 2001. A bi-level programming
approach for trip matrix estimation and traffic control problems withstochastic user equilibrium link flows. Transportation Research, 35B,
23-40.
Tobin, R.L. and Friesz, T.L. 1988. Sensitivity analysis for equilibrium
network flows. Transportation Science, 22, 242-250.
Yang, H. 1995. Sensitivity analysis for queuing equilibrium network flow
and its application to traffic control. Mathematical and Computer
Modeling, 22, 247-258.
Yang, H. 1997. sensitivity analysis for the elastic demand network
equilibrium problem with applications. Transportation Research, 31B,
55-70.
Yang, H and Yagar, S. 1994. Traffic assignment and traffic control in
general freeway-arterial corridor systems. Transportation Research,
28B, 463-486.
Yang, H, Yagar, S.,Iida, Y. and Asakura, Y. 1994. an algorithm for the
inflow control problem on urban freeway networks with user-optimalflows. Transportation Research, 28B, 123-139.
Yang, H.and Bell, M.G.H. 1998. Models and algorithms for road network
design: a review and some new developments. Transportation
Reviews, 18, 257-278.

28
28

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndCURRENT REMOTE SENSING
FOR LAND ENVIRONMENT AND DISASTER
HARUO SAWADA
International Center for Urban Safety Engineering
Institute of Industrial Science, The University of Tokyo, Japan
Department of Civil Engineering, The University of Tokyo, Japan
sawada@iis.u-tokyo.ac.jp

Current Remote Sensing for Land
Environment and Disaster
Disaster

ICUS, IISThe University of Tokyo
1

International Center for Urban Safety
Engineering (ICUS)(Established on April 1, 2001 for a 10-year period)
Director: MEGURO, Kimiro
The International Center for Urban Safety
Engineering (ICUS) was established in April 2001
with the objective of developing new systems andmethods to protect individuals from various
accidents and disasters that could possibly occurin everyday life, mostly in urban areas throughout
the world. ICUS works in collaboration withresearchers and engineers from around the world.

The research areas of this center are :1) Sustainable Engineering
2) Urban Safety and Disaster Mitigation
3) Infrastructure Information Dynamics

29
29
29

July 2009, Thailand
July 2009, Thailand

International Activities/ ICUS and RNUS

Construction Material Management –Kato lab
Study on providing durable infrastructures as well as maintenances ofexisting infrastructures while reducing cost, resource consumption, andenvironmental impact; A sustainable design philosophy is necessary todevelop the new concrete materials to meet this challenge.
Artifact formation

Artifact selection
Environmental
New
technology

Sustainable “triple bottom line” criteria
Economic

Socialgroup

Socialgroup

Socialgroup

Problems &solutions

Problems &solutions

Problems &solutionsSocialTraditional engineeringknowledge

Social groups’ input
DesignInformation
Industrial
waste
Recycled
materials

Artifact A

Artifact B

Artifact C

Determine form and function of new concrete materials
based on negotiation between different social groups

Localmaterials

Media

Artifact

Construct design information and select media based onsustainable criteria

Framework for sustainable concrete materials

30
30

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Life-Cycle Management of Urban
Infrastructure – Yokota lab
Study on the Life-cycle management which includes a series of actions
to evaluate the grade of deterioration and structural performancedegradation by inspection, to predict the future progress of performancedegradation, and to propose the alternatives of appropriate intervention.

Geotechnical and Geoenvironmental
Engineering – Kuwano lab
Studies on the mechanism of generation and the process of expansionof cavities and the loosening of the surrounding ground in order toevaluate the degree of ground looseness and also the resistance ofthe ground against being washed away.

Cave-in in the road caused by an old sewer pipe

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster

31
31

July 2009, Thailand
July 2009, Thailand

Urban Earthquake Disaster Mitigation
Engineering – Meguro lab
Study on Integrated Information System for Urban Earthquake Risk
Assessment as a tool to increase the disaster situation awareness of
individuals and to effectively reduce disasters; a 3D-GIS database inwhich each structure in an urban area are clearly identified.

Urban Earthquake Risk Assessment
using 3-D Micro GIS

Numerical Simulations – Huang lab
Studies on urban safety and environmental issues using numericalsimulations, experiments, and measurements; The research is designed
to simulate, explain, and design the urban safety and environment overmulti-scales from human scale, indoor, outdoor to meso-scale.

㪥㫌㫄㪼㫉㫀㪺㪸㫃㩷㫊㫀㫄㫌㫃㪸㫋㫀㫆㫅㩷㫆㪽㩷㪸㫀㫉㩷㫇㫆㫃㫃㫌㫋㪸㫅㫋㩷㪻㫀㫊㫇㪼㫉㫊㫀㫆㫅㩷

32
32㪜㫏㫇㪼㫉㫀㫄㪼㫅㫋㩷㫆㪽㩷㫌㫉㪹㪸㫅㩷㪽㫀㫉㪼㩷㫊㫇㫉㪼㪸㪻㩷

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Integrated Disaster Management
Engineering – Ohara lab
Development of the e-learning system to increase emergency medical
responses from doctors and nurses; ICUS is conducting a joint-research
program on the disaster manual of the hospital with the Division forEnvironment, Health and Safety and the Hospital of the University of Tokyo.
e-Learning

Disaster Drill

２ ．地 震 時 の 初 動 対 応 シ ミュ レ ー シ ョン状 況 ： 大 地 震 発 生 !!

平 日 の 午 後 2時 で す 。点 滴 の 差 し 替 え が 必 要 に な り、
あなたはその患者の病室への廊下を歩いています。
途 中 で 、お 見 舞 い に 来 た 70歳 代 の 女 性 に 、病 室 を 尋
ね られ ました。
ちょうどその 時 、� しい � れ が 起 こり、あ なた は 立 ってい る ことが で きま せ ん 。大 地 震 が 発 生 した よ うです 。
気 が つ くと 、 廊 下 に あ った 点 滴 ス タ ン ドの 幾 つ か が 横倒 しになってい るのが 見えました 。
7 0 歳 代 の 女 性 も 倒 れ て お り 、 “う ぅ っ ”と 弱 々 し く 唸 っ ています。

Q 1：あ な た は こ の 時 ど う し ま す か �
文 頭 の ボ タ ン を 一 つ 選 ん で ク リ ッ ク し て か ら 、 「次 へ 」の ボ タ ン を 押 し て くだ さ い 。
○○○「誰 か 来 て 下 さ い 」と 助 け を 呼 ぶ
倒 れ た 見 舞 い 客 の 反 応 を 確 認 した 後 ，呼 吸 を 数 え る
まずナ ースコール の あ った部 屋 へ 向 か う

次へ

Safety Check in HospitalＣ ＳＣＡＴＴＴに よ る 病 棟 で の 対 応ＣＳ� Ｃ o m m u n ic a tio n （ 情 報 伝 達 ）

Ａ

Ｔ

Ｔ

Ｔ

情 報 担 当 看 護 師 に より集 約 され た 病 棟 の 被 災 状 況 と傷 病 者 の 状 況 は 、リー ダ ー 看 護 師 が災害対策本部に報告します。
（写 真 は 平 成 18年 度 の 防 災 訓 練 で の 情 報 伝 達 の 様 子 で す 。 ）リー ダ ー 看 護 師
災害対策本部

役割分担

�
�病
病室
室担
担当
当
看
看護
護師
師

状況報告

情
情報
報担
担当
当
看
看護
護師
師
チ ェック リス ト
記入

リー ダー看 護 師

報告
前へ

次へ

Triage of Disaster Victims

Cave-in in the road caused by an old sewer pipe

Wood Engineering – Koshihara lab
Studies on the possibilities of wooden structures; By revising
the building standards, new medium-rise and high-risewooden buildings can be built.

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster

33
33

July 2009, Thailand
July 2009, Thailand

Urban Traffic Management– Tanaka lab
Study on the appropriate estimation of lost time in signal control that
affects the length of a signal cycle, which, in turn, affects the delay insignalized intersection. We estimated the total lost time by observing
the traffic flow using a video camera to shorten the signal cycle time.
W [m]first vehicle ofnext phasete [sec]tr [sec]

video observation

last vehicle ofcurrent phase

V [m/s]Observation and analysis of conflict points of traffic flows

Applied remote sensing – Sawada lab
Studies on systematic data processing methodologies spatially andtimely for remote sensing data such as satellite data, aerial photo, andLidar; Field observation systems are also integrated for vegetation
management on a global as well as on a local scale.

Vegetation environment map derived from satellite data (Indochina)

34
34

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Remote Sensing for FRA“Differences among data sets from the various countries can begreat owing to the methods applied, the terms and definitions
employed and the currency of the information in the individual
inventories. Therefore RS is introduced for standardize theinformation qualities.

•
•
•
•

Comparison of remote sensing and national reporting for Africa.
1: 1997 percent tree cover classification for Africa (Hansen, 2005).
2: Percent tree cover threshold which yields a forest area estimate for each country which matches that reported to FRA 2000.
This map was created by starting at the densest tree cover per country and sliding the threshold down from there until theforest areas matched that reported for FRA 2000 (Hansen, 2005).
3: Resulting forest map where per country forested area match FRA 2000 totals. Green = forest, white = non‐forest (Hansen,
2005). Pictures 1 through 3 confirm that national reporting and remote sensing analysis diverge for Africa (Mayaux et al., 2005).

Reliable and up-to-date informationon the sate of forest resources
Sustainably managed forests have multiple
environmental and socio‐economic functions which
are important at the global, national and local
scales, and they play a vital part in sustainable
development.
Reliable and up‐to‐date information on the state offorest resources is crucial to support decision‐
making for policies and programmes in forestry andsustainable development at all levels:‐ not only on area and area change,‐ but also on such variables as
growing stock,wood and non‐wood products,
carbon,protected areas,use of forests for recreation and other services,biological diversity andforests’ contribution to national economies.

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster

35
35

July 2009, Thailand
July 2009, Thailand

Sampling units of the FRA 2000 and the planned
FRA 2010 RSS.

1 degree by 1 degree lat./long. grid system• The latitude‐longitude grid is easy to understand and tocommunicate to national governments.
• Sample locations can be easily identified on every map.
• The FAO supported National Forests Assessments (NFA) use thislatitude/longitude grid; so information from field plots locatedwithin the sample units will be available to support theinterpretation for many countries.
・ The ‘degree confluence project’ isposting photos of each latitude andlongitude integer degree intersection
on the Internet (seehttp://www.confluence.org). These
photos can provide a substantial
support to the FRA 2010 RSS sample
interpret

36
36

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Further reasons to promote FRA2010• Accurate and timely information on forest and forest area change isessential for policy relevant at various levels.
• The new RSS approach promises to provide the best possible globallyconsistent data on forest cover by ecological zone which can provide
valuable input information to biomass and carbon stock assessments.
• Institutions involved in sector of wide policy and structuraladjustments can use frequently updated forest monitoring
information to check if changed policies do or do not have theintended impact on forests.
• The Survey is expected to advance science in global natural resources
monitoring.

National Forest Resources Database– NFRDB –•Two servers– Main system in Forestry agency• Ordinary use

NFRDB Server– Sub system in FFPRI• Backup system• Research and development

Database Room in Forestry agency

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster

18
18

37
37

July 2009, Thailand
July 2009, Thailand

Total Design of Accounting and Reporting System
Historical records
of management

FM monitoring
FM ratios
QA/QC

Models for Dead wood, Litter, SOC
BEF, R, D

Species
Age classSpecies
Age classSpecies

E stimation of C arbon flux
Forest registers

Improvingyield tables

Non CO2 GHG

Forest planning maps
Verification

Verification
Uncertainty

Orthophotos
Forest resources
monitoring survey

Field survey

TM

SPOT

ARD monitoring

ARD
19

Main Data on NFRDBBoundaries
forestplanning
maps
Attribute
data
ofmonitoring
forestregisters
Forestresources
surveySPOT
Orthophotos
images
with
with
2.5m
1m
resolution
resolution
LandsatTM
imageswith
28.5m
resolution
FM
monitoring
and
Stand
surveyARD
monitoring
by
500m
grids
with
970,000
polygons
withrecords
by 41M
4km
grids
Forest registers
Forest Planning maps
FM monitoring
ARD monitoring
Forest resources
monitoring survey
Orthophotos
SPOT images
Landsat TM images

38
38

20

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Natural Vegetation and Global Environment
Impact of NV(Forest) to Global
Environ.

If we monitor natural vegetation withappropriate way, we may get theenvironmental information as well.

Impact of Global
Environ. to NV(Forest)

CO2
Evaporation

Photosynthesis

Deforestation
CarbonBiodiversity
Monitoring ofland coverchanges

Water

Nutrients
Growth condition

Satellite Observation
YearlySeasonally

Remote sensing is the best way andnecessary tool to monitor these conditions.

Monitoring of
seasonal changes
21

Remote sensing data offers some indices which help us understanding
the situation of land covers and their environmental parameters.

Cyclic observation

Veget. Surface
Index Temp.

SeasonalchangeGeographicalcondition

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster

Water
Index

warming？

Temperature

Growing season
NPP

Sunshine

Snow
Index

warming？

Water

Healthiness of
ecosystem
22

39
39

July 2009, Thailand
July 2009, Thailand

Cloud-free dataset of 10 day interval
Surface Temperature

and

NDVI

23

Seasonal Changes observed by
the cloud-free10-day composite NOAA NDVI

１０

１１

１２

１

２

３

４

５

６

７

８

９

���������������������������������������������������������������������������������
�������������������������������������������������������������������
24

40
40

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Detection of Anomalies
Classification by the
Hidden Markov Model
Estimation of
HMMによる状態推定
state by HMM

MODIS data���band 1band 2band 7

Identification of
anomalies

Consolidation and classification of
状態記号列のSOMによる集約・分類
the state conditions by SOM

Redefinition of time

時系列モデルパラメータのsequential
再定義 model

parameters

スペクトル情報
SpectralEstimation of probability and
確率と状態の推定：
state

Informationp t   maxa 'ij b j ot j t   arg maxa 'ij b j ot jAnomalous score
�状��ス��：st    log2 maxp t ,2 40 スペクトルの記号化

encoding

400�の記号Other image

記号化����時�のデータ

Classification
results
分類プロダクト（36枚/年）(36 scenes/year)

記号ラスタ
Encodedraster image

�状��ラスタ

Detected raster

25

Time series processing and anomaly score
Plot.ID 19: 2006
10day composite(36 scenes)

Plot. ID 19: 2006
State image

Soil spectral pattern appeared
continuously on the images in
Dec. 2006

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster

Anomaly score image

Anomaly score started
increasing in December
2006

26

41
41

July 2009, Thailand
July 2009, Thailand

Detection of anomaly derived from the Discrete State Space Model
Original

1999(before fire）

State

Mid. June

Early July

Late July

Early Aug.

2000
(after fire)

OriginalState（estimated)

Anomaly
Score

Anomaly score
1

12
27

42
42

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster

43
43

July 2009, Thailand
July 2009, Thailand

AFFI RE: Autom atic Forest Fire Reporting Syst em by
農林水産省計算センター
FFPRI
DirectSatellite Database(SIDaB)

Hotspot

Fire detection

Fire reporting

MODIS受信

Network
NOAA

Hotspot
information

Fire risk mapping
NDII (Water stress)

E-mailMail

NDVI (Vegetation Index)

number of hotspot (NOAA/AVHRR):

Comparison of two
indiceslat(N)/lon(E) | continuity(days) |Level of dryness

44
44

6

---------------------------------

Dryness map

14.185 99.789

0

14.175 99.799

2

18.925 100.744

114.835 99.729

013.585 99.628

013.485 99.6180--------------END---------------

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

ALOS・PAＬSAR /Japan

Detection of Forest development by RADAR
Optical sensors (Landsat, CBRES, etc.)

Forest(Landsat2005) -> Cutover(RADAR2006) -> (CBRES2006年)

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster45
45

July 2009, Thailand
July 2009, Thailand

SCAN SAR (Cycle20 & Cycle21)

Flood monitoring by RADARSAT

Dec. 1997, ScanSAR Narrow

Aug. 1998 ScanSAR Narrow

46
46

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Four airborne sensors, aboard R99-B and R95-B aircraft,
operating in the microwave, visible spectrum and infrared
bands:・SAR sensor (Synthetic Aperture Radar);・HSS Hyperspectral sensor (Hyperspectral Scanner System);・MSS Multispectral sensor (Multispectral Scanner System);・OIS infrared sensor (Optical Infrared Sensor).

What is important…
• Integration of reliable RS information withevaluation system• Time series information as a simple layer of
GIS• Modeling for prediction• International collaboration for global
environment issues and disaster mitigation• International Friendship through RNUS

Current
DisasterCurrent Remote
RemoteSensing
Sensingfor
forLand
LandEnvironment
Environmentand
andDisaster47
47

July 2009, Thailand
July 2009, Thailand

48
48

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndPUBLIC TRANSPORTATION
IN BANGKOKTHIRAYOOT LIMANOND
School of Transportation Engineering
Suranaree University of Technology, Thailand
tlimanond@yahoo.com

Public Transportationin Bangkok
Thirayoot Limanond
Suranaree University of Technology
2nd Joint Student Seminar on Civil Infrastructures
AIT Center

6 July 2009

BangkokRanks as one of the top
twenty mega cities in theworld Population: 11 million people(15% of the country population ) 5.9 million vehicles registered Locates all gov. agencies andmany important businessenterprises
 50% of Country’s economic
activity Current Traffic Conditions:

Seriously congested!!!
Center for Transport Study and Technology Transfer (CT3)49
49

July 2009, Thailand
July 2009, Thailand

Size
History and City Expansion
Existing Land Use
Ring Road

35 km

CBD

Low-Density Residential
Med-Density Residential
Commercial & Hi-Density Residential
3
Center for Transport 60
Study
km and Technology Transfer (CT )Greater Bangkok Area
Bangkok +5 Adjacent Provinces
 Population: 15 million people
 6.8 million vehicles registered

Center for Transport Study and Technology Transfer (CT3)50
50

2ndJoint
JointStudent
StudentSeminar
Seminaron
onCivil
CivilInfrastructures
Infrastructures
2ndICUS Report 37
ICUS Report 37

Vehicle Registration
 In Greater Bangkok
Area (6.8 mill veh.)

165,556 , 2%

138,774 , 2%

104,000 veh.

84,000 veh.

42,000 veh.

12,000 veh.

241,494 , 4%
3,521,869 ,
51%
2,798,389 ,41%

Pas s enger cars
Motorcycles
Public trans port vehs
TrucksOthersCenter for Transport Study and Technology Transfer (CT3)Bus Service

Center for Transport Study and Technology Transfer (CT3)

Public Transportation
TransportationininBangkok
Bangkok51
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July 2009, Thailand

Bus ServiceService for the entire Greater Bangkok Area
Service Lines: 200+ major routes
Service Hours: 5:00 – 23:00
Bus Fare: 7 – 24 Baht
BMTA Bus Ridership (person-trips/day)

Bus Ridership

4,500,000
4,000,000
3,500,000
3,000,000
2,500,000
2,000,000
1,500,000
1,000,000
500,000
0
19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08
Year

Center for Transport Study and Technology Transfer (CT3)

Ferry along Chao Praya

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Suburban train

Center for Transport Study and Technology Transfer (CT3)

Current Heavy Rail System
GREEN LINE 2
Nat. Stadium – Thaksin Bridge(6.5 km)BLUE LINE
Bang Sue – Train Station(19.4 km)
GREEN LINE 1
Mohr Chit – On Nut(16.4 km)Current System : 3 lines – 42 km
Under Construction : 1 line – 28 km

RED LINE
Airport Rail Link
(28.5 km)Center for Transport Study and Technology Transfer(CT3)

Public Transportation
TransportationininBangkok
Bangkok53
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July 2009, Thailand
July 2009, Thailand

Current Heavy Rail System
 Green Lines (BTS sky train)

22.9 km
Service hours: 6:00 – 24:00
BTS Fare: 15 – 40 Baht
Ridership: 450,000+ Passengers/day

 Blue Line (MRTA subway)

19.4 km
Service hours: 6:00 – 24:00
BTS Fare: 16 – 41 Baht
Ridership: 200,000+ Passengers/day

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Center for Transport Study and Technology Transfer (CT3)

Heavy Rail Master Plan
10 Rail Lines
Total Length of over 400 km- SUBWAY in Inner City
- SKYTRAIN in Suburban

Center for Transport Study and Technology Transfer (CT3)

Public Transportation
TransportationininBangkok
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55

July 2009, Thailand
July 2009, Thailand

Bus Rapid Transit (BRT)
Other names
Bus Rapid Transit is a mass transit
system that mimics the rapidity
and performance of metros but
utilises buses rather than rail
vehicles.

Rapid Bus, Metro Bus, HighCapacity Bus Systems, High
Quality Bus Systems, Express
Bus Systems, Busway Systems,
etc.

Characteristics
9 Segregated busways
9 Rapid boarding and alighting
9 Efficient fare collection
9 Comfortable stations
9 Clean bus technologies
9 Modal integration
9 Competitively-bid concessions
9 Sophisticated marketing
9 Excellencein customer
Center for Transport Study and Technology
Transfer(CT3) service
Volvo

BRT Projects
BRT is an attempt to achieve a metro-levelof transit quality using bus technology.

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BRT Vs Heavy Rail“How much US$1 billion can buy?”

Bus Rapid Transit
 Less Expensive Fast ConstructionTime Greater Flexibility
 Greater Accessibility

426 km. of BRT

7 km. of Subway

14 km. of Skytrain

Center for Transport Study and Technology Transfer (CT3)

BRT Project
BRT was introduced in 2003
(Heavy Rail Master Plan
is available since 1997)-OTP (9 lines)-BMA(2 lines)
OTP:
9 lines of BRT
2004 – Feasibility study,
Prelim. Design
Current Status:
On-hold, waiting for a new
heavy rail system master
plan
BMA: 2 lines of BRT
2005 – Feasibility study,
Detailed Design,
Bidding
Current Status:
One line is recently
completed, but need toCenter for Transport Study and Technology Transfer (CT3)
wait for BRT buses

Public Transportation
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July 2009, Thailand
July 2009, Thailand

Bangkok BRTCenter for Transport Study and Technology Transfer (CT3)

Thank you

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2ndInvited Papers

METHODS FOR DETERMINING PHYSICAL,PARTICLE-RELATED CHARACTERISTICSRELEVANT TO FLUID FLOW PHENOMINAIN BIOWASTEECKHARD KRAFTBauhaus-Universität Weimar, Germany
eckhard.kraft@uni-weimar.de
ABSTRACTThe characterisation of biowaste and its influence on the fluid flow inpacked beds of biowaste has been researched in . For the first time aconsiderable number of experiments have been done to measure particle
density. The particle density can be used to estimate the amount of pores inthe biowaste. Particle size distributions have been examined from theorganic material. Possibilities have been tested to specify characteristicclassification numbers and effective particle diameters in order to study theproperties of the material. Using the knowledge of the specific surface,conclusions can be drown about interaction of Newton`s fluids in porous
media. Certainly there has been an interdependence between viscosity,
current properties, biodegradation and adsorption processes. At the
moment it is impossible to determine specific surfaces of packed columns byusing proved methods. Although this could help to improve processengineering in a range of technical fields. This study provides a statistical toestimate specific surfaces of packed columns. The results of a test of thismethod executed on defined materials and on organic waste are presented.
Based on these results the accuracy of the method is discussed.
1. INTRODUCTION
The amount of waste in Germany has been estimated 1993 in a range ofabout 338,5 Mio Mg (Umweltbundesamt, 1998). The mainly part of waste
accrues as a material mixture of different properties. This happens even tosource separated waste. Waste, containing or consisting of organic matter, isincinerated or treated physically and biologically, at the moment. In 1999,
594 biological treatment plants have been existing in Germany with acumulative capacity of about 8,4 Mio Mg (Wiemer and Kern, 1999). In
March 2000 395 biological treatment plants with an input of 4,5 Mio Mg
conformed with the RAL-quality standard (Bundesgütegemeinschaft
Kompost, 2000).
The properties of gases and fluids greatly influences the rate of
biological degradation of organic substances, the production rate of leachate
and gas as well as biological filters. The concerning materials like untreatedwaste, physically biological treated waste, source separated organic waste

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and biological filters are examples of packed beds and piles. The
characteristics of a material and its storage, which specify the behaviour of
fluids, vary in a big range of space and retention time in heterogeneous
waste materials.
In the domain of organic waste treatment a lot of forced aerated
composting systems exist, which have a high aeration demand. This courses
a high energy consumption. Often design and operation of composting
plants are heavily based on empirical knowledge lacking scientificreasoning.
The inhomogenity of the feedstock certainly has an influence to thissituation. Hence, it appears useful to study those characteristics of wastematerials which effect fluid flow in packed beds.
2. CONCEPTUAL CONSIDERATIONS
To research in fluid flow through packed beds of waste the following threefactors need to be taken in account:
1. the material; 2. the packed bed and 3. the flow pattern
The properties of these elements are strongly related to each other anddetermine in correspondence with the properties of the fluid the behaviourof the flow pattern. In packed beds of waste matrixes with huge pores andmatrixes which have small pores, like the soil matrix, could be expected.
Particles with internal pores can be expected additional.
Important characteristics in examining fluid flow phenomena inorganic waste are provided in table 1. It is vital to adequately define
procedures for sampling, sample handling and sample examination by using
appropriate analytical methods. This would enable to describe the degree
and quality of particle heterogeneity of organic waste. Such a description
would profoundly contribute in composting process design and management.
Table 1: Important characteristics relevant to fluid flow in waste materials
material

packed bedhydrophobility
water content
specific weightparticle size distribution
particle size
specific surface
roughnesssaturation of soil
pores
pore size distribution
particle distribution
Pore continuing
tortuosity

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effective poresspace, area load
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3. SPECIFIC WEIGHT OF ORGANIC WASTE
The specific weight ρS of a material is defined as the ratio of the mass thesolid single particles md to the volume of the solids Vk, including possibly
existing of inner pores, which cannot be reached from outside.
The specific weight ρS of a material can be measured by using thefollowing methods (Brüggemann, 1982):•••

estimation of the specific weight ρS by the common
pyknometer; (DIN 18124, 1989)
estimation of the specific weight ρS by submersible balance;
estimation of the specific weight ρS by air pyknometer.

The methods applied to soil mechanics of estimating the specificweight ρS of organic waste cannot be used without any adaptation. Related
to the German regulation DIN 18124 comparable experiments in four
variations have be realised:-heated in sand bath,heated in water bath,evacuated at 1000 ml Pyknometer
evacuated at 2000 ml and Pyknometer.

Caused by the properties and the composition of the organic waste
modifications are necessary (Kraft and Schwind 1996). According to themethod of pyknometer, 192 experiments to estimate the specific weight ρS
were realised. They included 96 experiments with a maximum particle sizeof 80 mm(original size) and 96 experiments with shredded material with amaximum particle size of 4 mm. Table 2 shows the results of theexperiments conducted to the material.
Table 2: specific weight ρS of organic waste for a maximum particle sizesof 80 mm and of 4mm
specific weight ρS

experimental method

heated in sand bathheated in water bathevacuated at 1000 ml
pyknometer
evacuated at 2000 ml
pyknometer[g/cm³]averageaverageshredded
4 mm
1,80
1,68

original
80 mm
1,76
1,62

1,87

1,74

1,80

1,61

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The specific weight of the shredded material is always higher than thespecific weight of original material. That has been expected, because innerpores, which could not be reached before shredding the material, could bereached after that. The difference regarding the results of the evacuation
method “water bath” is significantly wider than any of the methods. The
evacuation methods “sand bath” and “1000 ml pyknometer” estimate,independently of the particle size, the highest specifics of weight. This may
be assigned to an incomplete evacuation of the samples , when using these.
The differences of the results of the methods of “sand bath” and “waterbath” are caused by the different temperatures of the bath. The temperatureof the sand bath was 275°C and of the water bath 100°C. Even the quality ofheat transfer is varying with the material of the bath. The time of evacuation,
which is prescribed by the technical standard between 25 minutes to 1 day,has to be extend for the method of “water bath”, as a result the lower
maximum temperatures and the slower heat transfer involved.
Overall the aforementioned results indicate, that only the methods of“sand bath” and “1000ml pyknometer “ can reflect true results of specificweight of organic waste. Also, the experiments have to be done by using
original material. Hackling the material to a size smaller than 5 mm, asprescribed by the standard method, is not appropriate for the purposes of ourwork. The specific weight of the material examined in this study reached arange between 1,72 and 1,77 g/cm³. It should be mentioned that the methodof evacuating by using a pyknometer for estimating the specific weight oforganic waste and residual(MSW after separate collection of organic waste
and other secondary resources) waste has been established at most of theGerman universities. Mostly 2000 ml pyknometers are used, because theircontent can be evacuated by modern and stronger pumps within a working
day. Figure 1 pictures the relationship between the specific weight and theparticle size of an additional study. The results apply to material composted
for a period of 40 days. The average specific weight is 1,51 g/cm³.

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2,50

specific weight [g/cm³]

2,00

1,50

1,00

0,50

0,00
0,01

0,1

1

10

100

particle size [mm]Figure 1: Specific weight of different particle sizes in organic waste
5. PARTICLE SIZE AND PARTICLE SIZE DISTRIBUTION
At the composting process the material is usually stored like loose gravel or
as a packed bed. Generally, such groups of particles are named “disperse
systems”. They contain mostly of a lot of single particles, the so-called
disperse phase, and the surrounding media, the so-called continuos phase.
Both the disperse phase and the continuos phase can be solid or fluid.
Particle size and particle size distribution are strongly influencing theproperties of particle groups.
The figure 2 shows 7 different particle size distributions, i. e. five
single charges, the average of all charges and the corresponding distribution
as defined by Fuller(Lang et.al). Each line represents the average of four
replicates for each charge. Eight kg of material have been used for eachsingle particle size distribution, which, indeed, guarantees the representative
power of these results.

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100,00
90,00
80,00
70,00
60,00
50,00
40,00
30,00
20,00
10,00
0,00
0,01

Charge 1
Charge 2
Charge 3
Charge 4
Charge 5
Gesamtmittel
1100

Fullerkurve

10000

partcle size [mm]Figure 2: Particle size distributions of organic waste according to DIN
18123 (1996)
Using the particle size distribution the effective particle size dw, thecoefficient of uniformity CU, and the coefficient of curvature(CK) can beused as classifying parameters(Table 3). CU and CK of the particle size
distribution of the “Fuller” curve are given coefficients. The parameter dx
shows the diameter(d) of the particles, where x- mass % of the sieved
material passed.
Table 3: Classifying parameters of organic waste

d10
[mm]
d30
[mm]
d60
[mm]
CU
CK
dw
[mm]

charge 1 charge 2 charge 3
3,8
1,4
5,0

charge 40,6charge 5
3,8avera.
1,9

Fuller
1,3

10,0

5,3

15,0

3,0

10,0

7,0

11,7

21,7

13,5

28,3

8,0

19,0

18,0

46,8

5,71
1,21
6,65

9,64
1,49
3,97

5,66
1,59
5,03

13,33
1,88
1,54

5,00
1,39
5,51

9,47
1,43
3,48

36,00
2,25
1,22

At a next step the 5 particle size distributions of the charges arecompared to the one of Fuller(Table 4). They close a particle size
distribution to the particle size distribution as per Fuller, the higher shouldbe the estimated flow inhibition for this specific particle size distribution;
the particle size distribution as per Fuller indicates the most densiest packed
bed.

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Table 4: Comparison of specific particle size distribution to the particle size
distribution as per Fuller
charge 1 charge 2 charge 3 charge 4 charge 5 avera.
A=CU/C
0,159
0,268
0,157
0,370
0,139
0,263
U,Fuller

B=CK/C
K,Fuller

C=dw/dw,Fuller

D=(A+B+C)/3
E=D-1

Fuller
1

0,539

0,661

0,707

0,833

0,616

0,637

1

5,468

3,263

4,133

1,269

4,534

2,859

1

2,055

1,397

1,666

0,824

1,763

1,253

1

1,055
6

0,397
3

0,666
4

-0,176
2

0,763
5

0,253

0
1

The highest flow inhibitions could be expected at the charges 2 and 4,
followed by the charges 3, 5 and 1.
Generally, particle size distributions of heterogeneous materials
involve a substantial degree of error, when based on mass calculation. Using
the results of particle size-based specific weights of organic waste, theparticle size distributions, on a volume base, can be calculated. From those,
the effective particle size, on a volume basis, can be calculated. Table 5shows the comparison between effective particle size on a mass (dw) andvolume (dw,v) basis.
Table 5: Comparison between effective particle size on a mass (dw)
andvolume (dw,v) basis
dw [mm]dw,v [mm]Δd [%]

charge
1
6,65
8,51
27,9

charge 2 charge 3 charge 4 charge 5
3,97
4,79
20,6

5,03
7,16
42,3

1,54
1,90
23,4

5,51
7,24
31,4

Fuller1,22
1,220

The difference between dw and dw,v is apparently more then 20 %.
6. SPECIFIC SURFACE
A method developed by Chalkley et al. (1949), gave an estimation for theratio of volume to surface of particles. This method was originallydeveloped for speeding up the analysis of quantitative morphologic
properties of human cells. Also, this method moves along the lines, the idea
and results realised by Crofton (Encyclopaedia Britannica, 1875-1889).
If a room contains a number of each objects with different volume andsurface, the following equation is valid (Chalkley et al., 1949):

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r1 ⋅ h 4 ⋅ Σvolume=cΣsurface[1]

Where:

c is the number of cuts of an independent and randomly dropped bar
r1 length of the bar
Chalkley et al. (1949) verified the equation 1 by using geometricobjectives with given sizes. During the course of our experiments anadaptation of the method of Chalkley et al (1949) has been developed, inorder to apply the procedure to heterogeneous packed beds of organic waste.
At a packed bed the single particles are laying strictly side by side. That
requires a method to show clearly the border between the particles and thepores. The most suitable method is to have the print of a plane and coloured
surface of frozen material on paper. That makes sure that deeper parts of thematerial(pores) cannot leave their print at the paper(Kraft, 2000; Schreiber,
1998). As copy only the coloured particles will be seen.
After preparing a copy of distributed particles and pores, the picture
can be digitally scanned. The computerised interpretation of the specificsurface according to the principle of Chalkley et al. (1949) has be developed
and applied.
The program for simulation is able to create the print corresponding to
the randomly dropped bar. The simulation can include up to 10000 prints. It
can count the number of “cuts” and “hits”. The program produces a raster,which allows the localisation of every point at the co-ordinate system(Figure 3).

Figure 3: Program mask “establishing raster”(model of balls)
The prints of the bar is guaranteed to entirely fall in the designatedarea., because the main parameters, like the length of bar and the “hits” of66
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the ends of the bar, are contributing in the equation for estimating thespecific surface.
Figure 4 shows the result of experiments hold on a homogenous
packed bed of balls with a length of the bar of 100 mm. The dotted lines at
the figure illustrate the area of tolerance while showing the independentcalculated specific surface between 0,4 mm²/mm³ to 0,4615 mm²/mm³(theused balls have an own tolerance of fabrication). At this experiment theapproximation to one value due to the big number of independent samples isclearly to be seen. The estimation of the specific surface by the statisticalmethod fluctuates between the valid borders of tolerance and stops at 0,424
mm²/mm³. A second experiment as been done at the them picture with thethem length has been done in order to proof the reproduction andcorrectness of the procedure used. By comparing the result with the firstexperiment, almost the same estimation of the specific surface, which has
been 0,426 mm²/mm³, could be found.

0,5
0,49
0,48

Specific surface [mm²/mm³

0,47
0,46
0,45
0,44
0,43
0,42
0,41
0,4

0,39
0,38
0,37
0,36
0,35
0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

numb. of throws

Figure 4: Statistically estimated specific surface of a homogenous surface ofa packed bed of balls (length of the bar 100 mm)
7. CONCLUSIONS
Particle size distributions of organic waste according to DIN 18123 havebeen created. Possibilities of evaluating organic waste in consideration tocharacteristic numbers and effective particle size have been tested, toconclude to the properties of the packed bed.
The main results can be summarised as follows:-

the Kozeny-Köhler method is at the moment the most suitable one toestimate an effective particle size of particle size distributions,the uncritical derivative of an effective particle size on a mass basis
according to the Kozeny-Köhler method contains for organic waste

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at least a mistake of 20%, compared with an estimated effective
particle size on a volume basis,in the case of rest waste the divergence in calculating an effective
particle size on a mass basis will still increase, because of the evenhigher heterogeneity of rest waste,
the use of characterising numbers, for instance the coefficient ofcurvature and uniformity, is suggestive to evaluate particle size
distributions.

-

For practical use the following conclusions can be taken by the done
research of particle size distributions of organic waste:-

in the cause of problems running the treatment process a detailed
estimation of the particle size distribution and of the characteristic
numbers, while sieving the material at least on 10 sieves, should be
done,
closely particle size distributions should be used,
the recording of the test to the quality assurance system and theperformance of the test in monthly distances is recommended
especially in the case of recurring problems-

Knowledge about the specific surface could cause new possibilities
according to the calculation of the aeration system as well as to thedegradation process of organic waste. Whiteout specific to the effective
surface of organic waste, a further detailed calculation of the degradation
process seems to be impossible. Recently a big number of different methods
is available to estimate the specific surface of different materials,
nevertheless there are now experiences on organic waste. A speciality of theconsidered research is, that the specific surface should be possibly estimatedat the packed material and not at the loose gravel.
The estimated results, based on the introduced statistical method canbe summarised as follows:-

two different samples of packed beds of organic waste have been
searched,
the sample with the higher amount on fine particles had a specificsurface of 3,82mm²/mm³ to 4,22 mm²/mm³,
the sample with the lower amount on fine particles had a specificsurface of 1,34mm²/mm³ to 1,74 mm²/mm³.

For practical reasons a special importance approaches to theestimation of an effective particle size for planning the aeration system.
Because the limited number of tests, comparing to the investment costs, theestimation of an effective particle size, based on specific weights of theparticle size fractions should be done. Of course, the annual details of thecomposition of the organic waste according to the seasons of the different
catchment areas have to be accounted. For the planning of plants, therealisation of the proposed experiments is recommended as part of the basic
data’s. The tests should be take place in Europe at least in January and68
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September. In connecting with additional tests, like the water capacity, theproposed tests could be part of simple and fast optimisation of material
properties, in the wider contents of a quality assurance system.
REFERENCES
Brüggemann, K. (1982). Die Bodenprüfverfahren bei Straßenbauten,
Düsseldorf.
Bundesgütegemeinschaft Kompost e.V. (2000). personnel notice
Chalkley, H. W. (1949). A Method for Estimating Volume-Surface
Ratios.- In: Science, Vol. 110, pp. 295-297, September 23, 1949.
DIN 18123. Bestimmung der Korngrößenverteilung.-Normenausschuß
Bauwesen (NABau) im DIN Deutsches Institut für Normung e.V., 1996.
DIN 18124 T1. Bestimmung der Korndichte.-Normenausschuß
Bauwesen (NABau) im DIN Deutsches Institut für Normung e.V., 1989.
Encyclopaedia Britannica (1875-1889). Probabaility.- 9. Auflage, Vol.
XIX, S. 768-788, Edinburgh.
Kraft, E. (2000). Charakterisierung heterogener Abfallstoffgemischehinsichtlich ihres Einflusses auf das Strömungsverhalten Newtonscher
Fluide, Dissertation Bauhaus-Universität Weimar.
Kraft, E., Schwind, B. (1996). Korn für Korn - Materialspezifische
Kennwerte für Bioabfall ermöglichen Rückschlüsse auf das
Durchströmungsverhalten von Abfällen, Müllmagazin 4/96.
Lang (1996): Bodenmechanik und Grundbau, Das Verhalten von
Böden und Fels und die wichtigsten grundbaulichen Konzepte, Springer
Verlag, Berlin.
Schreiber, P. (1998). Erarbeitung und Test einer statistischen Methodezur Ermittlung spezifischer Oberflächen von Abfallstoffen, Diplomarbeit
GHS Essen, Fachgebiet Abfallwirtschaft.
Umweltbundesamt (1998) :Internet: www.umweltbundesamt.de,
Abfallaufkommen, letzte Aktualisierung 18.11.98, abgefragt 02.06.2000.
van Genuchten, M. Th., Nielsen, D. R. (1985). On Describing andPredicting the Hydraulic Properties of Unsaturated Soils. Annales
Geophysicae 3,5 S. 615-628.
Wiemer, Kern (1999). Bio- und Restabfallbehandlung III, Baeza
Verlag, Witzenhausen, 1999.

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2ndANALYSIS OF FLOORACCELERATION DEMANDSIN MULTISTORY BUILDINGSSEMİH KÜÇÜKARSLAN and YALÇIN KUMBASAR
Department of Engineering Sciences
Istanbul Technical University, Istanbul
kucukarslan@itu.edu.tr
ABSTRACTThe floor acceleration demands in multistory buildings response as
elastical when it is subjected to earthquake ground motion. In this paper, anapproximate method is proposed in order to estimate these floor
acceleration demands at any floor level. The dynamic characteristics of thebuilding are investigated by using an approximated model combining aflexural beam and a shear beam. The dynamic properties of continuum
system are considered to compute the modes of vibration of the building.
The formulation is modeled including the effect of change of lateral stiffness
along the height. Finite difference method with forward difference is used toestimate the acceleration demands in the building with non-uniform stiffness
along height.
1. INTRODUCTION
During occurred earthquakes, a huge amount of nonstructural elements ofthe buildings are subjected to floor acceleration demands which result indamages and economic losses even when buildings go through nonstructural
damage. As a result, it is important to estimate the distribution of maximum
acceleration demands along the height to design correct and efficient nonstructural components. Villaverde [1] states that not all elements, whichhave importance in the response of non-structural components, are taken
into account in present design-oriented methods. He proposes a designoriented method, which includes the dynamic properties of the first mode ofvibration, to cover these limitations [2]. This method does not consider thecontribution of higher modes of vibration to floor acceleration demands inbuildings.
This paper presents an approximate solution to estimate floor
acceleration demands in multistory buildings with non-uniform stiffness byusing a small number of variables. While providing this, the contribution of
the first three modes of vibration of the building are considered.

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2. MULTISTORY BUILDINGS WITH NON-UNIFORM STIFFNESSALONG HEIGHT
2.1 Simplified Model of Multistory Building
In the this method, the dynamic properties of multistory buildings areapproximated by using an equivalent continuum model consisting of aflexural cantilever beam and a shear cantilever beam deforming in bending
and shear configurations, respectively shown in Figure 1 [3-5].

Figure 1: Simplified model to estimate dynamic properties of multistory
buildings [5]Miranda used the model shown in Figure 1 to estimate the maximum
roof displacement and maximum interstory drift of uniform buildings
responding primarily in the first mode of vibration [3]. This approximate
method was more recently extended to buildings with non-uniform lateral
stiffness [4]. That work shows that reductions in lateral stiffness along theheight have a negligible effect on the ratio of spectral displacement tomaximum roof displacement and only a small effect on the ratio of
maximum interstory drift ratio to roof drift ratio. Non-dimensional ratios
computed from the uniform case are accurate enough for computing
approximate lateral drift demands in buildings subjected to earthquake
ground motions. Miranda and Taghavi [5] give the response of thecontinuum system shown in Figure 1, when subjected to a horizontal
acceleration at the base level, by the following equilibrium equation [5]7272

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Eq. 2.1.1

ρ ( x) u( x, t ) + c ( x) u ( x, t ) +1
1
EI ( x) u′′ ( x, t ) )′′ − 2 ( GA( x) u′ ( x, t ) )′ = −ρ ( x) ug ( t )4(HH

x: non-dimensional height varying between zero at the base of thebuilding and one at roof levelt: timeρ ( x ) : mass per unit lengthu(x,t): lateral displacement at height x and time t
H: total height of the buildingc(x): damping coefficient per unit length
EI(x): flexural rigidity of the flexural beam along the heightGA(x): shear rigidity of the shear beamu g ( t ) : ground displacement at time t

∂y
= y′
∂x
∂y
= y
For a function y(x,t), partial differentiation with respect to t:∂t
For a function y(x,t), partial differentiation with respect to x:

At the base level of the structure, the variation of flexural stiffness inthe flexural beam can be expressed as a function of the flexural rigidity.
Eq. 2.1.2
EI ( x ) = EI 0 S ( x )andGA ( x ) = GA0 S ( x )EI0 : flexural rigidity at the base of the structure
GA 0 : shear rigidity at the base of the structure
S(x): non-dimensional function which defines the variation of stiffness
along the height of the building
It is assumed that a uniform mass and damping coefficient exist along
the height of the building. Then, substitute Eq. 2.1.2 into Eq. 2.1.1 anddivide by EI 0
Eq. 2.1.3

ρEI 0u ( x, t ) +

α2
c
1
ρ
u ( x, t ) + 4 ( S ( x ) u′′ ( x, t ) )′′ − 04 ( S ( x ) u′ ( x, t ) )′ = −ug ( t )
EI 0
H
H
EI 0

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Eq. 2.1.4
1/2⎛ GA ⎞
α0 = H ⎜ 0 ⎟⎝ EI 0 ⎠where α 0 controls the degree of participation of overall flexural andshear deformations in Figure 1.
2.2 Approximate Earthquake Analysis
The structure is supposed to act with elastic behavior, so the response
can be calculated with superposition of the responses of all modes ofvibrations. The displacement can be computed as a linear combination of allmodal responses.
Eq. 2.2.1∞

u ( x, t ) = ∑ ui ( x, t )i =1

where u i ( x,t ) : contribution of the i th mode to the response
Assuming classical damping, Miranda and Taghavi propose thefollowing equation for solution [5].
Eq. 2.2.2
ui ( x, t ) = Γiφi ( x ) Di ( t )where Γi : modal participation factor of the i th mode of vibrationφi ( x ) : amplitude of the i th mode shape of vibration at x.

Di ( t ) : deformation response of a single-degree-of-freedom systemcorresponding to the ith mode to the ground motion
2.3 Dynamic Properties of Continuum System

For undamped (c=0) free vibration ( u g =0) Eq. 2.1.3 becomesEq. 2.3.1

ρEI 0u ( x, t ) +2
1′′ − α 0 S ( x ) u′ ( x, t ) ′ = 0
′′
,
S
x
u
x
t
(
)
(
)
(
)
(
)
H4
H4

Substituting Eq. 2.2.2 into Eq. 2.3.1 one can derive

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 ( t )ui ( x, t ) = Γiφi ( x ) Di,ui′ ( x, t ) = Γiφi′ ( x ) Di ( t ) and Γ i cancels out.

ui′′( x, t ) = Γiφi′′( x ) Di ( t ),Eq. 2.3.2

ρ
EI 0

 ( t ) +φi ( x ) Di2
Di ( t )′′ − α 0 D ( t ) S ( x ) φ ′ ( x ) ′ = 0
′′
φ
S
x
x
(
)
(
)
(
) H4 i (
)
i
i
H4

EI 0
. Using separation
ρφi ( x ) Di ( t )of variables two ordinary differential equations are obtained.

Then Eq. 2.3.2 is multiplied with

Eq. 2.3.3
 ( t ) + w2 D ( t ) = 0
a: D
i
i
i

b:

( S ( x ) φ ′′( x ) )′′ − α ( S ( x ) φ ′ ( x ) )′ − β φ ( x ) = 0
2
0

i

2
i i

i

1/2

⎛ ρH 4 ⎞where β i = ⎜ wi2⎟ and ωi : circular frequency of vibration of
EI 0 ⎠⎝
the system of i th mode of vibration.
2.4 Uniform Stiffness Along HeightEq. 2.4.1

φi′V ( x ) − α 02φi′′ ( x ) − β i2φi ( x ) = 0
Boundary conditions for the system, respectively. Zero displacement
and rotation at the bottom, zero moment and shear forces at the top.
Eq. 2.4.2
a: φi ( x ) x =0 = 0b: φi′ ( x )c: φi′′ ( x )d: ⎡φi′′′ ( x ) − α 02φi′′ ( x ) ⎤ = 0⎣⎦ x =1

x =1

=0

x =0

=0

From Eq. 2.4.1 and Eq. 2.4.2.a,b,c the solution for φi ( x ) is derived as
Eq. 2.4.3

φi ( x ) = A1e r x + A2 e − r x + A3e r x + A4 e − r x1

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α 02 + α 04 + 4βi2

r1 =

2

r2 =

α 02 − α 04 + 4 βi2222
r 2 + r22 − r1 r2 ( r1 + r2 ) − r2 r2 ( r1 − r2 ) r2
e +
e −
e
where A1 = − 1
2
2r1
2r1

r22 ( r1 − r2 ) − r2 r22 ( r1 + r2 ) r2
A2 = − r1r2 e −
e −
e
2r1
2r1
r1

r (r − r )r (r + r )A3 = 1 1 2 e r1 + 1 1 2 e − r1 − r22 e − r222
r (r + r )r (r − r )A4 = − 1 1 2 e r1 + 1 1 2 e − r1 − r22 e r222

and r12 + r22 = α 02 ⇒ r2 = α 02 − r12 , which gives a more simple equation.
Substituting Eq. 2.4.3 into Eq. 2.4.2.d gives the characteristic function
with parameters α 0 and βi .
Eq. 2.4.4⎡⎛ 2 r23 ⎞ 3
⎤
2
2
3
2
⎢⎜ r2 + ⎟ ( r1 − α 0 r1 ) + ( r1 + r1r2 )( r2 − α 0 r2 ) ⎥ cosh ( r1 − r2 )
r1 ⎠
⎣⎝
⎦
3
⎡⎛
⎤
r ⎞
+ ⎢⎜ r22 − 2 ⎟ ( r13 − α 02 r1 ) + ( r12 − r1r2 )( r23 − α 02 r2 ) ⎥ sinh ( r1 + r2 )
r1 ⎠
⎣⎝
⎦
2
⎡(r − r )⎤− ⎢ 1 2 ( r13 − α 02 r1 ) + 2r22 ( r23 − α 02 r2 ) ⎥ = 0
2⎢⎣⎥⎦
The three smallest roots of Eq. 2.4.4 correspond to the first, second
and third modes of vibration of the building, respectively.
For the case α 0 =3, one can compute the first three roots as follows:21

β =38.5 , β 22 =792.57 , β32 =4456.7
2.5 Non-uniform Stiffness Along Height

A close-form solution for a building with a non-uniform distributionof lateral stiffness along the height cannot be derived from Eq. 2.3.3.b.
Therefore, finite difference method with forward difference is used in orderto estimate the amplitudes of mode shapes for buildings with non-uniform
stiffness along height. Miranda and Taghavi propose the following formulafor variation of stiffness [5].

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Eq. 2.5.1
S ( x ) = 1 − (1 − δ ) x λwhere δ: ratio of the lateral stiffness at the top to the lateral stiffness atthe base
λ: non-dimensional parameter that controls the variation of lateral
stiffness along the height.
For the case, λ=0.5, δ=0.25 Eq. 2.5.1 becomes S ( x ) = 1 − 0.75 x1/2 .
Substituting this in Eq. 2.3.3.b gives
Eq. 2.4.2(1− 0.75x )φ′ − 0.75x φ′′′ + ⎡⎣0.1875x
1/2

i

V

−1/2

−3/2

i

−α02 (1− 0.75x1/2 ) ⎤⎦φi′′ +α02 0.375x−1/2 − βi2φi = 0

Finite difference method with forward difference:

φi → j yj

1 j +1y − j y)(h1jy′′ = 2 ( j + 2 y − 2 j +1 y + j y )h1
jy′′′ = 3 ( j +3 y − 3 j + 2 y + 3 j +1 y − j y )h1
jy′v = 4 ( j + 4 y − 4 j +3 y + 6 j + 2 y − 4 j +1 y + j y )
h
j

y′ =

Substituting these in Eq. 2.5.2 and taking h=1/16 (dividing the height
into 16 equal intervals, this number is optional, hence number of points canbe increased) gives
Eq. 2.5.3( a + b + c − d − β ) y + ( −4 a − 3 b − 2 c + 16 d )+ ( 6 a + 3 b + c ) y + ( −4 a − b ) y + a y = 0
j

j

j

j

j

j

j

2
i

j

j+2

j

j

j

j

j +3

j

j

j +1

y

j+4

where j a = 164 (1 − 0.75 j x1/2 )jjjb = 163 ( 0.75 j x −1/2 )c = 162 ⎡⎣0.1875 j x −3/2 − α 02 (1 − 0.75 j x1/ 2 ) ⎤⎦d = 16α 02 0.375 j x −1/2

Matrix form for Eq. 2.5.3: CY = 0 
where C : coefficients matrix , (21x21) square matrix
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⎡0y ⎤
⎡0 ⎤
⎢1 ⎥
⎢0 ⎥
⎢ y ⎥
⎢ ⎥
⎢. ⎥
⎢. ⎥
Y = ⎢ ⎥ : unknowns vector , 0 = ⎢ ⎥ : constants vector, (21x1)
 ⎢. ⎥
 ⎢. ⎥
⎢. ⎥
⎢. ⎥
⎢ ⎥
⎢ ⎥
⎢⎣ 20 y ⎥⎦⎣0 ⎦2.6 Numerical Implementation
Finite difference method applied into boundary conditions gives thefour extra equations needed, as follows:
Eq. 2.6.1
i)

0

y=0

ii) 4 ( 1 y − 0 y ) = 0

iii) 42 ( 18 y − 17 y + 16 y ) = 0
iv) 64 19 y − 192 18 y + 156 17 y − 28 16 y = 0

From Eq. 2.6.1.i,ii it is trivial that 0 y = 1 y = 0 . Thus, the first twocolumns and first two boundary conditions can be omitted. Coefficientmatrix reduces to a (19x19) matrix. Yet, a matrix with zero constants vector
cannot be applied into Cramer method. It is assumed that at the top
φi ( x ) x =1 = 16 y = 1 . Then, 14th (16th before elimination) column multiplied by-1 forms the new constants vector different than zero vector. In order tohave a square matrix one more row, which can be written as a linear
combination of the other rows, is eliminated. The most adequate row is thelast boundary condition: Eq. 2.6.1.iv. In the end, a (18x18) coefficientsmatrix and a (18x1) constants vector are proposed to be applied into Cramer
method.
2.6.2.1 First Mode of Vibration
Code used in command window in Matlab for β12 =38.5:

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Figure 2: Matlab Curve Fitting Tool showing the spline formode shape of 1st mode of vibration
In Figure 2, x-axis represents the height of the building (x), while yaxis represents the amplitude of the 1st mode of vibration ( φ1 ).
2.6.2.2 Second Mode of Vibration
Code used in command window in Matlab for β 22 =792.57

Figure 3: Matlab Curve Fitting Tool showing the spline formode shape of 2nd mode of vibration
In Figure 3, x-axis represents the height of the building (x), while yaxis represents the amplitude of the 2nd mode of vibration ( φ2 ).

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2.6.2.3 Third Mode of Vibration
Code used in command window in Matlab for β32 =4456.7

Figure 4: Matlab Curve Fitting Tool showing the spline
for mode shape of 3rd mode of vibration
In Figure 4, x-axis represents the height of the building (x), while yaxis represents the amplitude of the 3rd mode of vibration ( φ3 ).
Miranda and Taghavi propose an approximating function for the modeshapes of the simplified model with non-uniform stiffness along height asfollows [5]:Eq. 2.6.2:⎡⎤ ⎛ 1− δ ⎞⎛ πα 0 ⎞⎟⎟ + 0.4 ⎥ ⎜⎝ 60 ⎠⎦⎝ 9 ⎠

φi ,nu ( x ) = φi ,u ( x ) + ⎡( −1) i1.3 sin ⎣⎡( i − 0.1) π x ⎤⎦ sin (π x ) ⎤ x ⎢sin ⎜
⎣

i

⎦ ⎣

where φi ,u ( x ) is the function derived in Eq. 2.4.3. Besides, mentioned
approximation method using finite difference method, Eq. 2.6.2 can be used
to calculate the mode shapes of the building with non-uniform stiffness
along height.
3. CONCLUSIONSIn this paper, an approximated model of a multistory building is presented toestimate the mode shapes of vibration when the building is subjected to
ground motions. By using the model with uniform stiffness along height, the8080

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effects of first three modes of vibration are calculated. After that, finite
difference method with forward differences is applied to approximate thepoints of the curve which represents the mode shapes of buildings with nonuniform stiffness along height when exposed to ground motions. The first
three modes of vibration are considered separately. The calculations
followed during the paper are an application of a general method, which canbe applied into other situations with small modifications. Civil engineers
can use and improve this method to approximate floor acceleration demands
in multistory buildings with non-uniform stiffness along height.
REFERENCES
Villaverde, R. (1996). “Earthquake resistant design of secondary structures:a report on the state of the art.” Proc., 11th World Conf. on
Earthquake Engineering (CD-ROM), Elsevier Science Ltd., Oxford,
England.
Villaverde, R. (1997). “Method to improve seismic provisions for
nonstructural components in buildings.” J. Struct. Eng., 123(4), 432–
439.
Miranda, E. (1999). “Approximate lateral deformation demands in
multistory buildings.” J. Struct. Eng., 125(4), 417–425.
Miranda, E., and Reyes, C. J. (2002). “Approximate lateral drift demands in
multistory buildings with nonuniform stiffness,” J. Struct. Eng.,
128(7), 840–849.
Taghavi, S., and Miranda, E. (2005). “Approximate floor acceleration
demands in multi-story buildings. I: Formulation.” J. Struct. Eng.,
131(2), 212–220.

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IMPACTS OF BASE FLOW DISCHARGEON NITRATE IN SURFACE WATER QUALITYWOO SANGDEOK, HAN KYUNGMIN,
AND KIM GEONHA

Department of Civil and Environmental Engineering,
Hannam University, Ojungdong, Daedukku,
Daejeon, 306-791, Republic of Korea. 1
kimgh@hnu
ABSTRACT
It is a well known fact that baseflow discharge of rainfall runoff
impacts on water quality of surface water significantly. In this paper,
impacts of nitrate discharged as base flow on stream water quality werestudied by using a software, PULSE from USGS to calculate monthly
ground water discharge from hydrograph. We used water quality and flowrate data for Ghapcehon2 site in Daejeon city for year 2005 as well as
ground water quality data in the watershed acquired from government
agencies. Agricultural and forestry land use are dominant for upstream of
Ghapcheon2 in the watershed. Base flow contributes about 85~95% of
stream flows during spring and fall while 25~38% of stream flow was
induced by base flow during summer and winter. Monthly nitrate loading
discharged as base flow for Ghapcheon2 was estimated by using averaged
nitrate concentration of groundwater in the watershed. Nitrate loading
induced by base flow at Ghapcheon2 was estimated as 5.4 ton of NO3-N/km2, which is about 60% of nitrate loading of surface water, 9.2 ton of
NO3--N/km2. From this study, it can be understood that ground water
quality monitoring is important for the proper manage of surface water
quality.
Keywords
Base flow, Groundwater, Hydrograph separation, Nitrate, Water
quality.
1. INTRODUCTION
For the proper management of the surface water quality, pollutant loadings
are subjected to abatement. However, enough water quality improvements
may not be achieved without proper management of groundwater discharge(Freeze and Cherry, 1979; Schilling and Zhang, 2004). If a watershed iscovered by forestry and agricultural land use, surface water quality will bevulnerable to groundwater discharge of rainfall runoff.
Nitrogen, particularly in the form of nitrate, is the most common

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contaminants in aquifer systems (Freeze and Cherry, 1979; Kim et al. 2008).
Muhammetoglu et al. (2002) points to agriculture as being the mostsubstantial anthropogenic source of nitrate, and Burkart et al. (2002)
suggests that this is caused by the intensive and extensive land use activities
associated with crop production and animal raising. The Korean Ministry ofEnvironment monitored groundwater that was used as source water andreported that up to 5 % of the 4,534 wells were polluted by nitrates (KMOE,
2008). This fact indicates that nitrate is emerging as one of primarypollutant in the groundwater.
Nitrates in drinking water cause Methmoglobinemia (Blue baby
syndrome) and cancer. The US EPA promulgated nitrate concentration
criteria for drinking water as 10 mg NO3--N/L, and 1.0 mg NO2--N/L(Plumb, 1998). Nitrates originated from various sources which included
agricultural and industrial areas and percolated into ground aquifers. As
nitrates are hardly removed naturally from the subsurface areas, they
accumulate in the aquifers.
Flow rate of groundwater discharge may be separated from flow rateof surface water by hydrograph analysis. Frequently adopted hydrographseparation methodologies are master groundwater depletion curve method,straight line method, fixed base method, and variable slope method. Publicdomain software for hydrograph separation such as PULSE, PART,
RECESS, RORA, HYSEP, BFI is available, and can be downloaded fromthe internet (U.S. Geological Survey, 2008) for free.
As discussed above, groundwater quality is of concern, but
understandings about interactions between groundwater and surface water isstill not enough. In this research, impacts of nitrate discharged fromgroundwater on surface water quality were estimated by means ofhydrograph analysis for an agricultural-forestry watershed.
2. METHODOLOGY
For this study, one monitoring station named Ghapcheon2 was selected on
the Ghap stream in the Daejeon city, Republic of Korea. Fig. 1 show theland uses of the Daejeon city and the location of the Ghapcheon2 site.
Ghapchen2 station is located upstream of Ghap stream and before entering
the center of the city and the watershed includes large portion of agriculturaland forestry land uses. There are 4 groundwater monitoring wells locatedrun by the government and shown in Figure 1. A site and G site are close toforestry while B site and F site are relatively close to agricultural land use.
Water quality data were obtained from Water Environment
Information System (KMOE, 2008), groundwater quality data wereobtained from National Groundwater Information Center (KOWACO,
2008), water level data were obtained from National Water Resources
Management Information System (KMOLM, 2008). Rating curves between

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water level-flow rate were obtained from the Annual Report of Hydrology(KMOLM, 2008).
Hydrograph separation was carried out by PULSE developed by
USGS (USGS, 2002: Rutledge, 1997). PULSE separates groundwater
discharge from surface water flow rate based on master groundwater
depletion curve, which is deletion curves are isolated from hydrograph andsorted to compose main groundwater depletion curve. PULSE can calculate
flow rate of groundwater discharge and recharge rate based on recession
index derived from hydrograph and watershed area.

Figure 1: Land Use of the Daejeon city, Location of Ghapchen2, andGroundwater Monitoring Station for Domestic Use in Daejeon
City.
3. RESULT AND DISCUSSION
Figure 2 shows flow rate of Ghapcheon2 site in the year 2005 andgroundwater discharge calculated by PULSE. It can be understood thatgroundwater discharge largely constitute surface water. During January,
February and December, contribution of groundwater on surface water are
decreasing mainly due to defected flow rate data. During successive rainfall
event in July, contribution of baseflow discharge are decreasing as well. If
rainfall event was occur right after the previous one, groundwater discharge
contribution is low, implying that more precipitation discharged via surface
runoff.
Figure 3 is stream flow and groundwater discharge per unit area for
Ghapcheon2 for year 2005 calculated by PULSE. In the figure, contribution
Impacts
Impacts ofof BaseBaseFlow
FlowDischarge
Dischargeon
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NitrateininSurface
SurfaceWater
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July 2009, Thailand
July 2009, Thailandof baseflow discharge are shown on top of the graph by dividing
groundwater discharge by stream flow. During drought season, contributionof groundwater discharge is high while the contribution is low during wet
season. During April and May, 90% of flow rate of surface water are
discharged from groundwater while 30% of surface water was contributedby the groundwater during summer.
1000

Discharge (m3/s)

100

10

1

0.1

Groundwater Discharge
Streamflow

0.01
1

2

3

4

5

6

7

8

9

10

11

12

Month

Figure 2: Measured Stream Flow for Ghapcheon2 and Calculated Base
Flow Discharge by PULSE for Year 2005.

Streamflow, Groundwater Discharge (cm)

25

20

.38

Groundwater Discharge
Streanflow.28

Streanflow
Groundwater Discharge

15

.72
10

5

.35
.53

.25

.86

.96

.87

1

2

3

4

5

.83
.99

.35

11

12

0
6

7

8

9

10

Month

Figure 3: Stream Flow and Groundwater Discharge per Unit Area for
Ghapchen2 for Year 2005.

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Figure 4 shows nitrate and BOD concentration variations at
Ghapchen2. In general, BOD concentrations were high during Spring andSummer while nitrate concentrations were high during drought season.
Nitrates were leached more during winter as agricultural area is notenoughly managed and uptaked by plants.

5
BOD
Nitrate

2.5

4
2.0

1.5

3

1.0
2

BOD Concentration (mg/L)

Nitrate Concentration (mg/L)

3.0

0.5

0.0

1
2003

2004

2005

Time

Figure 4: Nitrate and BOD (Biochemical Oxygen Demand) Concentration
Variations at Ghapchen2 from September, 2002 to December,
2005.
Figure 5 is the comparison of nitrate loads of streamflow and baseflow discharge for the Ghapcheon2 for year 2005. Nitrate concentration ofground water was assumed as averaged nitrate concentration of groundwater,
1.62 mg/L (data not shown here). In the figure, estimated loadings ofbaseflow were higher than those of surface flow for months as the averaged
groundwater concentration were high. When converting data shown in Fig.
6 into annual loading, annual loading of surface runoff is calculated as 9.2
ton of NO3-N/km2·yr, while that of baseflow discharge is calculated as 5.4
ton of NO3-N/km2·yr, which is 59% of surface flow. While analyzing Fig. 6,
one should note that the subjected area possess significant portion ofagricultural land use, and groundwater concentration data contains highlevel of uncertainty (Diek, 2002).

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Impacts ofof BaseBaseFlow
FlowDischarge
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SurfaceWater
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Streamflow
Groundwater discharge

2

Discharge Load (10 ton/km )

40

-1

30

20

10

0
1

2

3

4

5

6

7

8

9

10

11

12

Month

Figure 5: Comparison of Nitrate Loads of Streamflow and Base Flow
Discharge for the Ghapcheon2 for Year 2005 (nitrateconcentration of ground water was assumed as 1.62 mg/L).
4. CONCLUSION
To assess the impacts of nitrate discharged from groundwater on surface
water quality, hydrograph analysis was carried out for an agriculturalforestry watershed. Nitrate loading for the year 2005 were 85-95% during
Spring and Fall while 25-38% of loading during Winter.
When converting data into annual loading, annual loading of surface
runoff is calculated as 9.2 ton of NO3-N/km2·yr, while that of base flow
discharge is calculated as 5.4 ton of NO3-N/km2·yr, which is 59% of surface
flow. In this study, it can be understood that base flow discharge impacts on
surface water quality, but lack of monitoring data of groundwater quality
cause significant level of uncertainty.
ACKNOWLEDGEMENT
The authors sincerely appreciate the financial support provided by theHan River Watershed Management Committee and Han River Environment
al Research Center

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REFERENCES
Burkart M. R., Stoner, J. D. (2002) Nitrate in aquifers beneath agricultural
systems. Water Science and Technology. 45 (9), 19-29.
Diek R. H. (2002). Measurement uncertainty: Methods and applications.
3rd ed., ISA, Research Triagle Park, NC.
Freeze A. R. and Cherry, J. A. (1979). Groundwater, Prentice Hall, London.
Kim T., Kim G., Kim S., and Choi E. (2008). Estimating riverine dischargeof nitrogen from the South Korea by the mass balance approach.
Environmental Monitoring and Assessment, 136(1-3), pp. 371-378.
KMOLM (2008). National Water Resources Information System, Korean
Mistry of Land and Marine. http://wamis.go.kr., accessed December,
2008.
KMOE (2008). Water Environment Information System, Korean MinistryofEnvironment
http://www.water.nier.go.kr/weis/,
accessed
December, 2008.
KOWACO (2008). National Groundwater Information Center, Korean
Water Company, http://www.gims.go.kr, accessed December, 2008.
Muhametoglu H., Muhametoglu A., and Soyupak S. (2002) Vulnerability ofgroundwater to pollution from agricultural diffuse sources: a casestudy. Water Science and Technology, 45 (9), 1-7.
Plumb D. (1998) Nitrates and Groundwater: A Public Concern; Freshwater
Foundation, 28 pp.
Rutledge A. T. (1997). Model-estimated ground-water recharge andhydrograph of ground-water discharge to a stream. U.S. Geological
Survey. Water-Resources Investigations Report 97-4253.
Schilling K. and Zhang Y. K. (2004). Baseflow contribution to nitratenitrogen export from a large, agricultural watershed, USA. Journal of
Hydrology, 295, pp. 305-316.
U.S. Geological Survey. (2002) User guide for the PULSE program. OpenFile Report 02-455. Reston, Virginia, U.S. Geological Survey.
U.S. Geological Survey. (2008) USGS Groundwater Information.
http://water.usgs.gov/ogw/gwsw.html.

Impacts
Impacts ofof BaseBaseFlow
FlowDischarge
Dischargeon
onNitrate
NitrateininSurface
SurfaceWater
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Quality

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SUGGESTION OF PSC BOX BRIDGEPERFORMANCE PROFILE ACCORDING
TO CORROSION OF TENDON.
KIM HEE JOO and KONG JUNG-SIK
School of Civil Engineering and Architecture
Korea University, Korea
smileball@korea.ac.kr
ABSTRACT
Generally, Bridge structure become superannuated by deterioration ofsystem and environmental factor during service life. Decision making
should reflects the maintenance management what prevent from physicallifetime of the bridge deterioration and damage. Making analysis system
state what is the life cycle condition/ performance analysis by member
deterioration is important part of decision making. Study of condition
valuation is being done, but the study of performance valuation does notrevitalize in domestic bridge system. Particularly, numerical analysis of thedeterioration and repair and reinforcement effect is necessary for the
maintenance Life Cycle Cost Profile (LCC) analysis. This study proposes
the performance profile by using quantitative analysis of deterioration
factor PSC Box bridge what is effected by corrosion.
1. INTRODUCTION
Generally, Bridge structure became superannuated by deterioration ofenvironmental factor during service life. Therefore, the decision making
should reflects the maintenance management what prevent from physicallifetime of the bridge deterioration and damage. Making analysis system
state what is the life cycle condition/performance analysis is important partof decision making. Actually, condition valuation of domestic bridge is
proposing the performance profile that reflected regression analysis and aprofessional advice to a basis to Database of the bridge. However, in casethe bridge DB is insufficient, it is difficult to the precise regression analysis.
In addition, the subjective nature of professional opinion, difficult topropose the objectively condition curve. The performance evaluation of thebridge which make up for the weak points and make a determination ofmaintenance management time is performed, but the research of thedomestic is still insufficient. In this study, performance profile of the bridge
using RSM(Response Surface Method) by apply to the deterioration factorof the principal member of the PSC Box bridge was proposed.

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2. DETERIORASION OF THE PSC BOX BRIDGE.
For get to the performance profile of PSC Box bridges, it is very
important that understood about the deterioration factor of bridge. There aremany factors that work to the deterioration factors of PSC Box bridges. In
the PSC Box, if the concrete and tendon are deteriorated, the intensity inwhich it operation of the bridge is decreased and it has an affect on adurability and reliability of the bridge. These deterioration of tendon are
occurs at post-tensioned by pitting corrosion.
2.1. Corrosion of Post-Tensioned Steel Wire.
Generally, corrosion to occur at concrete bridges can be sectioned to
Pitting Corrosion and General Corrosion. The pit is concerned toelectrolysis reaction included anodic and cathodic reaction. Comparison
with general corrosion, the pitting corrosion rate is higher four to eight times
and causes the sudden failure of steel reinforcement. There are manyreasons to cause the pitting corrosion in the tendon. Most of the popularmechanism is the presence of bleed water and chlorides at the surface oftendon. Chloride ions occurring in the tendon reduce the pH environment
and making the passive film unstable. And it`s disperse the passive film andmake the film permeable to Fe�� ionsullowing the anodic reaction to occur
as a catalyst. The rust will form from the presence of oxygen and water on
the tendon surface, Fe����� was created. Furthermore, Fe����� reacts
with oxygen and water again to create Fe����� . Hydrolysis of thecorrosion product in the pit causes a decrease in pH. In acid condition andsaline product, corrosion may occur rapidly. In this study, all corrosions are
assumed to be occurred in the post-tendsined. Figure 1 explained thecreative rust process and pit formation. The pitting process is selfpropagating process in the nature as illustrated in Figure 2.

Figure 1: Anodic and cathodic reaction on the tendon surface

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Figure 2: The self-propagating process of pitting corrosion
2.2. Analysis Model the Member of Major Deterioration.
(1) Pitting corrosion modeling.
PSC Box Bridge are when the pit propagates, the loss of tendon crosssection area will be.
AS,C �π�����ν�T�����
�(1)The equation is calculate the corrosion section according to time. The
corrosion rate of rebar is different depend on environment. The corrosion
rate specification in rebar was suggested by ting, Mori, Ellingwood`s eachother 100µm/yr, 50µm/yr.
In this paper, the authors used the suggested equation from Stewart &Rosowsky (1998)
υ � 0�0011����ί����(2)

Where, R=Pitting corrosion coefficient, suggested by Gonzalez(1994),
ranges from 4 to 6; ί���� =corrosion current density (µA/cm� ). Used in thisstudy, Zhao-Hui Lu (1998) proposed the following expression.
ί���� �T� H��ω⁄� ���(3)That is suggested by Zhao-Hui Lu(1998); T� =absolute temperature,
average temperature 16°C/yr in Korea.;
Hr=relative humidity;
w/c=water/concrete ratio, assume 0.5; d� =cover.
Suggestion
Accordingto Corrosionof Tendon
Suggestion ofofPSC
PSCBoxBoxBridge
BridgePerformance
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ProfileAccordingto Corrosionof Tendon

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July 2009, Thailand(2) Time variant reliability index.
Tendon and shear reinforcement corrosion lead to the deterioration ofreliability index for moment and shear are respectively. Used in analysis thelimit state equations are formulated base on AASHTO LRFD code. thedepth of equivalent stress block is less than the thickness of top flange.
Therefore, the distance between the neutral axis and the compressive faceand nominal moment is used to calculate the reliability index.
3. MODEL SELECTION OF PSC BOX BRIDGE.
(1) Variable sesction
A PSC Box bridge is have a lot of ratios in bridge formats during
domestic use. In this study, the bridge analysis to the bridges of 50m span
length. A PSC Box bridge was classified according to the number of boxes,
such as the single and multi-box, and etc. And selected the single box bridgein this research. Also, PSC Box bridge was classified into to various
methods according to constructive methods. It classified as two methods ofa FSM and ILM among many methods being applied to the national and it
analysis. PSC Box bridge was assumed to the cross section was constantdepth girger. And the bottom flange value separately classified the central
part and support of the PSC Box bridge and analyzed. When used toanalysis of bridge, The critical sections for moment of analysis used in thebridge is considered at the middle of the first span. The valuse of momentand random variables are listed in table 1.
Table 1: The valuse od moment of PSC Bos bridge
FactorMean

COV

Factor

Mean

COV

f��

1681.65 Mpa

0.014234.5 Mpa

0.15

295cm

0.047

f��

262.2cm

0.0054

Dead load

68230 KN.m

0.1

Live load

15700 KN.m

0.2d� (ILMmethod)d� (FSMmethod)(2) Select performance profile constitution variable
Consider of environment factor, PSC Box bridge of 50m span length
performance profile is calculated. It is different to the constitution variableaccording to constructive methods, but its kind is not different. RSM
configuration variable of two methods is composed of humidity,
reinforcement depth, corrosion factor R, and time. The diameter of posttensioned tendon at midspan are 8x19x12.7 mm and slab tendon 19x9x12.7
mm for ILM bridge. In case of FSM bridge the post-tensioned tendon is
22x15x12.7 mm. PS Steel kind that used in two methods is different, butthey are assumed to the average of 80mm and the deviation of 20mm.

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Figure 3: post-tensioned tendon of ILM bridge
In addition, the humidity of RSM configuration variable is assumed to
be 60~100%, corrosion coefficient R is assumed to the average of 5 and thedeviation of 1.
4. PERFORMANCE PROFILE.
Each variables to have been composed of to RSM operation as theindependent variable, and it calculate reliability index along each condition.
The coefficient values of RSM variables are calculated the performance
profile by using the coefficient according to the application method.
Following figure indicated performance profile of reliability index along thechange of humidity by a method. It is different to the method, but RSM
variable used the same values.

Figure 4: Performance profile as change of humidity in PSC Box
bridge using ILM method .

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Figure 5: Performance profile as change of humidity in PSC Box
bridge using FSM method.
This two graphs shows the performance profile by Pitting Corrosion,and it`s assumes to be reinforce depth 60mm, corrosion coefficient 5. The
performance profile calculated form the two method appears that initial
reliability and corrosion point along time is different. The bridges of used an
ILM method appears to reliability index and safety to be high by thicknessof girder deeper than the bridges of sued a FSM method. A maintenancereinforcement point of time of a FSM method appears more quickly an ILM
method. Because, that is the starting point of the initial performance index
lower than the ILM method.
5. CONCLUSIONSIn this study, the main member and deterioration factor of PSC Box bridge
suggested to the performance profile by RSM. In this study assumed pitting
corrosion in ILM method and FSM method from PSCB girder. It could be
the base of the study what about calculation of performance profile assumed
the various environmental factor that in this study calculation of
performance profile result assumed the factor of tendon corrosion. The
performance profile shown up in consideration of the tendon corrosion is
proposed time of repair and reinforcement will be able to be predicted.
REFERENCES
KOREA INSTITUTE OF CONSTRUCTION RECHNOLOGY,. 2003.
Annual Report of Hydrologic Date,2002.,
Doo-Kie,Kim, Ki Young Koo, Seo Hyeong Yeol. 2004 GY,. 2003. Annual
Report of Hydrologic Date,2002 Long- Term Effectiveness of96
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Posttensioning in bridges. Korea Institute for Structural MaintenanceInspection, KSMI 2004.,
R.M.Salas, A.J.Schokker, J.S. West, J.E.Breen, M.E.Kreger, 2004.
Conclusion, Recommendations and Design Guidelines for Corrosion
Protection of Post-Tensioned Bridges, Center for Transportation
Research, University of Texas at Austin, 2004.
Willian H. Hartt, Corrosion Evaluation of Post-Tensioned Tendons on the
Mid By Bridge in Destion Frorida, Frorida Department ofTransportation Research Center, 2002

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ENVIRONMENTAL PERFORMANCE INDICATORSFOR CONCRETE CONTAINING HIGH VOLUME
OF RECYCLED MATERIALSGERMAN A. PARDO R.
Department of Civil Engineering, University of Tokyo, Japan
ger-pard@iis.u-tokyo.ac.jp
MICHAEL HENRY
Department of Civil Engineering, University of Tokyo, Japanmwhenry@iis.u-tokyo.ac.jp
YOSHITAKA KATO
International Center for Urban Safety Engineering
Institute of Industrial Science, University of Tokyo, Japankatoyosh@iis.u-tokyo.ac.jp
ABSTRACTThe role of concrete throughout the history has been to provide
society with infrastructure and shelter. During this process, concrete itself
has been in constant development and new factors have appeared andplayed different roles, with the environmental impact as one of the mostimportant and recent. After becoming aware of the necessity of mitigating
the negative environmental impact of concrete, the use of new materials for
producing concrete with low environmental impact has been proposed.
To reflect this new consideration, it is necessary to implement a newassessment factor which considers the environmental effect of concrete aswell as the mechanical performance. This paper shows the results of theenvironmental performance indicator, which considers both the mechanicalperformance and CO2 emissions. It was found that implementing theenvironmental performance indicator results in a balance between
mechanical performance and environmental performance, becoming in a
useful assessment tool.
1. INTRODUCTION
To face the reality of global warming, the concrete industry can play animportant role. Carbon dioxide (CO2) is the primary GHG contributing toclimate change, and the concrete industry is a major generator, with someresearchers estimating that the manufacture of Portland cement is
responsible for roughly 7% of the world’s total emissions. Along withcement production, the utilization of natural resources like aggregates, waterand sand, is also an important environmental concern, and since the concrete99
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consumption in the world is enormous and increasing every year, theavailability of these resources is endangered.
In order to mitigate the environmental effect of producing concrete
and reduce the exploitation of natural resources, many researches on
replacing cement and raw materials with by-products and recycled materials
have been conducted. These works have shown that it is possible to obtain agood quality concrete with other benefits such us landfill reduction andconcrete durability.
However, until now the construction industry has not taken thesefactors, such as CO2 emissions, seriously into account or considered them asimportant as the mechanical performance when evaluating concretematerials. Therefore, is necessary to implement a new assessment factor,“environmental indicator,” which considers both the mechanicalperformance and the CO2 emissions.
2. RECYCLED MATERIALS
A durable concrete which replaces virgin materials with industrial waste or
recycled products could provide a sustainable option for construction
materials. Fly ash, a waste by-product of the coal industry, has been used toreplace up to 70% of Portland cement in some concretes (Malhotra, 1999).
Total replacement of normal coarse aggregates with recycled aggregates,
produced from demolition waste, has also been investigated, andsatisfactory concrete quality was observed (Meinhold et al., 2001). Fibers
made from recycled plastics have also been successfully applied in concrete(Moriwake et al., 2004). These applications demonstrate the different
possibilities which exist for utilizing alternative materials in develop
sustainable concrete.
However, these materials are often utilized
individually, so the effect of combining recycled materials is not clear.
3. EXPERIMENTAL PROGRAM
3.1 Materials
Cement mortar and concrete were prepared using tap water (W), Type
1 Portland cement (C), river sand (S), normal (NG) and recycled (RG)
aggregates, fly ash (FA), polypropylene (PP) and recycled fibers (RF), andair entraining (AE) and super plasticizer (SP) admixtures.
The fly ash used was type JIS II fly ash, which met requirements
specified by JIS A 6201, and the coarse aggregates were low-grade.
Fiber properties are given in Table 1. The recycled fibers were
manufactures from recycled polyethylene terephtalate (PET).

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Type
PP
Recycled

Table 1: Fiber properties
Dia.
LengthDensity(mm)(mm)(g/cm3)
0.75
32
0.91
0.70
30
1.35

Strength(MPa)450

3.2 Mix proportions and specimens
Mix proportions are given in Table 2. The term binder (B) is used torepresent all cementitious materials – in this case, fly ash and Portland
cement. All mixes used a constant water-binder ratio of 30%.
Series
SB60
SB80
SB100
FA30-PP
FA50-PP
FA70-PP
FA30-RF
FA50-RF
FA70-RF
FA50-sa60-NG
FA50-sa60-RG
FA50-sa80-RG
FA30-sa60-RG
FA70-sa60-RG

Table 2: Mix proportions
Material ratios
Admixtures
Fibers(%)(%binder) (%volume)
W/B S/B FA/B s/a
AE
SP PP
RF
60
0.04 0.43080
0.04 0.5
100
0.04 0.6
2.030
0.04 0.53050
0.06 0.4
70
0.08 0.4
8030
0.04 0.5
2.050
0.06 0.4
70
0.08 0.4
0.015 0.66050
0.015 0.63080
2.080 0.025 0.5300.015 0.66070
0.015 0.6

For mortar mixtures to investigate the effect of sand-binder ratio, a
constant fly ash-binder ratio of 30% was used with PP fibers at 2% volume,and three sand-binder ratios of 60%, 80%, and 100% were chosen. For
investigating the effect of fiber type and fly ash-binder ratio, mortar
mixtures containing either PP or recycled fibers at 2% volume were mixed
at a constant sand-binder ratio of 80% with three fly ash-binder ratios of
30%, 50%, and 70%. Testing was conducted 28 days after casting.
For concrete mixtures, sand-binder ratio (80%) and fiber type(recycled fibers) were selected based upon the results obtained from mortar
series tests at 28 days. The effect of aggregate volume was obtained byvarying the sand-aggregate volume ratio from 80 (lower volume ofaggregates) to 60 (higher volume). Mortar (no aggregates) was also
included. At the highest volume, both recycled and normal aggregates were
tested to evaluate the effect of aggregate type. Finally, the fly ash-binder
ratio was varied from 30% to 70% to see the effect of fly ash content.

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AE and SP were varied as necessary to maintain satisfactory fresh
mortar and concrete properties. Testing was conducted 28 and 91 days after
casting.
Cylinder (10Øx20cm) and beams (10x10x40cm) specimens were castfor each mortar and concrete mix following JSCE-F 552-1999. Cylinder
specimens were cast in two layers and beam specimens in one layer, with avibrator applied to the outside of the mold after each layer was placed.
After casting, molded specimens were covered in plastic wrap and cured in
the molds for 24 hour, after which they were removed from the molds andmoved to water curing.
3.3 Fresh mortar and concrete properties
The properties of fresh mortar and concrete are given in Table 3.
Slump flow, flowability and air content were measured according to JIS A
1150, JSCE-F 512-1999 and JIS A 1128-2005 respectively.
Table 3: Fresh mortar and concrete properties
SeriesSlump flow
Efflux time
Air content
(mm)
(s)
(%)
SB60
495
6.5
SB80
495
5.7
SB100
585
5.7
FA30-PP
600
4.7
11.6
FA50-PP
360
10.7
12.1
FA70-PP650
4.4
9.1
FA30-RF595
5.5
9.9
FA50-RF
415
6.7
11.6
FA70-RF660
4.6
8.6
FA50-sa60-NG
600
5.3
9.0
FA50-sa60-RG
570
5.4
10.0
FA50-sa80-RG
615
4.6
10.0
FA30-sa60-RG
600
5.3
10.0
FA70-sa60-RG
600
5.1
10.0
3.4 Specimen testingThree properties of the mortar mixes were tested experimentally.
Compressive strength fc was measured according to JIS A 1108-2006, andflexural strength fb was determined according to JSCE-G 552-1999. For all
tests, reported valued are the average of three specimens.
Air permeability K was measured for concrete series at 91 days inorder to better observe the effect of fly ash. Air permeability specimens
were taken from cylinders by cutting a 40 millimeter-thick section from thecenter of the cylinder. Specimens were then placed in a drying machine for
one week at 40ºC and checked by monitoring the weight change. After dry

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conditions were met, the specimens were set into an air permeability
machine and the volume of air flow was measured under steady state
conditions. The air permeability coefficient was calculated per Equation 1.

K=

2P2 ⋅ h ⋅ r Q⋅
P12 − P22 A(1)

Where:
K:
air permeability coefficient (mm/s)
loading pressure (MPa)P1:
P2:
atmospheric pressure (MPa)h:specimen thickness (mm)r:unit volume weight of air (1.205x10-6 MPa)Q:
air flow volume (mm2/s)A:sectional area (mm2)4. ENVIRONMENTAL PERFORMANCE INDICATOR
The environmental performance indicator was calculated as the ratio of themechanical performance to the CO2 footprint (MPa / kg of CO2/m3)(Nielsen, 2009). The CO2 footprint is calculated from the mix proportions(Table 2), and the emissions of Portland cement, sand, aggregates and flyash (Table 4). These values are determined from the emissions related tothe manufacture of concrete and its constituent parts. In this research, only
the contribution from cement, fly ash, sand, and aggregates were considered.
Table 4: CO2 emissions by material (JCI, 2008)
Material
CO2 emissions(kg CO2/ton)Portland cement
765.5
Fly ash17.9
Natural river sand
3.4
Normal/Recycled aggregate
2.8
5. RESULTS AND DISCUSSION
5.1 Compressive and flexural strength
5.1.1 Mortar series
For sand-binder ratio effect, compressive and flexural strength results
are shown in Figure 1. For compressive strength it can be seen that
increasing the sand-binder ratio resulted in a marginal decrease incompressive strength. For flexural strength this effect is more pronounced;
increasing the sand-binder ratio resulted in flexural strength increases.
From these results, a sand-binder ratio of 80% is considered optimal for
utilizing in concrete series, as it produces the best combination.

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Figure 1: Compressive and flexural strength results by sand/binder ratio(Mortar)For the fly ash-binder ratio and fiber type effect, compressive andflexural strength results are shown in Figure 2. For compressive strength adecreasing trend is observed when the fly ash-binder ratio is increased. For
flexural strength the behavior is slightly different, with a decreasing
tendency when fly ash content increases from 30% to 50% and no change
from 50% to 70%. The effect of fiber type is almost negligible. Based uponthis recycled fibers were selected for concrete series.

Figure 2: Compressive and flexural strength results by fly ash/binder ratio
and fiber type (Mortar)
5.1.2 Concrete seriesCompressive and flexural strength results for fly ash-binder ratio areshown in Figure 3. It can be seen that increasing the amount of fly ash has asignificant effect on strength behavior. Increasing the fly ash-binder ratio
results in a constant decrease in both compressive and flexural strength.
The slow but steady strength development for high fly ash content can also
be seen, as the slope is less for 91 days than for 28 days.

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Figure 3: Compressive and flexural strength results by fly ash/binder(Concrete)
On the other hand, increasing the amount of aggregates does notconsiderably effect either the compressive strength or flexural strength. The
compressive strength is highest for normal aggregate (Figure 4). The
strength development seems quite good in time being more noticeable forcompressive strength, showing the best increase the series with 6.4%
aggregate volume; however for 0% aggregate volume and normal aggregate,
there is slight unexplained reduction in flexural strength.

Figure 4: Compressive and flexural strength results by aggregate
volume and type
5.2 Air permeability
As mentioned before, the air permeability test was conducted for
concrete series at 91 days, and the results are displayed in Figures 5 and 6.
It can be observed that increasing the fly ash-binder ratio from 30% to 50%,
decreases the air permeability considerably, and increasing the fly ashbinder ratio from 50% to 70% results in a much smaller decrease in the airpermeability (Figure 5). This can be explained by the large amount of fly
ash content which reacts slowly but progressively as compared to thedecreasing cement content which reacts faster up to 28 days.

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Figure 5: Air permeability by fly ash/binder
Conversely, increasing the amount of aggregate results in a constantincrease in air permeability; in this case it can be explained by the constantincrease in coarse aggregates (Figure 6). However, for aggregate type thelowest air permeability is for normal aggregate, due to the lower density andlow grade recycled aggregate, which results in higher air permeability. It
can be seen also that for 15% normal aggregate volume the air permeability
is quite similar to that for 0% aggregate volume.

Figure 6: Air permeability by aggregate volume and type
5.3 Environmental performance indicator
The environmental performance indicators were calculated at 91 days
for concrete series, and the results are displayed in Figure 7. For variable
aggregate volume and type, the trend is similar to the mechanicalperformance, so there is little change when normalized by the environmental
impact. For variable fly ash-binder ratio normalized by the environmental
performance indicator, results in a reverse of the trend shown by themechanical performance.

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Figure 7: Environmental performance indicators by fly ash/binder (top) andaggregate volume and type (bottom)
The usage of fly ash has a more noticeable effect on the environmental
indicator compared with recycled aggregates. Recycled aggregates have thesame CO2 emissions as normal aggregates, so the effect of aggregate type isdifficult to evaluate by CO2 alone. However, the effect of fly ash can be
explained by the large difference in CO2 between Portland cement and theother materials, and the effect is greater when more cement is replaced.
6. CONCLUSIONSIn this paper, environmental performance indicators were used to evaluate
the mechanical & environmental performance of concrete containing highvolume of recycled materials. It was seen that implementing theenvironmental indicator is a useful tool in order to assess both themechanical performance and the CO2 emissions of the concrete, and make it
clear that concrete quality can be appraised taking into account theenvironmental impact as well.
The concrete with the highest environmental indicator for compressive
strength had a fly ash-binder ratio of 50% and normal aggregates. The
advantage of utilizing recycled aggregates is difficult to see in this casesince recycled aggregates have the same CO2 emissions as normal
Environmental
ContainingHigh
Volumeof Recycled
Materials
EnvironmentalPerformance
PerformanceIndicators
Indicatorsfor
forConcrete
Concrete
ContainingHigh
Volumeof Recycled
Materials

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aggregates, but generally lower performance; therefore, to evaluate theeffect of aggregate type it is necessary to introduce an additional factor
which considers the effect of preserving raw materials as well.
The tendency of the environmental performance indicator for fly ashbinder ratio concrete is similar to that of air permeability, suggesting that theenvironmental performance indicator may be comparable to the durability of
the concrete in this case.
The concrete with the highest environmental indicator for flexuralstrength had a fly ash-binder ratio of 70% and 100% replacement of normal
aggregates with recycled aggregates. This value was driven by theextremely low CO2 emissions achieved by replacing high volume ofPortland cement with fly ash.
ACKNOWLEDGEMENT
The authors would like to express their thanks to Hirata Wataru, whodeveloped a big part of the project; Shota Mizukami, who helped in theachievement of the final part; and all the other members of Kishi-Kato
Laboratory who generously assisted in conducting these research works.
REFERENCES
Japan Concrete Institute, 2008. Concrete innovation in the environmental
age. Technical Committee on Environmental Performance of Concrete
Structures. (in Japanese).
Malhotra, V.M., 1999. Making concrete greener with fly ash. ConcreteInternationa 21 No. 5, 61-66.
Meinhold, U., Mellamann, G., and Maultzch, M., 2001. Performance ofhigh-grade concrete with full substitution of aggregates by recycledconcrete. Sustainable Development of Cement and Concrete. ACISpecial Publication SP-202, 85-96,
Moriwake, A., Habuchi, T., Kushida, M., Dec. 2004. Short fiber forconcrete reinforcement produced from recycled PET bottles. Cement
Concrete 694, 20-25 (in Japanese).
V. Nielsen, Clauss, January 2009. Toward sustainable concrete structures.
Lecture on Sustainability in Concrete & Concrete Structures,
University of Tokyo, Japan.

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SEISMIC HAZARD MAPS OF THAILAND
CHITTI PALASRI and ANAT RUANGRASSAMEE
Department of Civil Engineering,
Chulalongkorn University, Bangkok, Thailand
p_chitti@yahoo.com, fcearr@eng.chula.ac.th
ABSTRACTIn this study, the probabilistic seismic hazard map of Thailand andneighboring areas is developed. Thailand is located close to the Andaman
thrust in the west and the Sunda arc in the south which are the interaction
between the Eurasian plate and Indo-Australian plate. Several active faults
in the region contribute to earthquakes in Thailand. It is important toevaluate seismic hazard in the area for seismic design of structures.
Earthquakes recorded from 1912 to 2006 by Thai Meteorological
Department and US Geological Survey are used in the analysis. Two
attenuation relationships developed for the western USA which give goodcorrelations with actual measured accelerations are used for active tectonic
zones in Thailand. Maps of peak horizontal accelerations at rock sites with
2% and 10% probabilities of exceedance in 50 years are developed. For the
peak horizontal acceleration with 10% probability of exceedance in 50
years, the maximum value in Thailand is about 0.25 g in the northern partof Thailand. The peak horizontal acceleration is 0.02 g in Bangkok. For the
peak horizontal acceleration with 2% probability of exceedance in 50 years,
the peak horizontal acceleration is about 1.6 – 2 times of the 10%
probability of exceedance in 50 years for most areas.
1. INTRODUCTION
In Thailand, earthquakes occasionally occur in the northern and westernparts. Fenton et al. (2003) investigated several faults in the northern andwestern parts of Thailand. Maximum credible earthquakes were determinedby using empirical relationships between rupture lengths of faults andmagnitudes by Well and Coppersmith (1994). They found that the
maximum credible earthquakes of the active faults in Thailand had moment
magnitudes of 7 to 7.5. A maximum moment magnitude of 7.5 wasestimated for Thoen, Mae Chan and Three Pagodas faults which werelocated in Chiang Mai and Kanchanaburi provinces, in the north and west of
Thailand respectively.
Some earthquakes caused structural damages in local areas. For
example, the September 11th, 1994 earthquake with a local magnitude of
5.1 had an epicenter around 15 km from Pan District, Chiang Rai province.
The earthquake caused structural damage to the Pan hospital. The May 16th,
2007 earthquake with a moment magnitude of 6.3 had an epicenter near the

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Thai-Laos border. The earthquake caused structural damage to a school in
Chiang Rai province, located 109 km from the epicenter.
Warnitchai and Lisantono (1996) proposed the seismic hazard map ofThailand using the earthquake data and the seismic source zone map fromSouth Asia Association of Seismology and Earthquake Engineering(Nutalaya et al., 1985) and the attenuation model of Esteva (1973). For the
peak horizontal acceleration with 10% probability of exceedance in 50 years,
the maximum value in Thailand is about 0.25 g in the northern part ofThailand. The peak horizontal acceleration is 0.05 g in Bangkok.
Petersen et al. (2004) developed seismic hazard maps for Sumatra,Indonesia and the southern Malaysian Peninsula using attenuation models
for the active tectonic region and subduction zones such as Sadigh et al.
(1997), Toro et al. (1997) and Youngs et al (1997). The researcher foundthat the peak horizontal acceleration with 2% probability of exceedance in
50 years was around 0.1 g to 1.0 g for Sumatra Island and less than 0.2 g forthe southern Malaysian Peninsula and the peak horizontal acceleration with
10% probability of exceedance in 50 years is about 60% of that with 2%
probability of exceedance in 50 years.
At present, up-to-date earthquake catalogs and accelerograms areavailable. So, the seismic hazard maps of peak horizontal accelerations canbe developed with sound attenuation models and updated information. The
seismic hazard map can be used as a guideline for earthquake-resistantdesign of structures in Thailand.
2. EARTHQUAKE CATALOGS
Thai Meteorological Department (TMD) has recorded earthquake eventsand complied earthquake records from several catalogs which consist oftime, location, depth, and magnitudes. The earthquake events in the catalogs
range from 1912 to 2006 in the area covering 0oN to 30oN and 88oE to
110oE. Magnitudes in the catalogs are presented in a body-wave magnitude(mb), a local magnitude (ML), a surface-wave magnitude (MS) and a moment
magnitude (MW). The catalogs are obtained from US Geological Survey(USGS) with 9,794 events since 1954, International Seismological Centre(ISC) with 1,074 events since 1964, TMD with 3,794 events since 1976 andothers with 98 events before 1964. Before using the earthquake data, theearthquake magnitudes were transformed to the moment magnitude (MW).
Various types of the magnitudes were converted to the moment magnitude
using the relations proposed by Campbell (1985), Sipkin (2003), Heaton etal. (1986), and Hanks and Kanamori (1997).
The assumption in the seismic hazard analysis is that all earthquake
events are independent. Thus, the earthquake events which are foreshocks
and aftershocks must be eliminated before further analysis. The method
proposed by Gardner and Knopoff (1974) was applied to eliminate
foreshocks and aftershocks. After eliminating aftershocks and foreshocks,

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the numbers of earthquakes in the area ranging from 0oN to 30oN and 88oE
to 110oE become 5,550, or about 37.6% of all events in the original catalog.
3. SEISMIC SOURCE ZONES
Seismic source zones are usually derived from tectonics, earthquake
catalogs, and studies on active faults. Seismic source zones in Thailand wasrecently proposed by Saithong et al. (2000) as shown in Fig. 1, based onstudies by Nutalaya et al. (1985) and Charusiri et al. (2000). Active tectonic
regions in the north of Thailand consist of Zones E, F and I which there are
Chiang Mai, Chiang Rai, Mae Hong Sorn provinces. Active tectonic regionsin the west of Thailand consist of Zone J which is located in Kanchanaburiprovince. The previous study notes that active faults in the zone cangenerate an earthquake with a maximum magnitude of about 7.5 (Fenton etal., 2003). The subduction zones are Zones A, N and O in the Andaman Arc.
The earthquake data which were recorded in the past are incomplete
because instruments could not detect small earthquakes. And incomplete
data can result in the under-estimation of recurrence rates. The completenessof earthquake data was considered in this study by using the method
proposed by Stepp (1972). The Gutenberg-Richter magnitude-frequency
relation is used for each source zone aslog λm = a – bm(1)

where λm is the mean annual rate of exceedance of a magnitude m, a and b
are parameters of the relation. The result can be summarized in Table 1
which indicates the a and b values of Gutenberg-Richter relation and the
maximum moment magnitude of each source zone. The maximum moment
magnitude of each source zone is selected from 1) the study by Fenton et al.
(2003) that estimated the maximum magnitudes of faults in Thailand fromgeomorphology, and 2) the maximum magnitude from the past seismic
record.
4. ATTENUATION MODELS
Attenuation models are used for determining the ground motion parametersfrom the magnitude and distance. The attenuation models are important inpredicting the peak horizontal acceleration at any sites. Petersen et al.
(2004) modified the Youngs et al. (1997) relation for the subduction zone in
Sumatra’s outer-arc ridge, Indonesia and the Southern Malaysian Peninsula.
The Youngs et al. (1997) relation and the modified Youngs et al. (1997)
relation can be expressed as Eq. (2) and Eq. (3), respectively.
lnY = 0.2418+ 1.414M + C1 + C2 (10 − M )3 + C3 ln(Rrup + 1.7818exp(0.554M )) + 0.00607H + 0.3846Z r (2)StandardDeviation= 1.45 − 0.1 M

ln YModified (M,R) = ln YYoungs (M,R) – 0.0038(R - 200)
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where Y is the median spectral acceleration for 5% damping (g), or peakground acceleration (g), M is a moment magnitude, Rrup is the closest
distance to the rupture plane (km), H is the depth of earthquake source (km),Zr = 0 for crustal interplate region, Zr = 1 for crustal intraplate region and C1,
C2,…,C7 are coefficients. In this study, the relation by Youngs et al. (1997)
is used for the distance less than 200 km and the modified Youngs et al.
relation is used for the distance over 200 km.

Figure 1. Seismic source zone map in Thailand and neighboring areas.
(Saithong et al., 2004)

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Table 1: Source zones and parameters of Thailand and neighboring areas.
SourceZones

No. of
Events(1912-2006)

Zone A
Zone B
Zone C
Zone D
Zone E
Zone F
Zone G
Zone H
Zone I
Zone J
Zone M
Zone N
Zone O
Zone P
Zone Q
Zone R
Zone W

457
432
207
89
645
316
97
42
534
81
31
120
908
439
360
43
119

Parameters ofGutenberg-Richter
Equation
a
b
1.148
6.111
0.616
3.430
0.700
3.177
0.616
2.745
0.582
2.927
1.159
5.178
0.805
3.629
0.961
4.294
0.923
4.032
0.752
2.892
0.883
3.387
0.439
2.771
0.784
4.953
0.982
5.512
0.981
5.112
0.906
3.548
0.825
3.775

Maximum
MomentMagnitude
7.2
7.4
7.7
7.0
7.3 (7.5)
7.9
6.6
6.7
6.7 (7.5)
6.2 (7.5)
6.7
7.5
9.0
7.4
6.5
5.6
6.7

Remarks the value in the parenthesis is the moment magnitude from the study by
Fenton et al. (2003).

Chintanapakdee et al. (2008) studied suitability of various attenuation
models for Thailand by comparing measured ground accelerations withvarious available models. Peak horizontal accelerations were obtained from
163 ground motions recorded by Thai Meteorological Department from 45
earthquakes with magnitudes between 4.7 to 6.3 and distance ranging from
231 to 2090 km. Measured peak horizontal accelerations were comparedwith attenuation equations which are used in active tectonic regions. They
found that Sadigh et al. (1997) and Idriss (1993) relationships gave peakhorizontal acceleration close to measured peak horizontal accelerations. So,
Sadigh et al. (1997) and Idriss (1993) equations are used for active tectonic
regions and weighed equally for the peak horizontal acceleration. Sadigh etal. (1997) and Idriss (1993) equations can be expressed as Eq. (4) and Eq.
(5), respectively.
ln Y = C1 + C 2 M + C 3 (8.5 − M ) 2.5 + C 4 ln[ Rrup + exp(C 5 + C 6 M )] + C 7 ln( Rrup + 2)
Standard Deviation = 1.39-0.14 M(4)ln Y = (α 1 + α 2 M ) − ( β 1 + β 2 M ) ln( Rrup + 10) + ϕF
;M ≤ 5⎧ ε max⎪
Standard Deviation = ⎨ε 1 − 0.12 M ;5 < M ≤ 7.25
⎪ ε
M > 7.25
min
⎩

(5)

where Y is the median spectral acceleration for 5% damping (g), or peakground acceleration (g), M is the moment magnitude, Rrup is the closest

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distance to the rupture plane in km and C1, C2,…, C7, α1, α2, β1, β2, ε1, εmax,
εmin, ϕ are coefficients.
5. PROBABILISTIC SEISMIC HAZARD MAPS
The effects of maximum magnitudes considered in each source zones onseismic hazard maps are investigated. The maximum magnitude is the upper
limit set in the magnitude-frequency relation. From Table 1, maximum
moment magnitudes from the study on active faults by Fenton et al. (2003)
for source zones E, I, and J are larger than those from the catalogs. Sadigh etal. (1997) attenuation relationship is used in this comparison. The hazard
maps using the maximum magnitudes determined from catalogs and active
faults together with a case having no limit on maximum magnitudes areshown in Fig. 2.
98° E

102° E

100° E

0. 2

0. 01

0. 15

0. 1
0.04

0.15

0. 08

0.15
0.
1
0. 04

0.02

0.1

0.2

0.2

0. 04

0.2
0. 0
1

0.
02

0. 0
1

0. 0
6

0. 2

0.
06
0.
02

0. 1

0. 25
0. 2

0.
02

0.
08

0.02

0.06

0.08

0. 08

0.2

0.04
0.1

14° N

0.1
0.15

0.02

0.06

0.04

5
0. 1

0.1

0. 1

0.06
0.08

0.06

0.08

0.080.1

0.15

14° N

8
0. 0

16° N
0. 2
5
0.1

0.15

5
0. 1
0. 1

0. 2

0. 0
4

0.
2
0.2

8
0. 0

8
0. 0

18° N
25
0.

0. 1
5

15
0.

0.3

0. 2

0. 15

16° N

°

14 N

18° N

5
0. 2

20° N

0.01

0. 1
5

0. 1
6
0. 04
0
0.

16° N

102° E

0. 06

08
0.

0. 04

0.25

100° E

0. 2

0.
15

0.2

18° N

98° E

0.
06

0.
25

20° N

8
0. 0

1
0.
6
0. 0

5
0. 2

0. 3
0.25

20° N

102° E

0.25

0.
01

100° E

0. 15

98° E

(a)
(b)
(c)
Figure 2: Sensitivity of hazard maps to the maximum magnitudes.
(a) Unbounded maximum magnitude(b) Maximum magnitudes from the earthquake catalog(c) Maximum magnitudes from study on active faults

The peak horizontal acceleration from the case with no limit on
maximum magnitudes of earthquakes in Figure 2(a) are larger than the peakhorizontal acceleration from the case with maximum magnitudes derived
from active faults in Figure 2(c). The difference is about 0.01g - 0.04g in
the north of Thailand and about 0.004g – 0.008g in the centre and west of
Thailand.
The peak horizontal accelerations from the case with maximum
magnitudes from earthquake catalogs in Figure 2(b) are smaller than that thepeak horizontal acceleration from the case with maximum magnitudes from
active faults in Figure 2(c) by 0.014g in the west of Thailand and 0.004g in
the north of Thailand. It is seen that the effect of maximum magnitudes onseismic hazard is less significant because large earthquakes in Thailand
have low probability. In developing hazard maps, the maximum magnitudes
obtained from study on active faults are used for the maximum magnitudes
in each zone.

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The seismic hazard curve for all sources which is the relation between
the annual rate of exceedance and the peak horizontal acceleration can be
developed for any site. The seismic hazard curves for Chiangmai (Point A)
Kanchanaburi (Point B) and Bangkok (Point C) can be obtained as shown in
Figure 3. It is seen that the seismic hazard curve at Point A in the northernpart of Thailand is close to the hazard of Zone E, while the seismic hazard
curves at Point B (in the western part of Thailand) and Point C (in Bangkok)
are governed by the hazard of Zone J. Therefore, the hazard in Bangkok iscontributed from earthquakes in the western part of Thailand.
The probabilistic seismic hazard maps by Sadigh et al.(1997) andIdriss (1993) attenuation models are equal weighted. The seismic hazard
maps using Sadigh et al.(1997) and Idriss (1993) attenuation models
showing peak horizontal acceleration with 10% and 2% probability ofexceedance in 50 years are shown in Figure 4. High seismic hazard isobserved in the north and the west of Thailand.

010Annual rate of exceedance (/year)

Annual rate of exceedance (/year)

For 10% probability of exceedance in 50 years, the maximum peakhorizontal acceleration in the northern part of Thailand is about 0.25g, in thewestern part of Thailand is about 0.15g and in Bangkok is about 0.03g. The
maximum peak horizontal acceleration for the 2% probability of exceedancein 50 years map increases in the same trend with the value of about 1.6 to 2
times the peak horizontal acceleration with 10% probability of exceedancein 50 years.

-10

10

All zones
Zone E
Zone F
Zone I
Zone C

-20

10

0

0.5
1
Peak Horizontal Acceleration (g)1.5Annual rate of exceedance (/year)(a) Hazard curves at Point A

0

10

All zones
Zone J
Zone F
Zone R
Zone E

-10

10

-20

10

0

0.5
1
Peak Horizontal Acceleration (g)1.5(b) Hazard curves at Point B

0

10

All zones
Zone J
Zone R
Zone F
Zone I

A
B

-10

10

C

-20

10

0

0.5
1
Peak Horizontal Acceleration (g)(c) Hazard curves at Point C

1.5(d) The positions of Points A, B and C

Figure 3: Seismic hazard curves

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96° E
22° N

98° E

100° E

102° E

104° E

106° E

96° E
22° N

98° E

100° E

102° E

104° E

106° E

0.3

0.2
0.4

5
0. 2

0.0

0.25

0. 25

20° N

0. 1
5

0.3

0.

15

1
0. . 04
0
02
0.

0.2

18° N

01
0.

0.02
0.01

0.

0.2

0.1
0.0
6

02

0. 02

0.15

0.30
.25

0.0 0. 2
600.1
.08
0. 04
1
0.0

0. 0

0.
01

0.
06

2

0.4

0.25
0.15

0.25

0.04

0.3
0.2
5

5
0. 1

12° N

00.0.1
06.08

0.01

14° N

12° N

2 8
0. 00..10
6 . 040. 0 0

16° N

0.04
0.02

8
0. 0

0. 1

0. 25

0. 3

0. 3

0.15

0.3

0.2
0. 3

0.08

14° N

5
0. 1

0.1

0.06
0. 04

0.2

16° N

0.25

0.4

0. 4

18° N

0. 3

20° N

0.
06

8

10° N

0.08

0. 0
4

0. 1

10° N

0.2

8° N

0.
2
0.40.20.50. 35

0. 1
5

0.08

6 N
0.0
1

04

0. 1

°

0.0
1

0.

6 N

5
0. 1

°

0.06

2
0.0

0.25

8° N

0.0
2

(a) 10% probability of exceedance in 50 years (b) 2% probability of exceedance in 50 years

Figure 4: Seismic hazard maps of peak horizontal accelerations (g)Comparing this result with Wanitchai and Lisantono (1996), themaximum PHA from this study is close to that in Wanitchai and Lisantono(1996) but contour lines of PHAs do not cover in the same area. In Bangkok,
the PHA from this study which is about 0.03g is less than the PHA from
Wanitchai and Lisantono (1996) which is about 0.05g.
6. CONCLUSIONSIn this study, The seismicity of Thailand is collected from 1912 to 2006 byusing the catalogs of Thai Meteorological Department and US Geological
Survey. Probabilistic seismic hazard maps of Thailand are developed. It canbe concluded that
1) Peak horizontal accelerations at a site are affected by the nearest
sources. Kanchanaburi and Bangkok are governed by the hazard of zone J
where Three Pagodas fault and Sri Sawat fault are located on this zone.
2) The seismic hazard maps of Thailand for the 2% and 10%
probability of exceeedance in 50 years are proposed. Peak horizontal
accelerations for 10% probability of exceedance in 50 years areapproximately 0.15g in the western part of Thailand, 0.25g in the northernpart of Thailand and 0.03g in Bangkok. For the peak horizontal accelerationfor 2% probability of exceedance in 50 years, the peak horizontal
acceleration is about 1.6 - 2 times of the 10% probability of exceedance in
50 years for most areas.

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Wells, D.L. and Coppersmith, K.J. (1994) “New empirical relationships
among magnitude, rupture length, rupture width, rupture area, andsurface displacement,” Bulletin of the Seismological Society ofAmerica 84(4), 974–1002.
Youngs, R.R., Chiou, S.J., Silva, W.J., Humphrey, J.R. (1997), “Strong
ground motion attenuation relationships for subduction zone
earthquakes,” Seismological Research Letters, Vol. 68, 58-73.

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2ndEFFECT OF OXYGEN AND WATER
ON MACRO-CELL CORROSIONVU VIET HUNG,OMINDA NANAYAKKARA and YOSHITAKA KATO
Department of Civil Engineering
University of Tokyo, Japanviethung@iis.u-tokyo.ac.jp
ABSTRACTIn reinforced concrete structures, macro-cell corrosion with locallyseparated anode and very large cathode is dominant in chloride
environment & repair patching. It causes very severe damage and suddenly
destroys the real structures. Oxygen and water play an important role incorrosion process in terms of accelerating the corrosion rate duringexposure period. Moreover, it was proposed that anodic-cathodictransformation of steel bar over the time was caused by spatial variation ofoxygen and water concentration along the steel bar. This experimental study
was designed to study the effect of oxygen & water on the behavior ofmacro-cell corrosion in concrete and repaired concrete based on different
surface coating conditions. It was observed that the properties of macro-cell
corrosion, such as maximum anodic current density and time dependent
variation, mainly occur in regions closed to oxygen and water support. And
macro-cell corrosion was clearly observed when there is a big variation ofamount of oxygen and water concentration along the steel bar.
1. INTRODUCTION
In reinforced concrete structures, macro-cell corrosion with local anode andvery large cathode (Elsener et al. 2002) is dominant in chloride environment& repair patching. It causes very local damage and finally destroys the real
structures.
Corrosion is an electrochemical process in which both cathodic andanodic reactions occur simultaneously at the steel surface. Oxygen andwater, consumed in cathodic reactions (1) when corrosion occurs, accelerate
the corrosion rate during exposure period. The rate of oxygen and water
diffused to concrete, however, is limited due to the sound of concrete
structures. As a result, the rate of corrosion is restricted by the cathodic
reaction rate (Shiyuan et al., 2006).
Anodic reaction:
Cathodic reaction:

Fe → Fe2+ + 2eH2O + 1/2O2 + 2e- → 2OH-

(1)

Moreover, according to our research (Ominda et al. 2009), it was
proposed that anodic-cathodic transformation of steel bar against the time

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July 2009, Thailand
July 2009, Thailand(time-dependent variation of macro-cell corrosion) was caused by spatial
variation of oxygen and water concentration along the steel bar. Thisbehavior of macro-cell corrosion produces maximum corrosion rates
periodically and causes misinterpreted by the transformation when corrosion
current measurements are taken.
From these backgrounds, the aim of this experimental study is to
identify the effect of oxygen & water on the behavior of macro-cell
corrosion of steel bar embedded in concrete structures.
2. EXPERIMENTAL PROCEDURE
2.1. Methodology
Macro-cell corrosion was observed in both concrete and repaired
concrete with different chloride content. Segmented steel bar was used to
study macro-cell corrosion. To study the effect of oxygen and water, epoxy
coating was used to disturb oxygen & water penetration into specimen.
2.2. Specimen(1) Concrete specimen
Concrete specimens of 100×100×576 mm were cast in this experiment(Figure 1) and the mixture proportion of concrete is shown in Table 1.

Figure 1: Outline of specimen
Table 1: Mixture proportion of concrete
W/C

s/a
(%)

Weight per unit volume (kg/m3)
W

C

S

G

AE(C xweight %)

0.55

45

190

345

770

966

0.008

Chloride ions were deliberately added during mixing the concrete inorder to accelerate the corrosion process. Pure sodium chloride (NaCl) wasused as the source of chloride ions.

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A plain steel bar having a diameter of 10 mm was used. Segmented
steel bar is composed of 28 elements, in which each has 15mm of exposure
length (Figure 2).
Element 1

Element 28

Acrylic
bar

Lead
wire
Figure 2: Segmented steel bar

In term of material aspect, the steel bar was separated by steel
elements, however on the view of electrical aspect, it was assumed
continuously by using lead wire connection. Two lead wires were welded toeach steel element and then connected by epoxy. The total length ofsegmented steel bar embedded in concrete was 600 mm, even though thelength of concrete specimen was 576 mm. Therefore both ends of steel bar
were connected with acrylic bars as shown in Figure 2.
(2) Repaired concrete specimen
Same dimensional specimens were used (Figure 3) in which side A
was repair material, EMACO ® S98P, with high resistance to oxygen andwater penetration.

Figure 3: Outline of specimen

Element 1

Element 10

Figure 4: Arrangement of segmented steel bar
2.3. Experimental cases
Different bottom surface coating conditions were applied toinvestigate the influence of oxygen and water on the properties of macrocell corrosion in concrete and repaired concrete structures (Table 2).

Effect ofof Oxygen
Oxygenand
andWater
WaterononMacro-cell
Macro-cellCorrosion
Corrosion

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Table 2: Experimental cases
Epoxy coating (bottom)
Specimen No
Side ASide B1
coveredConcrete 2
covered3
4
coveredRepaired5
coveredconcrete62.4. Exposure condition
After casting, the specimens were sealed curing at a temperature of
20oC in one week and then all the surfaces of the specimen except thebottom surface were covered by epoxy layer, to ensure that the penetrationof moisture and oxygen through the bottom surface only. Then, allspecimens were exposed to wet and dry cycling in chamber to accelerate thecorrosion progress. Table 3 shows exposure condition in one cycle. The
study was conducted on concrete & repaired concrete specimens for 16
weeks & 13 weeks, respectively.
Table 3: Exposure condition
DurationT
Condition(days)
(°C)
Wet
3
40
Dry
4
20
2.5.

RH
(%)
90
60

Item and method of measurement

By applying segmented steel bar inside concrete specimen (which wasintroduced by Miyazato. 2001, Thesis for Doctoral Degree), it is easy anddirect to measure the electric current flowing from element to element tostudy the macro-cell corrosion (Figure 5). However, the lengths of steel
element in the experimental cases were different therefore to compare andanalyze the results the current is converted to current density, as calculated
by:

ai =

I i ,i + 1 − I i −1,iSi(2)where, ai is macro-cell current density of element i (µA/cm2), Ii,i+1,Ii-1,I aremacro-cell currents flowing from element i to i+1, i-1 to i respectively (µA),
and Si is surface area of steel element i (cm2).
Periodic macro-cell corrosion current measurement was carried out
twice a week (the end day of wet and dry exposure) by using a data logger.

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The current values were then converted to current densities of each steel
element along the specimen.
Ii-1,iIi,i+1A

A

AmmeterSteel barConcrete
i-1

i+1

i

Epoxy
Figure 5: Macro-cell corrosion current measurement
3. RESULTS AND DISCUSSION
Macro-cell current was measured and then converted to current density.
Figure 6 shows the results of current densities of concrete under dry
exposure (a) (12th week), wet exposure (b) (12th week) and repaired concrete
specimens under dry exposure (c) (9th week), wet exposure (d) (9th week)with different surface coating conditions (Table 2).
Side A

Macro-cell current density(µA/cm2)

2

Side B
Region I

Region II

16

24

0

-2
No. 1
No. 2
No. 3

-4

-6

0

4

8

12

20

28

Element(a)Effect ofof Oxygen
Oxygenand
andWater
WaterononMacro-cell
Macro-cellCorrosion
Corrosion

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Side A

Macro-cell current density(µA/cm2)

2

Side B
Region I

Region II

16

24

0

-2
P11
P12
P13

-4

-6

0

4

8

12

20

28

Element(b)
Macro-cell current density(µA/cm2)

0.10

Side A

0.05

Side B

0.00
-0.05
-0.10
No. 4
No. 5
No. 6

-0.15
-0.20
-0.25

0

2

4

6

8

10

Element(c)
Macro-cell current density(µA/cm2)

0.10

Side A

0.05

Side B

0.00

-0.05
-0.10
No. 4
No. 5
No. 6

-0.15
-0.20
-0.25

0

2

4

6

Element(d)8

10

Figure 6: Macro-cell current density

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In concrete specimens, it was imagined that side B was divided into
two equal regions I & II, in which region I is close to the interface. Table 4shows the comparison of specimens with different surface coating
conditions based on maximum anodic current density & anodic-cathodictransformation in region I & II.
Table 4: Comparison of specimens with different surface coating conditions
Specimen
Criteria
No. 1
No. 2
No. 3
Maximum anodic
I>>II
I>II
I>II
density
Anodic-cathodic
mainly inmainly in I
mainly in I
transformationII
It is widely accepted that oxygen and water play an important role incorrosion process. Wherever oxygen and moisture are available, corrosion
becomes active. As can be seen from Figure 6 (a), local corrosion (anode)
occurs near the interface (region I, side B) in specimen No.1 where it is
supported by cathodic area in side A, while in specimen No.2 & 3, theappearance frequency of maximum anodic densities in region II is higher.
Furthermore, the difference between specimen No.2 & No.3 is in region II,
corrosive level in specimen No.3 is higher than that of in specimen No.2. It
could be explained that elements of region II in specimen No.3 are much
more supported from side A where cathodic reactions take place.
Time-dependent variation of macro-cell corrosion was also consideredin this research. It is inferred that once an element changes from anode tocathode, another or other elements will switch from cathode to anode at the
short time or simultaneously. In concrete specimen, generally, macro-cell
corrosion and anodic-cathodic transformation occurs near the interface(region I), high “competitive” or the highest electrochemical potential
difference zone. However, as can be seen from Table 4, time-dependent
property was mainly observed in region II of specimen No. 2. The reason is,
in author’s opinion, region II not only acts as cathodic supporting area forregion I but also contains anodic elements. Therefore, elements in region II
are vulnerable to change their potentials due to different amount of oxygen
and water along the steel bar caused by on-going corrosion process.
In repaired concrete, although magnitude is small, macro-cell
corrosion continues happening in repair part (Figure 6 (c), (d)) because of
the incompatibility between repaired and non-repaired parts. In thisexperiment, there are two factors causing potential imbalance between twosides of specimen: different material and different chloride content. Thiscondition is the same with real repaired concrete structures subjected tochloride attack in which repaired part is free chloride content while concretepart is still contaminated by chloride.
According to previous researches (Ominda et al.2007, Barkey.2004,
Ping et al.1997), it was proposed that different material induced macro-cell

Effect ofof Oxygen
Oxygenand
andWater
WaterononMacro-cell
Macro-cellCorrosion
Corrosion

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corrosion will be dominant and causes corrosion in repaired part whenchloride concentration is less than 1.2 kg/m3 while corrosion caused bydifferent chloride content becomes significantly in concrete part if chloride
content is higher than 1.2 kg/m3. In this research, chloride variation of twosides of specimen was chosen as 4.8 kg/m3 on the expectation that corrosion
will occur in concrete part (side B).
Theoretically, it was assumed that repair material has higher resistivity
to corrosive agents, such as oxygen and moisture, penetration compared toconcrete part, and epoxy coating is used to totally disturb substancestransporting inside specimen. On this basis, illustration of amount of oxygen
and water content was drawn in series of repaired concretes (Figure 7).

No.4

No.5

No.6

Figure 7: Image of oxygen and water content
The results from Firgures 6 (c) & 6 (d) confirm the above assumption.
The rate of cathodic reactions in side A (repaired part) are very small
compared to that of in side B (concrete part) in both dry and wet exposure.
In the case of total bottom surface was exposed to experimental
environment (specimen No. 6), corrosion occurred in concrete part.
However, since concrete part was under coating condition in specimen No.
4, corrosion zone was reaching towards the interface where oxygen andwater were supplied from side A. Another evidence to show the role ofoxygen and moisture in corrosion process, for example, in specimen No.5,
high variation amount of oxygen and water between two sides of specimen,
in side A almost no corrosion occurs (current density less than 0.1 µA/cm2)
because of lack of oxygen and water while macro-cell corrosion appears
clearly in side B. The most severe macro-cell corrosion observed in
specimen No. 5 can be explained by the increasing in potential imbalance
between two sides caused by oxygen and water concentrations.
4. CONCLUSIONS
The aim of this study was to identify the effect of oxygen and water onmacro-cell corrosion. The research results are presented below:
Macro-cell corrosion occurs in higher chloride content side of concrete
specimen. Depend on surface coating conditions, however, the behavior ofmacro-cell corrosion will different in both the position of maximum anodic
current density and anodic-cathodic transformation.

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Macro-cell corrosion is clearly observed when there is a big different
amount of oxygen & water content along the steel bar in repaired concrete
structures.
REFERENCES
Ominda N. and Kato Y., 2009. Macro-cell corrosion in reinforcement of
concrete under non-homogeneous chloride environment. Journal ofAdvanced Concrete Technology, 7-1, 31-40.
Elsener, B., 2002. Macro-cell corrosion of steel in concrete –implications
for corrosion monitoring, Cement and concrete composites, 24-1, 6572.
Barkey, D.P. 2004. Corrosion of steel reinforcement in concrete adjacent tosurface repairs. ACI Material Journal, 101-4, 266-272.
Ominda N. and Kato Y. 2007. Macro-cell corrosion of partially repaired
concrete members. Seisan-kenkyu (University of Tokyo), 59-3, 263266.
Ping Gu, J.J. Beaudoin, P.J. Tumidajski, and N.P Mailvaganam. 1997.
Electrochemical incompatibility of patches in reinforced concrete.
Concrete International, 19-8, 68-72.
Miyazato, S.2001. Macro-cell corrosion induced by chloride andcarbonation in reinforced concrete with defects. Thesis for Doctoral Degree.
Shiyuan. Q., Jieying Z., Deyu Q., 2006. Theoretical and experimental studyof microcell and macrocell corrosion in patch repairs of concrete
structures. Cement and concrete composites, 28, 685-695.

Effect ofof Oxygen
Oxygenand
andWater
WaterononMacro-cell
Macro-cellCorrosion
Corrosion

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CONSTRUCTION STEP ANALYSISAND COLLOSION SIMULATION OF CFTAYI SUN-AE, JUNG MIN-CHUL and KONG JUNG-SIK
School of Civil Engineering and Architecture
Korea University, Korea
capritio@korea.ac.kr
ABSTRACT
Recently, many studies about high-strength concrete and composite
structures are progressed to get more economic and stable result in theconstruction of structure all over the world. One of those studies is about
CFTA(Concrete Filled and Tied Steel Tubular Arch) girder that applies anarch structure and a pre-stressed structure to CFT(Concrete Filled Steel
Tubular) Structure which is filled with a concrete and improve the stiffness
and strength of the structure by the confinement effect of fillers to maximize
the efficiency of structure and economic. In this study, construction step
analysis of CFTA girders by using ABAQUS. And one of the weak points of
CFTA girder is pre-stressed tendon exposed outside of structure. Therefore,
collision simulation of heavy projectile with CFTA girder was also
performed to check the safety of tendons by using ABAQUS 6.5-1.
1. INTRODUCTION
In recent years, many studies about shape of bridge’s superstructure,efficiency of materials and complex structure are being progressed forbridges, which are more safety, economical and well harmonized into nature,in the construction for structure all over the world. SCP Girder, as an
example, which has similar cross section shape of PSC girder is filled with apre-stressed concrete. In this manner, studies about new types of bridge
superstructure which can minimize shortcomings of the existingsuperstructure of bridge by performance improvement from rearranging ofstructure shapes and materials, using tensile force, and being beneficial for
economic as well as aesthetic part are being progressed.
As mentioned above, the studies about new types of synthetic
structure and girder system of bridges are being progressed these days.
However, as it is hard to be used on actual construction field immediately, it
is needed to develop new structure types which can be applied without
further delay and minimize some weak points of existing structure stuffs.
‘Concrete Filled steel tubular Tied Arch composite (CFTA) girder, one of
the new types of complex structure girder, is studied for this purpose. In
CFTA girder, the construction step has 4-steps. And the constructor has toconsider about the stress and displacement in each step. In this study makethe process for construction by using ABAQUS program and check the limit
stress and displacement.

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July 2009, Thailand
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There is an increasing number of severe accidents, some of these are
the accident between the transportation means or the merchandises and our
structure systems such as bridge girders, bridge piers or barriers. In this case,
if no officially is considered, there will be seriously affect not only to theperformance of the structure but also the economic issue and public safety.
In this part of the research, numerical simulation of collision has therefore
an imperative method for testing the CFTA girder. A hypothesis projectile
was created from an I shape steel girders or wood trees which the truck is
carrying were thrown out and collides the girder. Both the object and the
CFTA girder were modeled using finite element method (FEM) to simulate
the dynamic impact performance. The results will then be used to invoke forthe on-site full scale experiments later on and for further studying.
2. CFTA GIRDER AND PROPERTIESThe top side of CFTA girder filled with concrete in steel tube maintains
constant height, while the bottom side of CFTA girder is the arch shape.
Besides, pre-stress is put on steel tendons exposed at the lower part of
CFTA girder to improve its safety and reduce its weight.
The solid drawing and the constituent material of CFTA girder are
showed figure 1 and 2. As shown figure 1, the height of CFTA girder atboth ends is 620mm and filled with concrete. As going to the center part, theshape of the inner filled concrete is the arch shape with maintaining 180mm
thickness. Tendons are inserted until 520mm from both ends, and then they
are exposed to outside because of the arch shape of CFTA girder. The steel
box on the concrete arch is the empty and transmits the load from upper partof steel box to the concrete arch. The length of the whole span is 12.2m, thewidth is 500mm and the displacement control load is applied to perform thenon-linear analysis. Besides, material properties and scale are as following
table 1.

림Figure 1: The solid drawing of CFTA girder Figure

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Figure 2: The constituent material of CFTA girder
Table 1: Material Properties and Scale

Concrete SlabConcrete Block
Steel Plate
TendonDensity(kg/m3)
2500
2500
7850
8000

Young’s
Modulus(Mpa)
21,50029,885210,000
210,000

Part

Length(mm)Wide(mm)

Height(mm)

Slab
Steel

30600
30500

3500

240

Concrete
Steel Plate
Tendon inside

30600
30600
30600

2000
2000

Tendon outside

30600

Part

Poisson’s Ratio
0.167
0.167
0.3
0.3

198.6 ㎟1476
1476
2635.5 ㎟
1664.4 ㎟

3. CONSTRUCTION STEP ANALYSIS
Construction step has 5 steps and each step has different stress and strain. If
the constructor does not consider about that actual state, it could be makes
critical fracture. In this study the stress and strain were checked in from top
to bottom at middle of bridge.
Figure 3.1 shows the displacement of CFTA in step 1 by color, andthe detail value appears in table 3.1. In the first step of CFTA girder
construction, there are only self-weight of concrete and steel plate. So thegirder has -124.16mm displacement. It is the largest displacement in wholeconstruction step.

Construction
Simulation
of of
CFTA
Construction Step
StepAnalysis
AnalysisandandCollision
Collosion
Simulation
CFTA

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1st tendon stress(Mpa)

775.9

There are slab self-weight in third step. In this step, the slab stiffness
July
2009,
Thailandisnotrevelation
yet, so the girder displaced caused by the slab weight.
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ICUS Report 37

Figure 3.3:
3.1: Displacement of Step 31
Table 3.2. Stress and Strain of Step 2
CenterTable 3.3:
3.1: StressStress andand Strain
Strain ofof Step
Step 31
Part Table
StrainCenter Stress(MPa)CenterPart
Part
Upside2.20E-05
0.617965
StrainStress(MPa)StrainStress(MPa)
Filled concrete
Downside-0.00018-5.1912Upside-0.00018-5.05392
Upside-0.00036-10.2477
Filled concrete
concrete
FilledUpside
5.81E-05
12.2881
Downside-9.37E-07-0.03545
Downside
0.000357
10.2567
Steel plate
Downside-0.00022-46.7539
Upside-0.00021-44.5875
Upside-0.00049-104.027
Steel
plate
Steel
plate
Displacement Downside
(mm)
37.94
0.000487
104.365
Downside
3.05E-05
6.4032st
1 tendon stress(Mpa)775.9
Displacement-124.16
Displacement(mm)(mm)-27.07
1st tendon
863.287
There
are
slab
self-weight
third step.
In thisstep, theslab stiffness
The
SecondStep
has first in
tensionfor inside
tendon775.9Mpa
each
is
not
revelation
yet,
so
the
girderdisplacedcausedbytheslabweight.
and the whole tension is 4300kN. So CFTA girder gets 37.94mm camber.
Self-weightsteel
and concrete thewillsecond
be applied
withapplied
the firston
tension.
At the oflaststepplateof construction,
tensionCFTA
girder 420.6MPa each of step. Total tension force is 1400kN. The forces in
the last step are the self-weight of concrete, steel plate, slab and the tensionof tendon. Figure 3.4 shows the displacement of step 4 by color. CFTA
model’s displacement shows blue color, it means the whole model is insafety displacement range.

133n Step Analysis and Collosion Simulation of CFTA

3.2: Displacement of Step
step 23
Figure 3.3:
Table 3.3: Stress and Strain of Step 3
CenterPart
Filled concrete

Steel plate

132
132

StrainStress(MPa)Upside-0.00018-5.05392

Downside-9.37E-07-0.03545

Upside-0.00021-44.5875

Downside

3.05E-05

6.4032

Displacement (mm)
1st tendon

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Table 3.2. Stress and Strain of Step 2
CenterPart
Filled concrete
Steel plate

StrainStress(MPa)Upside2.20E-05

0.617965

Downside-0.00018-5.1912Upside

5.81E-05

12.2881

Downside-0.00022-46.7539

Displacement (mm)37.94

1st tendon stress(Mpa)

775.9

There are slab self-weight in third step. In this step, the slab stiffness
is not revelation yet, so the girder displaced caused by the slab weight.

Figure 3.3: Displacement of Step 3
Table 3.3: Stress and Strain of Step 3
CenterPart
Filled concrete

Steel plate

StrainStress(MPa)Upside-0.00018-5.05392

Downside-9.37E-07-0.03545

Upside-0.00021-44.5875

Downside

3.05E-05

6.4032

Displacement (mm)-27.07

1st tendon

863.287

At the last step of construction, the second tension applied on CFTA
girder 420.6MPa each of step. Total tension force is 1400kN. The forces in
the last step are the self-weight of concrete, steel plate, slab and the tensionof tendon. Figure 3.4 shows the displacement of step 4 by color. CFTA
model’s displacement shows blue color, it means the whole model is insafety displacement range.
Construction
Simulation
of of
CFTA
Construction Step
StepAnalysis
AnalysisandandCollision
Collosion
Simulation
CFTA

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Figure 3.4: Displacement of Step 4
Table 3.4: Stress and Strain of Step 4
CenterPart
Filled concrete
Steel plate

StrainStress(MPa)Upside-36.7257-4.70566

Downside-25.7211-3.60818

Upside-0.00017-3.17482

Downside-0.00012-3.18767

Upside9.81E-05

2.43468

Concrete slab

Displacement (mm)23.8977st

1 tendon822.548

2nd tendon

420.6

4. COLLISION MODEL
PHYSICAL TESTINGNUMERICALSIMULATIONOFThe projectile model is allowed to impact the CFTA girder at the mostdangerous part, the tendon area through transverse to the longitudinal axisof the tendon. The impact event is simulated using ABAQUS/Standard 6.51 for a period of 20 ms using automatically time increment. Various
quantities of interest are extracted from the finite element results including:
impact force versus time relationship, stress and strain values and rates at
key points, deformations at critical sections.
According to the speed limit on expressways managed by the Korea
Expressway Corporation, the range is 100 km/h and 120 km/h. This velocity
will be used in the study to examine the performance of the tendon first andthen the working state of the whole structure.
4.1 Numerical Models
The numerical model of the impact test comprises of two main parts:
the projectile (object) and CFTA girder. The first part represents the steel

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rectangular beam which had a length of 3000 mm, width of 800 mm andheight of 400 mm (Figure. 4.1). The projectile contains 120 solid elements;
type C3D8R, an 8-node linear brick, reduced integration and hourglass
control have been used. The second part of the model, the CFTA girder, wasmaintained unchanged as in the static analysis behavior. The tendon wasconsidered as cable element, which is an inextensible cable that supports
only tensile loads (initial stresses). It can be simulated as hybrid beam
element type 3-D beam linear B31 or the 2-node linear 3-D truss, T3D2
element. The mechanical properties and values of the system are displayedin the same values in the static analysis step.
4.2 Contact analysis algorithm
When two objects collide, contact stresses are transmitted across theircommon interface. In the analysis of contact problems, the contact area
needs to be defined and the stress transmitted through the contact must be
calculated.
Contact problems can be simulated using contact elements. In
advanced numerical programs, it is not necessary to define contact elements.
Instead, the interaction between contacting surfaces consists of two
components: one normal to the surfaces and on tangential to the surfaces.
This study uses slave surface and master surface to simulate the interaction
between two objects. The properties interaction was also defined (with finite
sliding, small sliding or with friction).
Because in finite element analysis the bodies to be analyzed are
discretised, a discretised formulation should be used in which the nodes on
one surface (the slave surface on the softer body) contact the discretised
segments on the other surfaces (the master surface on the hard body). The
distances between the corresponding locations are determined to examine
whether they are in contact or not.

Figure 4.1: Impact simulation model between truck and girder

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4.3 Results of Numerical Simulation Test
For the impact analysis, an implicit direct integration dynamic
approach, called the Hilber- Hughes- Taylor operator, has been used tosolve the non-linear equations of motion. Dynamic impact responses areusually involved with severely discontinuous non-linearity. With the rangeof the velocity and duration of impact specified in this research, the event
can be specified as an intermediate dynamic impact phenomenon.
Figure. 4.3 shows the selection of frames taken from the finite analysis at
the time of collisions. These numerical figures will then be compared withthe corresponding experiment results.

Before Impact

Impact at 0.015 s

Impact at 0.02 s

Figure 4.2: Model representing Master and Slave surfaces on the
project tile and the tendon member respectively

Figure. 4.3: Impact performance of the projectile to the tendon of the girder
at (a) 0.0s, (b) 0.015s and (c) 0.02s

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Strain rate is one of the important factors in order to investigate thebehavior of the structure. In this study, the strain rate of the most criticalpoints on the longitudinal side of the tendon was observed. Figure 4.4
shows the strain of the tendon as time goes. The tendon yield strain assumed
0.002, so the tendon 1 and 4 will be within the plastic strain range. But,
there are not be rupture by crash.

Figure 4.4: the tendon strain for collision simulation

6. CONCLUSIONSThis study is carried out to verify the performance characteristic of theinnovation type of the composite girder, CFTA girder. In order tocomprehend the characteristic, ABAQUS 6.5-1 is used. The conclusion by
the FE analysis of the CFTA girder is summarized as follows:
1) CFTA construction step has different load and stiffness in each step.
In this study concern about the material will be rupture or large stress in thatstep, so check the stress and strain in 5 steps.
2) The first step displacement and stress are the largest value in wholesteps, it is not make rupture concrete and steel-plate. So the CFTA girder issafe in the construction state.
3).The tendons are deformed through the impact by truck. And thetendons deformed within the plastic range. But there are no rupture by thecrash, so CFTA is in service after the collision
An investigation into the impact behavior of the CFTA girder has beenpresented. Using the hypothesis projectile resulted from the assumed
accident of the truck in the expressway. The simulations presented in thisresearch demonstrate that numerical modeling could serve as a power tool toinvestigate the vulnerability of new types of bridge or to improve general

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design criteria. These results will be used effectively in comparison with thenext research steps including on-site full impact experiments, vulnerability
assessment and fragility analysis.
ACKNOWLEDGEMENT
This study was supported by the Construction Core Technology
R&D(05 Construction Core D11 and 06 Construction Core D14) of
Ministry of Construction and Transportation (MOCT).
REFERENCES
Hak, Lee., Ho, Park., Eun Ho, Lee., Jung Ho, Kim., Jung Sik, Kong., 2007.
Behavior of Concrete-Filled and Tied Steel Tubular Arch Girder.
Spring Conference Paper Book 668-693. Computational StructuralEngineering Institute of Korea.
Ho, Park., Myung Kyun, Park., KyungHun, Park., JungHo, Kim., 2006.
Development of Concrete-Filled and Tied Steel Tubular Arch Girder.
Conference Paper Book 2289-2292. Korean Society of Civil
Engineers.
SungWon, Yoo., KyungWook, Hong., 2000. Bending Analysis andImprovement of Bending Capacity of Pre-stress Concrete Beam Using
the Partly Attached Rigid Model. The collection of learned papers
813-821. Korean Society of Civil Engineers.
ByungHwan, Oh., SungWon, Yoo., 2000. Bending Behavior Test of PSC
Beam Which has the External Steel Wire according to the Shape of
Steel Wire and the Number of deflection side. The collection oflearned papers 795-804. Korean Society of Civil Engineers.
ABAQUS 6.5-1 analysis user's manual

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EXPERIMENTAL STUDYON THE EVALUATION OF A CAVITY
AND LOOSENING IN SOILMARI SATO
Graduate Student
Department of Civil Engineering, The University of Tokyo, Japanmsato@iis.u-tokyo.ac.jp
REIKO KUWANO
International Centre for Urban Safety Engineering,
Institute of Industrial Science, The University of Tokyo, Japankuwano@iis.u-tokyo.ac.jp
ABSTRACT
Damaged old sewer pipes are known to often cause void/cavity orloosening in the surrounding soil, which may lead to a cave-in in the road.
In this research, in order to understand the mechanism and governing
factors of cavity formation/expansion in the ground, a series of model tests
simulating flow out of soil through a crack/gap in a buried pipe wasconducted.
It was found that a cavity and surrounding loosening in the groundcan extend rapidly upward when the ground consists of poorly graded, highwater content, and low relative density sand.. Quantitative evaluation was
also given to the loosening around a cavity.
1. INTRODUCTION
An old deteriorated sewer pipe, when it is damaged, may eventually cause
local subside or a cave-in in the road. Recently, the number of pipes which
are older than the service life time of 50 years, has been rapidly increasing.
Accordingly, incidents of a cave-in are found to be more frequent in urban
areas. In spite of the significance, full investigation of the road cave-in isoften skipped as the urgent road restoration is usually priotised. It is difficult
to clearly identify the real cause by the fact that eventual cave-in is likely tooccur long after the initial formation of a cavity in soil. The congested
underground lifeline structures also make it complicated.
Kuwano et al. (2006a) conducted a survey to obtain basic information
on how the damaged sewer pipes were related to the collapses of road,which occurred from 2001 to 2003. The survey was performed by sending
questionnaires and interviewing local government officers in seven cities
where the sewerage system had started more than 30 years ago and the
management and maintenance of old sewer pipes are likely to be the

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concerned issues. It was found that even small gaps or cracks could lead toroad cave-in and the rainfall appears to be one of the most important factors,
as schematically shown in figure 1. Based on the survey results, Kuwano etal. (2006b) performed a series of model tests to investigate how a cavity
initially forms in soil and how it progresses up to the ground surface. In thispaper, the typical pattern of cavity formation/expansion in sandy soil andthe quantitative evaluation for the loosening surrounding the cavity is shown.

㪩㪸㫀㫅㪽㪸㫃㫃

Rise of Ground water level
LooseningcavitySoil loss

Figure 1: A cavity and loosening formed above the defect of buried pipe
2. TEST APPARATUS AND PROCEDURE
A test apparatus used in this study is shown in Figure 2. Model ground of
300m wide, 50mm long and 200mm high was made in a small soil chamberhaving an opening of 5mm in a base plate. A cavity was formed above theopening at the base, after the water flow in and out of the opening
repeatedly. Water of approximately 100cc was supplied into the opening
with 1m head difference. After pausing 1 minute, the drainage valve was
opened to let water and soil flow out. This cycle of water supply/drainage
was repeated until the cavity or loosening reached the ground surface.

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Water chamber

50mm

300mm
Head difference

BelloframcylinderModel ground

Water supply
Drainage

㕙

200mm

೨

⢛

㕙

acrylic plate

Water chamber
5mm wide Opening

Figure 2: Model test apparatus for generating cavity/loosening in soil
The details of experimental conditions are shown in Table 1.
Three sandy materials were used for the model ground; Toyoura sand,Natural sand and silica sand. Both Toyoura sand and silica sand are clean
uniform sand but average particle size of silica sand is larger than that of
Toyoura sand. Natural sand contains fines of around 3%. Particle size
distributions are shown in Figure 3.
Table 1: Experimental conditions
Relative
density㧔㧑㧕

CodeMaterials

Initialwater content㧔㧑㧕

T14,80
T14,60Toyoura sand

14
14

80
60

K10,80K0,80
K5,80
K10,60Silica sand

10
0
5
10

80
80
80
60

E15.5,80
E0,80
E7.8,80

Natural sand

15.5
0
7.8

80
80
80

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100

Toyoura sand80

Silica sand60
40

Natural sand

20
0
0.001

0.01

0.1

Particle size (mm)110Figure 3: Particle size distribution of tested materials
For the preparation of model ground, soil was placed into the chamberand compacted by eight layers. At each layer, colored soil was placed inorder to observe the deformation of ground. A load equivalent to an
overburden of 60cm was applied on the surface of model ground.
At each cycle of water supply/drainage, a photograph was taken to observe a
cavity and deformation of the model ground. The weight of soil frown outof the opening was also measured.
3. DEFINITION OF INDICES
In order to evaluate a cavity and loosening developed in the ground, someindices were proposed as shown in Table 2.
Table 2: Definition of Indices in this research
Index

Definition and description

Weight of soil
loss

Cumulative dry weight of lostsoil
One cycle is from the
Cycle
beginning of water supply tothe end of water out flow.
Area of deformation area, notArea of loosening
including a cavity
Area of a cavity
Area of cavity + area ofand loosening
loosening.

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4. TEST RESULTS AND EVALUATION OF CAVITY AND
LOOSENING AREA
4.1 common features observed in all the experiments
Typical forms of a cavity and loosening created in the model groundare shown in Figure 4. A cavity of fan-like shape, having slope at both sides
and arching ceiling, was generated above the opening. After repeating somecycles, cavity and loosening above cavity were expanded.

(a) T14,80 after 7 cycles(b) K10,80 sand after 6 cycles

Figure 4: Cavity and loosening formed in the model ground
4.2 Comparison between different materials
Three different material conditions (T14,80 K15.5,80 & S10,80) which had
optimum initial water content and same relative density (80%) werecompared. Weight of soil loss and cavity and loosening areas are plottedagainst the number of cycles in Figure 5 and Figure 6. From figure5, it isrevealed that soil drainage was most rapidly in silica sand and was mostslowly in natural sand. On the other hand, Figure 5 and Figure 6 suggested
that expansion of loosening tended to followed by rapid growth of a cavity.
Loosening area expanded even if cavity hadn’t expanded so much,especially in Toyoura sand.

Figure 5: Weight of soil loss in different materials

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July 2009, Thailand*Loosening area was not observed in Silica sand.
Figure 6: Process of cavity/loosening formation in different materials
4.3 Comparison between different initial water content
Weight of soil loss in three different water content conditions innatural sand (E15.5,80 E7.8,80 &E0,80) is plotted against cycles in Figure7 andthe details of Figure7 is shown in Figure8. Referring to Figure7 and Figure8,
it is clean that soil was drained more rapidly in larger amount of initialwater content especially in early stages. Similar result was shown in silica
sand (K10,80 K5,80 &K0,80). Cavity and loosening areas of plotted against
cycles in Figure9. Figure9 showed that cavity expanded rapidly after
loosening area expanded sharply at a certain cycle.

Figure 7: Weight of soil loss in different water content

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Figure 8: Details of Figure 7

Figure 9: Process of cavity/loosening formation in different water content
4.4 Comparison between different relative densities
Two different relative density conditions (T14,80 &T14,60) werecompared. Weight of soil loss and cavity and loosening areas are plottedagainst the number of cycles in figure11. Referring to Figure10, weight ofsoil loss increased rapidly through cycles in a high relative density condition.
Figure11 showed that speed of loosening expansion was similar in bothconditions, but that of cavity expansion was specifically different. Low
relative density condition caused large cavity more immediately.

Figure 10: Weight of soil loss in different relative density

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Figure 11: Process of cavity/loosening formation in different relative
density
4.5 Summary
Summary of observations and comparisons between different
experimental conditions is shown below.
z Weight of soil loss corresponded well to expansion of cavity.
z 1) Larger particle, 2) smaller relative density, 3) lower watercontent, and 4) clean uniform sand caused cavity rapidly.
z In Toyoura sand and silica sand, weight of soil loss rapidly
increased at a certain cycle.
After rapid expansion of cavity was caused, large part of the ground
was collapsed. This phenomenon was suggested from data of expansion ofcavity and loosening area in natural sand. (Referring to Figure9)
5. ADDITIONAL EXPERIMENTS FOR EVALUATING FINESDRAINAGE“Weight of soil loss from a cavity” was estimated from observed cavity areain photographs, and it was found that estimated soil loss from cavity wasmuch less than measured soil loss. This result suggested that soil was
drained not only from a cavity, but also from loosening. To reveal moredetails about soil drainage from loosening, Content ratio of fines in whole
drained soil in each cycle was examined.
The examined results are shown in Figure12. Initial content ratio of fines
means original content ratio of fines in natural sand. Figure12 referred thatfines were drained more easily than larger particle soil even in the earlystage. In addition, ratio of fines loss was increasing as the number of cycles.
These two results suggested that much fines were drained from the groundwhich hadn’t caused a cavity yet.

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Initial content ratioof fines

Figure 12: Change of content ratio of fines
6. PROCESS AND MECHANISM OF EXPANSION OF A CAVITY
AND LOOSENING
General process of expansion of cavity and loosening is suggested like asfollows.
A) Cavity is expanded due to soil collapse from a limited small areasurrounding a cavity, which happens mainly after stopping
water supply.
B) Deformation and cracks gradually developed in large area of theground as cycle progress.
C) Deformation area of the ground is collapsed. This collapse area
is wide and was not near to the cavity.
D) Fines drainage (Referring to chapter4)
Process (A) was observed from initial cycles. Process (B) and Process(C) often happened after cycles progressing. If once Process (B) happens,
cavity expands and weight of soil loss increases very rapidly.
Then, Principal factors which caused (A) ~ (D) Processes were proposed as
below.
z Factor of Process (A) and (B): Saturation
Due to water inflow, soil surrounding a cavity is saturated. Saturation
causes increasing of pore water pressure and decreasing of effective stress.
Ground collapse is almost prevented during water is supplying, because
upward water seepage force due to water inflow is still working. Whenwater supply is stopped, collapse occurs.
On the other hand, during initial cycles, ground is stable due to suction in unsaturated conditions. However, due to repetition of water inflow, watercontent of large area of the ground increases. This rise of water content
makes large part of ground unstable and causes deformation.
z Factors of Process (C): Water seepage
Unstable area in the ground collapses due to a downward penetration
which works most strongly during water outflow.

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z Factors of Process (D): Fines drainage
Even the ground is still stable and not caused collapses, fines are
drained due to water inflow and outflow. Fines drainages causes decrease ofsoil density and promote generation of a cavity and loosening.
Ground situations which caused by these four factors are shown in Figure13.

Process(A)Process (B)Process (C)Process (D)Figure 13: Ground situations caused by four main factors
7. SUMMARY AND FUTURE PLAN
Factors which cause a cavity and loosening are 1) saturation, 2) water
Penetration and 3) fines drainage. Combination of these factors causes
generation of cavity or loosening. In addition, it is revealed that there aresome particular conditions which cause cavity and loosening very rapidly.
On the basis of this research, development of effective method forinvestigating cavity and loosening is aimed.
REFERENCES
Kuwano, R, Yamauchi, K., Horii, T. and Kohashi, H. 2006. Defects ofsewer pipes causing cave-in’s in the road, Proc. of 6th International
Symposium on New Technologies for Urban Safety of Mega Cities in
Asia, Phuket, November 2006.
Kuwano, R., Horii, T. and Kohashi, H., 2006. Evaluation of loose groundsurrounding a cavity due to the outflow of soil, Proc. of 41th annual
conference, Japanese Geotechnical Society, Kagoshima, 1785-1786(in Japanese).

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2ndFOUNDATION SYSTEM ADOPTEDTO CONSTRUCT THE CIVIL INFRASTRUCTURESIN KHULNA UNIVERSITY OF BANGLADESHMD ABDUR RAZZAQUE
Deputy Chief Engineer, Khulna University
Ph.D. Student
Khulna University of Engineering & Technology (KUET)
Khulna, Bangladesh
MUHAMMED ALAMGIR
Professor
Department of Civil Engineering
Khulna University of Engineering and Technology (KUET)
Khulna, Bangladesh
ABSTRACTThis paper describes the case studies of different foundation systems
adopted in Khulna University campus for the construction of civil
infrastructures. The sub-soil of South-West region of Bangladesh, especiallyin the campus of Khulna University consists of very soft fine grained soil
with significant percentage of organics. The buildings in this region are
experienced huge amount of total and differential settlements whileconstructed on conventional shallow foundation, as a result the constructed
infrastructures lost their utility and abandoned in some cases. Geotechnicalengineers have been facing such difficulties for the last few decades as in
the recent years the rate of construction of civil infrastructures on suchground have been increased. Based on field experiences and requirements,
different types of foundation have been practiced in Khulna University
Campus for the construction of Academic Building-I, II and III, Students
Dormitory, Vice Chancellor’s, Teachers’ and Staffs’ Quarter and otherinfrastructures. Brief accounts of the construction and the findings on thepractical performance of the constructed buildings are illustrated here.
1. INTRODUCTION
Three mighty rivers - the Ganges, the Brahmaputra and the Meghna andtheir associated tributaries contributed very much in the formation of subsoil profiles of Bangladesh which is situated in the lower reaches of Bengal
Basin. The South-West costal region of Bangladesh contains fine grain soil
deposits with the presence of significant percentage of organics. The
presence of organics is due to the fact that vast of these coast regions werepart of the Sundarbans, the world largest mangrove forest which extends
over an area of 577,285 hectares as recorded (Vishnu & Gupta 1972, Sen &Banerjee 1984, Das & Siddiqui 1985, Naskar & Bakshi 1987, Mukharjee

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1992). During the geological changes in the past, some parts of the
Sundarbans
werepast,
submergedby theand sedimentary deposits
geological changes
in thesome partsof weatheredthein the andpresent
peat deposits
in these regions. In this region peatbmerged by resulting
the weatheredsedimentarydeposits
soils isdifferent
and inpeat
different depth. Also this soil has low
nt peat deposits
in foundthese inregions.
In layers
this region
bearing
capacitybelow
For that purpose all infrastructures
ent layers andin different
depth.
Alsothethispeatsoillayer.
has lowsettledamount.
Due to these inherent limitations, the foundation
w the peat arelayer.
For bythatlarge
purposeall infrastructures
for the constructionof civilinfrastructures is designed with special
mount. Due tosystemthese inherent
limitations, thefoundationconsiderationand isemployeduction of civil
infrastructuresdesignedvery
withcarefully,
special which leads to high cost for theof sub-structures
this forregionployed very preparation
carefully, whichleads to highincost
the of Bangladesh.
uctures in this region of Bangladesh.
Khulna University, located in the south-western region of Bangladesh
situatedalong the Khulna-Satkhira
Highways, just 4km from the city center,
ity, located in
the south-westernregion of Bangladesh
established
1991.
The
is situated in a low-lying topography
ulna-Satkhirawas
Highways,
just in4km
fromthecampus
city center,with isa verythickinsoft
and organic
soil deposits up to a great depth. The first
91. The campus
situateda low-lying
topography
newbuilding
named
Academic Building-I was started inand organicconstruction
soil depositsofupa toa greatdepth.
The asfirst
the yearof 1992 Building-I
in the designatedlocationas per the layout plan of thebuilding named
as Academic
was started
incampus.
This
sufferedlarge settlement, which leaded tothe designatedlocationas building
per the layout
planveryof theaboutsettlement,
the types offoundations.
ng suffered rethinkvery large
which
leaded toIn a series of construction, in the
recent years,
severalofnew
buildings havees of foundations.
In a series
construction,in thebeen and/or are being constructedsuch
as Academic
Building-II,
Academic Building-III, Khan Jahan Ali Hall,
new buildings
havebeen and/or
are being constructed
Khan Bahadur
Ahsanullahbuilding, Vice-chancellor’s,
ilding-II, Academic
Building-III,
Khan Hall,
JahanAdministrative
Ali Hall,ICUS
37 in which differentICUS
ReportTeacher’s andbuilding,staff’s quartersandReport
Mosque,types
of 37
ullah Hall, Administrative
Vice-chancellor’s,
foundation
are different
considered
baseds quarters andMosque,systems
in whichtypes
of on the sub-soil profiles, building
types,and the profiles,
consultant
preference. The performance of there consideredbasedpurposeon the sub-soilbuilding
mangrove forest, partthe Sundarbans.
course of forest,
time someof the biggestInmangrove
the Sundarbans. In course of time someconstructedbuildings
are evaluated anddiscussed here.
the consultant
preference.
The performanceof thewere submergedpartby ofsediment
depositsin theSundarbans
wereresulting
submerged
by sediment deposits resulting in theare evaluated and discussed here.
this region. Thepresent
layout peatof Khulna
University
Campusislayer inthis region.
The layoutof Khulna University Campus is
2. A lake passes presented
through thethe locationsofin campus
Figure 2.andA lakepasses throughthe campus and the locations of
2. LOCATION AND LAYOUT OF THE UNIVERSITY CAMPUS
infrastructuresthe
havebeen constructed
recently andimportantcivil infrastructures
have been constructed recently and
D LAYOUT OF THE UNIVERSITY CAMPUS
t in future are shown
figure andhence arelistedas in the figure and hence listed asplannedintotheconstructin futureshown
Khulna University is situated in Khulna, the South-West region ofo 14.
marked from no. 1 to 14.
as coastalregion
zone. Thes situated Bangladesh
in Khulna, considered
the South-Westof University Campus is located inwesternside of Khulna
Cityis Corporationd as coastal thezone.
The University
Campuslocated in (KCC) area. It covers 105 acresof land. It (KCC)
is beside
Khulna-Satkhira
highway. Many years ago it was thehulna City Corporationarea.
It covers 105 acres
Khulna-Satkhira highway. Many years ago it was the
KUET

KhulnaUniversity

Map ofKhulna City

KUET

KhulnaUniversity

Map ofKhulna City

Figure 1: Location Khulna university campus in Bangladesh map andyoutof
Khulnauniversity
campusand location
theofmapof Khulna
city campus and location ofKhulna university
campusin Bangladesh
andFigure2: Layout
Khulnauniversity
casesites.
the map
ofstudy
Khulnacity
case study sites.

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3. SUB-SOIL CONDITIONSICUS Report 37
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part of the biggest mangrove forest, the Sundarbans. In course of time somepart of Sundarbans were submerged by sediment deposits resulting in thepresent peat layer in this region. The layout of Khulna University Campus ispresented in Figure 2. A lake passes through the campus and the locations of
the important civil infrastructures have been constructed recently andplanned to construct in future are shown in the figure and hence listed as
marked from no. 1 to 14.

Figure 2: Layout of Khulna university campus and location ofcase study sites.

3. SUB-SOIL CONDITIONS
To investigate the sub-soil condition of the sites at Khulna University
campus, sub-soil exploration programme was conducted by different
geotechnical engineering consulting firms, soil samples were collected andtested through conventional laboratory tests (BRTC 1998, SAAR 1992 andNSE 1995). Despite the differences of sub-soil profiles and the values ofsoil parameters in different locations within the campus, a typical sub-soil
profile is shown in Table 1, which can be treated as general one from thegeotechnical viewpoint. In the sub-soil profile, it can be seen that the top
soil layer about 1.5m is soft clay. Next layer depth exists from 1.5 to 7.5m
i.e. 6m thick layer of dark grey organic clay with decomposed woods andvegetations. In the university campus, organic layer varies from 5 to 6m
thick. Here N value is very low. In some layers it shows zero penetration
resistance. Below this organic layer, at the depth of 7.5 to 19.5m, grey
medium plastic clay having N values vary from 3 to 6 exists. Next to this, alayer of light grey medium compressible silt, trace fine sand is encounteredat 19.5 to 23.5m depth with N value varies from 8 to 9. Here soil
consistency is medium to stiff. In the bottom of the sub-soil profiles as

Foundation
Foundation Systemsystem Adopted
adopted to
toConstructconstruct the
the Civil
civil Infrastructure
infrastructure in
inKhulna
KhulnaUniversity
UniversityofofBangladesh
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executed, 23.5 to 30.5m depth, soil of light grey of non plastic silt, trace finesand having N value varies from 18 to 28, is countered.
Table 1: Typical sub-soil profile at Khulna University campus

Depth
(m)

Thickness
(m)

0-1.5

1.5

1.5-7.5

6.0

7.5-19.5

Strata12.0

19.5-23.5

4.0

23.5-30.5

7.0Classification of Soil

Light brown medium
plastic CLAY (soft)
Dark grey organic
CLAY, trace decomposedwood (very soft)
Grey medium plastic
CLAY (Soft)
Light grey medium
compressible SILT, trace
fine sand. (medium stiff)
Light grey non plastic
SILT, trace fine sand(medium dense)

NValue
3
1

3-6

8-9
18-28

4. DESCRIPTION OF THE ADOPTED FOUNDATION SYSTEM
There is no typical or standard or unique foundation system that can beapplied for the construction of any civil infrastructures in Khulna region as
well as in Khulna University Campus. Moreover, different consultants werealso employed for different types of buildings for the construction at various
times. Performance of foundation system already adopted in the campus,
experiences in the similar situation, sub-soil condition in the particular site,
super-structures requirements and finally consultant preference, lead toselect the foundation type, those are described in the following sections.
4.1 Sand cushion with mat foundation
In Khulna University campus, the first major construction works wasstarted in 1992 with the adopt of mat foundation system with sand cushion
to build Academic building –I, a four storied building as shown in Figure 3.
The schematic diagram of the foundation system is presented in Figure 4.
From this figure it can be seen that the top soft soils layer was removed up
to 4.5m depth measured from the existing ground surface. Here peat deposit
situated at 1.5 to 4.5m from top was removed by excavation. Then
excavated area was refilled by compacted sand. Two types of sands having
the fineness modulus (FM) of 1.2 and 2.2 were mixed properly at a ratio of
1:1. The filling sands were compacted in layers and the required degree ofcompaction was achieved. At the top of the filled and compacted sand layer,
several mat foundations were provided. The thickness of the mat foundation
varies from 300 to 450mm. At the top of the mat, a foundation beam was

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provided to reduce mat thickness. Despite the consideration of such special
precaution in the foundation, the building suffered from very large
settlement which mostly occurred during the first three years after thecompletion of the building. The long term measurement showed that thebuilding was settled as much as nearly 800mm. However, it was found that
settlement is uniform, as a result there was no damage in the building
although the pipe lines and other utility services hampered due to large
settlement and needed to replace and reconstruct to ensure the proper
function of the system, the details are given in Razzaque & Alamgir (1999).
Similar foundation systems were also employed to construct the buildings ofKhan Jahan Ali Hall and Associate Professor Quarter.

Figure 3: Pictorial view of Academic Building-I

PL

FGL

FGL
Beam
Mat

4.25m Sand Cushion

Figure 4: Schematic diagram of sand cushion with mat foundation
4.2 Floating foundation
Academic Building–I showed huge settlement due to the implementation of
mat foundation with a sand cushion. As a result the university authority andthe consultant looked for alternative solution to minimize the settlement
while constructing the Academic Building-II. After looking on the various

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possible alternatives, finally consultant decided to use floating(compensated) foundation, hence designed the foundation and the building
was constructed accordingly as shown in Figure 5. The schematic diagramof the foundation system used for the construction of this building is alsopresented in Figure 6.

Figure 5: Pictorial view of Academic Bldg.-II

PL
FGL

FGL

Tie
Bracing
Bottom SlabFigure 6: Schematic diagram of floating foundation- Academic Bldg-II
Based on the design requirements, the sub-soil was excavated andhence removed till the depth of 4.5m measured from original ground level.
Due to the excavation till the designated depth, the peat deposits were
removed from the site. Then a concrete box which has the inner clear spaceof 5.2m was provided. In the bottom, 450mm thick base was constructed.
Side wall is 300 to 200 mm from bottom to top. Top slab is 125mm thickacted as ground floor. For water protection outside of the concrete box threelayers of Hessian cloth with tar was placed. Inside the box a height 5.2m isavailable which stabilized by providing column, concrete bracing andconcrete tie inside the foundation box (SAL 1996, Hossain & Razzaque1999). To provide bracing and tie, the section of column was reduced, sothat the box was not tilted. Top of the box is the Plinth level of the building.
The settlement of this building was observed as insignificant, which is about

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20mm. Similar type of foundation system was also used in the constructionof the Female student’s dormitory named as Aparajita Hall.
4.3 Pile foundation
Pile foundation is commonly practiced in Bangladesh for theconstruction of buildings in the soft soil region and even in moderate to hardsoil for high rise building. In Khulna, since there is an existence of very softcompressible soil layer and peat deposits in the upper layer, the pile
constructed in this region is subjected to negative skin friction due to thefaster compression of these compressible upper layers surrounding the pile.

Figure 7: Pictorial view Admin. Building

FGL

Figure 8: Pile foundation used for the construction of Admin Building

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Table 2: Information of pile specification used in different buildings

Sl
No

Name of the
Buildings

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.

K.B. Ahsanullah Hall
Administrative Bldg.
Asst. Prof. Quarter
Staff Quarter
Staff Quarter
Lecturer Quarter
Professor Quarter
Central Mosque
Academic Building III
Central Library

Plinth
Area
(m2)
581
1,488
242
223
316
214
218
1,115
10,446
5,562

Storie
d
4
4
4
4
4
4
4
3
4
4

Pile
length
(m)
26
24
30
30
30
30
30
26
26
27

Pile
DesignDiameter load(mm)
(Tons)
450
450
500
450
500
500
500
500
450
500

55
40
53
46
50
44
46
38
40
45

In the Khulna University Campus, the first pile foundation was
employed for the construction of student’s dormitory named as Khan
Bahadur Ahsanullah Hall. This is a four storied building constructed on castin-situ bored pile made of Reinforced Cement Concrete (RCC) having
cylindrical shape of diameter 450mm and length 24m. In the design, thepossible development of skin friction due to the compression of surrounding
soft soil layers and peat deposits and its impacts on the reduction of pile
capacity was considered. One of the constructed buildings on pilefoundation in the campus is shown in Figure 7. The piles are configured in agroup with RCC pile cap based on the layout of the locations of different
columns of the buildings and loads as shown in Figure 8. Later, additionalnine buildings named as Administrative Building, Academic Building-III,
Central Library, Central Mosque, Professor’s, Assistant Professor’s,
Lecturer Quarters, Staff Quarter-I and Staff Quarter-II were constructed andplanned to construct using pile foundation. Table 2 shows the comparison ofvarious aspects such as dimension and design load of the pile foundation
used and/or planned for the construction of different buildings, till now tenbuildings, in the Khulna University campus for pile foundation. Althoughpile types, materials and construction process are similar, there dimensions
and capacity are different. The plinth areas of the buildings vary from 214 to
10,446 sq.m, length, diameter and designed load of pile foundation arevaried from 24 to 30m, 450 to 500mm, 38 to 55 Tons, respectively. The
actual pile capacity were also established conducting pile load test after the
installation of pile. Sufficient days were allowed in between the pile
construction and load test due to the remolding influence of the clayey soil.
4.4 Sand cushion with continuous footing
Some buildings were also constructed using spread footing i.e.
Continuous type Reinforced Cement Concrete (RCC) footing resting on
4.25m compacted filling sand cushion. One of the such buildings is shownin Figure 9, the Vice Chancellor’s Quarter. The building was constructed
through load bearing wall resting on the footing. Sub-soils till the depth
around 4.5m from the existing ground surface were excavated and removed

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and then filled by sand in layers ensuring proper compaction to provide asand cushion of 4.25m thick. On the top of sand cushion, continuous footing
by RCC was constructed over which load bearing wall of 380mm below and
250mm above the ground level were provided. The performance of suchfoundation system is found satisfactory.

Figure 9: Vice Chancellor’s quarter
Load Bearing Wall
FGL

PL

4.25m

Sand Filling Cushion

Continuous
Footing

Figure 10: Sand cushion with continuous footing and load bearing wall
4.5 Strip footing using continuous brick wall
In Khulna University campus, very simple foundation is also used forthe construction of one to two storied buildings despite the existence of very
soft clayey soil deposition. One of such foundation system is continuous
Brick Wall Footing, known as brick foundation. For lower bearing capacity
higher width of brick wall is required at the base which gradually decreases
as it reached to plinth level and finally ended with 250mm thick brick wall.
Khulna University Mosque as shown in Figure 11 was constructed on suchfoundation and the schematic diagram of the employed foundation system isshown in Figure 12. Here, the bottom width of the foundation is 1.5m.
Despite the implementation of simple foundation mostly used in low-risebuilding, it is observed that the performance of the foundation is quitesatisfactory.

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Figure 11: Mosque at Khulna University
FGL

PL

CC

Brick Soling

Sand filling
Figure 12: Strip footing constructed by continuous brick wall

5. DISCUSSIONIn Khulna University campus different types of building was constructedwith various foundation system based on soil conditions, super-structuralrequirements, client’s choice and consultant preference. Despite thesimilarity of sub-soil conditions, super-structures types and requirements,
different types of foundation systems have already been constructed andplanned as described in the above sections. From the illustration it isrevealed that the settlement is quite large while mat foundation in
conjunction with soil replacement with a well compacted sand cushion
although differential settlement was not noticed and there no cracks
observed in the buildings. However, due to very large settlement which is as
much as about 800mm, the utility appliances lost their serviceability andneeded to replace. However, it was realized that such foundation technique
is easier than other counterparts to satisfy fundamental criterion for theconstruction of buildings in the similar sub-soil conditions. Significant
amount of settlements were also observed while continuous footing made ofReinforced Cement Concrete (RCC) and Brick masonry resting on sandcushion with varying thickness were used for the construction of even one
or two storied building.

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To overcome the inherent limitations of the shallow foundation(except floating foundation), pile foundations have been adopted in tenbuildings. Most of them are four storied except central mosque which will
be a three storied structure. It is observed that pile foundation served betterand fulfilled the requirements despite higher cost than the shallow
foundation counterpart. It is also observed that the floating foundation also
performed well and the settlement remains within the tolerable limit.
In Khulna University Campus, as an alternative of conventionalfoundation system (e.g. pile foundation), ground improvement technique
may be adopted to examine the applicability in such compressible groundcondition. In this region, sand compaction piles with a diameter of 200 to
300mm and the depth of 6 to 8m were constructed successfully in several
projects and the performance are satisfactory. In some cases, geosynthetics
was also used successfully to reinforce the ground under shallow foundation(Alamgir & Chowdhury 20003; Haque et al. 2001). In future, instead of
conventional pile foundation, ground improvement techniques suitable forthe sub-soil conditions exist in Khulna University Campus may beconsidered to overcome the inherent limitations of conventional foundation.
6. CONCLUSION
Special care need to be taken while choosing a foundation system toconstruct civil infrastructures in the site of soft compressible ground withthe existence of peat deposits, like Khulna University campus. It is notproper to implement a unique system considering general sub-soil profile,appropriate foundation system might be chosen based on the soil conditionsin the specific location, type of superstructures and relevant requirements.
Field investigation shows that the rate and amount of settlement is
insignificant while Pile foundation and Floating foundation were used.
However, on the other hand mat foundation with sand cushion resulting
very large total settlement and damaged the utility fittings, although it is lessexpensive and easy to construct with locally available logistics. Ground
improvement technique such as sand compaction piles, cement columns, etc.
may be adopted to judge the applicability of such techniques in this sub-soil
conditions.
ACKNOWLEDGEMENT
The authors express their deep gratitude to the Engineering Section ofKhulna University to provide required information on the design andconstruction of Civil Infrastructures illustrated in this paper.

REFERENCES

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Alamgir, M. and Chowdhury, K.H. 2003. Ground Improvement
Methods Recently Practiced to Solve the Geotechnical Engineering
Problems in Bangladesh. Proc. Fifth International Conference on Case
Histories in Geotechnical Engineering, April 13-17, 2004, New York.
BRTC 1988. Soil investigation and constructing building foundation
repost of proposed Khulna University of selected Gollamary area. Bureau
of Research, Testing and consultancy (BRTC), BUET, Dhaka.
Das, S. and Siddiqui, N.A. 1985. The mangroves and mangrove forestsof Bangladesh, Mangrove silviculture division Bullatin No. 2.
Haque, M.A., Alamgir, M., Salim, M. and Kabir, M.H. 2001.
Performance of Geotextile-Reinforced Shallow Foundations used in
Bangladesh. Proc. International Seminar on Earth Reinforcement (ISKyushu 2001), 16-18 Nov. 2001, Fukuoka, Japan, pp.565-570.
Hossain, M.M., and Razzaque, M. A., 1999. Structural Advantages of
Floating foundation over traditional mat foundation for soft clay: A casestudy. Proc. Pre-conference symposium or Ground improvement andGeosynthetics. 5 Nov, 1999, Bangkok. pp. 201-210.
Mukherjee, B.B 1992. Quaternary pollen Analysis as a possible
indicator of prehistoric Agriculture in the Deltaic part of West Bengal, J.
Playnol, Vol.8 : 144-51.
Naskar, K.R. and Guha Bakshi, D.N. 1987. Mangrove swamps of the
Sundarbans.
NSE 1995. Subsoil Investigation for proposed 4 storied 2nd Academic
Building at Khulna University., National Soil Engineering, Dhaka.
Razzaque, M.A. and Alamgir, M. 1999. Long-term settlement
observation of a building in a peat deposits of Bangladesh. Proc. of the Intl.
Conf. on Civil & Environmental Engineering – New Frontiers andChallenges, 8-12 November 1999, Bangkok. Vol.2, pp.III-85 to IV-94.
SAAR 1992. Consultants reports about the design and construction of
Academic Building-I at Khulna University, SAAR Association Ltd, Dhaka.
Sen, P. and Banerjee, M. 1984. On the occurrence of Raptilian
Remnants in the peat bed of Barrackpore, 24-Parganas, West Bengal,
Paleobot. Soc. Ind. Vol. 29: 47-51.
SAL 1996. Consultants reports about the design and construction of
Academic Building-II at Khulna University, Shaheedullah and Associates
Limited, Dhaka.
Vishnu M. and Gupta, H.P. 1972. Pollen Analysis study of
Quaternary Deposit in the Bangla Basin, Paleobotanist, vol, 19: 297-306.

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A REPRESENTATION
OF MOTORBIKE DOMINATED TRAFFIC
USING CELLULAR AUTOMATA
HOANG THUY LINH,
MARC MISKA, and MASAO KUWAHARA
Traffic Engineering Laboratory,
Department of Civil Engineering,
The University of Tokyo, Japan
ht-linh@iis.u-tokyo.ac.jp
ABSTRACTThis study aims to develop a network scale, microscopic simulation
model for mixed traffic, where motorbikes dominate the traffic composition(more than 70%) as in Vietnam, Malaysia, or Thailand. Therefore, cellular
automata, with modified cell size, time step and rule set are used to simulate
three different vehicle classes (motorbike, car and bus). Surrounding
vehicles (type, speed, distance) are considerably affecting the motorbikes’
behavior, they have more freedom in movements, and hence, can continue
their way, while other modes already came to a stop. To capture these
special behavior patterns, a utility function is introduced, to determine
preference positions and movements. A first prototype of the model is
implemented to investigate queuing and dispersion behavior of mixed trafficat an intersection approach. Future work will extend the model to network
scale
1. INTRODUCTION
Up to now, large numbers of traffic simulation models have been developed.
However, those are mainly for lane-based traffic, in which vehicles remain
in a certain restricted area where lateral movements are only consideredduring lane changes. This is not applicable for motorbike dominated trafficof some developing countries such as Vietnam, Thailand, etc. In thesecountries, the share of motorbikes in the traffic stream is more than 70% andlink utilization is not lane-based.
Motorbikes can travel with more freedom than other modes, therefore,
they can advance using small spaces between vehicles. Besides, being themost unsafe mode in the stream, their behavior is strongly affected bysurrounding vehicles. These circumstances cause differences in road space
utilization between normal lane-based traffic and non lane-based traffic(Figure 1).

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July 2009, Thailand(a):lane-based mixed
traffic(b): non lane-based
mixed traffic(c): non lane-based
mixed traffic

Figure 1: Mixed traffic types
Hence, this study aims to develop a network scale model using
Cellular Automata method (CA); to represent this type of traffic in order toinvestigate possible effects and to evaluate ITS measures in those countries.
2. SIMULATING MIXED TRAFFICConventionally, traffic models are divided into microscopic, mesoscopic,
and macroscopic models. The major difference is the detail of how vehicles
and their drivers are represented. In macroscopic models, single vehicles do
not exist, and instead, a traffic stream is considered with dynamics based ona fundamental diagram, describing the relationships between speed, flow
and density. On the other hand, microscopic models deal with individualvehicle driver combinations and the dynamics are based on individual
behavior. Most commonly, the behavior is incorporated into two separate
models for car following and lane changing. As the name suggests,
mesoscopic models are something between the other two models and exist
and various set-ups. However, usually they represent single vehicles or
platoons (microscopic), which move according to macroscopic relationships.
Since the specifics of mixed traffic - are hard to describe in macroscopic
terms - that is without extensive measurements a microscopic approach has
been chosen. CA models belong to microscopic approaches, using crude
microscopic parameters to obtain correct macroscopic patterns. Therefore,
the CA approach - a computationally efficient microscopic model(Maerivoet 2005) - is chosen for this study.
2.1. Network representation
The cellular automaton is a well known mathematical concept andused in many researches on traffic flow phenomena. The CA model is a
dynamic, discrete system in space and time. Road space is discretized into alattice of cells and each cell is either empty or occupied by a vehicle. In thissystem, vehicles move by hopping from cell to cell based on a rule set,reflecting car – following and lane changing behavior (see Figure 2).
Therefore CA models are sometimes called “particle hopping models”.

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x
Time
t
ΔT

Δx

t+1
Figure 2: Schematic diagram of the operation of a single-lane traffic CA
Applying above scheme to the mixed traffic situation, we chose a 2D
lattice which is a square grid of 0.25mx0.25m (see Figure 3) for aunidirectional link, 3.75m wide and 400m long. The time step is 1/20
second. Therefore the increment speed is 5m/s. Each line paralleling to roaddirection is defined as a “virtual lane”.

Figure 3: Illustration of Road stretch with 2D gridTraversing through the intersection, vehicles are assumed to bebounded within a “virtual link” - marked by two green curves in Figure 4a.
The virtual link will be straightened and discretized into a 2D grid as anormal road stretch, in this case the left turning grid. It will be connectedwith the grids of in-going and out-going links (see Figure 4b). The
movement of traffic on this grid is similar to that on a link. Now todetermine the actual situation inside the intersection, the traffic flow on eachturning grid is projected onto the intersection grid. See Figure 4c as an
example.

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(a)

(b)

(c)
Figure 4: Illustration of Intersection and traversing grid2.2. Representative vehicle classes
Our modified CA model is developed for the mixed traffic with three
representative vehicle-classes which are significantly different in their
specifications, such as length, width and speed. Table 1 shows the vehicle
class specification in CA model units.
Table 1: Parameters of model
Maximum speed
Dimension
Vehicle Type
Width
Lengthcells/time step
m/s
cells
m
cells
m
Motorbike
3
15
3
0.75
8
2
Car

6

30

6

1.5

15

3.75

Bus

4

20

10

2.5

40

10

2.3. Driving behavior
There has been research on behavior of mixed traffic, and we haveintegrated findings from studies by Hsu et al. 2003 and Hien 2007 into themodel. Those findings were:
•

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•
•
•
•
•
•

Motorbikes at low speed prefer the right road side and at highspeed they prefer middle to left road side
In congestion, buses, cars and motorbikes can be at any positionin lateral direction
Motorbikes do not have preference either following buses or carsor followed by buses or cars
At intersection approach, the vehicles have mandatory lanechanging therefore they will make their effort to be on thedesired position to perform the turn.
For each turning direction through the intersection, vehicles are
assumed to travel in an virtualFirst vehicles will traverse though intersection with higher speed
than the followers.

In opposite to traditional CA models, we substituted the rule set for a
psychological driving logic approach to describe the drivers’ decision
making process. This includes the following steps:•••

Scanning driving environment
Determining the safe speed
Determining possible direction

Scanning driving environment
Here, the driver scans his visible area to get information ofsurrounding vehicles: gap distance, speed and type (Figure 5).

Figure 5: Scan area
Determining the safe speed
Based on the driving environment, he or she will determine his or her
safe speed - maximum speed in order to avoid collision with other vehicles.
The safe speeds are representations of the gap acceptances to the left,
straight or right side of the vehicle (Figure 6).

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vR
vS
vL

Figure 6: Determining the safe speed
Determining possible direction
Changing direction to the left, or right depends on the preferred“virtual lane”. This lane is determined by a utility function taking into
account a set of possible reasons for lane changing. The next section will
describe this function in more detail.
Above process is represented in CA model as one evolution of eachvehicle in the traffic stream to the next time step. Within the scope of thismodel, the term “virtual lane” changing, which is used instead of lanechanging, explains any change in the lateral position of a vehicle. At every
time step, the longitudinal position x is increased by the vehicle’s speed
calculated in the current step t.
x (t ) = x (t − 1) + v (t )(1)

The lateral position y is adjusted to the left or right by a maximum of
one cell of the vehicle’s current position, with the boundary condition thatthe vehicle does not leave the road stretch, determined by the lane marking.
⎧min( y (t − 1) + vehicle ' s width; road width ) if move to left⎪y (t ) = ⎨max( y (t − 1) − vehicle ' s width;0) if move to right⎪ y (t − 1) if go straight⎩( 2)

Formula (1) describes the longitudinal position where:
safe
safe
safe
(*) : v(t ) = max(vleft∨ vright∨ vstraight
,0)

safe
safe
safe
vleft
, v right, v straight:respectively.

Speed if vehicle moves to the left, right side or straight,

2.4. Utility function and decision making rule
The utility function shows the level of satisfaction a driver perceives
at every “virtual lane” at a certain time; taking all the possible reasons oflane changing as its parameters to describe how the driver is attracted byeach position over the cross section. Virtual lane changing behavior is
performed based on two parts, which are the reasons to change and thesafety criteria (Nagel 2002). Both are differentiated between car/bus andmotorbike (see assumptions in section 2.3).
We assume that the decision of choosing a lateral position of
motorbikes depends on all the vehicles in his front and back within his

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observable area, and also depends on the distance to the next desired turning
point (Gipps 1986). Within a road stretch (without any effect of bus stop,
intersection…), in free flow traffic, motorbike drivers can freely use all road
space. However, from observation, motorbike drivers do not like following
big, slow vehicles (buses, cars). In congestion, vehicles attempt to use every
road space or in other words, they are stimulated by free space in front.
Vehicles want to adjust their lateral position when they do not likefollowing the leading vehicles or find appropriate free space. But thosechanges are not necessary to be performed – discretionary lane changing.
While at an intersection approach, both with and without clear turning split,
the turning direction forces the driver to change lane from a certain distanceto the intersection – mandatory lane changing.
Therefore, the utility function contains variables such as gap distance,
type and speed of those observed vehicles. Further, depending on the subject
vehicle type and speed, the effect of the desired lateral position in the link
and intersection approach is also taken into account by the lane factor andturning factor respectively. Variables of this function and their explained
factors are shown in Table 2.
Table 2: Variables and factors of Utility function
Variable
FactorSpeed, vehicle type
Lane_factor
Front vehicle speed+gap; type Front_factor
Back vehicle speed+gap; type
Back_factor
Turning direction
Turning_factor
The ranges or boundaries in which each factor varies due to personal
preferences is still to be determined (see future work) and is for now set
according to personal perception. The utility function can be written as:
U ( i ) = {( front _ gap + front _ speed ) × Front _ factor+ ( back _ gap − back _ speed ) × Back _ factor }× Lane _ factor × Turning _ factor
Where U (i) : Utility of virtual lane i

P is a preferred lane ⇔∀ U (P ± k) ≥U (i)Where:
k: integer; k= 0÷vehicle’ width (“+”: range at the left side; and “-“:rang at the right side)
i: current virtual lane
To make the contents of the utility function easier accessible let us
consider the situation at the bottom of Figure 7. A motorbike with the speedof 2 cells /time-step evaluates first the attractiveness of each virtual lane by
the gap size, incorporating the speed of leading vehicles (Figure 7a). Since
following a bus or car is less desirable for the motorbike than driving among
other motorbikes or without a leader, the utility function get adjusted onthese lanes (see Figure 7b). Additional, in this situation, the motorbikes get
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followed by a bus. Feeling unease about the situation as the weakest
member of the traffic stream, the utility gets lowered in the width of the bus(Figure 7c), resulting in the final shape of the cross section utility (seeFigure 7d).
(a)

(b)

(c)

(d)

Figure 7: Illustration of Utility Function
The vehicle will choose the position where not only the utilities arehigher than in current position but also the number of consecutive higher
utility cells must not be smaller than the vehicle’s width. The preferencelane is the most left or most right boundary of those cells. In this example,
the driver will set the new preferred lane to 15 and hence try to move to theright inside the road stretch.
3. PROTOTYPE APPLICATION AND FUTURE WORK
To investigate the possibilities of such a model, we have implemented a
prototype. This first application shows already realistic driving behavior
among all three vehicle types and is the basis for further developments (seeFigure 9).

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Figure 9: Model prototype
All in all, we could proof the feasibility of such a model and in thenext step it will be calibrated and validated with video footage from
Vietnam.
REFERENCES
Gipps, P.G., ‘A model for structure of Lane changing decisions’, Transport
Research 20B No. 5, pp. 403-414, 1986
Hien Nguyen, Frank Montgomery, Patterns of discharge flow at traffic
signals and implications in motorcycle traffic, Journal of the Eastern
Asia Society for Transportation Studies, Vol. 7, 2007
Hsu, Tien-Pen, Ahmad Farhan Mohd Sadullah, Nguyen Xuan Dao, A
comparison study on motorcycle traffic development in some Asian
countries – case of Taiwan, Malaysia and Vietnam, The Eastern Asia
Society for Transportation Studies (EASTS) 2003
Lawrence W. LAN, Chiung-Wen Chang, Motorbike’s moving behavior inmixed traffic: particle-hopping model with cellular automata, Journalof the Eastern Asia Society for Transportation Studies, Vol.5, October,
2003
Maerivoet Sven, Bart De Moor, Cellular Automata Models of Road Traffic,
http://phdsven.dyns.cx, 2005
Nagel Kai, Cellular Automata Models for Transportation Applications, S.
Bandini, B. Chopard, and M. Tomassini (Eds.): ACRI 2002, LNCS
2493, pp. 20–31, 2002.
Wenzhou Jin, Yingli Zheng, Jiang Li, Microscopic Simulation of Traffic
Flow at Signalized Intersection Based on Cellular Automata,
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00830635

A Representation
using
Cellular
Automata
RepresentationofofMotorbikeMotorbikeDominatedDominatedTraffic
Traffic
using
Cellular
Automata

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2ndDEVELOPMENT OF PEDESTRIAN SIMULATORFOR TRANSFER CENTER OPERATION ANALYSISMYUNG-JOO HAN, DAISIK NAM
YOUNG-EUN KANG and YOUNG-IHN LEE
Department of Environmental Planning
Graduate School of Environmental Studies
Seoul National University, Republic of Korea
nds1027@snu.ac.kr
ABSTRACT
A pedestrian simulator for analysis of transfer center operations wasdeveloped using novel pedestrian behavior algorithms. We developed apedestrian behavior algorithm (including an obstacle avoidance behavior
algorithm) and a transit center user behavior algorithm (including a transit
facility use behavior algorithm that included transit boarding and alighting
behavior). To evaluate the operational efficiency of the design of a newtransit center and improve the transit center plan, this study used measuresof effectiveness (MOE) for pedestrians, the facility, and the simulation. In
addition, a prototype was developed to verify the adequacy of the pedestrian
behavior and facility use behavior algorithms.
1. INTRODUCTION
1.1 Background and Purpose of the Study
The efficiency of the interconnection systems between means oftransportation in current mass transportation transit systems (MTTS) is often
inadequate. Long transit times may be required because MTTS havecomplicated pedestrian traffic lines and there is insufficient information
about the facilities in the system (e.g., transit and convenience facilities).
Consequently, there are plans to construct a transit center that will
minimize transit time by improving user convenience. However, the current
design standards for such a transit center are inadequate. Therefore it isdifficult to evaluate transit center design effectively. To improve the designand evaluate the operational plans for a new transit center, an analysis ofpedestrian characteristics is required. Accordingly, this study develops
algorithms to analyze pedestrian behavior at a microscopic level, including
such behaviors as movement, facility usage, boarding and alighting. Suchalgorithms will enable the evaluation of the operational efficiency of a newtransit center design, enable improvements to the design of the transit center,and allow the development of an operation analysis program.

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1.2. Scope of the Study
The major objective of this study is to develop pedestrian behavior
algorithms for such behaviors as obstacle avoidance, facility use, as well asboarding and alighting, that will allow analysis of transit center operations
at the microscopic level.
Thus, the scope of this study includes:• Development of an obstacle avoidance behavior algorithm• Development of a transit facility use behavior algorithm• Development of a means of assessing transit boarding and alighting
behaviors• Development of an operation analysis program (i.e., a pedestrian
simulator)• Presentation of the results of the study
2. STATE-OF-THE-ART OF PEDESTRIAN BEHAVIOR MODELING
2.1. Pedestrian Behavior Model
It is relatively easy to analyze the movement patterns of vehicles since
they are required to drive along specific lanes and follow specified
regulations, but there are no similar physical or behavioral regulation
systems for pedestrians. In addition, analyzing the patterns and regularity ofpedestrian behavior is quite difficult because various behavior patternschange according to the pedestrian's mental state and the purpose of their
walking behavior.
Few studies have reported on research in this area. J. Fruin (1991)
studied pedestrian behavior as it reflected pedestrian traffic flow
characteristics and pedestrian mental factors. Another study KHCM (2004),
in Seoul, Republic of Korea, analyzed pedestrian behavior and provided
pedestrian facility evaluation criteria by examining pedestrian traffic flow
characteristics. That study produced a macroscopic level analysis ofpedestrian characteristics including assessment of pedestrian speed, density,and volume. However, none of the available pedestrian traffic flow
characteristic analyses reflect pedestrian behavior at the microscopic level,such as the examination of sequential behavioral processes used in object
recognition and subsequent reaction.
2.2. Pedestrian Simulator Development Cases
The operation analysis program discussed here includes a pedestrian
simulator function that reflects facility use and pedestrian behavior
algorithms. To date, no local technologies have been developed to simulate
the interactions of pedestrian behavior, transportation links, and transit
systems. However, there have been a few foreign studies and somecommercially available software packages have been developed. SIMPED is

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a pedestrian simulator model developed in the Netherlands, which analyzes
various behaviors generated by pedestrian movements and the interactions
between pedestrians. SIMWALK, developed by Savannah Simulations AG,
Herrliberg, Switzerland, is a pedestrian simulation software program that
may be used to simulate pedestrian flows by traffic planners, urban
professionals, and safety managers, and to evaluate facility features. This
program analyzes such aspects as walking behavior possibilities, pedestrian
comfort, and traffic congestion in various environments. Another pedestrian
simulation program, the Uban Analytics Framework (UAF) software from
Quadstone Paramics of Edinburg, Scotland, was developed to analyze andevaluate the interactions between road traffic and pedestrians. There areother pedestrian simulators, such as NOMAD (Delft University ofTechnology, Delft, Netherlands), PEDROUTE (Halcrow Group, London,
UK), which were developed for various purposes. However, the analyses
and results available from these simulators was not deemed suitable for ourstudy. Thus a locally appropriate simulator was needed.
3. PEDESTRIAN SIMULATOR
3.1. Features and Configuration
The operation analysis program (pedestrian simulator) is an analytic
tool which can reproduce the behaviors of major facilities and means(pedestrian, train, etc.) of transit center in a three dimensional (3D) display.
Furthermore, it can be used to evaluate and verify a transit center's
operational efficiency, its design adequacy, and it can evaluate a variety ofpedestrian behavioral aspects at a microscopic level. This type of simulation
program can be used to perform transit center pedestrian's behavior analysis,
microscopic level analysis of service facilities, and transit center operation
evaluations for various situations (e.g., peak time, disaster, etc.). The
pedestrian simulator discussed here includes several definitions andalgorithms.
3.2. Operation Analysis Algorithm
3.2.1 Pedestrian Behavior Algorithm
Pedestrian Definition
Walking is the basic transportation system in all pedestrian activities
and the most important means to connect to other transportation means. The
characteristics that differentiate pedestrian traffic and vehicle traffic are:• Vehicles drive along a fixed lane; pedestrians do not have afixed route.
• Vehicles move in a single direction in one lane, but there may
be many different directions in pedestrian traffic.
• Similar vehicles exhibit similar acceleration rates, butpedestrians may exhibit marked differences in acceleration.

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Vehicles move along a route in a forward direction, butpedestrians may have many direction changes.
Pedestrians have greater acceleration and deceleration thanvehicles.
Pedestrians have the capability to change course, while vehicles
do not.

Pedestrian Characteristic and Class Definitions
We defined directional changes in, and avoidance related aspects of,pedestrians based on a pedestrian's physical body area (Figures 1 and 2).

Body Center Point : Base for movement
distance calculation, this is a point
Body Ellipse : Human's minimum
occupational space. body center point
centered ellipse

Data from : John J. Fruin『Pedestrian Space』,
1991
Figure 1: Body Ellipse

Buffer Space : Personal free action radiusbuffer (this is volatile)
Visual Field : Set by person's view
scope. (normal value is 70 degree,
reduces when using stairway or E/S)
Recognition Space : Recognition andjudgement range for obstacles and routechange. differs by individual andcongestion

Figure 2: Definition of PedestrianCharacteristic and Class
Pedestrians have various walking speed, and this may differ by sex,age, and physical disability. In addition, pedestrians may show different
walking speed according to their purpose. This program divides pedestrians
into classes according to sex, age, and other characteristics, and alsoclassifies pedestrians according to whether they are with or without
packages (i.e., normal pedestrian with no baggage, normal baggage,
backpack, or luggage). Thus, the pedestrians were divided into 64 classes bysex (male/female), ages (<10/10-20/20-60/>60), other characteristics (e.g.,
degree of haste), diverse physical occupancy characteristics, and recognition
range characteristics.
Pedestrian Route Selection Algorithm
The above characteristics may affect a pedestrian's subjective
decisions about route selection in a transit center. This study applied ashortest path algorithm and a space syntax theory route searching algorithm

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to create a mathematical model for pedestrian route selection analysis. The
shortest path algorithm (Moore Algorithm) was used to find the shortest
route and assumed that the pedestrian is familiar with the facility structure
and passage routes in the transit center. The Space Syntax Theory wasapplied when a pedestrian was not familiar with the facility structure androutes of passage.
Pedestrian Behavior Algorithm
Pedestrians may show instantaneous behavior changes using an
individual decision-making process, which may include decisions about
walking direction and acceleration, and deceleration changes. Therefore, analgorithm is required to express such microscopic level characteristicchanges. This study defined several physical area standards that includes apedestrian’s body ellipse area and a recognition area which, by recognizing
other pedestrians and obstacles, allows for obstacle avoidance and direction
and speed changes. In addition, we defined a pedestrian avoidance principal
according to those standards.

Figure 3: Diagram of direction change and possible speed changes

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Figure 4: Pedestrian behavior algorithm
Figure 3, which uses the color coding of Figure 2, shows that apedestrian has a body range, a recognition range, and a current direction. In
the pedestrian behavior algorithm, a pedestrian has a 180 degree avoidance
range which is based on its physical range. Furthermore, when anotherpedestrian or obstacle is within recognition area, the pedestrian may adjust
both direction and speed within the desired directional range.
3.2.2 Transit Center User Behavior Algorithm
Transit Center Definition
The types of transit centers considered were: large scale (inside andoutside), medium scale (general facility type), and small scale (specific area
within a Facility) types. In addition, facilities were divided into block types
in the operation analysis program, and an established node-link system wasused when using the facility usage and pedestrian behavior algorithms.

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Large
Category

InsideFacility

Table 1: Definition of transit center types

MediumCategory
Passenger
Facility

Small Category

Platform
Waiting Room
Indoor Stairway
Outdoor Stairway
Elevator
Facility
E/S
E/V
General Passage
Connecting Passage
PassageTransit Passage
New Traffic Connection
Other
Toilets
Facility

Figure 5: Block System

Large
Category

InsideFacility

OutsideFacility

MediumCategory

Small Category

Ticket Gate
LoungeOther
Function Room
Facility
Emergency Shelter
Flood Prevention
Facility for Disabled
Bus Stop
Connection Bicycle Storage
Facility Transit Parking Lot
Stop Station

Figure 6: Node-link System

Service Characteristics of the Facility and Queuing Algorithm
Application
A service facility can be divided into two types: single and multiple
services. Through characteristic analysis on transit customers who use afacility, applied the facility service using characteristic and passenger's
queuing algorithm, and reflected transit center user behavior algorithm.
Situations in which a pedestrian uses a facility for transit were based on apedestrian entering into a queue to use the facility. In the simulation model,
both entering and exiting the queue are included.

Fundamental factors included in the queuing algorithm

Figure 7: Schematic of queuing algorithm••

Input source: volume of customers entering the service facility
Queue: customer waiting line before service

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Service discipline: sequence of service
Service facility: service providing person or facility

The types of queuing sequence used are:•••First in/first out (FIFO) : Ticketing Office, Ticket Gate, E/S
LIFO: E/L
Random: Toilet, Entrance Gate, Stairway

Figure 8 Facility Usage Algorithm

Figure 9 Time-Space Diagram In Transfer Center

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Within the operation analysis program it is necessary use the above
algorithms to classify each facility's characteristics, define the service
patterns for the facility, and establish the sequence by which pedestrian's
enter and exit a queue to use the facility and receive service.
3.2.3. Space Expansion
Vehicle Simulation
An applied car-following theory and a lane change model that reflect
car and bus moving characteristics were used in the operation analysis
program. In addition, it includes simulation of pedestrians’ behaviors
outside of a pedestrian service facility (e.g. at a bus stop or on a sidewalk orpedestrian crossroad).
Mass Transportation Boarding and Alighting Model
The operation analysis program included both bus stop and subway
platform information to assess transit customer behavior and reflectpedestrian boarding and alighting behaviors. This included a boarding andalighting algorithm to model queuing behavior at bus stops, and included
information on boarding & alighting intervals.
3.2.4. MOE Setup
Measures of effectiveness (MOE) for transit center efficiency analysis
can be derived through the operation analysis program, as well as throughassessment of the pedestrian and facility information at each simulation time
step. This study establishes MOE for pedestrians, facilities, and for thesimulation.
•••

Pedestrian related MOE: based on basic information on thepedestrian (i.e., pedestrian ID, age, sex, propensity, baggage),and on the pedestrian's current speed, as well as the free flow
speed, travel time and distance, etc.
Facility related MOE : Facility basic information on the facility(i.e., facility ID, facility type, server number), and on the numberof users, the average service and queue times, and queue length,etc.
Simulation MOE : Assesses total simulation time, total
pedestrian occurrence, totals of pedestrian moving distance andmoving time, pedestrian average speed, pedestrian movement
traces, etc.

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Pedestrian related MOE
MOE

Route

PersonPersonal MOE
Person ID:
Current Speed:
Desired Speed:
Travel Distance:
Travel Time :

MOE

Route

PersonRoute Information
Person ID:
Origin Node:
Destination Node:
Current Block:
Block Type:

Current Point : (x, y, z)

Facility related MOE
MOE

Route

PersonPerson Information
Person ID:
Age:
Sex :
Personality:
Baggage:State:
Walk/Wait/Service

Figure 10: Simulator MOE
Simulation MOE

Facility

Facility MOE
ID :Facility type :# of Server :Mean Service Time :Mean Waiting Time :# of used person :Queue length :Occupancy :Pedestrian Trace Display

Figure 11: User Interface of Pedestrian Simulator
3.3. Program Development
3.3.1 Development of a Prototype for Algorithm Verification
A prototype was developed to verify the adequacy of the pedestrian
behavior and facility use behavior algorithms. The prototype was developedwith object oriented properties that divide the environmental components
into homogeneous characteristic and feature performing objects andresponds to each event specified for simulation. It was developed using the
Visual C++ Program from Microsoft Corporation, Redmond, WA, USA,and was based in the Microsoft Windows XP operating system.

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Establish Input Data

(Generate Pedestrian, OD, Facility Operation/Use Status)↓

Perform Simulation(Time Step)․ Pedestrian Generate/Disappear․ Network Update(Network → Transit Center Update → Each Floor Update → Facility
Block Update → Pedestrian within Block Update)․ Calculate MOE↓

Output Result

Figure 12: Flow of data through the simulation process
Pedestrian Characteristic(Recognition Space, Body Ellipse)

Facility Usage Behavior

Pedestrian Class(Pedestrian withbaggage)

Boarding and Alighting Behavior

Figure 8: Pedestrian Classification
3.3.2. Two-dimensional Transformation and Processing of the Prototype
A 3D model generating process is performed using pedestrian
behavior two-dimensional (2D) data analysis. An additional animation
process was required in order to produce a realistic 3D image. Motion data
(walking motion) is justified to a static 3D pedestrian model and displayed
as a 3D animation vision using this as a framework.
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3.3.3. 3D Operation Analysis Program Development
The 3D operation analysis program was developed using the
aforementioned pedestrian behavior algorithms.

Pedestrian 3D

Display Pedestrian Property

3D View of Sadang Station

Operation Analysis Program UIFigure 14: 3D Operation Analysis Program(Pedestrian Simulator)
Development

Write Normalized CAD File(including
3D Transform Guideline)↓
Design Operation Analysis Program
Framework↓
Design detailed Module, Functions↓
Develop Operation Analysis Program UI,
Establish Input Variable, MOE, Data
Storage System↓
Develop 3D Operation Analysis Program

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Figure 15: Pedestrian Simulator
4. STUDY AVAILABILITY
The transit center operation analysis program performs transit center
operation efficiency evaluations that can reflect microscopic level pedestrian
behaviors within the transit center. At present, this program is underdevelopment and is being designed to use or be based on the following
features.
•
•
•
•
•

Simulation tests and MOE analyses of various scenarios (e.g.,
unexpected incidents, peak time usage, future demand changes,
etc.)
Analyze transit behavior according to pedestrian demand (e.g.,transit time and transit distance)
Transit behavior analysis of various pedestrian types, including
those that are transportation vulnerable
Dynamic and microscopic level individual pedestrian simulation
tests for transit center design plan evaluations
Analysis of Korean style transit centers including theconsideration of local pedestrian characteristics

5. FUTURE STUDY DIRECTION
This transit center operation analysis program is under development, andthere are still many items to complete. First, there is a need to complete thepedestrian behavior algorithms. That step requires verification through fieldsurveys. Second, there is a need to reflect various pedestrian behaviors in
the model through the use of an appropriate pedestrian characteristicclassification system, and the need to complete an algorithm that effectively
considers various facility use behaviors. Third, the program should be
developed as a user friendly program. Accordingly, this study will use anormalized computer assisted design (CAD) writing guide function toautomatically extract program input data. The CAD function is currently
under development. Finally, this program should be able to produce various
MOE estimates. The following types of MOE are to be included.

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•
•
•
•
•
•
•

Transit passenger mobility (e.g., transit distance, transit time,
movement path, etc. )Transit passenger convenience (e.g., waiting time, boarding andalighting time, etc.)
Facility adequacy (e.g., facility usage density, capacity
adequacy analysis, disposition adequacy evaluation, etc.)
Moving convenience for the transportation vulnerable (e.g.,transit distance, transit Time)
Transit center walking (moving) space usage rate distribution
Number of pedestrian interactions
Time-space trace according to transit paths and the transit path
diagram

ACKNOWLEDGEMENTS
This research is one of several national transport projects in South
Korea sponsored by the ‘Korea Ministry of Land, Transport and Maritime
Affairs.’ The authors thank the members of the ‘Korea Ministry of Land,
Transport and Maritime Affairs’ for their assistance
REFERENCES
MOCT, Supplementary and Design Guideline for City Railway and Transit,
Convenience Facility, 2002.
MOCT, KHCM, 2004.
Park DongJu, Study on Pedestrian Traffic Flow Characteristic according toWalking Purpose, SNU Master's Thesis
Lee JinUk, Study on Pedestrian Traffic Flow Characteristic and its
application, Yeungnam University Master's Thesis
Transportation Research Board, Transit Capacity and Quality of Service
Manual, 2nd Edition, 2003.
John J. Fruin, Pedestrian Space : Theory and Design, 1991
Daamen, W., Modelling Passenger Flows in Public Transport Facilities,
PhD thesis, Delft University of Technology, 2004.

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EXPLORING IMPACTS OF COUNTDOWN TIMERS
ON THROUGH MOVEMEN AT A SIGNALIZEDINTERSECTION IN BANGKOKTHIRAYOOT LIMANOD,
PRAMUK PRABJABOK and KRAISI TIPPAYAWONG
School of Transportation Engineering
Suranaree University of Technology, Thailand
tlimanond@yahoo.com
ABSTRACTThis study aimed to explore the impacts of the countdown timer during
various stages of the signal cycle using two approaches: a traffic analysis
and a public opinion survey. The traffic analysis made a comparison of
traffic characteristics during an off-peak day time at a selected intersection
when the countdown timer was on operation against when it was switched
off. A public opinion survey was conducted among more than 300 local
drivers who drive regularly and are familiar with the timer. It was foundthat the presence of the countdown timers at the intersection would help
reducing the start-up lost time during the beginning of the green phase by
22 percent and reducing the number of red-light violations during the
beginning of the red phase by 50 percent. Furthermore, more than half ofthe local drivers consented that the timers would help relieving theirfrustration from stopping for uncertain amount of time during the red phase.
However, the timer was also found to slightly reduce the saturation flowrate during the green phase. The public opinion survey showed that the
majority of the local drivers were favorable towards the system, and would
support the municipality to install more of such device on the street network.
1. INTRODUCTION
The applications of signal countdown timers have been increasingly popular
in several traffic-congested cities in Asia. The timer is a digital clock,installed next to the signal head, continuously displaying the number of
seconds remaining for each phase of the cycle. It informs an exact cue ofthe onset of the next phase, so drivers are able to make a better decision on
how they should react to the signal. The countdown timers are often
claimed to offer many benefits, among others, to improve the vehicle flowat the intersection, to provide drivers extra times for making judgment tostop or to proceed through the intersection, to increase safety conditions at
the intersection, and to relieve frustration of those drivers waiting in thequeue (Kasetsart University, 2004). Nevertheless these claimed benefits
have been rarely justified in the field.

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It is less than a decade that the continuous countdown timers havebeen employed in the field, thus the research works on this device are still
limited. Kidwai et al (2005) found a reduction in the average throughput (ina unit of pcu/h) after the installation of the timers at a non-CBD intersectionin Malaysia. An experiment with an intersection in Singapore by Lum andHalim (2006) found that the installation of the device reduced the number ofthe red-light violations by 65% at 1.5 months of the installation. However,such impact does not sustain over time; the number of the red-lightviolations was rebounded to the usual level after 7.5 months of theinstallation. The device was also found to increase the number of vehicular
stops within 2 seconds into red, and this effect seems to sustain over thelong period. The study of Ibrahim et al (2008) comparing the queue
discharge patterns at the three intersections with timers and the threeintersections without timers, found that the timers tend to reduce thedischarge headways of the first six vehicles in the standing queue.
Although these studies demonstrate some evidence of countdown
timers’ benefits, each focused on different characteristics without
substantiation from another geographical area, where driving mentality andbehavior might be different. In addition, to authors’ knowledge, driver
opinions on countdown timers have never been reported anywhere. The
objectives of this research are to explore the impacts of the countdown
timers occurred during different stages of the full signal cycle, and toinvestigate opinions of the local commuters on the countdown timers
installed on the street network.
2. METHODOLOGY
Table 1 below summarizes the potential impacts of the countdown timer
anlayzed in this study as well as the methodologies used. This research
primarily investigated traffic patterns between the ‘with timer’ and the‘without timer’ conditions, and utilized descriptive statistics and statisticaltests to analyze whether the device instigates any impacts on traffic during
various stages of the cycle. Apart from the traffic analysis, this study also
conducted a public opinion survey among local drivers for their opinions on
the countdown timer device.
The intersection of Kaset-Navamin Road/Lad Pra Kao Road wasselected to be the project site since it met all of the pre-defined conditions.
It is a major intersection located in a non-CBD area in Bangkok andequipped with the countdown timers on all four approaches. The signal
countdown timers are on operation throughout the day except for themorning and evening peak hours when traffic polices take control andmanually adjusted traffic signals themselves. During these peak periods, thecountdown timers are switched off and display nil. It is a typical situationof all major intersections in Bangkok. This circumstance prevents theresearchers from making a comparison between the “with timers” and“without timers” conditions during the peak periods. The present study,

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thus, focuses on the comparisons of traffic characteristics during the offpeak day time, starting from 10:00 – 15:00.
Table 1: Potential impacts of the countdown timer at various stages of thesignal cycle
Potential impact ofcountdown timers

Stage during the cycle
Beginning of the green
phase

Reduce the start-up lost time

During the green phase

Improve the saturation flow rate

Beginning of the red
phase

Reduce the no. of vehicles
entering during the amber time
Reduce the no. of red-lightviolations

During the red phase

Relieve frustration of drivers

During the amber phase

Other benefits of countdown timers

Methodology
Descriptive statistics,
t-testDescriptive statistics,
t-testDescriptive statistics
Descriptive statistics
Public opinion survey
Public opinion survey

This study analyzes the through movement on the eastbound approach
along Kaset Navamin Road. This approach comprises of two left-turn lanes(free-flow), three through lanes and one right-turn lane. We particularly
focused on the vehicular flow on the second and the third through lanes,
since traffic stream on these lanes were not much affected by motorcycles(which primarily drive on the first lane). Furthermore, the researchers
carefully selected and removed the headways of those large vehicles, e.g.,large trucks, buses and the subsequent vehicles from the analysis to ensurethat our study strictly analyzed the headways of normal passenger vehicles.
A video camera was set up on a building located adjacent to theintersection, for recording the vehicular flow at the intersection during theoff peak day time (10:00 to 15:00). Exactly a week later, the researchers
requested the department of highways to switch off the countdown timers
without any modifications of the traffic signal and its timings, then recorded
the flow of traffic at the same intersection for the same period. The latercase was used to represent the without countdown timer conditions, as
drivers have no knowledge of the remaining time of the upcoming phasechange, similar to the situation when the timer was not installed. We
intended to experiment at the same intersection to avoid any uncontrollable
factors that could happen when two different intersections were analyzed.
Table 2 below summarizes the signal operations and some trafficparameters observed on the eastbound through lanes for both the “withtimer” and the “without timer” conditions. The signal consistently operated
with a cycle length of 230 seconds with a green time of 102 seconds for the
eastbound through movement during the off-peak day time. The ranges ofobserved queue length on both experiment days were comparable: 14 – 33
vehicls on the “with timer” day (25 April 2007) and 14 – 37 vehicles on the“without timer” day (2 May 2007) implying that traffic demand on bothdays were similar.

Exploring
Movement
at at
a Signalized
Intersection
in Bangkok
Exploring Impacts
ImpactsofofCountdown
CountdownTimers
TimersononThroughThrough
Movementa Signalized
Intersection
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Table 2: Summary of the signal operations and traffic parameters of theeastbound through lanes during the off-peak period (10:00 – 15:00)
Conditions
With timer
Without timer(or timer off)

25 Apr 2007

No. ofcyclesanalyzed
78

Cycle length/
green time(minutes)
230/102

Range of observed
queue length(vehs/cycle)
14-33

2 May 200778

230/102

14-37

Date ofsurveyIn this study, the saturation flow rate and the start-up lost time were
determined based on the conventional approach, presented in the Highway
Capacity Manual (Transportation Research Board, 2000). That is, the firstfew vehicles in the queue generally take a few extra moments to make adecision and react to the signal change by proceeding through theintersection. The headway of successive vehicles gradually decreases until
it finally reaches a relatively stabilized interval through the rest of the queue.
This stable interval is referred to as the saturation headway.
The determination of the ith vehicle in the queue that begins to sustain
the saturation headway state is a challenging task. This study utilized asimilar approach to the previous studies of Joseph and Chang (2005) and
Raksutorn (2004). It performed a series of statistical t-tests to determine
whether the headway of the first few vehicles in the queue is significantly
different from the average headways of the rest of all vehicles in thestanding queue. That is, it starts by comparing the headway of the firstvehicle with the average headway of the second vehicle to the last vehicle in
the queue. If they are significantly different, then we proceed to test thedifference between the headway of the second vehicle and the average
headway of the third vehicle to the last vehicle in the queue, and so on. The
test terminates when it finds the ith vehicle that its headway is notstatistically different from the average headway of all subsequent vehicles.
Then, the ith vehicle is presumably the first vehicle of the queue that sustains
the saturation headway. The start up lost time is determined as thesummation of the extra seconds over the saturation headway that the firstfew vehicles (exactly from the first to the (i-1)th vehicles) in the queue
experienced.
Apart from the traffic analysis, the researchers conducted an interview
of more than 500 local drivers randomly selected in front of the key spots,
e.g., large shopping mall,s, department of lane transport, in the city. Given
that the countdown timers have been utilized in Bangkok since 2002, andcurrently being installed at more than 400 intersections all over the Bangkok
area, local commuters are fairly familiar with the device and presumably
have a firm opinion on the system. The questionnaire requested thepersonal data of the respondents, the frequency of their driving in Bangkok,
and their specific opinions on the signal countdown device. The dataset waslater reduced to include only those who drive at least 2-3 days per week.
This study categorized the sample into two groups: passenger car
drivers and motorcycle drivers. Motorcycles are usually small, have

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maneuverability flexibility while driving, and have ability to sieve throughand stall in front of the standing queue while the approach receives a red
light. Thus, the two groups might have different opinions on the timers.
The final sample includes 368 respondents, composing of 116 regularmotorcycle drivers and 252 regular car drivers.
The distribution of sexes, age groups, income groups of the samples
are shown in Table 3. In general, the gender of the sampled car drivers isequally split between male and female, while that of the motorcycle drivers
are predonminantly male. The sampled car driver group tends to be older
and have higher income than the motorcycle driver group.
Table 3: summarizes general characteristics of the respondents.
Total no. of sample
Characteritics
Sex
FamaleMale
Age group<18 yrs.
18-22 yrs.
22-30 yrs.
30-45 yrs.
45-60 yrs.
>60 yrs.
Income group<5,000 baht
5,000-10,000 baht
10,000-20,000 baht
20,000-40,000 baht
40,000-60,000 baht
60,000-80,000 baht
80,000-100,000 baht
>100,000 baht

Motorcycle drivers
116

Car drivers
252

AmountShareAmount

Share

21
95

18.1%
81.9%

119
133

47.2%
52.8%

6
11
49
35
11
4

5.2%
9.5%
42.2%
30.2%
9.5%
3.4%

1
22
95
101
31
2

0.4%
8.7%
37.7%
40.1%
12.3%
0.8%

23
52
31
9
0
1
0
0

19.8%
44.8%
26.7%
7.8%
0.0%
0.9%
0.0%
0.0%

10
56
118
55
9
0
0
4

4.0%
22.2%
46.8%
21.8%
3.6%
0.0%
0.0%
1.6%

3. RESULTS
3.1 Impacts on saturation headway and saturation flow rate during thegreen phase
Table 4 summarizes descriptive statistics of saturation headway as
well as saturation flow rate under the “with timer” and the “without timer”
conditions. It was found that the average saturation headway of the “withtimer” condition (1.88 seconds per vehicle) was larger than the saturation
headway of the “without timer” condition (1.85 seconds per vehicle). One
plausible explanation is that, while the queue is proceeding through anintersection (without countdown timers) during the green phase, the drivers
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are uninformed of the exact termination of the green phase, thus, they likely
to follow the front vehicle closely to improve their chance of proceeding
through the intersection before the green phase ends. With the presence ofthe countdown timers, however, the drivers know the exact remaining time
before the green time ends, and can comfortably follow the front vehicle toproceed through the intersection with larger headways. Thus, we observed
larger headways on traffic stream at the intersection with countdown timers.
However, the t-test showed that the mean saturation headways between the“with timer” and the “without timer” conditions are not statistically different
at a 95% interval (with a p-value of 0.261).
Table 4: Comparisons of saturation headway and saturation flow rate
Studyperiodfirst veh. tosustainsaturation
headwayNo. of datain thesaturation
headway
stateWith timer
W/O timer

8
10

2,469
2305

Saturation
headway

Mean

Std
dev

1.88
1.85

0.82
0.80

Equivalent
Saturation flow(vehicles/hour)
1,918
1,946

The larger saturation headway of the “with timer” condition leads to adecrease in the saturation flow rate from 1,946 vehicles per hour without thepresence of the timer to 1,918 vehicles per hour when the countdown timer
is presented. It implies that the installation of countdown timers at asignalized intersection would not improve, but rather reduce the saturation
flow rate of the intersection. Although it is contradict to our presumption,
this is consistent with the result of Kidwai et al (2005) that found a
reduction in the average throughput (in a unit of pcu/h) after the installationof the timers at a non-CBD intersection in Malaysia.
3.2 Impacts on the start-up lost time during the beginning of the green
phase
The analysis of start-up lost time (SULT) requires some adjustments
to the previous computations in order to make a comparison between the“with timer” and the “without timer” conditions with the same basis. First,
the order of the vehicle in the standing queue that first sustains thesaturation headway has to be equal between the two conditions, otherwise
one sums more headway data than the other. Second, the saturation
headways of the two conditions must be assumed to be of the same
magnitude, or else it is difficult to justify whether the estimated SULTs of
one condition is higher than the other. The adjusted values utilized in theanalysis as well as the estimated SULTs are shown in Table 5.

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Table 5: Comparisons of start-up lost time

Cases
With timer
W/O timer

First veh. tosustainsaturation
headway (ith)88*weighted average
saturation
headway(sec./veh.)
1.87
1.87

No. ofcyclesanalyzed
139
122

Start-up lost time(seconds)
Mean

Std dev

6.53
8.32

1.76
2.06

*adjusted vehicle to sustain saturation headway state, to make the comparison based on the same basis.

As shown in Table 5, the mean SULT with countdown timers is lessthan its without timer counterpart. The average SULT decreases from 8.32
to 6.53 seconds (or 22 percent reduction) when the countdown timers arepresent at the intersection. A t-test proved that the mean SULTs between
the two conditions were statistically different at a 95% confidence interval(with a p-value of less than 0.001). This is logical given that queuing
drivers anticipate the upcoming phase change from the countdown timers,
so they are ready to proceed through the intersection without much delay
compared to when the countdown timers are in use. This results in adecrease in the SULT with the operation of the countdown timers. In fact,
this favorable effect of the countdown timer has been well perceived bymost of general public. According to the public opinion survey, 69.4
percent of the sampled motorcycle drivers and 63.2 percent of the sampled
car drivers agree that the timing information given by the countdown timers
assists them prepare to proceed through the intersection when the green
phase begins.
To further explore the impacts of countdown timers on individualvehicles in the standing queue, the researchers analyzed the paired
individual headways between with and without timers, as summarized inTable 6 and plotted on Figure 1. The impact of the countdown timers in
reducing individual headways are largest on the first vehicle (1.24 seconds).
The impact, however, quickly dissipated on the second vehicle, and wasfurther reduced for subsequent vehicles. A series of statistical tests
demonstrated that the differences in individual headways are significant at a
95 percent confidence interval for the first vehicle, while are insignificant
for the rest of the queue. This indicates that the countdown timers havesignificant effects on the headway of the first vehicle only. It should be
noted that this finding was disagree with the results from the Ibrahim et al(2008) study, which found that the countdown timers tended to reduce thedischarge headways of the first six vehicles in the standing queue. The
difference could be perhaps due to a number of locality factors, such as,
driving behavior, drivers’ mentality, level of traffic congestion.

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Table 6: The difference in mean individual headways of the first few
vehicles in the standing queue for the with and without timer conditionsDay

ith vehicle
With timer
Without timer
Difference
Sig. at 95%
CI?

1
5.66
6.90
-1.24

2
2.67
2.87
-0.20

mean headway (seconds)
3
4
5
6
2.50
2.33
2.26
2.05
2.44
2.46
2.24
2.25
0.06 -0.13 0.02 -0.20

7
2.10
2.19
-0.09

8
1.98
2.01
-0.03

Yes

No

No

No

No

Note: Sig. – significant , CI – confidence interval

No

No

No

mean headway (sec.)

8.00
7.00
6.00
5.00

W Timer

4.00

W/O Timer

3.00
2.00

sat. headway = 1.87 sec.

1.00
0.00
1

2

3

4

5

6

7

8

th

i vehicle in the standing queue

Figure 1: Comparison of headway patterns of the first few vehicles in thestanding queue
3.3 Impacts on traffic characteristics during the amber phase
Table 7 summarizes the number of vehicles entering the intersection
during the amber phase as well as the entering time observed under the two
conditions. It was found that the numbers of vehicles entering theintersection during the amber phase were comparable between the two
conditions, 101 vehicles under the “with timer” condition and 104 vehicles
under the “without timer” condition. It was also found that the average
entering time during the amber phase was 1.34 seconds after the beginningof the amber phase under the “with timer” condition, slightly less than 1.52
seconds under the “without timer” condition. Nevertheless, a t-test showedthat the difference was not statically different at 95% confidence interval(with a p-value of 0.153). Thus, from the finding of this research, thecountdown timers have no or little impact on traffic characteristics during
the amber phase.

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Table 7: Comparisons of the number of vehicles entering the intersection
during the amber phase and the entering time
Cases

No. ofcyclesanalyzed

Total no. ofvehiclesentering
during theamber lightWith timer
W/O timer

78
78

101
104

Avg. no ofvehiclesentering
during theamber light
per cycle
1.29
1.33

Entering time(seconds after the
beginning of amber)Mean
Std dev
1.34
1.52

0.87
0.92

.

3.4 Impacts on red-light violations during the beginning of the red
phase
Table 8 summarizes the number of red-light running occurrence andthe maximum violation time (in seconds) observed under the two conditions.
It was found that the red-light violations under the “with timer” condition
occurred 35 times, half of the number of red-light violations under the“without timer” case (70 times). Furthermore, we found a reduction in the
maximum violation time when the countdown timer is used. Under the“without timer” condition, the maximum violation time was 4.13 seconds
after the onset of the red phase, while it reduced to 3.08 seconds under the“with timer” condition. In summary, the countdown timers would help
reducing the number of red-light running occurrence as well as reducing the
maximum violation time a vehicle entering the intersection after the onset ofthe red phase. These two impacts of the timer would help creating a safer
environment for driving, and reduce the likelihood of the right-angle
collisions at the intersection.
Table 8: Comparison of the number of red-light violations and maximum
violation time between with and without timer conditions.
Cases

No. ofcyclesanalyzed

Total no. of
red-light
violations
observed

With timer
W/O timer

78
78

35
70

Avg. no ofvehiclesentering during
the red light per
cycle
0.45
0.90

Maximum violationtime(seconds after the
beginning of red)
3.08
4.13

Given that the countdown timers were installed at the study
intersection in early 2006, the researchers still found the reduction in the
red-light violations in 2007, which is approximately one year after the
installation of the devices. Thus, the impacts of reducing the number of redlight violations seemed sustain over at least one year. This contradicts to
the Lum and Halim (2006) study, which found that the countdown timers
effectively reduce the red-light violation incidents only for a short term (1.5month after the installation).

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3.5 Public opinions on countdown timers
From a public opinion survey conducted on 252 car drivers and 116
motorcycle drivers, it was found that the majority of local commuters arefavorable towards the countdown timers. More than 95 percent of the car
drivers interviewed recognized that the countdown timers are somehow
beneficial to them, and almost all of them would encourage Bangkok
Metropolitan Administration to install more countdown timers on the street
network. The attitude of motorcycle drivers is also highly positive, but to alesser extent. Approximately 74 percent of motorcycle drivers interviewed
appreciate the countdown timers, and would support the municipality to
install more countdown timers in the city.
The summary of local drivers’ opinions on the key impacts ofcountdown timers is reported in Table 9. The majority of both car driverand motorcycle driver groups consented that countdown timers would help
relieving their frustration from stopping for long and uncertain amount oftime during the red phase (64.4% and 51.8%, respectively), assisting themto promptly proceed through the intersection when the signal turns green(63.2% and 69.4%, respectively), and ensuring them confidence in driving
through intersection during the green phase (62.8% and 54.1%,
respectively). Approximately half of both groups also agreed that the timers
would assist them in making a better judgment to stop during the phasechange to red (54.4% and 47.1 %, respectively). Less than half of
respondents utilize information from the timers for better usage of waitingtime spent during the red phase, or for switching off the car engine while
waiting in the standing queue.
Table 9: Summary of public opinions on specific impacts of countdown
timers.
Opinion on specific impacts of timers
Relieve frustration from stopping for longand uncertain amount of time during the red
phase
Assist to promptly proceed through theintersection when the signal turns green
Ensure confidence in driving thru
intersection during the green phase.
Assist better judgment to stop when thesignal turns red
Better use of waiting time spent during thered phase
Turn off the engine while waiting in thestanding queue

Car drivers
Not
Agree
Agree

Motorcycle driers
Not
Agree
Agree

64.4%

35.6%

51.8%

48.2%

63.2%

36.8%

69.4%

30.6%

62.8%

37.2%

54.1%

45.9%

54.4%

45.6%

47.1%

52.9%

34.7%

65.3%

40.0%

60.0%

25.9%

74.1%

42.4%

57.6%

For those respondents who think that the countdown timers will help
relieving their frustration during the red phase, the interviewers further
requested them to rate the average level of frustration they usually

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experienced while driving under the “without timer” and the “with timer”
situations. The rating is in a scale of 0 to 4, where it denotes 5 levels offrustration from none, little, moderate, severe, to maximum, respectively. It
was found that when the countdown timers were in use, the average
frustration level of car drivers would reduce from the score of 2.68 to 1.34.
For motorcycle drivers, the level of frustration decreased from the averagescore of 2.41 to 1.71. That is, the countdown timers would help reducing
the overall frustration of the drivers due to the long and unknown waiting
during the red light from a “moderate to severe” level to a “little tomoderate” level.
4. DISCUSSION AND CONCLUSION
The present study aimed to explore various impacts of the countdown
timers occurred throughout the full signal cycle using two approaches. First,
the researchers made comparison of traffic flow characteristics of thethrough movement at an intersection in Bangkok when the countdown timer
was on against when the timer was switched off. Due to some technicaldifficulties preventing us from making a comparison of the traffic
conditions during the peak hours, the traffic analysis of this study focusedon the conditions during the off-peak day time (10:00 to 15:00). Second, apublic opinion survey was conducted to interview local drivers on theiropinions regarding the countdown timer device. The final data set include
opinions of those who drive regularly on Bangkok streets, comprising 116
motorcycle drivers and 252 car drivers. They all are supposed to be fairly
familiar with the device and presumably have a clear opinion on the system,
since the countdown timers have been utilized in Bangkok for more than 6
years, and currently being installed at more than 400 intersections all overthe Bangkok area.
The impacts of the countdown timers found in this study aresummarized in Figure 2 and described below per stage of the cycle.

Significantly reduce start-up lost time during early green
Slightlly reduce saturation flow during the green phase
R

G

Y

R

No obvious impact during the amber phase
Reduce red light running by half during early red
Reduce the frustration level of drivers during the red phase

Figure 2: Summary of the impacts of countdown timers

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At the beginning of the green phase, the installation of the countdown
timers at an intersection would help reducing the start-up lost times
experienced by the first few vehicles in the standing queue. It was foundthat the SULT of through movement decreased from 8.32 seconds under the“without timer” condition, to 6.53 seconds under the “with timer” condition.
This is equivalent to 22 percent reduction in SULT when the timer was on.
The saving in SULTs contributes moderate improvement on the overall flow
at signalized intersections. The countdown timers would reduce SULTs by
1.79 seconds per cycle per lane, or equivalent to 1 extra vehicle/cycle/lane.
Given a cycle length of 230 seconds utilized at the intersection, an hourcomprises of 15.6 repeated cycles. Thus, the countdown timers can bereasonably expected to increase the throughput of the through movement by
15 vehicles/hour/lane. An intersection with a total of 8 through lanes (6lanes on the east-west approach plus 2 lanes on the north-south approach)can reasonably expect to accommodate up to 120 extra vehicles per hour on
the through movement of all approaches combined.
During the green phase, the information from the countdown timer
will give drivers confident in proceeding through the intersection before thegreen phase ends, thus drivers tend to use a larger headway at theintersections where the countdown timers are presented. This leads to aslight reduction in the saturation flow rate. In this study, the saturation flowrate of the through movement under the “without timer” condition was
1,946 vehicles per hour, decreasing to 1,918 vehicles per hour under the“with timer” condition. This equals to a reduction of 28 vehicles/hour/lane.
Assuming a green time of 102 seconds for through movement with a cycle
length of 230 seconds, the throughput of the through movement is expected
to reduce by 12.4 vehicles/hour/lane. An intersection with a total of 8
through lanes can reasonably expect to accommodate 99 fewer vehicles perhour on the through movement of all approaches combined.
Note that the reduction in the saturation flow rate would offset thebenefits of saving in SULT, so that the overall benefits of the countdown
timers on traffic flow become marginal. From the calculation above, thepresence of a countdown timer would be able to accommodate 21 extra
vehicles on the 8 through lanes of the intersection in an hour.
This study does not found any obvious impact of countdown timers on
traffic characteristics during the amber phase. The numbers of vehicles
entering during the amber phase between the “without timer” and the “withtimer” conditions were comparable, and the average entering times between
the two conditions were not statistically different.
At the beginning of the red phase, the countdown timers would reduce
the number of red-light running occurrences, since it provides timing
information of the end of the green phase beforehand so drivers have moretime to make a proper decision. In this study, the number of red-lightviolations under the “with timer” condition was a half of the amount foundunder the “without timer” condition. The reduction in red-light runningincidents would help decreasing the likelihood of right-angle collisions at

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the intersection. Nevertheless, it should be noted that the countdown timers
could potentially cause another safety impact at the intersection. As
discussed above, the countdown timers help reducing the SULT so the firstvehicle in the standing queue enters the intersection earlier than before. It
could hit the vehicle running red light from the previous phase, causing aright angle collision. A personal communication with a traffic police
indicates no increasing trend of such accident. However, future studies areneeded to substantiate this presumption when adequate accident statistics
become available.
Finally, the countdown timers potentially help relieving drivers’
frustration from stopping for long and uncertain amount of time during thered phase. From the public opinion survey, 64.4 percent of the sampled car
drivers and 51.8 percent of sampled motorcycle drivers agree that the timers
help alleviating their frustration. The timing information given by thecountdown timer would let the drivers aware of the exact waiting time inadvance.
ACKNOWLEDGMENTS
The authors would like to thank Suranaree University of Technology
for the funding of this research project.
REFERENCE
Ibrahim MR, Karim MR, Kidwai FA. (2008). The effect of digital countdown display on signalized junction performance. American Journalof Applied Science 5 (5): 479-482.
Joseph J, Chang GL. (2005). Saturation flow rates and maximum criticallane volumes for planning applications in Maryland. ASCE Journal ofTransportation Engineering, December 2005: 946-952.
Kasetsart University (2004). The development, the administration and the
management of Intelligent Transportation System: a review report.
For the Office of traffic and transport planning and policy, Bangkok,
Thailand, 2004.
Kidwai FA, Karim MR, Ibrahim MR. (2005). Traffic flow analysis ofdigital count down signalized urban intersection. Journal of theEastern Asia Society for Transportation Studies, 5, 1301-1308.
Lum KM, Halim H. (2006). A before-and-after study on green signal
countdown device installation. Transportation Research Part F, 9, 2941.
Raksutorn W. (2004). Saturation flow rate and start-up lost time of
motorcycles at signalized intersection. Proceeding of the 6th National
Convention on Civil Engineering, Petchburi, Thailand.
Transportation Research Board. (2000). Highway Capacity Manual.
Transportation Research Board, National Research Council. Washington
D.C. 2000.

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A MATHEMATICAL MODEL
FOR TRANSIT NETWORK ASSIGNMENTYONGTAEK LIM
Dept. of Transportation & Logistics
Chonnam National University, Korea
limyt@chonnam.ac.kr
JUN-SEOK JANG
Dept. of Transportation & Logistics
Chonnam National University, Korea
jjs1982@live.co.kr
ABSTRACTIn this paper, a transit assignment model in stochastic environment is
proposed. The model is expected to overcome some existing limits of
conventional transit models, which have adopted the optimal strategy ofSpiess et al.(1989). The proposed model contains in-vehicle congestion
effect as well as in-vehicle travel time and waiting time. A simple example
was also given to test the model
1. INTRODUCTION
A public transport including railroad has been increasingly drawing
more attention amid growing interest in green growth, environmental
pollution and energy conservation. Since KTX launched its operation in
2004, the demand for railroad transport, not to mention high-speed rail, hasbeen on the rise in line with positive recognition to the transit. However, theeffect of transit network or effect on other transport means have yet to be
fully considered due to insufficient analysis model necessary for evaluating
and planning the behavior of the transit passengers, and even the existingtransit assignment model fails to fully incorporate the reality. On thecontrary, network-level analysis techniques considering the connectivity
between the highways and network effect developed for extended time havebeen practically applied to the expressway or highway, and consequently,
the projects have been planned based on prediction on potential effect innetwork aspect. The study was first intended to analyze the challenges withthe existing transit assignment model, thereby presenting the transit
assignment model that would deal with such challenges. The model
proposed in the study was structured in stochastic model to incorporate theperceived travel cost error that might be incurred when the passengers
choose the transit route. In the following Chapter, the challenges with thetransit travel cost function and the transit assignment model were evaluated,
and in Chapter III, a transit model proposed was introduced. Lastly in
Chapter IV, the model proposed in the study was assesses though theexamples.

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2. TRANSIT TRAVEL COST FUNCTION AND EXISTING TRANSITASSIGNMENT
2.1. Transit travel cost function
In general, the travel cost of the public transport such as railroad andbus is represented by generalized travel cost, which is a sum of travel timefrom the start point to end point and the out-of-pocket money paid fortransport means. the out-of-the-pocket-money refers to the cost in cash suchas fare and the travel time includes access time, in-vehicle travel time,transfer time and waiting time. A travel cost function has been presented inmany ways and in case of TransCAD, a commercial program, it includes
boarding time and dwelling time in travel time. De Cea et al.(1993)
proposed the travel cost function for route section as formula (1). The routesection refers to the section between transfer points.
(1)
Where, the first term refers to in-vehicle travel time of the routesection s and the second term refers to waiting time and the last term is thecongestion cost resulting from tin-vehicle passengers, which used to be
excluded because of difficulties in estimating.
2.2. Problems with transit assignment using optimal strategy
2.2.1 Transit assignment using optimal strategy
The optimal strategy widely used today was developed by Spiess etal.(1989), which is the transit assignment technique of EMME/2, thecommercialized transport demand program. This method comprises the 1ststage to seek the optimal strategy and the 2nd stage for loading the transportdemand to the section selected by optimal strategy. The optimal strategy is
to choose the route at the minimal cost when selectable routes were knownat the start point. When a route is determined using the optimal strategy, a
traffic demand is assigned to the section in proportion to the headway of theroutes passing each node. The optimal strategy is outlined as follows.
[Step 0] Definition of start node
[Step 1] Move from the start node on a vehicle arriving the earliest
among the routes belonging to attractive line
[Step 2] Getting off at the node predetermined according to optimal
strategy[Step 3] If not arrived at the destination, the getting-off node isdefined as new start node, and start again from [step 1] and repeat until
arrive at the destination.
2.2.2 Problems with transit assignment according to optimal strategy
The problems with transit assignment using optimal strategy could be
categorized into the two. As seen in [step 1] it's modeled to get on thevehicle arriving the earliest, causing frequent transfer against the reality,
which are indicated in other experimental studies, resulting from theprinciple of the optimal strategy.

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The second problems, which is more critical, is unrealistic assignment oftransport demand to the route. To explain such problem, the example using
optimal strategy suggested in Emme/2 in <Fig 1> was evaluated. The
figures at each section refer to in-vehicle travel time and section number andit was assumed that 2 routes are provided from start point (Node A) and end
point (Node B) and the unit transport demand exists. Fig 1(b) and Table 1
indicate the travel volume and travel time by route. As indicated in Table,
despite route #2 & #4 required less travel time, less assignment was given to
the section #4 & #5 where two paths are located as seen in Figure 1(b),indicating unrealistic transit assignment based on optimal strategy.
Line 1(hdw=12min)
V=0.50 (2)Line 2(hdw=12min)

V=0.50 (1)Line 1(hdw=12min)

V=0.50 (3)Line 2(hdw=12min)

V=0.00 (4)Line 3(hdw=30min)

V=0.08 (5)Node AX

Y

7mins (2)

6mins (3)4mins (4)Line 3(hdw=30min)

V=0.42 (6)Line 4(hdw=6min)

25mins (1)4mins (5)10mins (6)Line 4(hdw=6min)B(a) Example transit network

Node AXYB(b) Assigned transit volumes
by optimal strategy

Figure 1: An example transit network and its assigned volumes(in Emme/2 user manual)
Table 1: Travel times for each path
Travel time of the path
path

Route composition

1
2
3
4
5

line 1line 2-Y-line 3
line 2-Y-line 4line 2-X-line 3
line 2-X-line 3-Y-line 4

In-vejt/time(A)
25
7+6+4
7+6+10
7+4+4
7+4+10

Waitingtime(B)

Total
(A+B)

0
2.5
2.5
4.3
4.3+2.5

25.00
19.50
25.50
19.30
27.80

3. STOCHASTIC TRANSIT ASSIGNMENT MODEL
The study, in a bid to ease the limit with transit assignment using optimal
strategy, was aimed at proposing the new transit assignment model, which isstochastically structured using a logit model. A stochastic user equilibrium
assignment has the benefit to ease the unrealistic assumption which thedeterministic user equilibrium assignment has. That is, It enables to ease theassumption that all passengers have the same travel characteristics and
A Mathematical
MathematicalModel
ModelforforTransit
TransitNetwork
NetworkAssignment
Assignment

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determine the route when a complete travel information. In stochastic transit
assignment, unlike the deterministic user equilibrium assignment, the error
term or the difference in perceived cost by user, is added to the travel cost of
the link, and each user tends to choose the route to minimize their perceived
cost. Thus, in stochastic user equilibrium (SUE), it may be defined as thestate that any user is not able to reduce the perceived travel cost by changing
the route at its discretion. The stochastic transit assignment model proposedin the study is as formula (2) below.
(2)
Where,
is the travel volume using the route between the start andis travel demand between the start and end point, .
isend point, .
the probability of using the route between the start and end point, whichuses logit model as formula (3)(3)
in formula (3) is the scale parameter of logit model, and , is the travel
time of the route between , which is represented as formula (4)(4)
Where, is the in-vehicle travel time of the route between while,
is expected waiting time andis the capacity of the route between. Thus, the last term on right in formula (4) is the cost term reflecting in
vehicle congestion. (is the parameter) Thought there are several
methods to resolve the proposed model (formula 2), A direct logit
assignment method of Lim Yongtaek (2003) was adopted. This direct logit
method has a single balanced solution according to the fixed point theory
and seeks the solution through repeated procedures.
4. ASSESSMENT OF THE MODEL
4.1 Examples of transit assessment
The study, in a bid to assess the transit assignment model proposed,
was aimed at evaluating the methods in two different ways that considers
the in-vehicle congestion as well as without considering the congestion. The
case without considering the congestion has not the last term on the right of
formula (4) The parameter of logit model was, coefficient of the costfunction was,, and the travel demand was set as.
4.2 Outcome of the analysis
Figure 2 shows the result of the case without considering the invehicle congestion and when compared to the assignment using the optimal
strategy in Fig 1(b), the travel demand appeared to have been broadly

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distributed and particularly, a considerable travel volume was assigned tosection #4 and #5 where the route #2 and #3 requiring less travel time werelocated. And in case of the route #1 requiring more travel time, it wassignificantly reduced when comparing to the assignment based on the
optimal strategy, which indicates the proposed model assigned the travel
demand to the network more realistically. Fig 3 shows the result of the caseconsidering the in-vehicle congestion. (route capacity). As
indicated in figures, it looks similar with the case without considering thecongestion effect, but in case of section 5 to which the most travel volume
was assigned, the travel volume was reduced from 0.564 to 0.515 due tocongestion effect and part of the travel volume was shifted to other routes.
Table 2 compares the travel time by route in two cases. The cases
considering the capacity (congestion was considered) appeared to have had
extended travel time, which was attributable to the travel cost function
incorporating in-vehicle congestion caused by limited capacity into theLine 1(hdw=12min)Line 2(hdw=12min)

V=0.839 (2)V=0.161 (1)Line 1(hdw=12min)

V=0.432 (3)Line 2(hdw=12min)

V=0.407 (4)Line 3(hdw=30min)
Line 4(hdw=6min)

Node A

X

Y

V=0.564 (5)Line 3(hdw=30min)

V=0.275 (6)Line 4(hdw=6min)

B

V=0.176 (1)V=0.824 (2)Node A

V=0.425 (3)V=0.399 (4)V=0.515 (5)V=0.309 (6)

X

B

Y

travel cost.
Figure 2: Assigned volumes without
congestion effectFigure 3: Assigned volumes withcongestion effect

Table 2: Comparison of path travel times for each case
Capacity
Route
Route composition1line 1
2line 2-Y-line 3
Without
3
line 2-Y-line 4
considering
4line 2-X-line 35line 2-X-line 3-Y-line 41line 1
2line 2-Y-line 33line 2-Y-line 4
Considering
4line 2-X-line 35line 2-X-line 3-Y-line 4

A Mathematical
MathematicalModel
ModelforforTransit
TransitNetwork
NetworkAssignment
Assignment

Time
25.00
19.50
25.50
19.30
27.80
26.54
22.78
26.93
22.66
28.78

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5. CONCLUSIONS AND FUTURE STUDY
The study was intended to propose and evaluate the new transit model in an
attempt to ease the problems with the existing transit assignment model. The
travel time of the model proposed is in the generalized form which includes
in-vehicle travel time, waiting time and the congestion cost, and to consider
the perceived error by the passengers, it was structured in a stochastic user
balanced transit assignment model. This model could be considered themore realistic model than the transit assignment model using the optimal
strategy. As a result of evaluating the model proposed in the study throughthe example, the proposed model proved to be able to produce the more
realistic resolution that the method using the optimal strategy. However, thestudy, which is still at the early stage, has a various modeling limits, which
is first the clear interpretation of the travel cost function of the public
transport and the need of realistic estimate of the parameters included.
Furthermore, the field test to apply the transit assignment model to the large
scale real network is also needed.
REFERENCES
Yongtaek Lim, 2003. An analysis algorithm of the stochastic logit transit
assignment model, Journal of Koreran Society of Transportation, Vol
21, No2, 95-105, Apr 2003.
De Cea, J. and J. E. Fernandez, 1993. Transit Assignment for Congested
Public Transport System : An Equilibrium Model, Transportation
Science, Vol. 27. No. 2. 133-147.
Emme/2 user's manual, INRO, http://www.inro.ca/en/index.php
Spiess H. and M. Florian, 1989. Optimal Strategies : A New Assignment
Model for Transit Networks, Transportation Research 23B. No. 2, 83102.
TraveldemandmodelingwithTransCAD,
Caliper,
http://www.maptitude.co.uk/transcad.htm

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2ndHIERARCHICAL SHORTEST PATH ALGORITHM
WITH PROBABILISTIC TRAVEL TIME
THEONETTE RUBA MARIBOJOC and HYUNMYUNG KIM
Transportation Engineering
School of Engineering and Technology
Asian Institute of Technology
st106067@ait.ac.th
ABSTRACT
It has long been aimed by transportation researchers to select thepath which can make drivers move to their destination at a least cost. With
the development of Route Guidance System (RGS) and telecommunication
technology, travelers’ route choice behaviors change rapidly but theexisting modeling methods could not follow the speedy change. For example,
most route choice models regard travel time as a deterministic value so theuncertainty of travel time is ignored. In addition, many of the previous route
choice models were formulated without integrating the driver’s attitude andpreference in detail. Customizable route guidance system is not yet
available nowadays. In this paper, a new route choice model which deals
with the mentioned topics is proposed. With the combined concepts ofhierarchical road classification and resource constraints, non-overlapping
paths are enumerated in the real network. After then, the model calculates
the path choice probabilities with respect to the given preferred arrival time.
In addition, the idea is also expanded to the general case without preferredarrival time. Using a network in Bangkok, Thailand, the proposed model
was tested and the calculated path choice probabilities are compared to theTrue Success Rate (TSRk) and deterministic hierarchical MNL.
1. INTRODUCTION
1.1 Background and Purpose of the Study
A route choice problem has been one of the core study topics intransportation network analysis. In traffic assignment, forecasting the route
choices of drivers is a fundamental requisite. In addition, a route choiceproblem is a rising issue in Intelligent Transportation System (ITS). In thelast decade, a car navigation system (CNS) is prevalent and many
commercial companies are devoting themselves to develop a better routefinding algorithm. Although there have been significant developments on
this topic, the existing models for the route choice problem are not sosatisfactory and still have numerous flaws to be improved. The maindifficulty in modeling the route choice problem is that it consists of various
influencing factors. The influencing factors included in the route choiceproblem can be classified into two groups; 1) user behavior and 2) system
uncertainty.

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First, each driver in the real world has heterogeneous characteristics
and travel demand. For example, the attitudes of drivers to uncertainty or
danger are different from one another. Some drivers are risk-averse; whileothers are risk-taking. In addition, the travel time budgets of travelers differ
even if their origin and destination is the same. Second, the supply side ofroad system also has some uncertainties. As we can observe in the realworld, travel time between two locations is ever-fluctuating and thevariation depends on the time of day and day of week. There would bevarious recurrent and non-recurrent sources of the fluctuation. As a result,the variables included in the route choice problem are not deterministic but
probabilistic ones. The need for a better model which incorporates the
behavioral aspects into the route choice is among the highlights of recent
ITS applications (Beckhor et al, 2006).
This study aims to develop a hierarchical path enumeration algorithm
that overcomes the Independence of Irrelevant Alternatives (IIA) problem
while reducing the computational costs and to calculate the choiceprobabilities of the generated paths considering the driver’s arrival time
preferences.
1.2. Scope and Limitations of the Study
The study includes the formulation of the path enumeration algorithm,
the simulation of the link travel times and the determination of the pathprobability density functions, a survey and analysis of the OD(Suvarnabhumi Airport to Victory Monument route) travel time PDF, theformulation of the route choice model and the checking of the model’s
reliability. . The proposed model application is limited to transportation
networks where link travel times are stochastic but not time-dependent. The
study is applied to single OD pair and passenger car vehicle travel only. The
study does not include the evaluation of the computation efficiency of thealgorithm.
2. LITERATURE REVIEW
2.1. Shortest Path Problems and Algorithms
A shortest path algorithm plays an important role in route choiceproblems. Generally, shortest path algorithms are based on Bellman’s
principle of optimality which states that:“Any optimal policy has a property that whatever the initial state andinitial decisions are; the remaining decisions must constitute an optimal
policy with regard to the state resulting from the first decision.”
The most basic algorithm for finding the shortest path is Dijkstra’salgorithm (Dijkstra, 1959) which can identify paths from one origin to more
destinations. Contrarily, Floyd (1962) formulated an algorithm that finds

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paths from multiple origins to multiple destinations. Finding the shortest
paths from all origins to all destinations is beneficial to traffic assignment
problem with a non-sparse OD table.
However, in this study, a single OD pair is considered, so Dijkstra’salgorithm is employed as a path finding algorithm. In terms of therandomness of link attributes, shortest path algorithms can be classified into
two categories: deterministic and stochastic. The deterministic shortest path
(Dijkstra, 1959; Sharma, 2001; Nepal, 2002) uses one exact value, usually
the mean travel time for transportation networks, in determining the shortest
path. On the other hand, the stochastic shortest path (Frank, 1969;
Mirchandani, 1976) uses random variables, the link travel time probability
distribution function (PDF), in determining the shortest path. For realtransportation networks, the stochastic approach is recommended since it issimilar to how drivers perceive their path travel time. However, stochastic
problem is more complex and more difficult.
2.2. Path Enumeration Algorithms
In the route choice problem, multiple paths are provided and the
choice probability of each route is calculated in general. For the problem,the preparation of choice alternatives (i.e. routes) is the most important work
since a false definition of alternatives will bring out false choiceprobabilities. Therefore, it is important to enumerate the plausible
alternative paths for a given OD pair. In this study, the existing pathenumeration algorithms are grouped into two categories; namely, k-shortest
path algorithm and path enumeration algorithm.
The first k-shortest path algorithms was introduced by Bock et al(1957). All possible paths from an origin to a destination are enumerated
and sorted based on the travel cost. Pollack (1961) calculated the k -shortest
path set by subjecting the links of the k-1 links to infinity. Eppstein (1998)
formulated a k-shortest path algorithm using heap-ordered tree. To
overcome the excessive calculation burden of the aforementioned k-shortest
path algorithms, Yen (1971) introduced an algorithm that eliminates thenodes used by the enumerated shortest path to find the next shortest path. In
this algorithm, overlapping among enumerated paths, well-known as IIA(Independence of Irrelevant Alternatives) problem, arises.
Column generation method is popular in path enumeration algorithm.
This method uses a revised Simplex method in solving the problem over a
subset of columns. Bell (1995) suggested a capacity-oriented column
generation method while Thomas (1991) introduced a label collecting
algorithm that updates its predecessor nodes and finds the multiple paths by
combining predecessor nodes. Park and Rillet (1997) presented two
heuristic algorithms that identify multiple and reasonable alternative paths
defining reasonable alternative paths as those having acceptable attributevalues and dissimilar in terms of links used with respect to the previously
identified paths. The first uses link penalty to minimize the link overlaps.
The second classified the network according to road classification function
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or hierarchy and uses link penalty as well. Both algorithms use a node-based
Dijkstra algorithm in finding the shortest path. Sharma (2001) identified
multiple alternative routes by pruning the network using resource and route
constraints to maintain uniqueness of identified paths. Nepal (2002)
introduced algorithms that consider multiple and conflicting objectives inthe shortest path formulation. Lim and Hydecker (2006) illustrated a fullpath enumeration method but it cannot be applied to a practical problem
without constraints since the required memory for storing the full path set istremendous. Lim and Kim (2006) introduced a link-based algorithm thatconsiders path overlap and turn prohibitions in enumerating reasonable
paths.
2.3 Route choice problem
In this paper, the way for incorporating the uncertainty of travel time
into the route choice problem is the main concern. According to theinclusion of travel time uncertainty, the types of route choice problems canbe grouped into two categories; the deterministic and stochastic. The
deterministic approach assumes that the travel times of the available routes
are deterministic. The travelers in the network know exactly the times onthese routes and each traveler chooses a route with the least travel time fromhis origin to his destination. In reality, assuming travel times as
deterministic and exactly known to the travelers is not reasonable. On theother hand, the stochastic approach analyzes the network travel time andtravelers’ perception as random variables (Sheffi, 1985). Analytically theperceived travel time, C krs on route k between origin r and destination s, iscomputed as:
C krs = c krs + ξ krs(1)∀k , r , s, k ∈ K rs
where c krs is the measured or deterministic travel time and ξ krs is the random
error. K rs is the set of paths for OD pair rs. Mirchandani and Soroush(1987) proposed a generalized traffic equilibrium model with probabilistic
link travel times and perceptions. They classified the travelers into three
categories: namely, risk-neutral, risk-averse and risk prone.
Assuming that the travel time is the most important attribute ofconcern to travelers in their choices of travel routes, the probability that aroute is chosen, Pkrs is given by

Pkrs = Pr(C krs ≤ Clrs , ∀ l ∈ K rs )∀k , r , s; l ≠ k ∈ K rs

(2)

The probability that a given route is chosen is the probability that its
travel time is perceived to be the lowest among the alternative routes (Sheffi,1985).
The process of selecting alternative paths for a stochastic network can
be based on the theory of discrete choice models, where an individual’s
preferences towards each alternative is described by the attractiveness orutility measure associated with each alternative. The most common discrete
choice models are the multinomial logit (MNL) and the multinomial probit

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ICUS Report 37(MNP) models. The former is based on the assumption that the utilities of
the alternatives in the choice set are identically and independently
distributed (i.i.d) Gumbel variates and can be derived (assuming the utilityof using the kth path between origin r and destination s, U krs ) as follows.

U krs = −θ ⋅ c krs + ξ∀k , r , s
The logit choice probability is then computed as

(3)

rs

rs
k

P =

e −θ ⋅ck∑e θ− ⋅clrs∀k , r , s(4)lwhere θ is a coefficient that scales the perceived travel time. The
STOCH method or Dial’s algorithm is known to implement a logit route
choice model. However, the logit model does not account the correlation
between the perceived travel times of the various routes to properly reflect
the topology of the network. The probit route choice model alleviates manydifficulties associated with logit-based network loading at increased
computational costs. In this model, the perceived travel time is assumed to
be normally distributed that leads to a multivariate normal distribution of theperceived travel times on all paths of the given OD pair. Monte Carlosimulation is used in loading the probit choice probabilities.
2.4 Travel Time Reliability
The effectiveness of a route choice model is dependent on how close isimitates the real network. The drivers’ route preference in uncongested ideal
traffic condition may solely be based on the path travel distance. Oppositely,in congested real traffic condition, the drivers may consider travel time
variations due to the variation of travel demand and supply, accidents, poor
weather, etc. In this regard, the reliability of travel time on the path is an
important decision factor in the real route choice problem. The network
reliability has two aspects; namely, connectivity and travel time reliability(Wakabayashi and Iida, 1989). The concept of connectivity evaluates theprobability that the driver reaches a given destination by all means, while
travel time reliability evaluates the probability that the driver reaches agiven destination within a given time. In this study, the authors focus on thesecond aspect.
Asakura (1998) proposed a model that evaluates the travel time
reliability between an OD pair in the case of natural disasters. Using astochastic travel time, a reliability measure is defined as a probability that
one can travel between an OD pair within an acceptable level of travel time.
Lee et al. (2000) formulated a reliability traffic assignment model wheretravel time reliability is determined by the degree of travel time variation on
the paths chosen by the motorists. In their study, travel time reliability is afunction of both road capacity and traffic demand. Asakura and Hato (2000)
formulated a behavioral model in a deteriorated network focusing on the
difference between the recognized network and the actual network for noninformed and informed drivers. Fan and Nie (2006) proved the monotonic
property of successive approximation sequences for routing problems with
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recourse. The study further showed that the deterministic shortest path andk-shortest path problems are equivalent to the special case of the stochastic
on-time arrival (SOTA) problem where link travel time probability densities
are delta functions.
Kim (2008) used PDFs of OD and route travel time for an activity
scheduling and route choice problem, respectively. In the study, reliable
travel time and travel time margin were introduced in order to model therisk-averse behavior of travelers. A traveler who has a preferred arrival time
at a destination would consider it for deciding one’s departure time. If
necessary, the preferred travel time can be reflected in one’s route choice. In
this paper, the integration of personal travel preference will be discussed indetail.
3. MODELING FRAMEWORK
3.1

Path enumeration problem and Hierarchical Road Network
Classification

The concept of the hierarchical algorithm proposed in this study isbased on Park and Rillet’s (1997). The algorithm identifies k reasonable
shortest paths in the hierarchical network in which arterial and highway
network is denoted as a major network and drivers find independent
corridors on it. In reality, human beings recall major corridors first and only
consider them when choosing and changing their routes since the memory
capacity of human beings are not sufficient for storing a whole minor
network topology. Therefore, the first process of hierarchical pathenumeration is an independent corridor finding problem. In order to find thecorridors in the major network, a normal k-path algorithm is used. The link
similarity among enumerated paths has been an issue in the generation ofmultiple paths. In this study, a penalty method is employed (Lim and Kim,
2006) in which the links used by previously identified paths are penalized tominimize the chances of generating the k-shortest path set with similar links
used. As explained, the corridor finding process on the major network
copies the cognitive process of human beings. In addition to the hierarchicalcorridor finding, the maximum detour of path is restricted for building up areasonable path set. After enumerating all feasible paths, the enumerated
paths were further subjected to the travel time constraint. Based on thedriver’s perspective, paths with costs higher than a driver’s acceptable limit
would not be included in the k-reasonable path set. In this study, themaximum travel time is 2.5 times higher than the minimum OD travel time.
After finding a corridor, a driver has to connect one’s origin and destination
with the closest points on the corridor because most original origins anddestinations are not located on the highway or main corridor. The original
origin and the destination should be connected with the closest entering andexiting locations on the corridors. Dijkstra’s algorithm is used for finding
the shortest connection.

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The corridor-based hierarchical path searching algorithm has anadvantage for modeling the effect of traffic information via VMS (Variable
message sign) or radio. In reality, traffic information disseminated throughbroadcast systems only reports traffic condition along main corridors.
Accordingly, most drivers cannot perfectly identify travel time on theirpaths since traffic condition between their trip ends and corridor in/out
points is still unknown. If a hierarchical network structure is used formodeling traffic information evaluation, then the imperfect information canbe considered in such a way that the travel times of corridors on the major
network are updated and given to drivers. To check the properties of theidentified corridors, two measures of effectiveness (MOE) were conducted.
The total travel time ratio (TTTR) is the first, which is the ratio of the travel
time of alternative path p to the travel time of the fastest path k. The TTTR
shows a pair-wise comparison of relative efficiency between two routes k
and p in terms of travel times. The second is the route similarity (RS), whichis the ratio of the length of the route k repeatedly used while traversing
through the alternative path p.
TT p
TTTRkp =(5)
TTkn∑ d ⋅δ

RS kp = a∈Aaa , kp

(6)
rs
d min
where TTTRkp = TTTR between route k and p; TTk = travel time onrsroute k; RSkp = RS of route k to route p; da = distance on link a; d min=
distance of the shortest route for OD pair rs; δ a,kp = 1 if link a is used byroute k and route p, 0 otherwise; and n = number of links in the network.
(a) Corridor enumeration
OriginCorridor1
Destination
Corridor2

Corridor3(b) Connecting original O/D with corridors

Sub-paths from corridors to a destination

Figure 1: Hierarchical path finding process

Hierarchical Shortest Path Algorithm with Probabilistic Travel Time
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3.2 Path choice problem with probabilistic travel time distribution
3.2.1 Link travel time
The first step of the modeling procedure for probabilistic path travel
time is the generation of random link travel time. Generally, the link travel
time is dependent on the traffic flow on the link. However, incorporating
traffic flows to the route choice model requires a huge calculation burden,
thus affecting the efficiency of the model. To simplify the problem whileconsidering the link travel time variation, the actual link travel time ta is
considered as a random variable with a normal PDF N(TTa, σa), where TTa(7) is the average link travel time and σa (8) is the standard deviation of thelink travel time, which is dependent on the road functional classification of
the link, φRF(n). It should be noted that link travel times in arterial roads are
more variable compared to freeways.
da
TTa =
(7)
E ( SPDa )σ a = ϕ RF ( n ) ⋅ TTa(8)where da and E(SPDa) is the length and average travel speed of link a,
respectively.
In randomly generating the actual link travel time, ta, Monte Carlosimulation method is used. Monte Carlo simulation is a technique in which
an output of random numbers is related to an assumed probability
distribution (in this study, a normal distribution) so that a set of probablevalues for the basic variables of the function is obtained (Smith, 1986). Thismethod is used to generate a sample of link travel time, ta.
t a = TTa + σ a − 2 ln TTa ⋅ sin 2π ⋅ TTa(9)
3.2.2 Path travel time
In reality, drivers recognize a path as a whole instead of dividing it
into independent links. Therefore, the uncertainty of path travel time is alsomodeled with respect to a path instead of a link. It is therefore reasonable
not to include the perception errors of the links in computing the path travel
time. Instead, perception errors should be considered path-based not linkbased as in Mirchandani and Soroush’s (1987) formulation. Accordingly,
path travel time crsk is calculated by summing random link travel times ta

along path k as shown in Eq. (10). In other words, a sample of path travel
time is calculated as a sum of random link travel times.
nc krs = ∑ t a ⋅ δ a ,k
a =1

if
a∈k
⎧1
where δ a,k ⎨⎩0 otherwise(10)

The addition of the random link travel times might be questionablebecause it does not consider the correlation of subsequent links. In reality,
traffic congestion and delay between two connecting links are interrelated.
Due to computational complexities, however, the inclusion of linkcorrelation is left for the future study.

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After a number of simulation trials m, the range and frequencies ofeach path travel time are determined for estimating the distribution of pathtravel time. To test the goodness of fit of the distribution, a chi-square test
was conducted. All path travel time distribution functions should satisfy Eq.
(11).
m(ni − ei ) 2< c1−α , f
(11)
∑
ei
i =1
where ni is the observed frequency of ckrs,i values and ei is thecorresponding frequency from the assumed theoretical distribution; f = m –1; c1-α, f is the value of the approximation of Chi-square ( χ 2f ) distribution at
a cumulative probability (1-α) and; α is the significance level.
The mean travel time of path k, µk and the standard deviation, σk of theenumerated paths were determined using Eq. (12) and Eq. (13), respectively.
μk =1 m rs⋅ ∑ c k ,im i =1

m

σk =

(12)

∑ (c − μ )
rs
k ,i

i =1

k

(13)

m −1

With the calculated path mean travel time and standard deviation, thepath normal distribution function curve was drawn. The distributionfunction was calculated using Eq. (14).

f ( x) =
x

σ

1
k

2π

⎛
⎜
−⎜
⎜
⎜
⎝

e

( x − μ k ) ⎞⎟⎟
2

σ

2

2
k

⎟
⎟
⎠

(14)

where x = z ⋅ σ k + μ k and -4.0 ≤ z ≤ 4.0. The path travel time
distribution is used for generating random path travel time samples in thedeveloped route choice model.
A statistical test for a normal distribution of a path travel time would
be unnecessary because the path travel time is the sum of normal link travel
times. The main purpose of above the procedure is for the application to reallink travel time data. If link travel time is collected from the real road
network, the sum of the travel time would not follow a normal distribution.
Therefore, in that case, a statistical test for other probability distributions isrequired. In addition, neglecting the link travel time correlation is acceptable
when the surveyed link travel times are used for path travel time modelingsince the link travel times already include the interaction.

3.3 Formulation of a new route choice model
Two alternatives were considered in the calculation of the route choiceprobability; namely, 1) with travel time budget and 2) without travel time
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budget. For the with travel time budget case, it is assumed that there is aknown preference arrival time at the destination; while for the without travel
time budget case, the route choice probabilities of a typical driver iscalculated.
3.3.1 Route choice with travel time budget
Some drivers demand a specific arrival time or maximum acceptable
travel time for their travels. Existing route choice models have not reflected
this demand which is, in reality, an important influencing factor on the route
choice decision. In this study, the path choice probability is calculated basedon the user’s maximum acceptable travel time and it is denoted as the“reference time.” As hypothesized by Fuji and Kitamura (2004), the pathchoice probabilities can be calculated considering the reference travel timewithin the users’ time frame. Usually, the drivers recall the route travel timein ranges; that is, from a minimum to maximum value. To imitate theperception process of path travel time, in their study, it is assumed that thedrivers believe that the path travel time of path k would not be less than tminor greater than tmax..
In calculating the path choice probability, this study accounts for the
PDF function of the path travel time. As shown in Figure 2, the probability
that the traveler arrives at a destination within a reference time t isdetermined by the shaded area. The greater the shaded area, the higher is theprobability of arriving on time using the path. In addition, the reference time
t differs from one person to the other in reality. Accordingly, the perceived
travel time distribution is surveyed in order to reflect the real distribution of
the reference time. The path choice calculation is taken as the probability
that a given path k is the shortest path at a particular reference time t. Eq.
(15) shows the probability that path k becomes the shortest path given aminimum OD travel time πrs. That is, when path k’s travel time is shorter
than those of the other alternative’s and mathematically represented by(15)
pr (C krs = π rs ) = pr (c krs < c1rs and c krs < c 2rs and ....... c krs < c nrs ) ∀ 1,2,..., k ,..., n ∈ P rs
where P rs is the path set for OD pair rs. The probability that the path k
becomes the shortest path is calculated by Eq. (15). All paths found by ahierarchical finding algorithm do not have serious overlapping links, so theirtravel times can be assumed to be independent one another. Accordingly,
the choice probability is further simplified as follows:(16)
pr (c krs = π rs ) = pr (ckrs < c1rs ) ⋅ pr ( c krs < c2rs ) ⋅ ...... ⋅ pr (c krs < cnrs ) ∀ 1,2,..., k ,..., n ∈ P rs
Analytically, the calculation of choice probability pr (ckrs = π rs ) for pathk by Eq. (15) and (16) requires a big calculation burden especially in thereal-size network since all cases have to be compared pair by pair. Let’s
assume that there are only two independent paths. In order to calculate thechoice probabilities of the two paths, the probability distributions of twopaths should be considered simultaneously. In addition, the cases in which
the minimum travel time is shorter than the reference time ( c refrs ) are only
taken into account because travelers do not want to arrive later than theirmaximum acceptable time. If both of paths cannot give a travel time shorter

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than the reference time, then it is regarded as a fail. The choice probabilityof path 1 only in the success cases can be calculated as Eq. (17).
pr (c1rs < c 2rs | success ) =pr (c1rs < c 2rs )pr (c1rs < c 2rs )
=
rs
rs
rs
pr (min( c1rs , c 2rs ) < c ref) pr (c1rs < c ref) + pr (c 2rs < c ref)(17)

The calculation of Eq. (17) is not so difficult in the case of two paths.
However, a calculation burden increases sharply as the number of paths in
the network increases. In addition, the calculation of pr (c1rs < c2rs ) requires
analytical manipulation.

pr (c k ≤ t )rs

Reference timet
Figure 2 Path Probability of On-time Arrival
rs
Reference time( cref)rspr (c1rs < cref)

Path 1rs
pr (c2rs < cref)

Path 2OD travel time

Figure 3 Pair by Pair Path Choice Probability Comparison
In this study, a simplified approach in approximating the path choiceprobability of Eq. (15) is presented. Using the Monte Carlo simulation
technique, the travel time of each path in the path set is generated and thefeasibility of the minimum travel time under the reference time is checked.
In other words, if the minimum travel time of paths at a trial is longer than
the reference time, then the trial is considered fail and it would not be
included in the probability calculation. The number of times that a path

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travel time c krs is the shortest is denoted, N krs , where c krs ≤ πrs is counted andthe choice probability of path k is estimated as follows:
N krs
(18)
pr (c krs = π rs ) =N total _ successwhere Ntotal_success is the number of trials where the shortest path has a pathtravel time less than or equal to the reference time c refrs . The feasibility ofarriving at the destination within c refrs is also calculated as
N total _ success(19)
Total Trials
where SR(crefrs ) is the success rate where the shortest path has a travel time lessrs)=
SR (c refthan c refrs (also known as Expected Satisfaction Rate) and Total Trials is thetotal number of Monte Carlo trials.
3.3.2 Route choice without travel time budget
In some cases, the traveler does not declare a specific arrival or it isunknown to a route choice model. In this condition, the reference travel time
which has been introduced in the previous case cannot be defined as adeterministic value. If the reference time is uncertain, the driver’s reference
travel time is assumed as an interval or probability distribution. This
assumption is based on the study of Fuji and Kitamura (2004). As in theprevious formulation, the probability that the driver arrives at the destination
on time is calculated asrspr (ckrs = π rs ) = pr (ckrs = π rs | cref
= t)

(20)

In this case, t is unknown but has a probability distribution which canbe determined from an interview survey. Analytically, equation (20) could
be elaborated as follows:∫pr (c = π ) =
rs
k

rs

t max

t min

rs
pr (ckrs = π rs ) ⋅ pr (cref
= t)

∫

t max

t min

rs
pr (cref
= t)

(21)

For simplicity, the path choice probability by Eq. (21) can beapproximated by Eq. (22).
tmax

pr (c = π ) =
rs
k

rs

∑ pr (c = π ) ⋅ pr (c
tmin

rs
k

tmax

rs

∑ pr (c
tmin

rs
ref

rs
ref

= t)

(22)

= t)

where tmin and tmax represent a temporal framework for route choice problem
and they are set as tmin = 5 minutes and tmax = 120 minutes, respectively. In
other words, drivers only consider their reference travel time within thistime frame. The calculation of the term pr (ckrs = π rs ) is the same to thesolution in the previous section; while the value of the term pr (c refrs = t ) istaken from the probability distribution function of the perceived OD travel
time which is an Erlang distribution and is given by

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rspr (cref= t) =

where v and k are parameters.
3.4v(vx) k −1 −vxe(k − 1)!

(23)

Measure of the Model Performance

For the performance evaluation, the proposed model was compared tothe true success or hit ratio (TSRk) shown in Eq. (24) and the Multinomial
Logit (MNL) model. TSRk has the nearly same concept with Eq. (18) but thesuccess and the fail of arrival by the reference time is not considered in Eq.
(24). Another data set for calculating the true probability to be the shortest
path is generated for the model evaluation. The events of N krs is randomly
calculated using the path travel time distributions (see Eq. (14)).
N rs
TSRk = k(24)
N total
where, N total is the total number of events for a comparison.
On the other hand, the choice probability of path k by a conventional
MNL model Prsk was also calculated as
rs

rs
k

P =

e −θ ⋅ck∑ eθ

− ⋅clrs(25)l∈P rswhere the c krs is the mean travel time on path k and the optimal value of scaleparameter θ was calibrated based on TSRk in a heuristic way. Note that allpaths found by the hierarchical method should be sufficiently independent.
Therefore, a requisite in applying MNL, known as IIA (Independence of
irrelevant alternative), is satisfied and MNL can show desirable capability inthe route choice problem.
3.5 Survey design for perceived travel time
In order to determine the probability distribution function of thereference time, a survey was conducted on the test route (Suvarnabhumi
Airport to Victory Monument in Bangkok, Thailand). Respondents wereasked regarding their expected travel time for the OD pair and they gave
answers as an interval. The interval of travel time was standardized andplotted in a histogram. The number of bins, k is assumed using the following
criterion (Hahn and Shapiro, 1967).
nwhere: n = number of samples(26)
4 ⋅ [0.75(n − 1) 2 ]1 / 5 ≤ k ≤ ,5

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Figure 4: Perceived OD Travel Time Distribution
Distribution

Table 1: Chi-square goodness-of-fit Test Results

1. Normal
2. Lognormal
3. Gamma
4. Erlang
5. BetaParametersµ = 61.149
σ = 11.343
λ = 3.596
ζ = 0.967
α = 29.059
β = 0.475; 1/β = 2.104
α = 15β = 0.245; 1/β = 4.077
α = 2.0
β = 3.5

Chi-squared value (w)158.185 > 75.624; Rejected
50.808 < 75.624; OK
660.701 > 75.624; Rejected31.526 < 75.624; OK
44.224 < 75.624; OK

The chi-square goodness-of-fit test is used for a statistical test. The
chi-square test statistic, w is obtained using Eq. (27). Note that high valuesof w signify that the observed data contradicts the assumed model.
k(ni − ei ) 2w=∑< c1− s , f
(27)
ei
i =1
where: ni=observed frequencies, ei= corresponding frequencies from anassumed theoretical distribution, c1-α,f is the value of the appropriate chisquare distribution at the cumulative probability (1 - α), degree of freedom,f = k – r- 1 and r = number of parameters. The assumed test distribution
models include normal, lognormal, gamma, Erlang and beta. The
appropriate probability distribution for the perceived OD travel time is usedin the calculation of the route choice probability. The perceived OD pairtravel time was taken from the survey conducted. The perceived OD travel
time responses were in ranges. In increments of 1 minute; the data wereplotted and the histogram of the OD travel time was obtained as shown in
Figure 4 (a). The length of range can be different in person to person, so theweight of each response for the range is normalized. From the data gathered,a chi-square goodness-of-fit test was conducted. The results of the test is
shown in Table 1; where n = 37 samples, sample mean, µ = 61.149 min,standard deviation, σ = 11.343 and chi(0.05, dof = 57) ≤ 75.62375. The Erlang
distribution best represents the perceived travel time distribution function,
as shown in Figure 4 (b).

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4. TEST RESULTS
4.1 Test area and path enumeration
An OD pair in Bangkok, Thailand (see Figure 5) is selected as a test
network. The origin-destination (OD) pair chosen is the Suvarnabhumi
Airport-Victory Monument route. The network consists of 314 links and
102 nodes. The generalized cost is taken from the average travel speed andlength of each link. Using the hierarchical path enumeration model, five
sufficiently independent paths are found as shown in Figure 5. In order toaccess the performance of the path enumeration algorithm, two Measuresof-Effectiveness were used; namely, the total travel time ratio (TTTR) andthe route similarity (RS).
In the final path set, TTTR of the longest path is 2.003 with respect tothe shortest path; which means that the worst path is 200.3% longer than the
shortest one. Similarly, TTTR of the second, third, and fourth paths are
1.191, 1.284, and 1.448, respectively. In the case of the RS, most path pairs
have a trivial value. The biggest one is between path 3 and 1 in which the
RS value is 0.041; which implies that only 4.1% of the total distance of path3 overlaps with path 1. The second biggest RS is 0.039 and it is occurred
between path 5 and path 4. As a result, all five paths are sufficiently
independent.

Figure 5 Study area in Bangkok, Thailand
4.2

Travel time generation for paths

Using Monte Carlo technique, the travel time of the enumerated pathlinks were generated (φRF(1) = 0.75 and φRF(1) = 1.5). The road function
coefficient, φRF serves as the standard deviation of the link travel time
during the simulation process. In every trial, the sample path travel time istaken as the summation of the link travel times along the path. After then,the probability distribution of path travel time is assumed to be normal and achi-square goodness-of-fit test was conducted.
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The Chi-square value (w) for the test is (Chi(0.05,6) ≤ 23.685). In the test,
a normal distribution well fits for the five paths. The w value of path 2 is thelowest (6.175) and the biggest occurs on path 3 (15.514). Figure 6 depicts
the normal distribution of path travel time for the five paths. Path 1 shows
the smallest average travel time (22.664 min). In addition, the SD of travel
time on path 1 is also the smallest (6.831). The second best path is path 3.
The average and SD of travel time on path 3 are 28.650 (min) and 8.758,
respectively. Contrarily, Path 5 gives the worst travel time with an average
travel time of 60.842 and SD of 13.23 minutes. Therefore, path 5 has theleast possibility to become the shortest path.

Figure 6 PDF of path travel time
4.3 Calculation of path choice probability
4.3.1 Route choice probability with travel time budget
Some drivers demand a specific travel time to arrive at the destination.
For instance, travelers might have important appointments, meeting, or eventhe regular office check-in times. For this case, the probability that a traveler
arrives at the destination on time was determined by calculating the
cumulative distribution function (CDF) of the path actual travel time for aparticular reference travel. Accordingly, the probability of arriving on timeincreases as the reference time increases (see Figure 7). If the on-timearrival probability is very low, the driver has to delay his arrival time target
so as to have sufficient probability for on-time arrival.
The total number of events where the shortest travel time is less thanor equal to the given reference time is set to 500, also known as the total
number of successful trials in this study. The total number of trials
regardless of whether the shortest path is less than or equal to the referencetime is also noted to determine the feasibility of arriving at the destination
denoted as the Expected Satisfaction Rate (see Figure 7). If a traveler wants
to arrive at the destination within 35 minutes or less, the probability that atleast one path would have such travel time is less than 100%. On the other
hand, if the traveller demands a travel time greater than 35 minutes, at least
one path (mainly path 1) that could give such travel time.

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Figure 7 On-time arrival probabilities and Expected Satisfaction Rate withincreasing reference time

Figure 8 Path Choice Probabilities at Different Demand Travel Time

Figure 9 Coefficient of Variation of the path choice probabilities withincreasing successful trials
Figure 8 shows the probability of being the shortest path at a specificreference time using 500 successful trials. When the reference time is very
short, such as 5 minutes, paths 1, 2, and 3 have similar probabilities. As
shown in Figure 7, the on-time arrival probability of the three paths are very
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small, so the paths show similar performance. However, the superiority ofpath 1 increases as the reference time is extended. In Figure 7, path 1
provides more than 60% probability for arriving on time at 25 minutes
reference time. Oppositely, those of path 2 and 3 are less than 35%. The
superiority keeps increasing until the reference time reaches 25~40 minutes.
After then, path choice probabilities are stablized.
Moreover, some fluctuations in the path choice probabilities areobserved with increasing reference time (see Figure 8) mainly because thechoice probabilities are calculated using Monte Carlo simulation. When thenumber of trials in the simulation increases, the choice probabilty
fluctuation can be decreased. The variance change of the path choiceprobabilities is shown in Figure 9. Visibly, the variance is inversely
proportional to the number of successful trials; that is, increasing the
successful trials decreases the variation of the path choice probabilities. As
shown in Figure 9, 500 successful trials give a satisfactory path choiceprobability variance and is therefore used in this study.
4.3.2 Route choice probability without travel time budget
The path choice probability without travel time budget is calculatedusing Eq. (22). This case is particularly applicable for travelers who
perceive an uncertain travel time as an interval. In this case, the perceived
travel time is unknown but its probability distribution is available from thesurvey conducted. Figure 10 shows the trend of the path choice probabilities
with varying reference time. In the test, the path choice probabilities arecalculated from tmin = 5 minutes to tmax = 120 minutes. Drastic changes inpath choice probabilities were observed when the maximum acceptable
travel time is relatively low; 5 to 30 minutes. Since the calculation of the
choice probability is accumulated from tmin to tmax, the values are dependent
on the maximum reference time. The longer the maximum reference time;the more stable is the path choice probability. From the survey, themaximum perceived travel time from Suvarnabhumi Airport to Victory
Monument was known to be 120 minutes. Figure 11 shows the path choiceprobability of the proposed model, as well as the comparison to the True
Success Rate (TSRk) and Multinomial Logit (MNL) model results.
The path choice probabilities of the proposed route choice model werethen compared to the True Success Rate (TSRk) and multinomial Logit(MNL) model. As shown in Figure 12, both the proposed and thehierarchical MNL models have strong positive linear relationship to TSRk;
0.9975 and 0.9972, respectively. Moreover, the coefficient of determination,
r2 values are relatively close to 1; 0.9951 and 0.9943 for the proposed modeland Hierarchical MNL, respectively. This means that 99.51% and 99.43%of the variance or fluctuation in the path choice probabilities of the proposedmodel and the Hierarchical MNL respectively can be predicted from the
TSRk. Therefore, we can conclude that the route choice probabilities of TSRk,
MNL, and the developed method are nearly similar at the aggregate level.
Note that there is no IIA problem for MNL. It means the test condition isvery suitable and ideal for MNL model. Therefore, the performance of thedeveloped model is very promising. In addition, in the case of MNL, utility

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coefficients should be calibrated with the real choice of travelers. It meansthat the developed model can give very accurate forecasting result without
calibration. It is an attractive advantage when applying the developed modelto the real-time route guidance system.

Figure 10 Path choice probabilities with increasing maximum reference
travel time

Figure 11 Path Choice Probabilities between the Proposed Model, TSR and
MNL

Figure 12 Correlation of the TSR to the Proposed Model and MNL

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5. CONCLUSION
Understanding the variable transportation network characteristics andtravelers’ behavior are the keys to achieve an efficient route choice model.
Route choice models have played an important role in solving theunmanageable urban congestion problem. This study presents a route choicemodel consisting of two parts; the path enumeration algorithm and the route
choice formulation. The path enumeration algorithm is used for generating
the reasonable path set. Both efficiency and flexibility to transportation
network conditions are considered in the formulation of the algorithm.
Among the issues addressed in the establishment of the algorithm areexcessive link overlapping and path resource constraint.
On the other hand, a new route choice model is formulated in order toconsider the probabilistic definition of path travel time and the perceived
OD travel time. Through the Monte Carlo simulation technique, the link
travel times were generated and then the path travel time mean and varianceare calculated. From the Chi-square test, a normal distribution is selected forpath travel time distributions. Moreover, the perceived travel time of drivers
is formulated by a PDF (Erlang distribution) based on the survey data.
Taking both the path actual and the perceived travel time PDFs, path choiceprobabilities were calculated for the two case scenarios: with and without
travel time budget. The route choice model was evaluated and compared to
the True Success Rate (TSRk) and the deterministic Multinomial Logit(MNL) model.
There are several important academic contributions of this study. First,
a route choice model for the PDF of path travel time is proposed. Previously,
a discrete choice model assuming the uncertainty of path travel time has
been used. For example, a logit model assumes that an error term in theutility function follows a Gumbel distribution. Oppositely, the developedmodel does not need any assumption on the travel time uncertainty. Instead,
it can handle the travel time distribution without any manipulation.
Second, the developed model can calculate route choice probabilities
without calibration. Most route choice models have to be calibrated withreal survey data. Conversely, the developed model does not have any
coefficient to be calibrated. Even without calibration, however, thedeveloped model gives the same route choice probabilities with MNL. It is a
big advantage for practical applications.
Thirdly, the drivers’ time budget can be taken into account in the route
choice problem. Existing route choice models and route guidance systems
does not consider the arrival time demanded by drivers. In reality, however,many travelers have preferred arrival time in their travels. For example, if adriver has an appointment 50 minutes later, the driver’s reference time is 50
minutes. Hence, his request for route choice is a conditional problem inwhich he wants to find the shortest path for arriving at his destination within
50 minutes. The developed model also shows that the route choiceprobability is dependent on the reference time when the path travel time is

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uncertain. In addition, the developed model can calculate the success rate
for on time arrival with given reference time. If the methodology isembedded in the car navigation system, drivers can adjust their scheduled
appointments with the information.
Fourthly, a hierarchical multi-path enumeration method is presented.
All paths found by the model are sufficiently independent so it is useful for
applying MNL to a real-size network. The concept of building two-layer
network is also consistent with the cognitive process of human beings.
To further improve the proposed route choice model, it is suggested toextend the application to dynamic and stochastic traffic assignment problem.
It is further recommended to apply the model to integrated transportation
networks where turn prohibitions and/or multimodal conditions exist. A
mathematical approach in calculating the path utility choice function is also
recommended to eliminate the burden of Monte Carlo simulation.
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2ndDEVELOPMENT OF MULTI-CLASSPEDESTRIAN ASSIGNMENT ALGORITHM
JUNG-YUB LEE, HYUN-HO CHANG,
HYUN-SUB LIM, and YOUNG-IHN LEE
Graduate School of Environmental Studies
Seoul National University
urrruk@naver.com
ABSTRACTWalking is a fundamental means of transport for humans, and a
primary means to connect between other forms of transportation. Even
though the number of studies on pedestrians and on pedestrian demand
analysis have increased, there are few studies on pedestrian assignment.
The object of this study is to develop a pedestrian assignment algorithm thatis applicable to mega-buildings and general sidewalks, and to investigate its
application possibility. This paper comprises a literature review of vehicle
and pedestrian assignment, a section on pedestrian assignment methodology,
a section with experiment results and analysis, and closes with a conclusionand suggestions for future studies.
1. INTRODUCTION
Walking is a human's fundamental means of transport, and a primarytransport method used to connect with other forms of transportation. Most
existing studies have investigated traffic assignment in order to analyze thetraffic demands of passengers. Recently, there has been an increase in thenumber of studies on pedestrian traffic and an increase in the need forpedestrian demand analysis. However, there are few studies on pedestrianassignment within pedestrian demand analyses.
In the existing vehicle traffic assignment methods, the transportation
capacity used to calculate travel cost is fixed. However, the capacity of the
facilities that pedestrians use varies with facility type and pedestrian
demand. There are also equilibrium and non-equilibrium states that may coexist and may relate to pedestrian class. Such co-existence limits theapplication of existing traffic assignment methods that use the Node-Link
system. Accordingly, a multi-class pedestrian assignment algorithm, which
considers variation in capacity related to directional demands and mixed
directionality of pedestrians, was developed in this study.
The multi-class pedestrian assignment algorithm consideredpedestrian space characteristics and was configured using a Node-LinkBlock network system where the Block, a pedestrian space unit, has been
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developed. Furthermore, pedestrian’s preferred or enforced travel types
were included in the assignment algorithm. Using the developed NodeLink-Block network system, along with the travel cost function and thepedestrian assignment algorithm, a final pedestrian status is obtained inwhich user equilibrium or user non-equilibrium states related to eachpedestrian class can co-exist.
This paper consists of a review of the literature regarding vehicle andpedestrian assignment, a section on the development of pedestrianassignment methodology comprising five portions: 1) pedestrianassignment; 2) definition of multi class pedestrians and facilities; 3) a
description of the Node-Link-Block network system; 4) a definition of thetravel cost function; and 5) a pedestrian traffic assignment technique. This isfollowed by a section containing experiment results and analyses. The paper
ends with a conclusion and suggestions for future study.
2. LITERATURE REVIEWS
2.1 Existing Traffic Assignment
Existing studies on traffic assignment typically consider passenger
cars and/or public transit, and their objectives are to analyze traffic demand
to allow system optimization or to determine user equilibrium states. An
assumption in traffic assignment studies based on user equilibrium is that nopassenger can select another path from origin to destination (O-D) without
increasing the total cost of all vehicle passengers. A characteristic of user
equilibrium is that all travel path costs are the same for all O-D paths, andthat the cost of the selected path is less than that of the unselected path. The
purpose of system optimization is to minimize the system’s overall travel
cost rather than to optimize passenger travel cost. Study on the traffic
assignment algorithm is continuously on the progress to link base and pathbase, from static to dynamic traffic assignment.
A typical traffic assignment transportation system has specific paths
that are based on unidirectional directionality within a path. However, eachpedestrian may move with differential directionality in a pedestrian spaceand may move along an infinite number of paths. Accordingly, there are
significant limitations when applying existing Node-Link vehicle traffic
assignment systems to pedestrian assignment systems.
2.2 Pedestrian Assignment
Even though there are many local and foreign studies regarding traffic
assignment, there are very few studies on pedestrian assignment. Ahmed
Abdelghany studied an American football field stadium, a crowded facility,
using a cell unit based discrete space structure system and an A* algorithm.
However, there are no known reports on pedestrian assignment among thereviewed traditional traffic assignment studies. Accordingly, the object of

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this study is to develop a pedestrian assignment algorithm that is applicable
to mega-building and general sidewalk situations, and to investigate its
application possibilities.
3. PEDESTRIAN ASSIGNMENT METHODOLOGY
Development of a methodology for pedestrian assignment consisted of
establishing the main items of consideration based on the traditional traffic
assignment system, establishing a network system for pedestrian spaces,
establishing the criteria for multi-class pedestrians, developing a travel costfunction for specific facilities, and altering the traffic assignment algorithm
based on the aforementioned items.
3.1 Considerations for Pedestrian Assignment
There are four key limitations to applying existing assignment
methods to pedestrian traffic. Accordingly, the following items were
included during development of the pedestrian assignment algorithm.
• Pedestrians use various facilities. Therefore, facilities in thepedestrian space need to be defined.
• The capacity of the Block used in this study varies according topedestrian directionality and pedestrian demand varies by direction.
• Different from the unidirectional link system used for vehicle
traffic, a pedestrian's movement direction within the pedestrianspace varies from 1~∞.
• Pedestrians can be divided according to facility usage types, suchas those who are required to use a specific facility, those who prefer
to use it, as well as those who attempt to optimize the travel time toa specific facility.
3.2 Multi-Class Pedestrian and Facility Groups
Distinct from vehicle characteristics, pedestrians consist of a variety
of classes that may be grouped according to facility usage, as well as bysocial or biological characteristics. Pedestrian class has been divided mainly
based on types of items being carried or accompanying the pedestrian. In
addition, if there is a specific facility in a specific pedestrian space, then thepattern of use of that facility and the time to pass through that facility candiffer by pedestrian class. For example, a pedestrian with a specificaccompanying item may prefer to use an escalator rather than a stairway.
Thus, pedestrians were grouped into five classes to reflect thesecharacteristics (Table 1).

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Table 1: Multiple classes of pedestrians

There are various types of facilities in pedestrian use areas, and thepatterns of use of these facilities are different. These types were categorizedaccording to the travel cost similar to the approach used in vehicle traffic
assignment. A travel cost model was developed for each facility type toreflect these characteristics. Table 2 presents the list of facilities that was
included in the development of the pedestrian assignment.
Table 2: Facilities included in the pedestrian assignment process

3.3 Node-Link-Block System
The network system used in this study differed from the network
system used for vehicle traffic assignment in that specified size ofpedestrian using block which is called facility. Vehicles follow a fixed
direction path in the traffic assignment network system; however,pedestrians may use an infinite number of paths. Accordingly, the systemused in this study added the Block item to the Node- Link system used forvehicle traffic assignment.
Figure 1 presents the basic structure of the Node-Link-Block system
for use in multi-class pedestrian assignment, and shows the attributes used
for pedestrian assignment. There are more than two Nodes in the pedestrian
network system, and Links connect those Nodes. The number of Nodes andLinks also differ according to the Block’s characteristics. The Link indicatesthe passage within a Block along which pedestrians can move. There are
two Nodes and a single Link for some facilities, such as an escalator,
because the direction of pedestrian movement in that facility is only in one
directional.

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Figure 1: The Node-Link-Block system used in pedestrian assignment
The variables used to define the Node-Link-Block system, including
travel cost, pedestrian traffic demand, etc., are as follows:
•
•
•
•
•
•
•

•
•

� � ��� , �� , �� , �� � � �� : the pedestrian space which includes
four facility types
S� : Stairway, S� , � �s�laltor, S� : Moving Walk, S� : Passage
��,� : the b�� Block which constitutes a facility or pedestrian spaceS���,�,� : the length (m) of the line that constitutes the e�� of B�,���,�,� : a set of nodes at the e�� outline of B�,�
N: a Node set the consists of n components, N�,�,� � N
��,�,� : a Link length i which connects Node i to the Node of b�� Block,j�N� �,�,�,� : the k �� pedestrian class traffic at Node i and Node j connected
to a Link in b�� Blockk � �k� , k � , k � , k � � the pedestrian class group (see Table1)
��,�,�,� : free traffic flow speed (m/s) of the k �� pedestrian class at
Node I and Node j connected by a Link in b�� Block��,�,�,� : the travel cost of the k �� pedestrian class at Node i and Node jconnected by a Link in the b�� Block

3.4 Establish Travel Cost Function

As explained previously, the road capacity to calculate travel cost isfixed in a vehicle traffic assignment system. However, the Block capacity inpedestrian assignment varies according to the total of each possible
directional pedestrian demand in the pedestrian assignment system. To
consider the volatile characteristics of the Block capacity, the capacity anddemand of the Block at the each iteration is calculated through anincremental assignment, and the Link's travel cost is calculated using the
V/C of the Block, where the V/C of each Block iteration is calculated using
the accumulated pedestrian traffic demand in equations (1) and (2), and theDevelopment
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capacity calculation from above. Thus

(1)

(2)
(3)
•
•
•

�� : is the pedestrian demand using the b�� Block�� : is the capacity of the b�� Block��� : is the unit capacity of Block b by type (persons/minute/meter)

Pedestrian facilities were classified as in Table 2 and equation (4) wasused to determine the travel cost function in each pedestrian class according
to the facility classification.
(4)
where d� is the pedestrian demand for use of the b�� Block and α and β are
derived through pedestrian surveys. Here, the values were from survey dataobtained at the Sadang Station where subway lines 2 and 4 cross. A total of
6 h of surveying [2 h at the morning peak time (07:00~09:00), 2 h during theevening peak time (17:00~19:00), and 2 h of non-peak time (12:00~14:00)]
were used. The non-linear feedback analysis from the SPSS statistic
package, was used to estimate the α and β parameters.
Table 3: Estimates of the α and β parameter values of the travel costfunction for each facility type

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Figure 2: Travel time by facility type
3.5 Traffic Assignment Method
The pedestrian use of a Block's capacity varies according to thedirectionality of pedestrian travel and the pedestrian demand in eachdirection. One problem is that the assignment may not reach equilibrium
because of pedestrian class differences between enforced and preferred use
characteristics of the facility type being used. Therefore, applying existingvehicle traffic assignment methods to reach user equilibrium was deemed
inappropriate. Accordingly, a Block's capacity at the each iteration isrevised through an incremental assignment method. We speculated thatthose who are required to select a facility reach non-equilibrium, while
those showing a preference for a facility reach equilibrium. The variation incapacity at the each iteration is primarily related to the percentage of traffic
along the O-D multi class pedestrian path which requires minimum cost.
Based on this, the capacity is calculated according to the demands of thepedestrian's Block usage, and this capacity is used to update the travel costfunction. This takes into consideration the often marked and rapid changes
in pedestrian demand for facilities in a subway station, which in reality,
does not reach equilibrium.
Considering these disciplinary limitations and the applicability to thestudy site, an incremental pedestrian assignment method was implemented
in this study. There was little difficulty in the calculation operation because
the selected traffic assignment network was for a relatively small network.
The preference selection pedestrian class reaches equilibrium but thepedestrian assignment is made in a non-equilibrium state for mandatory orforced users of a facility.
Figure 3 shows the multi-class pedestrian assignment procedure. The
procedure performs pre-loading along the shortest initial path at a V/C=0
state using equation (5), and with the P����� initial assignment ratio fromeach class traffic between pedestrian classes origin (r) and destination (s) at
Iteration = 0. This is the initial traffic assignment stage for pedestrians who
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use the facility as a result of mandatory, forced, or preference reasons. At
the iteration >0 stage the shortest path for each pedestrian traffic is
calculated using equation (6) for each iteration.
The V/C at iteration (i), along with the demand and capacity arecalculated using the Link demand included in the Block, and the outline
length of the Block. The Link travel time at iteration (i+1) is calculatedusing the V/C at iteration (i). Equations (1) through (4) are used for these
iterations. The search for the shortest path uses C ������� and the Dijkstra
Algorithm, which reflects the Link cost by pedestrian class between each
iteration’s origin (r) and destination (s).
••••T����� : the k �� pedestrian class traffic from the r to sP����� : the k �� pedestrian assignment ratio for the initial shortest pathfrom r to s (where 0.0 ≤ P����� ≤1.0)
Initial (Iteration = 0) shortest path assignment value:(5)
Loading traffic per iteration:(6)

Figure3: The traffic assignment method used for multi-class pedestrians
4. IMPLEMENTATION RESULTS
4.1 Network and Input Data
Figure 4 presents a schematic of the network used to implement multiclass pedestrian assignment in a specified pedestrian space. The network has

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three types of facilities with the stairway and escalator forming two verticaltransport facilities. A pedestrian's vertical transport facility type selection
varies because of the pedestrian class characteristic. For example, apedestrian with accompanying children prefers the escalator to stairway. In
consideration of this variation, a pedestrian's initial shortest path selection(P����� ) was set close to actual.

Figure 4: Sample network
Table 4 Sample network facility components

The network used in this analysis consisted of 3 Zones, 14 Blocks, 16
Nodes, and 45 Links. The configuration ratio of traffic and pedestrian class
between origin and departure points for traffic assignment was randomly
selected (Table 5).
Table 5: Origin and destination information used for pedestrian assignment

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4.2 Traffic Assignment Result
Multi-class pedestrians were assigned to the sample network with the
maximum iteration number set at 60 in the incremental assignment method.
The O-D traffic demand was set to 1 h, and the fluctuation in pedestrian
demand was set to 1 min. The actual demand fluctuation, according to dataobtained from train arrivals in the subway facility, was longer than 1 minute.
Thus, the setting used in this study may be regarded as realistic.
Table 6 indicates the results for the final path travel time for a generalpedestrian and a pedestrian with a baby and travelling from origin point 1 todestination point 3.
Table 6: Traffic assignment result: path travel time for two pedestrianclasses.

The travel time using the escalator was longer than stairway for thebaby accompanied pedestrian. Regardless of the longer travel time,
restricted pedestrians with carriers or those with an accompanying child
prefer to use an escalator rather than a stairway. Furthermore, the restricted
pedestrians used the escalator more, even when there is increased pedestrian
demand for the escalator. Accordingly, the travel time for pedestrians
accompanying child does not reach equilibrium.
General pedestrians do not have any such restrictions, and they selectfacility type by preference. Accordingly, when there are many pedestrian
demands in the escalator Block, using a stairway results in the optimum
travel time since the escalator then requires more travel time. According tothese general pedestrian's characteristics, final travel time reaches
equilibrium within the measured error range, regardless of the presence ofan escalator.
Consequently, a pedestrian of a specific class may or may not reachtravel time equilibrium. User equilibrium or non-equilibrium status isrelated to pedestrian class and thus, the overall network includes a nonequilibrium system.
5. CONCLUSION AND FUTURE STUDY PROJECTS
5.1 Conclusion
Since Block capacity varies according to directional pedestrian
demands, the equation used to calculate capacity must consider such volatile

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characteristics. For parameter traffic assignment, the α and β parameters of
the travel cost function in each facility are estimated using a non-linear
feedback method. According to the pedestrian class characteristic, the travel
time for restricted pedestrians, such as those with carriers or with anaccompanying child, does not reach equilibrium along any of the available
paths. However, for general pedestrians that are without facility selection
restriction, travel time will reach equilibrium along each available path.
5.2 Future Study Projects
This study introduced an assignment algorithm for multi-classpedestrians in a specified pedestrian space. It is expected that pedestrian
traffic assignment may be possible following the analysis of pedestrian
traffic surveys in actual pedestrian spaces. In this study, the travel costfunction's α and β parameters were decided by facility type. It is expectedthat higher accuracy could be achieved by reflecting survey based estimatesof the α and β parameters by pedestrian class.
ACKNOWLEDGEMENTS
This South Korea national transport research project was sponsored by
the Korean Ministry of Land, Transport and Maritime Affairs. The authors
thank the members of the Korea Ministry of Land, Transport and Maritime
Affairs for that support.
REFERENCES
Ahmed Abdelghany, Khaled Abdelghany, Hani S. Mahmassani and Saad A.
Al-gadhi(2005)
Microsimulation
Assignment
Model
for
Multidirectional Pedestrian Movement in Congested Facilities:
Transportation Research Record pp.123-132
Srinivas Peeta and Hani S. Mahmassani(1995). Multiple User Classes Realtime Traffic Assignment for Online Operations: A Rolling Horizon
Solution Framework, Transportation Research Part B
Lee Jun, Lim KangWon, Lee YoungIn, Lim YongTaek(2004) Developmentof Multi-Class Assignment For Dynamic path Guidance Strategy,
Korean Society of Transportation Journal, Vol. 22-7, Korean Societyof Transportation pp. 91-98
Lim YongTaek(2002), Multiple User Class Traffic Assignment based on
Variational Inequality Formulation in Variable demands , Korean
Society of Transportation Journal, Vol. 20-5, Korean Society ofTransportation, pp. 153-161

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TO NETWORK DESIGN PROBLEMSHYUNMYUNG KIM1 and TAWIN TIRATANAPAKHOM2Transportation Engineering
School of Engineering and Technology
Asian Institute of Technology, Thailand
1
khclsy@gmail.com, 2 tawinhkt@gmail.com
ABSTRACTIn this study, the effect of demand uncertainty in the transportation
system is investigated with network design problems. A network design
problem (NDP) finds a solution for the best network improvement withmaking use of optimization techniques. It provides important information
for building up the best investment plan with a given budget constraint.
Therefore, it has been a core topic in transportation planning. However,existing frameworks for NDP have some limitations. Among various
shortcomings, the most important flaw is that the uncertainty existing in thereal transportation system has been ignored and all attributes included in
the model are considered in a deterministic way. In this paper, one of theuncertainties, the probabilistic property of travel demand is incorporated
with NDP. In order to deal with the probabilistic travel demand, Monte
Carlo simulation technique is employed and new probabilistic definitions of
OD and link flows are also given. The main academic contributions of thisstudy are following three. 1) Traffic congestion is defined in terms of aprobability concept, 2) A new objective function for NDP in the stochastic
network analysis is developed, and 3) The probabilistic version of Braess’paradox is found.
1. INTRODUCTION
A network design problem (NDP) is a popular research topic intransportation planning. Starting from building a new road to find the
optimal congestion toll, it has provided valuable information with decision
makers. Nonetheless, the existing methodologies for transportation network
design have some limitations. First, most of them do not consider theuncertainty existing in the transportation system. A transportation system
consists of two parts, the supply side and the demand side of the system. In
reality, the attributes of the two transportation system components havevariations everyday. For example, the travel demand realized in the network
has a daily fluctuation. Kim (2008) explained the reason of the daily
demand fluctuation from an activity-based perspective. Even in the morning
peak in which daily mandatory trips take more than 50% out of a whole
travels, the number of total travels and the departure profiles of them are

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ever-changing. Moreover, the supply side of transportation system also has
a variation. The representative example is the drop of link capacity becauseof rainfall. In a rainy day, the surface of road is slippery and it makes
running vehicles slow down (Lam et al., 2008). Among the both, a demandvariation is more important factor for explaining uncertainty in thetransportation network. The variation of link flow, density, and travel speed
mainly comes from the daily change of incoming travel demand to the
network.
Many researches have realized the importance of demand variation fortransportation planning problems (Waller et al., 2001; Chen et al., 2003;
Clark and Watling, 2005; Sumalee et al, 2006; Siu and Lo, 2007; Ukkusuri
et al., 2007). They have assumed that travel demand has a probabilistic
property and proposed several methodologies in order to deal with thestochastic behavior of network. However, most of the studies do not comeup with a comprehensive framework for analyzing the stochastic network
problem. Most transportation planning studies still regard that network
attributes such as link flows and travel times as deterministic values in theanalysis. The elements in the OD table are still defined as deterministic
values. In addition, no proper objective function for NDP in the stochastic
case has been developed.
In this study, the authors develop a new comprehensive framework fora stochastic network analysis. A new definition for probabilistic traffic
congestion is proposed by means of a probabilistic link flow distribution. In
order to estimate the probabilistic distribution of link flows, Monte Carlotechnique is used. After then, a new objective function which can consider
the probabilistic properties of link flows is proposed for a stochastic NDP.
The capability of the developed framework is investigated with test
networks. In addition, a probabilistic Braess’ paradox is also found. The
static version of the paradox is well-known to the transportation field but theprobabilistic version of it is newly identified in this paper.
2. HISTORICAL REVIEW
2.1 Stochastic traffic assignment
The stochastic traffic assignment so far has been paid attention to theuncertainty of travelers’ behavior such as the perception error of travel time.
One of the stochastic techniques that play a major role in traffic assignment
model is the stochastic user equilibrium (SUE). In the SUE, travelers choose
their route based on their perceived travel time. This travel time isconsidered as a random value. In addition, unlike the deterministic user
equilibrium (DUE), the assumption, that travelers have perfect information
about travel time and they make correct decisions regarding route choice,can be relaxed (Sheffi, 1985). To load the OD demand onto a transportation
network, the discrete choice model associated with a random utility is
generally adopted for the SUE basic. Generally, there are two different
forms of discrete choice model: logit model and probit model. The logit

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model assumes that the random components of travel utility are independentwhile the probit model considers the joint density function of these random
components. By using the stochastic network loading mechanism (Sheffi,1985) associated with either logit or probit model, the assignment under
SUE can be achieved.
2.2 Network design problem
Network design problem (NDP) plays a major role in transportation
planning and decision making processes. The solutions of NDP can tell us
which segments of the transportation network can be optimally improved or
added (Abdulaal and LeBlanc, 1979). From Bell and Iida (1997), network
design problem can be considered into two forms: the continuous network
design problem and the discrete network design problem. Given network
topology, the continuous network design, taking link parameters such as
capacity and user charge into account, generally considers the trade off
between user benefit and costs of introducing network improvement. For
example, the continuous network design problem is used to determine theimprovement of a link such as capacity expansion by adding new lanes into
the network. On the other hand, the discrete network design problem
considers the topology of the network into such trade off. For example, thediscrete network design problem is used to find the addition or removal of alink in the network. Therefore, the problem of transportation network design
is defined as to select which links in the network should be improved or
added in order to maximize social welfare while accounting for the route
choice behavior of network users (Suwansirikul et al., 1987) or in order tominimize total network cost subject to a budget constraint while the flows
satisfy the user equilibrium (UE) condition (Ukkusuri et al., 2007). In
addition to the deterministic user equilibrium (DUE), other objectivefunctions such as the nonlinear objective functions whose solutions satisfy
the system optimal (SO) condition and the linear objective functions are also
exist in NDP (Abdulaal and LeBlanc, 1979; Chen and Alfa, 1991).
Furthermore, the SUE discussed in previous section can also be applied to
NDP as presented by Chen and Alfa (1991). They stated that considering the
SUE is more realistic than the DUE but it requires more computational
effort. In their study, the heuristic approach was adopted to solve the NDP
with logit based SUE problem.
2.3 Uncertainty in the network analysis
The uncertainty on the road network comes from various reasons suchas uncertainty in OD demand, link capacity, travel time, etc. In this study,
the uncertainties are categorized into three groups in terms of the source ofthem; supply uncertainty, demand uncertainty and travel time uncertainty.
2.3.1 Uncertainty in supply side
Supply uncertainty comes from several uncertain disturbances on theroad such as accident, road work, illegal parking, rainfall, etc. In addition,such phenomena affect a driving condition and cause a variation in the roadcapacity (Lam et al., 2008). For example, road capacity may degrade in a
The Application of Stochastic Assignment Technique to Network Design Problems
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rainy day because of poor driving conditions such as visibility and pavement
friction. Link capacity degradation would also happen as a result of somemajor and minor events and incidents (Siu and Lo, 2007). On one hand,
major events such as earthquakes cause a network connectivity problem. On
the other hand, some minor events such as accident, vehicle failure cause
stochastic link capacity degradation.
Likewise, Chen et al. (2002) considered such events as the sources of
network disruption. Recurrent and non-recurrent disturbance are used torefer these events. For example, congestion is referred as recurrent
disturbance since it could repeat almost everyday. On the other hand, nonrecurrent disturbance can be further classified into two types; an abnormal
and a normal failure. An abnormal failure such as earthquake causes systemdamage whereas a normal failure such as a traffic accident does not involve
system damage. In addition, Lam et al. (2008) classified the sources of roadcapacity uncertainty into two categories, predictable and less-predictable. In
their study, events such as a poor weather condition or a road work areconsidered as a predictable source. On the other hands, a road accident or avehicle breakdown is regarded as a less-predictable source.
2.3.2 Uncertainty in demand side
Recently, demand uncertainty has become fashionable intransportation planning. For example, Chen et al. (2003) developed a meanvariance model for finding the optimal toll and road capacity of the buildoperate-transfer roadway project by considering the uncertainty of demand
for travel. Ukkusuri et al. (2007) introduced a robust network design
problem (RNDP) which takes demand uncertainty into account. According
to Sumalee et al. (2006), travel demand variation mainly results fromtravelers’ behavior uncertainty and day-to-day variation in activity patterns.
Lam et al. (2008) proposed a new traffic assignment model which canconsider the impact of weather condition with uncertainty in both supply
and demand.
Travelers’ Behavior Uncertainty
Uncertainty from travelers’ behavior such as route choice variability
plays an important role in demand uncertainty (Sumalee et al., 2006). For
example, Asakura and Hato (2001) addressed that in degraded network, atraveler’s knowledge of the network influences his or her route choice
behavior. In their study, when network is degraded, non-informed travelers
will randomly recognize whether a link will be closed or not. He or she may
change routes several times or may not continue to finish their trips aftersome wasted trials.
Day-to-Day Variation
Daily variations in activity patterns are one of the main sources of
uncertainty in travel demand (Clark and Watling, 2005; Kim, 2008). For
example, Siu and Lo (2007) classified travel demand into two main groups:
commuters and infrequent travelers. They assumed that the traffic flows ofcommuters are stable since they travel regularly on the same OD pair.
Conversely, infrequent travelers irregularly make their trips, so their traffic

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flows are subject to higher uncertainty. Kim (2008) explained the propertiesof travel demand variation over time of day from an activity perspective. As
researched by Lam et al. (2008), weather also can be an important factor forthe variability of day-to-day OD travel demand. For example, to avoid apossible congestion during raining, travelers may change their plans totravel after receiving the weather forecast information.
2.3.3 Uncertainty in travel time
Uncertainty in travel time results from the interaction between supply
and demand variations of transportation system. The mean and standard
deviation of OD travel time increase as a result of higher travel demand(Chen et al., 2002). Lo and Tung (2003) considered the variation of daily
travel time caused by minor traffic incidents. In addition, they also showedthat in heavy traffic, travel time variability is higher than that in light traffic.
Travel time variation or travel time reliability influences travelers’ route
choice behavior (Siu and Lo, 2007; Lee et al., 2000, Kim, 2008). Hence,
travel time uncertainty can also influence the travel demand. As stated by
Lee et al. (2000), travelers prefer the path on which the travel time variation
is minimized. Furthermore, Lo and Tung (2003) introduced probabilistic
user equilibrium (PUE) to characterize route choice behavior in the face of
uncertainty travel time. They believed that travelers settle into a long-term
equilibrium pattern after they learn and experience their trips. Kim (2008)
introduced the concept of travel time uncertainty into the generation ofactivity schedule. In his study, the reliability of travel time is defined as aprobability that an OD travel time is less than a reference value. In addition,
he found that the variation of travel time is proportional to the length oftravel and the level of congestion.
3. MODELING FRAMEWORK
3.1 Modeling method of demand variation
A stochastic OD table is proposed to capture the variation of OD
travel demand. For a given OD pair, the number of trips is regarded as arandom variable. In the previous studies (Hazelton, 2003), the same
assumption was used and a normal distribution was proved as the mostprevalent probability distribution for modeling a variation in travel demand(Chen et al., 2003; Lam et al., 2008). Other probability distributions such as
Poisson distribution (Hazelton, 2003; Sumalee et al., 2006), uniform
distribution (Ukkusuri et al., 2007) also have been used for modeling ademand variation. Furthermore, Lam et al. (2008) assumed that the standard
deviation of OD travel demand is an increasing function with respect to themean OD demand. Hazelton (2003) proposed the mean-variancerelationship modeled by Var(Qrs) = αE[Qrs]β where Qrs represents thenumber of random day-to-day OD trips, and α, β are positive parameters(see Hazelton, 2003). Ukkusuri et al. (2007) and Waller et al. (2001)
assumed that OD demand varies uniformly around mean value. Chen et al.
(2003) used a half of expected OD demand as a standard deviation. In this
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study, a stochastic OD table is assumed to consist of random elements
following a normal distribution.

N (q rs , σ rs2 )(1)

where, qrs is a mean of travel demand between OD pair rs and σ rs2 is avariance of travel demand between OD pair rs. The OD table, as shown in
Table 1, is called a stochastic OD table. Note that the correlations among
OD flows are ignored when generating a random OD table. In reality, some
OD pairs have correlations because they share a common origin or
destination. However, no information regarding the correlations is available
for the hypothetical networks used in this study, so all OD flows are
assumed independent.
Table 1: A general form of a stochastic OD table
Destination
Origin 1

2

3

…

…z

1

2
N (q11 , σ 11)

2
N (q12 , σ 12)

2
N (q13 , σ 13)…

… N (q1z , σ 12z )22
N (q 21 , σ 21)

2
N (q 22 , σ 22)

2
N (q 23 , σ 23)…

… N (q 2 z , σ 22z )3

2
N (q 31 , σ 31)

2
N (q 32 , σ 32)

2
N (q 33 , σ 33)…

… N (q 3 z , σ 32z )N (q i1 , σ i21 )N (q i 2 , σ i22 )

N (q i 3 , σ i23 )…

… N (q iz , σ iz2 )N (q1z , σ 12z )N (q 2 z , σ 22z )N (q1z , σ 12z )…

… N (q zz , σ zz2 )

#

i
#

z

3.2 Stochastic traffic assignment by Monte Carlo technique

To simulate the demand uncertainty, Monte Carlo technique is used
for producing the number of travels in each element of OD table. For
generating a random variable with assumed probability distribution, Monte
Carlo simulation has been used as a sampling technique (Sumalee et al.,2006; Waller et al., 2001; Clark and Watling, 2005). In addition, othersampling techniques also can be employed. For example, Chen et al. (2003)
used Latin hypercube sampling (LHS) technique to simulate demand
uncertainty. To generate multiple OD tables, the mean and standard
deviation of each OD demand are assumed to be known. In this paper, 30%
of OD flow volume is assumed as a standard deviation.
Figure 1 shows the framework of traffic assignment used in this study.
From the given stochastic OD table, multiple OD tables are generated
randomly and loaded onto the network. In the tests, the number OD table isset as 30. As a result, each link has 30 OD flow samples. In this study, astandard traffic assignment method, UE (user equilibrium) model is used for
the OD flow loading with BPR (U.S. Bureau of Public Roads) function is

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used as a link performance function. Finally, a probability distribution oflink flow is estimated from the sample link flow set.
In the study, a link flow is assumed as a random variable. Accordingly, alink flow distribution can be estimated from the link flow samples. For the
purpose, the histogram of sample link flows should be constructed. Afterthen, a best-fit distribution would be found by a Chi-square (χ2) test. From
Blank (1980), the general form of hypothesis for the test is shown as follows.
H0: sample is from a specified distribution
H1: sample is not from a specified distribution
A goodness-of-fit test is a test of H0 versus H1. The test can be performed bymeasuring the relative difference between observed (Oi) and expected (Ei)frequency values as shown in Eq. (2).
(Oi − Ei ) 2
Ei
i −1
k

χ2 = ∑(2)

where (O1, O2,…, Ok) are observed frequencies and (E1, E2,…, Ek) are
expected or theoretical frequencies from assumed distribution.
The frequency values, Ei, are:Ei = nPi(3)

where n is the sample size and Pi is the probability from the assumed
distribution.
Moreover, to test the fit by determining the acceptance region, thedegree-of-freedom parameter (4) of the χ2 probability distribution function(PDF) must be determined.
v = k − r −1

(4)

where r is the number of parameters of the assumed distribution. The
acceptance region for H0 is area to the left side of the Chi-square value (C1-α,2v) or the CDF at 1-α. That is, if χ < C1-α, v, then the assumed theoretical
distribution is acceptable with the significant level, α. For this experiment,
the significant level, α, is set to 5 percents and the best fit distribution
provides the lowest value of χ2.

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OD flow

Daily variation of
OD travel demand

Day

Assumed a normal
Assumed a normal
distribution
distribution
frequency

Simplified model by aprobability distribution

OD flow

Monte Carlo simulation techniqueMonte Carlo simulation techniqueSampling OD tables
Sampling OD tables
OD1
OD1

OD2
OD2

OD3
OD3

….
….

ODn
ODn

Traffic assignment
Traffic assignment
x1
x1

x2
x2

x3
x3

….
….

xn
xn

Link flow
frequency

Probability
distribution

Link flow
distribution

Figure 1: Monte Carlo simulation framework for link flow distribution
3.3 Probabilistic definition of traffic congestion
In the developed framework, a new definition for traffic congestion
arises. As shown in Figure 2, each link has the distribution of flow as aprobability density function. In the conventional traffic assignment, a linktraffic flow is defined as a deterministic value and the level of congestion
can be assessed by comparing the flows to the deterministic link capacity(ca). However, in the stochastic assignment, link flow has randomness, so
comparing the average flow to capacity is insufficient for assessing the levelof congestion. Therefore, the probability of congestion at the link a isintroduced and denoted as P(xa>ca).

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P(xa>ca)xa

Ca

Link Flow (Xa)

Figure 2: The probability of traffic congestion
For a conventional definition of traffic congestion, v/c ratio (xa/ca) has
been used prevalently. In this case, xa is the average link flow. Accordingly,
the conventional method only uses the average link traffic for evaluating thelevel of congestion on the link. However, in the stochastic traffic assignment,
not only an average link flow rate, but also a link flow variance should betaken into account. The new definition for traffic congestion can capture theprobabilistic property of link flow. The probabilistic definition of traffic
congestion is useful for explaining the behavior of real traffic congestion. In
the real world, we have different levels and locations of traffic congestion.
The fluctuations in the supply and demand side of transportation system
makes very complex demand-supply interactions on the road network.
However, if an average OD table is used with deterministic network
attributes, the fluctuation of traffic congestion cannot be explained. In
addition, the v/c ratio (xa/ca) cannot explain the feature of probabilistic
traffic congestion comprehensively.
Figure 3 shows the difference in the definition of traffic congestion
between probabilistic and deterministic cases. If the variances of flows on
the two links are the same, then v/c is a complete measure for evaluating acongestion level. However, if two link flows have the difference magnitudes
of variances, the level of congestion cannot be compared in terms of v/c. In
the figure, the path 1 has a higher average flow than the path 2 but theprobability of congestion of the link 1 is smaller than that of the link 2.
Note that the probability of congestion is a more user-oriented
measure. Let’s assume that there are two daily commuters who use thegiven two paths, respectively. In that case, the commuter on the path 2
would experience traffic congestion more frequently than the commuter on
the path 1. However, the v/c ratio cannot reflect the reality and expects thepath 1 is more congestion-prone. Considering a current traffic surveillancesystem, the calculation of v/c is easier than that of congestion probability
because the variance of flows requires traffic surveillance for a sufficient
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number of days. This is a reason why v/c has been preferred to date.
However, the consideration of congestion probability should be included intransportation planning and engineering because it show the actual traffic
situation with which drivers face in reality.

v/c=1

Path 1

Path 2Pr( x2 > c2 )x 2avex1ave

Pr( x1 > c1 )Figure 3: Deterministic and probabilistic definitions of traffic congestion
3.4 Formulation of objective function for a stochastic NDP
As mentioned above, in the conventional NDP, the impact of new
network design is evaluated by means of the average link flows and theaverage link travel time. However, in the real world, since travel demandrandomly changes consistently, the flow on each link cannot be adeterministic value but a random value. In this stochastic case, how can weevaluate the performance of new network design? How can we minimize therandom traffic congestion with the network design change?
In order to improve the performance of network under uncertainty, a
new objective function for a NDP is proposed. The concept of new objectivefunction for a stochastic NDP is to determine the additional capacity of eachlink which can be improved so that the probability of congestion in thesystem is minimized. At first, the probability of congestion on the link ashould be defined in terms of the CDF of normal distribution.
⎛ c a − x a* ⎞⎟P ( x a > c a ) = 1.0 − Φ⎜⎜
⎟
σ
a
⎝
⎠

(5)

where, Φ ( ) is the cumulative distribution function (CDF) of normal
distribution. x a* and σa are the average and standard deviation of link flowon the link a.

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After then, the probability of congestion in the system representing thewhole network’s congestion situation can be defined mathematically by
adopting the concept of weighted average. That is, in order to reflect the
average probability of congestion for a whole network, the probability ofcongestion of all links need to be taken into account. In addition, since eachlink has different amount of flows, the average link flow ( x a* ) is taken into
account as a weighting parameter to the probability of congestion. For
finding the optimum capacity improvement, as stated above, themathematical optimization program is established as presented in Eq. (6).

⎡⎛ (c a + y a ) − x a* ⎞⎤*⎡⎛ (c + y a ) ⎞ ⎤⎜⎟⎥ + ∑ x a* ⎢1.0 − Φ⎜⎜ a
⎟⎟⎥
1
.
0
x
−
Φ
⎢
∑
a
⎜
⎟
σa
a∈P
⎢
⎝ σa
⎠⎦
⎝
⎠⎦⎥ a∈E ⎣⎣min Z ( y a ) =
*
∑ xa
a

(6)

where, y a is additional capacity for improvement, P is the set of links
proposed for improvement, and E is the set of remaining links (noimprovement). In this regard, y a is a decision variable for link a.
3.5 Solution algorithm for NDP

To solve the above objective function, several global optimum
searching methods are available nowadays. In this paper, the Hooke andJeeves’ method is adopted since it is simple and sufficient for theexperiments.
Hooke and Jeeves’ Method

Hooke and Jeeves algorithm consists of two main steps: exploratory
move and pattern move, running repeatedly in order to search for optimum
objective value. Briefly, first, exploratory move searches the better objectivefunction value along the decision variable axes either positive or negative
direction with a fixed step size. Second, pattern move searches the better
objective function value by stepping off from the best point of exploratory
move. This is in the trend of optimizing the objective function by using theextrapolation technique with a fixed step size. Then, this point is considered
as a start point for the next exploratory move. Following the steps discussed
in Abdulaal and LeBlanc (1979), the overview of Hooke and Jeevesalgorithms is dicussed as follows.
Step 1: Initialization: Choose a starting point ( y10 , y 20 ,..., y i0 ) , andinitial step size (Δ0i ) which is equal to a current decision
value multiplied by step size reduction factor.
Step 2: Exploratory move:
2.1 Move to positive direction with a step
length ( y ik +1 = y ik + Δki ) .
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Evaluate objective function value. If Z ( y ik +1 ) < Z ( y ik ) ,go to step 3. Otherwise go to step 2.3.
2.3 Move to negative direction ( y ik +1 = y ik − Δki ) and thenevaluate objective value like in step 2.2. If the result is
successful, go to step 3. Otherwise, go to step 2.4.
2.4 Go back to origin point and reduce the step size by
reduction factor. After that start step 2.1 again.
Step 3: Pattern move:
3.1 Set y ik = y ik +1
2.2

3.2
3.3

Extrapolation by y ik +1 = 2 y ik − y ik −1 and evaluate theobjective function value. If success, go to step 3.
Otherwise, go back to origin like step 2.4.
If step size is satisfied the convergence criteria, stop.
Otherwise, reduce the step size by reduction factor and
go to step 2.

The solution here is the additional capacity which will be added to thelinks proposed for improvement. After improvement, the link’s
characteristic has been changed. Accordingly, the equilibrium of the system
is replaced. Then, the traffic assignment problem needs to be conducted forfinding the new equilibrium of the network again. Consequently, the solving
procedure returns to find the new global optimum over again. This solving
procedure conducts iterative process between global searching method andtraffic assignment problem until satisfying the convergence criteria.
4. SIMULATIONASSIGNMENTRESULTSOF

STOCHASTIC

TRAFFIC

4.1 Test network 1

Nguyen-Dupius network (Ukkusuri et al., 2007) is selected as the firsttest network. It has 13 nodes and 19 links and is shown in Figure 4. There
are four OD pairs: 1→2, 1→3, 4→2 and 4→3. The outcome of stochastic
traffic assignment is the probabilistic flow distributions of links. In Monte
Carlo simulation, 50 samples of link traffic flows are generated for each link.
From the samples, a probabilistic distribution of link flow is estimated.

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5.25 km.
( 35 kph, 2200 vph)1
4.08 km.
1( 35 kph, 2200 vph)

4

5

3

7.00 km.
( 35 kph, 2200 vph) 4

6181.75 km.
( 35 kph, 2200 vph)5.25 km.
( 35 kph, 2200 vph)1225.25 km.

5.83 km.
( 35 kph, 2200 vph)9125.25 km.

( 35 kph, 2200 vph)2.91 km.

2.92 km.

( 35 kph, 2200 vph)

( 35 kph, 2200 vph)

( 35 kph, 2200 vph)

10103.50 km.
(35 kph, 2200 vph)
14

8

9

5.25 km.
( 35 kph, 2200 vph)5.25 km.

11( 35 kph, 2200 vph)5.25 km.

11( 35 kph, 2200 vph)1524.67 km.

16

13( 35 kph, 2200 vph)777.58 km.

8( 35 kph, 2200 vph)

8.17 km.

( 35 kph, 2200 vph)

6

5

17

4.08 km.

( 35 kph, 2200 vph)6.42 km.

13( 35 kph, 2200 vph)
19

3

Figure 4: Nguyen-Dupius Network
In order to determine the appropriate distribution of link flows, threeplausible candidates for the probability distribution: normal, log-normal,
and Gamma distribution, are selected. After then, each distribution is tested
by means of the Chi-square goodness-of-fit.
60
Area of acceptance (Chi-square)
Gamma
Log-Normal
Normal50

Fitness value

40
30
20
10
0
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

Link

Figure 5: Comparison of the results from the Chi-Square Goodness-of-Fit
test
According to Figure 5, a normal distribution is selected as the mostappropriate distribution. At the all links except link 8 and 17, a normal
distribution is accepted. There are twelve links showing that a normal
distribution is superior to other two distributions. In summary, 63% of thebest fit cases in this network are achieved by a normal distribution. In the
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Chi-square test, a normal distribution is accepted at the 89% of links. The
probability of congestion, herein, is defined as the probability that traffic
flow exceeds capacity on the particular link. Figure 6 shows the relationshipbetween the probability of congestion and v/c. As illustrated in the figure,there are two independent tendencies. Up to 0.6 v/c level, the probability ofcongestion is nearly zero, so v/c and congestion probability have nocorrelation up to the level. Beyond 0.6 v/c level, however, the probability ofcongestion steeply goes up with the increase of v/c. This figure implies there
is a critical v/c ratio in order to keep the probability of congestion low.
1.20
y = 1.2201x - 0.7397

Prob. of congestion

1.00

2

R = 0.9618

0.80
0.60
0.40
0.20
0.00
0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

v/c ratio

Figure 6: v/c ratio and probability of congestion for Nguyen-Dupius
Network
4.2 Test network 2
In order to test the developed model under more general condition,
Sioux Falls network shown in Figure 4 is selected. It consists of 24 nodes,
76 links, and 552 OD pairs. The network characteristics and OD demands
follows Suwansirikul et al. (1987). In order to find the best probability
distribution for link flows, a normal and a log-normal distribution are tested.
The results of test are summarized in Figure 8 in which both distributions
are accepted for all links beside the link 28 and 48. In addition, a log-normal
distribution is rejected at the link 21 and 59. In summary, the results
demonstrate that most of the best fits (i.e. lowest of fitness value) areachieved with a normal distribution which is accepted for 97 percents ofnumber of links.

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3.40 km.
(56.67 kph, 25900.20 vph)12.30 km.
(57.50 kph,

2

1
2

4

1.80 km.
523403.50 vph)

(45 kph,17110.50 vph)831.00 km.

1.50 km.

(50 kph, 17782.80 vph)(37.50 kph, 4948 vph)

11

4
9

6
2.40 km.

37

1.10 km.
(22 kph,10(60 kph,35

23403.50 vph)

31

(25 kph,(13.33 kph,
13915.80 vph)

33

12

36

11

2 km.

24

25

2 km.

4908.8 vph)

10000 vph)34(22.50 kph,

4313512 vph)4876.50 vph)0.90 km.

714924.80 vph)

(25 kph, 5000 vph)

0.60 km.

(25 kph, 73(30 kph,

74

76

66
75

45 1.10 km.
(36.67 kph, 15650.8 vph)
1.20 km.

59(40 kph,(60 kph,540.90 km.

1850
0.40 km.

52 (20 kph,

23403.50 vph)

(30kph,19679.90vph)

5229.90 vph)

56

69(62.50 kph,

68

64

60

1.10 km.

61

63
62

21

1 km.

23403.50 vph)

195002.60 vph)

(28 kph, 5075.70 vph)65

2.50 km.

(27.50 kph,

10315 vph)(33.30 kph, 4885.40 vph)(37.50 kph, 5091.30 vph)

1.20 km.

(24 kph, 5045.80 vph)55

58

1.40 km.

5229.90 vph)24

1.50 km. 39

2270

0.50 km.
5078.50 vph)

67

72

23

16

7
18

57

46

1 km.

(22.50 kph, 4220
47 1.20 km.

(30 kph, 53

15

44

17

8

17

4824 vph)(22 kph, 5127.50 vph)
41

14

49

51

(36.67 kph, 7841.80 vph)

0.60 km.

1.10 km.

38(27.50 kph,

(20 kph, 4993.50 vph)(18.33 kph,40

29

1.60 km. 30

1.10 km.

0.90 km.

1.10 km.
19

1.10 km. 22

5133.50 vph)28

(45 kph, 4898.60 vph)(27.50 kph, 5133.50 vph)48
1.10 km.

26

10(66.67 kph,

13

(19 kph, 5050.20 vph)21

90.8 km.

32(16 kph,37

23(42 kph, 4958.20 vph)0.90 km.

16

1.90 km.

27(33.30 kph,25900.20 vph)130.40 km.

4908.80 vph)

6

1210000 vph)

1.50 km.

15

52.10 km.
1420

1.30 km.

(21.67 kph, 5059.90 vph)

Figure 7 Test network 1
18

Area of acceptance (Chi-square)

16

Normal

Log-Normal

14
Fitness value

12
10
8
6
4
2
0
1

11

21

31

41

51

61

71

Link

Figure 8 Comparison of the Results from the Chi-Square Goodness-of-Fit
Test

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As shown in Figure 9, v/c and congestion probability have a
proportional relationship when v/c is near or beyond capacity range.
However, the two values might be inconsistent each other. In order toexamine it, the probability distribution of link flow is plotted against the v/cratio of link as shown in Figure 9. The link 26 and the link 51 are selected
for illustration. The v/c ratios of link 26 and link 51 are 0.9234 and 0.8603
respectively, so the link 26 shows the higher congestion level. However, thearea of PDF curve beyond capacity (i.e., v/c>1.0) for the link 51 is larger
than that for the link 26. That is, the link 51 is more likely to have traffic
congestion than the link 26. The probability of congestion for the link 51
and the link 26 are 0.1599 and 0.1197, respectively. This example provesthat the probability of traffic congestion and v/c are different measures. As
explained, drivers on the link 51 will undergo traffic congestion more
frequently than drivers on the link 26. Therefore, the consideration ofcongestion probability is more appropriate and user-oriented measure wheninvestigating a traffic congestion problem.
7
6

PDF

5
4
3

Probability of congestion

2
1
0
0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

v/c ratio
Link26

Link51v=cFigure 9: v/c ratio and the probability of congestion
5. NDP IN STOCHASTIC TRAFFIC ASSIGNMENTIn this chapter, the features of the new traffic assignment model areinvestigated. In order to consider the probabilistic property of link flow, anew objective function for NDP is proposed as shown in Eq. (6). The
comparison between a conventional objective function (i.e., total network
travel time, TNTT) and the Eq. (6) will be given.

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5.1 Test network 1: Harker-Friez (HF) Network
As shown in Figure 10, the HF network consists of 6 nodes and 16
links and there are two OD pairs; from the node 1 to the node 6 with 15 flow
units and from the node 6 to the node 1 with 30 flow units. In thisexperiment, three test cases were conducted. First, the conventional NDP
with a deterministic traffic assignment is denoted as “Case 1.” Theconventional NDP means the optimal network design is calculated in termsof the minimization of TNTT. In the Case 1, the average OD flows of themultiple OD tables used for the Case 2 and 3 is used. The number of OD
tables is 30. Second, the conventional NDP with the new stochastic
assignment technique is denoted as “Case 2.” For the purpose, multiple
traffic assignments are executed and the average link flow value is used forminimizing the TNTT. It means that the average TNTT is minimized in theCase 2. Third, the objective function (6) with the new stochastic assignment
technique is named as “Case 3.”
2 km.
(60 kph, 45 vph)
1 km.
(60 kph, 3 vph) 1

1
2 km.
(60 kph, 10 vph)

2

9

4

2
5

3 km.

(60 kph, 9 vph)

4

3

4 km.

1 km.

(60 kph, 1 vph)2 km.
7(60 kph, 2 vph)

4 km.

(60 kph, 2 vph)

13

10(60 kph, 44 vph)

(60 kph, 4 vph)

6
2 km.

14
12

3
8

11

3 km.

(60 kph, 3 vph)
(60 kph, 20 vph)

4 km.
(60 kph, 6 vph)

15 (60 kph, 1 vph)

9 km.

(60 kph, 3 vph)

6

5 km.

5 km.

5

16

6 km.
(60 kph, 4.5 vph)

1 km.
(60 kph, 10 vph)

Figure 10: Harker-Friez (HF) network
The scenario in this experiment is that all links in this network canhave additional capacity at the same time and the maximum capacity to be
added for each link is assumed as 5 flow units. In order to find the optimal
capacity improvement, Hooke and Jeeves algorithm is employed. For
simplicity, no budget constraint is considered. The results of this experimentare depicted in Figure 11.

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4.5

Added capacity

4
3.5
3
2.5
2
1.5
1
0.5
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Link
Conventional NDP with a deterministic OD table

Conventional NDP with a stochastic OD table

Proposed NDP with a stochastic OD table

Figure 11: The comparison of the three cases
The first interesting finding is that the optimal increases of links are
significantly different among the cases. For example, the capacity of link 7
is fully increased in the Case 2, but other cases do not add capacity. At thelink 5, the link capacity is increased in the Case 1 and 2, but there is no
capacity increase in the Case 3. It means that the extra capacity on the link 5
can decrease TNTT, but would increase or at least cannot decrease theprobability of traffic congestion.
Table 2 Comparison of values of system travel time (TSTT) and probabilityof congestion in the network (PCN) between Case 1, 2 and 3
TNTT
PCN
Case
Before1
After1
BeforeAfter412.52114.7055
Case 1(796.8341)(21.57211)(0.61871)(0.57751)
796.834
21.2802
0.61870.5702
Case 2796.834
71.7505
0.6187
0.4813
Case 3
1

Note: Originally, the PCN of Case 1 cannot be calculated because it executes a single
assignment. The above number in the cell shows it. For comparison, the TNTTand PCN value for the Case 1 is calculated with the multiple OD tables used in theCase 2 and 3. It is shown in the parenthesis.

Furthermore, the values of TNTT and the probability of congestion in
the network (PCN) in each case were compared in Table 2. According to thetable, the TNTT of all cases are substantially reduced after the improvement.
In term of improved TNTT, the Case 2 provides the best solution. On theother hand, if solely the PCN is taken into account, Case 3 gives the bestsolution. In addition, as observed from this table, the improved TNTTs of
Case 1 and Case 2 are very similar. Note that the objective function of theCase 1 and 2 are the minimization of TNTT. This result implies that the roleof objective function is critical in NDP.

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Another important thing to be discussed is the TNTT calculated fromthe average OD table is significantly lower than the average TNTT
calculated from multiple OD tables. The distinct between deterministic
TNTT from the average OD table and the expected TNTT from multiple
OD tables is consistent with the study of Waller et al. (2001). In the study,
thissituation
was
explained
throughJensen’s
inequality(i.e. E[ g ( x)] ≥ g[ E ( x)] ) in their study.

In this study, another reason is provided by means of the nonlinear
characteristic of BPR function. The underestimation of TNTT with theaverage OD table can be explained as shown in Figure 12. From the average
link flows (X-AVE), the average link travel time can be decided as T-AVE.
Let’s assume that link flow has some fluctuations. In that case, the increaseof link flows gives a bigger impact on link travel time than the decrease oflink flows as shown in Figure 12. In other words, (T-max – T-AVE) islarger than (T-AVE-T-min) although (X-max – X-AVE) is equal to (X-AVE– X-min).
Travel time

BPR function

Link flow distribution

T-max

T-AVE
T-min

Δt1

Δt

Δt 2

Δt

Average travel time E(T)Steep slope
Gentle slope

X-min

Δx

X-AVE

Δx

Link flows
X-max

Figure 12: The effect of link flow variation on travel time in BPR function

It is because of the non-linearity of BPR function. As shown in Figure
12, the slope of BPR function in the right side is always steeper than that in
the left side. Therefore, Δt1 is always larger than Δt 2 and the average
travel time, E(T) is also always bigger than T-AVE. In the same vein, thebigger travel time from multiple OD tables is reasonable and thediscrepancy between the two TNTT in the Case 1 can be increased as thevariance of link flows goes up. In reality, the monotonic increase of travel
time with respect to link flows is plausible. It implies that the use of average
OD table in transportation planning can underestimate the level of traffic
congestion significantly.

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The superiority of Eq. (6) to TNTT is worth noting. In TNTT, there isno big difference among the three cases. Oppositely, the Case 1 and 2 giveover than 57% congestion probability but the congestion probability of Case
3 is 45%. Therefore, the new objective function can give a similar amountof TNTT reduction and significantly bigger reduction in SPC.
5.2 Test network 2: Nguyen-Dupius Network
In this experiment, the maximum extra capacity to be added to eachlink is equal to 2200 flow units. All attributes of the Nguyen-Dupius
network are the same with previous case beside OD travel demand. ThreeOD demand levels of four OD pairs were selected for this experiment: Q12 ={2100, 2600, 3100}, Q13 = {2300, 2800, 3300}, Q42 = {2500, 3000, 3500},
and Q43 = {1900, 2400, 2900}, respectively.
8500
8000
7500

TNTT

7000
6500
6000
Case1: Deterministic TNTT

5500

Case1: Average TNTT
Case2

5000

Case3

4500
4000
8000

9000

10000

11000

12000

13000

14000

Total ave. OD Flow

Figure 13: Comparison of Average TNTT between Case 1, 2 and 3 atdifferent OD demand levels
Figure 13 shows the discrepancies of the three cases in terms of TNTT.
As expected, there are trivial discrepancies so the performances of TNTT
minimization and congestion probability minimization are similar.

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0.4500

Probability of congestion

0.40000.35000.30000.25000.20000.1500
Case1: using a stochastic OD table

0.1000Case2

0.05000.0000
8000

Case3
9000

10000

11000

12000

13000

14000

Total ave. OD Flow

Figure 14: Comparison of PCN between Case 1, 2 and 3 at different OD
demand levels
However, the two objective functions reveal significantly different
performances in resulted PCN. At all travel demand level, the Case 3
reduces the probability of congestion greater than other two cases do.
Nonetheless, the Case 1 and the Case 2 show nearly the same performance.
It confirms that the objective function plays a crucial role in NDP and theconsideration of multiple OD table (i.e., OD flow variance) without making
use of the new objective function would not bring a big improvement in the
network design. Based on these properties, the solution for NDP from thenew objective function seems to be the best for both aspects.
6. BRAESS’ PARADOX IN STOCHASTIC TRAFFIC ASSIGNMENTIn order to check out the properties of the new assignment technique further,a famous problem in transportation planning is tested with a stochastic
traffic assignment. The problem is Braess’ Paradox in which the addition of
new link aggravates the travel time of drivers. The classical Braess’ paradox
observes the failure of improvement in term of increasing total travel timeafter adding new link. In this study, differently, the failure of improvement
can be explained in terms of congestion probability. This new paradox,
namely “Probabilistic Braess’ Paradox” is described as follows.
Figure 15 presents the improvement of simple network from four-link
network to five link network, and the network characteristics, length, freeflow speed and capacity are also shown in the figure. The improvement
scenario is to add a new link between node 2 and node 3 with intention to
reduce the system probability of congestion.

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0.75 km.
(70 kph, 920 vph)

Q1Æ4

2

1

3

0.75 km.
(70 kph, 920 vph)

1

Q1Æ4

4

1.56 km.
(60 kph, 830 vph)

Q1Æ41.56 km.
(60 kph, 830 vph)20.75 km.
(70 kph, 920 vph)

1.56 km.
(60 kph, 830 vph)

1.56 km.
(110 kph, 1,110vph)

Before improvement

After improvement4

1.56 km.
(60 kph, 830 vph)

3

Q1Æ4

0.75 km.
(70 kph, 920 vph)

Figure 15: Improvement scenario for showing Braess’ Paradox principle
To show the probabilistic Braess’ Paradox, suppose that the demand
from the node 1 to the node 4 (Q1Æ4) is 1200 flow units. By performing thetraffic assignment with a stochastic OD table, the results show that before
improvement, the probability of congestion in the network (PCN) is equal to0.0826, but after improvement the PCN is 0.23. That is, the improvement
makes the occurrence of congestion more likely to happen than before.
6.1 Experiment on Different Demand Levels
The additional experiment was conducted to several demand levels
and the results were compared with the classical Braess’ paradox from both
deterministic OD table and stochastic OD table. Table 3 presents the resultsfrom this experiment. According to Table 3, the shaded cells in the tableindicate Braess’ paradox occurrence. For the deterministic case, the paradox
occurs in the demand range between 800 and 1200, and for stochastic case,the demand around 800 presents the paradox situation. On the other hand,the probabilistic Braess’ paradox happens in wider range (600 to 1600) andcovers the range of classical Braess’ paradox as presented in this table.
Table 3 Comparison of TNTT and PCN between before and after
improvement
Demand(Q1Æ4)
400
600
800
1000

260
260

Deterministic TNTT
(hrs)
BeforeAfter
14.6924
14.304422.079421.8241
29.5857
29.951237.368137.6485

Average TNTT
(hrs)
BeforeAfter
14.7861
14.4588
22.268122.162530.0100
30.071638.353938.3103

PCN
Before000.00020.0119After
00.01050.04000.1036

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1200
1400
1600
1800
2000
2200

45.684154.9164
65.5987
78.4401
94.3508
114.4669

45.709154.4577
64.570276.331990.7065
108.72547.8606
59.364874.0292
93.3999
119.4605
154.6874

47.4279
58.231071.592788.7467
111.4089
141.6219

0.08260.22540.39250.54170.65850.74470.23000.36270.4546
0.52910.59530.6541

Note that the occurrence of Braess’ paradox is dependent on the levelof travel demand. This is not a new finding in transportation study. Pas andOrincipio (1997) asserted that the occurrence of Braess’ paradox is closely
related with the level of travel demand and the behavior of network supply.
More comprehensive investigation was done in Yang and Bell (1998).
They found that the occurrence of Braess’ paradox is dependent not
only the level of travel demand but also the route choice behavior of traveler.
According to the study, the range of Braess’ paradox is wider when SUE(Stochastic user equilibrium) is used as a route choice principle. Anotherimportant finding is that the potential or reserved capacity might be reduced
by a link addition.
More important finding from this test is that the probabilistic Braess’paradox exists. When travel demand is 1200~1600 flow unit, the probabilityof traffic congestion increases significantly after the link addition. In
addition, the amount of aggravation in congestion probability is also
dependent on the level of travel demand. In this example, the probability
increase reaches at maximum when travel demand is around 1200~1400
flow unit.
In order to investigate the influence of OD demand variance on
Braess’ paradox, above test is executed at the various levels of OD flow
variance. In addition, three different demand levels: 800, 1200 and 1600 are
selected and loaded in order to compare the change of the paradox situation
on various demand levels. For comparison, the changes of PCN after
improvement used to identify the probabilistic Braess’ paradox situation insuch that the positive value indicates the occurrence of paradox while thenegative value presents no paradox situation. These PCN changing values
were plotted against the levels of OD variation as demonstrated in Figure 16.

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Demand 800

Prob. Braess’s Paradox

0.4
0.3
0.2

Demand 1200
Demand 1600

0.1
0
0

10

20

-0.1
-0.2

30

40

50

no Paradox

Change of SPC after improvement

0.5

60

70

80

90

100

% of q14 for Var(Q14 )-0.3

Figure 16: Probabilistic Braess’s paradox with various demand levels andvariations
According to Figure 16, when the demand is low (demand: 800 flowunit), the peak of PCN change can be observed at the greater level of OD
variation (around 50% of q14). Conversely, when the level of demand is high(demand: 1600 flow unit), the highest change of PCN can be observed at thelower level of OD variation (close to 0% of q14). In addition, when demand
equals to 1200 flow unit, the peak of PCN changing value can be observed
when the level of OD variation is about 23 %. Moreover, it was observedthat when the OD variation is high, the severity of probabilistic Braess’paradox tends to decrease. For higher demand level (e.g. 1600 flow unit),
the probabilistic Braess’ paradox disappears when the OD variation isaround 47%, while for lower demand level (e.g. 1200 flow unit), theparadox situation cannot be observed when the OD variation is almost 100%.
7. CONCLUSION
In this study, uncertainty of travel demand is modeled in a way that OD
flows are defined as random variables. From the stochastic framework, thenew definitions of link traffic flow and congestion for a stochastic network
analysis are also introduced. The new measure for probabilistic traffic
congestion can reflect the real experience of travelers in the congested
network, so it is more user-oriented congestion measure than v/c.
The concept of the probabilistic traffic congestion is employed in the
NDP. A conventional NDP only considers TNTT, so the uncertainty oftravel demand has not been taken into account in the optimal design
calculation. For overcoming the flaw, a new objective function which canreflect the probabilistic network congestion is developed. The new objectivefunction shows similar performance to a conventional TNTT minimization
function in terms of TNTT. However, it shows a significant superiority to

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the conventional function in terms of PCN. As a result, the new objectivefunction is a more appropriate measure for a NDP under travel demand
uncertainty.
Another important contribution of this paper is the identification ofBraess’ paradox existence in the stochastic network case. The probabilistic
Braess’ paradox is defined with probabilistic traffic congestion and the
behavior of the paradox is investigated with the various levels of travel
demand and its variance. According to simulation tests, the range of Braess’paradox occurrence is wider in the case of stochastic network. In addition,
the range of probabilistic Braess’ paradox is wider than that of deterministic
one. It implies that if travel demand is fluctuating, then more Braessparadox can be happen and the identification of occurrence condition can befound more efficiently with the probabilistic definition of traffic congestion.
In other words, if we use TNTT for finding Braess’ paradox occurrence,then some cases which increase the probability of network congestion canbe unidentified.
Although many significant contributions are achieved in this study,
there are some tasks left for the future study. First, a more comprehensive
stochastic traffic assignment model should be developed. In this study,Monte Carlo technique is used with static UE traffic assignment, but it
requires multiple assignment runs. In addition, the used simulation
framework cannot consider the correlation of OD flows. Second, if the
proposed probabilistic concept of traffic congestion is combined with adynamic traffic assignment, a complete network analysis would be possible.
One important factor for the occurrence of traffic congestion is the temporalconvergence of travel demand. Therefore, a dynamic framework formodeling traffic congestion is inevitable. Third, the probabilistic
characteristics of OD travel demand should be investigated with the realdata. The variance, appropriate distribution, and correlations among OD
flows are the most basic prerequisites for applying the developed model andconcepts to in practice.
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Asakura, Y. and Hato, E. (2001). A route recognition model for transport
network reliability analysis. In Bell, M. G. H. and Cassir, C. (Eds.),
Reliability of Transport Networks (pp.203-216). Research Study Press.
Bell, M.G.H. and Iida, Y. (1997). Transportation network analysis.
Chichester: John Wiley and Sons.
Blank, L. (1980). Statistical procedures for engineering, management, andscience. McGraw-Hill, Inc.
Chen, A., Subprasom, K., Ji, Z.W., (2003). Mean-Variance model for the
Build-Operate-Transfer scheme under demand uncertainty.
Transportation Research Record 1857, 93-101.
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Chen, A., Yang, H., Lo, H. K., and Tang, W. H. (2002) Capacity reliabilityof a road network: an assessment methodology and numerical results.
Transportation Research Part B, 36:225-252.
Chen, M. and Alfa, A.S. (1991). A network design algorithm using astochastic incremental traffic assignment approach. Transportation
Science, 25(3), 215-224.
Clark, S.D., and Watling D.P. (2005). Modelling network travel time
reliability under stochastic demand. Transportation Research, 39 (B),
119-140.
Hazelton, M.L., 2003. Some comments on origin-destination matrix
estimation. Transportation Research Part A, 37, 811-822.
Kim H., 2008. New dynamic travel demand modeling methods in advanced
data collecting environments, Ph.D. dissertation, University ofCalifornia, Irvine.
Lam W.H.K., Shao H., Sumalee A. (2008). Modeling impacts of adverse
weather conditions on a road network with uncertainties in demandand supply. Transportation Research Part B, 42, 890-910.
Lee, S., Moon B. and Asakura Y. (2000). Reliability Analysis andCalculation on Large Scale Transport Networks. In Bell, M.G.H. andCassir, C. (Eds.), Reliability of Transport Networks (pp.173-190).
Research Study Press.
Lo, H.K. and Tung Y.K., (2003). Network with degradable links: Capacity
analysis and design, Transportation Research Part B 37, pp. 345–363
Pas E.I., S.L. Principio (1997) Braess’ paradox : some new insights,
Transportation Research Part B 31 (3), pp. 265-276.
Sheffi, Y. (1985). Urban transportation networks. New Jersey: Prentice-Hall.
Siu, B.W.Y., Lo, H. K. (2007). Doubly uncertain transportation network:
Degradable capacity ans stochastic demand. European Journal ofOperational Research, 191, 166-181.
Sumalee A., Watling D.P., and Nakayama S. (2006) Reliable Network
Design Problem: the case with stochastic demand and total travel time
reliability, Transportation Research Record, 1964, p 81-90.
Suwansirikul C., Friesz, T.L., and Tobin, R.L. (1987). Equilibrium
decomposed optimization: A heuristic for the continuous equilibrium
network design problem, Transportation Science, 21, No.4, 254-263.
Ukkusuri, S.V., Mathew, T.V., Waller, S.T., 2007. Robust transportation
network design under demand uncertainty. Computer-Aided Civil andInfrastructure Engineering, 22, 6-18.
Waller, S.T., Schofer J.L. and Ziliaskopoulos A.K. (2001). Evaluation withtraffic assignment under demand uncertainty. Journal of theTransportation Research Board, 1771, 69–75.
Yang H., M. G. H. Bell (1998) Models and algorithms for road network
design: a review and some new developments, Transportation 18 (3),pp. 257-278.

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ESTIMATION OF LAND SURFACE WATER
COVERAGE (LSWC) WITH
AMSR-E AND MODISSHUMON MORI, WATARU TAKEUCHI and HARUO SAWADA
Institute of Industrial Science, University of Tokyo
morishumon@gmail.com
ABSTRACTThis research focuses on estimation of land surface water coverage byusing AMSR-E and MODIS sensors. The main point in this study is tocalibrate the two data, NDPI (normalized difference polarization index) and
LSWC (land surface water coverage) obtained by AMSR-E and NDWI
(normalized difference water index) of MODIS correspondingly and finally
we mapped the inundated land surface distribution.
1. INTRODUCTION
In recent years, overpopulation and human activities are causing water
problems such as water shortage, increase of flood disasters. Also, climatechanges influence the water cycle and water resources due to global
warming. Paddy fields and marshes are presumed to be a major emission
source of methane recognized as global warming gases (IPCC), and they are
distributed extensively in global scale. It requires the observation methods
for paddy fields and marshes and scientific analysis solution for water cyclein global scale. Since it is difficult to investigate the natural field over theextensive area, remote sensing is one of the very useful tools to observe theland surface conditions globally.
AMSR-E is a microwave sensor and its spatial resolution is in scale ofkilometers, not high resolution but it can observe the land surface with low
cloud interruption (that means high temporal resolution). On the other hand
MODIS sensor is a visible infrared sensor and its spatial resolution is inscale of hundreds meter (high spatial resolution) but its observation isfrequently interrupted with cloud contamination. In order to overcome theabove mentioned problems, the combined utilization of AMSR-E and
MODIS is conducted in observing the distribution of the paddy field at the
global scale (Takeuchi 2006). These researches focus on the spatialtemporal features of AMSR-E and MODIS that can complement their
spatial-temporal weak point each other.
The objective of this study is to investigate the spatial-temporal
difference of inundated land surface properties obtained from both AMSR-E
and MODIS sensors. The more precise calibration between AMSR-E and
MODIS is conducted to find the relationship between AMSR-E NDPI and

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MODIS LSWC. The uniqueness of this study is found on the calibration ofindex (AMSR-E NDPI) and physical quantity supplemented by MODIS.
2. METHODOLOGY
2.1. Flowchart to produce LSWC distribution map with AMSR-E and
MODIS
Figure 1 shows a flowchart to produce land surface water coverage(denoted as LSWC) distribution map by combining AMSR-E and MODIS.
AMSR-E10km mosaics(2006, 365 scenes)

NDPI

MODIS
2 km mosaics(2006, 8 day composite,
46 scenes)

NDWI

Calibrating
NDPI and LSWC [%]with logistic function.
LSWC = F(NDPI)LSWC [%]
25 pixels = 5×5

Mapping the LSWC distribution
by using AMSR-E NDPI

Figure1: Flowchart to produce LSWC distribution map by combining AMSR-E and MODISAMSR-E data was obtained from National Snow and Ice Data Center(NSIDC) in United States of America, and a series of preprocessing was
carried out including radiance calibration, geometric correction, spatial
mosaic and subset. Normalized difference polarization index (NDPI) was
computed as shown in Equation (1).

Tv − Th
Tv + Th
Tv : 36.5 GHz brightness temperature vertical
Th : 36.5 GHz brightness temperature horizontal
NDPI =(1)

MODIS data rebuilding was carried out by Linlle-University group(Dr. Louis Gonzalez, personal communication) based on NASA’s 8-day
global mosaic composite product (MOD09A1). The idea is to remove
clouds by spatio-temporal filtering technique on a pixel-basis processing.
Normalized difference water index (NDWI) was computed as shown inEquation (2).

VIS − SWIR
VIS + SWIRVIS : Reflectance of visible (630 nm, channel 1)
NDWI =

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SWIR : Reflectance of short wave infrared (1620 nm, channel 6)
2.2. Study area
Figure 2 shows an area of interest in this study highlighted by red
square, centered at latitude 54-17`59, 95”N, longitude 90-52`0.20”E. The
area is 10,000 square kilometers (10x10 pixels) covered by seasonal
dynamics of open water, natural wetlands and paddy fields. In this study,
above ground vegetation contribution is neglected.

Figure2: Area of interest in this study is highlighted by red square, centeredat latitude 54-17`59, 95”N, longitude 90-52`0.20”E and the area is 100
square kilometer known as open water area.
2.3. Setting the inundated pixel thresholdIn this study land surface water coverage is strongly dependent on theinundated threshold. To estimate the precise inundated threshold we build
the global land coverage histogram by utilizing MODIS NDWI images.
After that, we simulated the histogram’s curve behavior with Gaussianfunction:f ( x) =a
2π c 2exp(−( x − b) 2
)
2c 2

(3)

The parameter ‘a’ expresses the height of the curve’s peak and ‘b’expresses the center of curve’s peak and ‘c’ controls the width of peak. In
this study we simulated 36 NDWI histograms.

Estimation
with
AMSR-E
andandMODIS
Estimation ofof Land
LandSurface
SurfaceWater
WaterCoverage
Coverage(LSWC)(LSWC)with
AMSR-E
MODIS

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Then we processed 3 steps:
First step is to classify the part of histogram such as vegetation, soil,
and mixture of water and soil.
Second step is to confirm the dependence of temporal series of thethreshold by verifying the fluctuation of the peaks.
Third step is to classify the distribution of inundated area in order to
set the inundated threshold more precisely.
2.4. LSWC (Land Surface Water Coverage)
Figure 3 shows a schematic diagram between AMSR-E and MODIS
footprints. One pixel of AMSR-E (10 square km) corresponds to the 5x5
block of MODIS pixels.

10km
Inundated pixels

10km =2[km/pixel] ×5[pixels]
Total = 5×5 = 25pixels(b) MODIS NDWI(a) AMSR-E NDPI

Figure 3: Spatial correspondence between AMSR-E and MODIS.
LSWC between AMSR-E and MODIS was estimated by following thethree steps:
1. To select the threshold of MODIS NDWI images to distinguishbetween inundated and non-inundated pixels aided by histogram curve.
2. To produce the inundated distribution map in 2 km and spatiallyaggregate the inundated distribution map 2 km into the fractional
coverage of land surface water coverage in 10 km (MODIS LSWC).
3. To find out the relationship between AMSR-E NDPI and MODIS
LSWC. Logistic function as shown in Equation (4) is expected to fit
well between those (Takeuchi, 2006). The coefficients a, b and c are
estimated by using the least squares method.
LSWC =

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3. RESULTS
3.1. Spatial correspondence between AMSR-E NDPI and MODIS
NDWI
Figure 4 shows comparison of MODIS NDWI 2 km, MODIS LSW
map 2 km, MODIS LSWC 10 km and AMSR-E NDPI 10 km. Brighter
pixels indicate high abundance of inundated area.
MODIS NDWI 2km

MODIS LSW map 2km MODIS LSWC 10km

AMSR-E NDPI 10km

Jan

Apr

Oct

Figure 4: Comparison of MODIS NDWI 2km, MODIS LSW map 2km, MODISLSWC 10 km and AMSR-E NDPI 10km. Brighter pixels indicate highabundance of inundated area.
3.2. Select the inundated threshold
Figure 5 shows a MODIS NDWI histogram in 2006 characterized by
4 peaks including vegetation (peak1), soil (peak2), mixture of soil and water(peak3), and water and snow (peak4).

Estimation
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Estimation ofof Land
LandSurface
SurfaceWater
WaterCoverage
Coverage(LSWC)(LSWC)with
AMSR-E
MODIS

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2kmMODIS NDWI global �istogram
140000
peak1 �vegitation�Number of pixels/25

120000

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Averagepeak 4��ater an� sno��

10000080000
peak 2 �soil�

60000
40000

peak 3 �soil an� �ater�
20000
0
0

50

100

①

150
MODIS NDWI

②

200

③

250

④

Figure 5 : MODIS NDWI histogram in 2006
In first step, we classified the peak1, 2 and 3 by matching theGaussian function with them and we demonstrated approximately their land
cover type according to NDWI values. Then, the result was that peak1
represents the vegetation area, that peak2 represents the soil area and thatpeak3 represents the mixture of soil and water area. The results are
summarized in Table 1 and in Figure 6.
Table 1: details of 3 peaks

Peak1
Peak2
Peak3

averaged centerof peak
83.0
100.3
126.8

S.D. of peak

S.D. of center

5.2
7.8
3.3

0.7
1
1.5

Figure 6: Global NDWI histogram mapping. The green pixels whose rangeo f [78, 81] shows vegetations, The brown pixels whose range of [92, 108]shows soils and the yellow pixels range of [123, 130].

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In second step, we found the slight-fluctuation of the peaks as shownin Table 1 and in Figure 5. Therefore we suggested the independence of theinundated threshold on temporal series.
In third step, we investigated the inundated range, NDWI = [121, 255],
to determine the precise inundated threshold by classifying the range to 4
areas, NDWI = [121, 136], [137, 178], [179, 239], [240, 255], as mentioned
in the above Figure 5. The results are shown in the figure 7- (b).
Then we comprised the NDWI maps with MODIS visible map in
Bangladesh and we determined that inundated threshold=121 represents theinundated areas most precisely as shown in MODIS visible image.

(a) MODIS visible image 20060106 as a reference(b) MODIS NDWI image 20060101 (8 day composite) red pixels show
range of [240, 255], green pixels show range of [179, 239], cyan pixels
show range of [137, 178] and white pixels show range of [121, 136].
Figure 7: Comparison between MODIS visible image and MODISNDWI image

Estimation
with
AMSR-E
andandMODIS
Estimation ofof Land
LandSurface
SurfaceWater
WaterCoverage
Coverage(LSWC)(LSWC)with
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3.3. Relationship between NDPI and LSWC
Figure 8 shows the scatter plot representing the relationship between
AMSR-E NDPI and MODIS NDWI. We used the data on 2006, Jan 01 andon 2006, July 04 in area mentioned above and after applying least squares
method we received the following scatter plot, coefficients and standard
deviation:����a����n �� ���������n �u��� ��t���n Jan and Jul
120
100����� ���� ���

80
Jan
Jul
data Jandata Jul

60
40
20
0
0

20

40

60

80

100

120

140

-20
����-� ����Figure 8 : Scatter plot representing the relation between AMSR-E NDPI
and MODIS LSWC
Table 2 : Results of coefficients a, b, c and standard deviation after
application of least method

20060101

a
b
c
S.D.

100.0
323.5
0.1
7.1

20060704

a
b
c
S.D.

100.0
404.6
0.1
6.0

For your attention, the data is quantized to 4%. Then, the minimum
standard error is 4 % (because MODIS image has 25 pixels in 10 square km
area and then minimum scale of data is 4%).
We can find that the two regression curves are modeled similarly
within the standard error 4%. Thus, we can hypothesize that theses
regression curves represent precisely the LSWC in independent on temporalseries.

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3.4. Spatial correspondence between AMER-E NDPI
distribution map and MODIS NDWI LSWC distribution map

LSWC

Figure 9 shows the spatial correspondence between AMSR-E NDPILSWC distribution map and MODIS NDWI LSWC distribution map.
Brighter pixels indicate high abundance of inundated area. There is highcorrespondence between two maps as shown.

(a) LSWC distribution map of AMSR- (b) LSWC distribution map of MODISE NDPI
NDWI
Figure 9 : Spatial correspondence of AMSR-E NDPI LSWC distribution map
with MODIS NDWI LSWC distribution map
4. CONCLUSION
In this study we built the NDWI histogram in global scale by MODIS
8-day composite data and we classified the main 3 peaks to assume thewater-soil mixture NDWI range. Then, we classified more precisely thewater NDWI range. Finally, we succeeded to hypothesize the inundated
threshold with comparison of MODIS visible image.
Based on the threshold we processed the sub-pixel analysis in 10,000
square km open water area, and then we modeled the logistic regression
curve by using 100 points data of this object area.
By comparing with two days regression curve, we found that theirdifference distance is closely 4% (the our data is quantized to 4% then theminimum standard error is 4 %) then we arrived to the conclusion that ourlogistic regression curve represent precisely and independently of temporalseries Land Surface Water Coverage in open water area.
Finally we mapped the LSWC distribution by using AMSR-E. By
applying these results we can assume the inundated period and produce
flood disaster map and so on.

Estimation
with
AMSR-E
andandMODIS
Estimation ofof Land
LandSurface
SurfaceWater
WaterCoverage
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In this study we focus on only open water area. Then for the future
work we need to investigate the arboreal vegetation influences on
microwaves and visible infrared waves to improve and apply the logistic
regression curve for Amazon or other similar area.
REFERENCES
Wataru Takeuchi, Daisuke Komori, Taikan Oki and Yoshifumi Yasuoka,
2006. An integrated approach on rice paddy irrigation patternmonitoring over Asia with MODIS and AMSR-E . American
geophysical union fall meeting 2006 (AGU): San francisco, USA,
2006 Dec. 5.
Sippel, S. J. et al., 1994. Determination of inundation area in the Amazon
river flood plain using the SMMR 37 GHz Polarization difference,
Remote Sens. Environ, 70-76, 48.

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COMPARATIVE ANALYSIS OF PLANNEDAND ACTUAL DISASTER MANAGEMENTOPERATIONS PERFORMED DURING
AND JUST AFTER THE 2007 CYCLONE SIDR
IN BANGLADESH
TAIKI KOU1) and KIMIRO MEGURO2)
Department of Civil Engineering, The University of Tokyo
2)
International Center for Urban Safety Engineering,
Institute of Industrial Science, The University of Tokyo, Japan
kou@risk-mg.iis.u-tokyo.ac.jp
1)

ABSTRACTThe authors have analyzed “Standing Orders on Disaster”, which isthe basic plan to implement the disaster management operations in
Bangladesh, and compared it to actual operations performed during andjust after the Cyclone Sidr in 2007. Information on actual operations wascollected by the authors and researchers in the Bangladesh University ofEngineering and Technology based on briefing papers at that time and fieldsurveys. The Orders were also sorted out according to the categories of
Basic Disaster Prevention Plan in Japan in order to figure out a frameworkof the Orders. Further, by using the framework, the authors have proposed
a continual revision process of the disaster management plan.
1. INTRODUCTION
1.1 Background
Standing Orders on Disaster (SOD), which is equivalent to the Basic
Law on Natural Disasters, was published in 1997 in Bangladesh. The
principal thinking behind the formulation resulted from the experiences ofdevastating flood and cyclone damages during the 1980s and 1990s. The
experiences intensely highlighted the insufficiencies of disaster management
operations that mainly stressed on response and rehabilitation.
More than 10 years have passed since the Orders were put into effect;
several controversial points have been noted in that time, and themomentum of revision that has recently gathered.
1.2 SOD –Bangladesh Disaster Management Plan–
What is SOD?
SOD is a basic disaster management plan in Bangladesh to implement
the disaster management operations. The Orders comprehensively describe
the total disaster management plan for each of the 38 actors which are

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related to the disaster management operations, and each phase such as“Normal Times”, ”Alert Stage”, ”Warning Stage”, “Disaster Stage” and“Rehabilitation Stage”.
Significance of formulating SOD
Before formulating SOD, it should be remembered that each disastermanagement plan (such as flood or cyclone) rarely has uniformity in theplan. So the total disaster management plan was formulated, which was
adoptable even though he types of disasters are different. This plan clarified
the disaster management operations actors, and the proposed operations
which were described based on the phase made it straightforward for theactors who implement the disaster management operations to grasp thecontents of operations easily by checking up the papers.
Recent Controversial points of SOD
Ten years have passed after publishing the document, and SOD has
noted following controversial implementation points immediately preceding
and following the disaster management operations. These arguments
increase the momentum towards upgrading the Orders into further practicalsteps.
z Planned operations are only listed, and a time axis is notconsideredz Obscurity of coordination and linkage of operations / information
with other agencies
z As the total picture of disaster management operations is notdescribed in the Orders, the orientation of each disastermanagement operation is hard to figure out in the total frameworkof disaster management.
z Not updated since the publication in 1997
1.3 Purpose of the StudyIn this study, the authors have analyzed Standing Orders on Disaster,which is the basic plan to implement the disaster management operations in
Bangladesh, and compared it to actual operations during and just after the2007 Cyclone Sidr. The study focuses on public sectors, Cyclone
Preparedness Programme (CPP), Bangladesh Red Crescent Society(BDRCS), and is based on briefing papers at that time and field survey
conducted by the authors and Bangladesh University of Engineering andTechnology (BUET). Based on the Comparison, the authors haveoverviewed the actual disaster management situations mainly focusing on
Public Sectors in Bangladesh.
In addition, the contents of the disaster management plan described in
the Orders have been sorted out by the disaster management operations’categories of Basic Disaster Prevention Plan in Japan. This analysis made it
possible to figure out a framework of the Orders by comparing it withJapanese disaster management. Further, by using the framework, this study
put forward a proposal of a continual revision process of the disastermanagement plan.

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2. METHODOLOGY
2.1. Database Setting
Database setting of SOD
The authors built up the database of SOD and newly added two
attribution data noted on Figure 1 to overcome the controversial points thatare illustrated above.

Figure 1: Two attribution data newly added
Introduction of relative time axis
To express the disaster management operations in time axis, werecently added the seven phases, from disaster foreseeing phase to
assistance beginning phase.
Adoption of operation categories
The proposed operations in SOD were sorted out by the categories ofdisaster emergency management and recovery and rehabilitation describedin the Basic Disaster Prevention Plan in Japan.
2.2. Three Dimensional Analysis
Three dimensional analysis was done by choosing the actor, time axis,
amounts of operations’ description from the Database illustrated above. This
analysis made it possible to visualize the each actor’s amounts of operations
relative to time.
2.3. Operation Category Analysis
Operation category analysis was done by choosing an actor andoperation category from the database. This analysis made it possible tofigure out each description of operation in the total framework of disaster
Comparative Analysis of Planned and Actual Disaster Management Operations Performed
Comparative
Analysisof Plannedand Actual
DisasterManagement Operations Performed
during and justafter the2007 Cyclone
Sidr in
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management operations and actors who would implement cognate disastermanagement operations.
It was important to set an empirically-selected category to embracedisaster management operations, therefore this study adopted the categoryof Japanese Basic Disaster Prevention Plan. It would be preferable to adopt
other categories to analyze the SOD in different ways too.
3. ANALYSIS AND RESULT
3.1 Analysis of SOD
Estimation of operation amounts by three Dimensional Analysis
From the analysis (Figure2), the following controversial points
became apparent.

Figure 2: Estimation of operation amounts by three dimensional analysis
1: Planned operations were described based on the four disaster phases
as mentioned in section two, so it is difficult to be clear about theorders of operation in each phase
2: Existence of terms when sudden increase of operations are required.
It would be better to implement some operations in advance which
can be done before the arrival of the hazard
3: The amounts of descriptions of operations are significantly different
from each actor. These disagreements come from the concreteness ofoperations’ contents. These disagreements make the responsibility ofeach actor ambiguous.
Clarification of disaster management operation field of each actor
based on Operation Category Analysis
To grasp the total framework of disaster management, which is hard tojudge only by the text written in the Orders, we conducted the operation
category analysis. Adopting the Disaster management operation actors in
Bangladesh and categories of disaster management operations in Japan
illustrated in the Basic Disaster Prevention Plan as two axes, this analysis
made it possible to grasp the disaster management operation categories of

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each actor. From the Figure 3, it can be seen that some plans of disastermanagement operations were fully described to implement comprehensive
disaster management operations, such as Ministry of Home Affairs (MoHA),
Ministry of Shipping(MoS), Ministry of Health and Family
Welfare(MoH&FW), Ministry of Local Government(MoLG). At thedisaster stage during which every actor faces the scarcity of both theartificial and the physical, it is quite important for each actor to describe not
only disaster management operations related to its usual operations but also
disaster particular operations such as rescue, relief operations excepting theparticular operations which requires some authorities or abilities, such as the
distribution of relief aid, rehabilitation of the infrastructure system ormedical relief as a description of orders.

Figure 3: Clarification of disaster management operation field of eachactor based on operation category analysis
However, the problem is that “Is it possible or not to implement thewhole operations illustrated in the Orders during the disaster stage whichfaces the crucial scarcity”
If not, the Orders would be an all-round less effective plan. At thestage of comparison with actual disaster management operations, the Orders
have to be checked against whether the Orders was formulated with taking
consideration of that point.

Comparative Analysis of Planned and Actual Disaster Management Operations Performed
Comparative
Analysisof Plannedand Actual
DisasterManagement Operations Performed
during and justafter the2007 Cyclone
Sidr in
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3.1 Actual Disaster Management Analysis in 2007 Cyclone Sidr
Follow-up period
This study sets from 13th to 22nd November as a follow-up period ofdisaster management operations led by the government and public
administration of Bangladesh for the following reasons:Situation Report about Cyclone Sidr had been published on 13th
November (15:30, 13th Nov, ‘07). After 22nd November, international
coordination relief operations called “Cluster Approach” which both
described actors and not described actors in SOD committed had started.
This was triggered by the Disaster Emergency Response (DER) Meeting
which government, donor, international organization and NGO participatedin.
Limited information of disaster management operation
Before starting the analysis, there are some controversial points about
disaster management operation records: partial and limited records ofdisaster management, no detailed description of time and date, amount oftime required or head-count required.
For setting the feedback and update system of disaster management
operation in place, it will be also required to amend how to the data. isrecordedComparative analysis of planned and actual disaster managementby three Dimensional Analysis
In this analysis related to the above example, some problems
occurred.There were several records which could not count as actual
disaster management operation records since they lacked description ofoperations’ detail, which made it difficult to do comparisons with the Order“SOD”. These ambiguous points were confirmed by the field surveys
conducted by the authors and BUET research members. The results of fieldsurveys were reflected to this analysis as much as possible along with theoperations defined in SOD.

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Figure 4: Comparative analysis of planned and actual disastermanagement by three dimensional analysis
Figure 4 is the result of comparison of actual and planned operations(Left: planned, Right: actual).
From the result of analysis and field survey, the following points canbe indicated:
1) Disaster management operations were not or could not be
implemented, nor was there a mechanism of informationtransmittance to the upper organization even though the operations
were done
2) Generally, operations were not fully achieved3) A small portion of central organizations were forced to implement
more operations than the Order provisioned
From the insight of field survey, the reason for point 1) and 2) could
be divided into two aspects. One was a human resource problem, involving
insufficient numbers of public officers or officer’s affliction. The other was
physical problem: insufficient number of motor boats to distribute the reliefmaterials or shortage of budget to keep up a distribution operations.
Situation like point 3) could be seen at the central disastermanagement organizations such as Ministry of Food and Disaster
Management (MoFDM) or Disaster Management Bureau (DMB). It was
confirmed by the field surveys, coordination operations of foreign assistanceComparative Analysis of Planned and Actual Disaster Management Operations Performed
Comparative
Analysisof Plannedand Actual
DisasterManagement Operations Performed
during and justafter the2007 Cyclone
Sidr in
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by several donors or UN agencies, operation works at Barisal wereconcentrated on these organizations.
Comparison of actual operations and planned operations by using
Operation Category Analysis

Figure 5: Comparison of actual operations and planned operations
by using operation category analysis
Figure 5 is the result of a comparison of actual and planned
operations sorted out by the categories of disaster management operations(Left: planned, Right: actual).
From the result of analysis and field survey, the following points canbe indicated:
1) Disaster management operations were not or could not be
implemented, nor was there a mechanism of informationtransmittance to the upper organization even though the operations
were done
2) Disaster management operations were mainly done within normal
time’s operation fields of each organization
3) Only limited organizations were able to implement measures in theoperation field while several organizations were supposed to
implement the operation.
About points 1) and 2), the reason the organization could not
implement operations were due to the human and physical issues.

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Related to 2), it could be indicated that there was no room to enlarge
the operation field to disaster-particular operation fields, such as rescue or
relief operations for the problem of the scarcity of human or physicalresource problem.
About 3), in 2007 Cyclone Sidr case, Armed Force Division (AFD)
played a leading role of operations in the field just after the cyclone.
Especially, long-range transportation of relief materials was almost totally
implemented only by AFD.
Besides, the blank areas of the right figure, which means that planned
operations were not done, were substituted by internal/international aid
agencies, NGOs or NPOs.’ relief operations. These situations could be
realized by analyzing not only records but also field surveys.
4. CONCLUSIONS
Comparison of planned and actual operations by using category analysis
enables us to review and evaluate the total disaster management figure in
Bangladesh, which had not been done sufficiently before.
The approach to effectively verify the causes of unfulfilled operations
was set, such as clarifying the actors which could not fully operate, or the
categories which were not fulfilled, by checking up the controversial pointsof both orders and organizations (Figure 6).
At the present stage such disaster management operations described in
the Order could not be achieved, therefore it is necessary to set priorities on
the Orders under the human, physical resource and time limitation.
In concrete terms, conducting the following validations enables us torenew the orders into more actual suited orders:
•
•
•

•

Figuring out whether each actor could conduct the disastermanagement operations along with the Orders or not, throughlooking around the whole situation of disaster management
About the blank areas which can be seen in right side of
Figure*, confirming the capacity of implementing disastermanagement operations of each actor planned in the OrderIf there is sufficient capacity, clarifying and ensuring theimplementation framework of the orders for upcoming
disaster. If not, confirming which actor could substitute theoperations, and re-allocating the operations
Considering which is adoptable and efficient for the actual
situation, coming to terms with the concentrated situation or
seeking alternative solutions (Ex: concentration on AFD totransform the relief materials)

Comparative Analysis of Planned and Actual Disaster Management Operations Performed
Comparative
Analysisof Plannedand Actual
DisasterManagement Operations Performed
during and justafter the2007 Cyclone
Sidr in
Bangladesh
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Figure 6: Reviewing Process of disaster management operations
by using Operation Category Analysis
Further, as illustrated in Figure 7, continual implementation of thisevaluation process whenever the experience is accumulated can reflect
sequentially the disjunction of plan and fact. This evaluation process enables
us to continually revise the disaster management plan.

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Figure 7: Continual Revision Process of Disaster Management Plan
ACKNOWLEDGEMENT
The authors would like to express their sincere gratitude for all the
support by Prof. Mehedi Ahmed Ansary and Ms. Researcher Afifa Imtiaz,
Urban Safety Engineering Department, BUET for conducting the fieldsurveys
REFERENCES
Shinya KONDO, 2002. Development of New Style Disaster Information
Database for Implementation of Efficient Disaster Countermeasures
Yoshiki HIRUMA, Kimiro MEGURO, 2007. Analysis of Record forDisaster Response Activities of Local Government Officials of
NIIGATA Prefecture after The 2004 NIIGATAKEN-CHUETSU
Earthquake
Mehedi Ahmed Ansary, Rubel Das, Afifa Imtlaz, 2008. Role of Logistics
during Cyclone SIDR, BNUS, BUET, USMCA 2008, 249-254
World Bank Report, 2008. Cyclone SIDR in Bangladesh, April, 2008.
Government of the People's Republic of Bangladesh, 1999. Standing Orders
on Disaster

Comparative Analysis of Planned and Actual Disaster Management Operations Performed
Comparative
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DisasterManagement Operations Performed
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ENVIRONMENTAL FLOWIN WATER RESOURCES MANAGEMENT:STATUS IN SOUTH ASIA
MD. REAZ AKTER MULLICK and MUKAND S BABEL
School of Engineering and Technology
Asian Institute of Technology, Thailand
reazmullick@gmail.com
ABSTRACTThe concept of environmental flow (EF) is central to achieving
sustainable water resources management. This paper reviews the current
status on adoption and use of EF approach in water resources managementin South Asian countries. Issues explored in this study also include theongoing challenges in achieving sustainable water resources management
since the region is growing fast and freshwater becomes scarce which
pushes the region towards environmental water scarcity. The review finds
some progress in assessment and adoption of EF into water resourcesmanagement in Sri Lanka, Pakistan and India; however, Bangladesh and
Nepal show an early stage progressing status. At the end, suggestions are
made on some key issues to be considered for recognizing EF as an effective
tool in integrated water resource management while meeting the waterneeds for South Asian countries.
1. INTRODUCTION
Water pervades life on earth, determines the ecosystem pattern and deeply
embeds civilization while providing numerous services to mankind. The
multi-sectoral development and multi-interest utilization criteria of waterneed to be acknowledged and managed in an integrated approach. Agenda
21 recognizes the issue of ensuring water supply for societal need while
preserving the functions of ecosystem. The Millennium Ecosystem
Assessment (MEA) identifies “any progress achieved in addressing theMillennium Development Goals (MDGs) of poverty and hunger eradication,
human health and environmental protection is unlikely to be sustained if theecosystem services on which humanity relies continue to be degraded” 1 .
However, increasing population, growing urbanization and accelerated
industrial activities result an enormous appropriation of freshwater
resources leading to serious degradation of aquatic ecosystem in much of
the world (King & Brown, 2006; Tharme, 2003; Bunn & Arthington, 2002).
Degradation of aquatic ecosystem bears a far reaching impact not only bylosing ecosystem integrity but also to a large community who directly
depends on instream flow for livelihood. Growing demand and competition
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for water in developmental needs in one hand with increasing concern on
environmental sustainability in another, the concept of ‘environmental flow’(EF) is central to the dilemma in achieving sustainable water resourcesmanagement (WRM).
South Asia, the home of about one-fifth of the world’s population, isthe most populous and densest yet economically poor region in the world.
Dissimilarities observed among different organizations in delineation ofSouth Asia’s boundary; however, considering present member states of
SAARC (South Asian Association for Regional Cooperation;
http://www.saarc-sec.org/) is considered as the basis to find the region. The
entire region has many diverse physical characteristics offering ageographical variety from deserts to swamplands, forests to grasslands,
mountains to coastal areas. Bounded by the Himalayas in the north, South
Asia's southern border is the Indian Ocean. The region spreads up to
Afghanistan in the west where as it bounds India-Myanmar border and the
Bangladesh-Myanmar border to the east. Several large rivers keep thisregion ‘live’. With the agro based economy of the region, more than fifty
percent of the countries population is directly or indirectly dependent on
agriculture where water is the key factor to production. Rivers are the major
sources of water for agriculture in these countries. Rivers are also vital as
the cheapest mode of communication and transportation. However, theregion is faced with significant changes and challenges in sustainable water
resource management. At the national level, it is expected that Afghanistan,
India, Iran, and Pakistan will fall below the threshold of 1500 m3 per capita
of water availability by the year 2030 (Yang, 2003). With the overall growthof population and urbanization in these countries, freshwater ecosystems isunder continued pressure due to water withdrawals for increased foodproduction, by building of dams for hydropower to meet energy demand, bymaking reservoirs to supply drinking water to large cities and by
navigational improvement projects to improve trade between countries.
This paper provides a quick overview on environmental water need inwater resources management and reviews the status of EF approach in water
resources management in South Asia Region including its adoption andimplementation. The paper describes briefly on consideration of EF as a toolin Integrated Water Resource Management (IWRM) within this region fromcurrent available literature. Issues explored in this study also include theongoing challenges of achieving sustainable water management since
freshwater becomes scarce and the region moves towards environmental
water scarcity condition.
2. WATER FOR HUMAN AND NATURE
In late ninth century, Queen Sammu-Ramat (who ruled Assyria, presently
northern Iraq), inscribed on her tomb (cited in Postel & Richter, 2003): “I
constrained the mighty river to flow according to my will and led its water
to fertilize lands that had before been barren and without inhabitants”.
Alike the history, political leaders still are in practice of showing their

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power and legitimacy with endeavor of winning their constituencies’ favor
by creating the provision of economic development of their country using
the water resources. Globally, fresh water supports 40% of the foodproduction, 20% of fish yield through aquaculture (FAO, 1996) and 20% of
power supply (Gleick, 1998). However, direct economic contribution ofwater resources at regional level can still be higher (Saleth and Dinar, 2004).
To make increase the gross benefit from water resources human intervention
on the natural flow is a common phenomenon. Following the developed
world, natural flow alteration is growing rapidly in the developing region(Richter et al., 2006). Flow that meets the sea is often viewed as wastage
from an effective management perspective and in several places water isshared between the offstream users leaving the rivers dry proves once again
the well known quote from Winston Churchill in 1908 (cited in Postel &Richter, 2003), “One day, every last drop of water which drains into thewhole valley of the Nile … shall be equally and amicably divided among theriver people, and the Nile itself … shall perish gloriously and never reachthe sea.”
Human interventions on natural flow through dams, reservoirs,
diversions or commercial afforestation and land use change have serious
and far reaching consequences on the original flow regime of rivers (Hughes,
2003; King & Brown, 2006). These activities change the dynamic
movement of water and sediment of a free flowing water body, which result
an alteration of habitats for aquatic and riparian species with an ultimate
loss of ecosystem goods and services those flowing river provides (Poff etal., 1997; King & Brown, 2006). Richter et al. (2006) cited several
literatures on river ecosystem health deterioration due to alteration ofnatural flow in the rivers.
However, water requirement for the natural environment has rarely
been considered and almost ignored in the traditional water management
and planning (IWMI, 2005; Gleick, 2003; Gleick, 1996) even though theenvironment is showing a continuous degradation. The circumstances urged
the scientists started thinking on provision of flow for the natural water
bodies with the view of defining a development space and imposing a limit
up to which flow can be diverted for human use while maintaining the
integrity of the ecosystem or an accepted level of degradation (Tharme,
2003). Hence, environmental flow (also termed as minimum flow or
instream flow) is the provision of flow in a regulated river towards
maintaining a specified level of ecosystem integrity through mimicking thenatural flow regime in order to ensure ecosystem goods and services on
which human rely in myriad ways.
3. CONSIDERING ENVIRONMENTAL FLOW IN THE
FRAMEWORK OF IWRM
IWRM is a process that ‘promotes the coordinated development andmanagement of water, land and related resources, in order to maximize theresultant economic and social welfare in an equitable manner without
Environmental
Management: Status
EnvironmentalFlow
flow in Water
water Resources
resources management:
Status inin SouthSouthAsia
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compromising the sustainability of vital ecosystems’ (Global Water
Partnership 2000). Environmental flow identifies the environment as anindividual sector with its own right of using water at basin scale.
In addition to its intrinsic value, flows in river carry significant
economic and socio-cultural values. In particular the riparian communities
crucially depend on river flow for their livelihood. Environmental flow istherefore an important tool for balancing the relationship between
functioning healthy ecosystems and sustainable livelihoods for a large
community. Within the framework of IWRM environmental flow helps
ensuring water allocation to the environment in development planning,
especially involving large infrastructure, and is seen as an integral tosustainable water management. With a better understanding ofenvironmental requirements and the potential trade-offs with other uses,
decision-makers are able to make informed decisions on water allocation tocompeting users. Environmental flow approach provides a roadmap for
defining negotiated instream flow requirements, considering theimplications for infrastructure development, assessing costs and benefits,
nurturing a supportive institutional and policy framework and generating
political consensus for changes.
4. WATER NEED – CONFLICT BETWEEN HUMAN AND NATURE
Despite a substantial work has already been done on instream waterrequirement, rivers are still drying up. Inefficient, over appropriation andmisuses of water resources are still in practice within all off-stream water
use sectors generate extra pressure on increasing demand for water. As aconsequence, water resources sector is clearly showing an era of scarcity(Saleth, 2002) which results severe ecological degradation with reduced
flow of ecosystem goods and services including loss of industrial andagricultural production (Postel et al., 1996). Reallocation of water from offstream use to environmental use has also been taken place in different
places (Hollinshead & Lund, 2006); however, conflicts based on theperceived needs of ecosystems versus human for fresh water are
increasingly reported in the news and came in several literatures e.g.
Johnson & Adams, 1988; Poff et al., 2003; Hollinshead & Lund, 2006.
Aquatic ecosystems are under a real threat all the world around in spite oftaking some measures. Gleick (1996) noted that in global scale there are
more than 700 species of fish which are considered to be threatened withextinction. Smakhtin et al. (2004) might be the first in estimating the global
environmental water scarcity; they stated that about 1.4 billion people livein river basins where current water uses are in conflict with environmental
water demand. Higher demand for the growing population anddevelopmental needs along with climate change effects are exacerbating thesituation by acting synergistically. In particular higher agricultural water
demand results environmental water scarcity for developing countries and it
becomes a critical concern to meet about a double irrigation water demandby 2050 as forecasted by some estimates (Falkenmark & Galaz, 2007). In
second World Water Forum 2000, this issue was raised by the two groups of

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people, one ‘water for food’ and another ‘water for nature’ – both stressedon significant water requirement for their respective fields. The Global
Water Partnership’s Framework for Action concluded that (GWP, 2000):“On the one hand, the fundamental fear of food shortages encourages evergreater use of water resources for agriculture. On the other, there is a needto divert water from irrigated agriculture to other users and to protect theresources and the ecosystem. Many believe this conflict is one of the mostcritical problems to be tackled in the early 21st century.”
5. USE OF ENVIRONMENTAL FLOW IN WRM IN SOUTH ASIAThe South Asian rivers, mostly originated from the Himalayas act as thecultural and economic backbone of this region while support livelihood tomillions of people and irrigate millions of hectares of land as well as theyown very rich ecosystem. These rivers are, however, also a source of
conflict between countries and people in the region because of theirtransboundary nature. Ganges, Indus, Brahmaputra, Krishna, Godavari,
Cauvery, Mahi Tapti are some of the major rivers among lots in this region.
Almost all rivers in South Asia are heavily exploited mainly for irrigationand hydropower neglecting the environment. India alone has 4011 large
dams (registered at ICOLD [International Commission On Large Dams]) on
several of its rivers sharing 9% of world’s total number of dams (Tharme,
2003). Almost all the river basins in South Asia therefore fall in the
category of environmental water scarcity defined by Smakhtin et al., 2004.
In Pilot Global Assessment of Environmental Water Requirements andScarcity, Smakhtin et al. 2004 provided a preliminary estimate of mean
Environmental Water Requirements (EWR) as a percentage of mean annual
runoff (MAR) for the major basins and drainage regions of the world. The
estimated EWR indicates the basic flow requirement to maintain a river
system at “fair” on a conservation status scale of natural-good-fair-poor.
The estimated EWR for highly variable monsoon-driven rivers in South
Asia are lower (20 to 30 percent of the MAR) which is presented in Table 1.
Basins to be managed for higher levels of ecosystem health would require
higher EWRs. One critical justification for greater concern for aquatic
ecosystem health for this region is the dependence on fisheries as a sourceof food and local economy. The share of fish protein in total animal protein
consumption in Sri Lanka and Bangladesh in 1993 was high (51.5% and46.7%, respectively), while that in India was relatively low (12.7%). The
per capita fish consumption in South Asia in 1993 ranged from 0.2 kg(Bhutan) to 15.4 kg (Sri Lanka) (de Silva and Smith, 2005).
River basin
Brahmaputra
Brahmari
Cauvery
Chotanagpui
Eastern Ghats
GangesTable 1: EWR for some major rivers in South Asia
Countries
Bangladesh, China, India
India
India
India
India
Bangladesh, India, Nepal

EWR (% of MAR)
27
24
25
25
26
23

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River basin
Godavari
India East Coast
Indus
Krishna
Luni
Mahi Tapti
SahyadaSri Lanka

Countries
India
India
India, Pakistan, Afghanistan
India
India
India
India
Sri Lanka

Source: adopted from Smakthin et al., 2004

EWR (% of MAR)
24
25
25
24
21
23
21
26

There are enough evidences of EF adoption into IWRM approachparticularly in developed world; however, developing countries are also
showing interest in this field (Moore 2004). South Africa is the pioneer fromthe developing world in adopting EF in water resources management.
Tharme and Smakhtin (2003) reported clear evidence of increasing researchand practice in EF assessments in south Asian countries of Nepal, Pakistan
and Sri Lanka, where as India is in the early stage. This paper reports theinterest, adoption and implementation of EF approach in water resourcesmanagement for different countries in South Asia in detail (Table 2). The
South Asian countries of India, Nepal and Pakistan have adopted theapproach and, in some cases, have included EF in national legislation andpolicies. Emerging areas of interest and adoption in EF include Bangladesh
and Sri Lanka. However, evidence of implementation is generally limited.
Countries where there was no evidence of EF included Afghanistan, Bhutan,
and Maldives.
Table 2: Status and use of environmental flow in South Asian countries

Country
EF status and use
Afghanistan No evidence of using EF is found as a tool for IWRM. One recent
study is found on estimating environmental flow for Hari Rod riverand subsequent simulation of Salma dam operation to satisfy
environmental requirement by Atef, 2009.
Bangladesh Evidence of interest in adopting the EF approach in Bangladesh isfound; however, the approach is not mainstreamed into river basin
management. Three studies are found estimating EF requirements for
Ganges by Rahman (1998), for Surma by Zobeyeer (2004) and forSurma-Kushiyara, Teesta and Gorai by Bari & Marchand (2006).
The National Water Policy 1999 and National Water Management
Plan 2001 do not explicitly mention EF but strongly mention therequirements of ecosystem to be provided. However, National Water
Plan (1986, 1991) argued that if discharge is maintained fornavigation, then water for environment and ecosystem would be met.
BhutanThe review did not find any evidence of EF being used as a tool for
IWRM.
India
Environmental flow approach has been adopted in India to someextent. First scientific attempt to assess EF (in fact volume of EF) is
by Amarasinghe et al., 2005. Smakhtin & Anputhas, 2006 examineand evaluated the EF demand for Brahmaputra, Cauvery, Ganga,
Godavari, Krishna, Mahanadi, Mahi, Narmada, Pennar, Periyar,
Tapi, Sobarmati and Subarnarekha. The National Water Policy 2002
provides for EF as does the National Environmental Policy 2006.

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Country

Maldives
Nepal

Pakistan

Sri Lanka

EF status and use
Adoption of EF is occurring at varying scales and levels ofgovernance. Barriers to the implementation of the EF approach
include, lack of funding and institutional mechanisms for IWRM,
and awareness about the approach.
No evidence of EF is found being used.
Environmental flow approach is considered in the Water Resources
Strategy Nepal 2002, the National Water Plan, and the National
Wetland Policy 2003. Smakhtin et al., 2006 estimated EF for East
Rapti River using desktop method. However, using EF as a tool is in
its infancy in terms of implementation.
Environmental flow approach has been adopted in Pakistan inparticular for lower Indus basin (Acreman et al., 2000). The National
Water Policy (draft) takes note of the necessity of EF for the Lower
Indus delta in no ambiguous terms. However, the adoption andimplementation of EF appears to be occurring slowly.
Progress of Sri Lanka in adoption of environmental flow is observed
well with several evidences of interest. Tharme (2003) mentioned
different methods under use in Sri Lanka to assess EF. However,national legislation or policies do not reflect yet.

6. CONCLUSION AND WAY FORWARD
This study attempted to review the trend and current status of EF approachin water resources management in South Asian countries. Some progress inassessment and adoption of EF into water resources management isobserved in Sri Lanka, Pakistan and India; however, Bangladesh and Nepal
are in early stage progressing. It is a contemporary need to initiate EF
assessment and adopt EF in national water resources management strategy
for all countries. In particular for India, EF assessment projects need toinitiate at several sites in cooperation with downstream countries like
Bangladesh which is most likely to be affected by the planned National
River Linking Project (NRLP) inter-basin water transfer.
Several methods are already developed to assess EF; however, itneeds to be tested in a specific context of flow regime, ecology and water
resources development at a particular area. Type of EF assessment methods
have to be selected based on the type of proposed development, level ofimpact of the proposed development, ecological importance and sensitivitiesof the river, the degree to which it is already developed, the socioeconomic
importance of the river and its proposed development. Large transboundary
river basins present a special challenge in assessment and application of EF.
Cross-border collaboration is therefore important, but also results in an
increased expenditure of effort in addressing EF in river restoration andother projects (Tharme 2003).
Factors likely to limit the adoption of EF in the region include lack ofawareness from the key stakeholders. In addition to just recognition of theconcept, awareness should extend to various aspects including awareness ofthe costs and benefits of implementation, the consequences of not

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considering EF and the trade-offs between social, economic andenvironmental objectives within a river basin. It is therefore extremely
important to realize the societal cost and benefit from environmental water
allocation by all the stakeholders. This concept gets reinforcement whenanalyzing the reasons behind successful implementation of EF in somecountries where importance of flows to local livelihoods is taken with due
regards (Moore 2004). Community members and social organizations, aswell as other key actors within a river basin, therefore need to bemeaningfully engaged in the development of an EF program.
REFERENCES
Acreman M.C., Farquharson F.A.K., McCartney M.P., Sullivan C.,
Campbell K., Hodgson N., et al., 2000. Guidelines for artificial flood
releases from reservoirs to restore and maintain downstream wetland
ecosystems and their dependent livelihoods. Final report to DFID andWorld Commission on Dams. Centre for Ecology and Hydrology,
Wallingford, United Kingdom.
Amarasinghe, U. A., Sharma, B. R., Aloysius, N., Scott, C., Smakhtin, V. U.,
De Fraiture, C. et al. 2005. Spatial variation in water supply anddemand across the river basins of India. Colombo, Sri Lanka:
International Water Management Institute. 37 pp. (IWMI Research
Report 83).
Atef, S. S., 2009. Reservoir operation considering environmental flows: acase study in Hari Rod river basin, Afghanistan. M. Engg. Thesis,
Water Engineering and Management, Asian Institute of Technology,
Thailand.
Bari, F. M. & Marchand, M., 2006. Introducing Environmental FlowAssessment In Bangladesh: Multidisciplinary Collaborative Research.
BUET-DUT Linkage Project, Phase III, Capacity Building in the Fieldof Water Resources Engineering and Management in Bangladesh.
Research Project No. 2 – Management of Rivers for Instream
requirement and ecological protection.
Bunn, S. E. & Arthington, A. H., 2002. Basic Principles and EcologicalConsequences of Altered Flow Regimes for Aquatic Biodiversity.
Environmental Management. 30 (4), 492–507
De Silva, S. S. & Smith, S. F., 2005. A review of stock enhancement
practices in the inland water fisheries of Asia. Asia-Pacific Fishery
Commission. RAP PUBLICATION 2005/12. Food and Agriculture
Organization of The United Nations Regional Office for Asia and The
Pacific, Bangkok, Thailand.
Falkenmark, M. & Galaz, V., 2007. Agriculture, Water and Ecosystems.
Swedish Water House Policy Brief Nr. 6.
FAO, 1996. Food production: the critical role of water. Technicalbackground document for the world food summit: 7, Rome: FAO.
Gleick P. H., 1996. Basic water requirements for human activities: Meeting
Basic Demand. Water International., 21 (2), 83 – 92.
Gleick, P. H., 1998. World’s water 1998–1999: The biennial report on
freshwater resources. Island Press, Washington, DC, 319 pp.

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Gleick, P. H., 2003. Water Use. Annual Reviews on Environmental
Resources. 28, 275-314.
Global Water Partnership (GWP), 2000. Framework For Action: Towards a
Water Secure World. Global Water Partnership, Stockholm.
Hollinshead, S. P. & Lund, J. R., 2006. Optimization of environmental
water purchases with uncertainty. Water Resources Research., 42 (8),art no. W08403.
Hughes, D., 2003. Environmental flow requirements in water resource
planning and operation. Water Resources Systems—Hydrological risk,
management and development. (Proceedings of symposium HS02b
held during IUGG2003 at Sapporo, July 2003). IAHS Publ. no. 281,
2003.
IWMI, 2005. Environmental flows: Planning for environmental water
allocation. Water policy briefing, Issue 15. International Water
Management Institute. October 2005.
Johnson, N. S. & Adams, R. M., 1988. Benefits of Increased Streamflow:
The Case of the John Day River Steelhead Fishery. Water Resources
Research., 24 (11), 1839-1846.
King, J., & Brown, C., 2006. Environmental Flows: Striking the Balance
between Development and Resource Protection. Ecology and Society.
11 (2): 26.
Millennium Ecosystems Assessment, 2005. Ecosystem and Human Wellbeing Synthesis, Island Press, Washington DC, USA.
Moore, M., 2004. Perceptions and interpretations of Environmental Flows
and implications for future water management – A Survey Study.
Master Thesis, Department of Water and Environment Studies,
Linkoping University Sweden.
Poff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L., Richter,
B. D., et al., 1997. The natural flow regime. BioScience. 47, 769–784.
Poff, N. L., Allan, J. D., Palmer, M. A., Hart, D. D., Richter, B. D.,
Arthington, A. H., et al., 2003. River flows and water wars: Emerging
science for environmental decision making. Frontiers in Ecology andthe Environment. 1 (6), 298-306.
Postel, S. L., & Richter B. D., 2003. Rivers for Life: Managing Water for
People and Nature. Island press, Washington DC.
Postel, S. L., Daily, G. C. & Ehrlich, P. R., 1996. Human appropriation ofrenewable fresh water. Science. 271, 785 – 788.
Rahman, M., 1998. A hydrologic determination of instream flow
requirement of the Ganges River. M. Engineering Thesis, Departmentof Water Resources Engineering, Bangladesh University ofEngineering and Technology, Dhaka.
Richter, B. D., Warner, A. T., Meyer, J. L. & Lutz, K., 2006. A
collaborative and adaptive process for developing environmental flow
recommendations. River Research and Application. 22, 297-318.
Saleth R. M. & Dinar A., 2004. The Institutional Economics of Water A
Cross-Country Analysis of Institutions and Performance. A copublication with the World Bank. Edward Elgar, UK & USA. pp. 398.
Saleth R. M., 2002. (Edited) Water resources and economic Development.
Edward Elgar publishing limited, UK & USA. pp527.

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Smakhtin, V. & Anputhas, M. 2006. An assessment of environmental flow
requirements of Indian river basins. Colombo, Sri Lanka:
International Water Management Institute. 42p. (IWMI Research
Report 107).
Smakhtin, V., Shilpakar, R. L. & Hughes, D. A. 2006. Hydrology-based
assessment of environmental flows: an example from Nepal.
Hydrological Sciences Journal. 51 (2), 207 – 222.
Smakhtin, V., Revenga C. & Doll, P., 2004. A pilot global assessment ofenvironmental water requirements and scarcity. Water international.
29 (3), 307 – 317.
Tharme, R. and Smakhtin, V., 2003. Environmental Flow Assessment in
Asia: Capitalizing on Existing Momentum. Proceedings of the FirstSoutheast Asia Water Forum, 17-21 November 2003, Chiang Mai,
Thailand.
Tharme, R. E., 2003. A global perspective on environmental flow
assessment: Emerging trends in the development and application ofEnvironmental flow methodologies for rivers. River Research andApplications. 19, 397–441.
Yang, H. P. Reichert, K. Abbaspour and A.J.B. Zehnder, 2003. A water
resources threshold and its implications for food security. Environ. Sci.
Technol. 37, 3048–3054.
Zobeyer, A. T. M. H., 2004. Application of Physical Habitat Simulation
Approach for instream flow requirement in the Surma river, M. Sc.
Engineering Thesis, Department of Water Resources Engineering,
Bangladesh University of Engineering and Technology, Dhaka.

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EXPERIMENTAL STUDYON SHAKING TABLE TEST
OF A CONCRETE GRAVITY DAMSUMETEE CHAROENWONGMIT,
EKACHAI YOOPRASERTCHAI, BUPAVECH PHANSRI,PENNUNG WARNITCHAI and KYUNG-HO PARK
School of Engineering and Technology
Asian Institute of Technology, Thailand
sumethee_joe@hotmail.com
ABSTRACTThis study deals with the shaking table testing conducted on smallscale concrete gravity dam models. Two models of the prototype dam with
118 m high and 94 m width at 1:100 scale are tested on a 1.2 m x 1.0 m
shaking table. The bentonite-concrete material is developed to match thematerial similitude requirements between the prototype and the model. The
scaled models typically employ two types of excitation: resonance andambient testing. The resonance frequency of two models showed obviously
different mode due to the effect of material properties. The propagation of
the cracks was observed in the neck area until the failure at the baseacceleration of about 0.55g-0.60g.
1. INTRODUCTION
Many concrete gravity dams have been designed with static and seismic
loading, to make sure that this structure will be able to resist a maximum
earthquake without the full and uncontrolled cracks. However, someconcrete gravity dams have been damaged due to the strong ground motion,for example Koyna Dam, India, 1967; Hsingfengkiang Dam, China, 1962;
Sefi-Rud Dam, Iran, 1990 and Pacoima Dam, California, 1971 and 1994.
For concrete gravity dams designed according to current design
criteria, the static and earthquake compressive stresses are generally muchless than the compressive strength of the concrete. However, linear dynamic
analyses of gravity dams show that the earthquake ground motion can
produce tensile stresses that exceed the tensile strength of the mass concrete.
Therefore, the nonlinear tensile cracking must be considered in the seismic
response of concrete gravity dams.
In order to predict the earthquake response of gravity dams, theexperimental shaking table tests have been conducted on small-scale models(Bakhtin & Dumenko 1979, Niwa & Clough 1980, Donlon 1989, Hall 1989,

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Donlon & Hall 1991, Lin et al. 1993, Zadnik & Paskalov 1992, Zadnik 1994,
Mir & Taylor 1995, 1996, Harris et al. 2000, Tinawi et al. 2000). Thesestudies considered special materials and length scale for investigation of thenonlinear aspects of dam response (cracking, joint opening, sliding behavior
under high compression, and cavitation in the water).
This study deals with the shaking table testing of a concrete gravity
dam, which is planed for the construction in the northern Thailand or
Myanmar. Two small-scale models were designed to maintain similituderelationships and be constructed and tested on the shaking table.
2. MODEL DEVELOPMENT
2.1 Similitude requirements
To simulate the nonlinear behavior of a small scale model
corresponding of its prototype or full scale, it is necessary follow the laws ofsimilitude requirements. These laws are determined by dimensional analysis
according to Buckingham’s Pi thorem. Using the scale factor definitions,
Fp / Fm = S F , t p / tm = St , m p / mm = Sm and l p / lm = Sl , the model andprototype relation give the formulas:
S F = Sl S m / St2(1)
S r = Sl 2 Sm / St2(2)

where F, t, m and l are force, time, mass and length, respectively. Subscript
p and m represent the prototype and model. A summary of the similitude
requirements established for this investigation is presented in Table 1.
Table 1: Scale factors for nonlinear seismic response analysis
Notation

Dimensions

Scale Factor

Acceleration due to gravity

g

LT-2

1

Linear dimension

l

L

Sl

Modulus of elasticity

E

FL-2

Sr

Stress

σ

FL-2

Sr

Strainε-1Poisson's ratio

υ

-

1

Ultimate strength

σcu, σtu

FL-2

Sr

T

T

Sl

Physical Quantity

Time

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1
Sl

T-1

f

Frequency

In this study, the length scale is set at 1/100 by considering a model
size that could be constructed and tested on shaking table. According tosimilitude requirement, the properties of prototype and model are shown in
Table 2.
Table 2: Similitude requirements and model material properties
Physical Property

Unit

Prototype

Scale Factor

Model

Density

kg/m3

2400

1

2400

compressive, f'c

MPa

10-15

100

0.1-0.15

tensile, ft

MPa

2.5

100

0.025

Young’s modulus, E

MPa

18200

100

182

compressive, εc

-

0.003

1

0.003

tensile, εt-

0.0001210.00012Ultimate strength

Ultimate strain

2.2 Development of material
One of the important works is to develop a material having
appropriate strength and elastic modulus. The unit weight is not consideredin the material development. This study used bentonite as a component to
reduce strength and elastic modulus.
2.2.1 Concrete mix design and material properties
In order to find the suitable mixture proportions, the trial mix was
separated into two phases. In the first phase, five mixtures were
proportioned with zero (control mixture), 10, 15, 20, and 25 % bentonite bymass of cement plus bentonite (Table 3). The trial mixes were made in thelaboratory. Standard 10×20 cm cylinders were made from each batch toidentify required properties. The modulus of elasticity and Poisson’s ratio
are determined by attaching two strain gages (vertical and horizontal
direction) at 7 and 28 day’s age.
Based on the test results of the first phase, the second concrete
mixture proportions were prepared to develop the materials such that theirExperimental
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a Concrete
GravityDam
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properties are matched to similitude requirements by varying othercomponents, for example, content of fine or coarse aggregate. The mix andproperties used for the model are shown in Table 4.
Table 3: Bentonite-concrete mixture proportions
Mix1(kg/m3)(Control)

Mix2

Mix3

Mix4

Mix5

(kg/m3)(kg/m3)(kg/m3)(kg/m3)

Water

258

309

314

332

332

Cement

178

160

151

142

95

Bentonite

0

18

27

36

24

Bentonite (%)

0%

10%

15%

20%

25%

Fine Aggregate

866.00798.00797.00773.00810.00Coarse Aggregate

873.00806.00804.00780.00820.00

w/c

1.45

1.93

2.08

2.34

3.49

w/b

1.45

1.74

1.76

1.87

2.79

Weight (kg)

2175

2091

2093

2063

2081

Table 4: Model concrete mix components
Actual model mix
Model
mix,
kg/m3

Volume inmix per 0.065
m3 batch

Water

404.46

26

Cement

81.23

5.28

Bentonite

36.46

2.37

Fine Aggregate

1056.92

68.70

Coarse Aggregate (No.4-3/8"): wet

1060.00

68.90

Component

w/c

4.98

w/b

3.44

Weight(kg)

300
300

2639.08

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In this study, two dam models were constructed. Three batches weremixed in each model and nine standard 10cm x 20cm cylinders werecollected from each batch to test the physical properties. Compressive
strength was given from compression test at 28 days after casting. Ultimate
tensile strength of specimens was obtained using two different methods:
direct tensile method and splitting tensile method. Also, the physicalproperties of the mix are shown in Table 5.
Table 5: Properties of model materials

Physical property

Unit

Targetvalue

Actual model mix
at 28 days
Model 1Model 2

0.317

0.399

- Direct tensile

0.029

0.054

- Splitting tensile

0.01350.0103- Ec : Compressive test

1,797.65

905.82- Et : Tensile test

11.56

14.16

Ultimate compressive strength, f'cMPa

0.1-0.15

Ultimate tensile strength, ft

MPa

0.025

Young's modulus, E

MPa

182

kg/m3

2,400

1,907.44

1,936.87

Ultimate compressive strain, εc

-

0.003

0.001390.00262

Ultimate tensile strain, εt-

0.000120.00495

0.00429

Poisson ratio--

0.2026

0.1930Mass density, ρ

3. EXPERIMENT SET-UP AND PROCEDURE
3.1 Model construction and instrumentation
For the experiment the model was constructed on the floor, mounted
on a shake table, and then tested at the time of 35 days for model 1 and 28
days for model 2. The 1/100 scale chosen resulted in a 1.18 m tall model
weighing 263 kg. Figure 1 shows the dam model mounted on the shake
table. Instrumentation was designed to measure displacements andaccelerations on the model and from the input actuator. The lateral bracingwith two plastic rollers were conducted to prevent transverse vibrating. Also,
the general instrumentation locations and detailed is shown in Figure 1.

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0.94 m.
0.26 m.
0.225 m.

0.68 m.

0.13 m.
ACC-3
LVDT-40.21 m.

STR-1

0.28 m.

STR-2,4

LVDT-30.455 m.
1.18 m.

ACC-2

LVDT-20.325 m.

STR-3,5ACC-10.12 m.

LVDT-10.31 m.

0.47 m.

Figure 1: Configuration of a small-scale concrete gravity dam
3.2 Input motion
In order to determine resonant frequencies of the modal, model
response was recorded at even frequencies from 2 to 30 Hz with a constant
input acceleration of 0.05g. Figure 2 shows the acceleration of the top of themodel along the excitation axis at even frequencies from 10 to 30 Hz. The
normalized displacement at the top of model at even frequencies from 2 to30 Hz is shown in Figure 3.
0.30

0.40

0.25

0.30
0.20

0.15

Accerelation, g

Accerelation, g

0.20
0.10
0.05
0.00
-0.05

0.10
0.00
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0

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3

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Time, sec

Time, sec

140

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Figure 2: Resonance testing: horizontal acceleration at the top of the model

100
80
60
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0

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10 12 14 16 18 20 22 24 26 28 30 32
Frequency, Hz

2500.00
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0

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4

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10 12

14 16

18 20

22 24 26 28

30 32

Frequency, Hz(a) Model 1(b) Model 2
Figure 3: Normalized displacement at the top of model at frequencies
from 2 to 30 Hz

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In the model 1, the first excitation frequency which showed an
amplification of acceleration above the input was 14 Hz. A sinusoidal
motion of 14 Hz was chosen as the excitation frequency for all subsequent
tests. For the model 2, acceleration with the frequency 24-28 Hz at base ofmodel showed the excitation of the model. Therefore, the sinusoidal motionof 28 Hz was chosen as the excitation frequency for all subsequent tests. In
summary, the test program consisted of two phases: (1) the determination of
the lowest resonant frequency by shaking the model at 0.05g at evenfrequencies from 2 to 30 Hz, and (2) the failure of the model by shaking it at
the lowest resonant frequency, increasing the acceleration amplitude from
0.05g to failure in 0.0125g steps holding at each step for 5 seconds.
4. TEST RESULTS
For ambient testing, the maximum amplitude accelerations at the baseare 0.55g-0.60g and 0.60g-0.65g for the first and second models,
respectively. Crack was initiated at the changing slope point and developedaround the neck of model below the crest 30-40 cm. Figure 4 shows crack
patterns of two models.

(a) Model 1(b) Model 2

Figure 4: Crack pattern at the top of dam model
5. CONCLUSION(1) A new concrete mix design was proposed. The mix used benotite to
reduce strength properties of the concrete. From the results of mix
properties, it is obvious that not all parameters meet the similitude
requirement simultaneously.

Experimental
ofof
a Concrete
GravityDam
Experimental Study
Studyon
onShakingShakingTable
TableTest
Test
a Concrete
Gravity
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July 2009, Thailand(2) An experimental study was conducted on the shaking table. The
propagation of the cracks was observed in the neck area until the
failure at the base acceleration of about 0.55g-0.60g.
REFERENCES
Bakhtin BM, Dumenko VI, 1979. Seismic stability of a concrete gravity
dam having a lightweight profile. Hydrotechnical Construction. 5,445-450.
Donlon WP, 1989. Experimental investigation of the nonlinear seismic
response of concrete gravity dams. Earthquake Engineering ResearchLaboratory. Report No. 89-01. California Institute of Technology,
Pasadena.
Donlon WP, Hall JF, 1991. Shake table study of concrete gravity dams:
Shake table tests and numerical analyses. Earthquake Engineeringand Structural Dynamics. 20, 769-786.
Hall JF, 1989. The dynamic and earthquake behaviour of concrete dams.
Soil Dynamics and Earthquake Engineering. 7, 58-121.
Harris DW, Snorteland N, Dolen T, Travers F, 2000. Shaking table 2-D
models of a concrete gravity dam. Earthquake Engineering andStructural Dynamics. 29, 769-787.
Lin G, Zhou J, Fan C, 1993. Dynamic model rupture test and safety
evaluation of concrete gravity dams. Dam Engineering. IV(3), 769786.
Mir RA, Taylor CA, 1995. An experimental investigation into earthquakeinduced failure of medium to low height concrete gravity dam.
Earthquake Engineering and Structural Dynamics. 24, 373-393.
Mir RA, Taylor CA, 1996. An investigation into the base sliding response of
rigid concrete gravity dams to dynamic loading. Earthquake
Engineering and Structural Dynamics. 25, 79-98.
Niwa A, Clough RW, 1980. Shaking table research on concrete dam models.
Earthquake Engineering Research Center. Report No. UCB/EERC-8005. College of Engineering, University of California, Berkeley,
California.
Tinawi R, Leger P, Leclerc M, Cipolla G, 2000. Seismic safety of gravity
dams: from shake table experiments to numerical analyses. Journal of
Structural Engineering. 126(4), 518-529.
Zadnik B, Paskalov T, 1992. Dam stability and time-dependent coefficientof friction. Proceedings of the 10th World Conference on Earthquake
Engineering, Balkema, Rotterdam, The Netherlands. 4633-4638.
Zadnik B, 1994. Motions of rigid unsymmetric bodies and coefficient offriction by earthquake excitations. Structural Engineering and
Mechanics. 2(3), 257-267.

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TUNNELING-INDUCED GROUND MOVEMENTIN TWIN-TUNNELS OF BANGKOKMRT SUBWAY PROJECTTRAN VIET DUNG and KYUNG-HO PARK
School of Engineering and Technology, Asia Institute of Technology
vietdungtran82@gmail.com
ABSTRACTThis study deals with tunneling-induced ground movement in twintunnel of Bangkok MRT Blue Line subway project. First the measured
longitudinal surface settlements are evaluated. Second the simplified 3D
numerical simulations, based on 2 main parameters ( the partial stressrelease factor αdec and the length of unlined zone Ldec), are examined fordrained and undrained cases. The results of the numerical simulation may
be used for the prediction of tunneling-induced ground movements in theplanned South Blue Line Extension subway project.

1. INTRODUCTION
Generally three methods (analytical, empirical and numerical methods) havebeen used for the prediction or estimation of tunneling-induced groundmovements. Among those methods, numerical simulation of EPB (Earth
Pressure Balance) tunneling requires complex aspects, such as the soil
excavation, the overcut or annular space, the application of the face pressure,the installation of the definitive supports, the grouting of the annular space,
non-linear behavior of the soil, and the soil-structure interaction. In
particular, in order to take into account the boundary condition around theopening during the tunnel construction process, two approaches have beenused (Cheng et al., 2008): the force controlled approach and thedisplacement controlled approach (Sagaseta, 1987; Longanathan and Poulos,
1998; Park, 2004, 2005).
The force controlled approach simulates tunneling by removing nodal
forces corresponding to the initial soil stress state. However, the method
predicts wider surface settlement trough accompanied with higher far field
settlement than field or centrifuge test (Simpson et al., 1979; Dasari, 1996;
Leca, 1996; Stallebrass et al., 1996). Recently Mroueh and Shahrour (2008)
proposed a simplified modeling technique by applying the convergenceconfinement concept to 3D numerical simulation. In this way, the out-ofbalance forces, developed by tunnel excavation, are not entirely released but
partially released at each construction stage.

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The measured longitudinal surface settlements in the single tunnel ofthe MRT Blue Line subway project were evaluated and the corresponding
3D numerical simulations were conducted (Tran, 2008). This study deals
with the evaluation of the measured longitudinal surface settlement and thesimplified 3D numerical simulation for twin-tunnel.
2. EVALUATION OF THE MEASURED SURFACE SETTLEMENTDATA
In Zone 23 from Thiam Ruamit to Pracharar Bumphen, there were total 46
ground surface makers, installed along the centreline of the tunnel alignment.
The primary object of the measurements with settlement makers was tomeasure the maximum surface settlements above the centreline. In addition,
the reading was taken over time covering a period before shield approaching
and after shield passing. The location of the ground settlement maker isshown in Figure 1.

Figure 1: Location of ground settlement makers
The surface settlement data of total 46 points are collected. Among
those, the data of only 23 points are selected for the further study to avoid
the possible measurement error from other points.

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5
0-5
Surface settlement (mm)

-10
-15
-20
-25
-30
SB
-35

NB

-40
-50

-40

-30

-20

-10
0
10
Shield position (m)

20

30

40

50

Figure 2: Measured surface settlement
Figure 2 shows the summary of the surface settlement measurementsof the Southbound (SB, the first excavated tunnel) and the Northboundtunnel (NB, the second excavated one). In both tunnels, the longitudinalsurface settlement mostly occurred between 40 m (≈ 7D) to – 40 m (≈ 7D)
and can be divided into three zones: Zone 1, about 30 m distance, theground begins to deform but the shield not reached; Zone 2, about 15 m
distance, starts from the distance in front of the shied face and develops
significantly during the shield passing; Zone 3 begins after the performanceof the tail void grouting. For the Southbound tunnel, the maximum surface
settlement varied from 14 mm to 33 mm and the surface settlement at thetunnel face (wa), wa = 4 ~ 16 mm, the surface settlement induced in theunlined zone (wb), wb= 2 ~ 6 mm and the surface settlement due to complete
released stress of the confinement (wc), wc = 8 ~ 22 mm. And for the
Northbound tunnel, the maximum surface settlement varied from 14 to 36
mm. The value of wa = 6 ~ 19 mm, wb = 2 ~ 7 mm, and wc = 5 ~ 18 mm.
Due to the excavation of the Southbound tunnel (the first tunnel) the initial
settlement of about 4 ~ 6 mm was recorded.
3. 3D NUMERICAL SIMULATION
Numerical simulation has been conducted using the commercial finite
different code FLAC3D. Figure 3 and 4 show the ground condition and themodelling mesh. The constitute law used for the soil element is the elastoplastic associated Mohr-Coulomb model. The left right boundaries are
hinged to prevent the movement in horizontal direction but are free to allow
Tunneling-Inducedof of
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vertical direction. The width of the left through the right boundaries is set toprevent boundaries effect on the perimeter of the tunnel. At the bottom, theboundary is fixed against vertical movement.

(a) Drained case(b) Undrained case
Figure 3: Typical subsoil conditions in Zone 23

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Figure 4: Modelling mesh
Excavation and the shield advance are simulated through the step bystep procedure as follows:
In the first step of excavation, (a) remove the ring of soil element
equal to 7 m ahead of the shield (6m for length of the shield and the lengthof the unlined zone and 1 m for the supported zone where the lining will be
installed), (b) release stress around the tunnel, (c) grout the removed area,(d) install lining 1 m at the end of the excavated and measure thedisplacement at the tunnel boundary and at the surface, (e) back fill material
and apply face pressure to prepare for the next excavation.
In the step of tunnel face advance, (a) remove the ring of soil element
ahead of the tunnel face, (b) release the stress using αdec and Ldec, (c) grout
the removed area, (d) activate the lining element in removed are with thelength of Llin and take the measurement for the element displacement, (e)apply the pressure at the tunnel face. Then repeat from (a) to (e).
4. RESULTS
4.1 Drained case
Figure 5 shows the numerical results of the longitudinal surface
settlement using αdec = 0.4 and 0.6, together with the measured one. Ldec isfixed to 1D (D: diameter of the EPB). Similar to the measured surface
settlement, a portion of ground settlement occurred due to the excavation of
the first tunnel. The value of αdec highly affects to the simulated results(value of wa, wb, wc or the maximum surface settlement value). The
maximum surface settlements vary from 27 to 37 mm.

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50-5Surface settlement (mm)

-10
-15
-20
SB

-25

NB
SB (0.4)-30

SB (0.6)
NB (0.4)-35

NB (0.6)

-40
-50

-40

-30

-20

-10
0
10
Shield position (m)

20

30

40

50

Figure 5: Simulated results (Drained case)
4.2 Undrained case
Figure 6 shows the numerical results for undrained case. In undrained
case, the value of αdec = 0.4, 0.5, 0.6 and Ldec is fixed to 1D. Similar to thedrained case, the ground settlement mostly occurred between -4D ~ 4D anda small portion settlement occurred at the beginning of the excavation due tothe excavation of the first tunnel. The value of αdec is also the key parameterand mainly affect to the simulated results. The maximum surface
settlements vary from 27 to 40 mm.

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5
0-5

Surface settlement (mm)

-10
-15
-20

SB
NB

-25

SB (0.3)-30

SB (0.4)
SB (0.5)-35

NB (0.3)
NB (0.4)-40

NB (0.5)

-45
-50

-40

-30

-20

-10
0
10
Shield position (m)

20

30

40

50

Figure 6: Simulated results (Undrained case)
5

CONCLUSION

In this study, measured longitudinal surface settlements were evaluated andthe corresponding 3D numerical simulations were conducted for twin-tunnel.
The following conclusions can be drawn:(1) The maximum surface settlements varied from 14 mm to 36 mm:
wa = 6 ~ 9 mm, wb= 2 ~ 7 mm and wc = 5 ~ 18 mm. The excavationof the first tunnel causes 4 ~ 6 mm in the settlement of the second
one.
(2) The longitudinal surface settlement mostly occurred between 40
m (≈ 7D) to – 40 m (≈ 7D) and can be divided into three zones.
(3) The parameter αdec highly affects the maximum surface
settlement. As αdec increases, the maximum surface settlement
increases. The measured surface settlements can be ranged withinαdec = 0.4 ~ 0.6 in drained case and 0.3 ~ 0.5 in undrained case.

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REFERENCES
Cheng, C.Y., Dasari, G.R., Chow, Y.K., Lueng, C.F., 2008. Finite
element analysis of tunnel-soil-pile interaction using displacement
controlled model. Tunnelling and Underground Space Technology, 22, 450466.
Leca, E., 1996. Modelling and prediction for bored tunnels. Proceedings ofInternational Symposium on Geotechnical Aspects of UndergroundConstruction in Soft Ground, Balkema, London, 27–42.
Longanathan, N., Poulos, H.G., 1998. Analitical prediction for tunneling
induced ground movement in clay. Journal of Geotechnical andGeoenviromental Engineering, 124(9), 846-856.
Mroueh, H., Shahrour, I., 2008. A simplified 3D model for tunnel
construction using tunnel boring machine. Tunnelling andUnderground Space Technology, 23, 38-45.
Park, K.H., 2005. Analytical solution for tunnelling-induced groundmovement in clays. Tunnelling and Underground Space Technology,
20(3), 249-261.
Park, K.H., 2004. Elastic solution for tunnelling-induced ground movement
in clays. International Journal of Geomechanics ASCE, 4(4), 310-318.
Sagaseta, C., 1987. Analytical of undrained soil deformations due to groundloss. Geotechnique, 37(3), 301-320.
Simpson, N.J., O’Riordon, Croft, D.D., 1979. A computer model for the
analysis of ground movements in London clay. Geotechnique, 29(2),149–175.
Stallebrass, S.E., Grant, R.J., Taylor, R.N., 1996. A finite element study ofground movements measured in centrifuge model tests of tunnels.
Proceedings of International Symposium on Geotechnical Aspects ofUnderground Construction in Soft Ground, Balkema, London, 595–
600.
Tran, V.D. 2008. A simplified 3D numerical simulation for subway tunnel
construction in Bangkok. AIT Master Thesis.

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2ndStudent ReportREPORT FROM STUDENT PARTICIPANTS
ON 2nd JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURES
Venue: Asian Institute of Technology, Pathumthani, Thailand
Date: July 6-7, 2009

AIT Center

BRT terminal
Participants (from left to right, above picture): Shumon Mori;
Dr. Muyeyoshi Numada; Mari Sato; German Alberto Pardo Rios;
Hoang Thuy Linh; Tae Soo Tang; Vu Viet Hung; Dr. Shinji Tanaka

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July 2009, Thailand
July 2009, Thailand

PRESENTATION SESSION
On 06 July, 2009, 2ndJoint Student Seminar on Civil
Infrastructures was held at
Asian Institute of Technology(AIT) by International Centerfor Urban Safety Engineering(ICUS) with great success.
Although prevalence of new
influenza, 34 peoples weregathered from Thailand, Korea
Japan team at AIT
and Japan at AIT and shared
fruitful time together. The Japan team consisted of 6 members from various
nationalities: 1 Colombian, 1 Korean, 2 Vietnamese and 2 Japanese.
Although this was first time to meet each other, we shared all the time
together in good friendship.

Joint Student Session
theme successfully with great efforts.

In
thepresentation
session 5 professors gave us
lectures and then 18 students
presented their researches withvarious fields such as Nanotechnology,
Transportation
Engineering,
GeotechnicalEngineering,
Concrete
Structure Engineering, Disaster
Mitigation Engineering andApplied Remote Sensing. Eachof us presented their researchAfter
presentation
session all members wereinvited to the party held by
ICUS. At the opening of party
3 students from each country
were awarded as best presenter
voted by all students. The party
was so lively and gave us goodchance for cultural exchange
and friendship.
In this conference we
The party held by ICUS
had very precious experiences
to study various interesting topics related on civil infrastructure field.
Furthermore, toward this conference, we had good opportunities to practicepresentation skill in English. Although the session was short, we spent sovaluable and unforgettable day.
(by Shumon Mori)

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On The Visit Bangkok’s
Public Transportation Facilities
At the seminar, Dr. Thirayoot Limanond gave us a very fruitfulintroduction about the traffic situation and public transportation system
in Bangkok. And on the next day, we had a great opportunity toexperience this system and visit its facilities. On one day, we had ridden
on most of public transportation modes in Bangkok including MRT(subway), BTS (skytrain), taxi, bus, tuktuk and boat. It was very
interesting to all of us.

Picture1: Tuk tuk

Picture 2: Taxi

Bus and Taxi are quite popularly used with reasonable price but they facecongestion at the peak hour. Tuk tuk - a small size taxi, which made usvery impressed and excited for running fast and threading movement, isalso very popular. But, frankly speaking, it was quite scary!

Picture3: Boat and Boat Pier in Chao Phraya River
The Chao Phraya River and channel system in the city are also utilized asa very convenient way of transport. The boat which took us from
Asokepetchaburi boat pier to Pratunam boat pier was very crowded not
only by the tourists but also by local people.
Transportation infrastructure in Bangkok is quite well developed but
congestion still occurs everyday. Two BTS routes and one MRT routehas been constructed and brought in use. And due to budget constraints,
Bangkok is promoting Bus Rapid Transit (BRT) system in near futureplan.
To keep our good memories of this trip, we record the route we took byusing Bangkok map as following.

Reprot from Student Participations on The 2nd Joint Student Seminar on Civil Infrastructures

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July 2009, Thailand
July 2009, Thailand
List of locations:
A: Chatuchak Park MRT station
B: Phetchaburi MRT station; and Asokepetchaburi boat pier
C: Pratunam boat pier
D: Siam BTS station
E: Non changsri BTS station
F: Sathon Express Boat Pier
G: Tha Chang Wang Luang Express Boat Pier316

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July 2009,July
Thailand
2009, Thailand
July 2009,2009, Thailand
ThailandJuly

ICUS Report 37

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all════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════The 2nd The
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2nd Joint
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2nd

July 2009, Thailand

ICUS
ICUS Report
37Report 37
ICUS Report 37
ICUS Report 37

The inseminarin AITand thewholetrip in Bangkok
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══════════════════════════════════════════════════════════════════════════════════════nd═══════════════════════════════════════════
Firstof allwas my pleasure
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═════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════
Joint Seminar
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Civil Infrastructures
The JointThe
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on Civil on
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2ndSeminar on Civil Infrastructures
318
Reportformparticipants
student participants
on 2ndJointSeminar
Student on
SeminaronJoint
CivilStudent
Infrastructures
317
Report form
student
on 2nd Joint
Student
Civil
Infrastructures
317
Report form student participants on 2nd Joint Student Seminar on Civil Infrastructures
317
Report form student participants on 2nd Joint Student Seminar on Civil Infrastructures317