3rd Joint Student Seminaron Civil Infrastructures
29-30 July 2010
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
Myongji University, Korea
Chonnam National University
andThe University of Seoul

Organization Teams
Dr. Kyung-Ho Park (AIT, Thailand)
Dr. Kunnawee Kanipong (AIT, Thailand)
Dr. Jaroon Rungamornrat (Chula Univ., Thailand)
Dr. Kasem Choocharukul (Chula Univ., Thailand)
Dr. Anat Ruangrassamee (Chula Univ., Thailand)
Dr. Pison Udomworrarat (KMIT-Thonburi, Thailand)
Dr. Chayanon Hansapinyo (Chiangmai Univ., Thailand)
Dr. Thirayoot Limanond (Suranaree Univ., Thailand)
Dr. Ampol Karoonsoontawong (KMIT-Thonburi, Thailand)
Dr. Shinji Tanaka (UT, Japan)
Dr. Akiyuki Kawasaki (UT, Japan)
Dr. Wataru Takeuchi (UT, Japan)
Dr. Kato Hironori (UT, Japan)
Dr. Uchida Kenetsu (Hokkaido U, Japan)
Dr. Shinya Hanaoka (TIT, Japan)
Prof. Atsushi Koizumi (WU, Japan)
Dr. Sano Kazushi (Nagaoka Univ. of Tech, Japan)
Prof. Mitsutaka Sugimoto (Nagaoka Univ. of Tech, Japan)
Dr. Hyunmyung Kim (MJU, Korea)
Prof. Yongtaek Lim (CAU, Korea)
Prof. Ikki Kim (HYU, Korea)
Prof. Young-Ihn Lee (SNU, Korea)
Prof. Kee-Choo Choi (Ajou U, Korea)
Dr. Jung-Hoon Ryu (Ajou U, Korea)
Prof. Young-Uk Kim (MJU, Korea)
Prof. Ki-Jung Kum (MJU, Korea)
Prof. Young-Tae Son (MJU, Korea)
Dr. Jung-Sik Kong (KU, Korea)
Prof. Hee-Jung Kang (KKU, Korea)
Dr. Seok-Won Lee (KKU, Korea)
Dr. Gye-Chun Cho (KAIST, Korea)
Dr. Gun-Ha Kim (HNU, Korea)
Dr. Jin-Hyuk Jung (YSU, Korea)
Dr. Tae-Wan Kim (CAU, Korea)

Dr. Chang-Yong Kim (KICT, Korea)
Dr. Seong-Won Lee (KICT, Korea)
Dr. Dong-Hoon Shin (K-water, Korea)
Dr. Ik-Soo Ha (K-water, Korea)
Dr. Seungkirl Baek (KHC, Korea)
Dr. Jun-Seok Oh (Western Michigan Univ., USA)
Dr. Borinara Park (Illinois State Univ., USA)
Prof. Keh-Jian Shou (NCHU, Taiwan)
Prof. Muhammed Alamgir (KUET, Bangladesh)Dr. Eckhard Kraft (BU Weimar, Germany)
Dr. Semih Kuchkarslan (ITU, Turkey)

3rd JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURES
July 2010
PREFACEIn this era of rapid globalization, having an international sense andbroad human resource network is essential. In particular, building up a good
relationship with various communities during young school days will be ofadvantage in the future. To give such an opportunity to students from Asian
countries, we held the 1st and 2nd joint student seminar in July 2008 and July
2009, respectively, and following this success the “3rd Joint Student Seminaron Civil Infrastructures” was held on 29-30th July, 2010.
The objectives of this seminar are:1) to experience the international seminar,
2) to improve the presentation skill,3) to share the research information and friendships.
The number of participants was 62, consisting of 6 faculties and 56
students from Konkuk University, Chonnam National University, University
of Seoul, Myong Ji University, Khulna University of Engineering &Technology, The University of Tokyo and Asian Institute of Technology.
On the first day, we had a presentation session, having 3 faculties’
lectures and 16 students’ presentation. The topics covered wide range areasof civil engineering and every student did their best in his/her presentation.
During the seminar, students and faculties had lively exchange of views
beyond their specialty, culture and nationality. At the end of the seminar,
excellent presentation awards were given to the following 4 students.
1. Mr. Akira Kodaka from Thailand
2. Mr. Hirotoshi Kishi from Japan
3. Ms. Parichart Pattanamekar from Korea
4. Mr. Sattrawut Ponboon from Thailand
On the second day, we had a field visit to the PTT Public Company
Limited (a gas transmission pipeline company); the Sea Turtle Conservation
Center and the Thai Island and Sea Natural History Museum by the Royal
Thai Navy and Pattaya Floating Market.
The seminar was quite successful and fruitful: this seminar gave not
only knowledge and information but also a lot of other stimuli to thestudents. 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.
HYUNMYUNG KIM, AKIYUKI KAWASAKI,KYUNG-HO PARK, SHINJI TANAKA AND KUNNAWEE KANIPONG

ICUS Report 45
ICUS Report 45

Table of Contents
Page

Opening Session
SECTION 1

SECTION 2

Invited Lectures
Some Key Issues on Disaster Risk Reduction
Pennung Warnitchai

1

Developing a Landslide Disaster Management System
Akiyuki Kawasaki

27

Korea's Low-Carbon Green Growth Strategy and Implications
for the Industrial Sector
Hee-Jung Kang

41

Student Presentations
Comparison between Node-based and Link-based Shortest Path Algorithms
Yongtaek LIM

51

Spatiotemporal Patterns Analysis of Biomass Burning PM2.5 over Asia with
MODIS
Hirotoshi Kishi

63

The Development of Land Use and Transportation Integrated Model for
Urban Sustainability Evaluation
Ajay Khatri Chettri

71

A Study on Municipal Urban Planning Regulation and Guidance for Flood
Disaster Mitigation in Japan
Yuto Shiozaki

83

Analysis of Motorcycle of Accident Cost in Thailand by Willingness-to-Pay
MethodPreeda Chaturabong

93

Trial Test of Microbaial Cementation on Toyoura Sand Filled in a Syringe
Makoto Hosoo

103

Foundation System Applied to Construct the Civil Infrastructures in theSouth-West Region of Bangladesh
Md. Julfikar Hossain

111

Development on Precast Hybrid Frame-Wall Buildings for Seismic Resistance
May Phyo Aung

127

Estimating Vehicle Trajectories on Urban Arterials by Data Fusion
Farhana Naznin

145

Estimating Intermodal Freight O-D matrix from Observations
Parichart Pattanamekar

155

3rd Joint
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Infrastructures
3rd
Joint Student

July 2010, Thailand
July 2010, Thailand

SECTION 3

Pedestrian flow Simulation on Peak Time
Chalat Tipakornkiat

167

Problems of Existing Sanitation System in Khulna City of Bangladesh: A CaseStudy
Sk. Ariful Islam

181

Roadside Hazards through Accident In-Depth Study
Sattrawut Ponboon

191

A study on the Optimal Selection of Installation of the Point Detector
Considered the Characteristics in the Congested Section in Freeway
Sanghwa Lee

205

Development of Chemically Prestressed Ferrocement
Akira Kodaka

219

Experimental Study of Seismic Behavior of Scaled Non-Engineered Masonry
Structures Retrofitted by PP-Band MeshShahid Nazir

231

Student Report
Report from Student Participations on the 3rd Joint Student Seminar on
Civil Infrastructures
Yongtaek Lim, Hirotoshi Kishi, Yuto Shiozaki, Makoto Hosoo, Farhana Naznin,
Akira Kodaka and Shahid Nazir

241

3rd
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ICUS Report 45
ICUS Report 45

Registration

Opening ceremony (Dr. Shinji Tanaka)

Dr. Pennung Warnitchai

Dr. Akiyuki Kawasaki

Prof. Hee Jung Kang

Mr. Dae Seong Kim

Mr. Hirotoshi Kishi

Mr. Ajay Khatri Chettri

3rd
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Civil Infrastructures
Infrastructures
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July 2010, Thailand
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ii
ii

Mr. Yuto Shiozaki

Mr. Preeda Chaturabong

Mr. Makoto Hosoo

Md. Julfikar Hossain

Ms. May Phyo Aung

Ms. Farhana Naznin

Ms. Parichart Pattanamekar

Mr. Chalat Tipakornkiat
3rdSeminar on
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Civil Infrastructures
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ICUS Report 45
ICUS Report 45

Sk. Ariful Islam

Mr. Sattrawut Ponboon

Ms. Sanghwa Lee

Mr. Akira Kodaka

Mr. Shahid Nazir

Excellent Presentation from Japan

Excellent Presentation from Thai

Excellent Presentation from Thai

3rd
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July 2010, Thailand
July 2010, Thailand

Presentation at Gas Transmission Pipeline Company

Presentation at Gas Transmission Pipeline Company

Presentation at Gas Transmission Pipeline Company

iv
iv

3rd
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3rd Joint
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ICUS Report 45
ICUS Report 45

Seminar: Group Photo at AIT CC

Field Trip: Group Photo at PTT Public Company Limited

3rd
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on Civil
Civil Infrastructures
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3rd Joint
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Invited LecturesÍÑÓÛ ÕÛÇ ×ÍÍËÛÍ ÑÒ Ü×ÍßÍÌÛÎ Î×ÍÕÎÛÜËÝÌ×ÑÒPENNUNG WARNITCHAI
School of Engineering and Technology
Asian Institute of Technology, Thailand
pennung@ait.ac.thÌ¸» í®¼ Ö±·²¬ Í¬«¼»²¬ Í»³·²¿® ±² Ý·ª·´ ×²º®¿­¬®«½¬«®»­
ß×Ì Ý»²¬»®ô îç Ö«´§ îðïðÐ»²²«²¹ É¿®²·¬½¸¿·Ý±±®¼·²¿¬±® ±º Í¬®«½¬«®¿´ Û²¹·²»»®·²¹
Ý±±®¼·²¿¬±® ±º Ü·­¿­¬»® Ð®»°¿®»¼²»­­ô Ó·¬·¹¿¬·±² ¿²¼ Ó¿²¿¹»³»²¬
ß­·¿² ×²­¬·¬«¬» ±º Ì»½¸²±´±¹§

Wenchuan Earthquake (2008), China
Magnitude = 7.9
Death Toll > 70,000

11July 2010, Thailand
July 2010, Thailand

Haiti Earthquake (Jan 12, 2010)
M 7.0, focal depth 13 km
Downtown Port au Prince
Death Toll > 200,000

22

3rdJoint
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3rdICUS Report 45
ICUS Report 45

Ç±¹§¿µ¿®¬¿ Û¿®¬¸¯«¿µ» øîððê÷
Ó¿¹²·¬«¼» ã êòîÜ»¿¬¸ Ì±´´ ã ëôððð

Some KeyKey Issues
IssuesononDisaster
DisasterRisk
RiskReduction
Reduction

33

July 2010, Thailand
July 2010, ThailandÊËÔÒÛÎßÞ×Ô×ÌÇ ÚßÝÌÑÎÍ
Ò±²ó­»·­³·½ Ü»¬¿·´·²¹Í¬®±²¹ Þ»¿³ŠÉ»¿µ Ý±´«³²
Í±º¬ñÉ»¿µ Ú·®­¬ Í¬±®§Ì±®­·±²¿´ ×®®»¹«´¿®·¬§

íÜóª·»© ¹»²»®¿¬»¼ ¾§ ÍßÐîððð
ïð

Building Response to EQ Ground Shaking

Lateral-Torsional Movement (period = 0.50 sec)44

3rdJoint
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3rdICUS Report 45
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9Some KeyKey Issues
IssuesononDisaster
DisasterRisk
RiskReduction
Reduction55

July 2010, Thailand
July 2010, Thailand

Cyclone Nargis
May 2, 2008
146,000 Deaths
in Myanmar

Storm Surge by Cyclone Nargis
Archarlay Village, Myanmar

66

3rdJoint
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3rdICUS Report 45
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Ì¸» îê Ü»½»³¾»®
îððì Ó»¹¿¬¸®«­¬Û¿®¬¸¯«¿µ»Ó¿¹²·¬«¼»æ çòíÎ«°¬«®» Ô»²¹¬¸æïîðð µ³

Ishii et al., 2005
Nature¸¬¬°æññ­¬¿ººò¿·­¬ò¹±ò¶°ñµ»²¶·ò­¿¬¿µ»ñÍ«³¿¬®¿óÛò¸¬³´

Some KeyKey Issues
IssuesononDisaster
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77

July 2010, Thailand
July 2010, ThailandÌ­«²¿³· Ú´±±¼·²¹Õ¿³¿´¿ Þ»¿½¸ô Ð¸«µ»¬Õ¸¿± Ô¿µô Ð¸¿²¹óÒ¹¿
Ó¿¨·³«³ É¿¬»® Ô»ª»´

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CivilInfrastructures
Infrastructures
3rdICUS Report 45
ICUS Report 45

×²¼·¿² Ñ½»¿² Ì­«²¿³· É¿®²·²¹ Í§­¬»³Ì¸» Ü»ª»´±°³»²¬ ±º ×²¼·¿² Ñ½»¿² Ì­«²¿³· É¿®²·²¹ Í§­¬»³
ø×ÑÌÉÍ÷ ·­ ½±±®¼·²¿¬»¼ ¾§ ×ÑÝ ±º ËÒÛÍÝÑò

Some KeyKey Issues
IssuesononDisaster
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RiskReduction
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99

July 2010, Thailand
July 2010, ThailandÒ¿¬·±²¿´ Ý»²¬»®­

Î»¹·±²¿´ Ý»²¬»®

ß¬ Î·­µÓ±²·¬±®·²¹ Ò»¬©±®µÍ±«®½»æ ß­·¿² Ü·­¿­¬»® Ð®»°¿®»¼²»­­ Ý»²¬»®

Í¿¬»´´·¬» Ì®¿²­½»·ª»®

Í»¿ Ô»ª»´ Ñ¾­»®ª·²¹ Í¬¿¬·±² ·²Ì¿°¿± Ò±· ×­´¿²¼ ²»¿® Ð¸«µ»¬Î¿¼¿® Í»²­±®

Ü¿¬¿ Î»½±®¼»®

Í¬¿ºº Ù¿«¹»Ð®»­­«®» Í»²­±®

10
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3rdJoint
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Seminaron
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3rdICUS Report 45
ICUS Report 45Ü»»° Ñ½»¿² Í»²­±®­Ü¿¬¿ Ý±´´»½¬·±²ñÐ®±½»­­·²¹ Ý»²¬»®

Some KeyKey Issues
IssuesononDisaster
DisasterRisk
RiskReduction
Reduction

11
11

July 2010, Thailand
July 2010, Thailand

12
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3rdJoint
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3rdICUS Report 45
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The 1995 Kobe EarthquakeÍ»·­³·½ Ü¿³¿¹» ß­­»­­³»²¬ «­·²¹ Ù×ÍÞ«·´¼·²¹×²ª»²¬±®§ïòððòè

Í³¿´´ Ü¿³¿¹»
Ó±¼»®¿¬» Ü¿³¿¹»

ðòêÛ¨¬»²­·ª» Ü¿³¿¹»

ðòìÝ±´´¿°­»

ðòî
ðòð
ð

íð

êð

çð

ïîð

ïëð

ÐÙÊ ø½³ñ­÷Ù»±´±¹·½¿´
Ü¿¬¿Í»·­³·½ Ø¿¦¿®¼

Some KeyKey Issues
IssuesononDisaster
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ReductionÊ«´²»®¿¾·´·¬§Ü¿³¿¹»

13
13

July 2010, Thailand
July 2010, Thailand

Seismic Vulnerability Assessment & Loss Estimationof 3 Major Cities in Bangladesh

DHAKA

91 Wards

14
14

3rdJoint
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3rdICUS Report 45
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Building GIS Database(Existing Data)

Number of buildings in each city

Dhaka
Chittagong
Sylhet: 326,825: 182,277: 52,176

Dhaka City
Number of Wards = 91
Number of Clusters = 552
Number of Buildings = 326,000
Population = 7 Millions

552 Clusters

91 Wards

Some KeyKey Issues
IssuesononDisaster
DisasterRisk
RiskReduction
Reduction

15
15

July 2010, Thailand
July 2010, Thailand

Building Density & Occupancy in Dhaka CityÞ«·´¼·²¹ Ü»²­·¬§
øÒ«³¾»® °»® µ³î÷Þ«·´¼·²¹ Ñ½½«°¿²½§ Ý´¿­­

0 15001500 3000

Residential

3000 4500

Commercial

4500 6000

Industrial

6000 15000

Ward 48

Ð±°«´¿¬·±² Ü»²­·¬§øÌ¸±«­¿²¼­ °»® µ³î÷Population in Dhaka

0 30
30 50

30 50

50 10050 100100 150

100 150150 350150 350

Daytime Population

16
16

Ð±°«´¿¬·±² Ü»²­·¬§øÌ¸±«­¿²¼­ °»® µ³î÷
0 30Nighttime Population

3rdJoint
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3rdICUS Report 45
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Medical Care Facilities in Dhaka CityØ±­°·¬¿´Ó»¼·½¿´ Ý´·²·½

Road Network

Some KeyKey Issues
IssuesononDisaster
DisasterRisk
RiskReduction
Reduction

Natural Gas Distribution Network

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July 2010, Thailand
July 2010, Thailand

A Scenario Earthquake

Mw = 6.2

Earthquake magnitude = 6.2
Location of Epicenter = 20 km
North of Dhaka city

PGA (g)0.14  0.17
0.17  0.20
0.20  0.23
0.23  0.26
0.26  0.29
0.29  0.32

Damage to Concrete Buildings in Dhaka City

Scenario 1Ò«³¾»® ±º Ý±²½ò Þ«·´¼·²¹­ ©·¬¸³±¼»®¿¬» ¬± ½±³°´»¬» ¼¿³¿¹» ½±²¼·¬·±²
0 50
50 100100 150150 250250 450Ü¿³¿¹» Ô»ª»´

Ó±¼»®¿¬»
Û¨¬»²­·ª»Ý±³°´»¬»

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3rdJoint
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Damage to Concrete Buildings in Dhaka City

Scenario 1Ò«³¾»® ±º Ý±²½ò Þ«·´¼·²¹­ ©·¬¸³±¼»®¿¬» ¬± ½±³°´»¬» ¼¿³¿¹» ½±²¼·¬·±²
0 50
50 100100 150150 250250 450Ü¿³¿¹» Ô»ª»´

Ó±¼»®¿¬»
Û¨¬»²­·ª»Ý±³°´»¬»

Casualties in Dhaka City
Scenario 1

Number of Injuries
Levels 2+3+4
0 50
50 100100 300
300 600
600 1200

Severity Level 1: Injuries will require medical
attention but hospitalization is not needed
Severity Level 2: Injuries will require
hospitalization but are not considered lift
threatening
Severity Level 3: Injuries will require
hospitalization and can become lift threatening if
not promptly treated
Severity Level 4: Victims are killed by theearthquake

Some KeyKey Issues
IssuesononDisaster
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Reduction

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July 2010, Thailand
July 2010, Thailand

Casualties in Dhaka

Severity Level 1: Injuries will require medical attention but hospitalization is not needed
Severity Level 2: Injuries will require hospitalization but are not considered lift threatening
Severity Level 3: Injuries will require hospitalization and can become lift threatening if notpromptly treated
Severity Level 4: Victims are killed by the earthquake

Scenario 1

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Damage to Medical Care Facilities
in Dhaka City

3rdJoint
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Direct Economic Loss in Dhaka
Scenario 1

Building Occupancy Class

Residential
Commercial
Industrial
Government
Education

Direct Economic Loss(thousands of dollars)
0 30003000 5000
5000 10000
10000 20000
20000 30000

Scenario 1

Some KeyKey Issues
IssuesononDisaster
DisasterRisk
RiskReduction
Reduction

Potable Water System in Dhaka City

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July 2010, Thailand
July 2010, Thailand

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Scenario 1

Highway Transportation System
in Dhaka City

Scenario 1

Natural Gas Distribution System in
Dhaka City

3rdJoint
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3rdICUS Report 45
ICUS Report 45Ú·®»­ ®»­«´¬·²¹ º®±³ ¬¸» Û¿®¬¸¯«¿µ»Õ±¾» ÛÏô ïççëScenario 1

Some KeyKey Issues
IssuesononDisaster
DisasterRisk
RiskReduction
Reduction

Fire Following Earthquake
in Chittagong City

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July 2010, Thailand
July 2010, Thailand

Natural Disaster Simulation

FLOOD

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HURRICANE

EARTHQUAKE

3rdJoint
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Some KeyKey Issues
IssuesononDisaster
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July 2010, Thailand
July 2010, Thailand

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3rdJoint
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3rdÜÛÊÛÔÑÐ×ÒÙ ß ÔßÒÜÍÔ×ÜÛ Ü×ÍßÍÌÛÎ
ÓßÒßÙÛÓÛÒÌ ÍÇÍÌÛÓAKIYUKI KAWASAKIInternational Center for Urban Safety Engineering (ICUS),
Institute of Industrial Science, The University of Tokyo;
RNUS, Asian Institute of Technology
kawasaki@ait.ac.th, akiyuki@iis.u-tokyo.ac.jp

The utilization of existing dataset toward e-municipal governmentßµ·§«µ· Õ¿©¿­¿µ·
×²¬»®²¿¬·±²¿´ Ý»²¬»® º±® Ë®¾¿² Í¿º»¬§ Û²¹·²»»®·²¹ ø×ÝËÍ÷ô
×²­¬·¬«¬» ±º ×²¼«­¬®·¿´ Í½·»²½»ô Ì¸» Ë²·ª»®­·¬§ ±º Ì±µ§±åÎÒËÍô ß­·¿² ×²­¬·¬«¬» ±º Ì»½¸²±´±¹§

Acknowledgements
Data was provided by:- City of Yokohama, Japan- Kanagawa Prefecture, Japan
This work was funded by the Researchand Development Grant ofConstruction Technologies by MLIT
(Ministry of Land, Infrastructure andTransport), JAPAN.

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July 2010, Thailand

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Developing
Developing aa Landslide
LandslideDisaster
DisasterManagement
ManagementSystem
System29
29

July 2010, Thailand
July 2010, Thailand- Many landslides in Japan.
- Recent efforts to develop management-technique using
computers are underway because engineering measures,
like covering hillsides with concrete, are not always
applicable due to economical and environmental concerns.
*) Develop algorithms for landslide prediction*) Create a user-interface (website) for daily landslide
observation by city officials*) Predict location and extent of damage-prone slopes
for emergency response

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Methodologies and technologies- Statistical analysis of government data (inventories,
landslide records, and precipitation)- Spatial analysis of precipitation, DEM, Land-use, andRemote sensing data (QuickBird and Laser Profiler)

Landslide mitigation in Yokohama, Japan.
- Predict landslide-prone areas in near-real time using GIS
and rainfall data.
- Compile digital and paper landslide data into a database.

(1) Predicting and evaluating landslides
A Automatically specify “steep slopes” and “damage prone sites”
B. Analysis of Primary cause Basic factor for collapse
C. Analysis of Provoking cause Trigger factor for collapse
D. Automatically specify “steep slopes” and “damage prone sites”
E. Create comprehensive methods for A, B, C, and D(2) Landslide disaster management system development
A Slope Information Management System
B. Real-time landslide Prediction System(3) Conclusions

Developing
Developing aa Landslide
LandslideDisaster
DisasterManagement
ManagementSystem
System31
31

July 2010, Thailand
July 2010, Thailand“”

D.E.M.

Damage prone sites

Steep Slopes

Land Use

Overlay
QuickBird

LaserProfiler

“

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Steep slope inventory in Yokohama.

“

Collapse ”record- Height- Slope- Overhang- Surface
thickness-Leaking water- Vegetation- Looseness,relaxation- Surface water“”- Drainage on the
top

Discriminate Value – 0.06
(Accuracy 85.1%)
Never collapsed
Actually collapsed

Sample score

Frequency distribution of
distinction result

Surface
Roam

Sample score : Total of weighted coefficient of a slope
0.086+(-0.107)+(-0.078)+1.296+……=0.677

Pattern 1

Results of Quantification Theory Type

Developing
Developing aa Landslide
LandslideDisaster
DisasterManagement
ManagementSystem
System33
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July 2010, Thailand
July 2010, Thailand

Collapse

Y = 0.174X + 28.4

Relationship between Collapse and Rainfall in Yokohama(Pattern 1)

Y = 0.174X +28.4(“RasterCalculation” )2.00 am

6.00 am

10.00 am

2.00 pm

Dangerous area at the time by rainfall data

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CivilInfrastructures
Infrastructures
3rdICUS Report 45
ICUS Report 45“”¼»¬»®³·²¿¬·±² ½±»ºº·½·»²¬ ã ðòéçë

“

”

“

”
“

“

“

”

”

”

Daily

A. Specify dangerous slopes and damage area
Slope inventory
Steep slopes
Collapse records

B. Collapse and slope type relationships
Slope inventory

Primary cause

Emergency Response
C. Analyze collapse and rainfall relationships
RainfallSlope inventory

Provoking cause

Collapse records

Damage pronebuilding

Developing
Developing aa Landslide
LandslideDisaster
DisasterManagement
ManagementSystem
System35
35

July 2010, Thailand
July 2010, ThailandÍ´±°» Ü¿¬¿Slope Information
Management SystemÍ´±°» Ü¿¬¿É¿®»¸±«­»

Management

Real-time
Precipitation

Management GDB
landslide Prediction
SystemAdjustment oflandslidediscriminant

……
1982
1981
1980

Analysis

Prediction
Response
Results of
Prediction

Y=

Pattern0.112 X +14.06

PatternY=

0.605 X +96.8

PatternY=
140.140.98 X +PatternY=Û³»®¹»²½§
Î»­°±²­»

Municipality

É¿®²·²¹

Fire Department

Ñ°»²·²¹ ±º×²º±®³¿¬·±²

3636

ÜÉØ ß²¿´§­·­

now
2003

0.66 X +77.73

Residents

3rdJoint
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3rdICUS Report 45
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Developing
Developing aa Landslide
LandslideDisaster
DisasterManagement
ManagementSystem
System37
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July 2010, Thailand
July 2010, Thailand

38
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3rdICUS Report 45
ICUS Report 45

Daily work

High-resolution Steep Slope

Existing Data

Data Capturing Systemcellular phone

Slope Management System
Retrieving

Management

StorageAnalysisEmergency Response
landslide Prediction
Real-time rain data

Analysis

Prediction(1) Predicting and evaluating landslides
A Indicate “steep slopes” and “damage prone sites”
B. Analysis of Primary causes Basic factor for landslide
C. Analysis of Provoking cause Trigger factor for landslide
D. Create methods for A, B, and C(2) Landslide management system development
A Slope Information Management
B. Near real-time Landslide Prediction

Developing
Developing aa Landslide
LandslideDisaster
DisasterManagement
ManagementSystem
System39
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July 2010, Thailand
July 2010, Thailand

Aim for “Digital-government” and “Digital-city”
according to the national “e-Japan” policy
This project is a concrete example of using city documents for
e-Government…
…Our “Landslide Prevention Law” requires collecting
landslide data nationwide, but its only being collected, andnot really being used...
Field survey data is more valuable if it is used for predicting
danger instead of simply stored in some government office.

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3rdJoint
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CivilInfrastructures
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3rdÕÑÎÛßŽÍ ÔÑÉóÝßÎÞÑÒ ÙÎÛÛÒ
ÙÎÑÉÌØ ÍÌÎßÌÛÙÇ ßÒÜ×ÓÐÔ×ÝßÌ×ÑÒÍ ÚÑÎ ÌØÛ ×ÒÜËÍÌÎ×ßÔ
ÍÛÝÌÑÎ
HEE-JUNG KANG
Konkuk University,
Senior Research Fellow, KBCSD
kanghi@konkuk.ac.kr

JULY 29, 2010µ¿²¹ô Ø»» Ö«²¹
Ð®±º»­­±®ô Õ±²µ«µ Ë²·ª»®­·¬§
Í»²·±® Î»­»¿®½¸ Ú»´´±©ô ÕÞÝÍÜ

DEDICATED TO MAKING A DIFFERENCE

1. Background for Koreas Low-Carbon Green GrowthÝ´·³¿¬» ×³°¿½¬
Disproportionate impact on Korea
 average temperature rose by 1.7 over the past century (twice as much as the global average)
 annual cost incurred by natural disaster (billion USD): (80s) 0.5(90s) 0.7(00s) 2.6

Korea is one of the Worlds largest GHG emittersØ·¹¸ »²»®¹§ ½±²­«³·²¹ ­¬®«½¬«®»
10th largest energy consumer, Import 97% of total energy vulnerable to the oil price fluctuation and Middle East political crisis
 slowdown of energy growth since 2000, but sharp increase in energy import bill in recent years

1

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July 2010, Thailand
July 2010, Thailand

DEDICATED TO MAKING A DIFFERENCE

1. Background for Koreas Low-Carbon Green Growth

2

DEDICATED TO MAKING A DIFFERENCE

1. Background for Koreas Low-Carbon Green Growth

3

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3rdJoint
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DEDICATED TO MAKING A DIFFERENCE

1. Background for Koreas Low-Carbon Green GrowthÛ½±²±³·½ Í·¬«¿¬·±²
Growth rate declining since the Asian economic crisis in late 1990s
Growth without rise in employment
Depending heavily on energy intensive manufacturing
 energy intensive industries as a major sources of GHG emission(more than 85% of GHG emitted from fossil fuel combustion)
 energy intensive industries as the major sources of economic growth for the last 30 years[Economic Portion of Energy Intensive Industries]Í¬»»´
Ò±²ó³»¬¿´´·½ Ó·²»®¿´­Ð»¬®±óÝ¸»³·½¿´

Õ±®»¿

Ö¿°¿²

ËÍ

Ù»®³¿²§

ËÕ

4

DEDICATED TO MAKING A DIFFERENCE

2. Koreas Legal & Institutional Framework on Green Growthøï÷ Ô»¹¿´ Ú±«²¼¿¬·±²æ Ú®¿³»©±®µ ß½¬ ±² Ô±©óÝ¿®¾±² Ù®»»² Ù®±©¬¸
Has priority over all relevant laws; Effective as of April 14th, 2010
Key Provisions:
Mandates government to foster green industry and the transformation of
conventional industry
Mandates government to set ¬¿®¹»¬­ º±® ÙØÙ »³·­­·±²­ ®»¼«½¬·±²ô »²»®¹§­¿ª·²¹ ¿²¼ ®»²»©¿¾´» »²»®¹§ ­«°°´§
Energy-intensive, large CO2-emitting companies responsible for mandatory
reporting of GHG emissions
Provides the legal framework to introduce a cap & trade system in Korea
Provides articles on environment-friendly land-use, green buildings, low carbon
transportation, green consumption and production, etc.
5

Korea'sandImplications
forforthetheIndustrial
Sector
Korea’sLow-Carbon
Low-CarbonGreen
GreenGrowth
GrowthStrategy
Strategy
andImplications
Industrial
Sector43
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July 2010, Thailand

DEDICATED TO MAKING A DIFFERENCE

2. Koreas Legal & Institutional Framework on Green Growthøï÷ Ô»¹¿´ Ú±«²¼¿¬·±²æ Ò¿¬·±²¿´ Í¬®¿¬»¹§ ú ëóÇ»¿® Ð´¿² º±® Ù®»»² Ù®±©¬¸

Visionsß Ù®»»² Ô»¿¼»®
ó Ø¿®³±²§ ¾»¬ò »²ª·®±²³»²¬¿´ ½±²­»®ª¿¬·±² ¿²¼ »½±²±³·½ ¼»ª»´±°³»²¬Åí Ð±´·½§ Ñ¾¶»½¬·ª»­ ¿²¼ ïð Õ»§ Ð±´·½§ ×¬»³­ÃÓ·¬·¹¿¬·±² ±º Ý´·³¿¬» Ý¸¿²¹»
ú ×³°®±ª»¼ Û²»®¹§ Í»½«®·¬§

Ý®»¿¬·²¹ ±º Ò»© Û²¹·²»­º±® Û½±²±³·½ Ù®±©¬¸Û²¸¿²½»¼ Ï«¿´·¬§ ±º
Ô·º» ú ×²¬»®²¿¬·±²¿´ Ô»¿¼»®­¸·°

1

Effective mitigation of GHG

4

R&D for green technology

82Reduced use of fossil fuel5

Greening for existing industries

Green transportation &land management

6

Advancement of industrial
structure

9

Green lifestyle7

Laying the foundation forgreen economy

3& improved energy security
Adaptation to climate change

10 Role-model of green growthfor Intl community

6

DEDICATED TO MAKING A DIFFERENCE

2. Koreas Legal & Institutional Framework on Green Growthøï÷ Ô»¹¿´ Ú±«²¼¿¬·±²æ Ò¿¬·±²¿´ Ó·¼ó¬»®³ Ù®»»²¸±«­» Ù¿­ Î»¼«½¬·±² Ì¿®¹»¬íðû ®»¼«½¬·±² º®±³ ¬¸» èÞßËŽ ­½»²¿®·± ¾§ §»¿® îðîðTranslates into 244 million tons less emissions compared to the BAU level
Highest level recommended by IPCC for non-Annex 1 countries
Estimated cost: 0.49% of GDP[Mid-term Reduction Goal](Unit: MMT CO2-e)

Voluntary NAMA registration+
Domestically binding+
International MRV
An alternative to becoming Annex 1 oraccepting Internationally binding reduction target

7

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3rdICUS Report 45
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DEDICATED TO MAKING A DIFFERENCE

2. Koreas Legal & Institutional Framework on Green Growthøî÷ ×²­¬·¬«¬·±²¿´ Ú®¿³»©±®µæ Ð®»­·¼»²¬·¿´ Ý±³³·¬¬»» ±² Ù®»»² Ù®±©¬¸Û­¬¿¾´·­¸»¼ «²¼»® ¬¸» ¼·®»½¬ ½±²¬®±´ ±º ¬¸» Ð®»­·¼»²¬ øÚ»¾ò èðç÷Ý±ó½¸¿·®»¼ ¾§ ¬¸» Ð®·³» Ó·²·­¬»® ¿²¼ Ð®·ª¿¬» Í»½¬±® Î»°®»­»²¬¿¬·ª»Ó¿²¼¿¬»¼ ¬± ­»¬ «° ¿½¬·±² °´¿²­ ¿²¼ °±´·½§ ³»¿­«®»­ º±® ¹®»»² ¹®±©¬¸Ø¿­ ë ½±²­«´¬¿¬·ª» ½±«²½·´­ ©·¬¸ ´»¿¼»®­ ·² ¾«­·²»­­ô ­½·»²½»­ô ½·ª·´ ­±½·»¬§ô×Ì ¿²¼ º·²¿²½»ô ¬± °®±ª·¼» »¨°»®¬ ¿¼ª·½» ¿²¼ °®±³±¬» °®·ª¿¬» ­»½¬±® ·²ª±´ª»³»²¬[Ex. Structure of Business Dialogue]

8

DEDICATED TO MAKING A DIFFERENCE

2. Koreas Legal & Institutional Framework on Green Growthøî÷ ×²­¬·¬«¬·±²¿´ Ú®¿³»©±®µæ ÙØÙ ×²ª»²¬±®§ ú Î»­»¿®½¸ Ý»²¬»® ±º Õ±®»¿×²¼»°»²¼»²¬ ú °»®³¿²»²¬ ®»­»¿®½¸ ½»²¬»® ¬¸¿¬ ½¿² ­¬®»²¹¬¸»² ¬¸» º±«²¼¿¬·±²¬± ¿½¸·»ª» ²¿¬·±²¿´ ³·¼ó¬»®³ ÙØÙ ®»¼«½¬·±² ¬¿®¹»¬ øÖ«²» ïë¬¸ô îðïð÷
Ó¿·²¬¿·²­ ²¿¬·±²¿´ ÙØÙ ×²ª»²¬±®§ ¼¿¬¿¾¿­»å ß²¿´§¦» ²¿¬·±²¿´ ú ­»½¬±®¿´ ÙØÙ°±¬»²¬·¿´ ¿²¼ ­»¬ ¬¿®¹»¬­å ß­­»­­ ¬¸» °®±¹®»­­ ±º ÙØÙ ³·¬·¹¿¬·±² ·² Õ±®»¿Vision: To Act as a Global Think Tank for GHG Reduction,in support of Low-Carbon Green Growth

Information Hub

Accelerating
Green Growth

Global Networking& Outreach Comprehensive & effective
management of GHG
inventory data

 Setting national & sectoral
GHG reduction targets

 Enhancement of cooperative
network for GHG reduction

 Research & analysis on
GHG reduction within
nation and beyond Enhancement of global
cooperation for green growth Nationally integrated GHG
info management system

9

Korea'sandImplications
forforthetheIndustrial
Sector
Korea’sLow-Carbon
Low-CarbonGreen
GreenGrowth
GrowthStrategy
Strategy
andImplications
Industrial
Sector45
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July 2010, Thailand

DEDICATED TO MAKING A DIFFERENCE

2. Koreas Legal & Institutional Framework on Green Growthøî÷ ×²­¬·¬«¬·±²¿´ Ú®¿³»©±®µæ Ù´±¾¿´ Ù®»»² Ù®±©¬¸ ×²­¬·¬«¬» øÙÙÙ×÷
×²¼»°»²¼»²¬ ·²­¬·¬«¬» ¼»­·¹²»¼ ¬± ¾«·´¼ °®¿½¬·½¿´ °±´·½§ ±°¬·±²­ ¿²¼ ­«°°±®¬¼»ª»´±°·²¹ ½±«²¬®·»­ ·² ¬¸»·® »ºº±®¬ ¬± ¿½¸·»ª» ¹®»»² ¹®±©¬¸ øÖ«²» ïê¬¸ô îðïð÷
Serving as a lead funder , Korea to fund 10 million USD annually for the first 3 years(10~12)

1 Í§­¬»³·¦·²¹ Ì¸»±®§

Achieve theoretical systemization of green growth as a new
global growth paradigm

2 Í°®»¿¼·²¹ Ù®»»²

Hold annual Global Green Growth Conference in connection
with the East Asia Climate ForumÙ®±©¬¸ Ó±¼»´

3 Í«°°±®¬·²¹

Develop GGGI model for GHG emissions analysis

Issue an annual Global Green Growth Report
Establish green growth master plans for developing countriesÜ»ª»´±°·²¹ Ý±«²¬®·»­

Establish detailed development plan for green growthindustries and technologies

10

DEDICATED TO MAKING A DIFFERENCE

2. Koreas Legal & Institutional Framework on Green Growthøî÷ ×²­¬·¬«¬·±²¿´ Ú®¿³»©±®µæ Û¿­¬ ß­·¿ Ý´·³¿¬» Ð¿®¬²»®­¸·°Ô¿«²½¸»¼ ·² îððè ¬± ­«°°±®¬ ¿²¼ ½±±°»®¿¬» ©·¬¸ Û¿­¬ ß­·¿² ½±«²¬®·»­ ·² ¬¸»·®
»ºº±®¬ ¬± ·³°®±ª» ½´·³¿¬» ½¸¿²¹» ¿¼¿°¬¿¬·±² ½¿°¿½·¬§ ¿²¼ ®»¼«½» ÙØÙ »³·­­·±²­

11

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Í³¿®¬ Ô±©
Ý¿®¾±² Ý·¬§Lead low-carbon development across Asian cities to boost urban energy
efficiency and to lower greenhouse gas emissions

É¿¬»®
Î»­±«®½»­
Ó¿²¿¹»³»²¬

Support water control infrastructure and relevant policy development to help
prevent disasters and ensure accessibility to fresh water to enhance thecapacity to handle increasing water-related natural disastersÔ±© Ý¿®¾±²
Ð±©»®
Ù»²»®¿¬·±²

Assist those Asian countries experiencing high dependency on fossil fuels
and severe power shortages, while cutting down on carbon emissionsÚ±®»­¬®§ ¿²¼
Þ·±³¿­­

Support the entire value chain from forestation to biomass through projects
to promote carbon capture & storage (CCS)

Í±´·¼ É¿­¬»Ì®»¿¬³»²¬

Take the lead in Asia regarding waste-related issues by developing a
database of waste information of other countries and using it to support
optimal waste management / reuse and waste-to-energy efforts

3rdJoint
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DEDICATED TO MAKING A DIFFERENCE

3. Impacts & Implications for Korean Industrial SectorÐ®±³·­·²¹ Í·¹²­æ ²±¬·½»¿¾´» ·²½®»¿­» ·² ¹®»»² ·²ª»­¬³»²¬ º®±³ ¾«­·²»­­
Top-30 Korean conglomerates responded positively to ·²½®»¿­·²¹ ¬¸»·®
·²ª»­¬³»²¬ ·² »²ª·®±²³»²¬óº®·»²¼´§ º¿½·´·¬·»­ ¿²¼ ÎúÜ­ ¾§ ë¢îðû per year
[Green Technology Investment by Koreas Major Companies]
Company
POSCO

Major Areas of Investment$ 0.17 billion

SK Marine Biofuel, Solar Cell, Green Car,
Hydrogen Fuel Cell, Green U-CityGS

 Fuel Cell Power Plant

Hyundai/Kia Motors
Samsung Elec.
STX

Budget Fuel Cell

$ 1 billion$ 1.7 billion

 Green Car, GHG Mitigation$ 4.1 billion

 Environment-friendly Products & Fibers,
Low-Carbon Sites$ 5.4 billion

 Photovoltaics, Wind Power$ 0.2 billion

Samsung Heavy Industries

 Wind Power$ 0.6 billion

LS Industrial Systems

 Smart Grid

$ 0.2 billion

12

DEDICATED TO MAKING A DIFFERENCE

3. Impacts & Implications for Korean Industrial SectorÞ«®¼»²­ ±² Þ«­·²»­­æ è¹®»»² ¹®±©¬¸ ½±³»­ ¿¬ ¿ ¸·¹¸ ½±­¬ŽAccording to Article 29 of the Framework Act on Low-Carbon Green Growth,
businesses subject to governmental control on GHG emission & energy consumption:
GHG Emissions

Energy Consumption

2011

125,000 CO2tons

500 terajoules

2012

87,500 CO2tons

350 terajoules

2014

50,000 CO2tons

200 terajoules

Approx. 70% ofnational GHG emissions
will be subject togovernment control
13

Korea'sandImplications
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Korea’sLow-Carbon
Low-CarbonGreen
GreenGrowth
GrowthStrategy
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andImplications
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Sector47
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July 2010, Thailand

DEDICATED TO MAKING A DIFFERENCE

3. Impacts & Implications for Korean Industrial SectorÞ«®¼»²­ ±² Þ«­·²»­­æ è¹®»»² ¹®±©¬¸ ½±³»­ ¿¬ ¿ ¸·¹¸ ½±­¬ŽAs a Follow-up step, Korean Govt now formulating measures to set sectoral
reduction targets and allocate allowances for industrial sectors and sitesÞ«­·²»­­ ­»½¬±® ²±© ¾«®¼»²»¼ ¬± ½±³» «° ©·¬¸ ±©² ®»¼«½¬·±² °±¬»²¬·¿´¿²¼ ¿­­»­­ »ºº»½¬·ª»²»­­ ±º °±­­·¾´» ®»¼«½¬·±² ³»¿­«®»­¬± ³·²·³·¦» »½±²±³·½ ½±­¬­

14DEDICATED TO MAKING A DIFFERENCE

4. Actions and Achievements by Industrial SectorÕ±®»¿Ž­ ·²¸»®»²¬ ·²¼«­¬®·¿´ ­¬®«½¬«®»Õ±®»¿Ž­ ³¿²«º¿½¬«®·²¹ °±®¬·±² ±º ÙÜÐæ îèòçû (US: 21%, Japan 14.4%)
main cause of Koreas higher energy intensityÕ±®»¿Ž­ ³¿¶±® »²»®¹§ó½±²­«³·²¹ ­»½¬±®­ ø»¨ò ±·´ó®»º·²·²¹ô ­¬»»´÷ ¿´®»¿¼§¿½¸·»ª»¼ ¸·¹¸ »²»®¹§ó»ºº·½·»²½§ ´»ª»´­Ó¿®¹·²¿´ ¿¾¿¬»³»²¬ ½±­¬ º±® ÙØÙ »³·­­·±²­ ·² Õ±®»¿ ¸·¹¸»® than other countries

Energy Intensity Index

Korea

Steel (06)

99

100

110

120

Cement (05)

100

100

-

177

Chemical (03)

100

100

-

110

Oil Refining (02)

100

100

-

113

15

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DEDICATED TO MAKING A DIFFERENCE

4. Actions and Achievements by Industrial Sectorøï÷ ß½¬·±²­ º®±³ Ó¿²«º¿½¬«®·²¹ Í»½¬±®
Fuel Cell Vehicles

Independent development of fuel cell hybrid vehicle (2003) & fuel cell electric bus (2006)
Start of monitoring project in Korean market, Actual application of independently-developed fuelcell electric architecture (2006~2010)
Production in small quantity & commercialization of fuel cell electric vehicles by 2012

Development of Top-notch TechnologiesÐÑÍÝÑŽ­ Ú×ÒÛÈ ¬»½¸²±´±¹§: Next-generation eco-friendly iron making process that does not
require preparation stage of iron ores or other source materials including Cokes & sintering processÐÑÍÝÑŽ­ Í¬®·° Ý¿­¬·²¹: State-of-the-art process that combines casting and rolling in a single step
to reduce flat product manufacturing time by 90%

Lowest CO2 reduction potentials due to worlds highest energy efficiency level

16

DEDICATED TO MAKING A DIFFERENCE

4. Actions and Achievements by Korean Industrial Sectorøî÷ ß½¸·»ª»³»²¬­ ·² Ó¿²«º¿½¬«®·²¹ Í»½¬±®æ ·³°®±ª»³»²¬ ·² »²»®¹§ »ºº·½·»²½§
Constitutes ëìòðû ±º ¬±¬¿´ »²»®¹§ »²¼ó«­»
Recent ¼±©²¬®»²¼ ·² »²»®¹§ ·²¬»²­·¬§ ¾§ íçû from 1998 level[Energy Increase Rate & Intensity in
Manufacturing Industry]

Key Drivers to Improvementin Energy Efficiency
Promotion of high value-added
products & services
Adjustment of existing structure tolower energy consumption
Expansion of nuclear powerin supply chainÙØÙ ·²¬»²­·¬§ ¿´­± ·³°®±ª»¼ ¾§ íìòçû from 1992
17

Korea'sandImplications
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Low-CarbonGreen
GreenGrowth
GrowthStrategy
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DEDICATED TO MAKING A DIFFERENCE

5. Challenges & Issues for ImprovementÝ«®®»²¬ Í¬¿¬«­
Foundation for low-carbon green economy laid from both public & private sector
However, challenges remain to ensure we are headed in the right directionÕ»§ Ý¸¿´´»²¹»­Ý¸¿´´»²¹» ï Ú´»¨·¾´»ô ­»´ºó®»¹«´¿¬·²¹ º®¿³»©±®µ needed, to embrace various reduction
measures including intensity target relative to value-added
 ×²½»²¬·ª»ó¾¿­»¼ ·²­¬®«³»²¬­ criticalÝ¸¿´´»²¹» î Î»¿´ ½¸¿²¹»­ ­¸±«´¼ ½±³» º®±³ ¬®¿²­°±®¬¿¬·±² ú ¸±«­»¸±´¼ ­»½¬±®­,
as the cost of reducing their emissions is relatively lowerÝ¸¿´´»²¹» í Í¸·º¬ ¬± ¼»³¿²¼ó±®·»²¬»¼ °±´·½·»­ needed, to speed up green market take-up

18

DEDICATED TO MAKING A DIFFERENCE

Low-Carbon, Green Growth is not a fixed, but a continuously evolving concept.
Therefore Korea and like-minded countries can maximize mutual cooperation
by exploring numerous green growth opportunities tailored to meet their needs
and conditions!

Thank You for Your Attention.

19

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3rdStudent PresentationsÝÑÓÐßÎ×ÍÑÒ ÞÛÌÉÛÛÒ ÒÑÜÛóÞßÍÛÜßÒÜ Ô×ÒÕóÞßÍÛÜ ÍØÑÎÌÛÍÌ ÐßÌØßÔÙÑÎ×ÌØÓÍ
Yongtaek LIM 1 , Hyungwoo LIM 2 , Dae Seong KIM 3
and Hyeon Jin CHEONG 3
1

Chonnam National University, Korea
limyt@chonnam.ac.kr
2

3

Yonsei University, Korea

Chonnam National University, Korea

ABSTRACTThis paper compares conventional 4 shortest path algorithms ; 3
node-based algorithms which are the Tree Building Algorithm, the Vine
Building Algorithm, and the Modified Vine Building Algorithm, and 1 linkbased Algorithm. Through looking at example network, we will try to findout whether these algorithms can find the right shortest path, and if not,examine the reasons why these are wrong and how to solve the problem.
Also, through complexity index, investigate performance of each algorithm.
Keywords: Shortest path algorithm, node-based, link-based, optimal
condition, turn-restriction, big O
1. INTRODUCTION
The main goal of solving a shortest path problem is to find an algorithm
which can consider turn prohibition and turn penalty. In case of Dijkstra
Algorithm (a kind of Tree Building Algorithm), it cannot hold turnprohibitions such as restricting left-turn, P-turn, U-turn. It is because Tree
Building Algorithm cannot satisfy Bellmans optimal condition in searchingsteps. To solve the turn prohibitions, Vine Building Algorithm was
proposed. It can consider these problems by adding extra nodes; however, italso has limitation. It is shown that this algorithm cannot extract the rightshortest path in case of conventional sequential turn prohibitions (Kim,
1998). This happens because Vine Building Algorithm satisfying optimal
condition restricts the Feasible Searching Area. To resolve this problem, it
needs to expand network which takes extra time and cumbersome work.
In this paper, we will discuss conventional node-based algorithms;
Tree Building Algorithm, Vine Building Algorithm, and Modified Vine
Building Algorithm proposed by Kim (1998) with a newly suggested Link5151

July 2010, Thailand
July 2010, Thailand

based Algorithm. Through looking at an example network, we will examine
whether these algorithms can find the right shortest path, and if not,examine why these cannot find it and how to solve the problem. Also,
through complexity index, we will examine the performance of eachalgorithm.
2. BELLMAN’S PRINCIPLE OF OPTIMALITY AND ITS LIMIT
Common Shortest Path Problem consists of linear program and its specificform, which has a unique solution proved by Bellman (1957). The minimum
path cost for each origin-destination (OD) pair consists of a sum of aminimum cost of preceding node and link cost. Moreover, its form is linear.
In normal search algorithm, minimum path cost for OD pair is decided by
calculating each node cost in a sequence or modifying a minimum cost of anode at calculating stage. Moreover, by storing a predecessor node of eachnode, we can establish the minimum path of each OD pair. Bellmans
optimal condition is shown below.
[Bellmans optimal condition]c(h) 0c * ( j ) min j h [c * (i ) c(i, j )]This condition can be rewritten as follows.
c * ( j ) c * (i ) (i, j )forall (i, j )

(1)
A

(2)

The inequality means that for link(i,j) in road network, the minimum
path cost of node j should not be bigger than the sum of the cost from origin
to node i and link(i, j). If this condition does not satisfy, there exists at least
one path which can reduce more path cost.
Conventional shortest path algorithms based on Bellmans principle,
however, have some limitations. In the case of considering the network
consists of several turn prohibitions such as restricting left-turn, which are
popularly adopted in real world network (see Figure 1), it makes difficult for
the traditional network optimization technique to deal with. Banned andpenalized turns may not be described appropriately in the standard node/link
method of network definition with intersections represented by nodes only.
The reason is that Bellman`s Principle of Optimality` does not hold in that
case.

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ø¾÷ Ëó¬«®²

Figure1: Examples for P-turn and U-turn
To solve the turn restriction problem, Vine Building Algorithm was
proposed. Vine Building Algorithm can include the same node many times.
Thus, it is able to find an optimal path even if the road has turn penalties
like left-turn prohibition. However, it was shown that in case of existing
sequential turn prohibitions on road network, conventional algorithms
cannot find the right shortest path(Kim, 1998). Kim(1998) proved that insequential left-turn prohibition road network like Figure 2, Vine Building
Algorithm is not able to find the right shortest path. In this case, traditional
Vine Building Algorithm calculates a wrong path shown in Table 1. It isbecause Vine Building Algorithm satisfying the optimal condition isrestricting the Feasible Searching Area.

Figure 2: Example network by Kim (1998)
Network expansion is commonly used to solve this problem. In other
word, we add links to each turn directions at a node in geometric way.
Following this way, conventional search algorithms can find the right
optimal path, but it has a burden to expand network, especially in dynamiccase.
3. NODE-BASED AND LINK-BASED ALGORITHMS
This paper would like to compare node-based shortest path algorithms withlink-based algorithm in general traffic network which has turn limitations
such as left-turn prohibition, U-turn, P-turn. The compared node-basedalgorithms include Tree Building Algorithm, Vine Building Algorithm andnewly suggested Modified Vine Building Algorithm.

Comparison
Comparison between
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link-basedShortest
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3.1 Node-based shortest path search algorithms
3.1.1 Tree building algorithm
Tree Building Algorithm follows the procedures by Bellmans
optimal condition. Assume that a traffic network is made of node i, j,k, link(i, j), cost of link(i,j) and cost c(i,j). Then let origin as h,cost of the shortest path from origin to node j as c*(j), labeled node set as R
and unlabelled node set as R . The algorithm is as follows.
[step1] Initialization
Pj , c(h) (0,0)
c( j )
Where, Pj is predecessor node, and h is origin.
[step2] Search optimal pathc * ( j ) min j h [c * (i ) c(i, j )]Where, i
Pj i

R, j

R

[step3] Label node j as ( Pj , c * ( j )add j[step4] If R

R and delete jRthen stop, otherwise go to [step2]

Once reached destination, through tracking predecessor node Pj thealgorithm can calculate the shortest path and the cost of the path.
3.1.2 Vine building algorithm
Tree-based Algorithm is a graph which all nodes are connected to allother nodes without a cycle in a path. It is efficient in simple network
however, as it can only memorize a predecessor node, it cannot solve theproblems like turn prohibition problems. To overcome this limit, Vine
Building Algorithm was designed. As Vine-based Algorithm store apredecessor node and a pre-predecessor node as search range, it canconsider not only node to node relation but link to link relation. As a result,
this is able to consider turn prohibition problems. Vine-based Algorithm
stores predecessor node and pre-predecessor node to consider turn penalty.
Basic process of search for shortest path procedure is as same as the treebased algorithm except the [step2].
[step2] Search optimal pathc * (r ) min h r [c * (i ) c(i, r )Pk i r ]Pr i, PPr k
Where, i is predecessor node of node r , and k is pre-predecessor node ofnode r . Pk i r is turn penalty at node i coming node k to node r , or transfer
cost.
3.1.3 Modified Vine Building Algorithm

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Most of the turn penalty problems can be solved by Vine Algorithm.
However, when turn penalty exists in a row, such that transportation
network has many nodes which have turn penalty or U-turn, P-turn, Vine
Algorithm cannot find the right shortest path (Kim, 1998). Modified Vine
Algorithm was designed to solve this problem. It considers all turn penalties
by giving extra labels to approaching directions to a node, so it doesnt needto expand network. This is a method considering turn penalty by setting up
many labels to directions of each predecessor and pre-predecessor nodes.
Conventional Tree Algorithm marks only one label to a node, while on theother hand, the Modified Vine Algorithm set many labels to a node. For
instance, in 4-way intersection, maximum number of labels is 4. Differencewith Vine Algorithm is that the number of labels is equal to the number ofpredecessor nodes and all other procedures are the same.
3.2 Like-based Shortest Path Search Algorithm
Link-based Algorithm was designed by Potts & Oliver (1972). Let
LC(i,j) be the nonnegative link cost required to travel from node i to node j
and LEC(o,i) be the link end cost, or minimum path cost from origin to node ithrough link(o,i) which refer to the directed link leading from node o to node i.
TP[link(o,i),link(i,j)] is the turn penalty which implies the additional cost at node i
between from link(o,i) to link(i,j) when turning prohibitions exist as shown in
Figure 3. With these notations we can define the link-based shortest path
optimality condition with turn penalty as follows.
LEC(o,i)+TP[link(o,i),link(i,j)]+LC(i,j) LEC(i,j) ,FORALL o,i,jN(3)

Figure 3: Basic concept of the link-based shortest path algorithm
The optimality condition in equation (1) has a unique solution,
because we can easily take over the optimality theorem already proved fornode-based case by simply replacing link costs by the sum of link costs andturn penalty, TP. The optimality theorem for node-based searching method
is explained fully in Potts & Oliver(1972). In the paper, instead of preceding
nodes in conventional shortest path algorithms, preceding links are used tomemorize the track of shortest path. The preceding link from node i to nodej , PL(i,j) , of Figure 3 is defined as
PL(i,j)=link(o,i)
ie. PL(i,j) is the link immediately before link(i,j) on the shortest path.

Comparison
Comparison between
betweenNode-based
node-basedandandLink-based
link-basedShortest
shortestPathpath Algorithms
algorithms

(4)

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Based on the equation (1) and (2), the steps of link-based shortest
path algorithm are as lists. Let R be the set of all labeled links, Rºset of
unlabelled links and O the set of all connected nodes with origin.
[Step 1] Label the link(h,i) , connecting origin node h with node i ,enter link(h,i) into set R , i.e. R= { link(h,i) }set LEC(h,i)=LC(h,i) and LEC(h,j)=INF j i
PL(h,i)=[Step 2] Find an unlabelled link
If LEC(i,j)+TP[link(i,j),link(j,k)]+LC(j,k) LEC(j,k)
Then, LEC(j,k)=LEC(i,j)+TP[link(i,j),link(j,k)]+LC(j,k)
PL(j,k)=link(i,j)[Step 3] Label the link(i,j)
Add the link(i,j) to the set R , and delete it from the set Rº[Step 4] If Rº= stop, otherwise go to [Step 2].

O

The main advantage of the algorithm described above is that it is easy
to code and possible to consider the turn restrictions. The Algorithm is very
similar to conventional Tree Building Algorithms. The only difference isthat it is link-based search, not node-based. Thus with a little modification,
the conventional algorithm can be used.
4. COMPARISON OF SHORTEST PATH SEARCHING
ALGORITHM
4.1 Comparison with example
4.1.1 Example transportation network
To compare Node-based and Link-based Algorithms, consider asimple transportation network as shown in Figure 4. The network has one
left-turn prohibition (node2), one U-turn (node3). This example tries tosearch a shortest path from node1 to node6. The link costs are marked oneach link. The shortest track of this example is node1-2-3-2-5-6 andminimum cost of the path is 20.

Figure 4: Example transportation network

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4.1.2 Procedure of shortest path search(1) Tree Algorithm
Tree Algorithm cannot consider turns inherently because of Bellmans
optimal condition, thus it cannot find the shortest path in this example which
has left-turn prohibition and U-turn. However, through expanding networkby adding dummy-nodes and dummy-links (expand node 2 to node 2.1, 2.2,
2.3, 2.4), it is able to find the right shortest path. Number above each node iscost of shortest path. From the expanded network, the algorithm cancalculate node 1-2-3-2-5-6 and path cost is 20 so it found the right shortest
path.

Figure 5: Expanded network for Tree algorithm(2) Vine Algorithm
Figure 6 shows the path found by Vine Algorithm, which consists oftrack node 1-4-5-6 and the path cost is 22, showing wrong path cost. This
happens because the path cost from node 1 to node 2 directly (10) is lessthan the path cost through U-turn by node 3 (14). Then the algorithm set theformer as a label. That is, the path node 2-3-2 (U-turn) is excluded because
it doesnt coincide with Bellmans Optimal Condition. As a result, Vine
Algorithm cannot find the shortest path in this example network which isalso found in Kim (1998). Thus, Vine Algorithm may not find the rightshortest path if the network has these kinds of turn restrictions. Expanding
network or using Modified Vine Algorithm is needed to overcome thisproblem.

Figure 6: Vine algorithm

Comparison
Comparison between
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link-basedShortest
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July 2010, Thailand
July 2010, Thailand(3) Modified Vine Algorithm
Vine Algorithm cannot find the right shortest path because it only has
one label for a node. That is, although many links are approaching to a node,
only one label is stored, thus, every other approaching link information onthat node is lost. To cope with this limit, Kim (1998) presented modified
vine algorithm that stores every approaching node data as labels. Figure 7
show a procedure of search for a shortest path using Modified Vine
Algorithm. Node 2 has not only a label from node 1 but a label from node 3
as well. Moreover, node 5 has 3 labels including one from pre-predecessor
node. Among these, only minimum cost is selected. The optimal pathcalculated from Modified Vine Algorithm is node1-2-3-2-5-6 and the pathcost is 20 which are right.

Figure 7: Modified vine algorithm(4) Link-based Algorithm
Link-based Algorithm puts labels on links not on nodes as otheralgorithms did. This method does not expand network or not label eachapproaching links by directions as Modified Vine Algorithm did. As shownin Figure 8, path costs are labeled on the end of link. Thus, it needs only one
label for a link. The calculated results by this method are presented in the
Figure8; track of node 1-2-3-2-5-6 and path cost is 20.

Figure 8: Link-based algorithm

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4.1.3 Comparison of searching algorithms
Every outcome of 4 different algorithms is summarized in Table 1.
Tree Algorithm cannot find the right solution for the example traffic
network unless it expands the network. Vine Algorithm has a defect andmay not find the shortest path in some cases, so it has to expand its networkor use Modified Vine Algorithm. Modified Vine Algorithm doesnt expand
network, however, it needs to put many labels on one node. On the contrary,Link-based Algorithm put a label on a link so it is the most efficient andsimple method.
Table 1: Comparison of shortest path searching algorithms
Original network
Expanded network
MethodCalculated Path Optimal
Path Optimal
Calculated path
path
cost path ?
cost path ?
node1 2.1

Tree

-

-

No

2.2 3 2.3

20

Yes

20

Yes

2.4 5 6
node1 2.1

Nodenode1 4 5
22
Vine
based
6

No

Modified node1 2 3
20
Vine
2 5 6

Yes

-

-

-

Yes

-

-

-

Link-based

node1 2 3
2 5 6

20

2.2 3 2.3
2.4 5 64.1.4 Application to larger transportation network
Consider a second example given by Kim (1998). The outcome of
Modified Vine Algorithm was presented in Kim (1998) and we only have tolook for a shortest path by Link-based Algorithm which has proven to be themost efficient one. The example network has one OD pair from node r tonode d, consists of 10 nodes and 23 links. Also, it has 1 left-turn prohibition,
3 turn restrictions and 1 U-turn. Shortest path calculated by Link-based
Algorithm is presented in Figure (b). It takes a U-turn at node 10 and P-turnat node 4. The cost of shortest path is 25 which are equal to the result of
Modified Vine Algorithm. That is, it found the right shortest path.

Comparison
Comparison between
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link-basedShortest
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July 2010, Thailand(a) example network(b) Found shortest path by Link-based algorithm
Figure 9: Large-scaled transportation network
4.2 Performance of computing time
Computing time to find a shortest path mainly depends on topology oftransportation network. Computing time requirement for an algorithm is afunction of the problem size and specifies the largest amount of time neededby the algorithm to solve any given size problem. In other words, the timerequirement measures the rate of growth in solution time as the problem size
increases. To compare the efficiency of the shortest path algorithms, we use
the notation, O(n) , with the network parameter n . O(n) means the
maximum time requirement to solve the problem and the complexity of thealgorithm (see Ahuja,et.al.,(1993) for more detailed).
With the notation O(n) , we can compare the efficiency of thealgorithms. The conventional Tree-based Algorithm has the timerequirement of O(n²) with parameter n, number of nodes. The Vine-based
Algorithm, however, has O(n³) . On the other hand, if the example network
composed of n node and l links has bi-directional rectangular topology,
there exists a relationship between n and l; l=4[ n- n ] . Because the Link-

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based Algorithm presented in the paper is based on links, not nodes, it hasO(l²= [ 4(n- n) ]²) of time requirement, which is greater than Tree-based
Algorithm but less than the Vine-based one. This implies that the algorithm
dose not requires too much running time to find the path, compared with theconventional algorithms. In special, when it applies to a network with turnprohibitions or to an integrated network with several transportation modes
on it, less computing time is required because it does not need to expand the
network.
5. CONCLUSIONSIn this paper, four Shortest Path Search Algorithms such as three Nodebased and one Link-based Algorithm have been compared with two
example transportation networks. From the results of the examples, we may
conclude that the Link-based Algorithm is more efficient for finding
shortest path under some turn-prohibited network.
Link-based algorithm may be applied to a variety of network analyses.
In first, it can be used for solving multi-mode traffic assignment with ease;such assignment model should be considered transfer behaviors of users. It
may also apply to finding evacuation route in emergency.
REFERENCES
Ahuja,R.K., T.L. Magnanti, J.B. Orlin, 1993, Network Flows: Theory,
Algorithms and Applications, Prentice Hall
Akgun,A.,Erkut,E.,Batta,R., 2000, On finding dissimilar paths, European
Journal of Operational Research 121, 232-246
Bellman,R.E., 1957, Dynamic programming, Princeton University Press,
Princeton
Caldwell,T., 1961, On finding minimum routes in a network with turn
penalties, Commum, ACM4, 107-108
Dijkstra, E. W.,1959,A note ,on two problems in connection with graphs, N
ume Math.1, 269-271
Jayakrishnan,R.,W.K.Tsai, A.Chen, 1995, A dynamic traffic assignment
model with traffic-flow relationships, Transportation Research (C),
Vol3, 51-72
Kim,I., 1998, Development of a modified vine building shortest path
algorithm for ATIS, Journal of Korean society of transportation,
Vol.16, No.2, 157-167
Potts,R.B., R.M. Oliver, 1972, Flows in transportation networks, Academic
press
Shier, R.D.,1979, On algorithms from finding the k shortest paths in a netwo
rk, Networks, Vol.9, 195-214
Yen J.Y,1971, Finding the K shortest loopless paths in a network, Managem
ent Science, Vol 17, No. 11, 712-716

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link-basedShortest
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3rdÍÐßÌ×ÑÌÛÓÐÑÎßÔ ÐßÌÌÛÎÒÍ
ßÒßÔÇÍ×Í ÑÚ Þ×ÑÓßÍÍ ÞËÎÒ×ÒÙ
ÐÓîòë ÑÊÛÎ ßÍ×ß É×ÌØ ÓÑÜ×Í
Hirotoshi KISHI1, Wataru TAKEUCHI2 and Haruo SAWADA3
1
Graduate school of Civil Engineering, The University of Tokyo, Japan
kisshi@iis.u-tokyo.ac.jp
2

Associate Professor, Institute of Industrial Science, The University ofTokyo, Japanwataru@iis.u-tokyo.ac.jp
3

Professor, International Center for Urban Safety Engineering,
Institute of Industrial Science, The University of Tokyo, Japan
sawada@iis.u-tokyo.ac.jp
ABSTRACTOne of the major pollutants, particulate matter whose aerodynamic
diameters is less than 2.5 m (PM2.5), remains in the inner part of lung andresults in asthma, bronchitis and cancer. However, its emission source andspatial distribution are difficult to be understood because of the variation
temporally and spatially. This study aims to estimate biomass burning PM2.5
emission over Asia with MODIS. Biomass burning PM2.5 emission isdetermined by burned area, fuel loading, combustion factor and PM2.5
emission factor. Firstly, burned area is estimated by MODIS fire data.
Secondly, fuel loading is developed by MODIS LAI, SLA, vegetation
continuous field. Thirdly, combustion factor and PM2.5 emission factor isdetermined by MODIS NDVI, FEPM and FOFEM model parameters.
Finally, biomass burning PM2.5 is compared to MODIS AOD. The maximum
value of biomass burning PM2.5 was captured by MODIS AOD observation,
however there seems to be another contribution to account for MODIS AOD.
Keywords: Particulate Matter less than 2.5 m (PM2.5), Aerosol
Optical Depth (AOD) and field burning
1. INTRODUCTION
1.1 Backgrounds
Air pollution is a serious problem because of emissions from factories,
vehicles, forest fires, and poor legal regulations, which are not prepared
precisely or poor force (JICA, 2005). Particulate matter (PM) is a complex
mixture of solid and liquid particles which remain suspended in the air and
it is one of the major pollutants that affect air quality in urban and even rural
areas of the world (Gupta, et al., 2006). Especially, PM whose aerodynamic
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diameters is less than 2.5 m (PM2.5) remains in the inner part of lung andresults in asthma, bronchitis and canceri. It is reported that PM2.5 mainly
produced by forest fire events about 30% for all PM2.5 emission in USAii,
about 20% in Canadaiii, however the investigation for its inventory andpatial distribution especially in Asia is not enough. Satellite based remotesensing is one of the most promising way to observe the fire events forsafety, immediacy and cost effectiveness rather than by field survey, and tounderstand the dynamics of pollutants spatially and temporally than by point
measurement equipments. Moderate resolution imaging spectroradiometer(MODIS) measures aerosol optical depth (AOD) at 0.55 m wavelength
corresponding to suspended particle matter whose aerodynamic diameters is
less than 2.5 m (Gupta, et al., 2006). It is important not only to monitor theatmospheric pollutants by satellite remote sensing but also to estimate theemission of PM2.5 from biomass burning events.
1.2 Objective
The objective of this study is to estimate biomass burning PM2.5 over
Asia with MODIS.
2. METHODOLOGY
Figure 1 shows a framework of estimation of biomass burning PM2.5 over
Asia with MODIS. Zhangs approach for monitoring of biomass burning
particulate emission across contiguous United States (Zhang et al., 2008) isapplied to Asia eatser than India with MODIS by following formula ;(1)
where E is the PM2.5 emission in a certain time period and location(Mg), A is burned area(Km2), B is the amount of fuel mass available for
combustion (Mg/Km2), C is the combustion factor, F is the emission factor
for PM2.5, k is the time period, l is the fuel type. They are determined with
MODIS land products shown by table 1.

Figure 1: Flowchart of an exhaust emission assessment with satellite remotesensing and city traffic modelingi

Ministry of the environment (http://www.env.go.jp/)
Environmental Protection Agency (http://www.epa.gov/)
Environment Canada (http://www.ec.gc.ca/)

ii

iii

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2.2 Burned area estimation
Burned area is estimated by MODIS active fire observation product(Takeuchi, et al., 2006). This fire product represents how many times hot
spots are detected in 1 km resolution during one month in 2007. We assume
one fire detection is equivalent to 1kmburned area in this study.
Table 1: Satellite data used for estimating biomass burning PM2.5 emission
Corrected satellite
Temporal and spatial
Data source
data with MODIS
resolution
Fire data
Monthly and 10 km
University of Tokyo
Land cover type
Static and 10 km
Boston university
Leaf area index
Monthly and 4 km
Boston university
Specific leaf area
Static and 60 km
University of Tokyo
Vegetation continuous
Static and 1 km
Global land coverfield
facility
NDVI
Monthly and 10 km
Lille University
2.3 Calculation of fuel loading
Foliage, branch, and aboveground biomass density are determinedaccording to Zhangs approach using regression model (Zhang et al., 2006)
shown by equation 2 and 3;(2)(3)
where, Bl is biomass density (g/cm2) in certain land cover type forfoliage (f), broadleaf and aboveground, SLA is specific leaf area (cm2/g)(Yasuoka, Y. 2007), LAI is leaf area index, and and x are coefficients
according to land cover type for needleleaf and broadleaf shown by Table 2(Zhang et al., 2006). Mixedleaf forest is regarded as no needle and broadleaf
forest, and that of percentage is regarded as fifty and fifty for needle andbroadleaf forest.
Shrub biomass density Bs is determined by this regression model
developed in the western US (Chojnacky et al., 2004) is applied;(4)
where, Bs is shrub biomass density (Mg/km2) and Vs is percentage ofshrub cover (0 - 1).
Litter and course woody detritus biomass density (Bl and Bw) aredetermined by the relationship of vegetation type with litter and course
woody detritus (Matthews, 1997). They are reclassified according to
MODIS land cover properties (King, et al., 1997) shown by Table 3, andSpatiotemporal
Spatiotemporal Patterns
patternsAnalysis
analysisofofBiomass
biomassBurningburningPM2.5
PM2.5over
overAsia
Asiawith
withMODIS
MODIS

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refined using the following equation to estimate them more realistically(Zhang et al., 2008);(5)
where, Blw is biomass density (g/m2) for litter (l) and course woody
detritus (w), Vc is percentage of vegetation cover (0 - 1), f and n are forest or
non-forest vegetation cover.
2.4 Determination of combustion factors
Firstly, vegetation condition index (VCI) is determined with MODISNDVI by following formula (Kogan et al., 1995) ;(6)
Secondly, moisture category factor (mcf) which is obtained by Fire
Emission Production Simulator (FEPS) (Andelsen et al., 2004) isdetermined by VCI equally divided into six categories,
Table 2: Coefficients for the estimation of branch biomass
Land cover type
Branchbiomassx
Needleleaf forests
1.46080
1.0592
Broadleaf forests
4.08676
1.1555
Table 3: Litter and coarse woody detritus biomass density according to
MODIS vegetation type
ID Land Cover
Litter Course woody detritus(g(dm)/m2)(g(dm)/m2)
0 Water
00
1 Evergreen Needleleaf Forest
2,730
3,200
2 Evergreen Broadleaf Forest
2,936
2,934
3 Deciduous Needleleaf Forest
1,955
1,700
4 Deciduous Broadleaf Forest
1,955
1,700
5 Mixed Forest
2,394
2,384
6 Closed Shrubland0184
7 Open Shrubland0184
8 Woody Savannas
0180
9 Savannas
0180
10 Grasslands
0120
11 Permanent Wetlands
00
12 Croplands
00
13 Urban and Built-Up00
14 Cropland/Natural Vegetation Mosaic
00
15 Snow and Ice00
16 Barren or Sparsely Vegetated00
17 Unclassified
0
0

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Table 4: Moisture category factor (mcf)
Moisture condition
Canopy Shrub
Duff

CWD

Very dryDry
Moderate
Moist
Wet
Very wet

0.08
0.12
0.15
0.22
0.31
0.75

0.33
0.50
1.00
2.00
4.00
5.00

0.25
0.33
0.50
1.00
2.00
4.00

0.33
0.50
1.00
2.00
4.00
5.00

which represents moisture conditions of very dry, dry, moderate,
moist, wet, and very wet respectively shown by Table 4 ; Thirdly,
Combustion factor is calculated by following model (Andelsen et al., 2004).
Equation 7 is aplied to foliage, branch, shrub and grass, and equation 8 is forcourse woody detritus. Litter fuel loading is regarded as completely
comsumed in various moisture condition (Andelsen et al., 2004) ;(7)(8)
where,Cc is combustion factor (C) for each land cover type (c), (Cw) isthat for course woody detritus, mcf is moisture condition factor shown by
Table 4.
2.5 Determination of PM2.5 emission factor
Emission factors for smoke from wildland fires are strongly related to
the fire condition associated with the combustion of a given fuel component(Hardy et al., 1996). PM2.5 emission factor is obtained from the Fire OrderFire Effects Model (FOFEM) (Reinhardt et al., 1997) shown by Table 5.
3. RESULT
3.1 Biomass burning PM2.5
Figure 2 shows estimated biomass burning PM2.5 in 2007. Figure 3
shows monthly mean and standard deviation AOD at 0.55 m derived from
MODIS level 3 atmosphere joint product (MOD08) and biomass burning
PM2.5 emission in 120km resolution in 2007. East Siberia (a), Middle China(b), North Laos (c) and South Kalimantan (d) are focused on in this study
because many fire events were found and they have various land coverproperties. These points can be detected below according to Figure 2 andFigure 3;Figure 2 shows large PM2.5 emission is estimated over East Siberia,
Middle China, South China, North Laos, North Thailand andMyanmar.

Spatiotemporal
Spatiotemporal Patterns
patternsAnalysis
analysisofofBiomass
biomassBurningburningPM2.5
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July 2010, ThailandFigure 3 shows large PM2.5 emission is estimated over East Siberia
in June and July, Middle China in August, North Laos in March andSouth Kalimantan in September.
Figure 3 shows large PM2.5 season shows high AOD mean andstandard deviation.
Figure 3 shows high AOD is observed over Middle China in no fireseason.

It is found that temporal behavior between biomass burning PM2.5 and
MODIS AOD is tolerable shown by Figure 3. Biomass burning has a large
impact to PM2.5 emission and MODIS AOD. On the other hand, there seems
to be another contribution to MODIS AOD except for biomass burning.
4. SUMMARY
This study is to estimate biomass burning PM2.5 over Asia with MODIS. It
is found that the maximum value of biomass burning PM2.5 was captured by
MODIS AOD observation. However, there seems to be another contribution
to account for MODIS AOD. In addition to this, the accuracy of estimatedbiomass burning PM2.5 and observed MODIS AOD is need to be checked.
The near future works in this study are refinement of biomass burning PM2.5
estimation model and checking MODIS measurement quality.
Table 5: PM2.5 emission factors
Moisture condition
CanopyShrub
LitterCWD(Kg/Mg) (Kg/Mg) (Kg/Mg) (Kg/Mg)
Dry
Moderate
Wet

10.65
10.65
10.65

10.65
10.65
10.65

3.95
3.95
3.95

8.10
9.15
9.15

Figure 2: Estimated annual mean biomass burning PM2.5 density in 2007. 4
areas are indicated to be compared to time series of MODIS AOD.

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Figure 3: Temporal patterns of observed monthly mean and standard
deviation AOD with MODIS, and estimated biomass burning PM2.5 in 2007.
REFERENCES
Anderson, G.K., Sandberg, D.V., Norheim, R.A., 2004. Fire Emission
Production Simulator (FEPS) User s Guide (version 1.0).
Chojnacky, D.C, Mickler, R.A., Meath, L.S., Woodall., C.W., 2004.
Estimates of Down woody materials in eastern US forests,
Environmental Management 33, S44-S55.
Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Leed, Yc. and Kumare,N. 2006. Satellite Remote Sensing of Particulate Matter and Air
Quality Assessment over Global Cities, Atmospheric. Environment.,
40, 5880-5892.
Hardy , C.C., Burgan, R.E, Ottmar, R.D, Deeming, J.E., 1996. A Databasefor Spatial Assessments of Fire Characteristics, Fuel Profiles, andPM10 Emissions, CRB Landscape ecologystars report chapter 19B.
Japan International Cooporation Agency, 2005. An effective approach fordevelopment agenda -Air pollution-.
Strahler, A., Muchoney, D., Borak, J., Friedl, M., Gopal, S., Lambin, E.,
Moody, A. 1999. MODIS Land Cover Product Algorithm Theoretical
Basis Document (ATBD) Version 5.0 MODIS Land Cover and LandCover Change.
Kogan, F.N., 1995. Application of vegetation index and brightness
temperature for drought detection. Natural Hazards: Monitoring And
Assessment Using Remote Sensing Technique Advances In Space
Research 15 (11), 91-100.
Matthews, E., 1997. Global litter production, pool, and turnover times :estimates from measurement data and regression models, Journal ofGeophysical Research,102, D15,18, 771-18800.
Spatiotemporal
Spatiotemporal Patterns
patternsAnalysis
analysisofofBiomass
biomassBurningburningPM2.5
PM2.5over
overAsia
Asiawith
withMODIS
MODIS

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Reinhardt, E.D., Keane, R.E., Brown, J.K., 1997. First Order Fire Effects
Model: FOFEM 4.0, User s Guide (General Technical Report INTGTR- 344). U.S. Department of Agriculture, Forest Service,
Intermountain Research Station,Ogden, UT, 65 pp.
Wataru Takeuchi and Yoshifumi Yasuoka, 2006. Near-real time active firemonitoring over Asia using Aqua/Terra MODIS. Journal of the Japan
Society of Photogrammetry and Remote Sensing, 45(5), 59-63.
Yasuoka, Y. Parameterization for land ecosystem model. Summary reportfor joined research presentation in 2007. Tokyo, March, 2007,
Ministry of Education, Culture, Sports, Science and Technology, 83107.
Zhang, X., Kondragunta., S. 2006. Estimating forest biomass in the USA
using generalized allometric models and MODIS land products,
Geophysicalresearchletters,
33,
L09402.
doi:10.1029/2006GL025879.
Zhang, X., Kondragunta, S., Schmidt, C., Kogan F. 2008. Near real time
monitoring of biomass burning particulate emissions (PM2.5) acrosscontiguous United States using multiple satellite instruments,
Atmospheric Environment, 42, 6959-6972.

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3rdÌØÛ ÜÛÊÛÔÑÐÓÛÒÌ ÑÚ ÔßÒÜ ËÍÛ ßÒÜÌÎßóÒÍÐÑÎÌßÌ×ÑÒ ×ÒÌÛÙÎßÌÛÜ ÓÑÜÛÔÚÑÎ ËÎÞßÒ ÍËÍÌß×ÒßÞ×Ô×ÌÇÛÊßÔËßÌ×ÑÒ
1

Ajay KHATRI CHETTRI1 and Dr. Hyunmyung KIM2
Master’s Student, Transportation Engineering, Asian institute ofTechnology, Bangkok, Thailand.
visitajay@hotmail.com
2
Assistant Professor, Ph.D.&P.E., Myong Ji University, Korea.

ABSTRACT
Sustainability is a growing concern among policy makers andplanners in every field and transportation is no exception. The nexus
between the sustainability and transportation can be established through arelationship between land use and transportation. So, the first step towards
defining sustainability is to define a relationship between land use andtransportation. The notion how land-use and transportation are related witheach other has been the topic of interest from past many years. Number ofstudies in the past has established a two-way interaction between the land
use and transportation system. However, the precise relationship between
land use and transportation has always eluded us. This relationship if
properly defined can give important parameters or indicators, which can be
useful in determining the sustainability of an urban area. In this study wedeveloped a simple land-use and transportation interaction model using
variables like zonal attributes (land price, attraction, and household) andtravel cost or time between zones. With the developed model we evaluateddifferent transportation scenarios from the sustainability aspect.
Keywords: accessibility, land use and transportation, sustainability,
sustainability evaluation, urban transportation scenarios.
1. INTRODUCTION
The concept of sustainable development first appeared because of thegrowing concern for the conservation of natural environment. The
discussion of sustainability is impossible without referring to a transport
sector as the pollution from the transport sector overwhelms that from othersector. For incorporating the concept of sustainability in transportation it isnecessary to find a link which can connect transportation with thesustainability issue. Himanen et al (2004) argued that transport plays “anintricate role in any quest for global sustainable development”. Theinteraction that takes place between land-use and transportation can provide
this link to realize the concept of sustainable transportation. There is a two

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way interaction between land-use and transport, which determine the pattern
in which land is used with the changes in transportation system. The early
work on the formulation of model linking land-use and transportation
started in the early 1950. It was only in early 1960, that the first operation
land-use and transportation model ‘The Model of Metropolis’ wasdeveloped by Lowry. After this number of models of land use andtransportation emerged, which can be categorized into three sections
namely: spatial interaction /gravity based models (these models aredeveloped based on the spatial impendence provided by the geography of
the area, eg: LILT, ITLUP, Lowry Model); random utility/discrete choicemodels (these models are based on random utility, eg: MEPLAN, TRANUS,METROSIM) and microscopic/disaggregate models (these attempt torepresent processes of change from the bottom up, that is, they account forthe behavior of individual agent in space and/or time, along withinteractions between agents (Iacono, et al, 2008)., eg: ILUTE, UrbanSim).
An integrated approach has always been applied to integrate land-use
policies with the transport infrastructure development to better understandthe spatial interaction between these two aspects. The parameter or indicatorobtained from this interaction can help bridge the gap between
transportation planning and sustainable planning. This study is focused on
development of integrated land use and transportation model, but the idea
here uses simple variables and concept that can suitable represent thisrelationship. The important variable in the model is land price from land use
and travel time or cost from the transportation. Besides, there are some
auxiliary variables in the model. The data requisite of the model is simple,
which are readily available for most metropolises. All the coding involvedin the model development were done using C-program.
2. METHODOLOGY
2.1 Problem formulation
The proposed model is formulated as a bi-level problem, where landuse is an upper level problem, where it is desired to maximize the total
utility for the residential location and non-mandatory location choice andtransportation is a lower level problem, where it is desired to minimize
travel time or distance, i.e.
Upper level problem (Land Use Model): max Utility
Lower level problem (Travel Demand Model): min Travel time
2.2 Framework
The proposed land-use and transportation integrated model starts witha basic premise that employment in each zone is fixed and given. Next stepis to locate the household for given employment in the region. Using given
and fixed employment and zonal attributes (Land price, attraction, Nonmandatory opportunities, etc.) in the Residential Location Choice Model(RLCM), the number of household in each zone is calculated. With

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calculated household, number of non-mandatory activities in each zone iscalculated using Non-mandatory opportunity Location Choice Model(NmoLCM). These allocations will give rise to new demand, which is fed
into Travel Demand Model (TDM). The TDM includes Trip Generation
Model (TGM), Trip Distribution Model (TDM), Combined Mode Choice
and Traffic Assignment Model (CMCTAM). Output of TDM is the travel
time for three different time of the day; morning peak, non-peak andevening peak. This gives new accessibility, which is used in the Land price
Model (LpM) to calculate new set of zonal land price. The average of zonal
land price is compared with average of the (ith-1) zonal land price. If thedifference is insignificant then the program is terminated, else new set ofzonal land price is used to re-allocate household and non-mandatory
opportunities, and the process continues. The conceptual framework of thedeveloped model is shown in the figure-1 below:

Figure 1: Land-use and transport model
2.3 Brief description of sub-models
2.3.1 Land Use Model (LUM)
First sub-model under LUM includes Residential Location Choice
Model (RLCM) where household is allocated to different zones and thennumbers of non-mandatory activities in each zone are obtained from Nonmandatory opportunity Location Choice Model (NmoLCM). Final submodel in the LUM is the Land price Model (LpM), which is performed after
the TDM.
i. Number of household in each zone is calculated as

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Hi = ∑nj = 1 Ej * Pji for i = 1,…, nWhere, Ej is fixed and given total employment (includes both workand school trips); Pji is the probability that the employment in zone j will
choose zone i as home location. If Lpi is the land price of zone i, t  ji is thetravel times between zone, Mi is the opportunities to participate in otheractivities (number of non-mandatory activities) in zone i, then Pji can beformed as the multinomial logit model as ( αi , βi , γi are the parameters ofthe multinomial logit model);Pj i =(2)e− ( αi L pi +βi t ji − γi Mi )

Σ kN=1e− ( αk −Lpk +βk t jk −γk Mk )ii. Number of non-mandatory opportunity in each zone is obtained as
Ni = ∑nj = 1 POPj * Pji(3)fori = 1,… n

Where, POPj is the population in zone j; Pji is the probability that the
population in zone j will choose zone i as non-mandatory activity location.
If t ji is the travel time between zones and Wi is the attraction for zone i,then Pji can be formed as the multinomial logit model as ( βi , γi are theparameters of the logit model);Pj i =(4)e− ( βi t ji − γi Wi )

Σ kN=1e− ( βk t jk −γk Wk )iii. Total zonal land price is obtained as
Land Price = Residential Land Price (RLP) + Commercial Land Price(CLP)
Where,
a. Residential Land Price in zone i (RLP)i = (Number of Household)i +(Mandatory Accessibility)i + (Non-mandatory accessibility)ib. Commercial Land Price in zone i (CLP)i = (Number of Household)i+ (Overall Accessibility)i + (Number of non-mandatory activities)i
Mandatory, non-mandatory and overall accessibility is obtained as(Ai)man = ∑Nj= 1 Djman * f (tij) , ,(5.1)(Ai)non = ∑Nj= 1 Djnon * f (tij) , ,(5.2)(Ai)overall = (Ai)man + (Ai)non . .
(5.3)

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Where, (Ai)man and (Ai)non is the accessibility for mandatory and nonmandatory activities for zone i, Djman and Djnon is the number of
mandatory and non-mandatory activity opportunities in zone j (number of
mandatory activity opportunities is equivalent to number of employment in
the zone while number of non-mandatory activity opportunities is equivalent
to number of non-mandatory activities), and f (tij ) is the impendence
function which is the function of travel time ((tij ) between two zones.
2.3.2 Travel Demand Model (TDM)
TDM has three separate sub-model namely; Trip Generation Model(TGM), Trip Distribution Model (TDM) and Combined Mode Choice andTraffic Assignment Model (CMCTAM).
i. The trip production and attraction are broken down according to trip
purpose and time of the day. According to trip purpose the trips are
categorized into mandatory and non-mandatory activities. According to timeof the day the trips are categorized into morning peak, non-peak andevening peak period. The table-1 below gives the details of trip production
and trip attraction for the zone.
Table 1: Trip production and attraction for the zone
Time ofTrip Production
the dayMan
Non-manMor. Peak 2.5 * Hj
0.1 * Hj
Non-peak 1.0 * Hj 0.2 * Ej + 0.2 * Hj
Eve. Peak 2.5 * Hj
0.1 * Hj

Trip AttractionMan
Non-manEj
0.1 * Hj + 0.1 * Nj
0.1 * Ej Hj + Nj
0.1 * Hj + 0.1 * Nj
Ej

Where, Ej is total employment, Hj is total household, and Nj is total
Non-mandatory opportunity in each zone. This given 6-OD table, that is one
for mandatory and other for non-mandatory activities for three different
time of the day. The total number of all the trips originating at all the zones
has to be equal to the total number of trips attracted to them. This isachieved applying a correction factor assuming that the total number of trips
arising from summing all trips originating from zone i, Oi is correct (i.e. trip
production are more reliable).
ii. Trip distribution is concerned with determining how the total trips
generated will be distributed among different zones. Trip distribution isdone separately for mandatory and non-mandatory activities and for
different time of the day, i.e. morning peak, non-peak and evening peak.
This gives six OD trip tables. While traffic assignment, mandatory and nonmandatory OD tables are combined to get travel time during morning peak,
non-peak and evening peak hour. Here, doubly constrained gravity model isused to distribute trips between different zones. The variable used for the
distance decay function is the travel time between the zones (Ortuzar, J. D.
and Willumsen, L. G., 2000).

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The total number of trips from zone i to zone j is given by(6)

Tij = Ai Oi Bj Dj f (cij )Where, Ai and Bj are the balancing factors, Oi is the total trip
originating from zone i, Dj is the trip attracted to zone j andf ( cij ) = exp ( − β * ti j ) is the distance decay function.
iii. Combined Mode Choice and Traffic Assignment Model(CMCTAM).
The model was developed by Lim Y. et al in 2001. The model wasdeveloped by combining Mode choice and Traffic demand model. Model
incorporates the transfer cost between modes (auto & transit).It can describe
the transfer behavior between auto and transit and it can depict mode choice
behavior without standard formula, such as logit mode choice function. The
model is based on persons’ trip not vehicle trip, where shortest path isdetermined with a link-based Shortest Path Algorithm based on Dijkstra’salgorithm. The model analyzes equilibrium problem over the multi-model
network. A feasible flow set is called an equilibrium solution if it satisfies
the conditions proposed by Wardrop(1950), i.e., “for each OD-pair, thetravel times on both the transit and road network should be equal if both areused”. The model considers that transportation modes are dependent ofeach other i.e., the link travel cost is asymmetric, for which no mathematicalequivalent program exist. Therefore, it is formulated as a variational
inequality problem. These kinds of formulations are proposed by De Cea etal (1993 &1996) and Lee S. et al (1996). It is formulated as follows:
G(V ) ≡ t (Vp*)T * (Vp* − Vp) ≤ 0(7)

Where, G (V ) is the gap function, t (V ) is the time vector,{ tinveh,p (in-vehicle travel time); twait,p (waiting time); ttran,p (transfertime)} and Vp = { Vstart,p (starting volume); Vthr,p (through volume); Vtran,p(transfer volume)} is the link flow vector. Vp* is the link flow vector atequilibrium state. Hearn (1982), stated that if the gap value G (V ) is identicalto zero, then Wardrop’s equilibrium is obtained. The equilibrium conditionis tested for the two test network before being applied to the real network.
The difference in equilibrium travel time for 3 node, 6 node and 25 node
network (real network) were found to be less than 1%, 5% and 10%,respectively, which is well within the permissible limit.
3. RESULT AND ANALYSISThe study is conducted on the test network resembling the Bangkok
metropolitan area. The network contains 25 node and 124 links with 141
origin-destination pair. The first part of the study deals with output of the76
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model and its analysis, while later section is concerned with the different
scenario analysis using the developed model.
3.1 Presentation of results of the model
The result shows that the distribution of household is largely
dependent on the utility resulting from the zonal land price. Besides, zonal
attraction, accessibility and number of mandatory & non-mandatory
opportunities play an important part. Zones with higher number ofhousehold have higher accessibility, attraction and opportunity to participate
in other non-mandatory opportunity, due to which the land price for thatzone is significantly higher (Table -2).
Table 2: Trip production and attraction for the zone
Attra
-ctionEmployment

N-man
Opp
x100

Land
price/
km2

Household

3
3
3
2
2
3.5
3
3.5
2
3.5
3.5
2
5
3
5
4
3
2
4
3
5
3
3
3
4

5000
5000
5000
4000
4000
6000
5000
6000
4000
6000
6000
4000
10000
5000
10000
7500
5000
4000
7500
5000
10000
5000
5000
5000
7500

4.33
5.51
4.98
2.02
2.31
7.59
5.71
6.34
1.57
4.74
5.29
0.9
20.88
2.3
13.94
9.8
4.25
1.83
5.86
2.3
19.61
2.8
2.98
2.5
6.15

10.17
11.27
10.38
9.25
8.67
13.25
10.95
13.17
8.84
12.53
12.41
9.77
23.33
10.46
20.24
15.89
11.46
8.51
14.12
10.53
23.37
10.7
10.63
11.17
14.53

4550
5346
4681
4703
4293
6117
5119
6065
4553
5617
5621
4434
11074
5159
8188
6896
5605
4239
5946
5260
10490
5270
5225
5871
6179

Accessibility
NonManOverall
man

50.04
51.41
50.01
42.09
40.36
60.57
50.28
61.16
40.29
62.05
60.27
42.12
101.6650.18
100.01
76.37
52.08
40.06
75.07
50.2
100.38
50.36
50.19
50.99
75.45

4.33
5.58
4.98
2.25
2.35
7.64
5.73
6.45
1.6
4.98
5.3
1.34
20.94
2.34
13.94
9.91
4.52
1.84
5.87
2.32
19.63
2.83
2.99
2.61
6.18

54.37
56.99
54.98
44.34
42.72
68.21
56.01
67.61
41.89
67.03
65.57
43.46
122.6
52.52
113.95
86.28
56.6
41.9
80.94
52.52
120.01
53.19
53.18
53.6
81.63

The figure -2 below shows the graphical representation of the effect of
the land price and non-mandatory opportunity in the distribution ofhousehold. Zones with higher household have higher land price and nonmandatory opportunity, which signifies the attractiveness of that zone. Thisis plausible because as the attractiveness of any zone increase, the number

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July 2010, Thailandof household will increase. This will result in increase in the land price inthe zone, and the number of non-mandatory activity trying to control thehigher influx of household into the zone and redistributing the household toother likely zones. This process is repeated until a certain sort of equilibrium
is reached between the utility coming from the land price, non-mandatory
activity and the travel time and the household distribution such that there islittle or no change in the distribution of household between successive
iteration. Here, land price is chosen as the variable to check this stability. If
there is little or no difference in the average value of land price insuccessive iteration, then the stability is assumed to have achieved.

Figure 2: Result of the Land Use and Transportation Model
3.2 Scenario analysis
The scenario analysis is performed to test the applicability of themodel to address problem relating to sustainability of an urban area. The
proposed model here uses the economical and social aspect of thesustainability to evaluated different scenarios or policies related tosustainable transportation. The different scenario proposed here are do
nothing, construction of new subway joining selected zones, increasing
capacity of road network by 30% and increase Transit speed by 30%. The
outputs for different scenarios are analyzed from the sustainable
development aspect and the best of the proposed scenario is suggested. The
parameter or variables used for the scenario analysis are total system travel
time (person-hour) i.e. summation of the product of the link volume and link
travel time; total land price per unit capacity of the zone (per km2 per
Population); total land price (per km2); total non-mandatory activities perunit household in the zone (number); and total mandatory, non-mandatory
and overall accessibility (monetary or time). The variables like total system
travel time (less is better), total land price per unit capacity of zone (less isbetter), total land price (less is better) and total non-mandatory activities perunit household in zone (more is better) can explain the economic aspect;

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while variable like mandatory, non-mandatory and overall accessibility(more is better) can explain the social aspect of sustainability.
3.2.1 Inference from scenario analysis
In this section the values of variables used for the sustainability
evaluation is calculated from different scenarios. The current scenario
analysis is more concerned in analyzing the different scenarios and finding
the best among them. So, an alternative method was necessary to best
represent the infer. Here, ranking method is used for this purpose. The
ranking method employs ranking values of the variable obtained fromdifferent scenario based on their performance. Rank Ist represent the best
scenario, while rank in increasing order represent the 2nd best, 3rd best andso on. The detail of the scenario analysis is presented in table -3.

Variable\Scenario
Total System
Travel time
Total land
price/capacity
Total land priceTotal non-man
active./household
Mandatory
accessibility
Non-mandatory
accessibility
Overall
accessibility

Table 3: Scenario evaluation
Do
New
Road Capacity
Nothing Subway>30%

Transit Speed
> 30%

III

IV

II

I

II

IV

I

III

II

IV

III

I

III

II

IV

I

IV

II

III

I

III

II

IV

I

IV

II

III

I

From the table 3 it is clear that from the different scenario analyzed
the fourth scenario i.e. increasing the travel speed of the transit network by
30% is the best scenario. The only parameter which is higher than otherparameter is total land price per capacity of zone, but the total land price isbetter than other scenarios. Increase in the speed in the transit network,
increases the model share of the transit. A little increase in transit model
share means there is huge passenger shift from the auto. This increases theefficiency of the transit as well as highway network as well as the wholenetwork. This also facilitates easy movement among zones, which isreflected by the accessibility performance of the scenario. Better total
system travel time, total land price, total non-mandatory activities andaccessibility ensures economical and social sustainability of the scenario.
The second best scenario is new subway construction as it has increased
accessibility even though the overall economic efficiency is very poor.
Increasing the capacity of the road network is the third best scenario. As
obvious, do nothing is the worst of the entire proposed scenario. The
simplicity of the integrated model has made the result of this analysis easy
to analyze and interpret. Here, while considering the different scenario the
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cost/benefit analysis or the economic cost involved is not considered, as it isout of the scope of the current study.
4. CONCLUSION
Here, with the use of most important transportation variables (like traveltime) and land use variables (like employment, land price, etc.) a simple
integrated land use and transportation model is developed. The developedmodel is used for the test network. The attraction of the zone, travel time
between zones, and land price seems to be the most important factor for the
distribution of household and non-mandatory activities. The zones withhigher number of household has higher land price, which acts a disutility,
but it also has higher accessibility and attraction, which acts as utility forhousehold distribution. Also, using the variables from the land use andtransportation interaction model the performance of different transportation
scenarios based on the sustainability is analyzed, which helped us to selectthe best of the proposed scenarios.
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Allen, W.B., Liu, D., and Singer, S., 1993. Accessibility Measures of U.S.
Metropolitan Areas. Transportation Research –B, Vol. 27B, No 6, pg.
439-449.
Bertolini L., le Clercq F., and Kapoen L., 2005. Sustainable Accessibility: A
Conceptual Framework to Integrate Transport and Land Use Planmaking. Two Test-applications in the Netherlands and a Reflection on
the Way Forward. Transport policy-12, pp. 207-220.
De Cea, J., and Fernandez, J. E., 1993. Transit Assignmnet for Congested
Public Transport System : An Equilibrium Model, Transportation
Science ,Vol. 27, No. 2, pg 133-147.
De Cea, J., and Fernandez, J. E., 1996. An Empirical Comparison of
Equilibrium and Non-Equilibrium Transit Assignment Models, Traffic
Engineering Control, pg. 441-445.
Dong, X., Moshe E., Ben-Akiva, Bowman, J. L., and Walker, J. L., 2006.
Moving From Trip-based to Activity-based Measures of Accessibility.
Transportation Research Part A 40, pg. 163–180.
Foot, D., 1981. Operational Urban Models, An Introduction; Matheun,
London.
Hearn, D. W., 1982. The Gap Function of a Convex Program, Operations
Researches, Let. 1, pg. 67-71.
Himanen, V., Lee-Gosselin, M., Perrels, A., 2004. Impacts of Transport on
Sustainability: Towards an Integrated Transatlantic Evidence Base.
Transport Reviews-24 (6), pg. 691-705.
Iacono, M., Levison, D. and El-Geneidy, A., 2008. Models ofTransportation and Land-Use Change: A Guide to the Territory;
Journal of Planning Literature- 22, pg. 323-340.

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Lee, S., Rhyu, K., Chon, K., 1996. A User Equilibtium Transit Aassignment
Model for Vehicle Capacity Constraint, Journal of Korean Society ofTransportation, Vol. 14, No.3, pg. 27-44.
Lim, Y., Kim, H. and Lim, K., 2001. Development of an integrated networkmodel with considering explicit transfer behavior between
transportation modes. World Conference on Transport ResearchSociety, COEX Convention Center, Seoul, Korea.
Ortuzar, J. D. and Willumsen, L. G., 2000. Modeling Transport; John Wiley& Sons Ltd., England.
Sheffi, Y. (1985); Urban Transportation Network, Equilibrium Analysis
with Mathematical Programming Methods. Prentice-Hall, Inc.,
Englewood Cliffs, New Jersey 07632.

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1

Yuto SHIOZAKI1, Takaaki KATO2 and Hitoshi NAKAMURA2
Department of Urban Engineering, The University of Tokyo, Japanyuto@city.t.u-tokyo.ac.jp
2
International Center for Urban Safety Engineering,
Institute of Industrial Science, The University of Tokyo, Japan

ABSTRACTIn Japan, in preparation for large-scale flood, municipalities arelegally obligated to make and publish “Flood Hazard Map” which showsthe estimated flooded area, flood depth and refuges in case of a specificlarge-scale flood. In the short term, the improvement of evacuation isimportant. However, from a long-term perspective, the concept of city lessvulnerable to floods is needed because it is estimated that climate change
will increase the occurrence frequency of large-scale flood in the future.
In this study, the authors investigated the actual situations ofmunicipal urban planning regulation and guidance for flood disastermitigation. The effects and problems of each case are qualitatively
evaluated. Additionally, the condition for developing flood risk control inurban planning is discussed.
Keywords: flood disaster mitigation, urban planning, flood hazard
map, climate change adaption.
1. INTRODUCTION
In Japan, flood countermeasures have been mainly held by building water
works like dams, levees and so on to completely prevent floods since theRiver Law was established in 1896. However, recent some floods in urban
area including the Fukuoka Flood Disaster in 1999, Tokai Flood Disaster in2000 were wake-up call to remind that the countermeasures were needed in
preparation for flood disaster. The Flood Prevention Law was revised in2003 and 2005 to obligate municipalities to make and publish flood hazard
map on rivers which would damage the municipality in case of flood. Floodhazard map shows the estimated flooded area, flood depth and refuges incase of a specific large-scale flood.
In the short term, it is important to improve evacuation planning by
flood hazard map. At the same time, it is also needed to build a city lessvulnerable to floods from a long term perspective because climate change

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will increase the occurrence frequency of large-scale flood in the future.
Though flood risk control is should be considered in urban planning process
for building a city vulnerable to floods, the concept of flood risk control has
been lacked in Japanese urban planning system. However, somemunicipalities implement urban planning regulation and guidance for flood
disaster mitigation.
The purposes of this study are as follows: 1) to investigate the actual
situations of municipal regulation and guidance for flood disaster mitigation,2) to qualitatively evaluate the effect and problem of each case and 3) todiscuss the condition for developing flood risk control in urban planning.
2. CASE STUDY
Through the Internet and literature review, ten cases of municipal urban
planning regulation and guidance for flood disaster mitigation weresearched. Interviews were conducted on the municipal officers in charge often cases. The cases are classified into four types depending on
implementation systems (Table 1).
Table 1: Investigated cases in this study
Implementation type
Municipal regulation and guidance
Regulation
Nagoya City Building Ordinance
Setagaya City Building Guideline
Guidance
Suginami City Building Guideline
Shinjuku City Building Guideline
Regulation and guidance
Kusatsu City Building OrdinanceNakano City Subsidy to High-floored Building
Suginami City Subsidy to High-floored
Building
Shinagawa City Subsidy to Flood Protection
Subsidy
ShieldSuginami City Subsidy to Flood Protection
ShieldKomaki City Subsidy to Flood Protection Wall
2.1. The case of flood disaster mitigation by municipal regulation
2.1.1. The outline of the building ordinance in Nagoya CityIn 1959, the storm surge of Isewan typhoon broke down coast andriver levees causing ruinous damages to Nagoya city. The dead anddisappeared were 1,851, the afflicted people were 530,000, and the damaged
houses were 118,000 in the city. Learning from the Isewan typhoon disaster,
Nagoya City established a building ordinance in 1961 to promote flood
disaster mitigation in preparation for the case that coast and river levees
were breached by river flood or storm surge. This building ordinance isbased on a provision of Building Code, “designation of disaster risk zones”,
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disaster risk zones and to establish building restrictions on the area. Nagoya
City Building Ordinance is the only case in Japan that flood disastermitigation is implemented in a wide urban area by the designation ofdisaster risk zones.
The area in application of Nagoya City Building Ordinance extends
6,000ha. The area is classified into four districts depending on the estimatedflood damage and the land use situation. The regulation of ground-floor
level and building structure is established on each district (Table 2, Figure1). The aim of this regulation is to secure safety refuges for saving human
lives in case of large-scale flood including storm surge.
The 1st district is in danger of being swept away by storm surge
because this district is located outside tidal barriers. Hence, in the 1st district,the ground-floor level of building must be higher than N.P.1 +4m, andwooden structure is basically banned to build.
Table 2: The restrictions of Nagoya City Building OrdinanceDistrict
Ground floor Height
Structure
Wooden construction is
1stHigher than N.P. +4.0m
banned.
2ndHigher than N.P. +1.0m

3rdHigher than N.P. +1.0m

4thHigher than N.P. +1.0m

A room must be established on
second floor and above.

A room must be established on
second floor and above.
(Source: Nagoya City)

Figure 1: Four districts of Nagaya City Building Ordinance(Source: Nagoya City)
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In the 2nd, 3rd and 4th districts which are located inside tidal barriers,
the ground-level of building must be higher than N.P. +1m. The 2nd districtis in danger of being deeply flooded by storm surge. In the 4th district, since
it is urbanization-restricted area, buildings are so scattered and safety
refuges are few. Hence, safety evacuation spaces are needed to be secured inindividual buildings. For these reasons, making a room on second-flood andabove is obligated in the 2nd and 4th district. In 3rd district, restriction onbuilding structure is not imposed because this district is located inland, and
it is estimated to be not deeply flooded comparing with other districts.
Additionally, in the 2nd, 3rd and 4th district, the building ordinanceputs further restrictions on public buildings like schools, hospitals,
community centers and government offices which are refuges in case offlood disaster. The restrictions are as follows: 1) the ground-floor level mustbe higher than N.P. +2m, 2) one or more rooms must be established higher
than N.P. +3.5m and 3) wooden structure is banned to build.
The observance of restrictions is verified in the process of building
certification base on Building Act. Correction order is given to the buildingin which the restrictions are violated. The punishment based on Building
Act is applied in case that the correction order is also neglected.
2.1.2. Effect and problem
The effects of Nagoya city building ordinance are as follows: 1) thesafety level in the area has been improved up to now in preparation for thebreach of flood barriers and dikes by storm surge, and 2) the building
ordinance leads to the enlightenment for promoting further flood disastermitigation than the restrictions.
The ground floor height of building is settled in the ordinance, but it is
not strict comparing with ground level. Additionally, the most of recent
buildings are higher than two-story. For these reason, it can be said that theordinance has already fulfilled a roll appropriate to the needs of the times
after Isewan typhoon. Another ordinance appropriate to current situation isneeded for developing further flood disaster mitigation.
Another problem of the building ordinance is that some flood disastermitigation measures are not suitable for barrier-free building. For example,
raising ground-floor makes the length of wheelchair ramps longer.
2.2. The cases of flood disaster mitigation by municipal guidance
2.2.1. The outline of municipal guidance cases
The number of flood damage to basement room by local heavy rain
has risen for these years. For this reason, Setagaya, Suginami and Shinjuku
City in Tokyo established each building guideline for flood damage
mitigation. These guidelines aim to let building owners know flood risk andflood damage mitigation measures.

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These cities employ notification procedure as the following. The
owner who will construct a building shown in table 2 is supposed to turn ina document for showing what measure the owner will employ for flood
damage mitigation in the process of building certification. In Suginami Cityand Shinjuku City, the notification procedure is applied to the area which isin danger of being flooded according to the flood hazard map of each city.
Table 2: The target of notification procedure
The target of notification procedure
New-built or rebuilt building
which has a floor under the surrounding
Setagaya Building
ground or road,
Guideline
or in which discharge water might flow backbecause of the surroundings
New-built
or rebuilt building
Suginami Building
which is located in the flood-hazard area
Guideline
shown on the flood hazard map,
Shinjuku Building
and which has a flood under the surrounding
Guideline
ground or road

(Source: Setagaya, Suginami and Shinjuku City)

These cities advise the owners to mound up the entrance to basement,
put in a mobile flood protection shield or a drainage pump and so on.
However, the scale of flood mitigation measure is left to the judge of theowner in all of these guidelines.
The owners are not legally obligated to employ a flood mitigation
measure because these guidelines are not based on any law. These cities aremaking efforts so that flood mitigation measures are devised on the target
buildings through guidance.
2.2.2. Effect and problem
The targets of the building guidelines are limited to part of buildings,
but they have functions to inform flood risk and promote flood mitigation
measures in the process of urban planning. If an owner does not employ theappropriate flood mitigation measure following the guideline, it is importantthat this system gives the owner the information on flood risk and an
opportunity to make a decision whether he or she employs flood mitigation
measure.
2.3 The case of the combination use of regulation and guidance
2.3.1. The case of building ordinance in Kusatsu City
Kusatsu City is located in Shiga Prefecture, and facing Biwa Lake
which is the largest lake in Japan. Some ceiling rivers run through the city.
It had suffered form floods of Biwa Lake and ceiling rivers. Since river
improvement work was recently completed, Large-scale flood has neveroccurred in Kusatsu City. However, in 2006, Kusatsu City established abuilding ordinance for promoting flood disaster mitigation in reaction to the

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fact that the flood danger area maps of Biwa Lake and some rivers weremade public after 2002. This ordinance doesn’t have legal obligation
because it is not based on any national law.
In this building ordinance, the areas in danger of being flooded andthe estimated flood depth are shown on the map which is made byoverlapping the flood danger area maps of Biwa Lake and some rivers. The
depth in map is the deepest water level in the flood danger area maps.

Figure 2: The areas in danger of being flooded (Source: Kusatsu City)
Disaster-prevention facilities and refuges are designated “specificbuilding” in the building ordinance. When a specific building is built, it isobligated to employ flood mitigation measure considering the estimatedflood depth shown on the map. The specific buildings are mainly publicbuildings like government office, school and hospital. Hence, the regulation
on specific buildings is implemented as self-imposed regulation of public
sector.
Notification procedure is employed in regard with ordinary buildings.
The owner who will build a building with a basement or an emergency
elevator is supposed to turn in a document for showing a flood mitigation
measure employed in the process of building certification. However, theowner is not obligated to employ flood mitigation measure in the same way
as Setagaya, Suginami and Shinjuku City.
2.3.2. Effect and problem
Kusatsu City is preparing for large-scale flood by introducing flood
mitigation measure into disaster-prevention facilities and refuges
considering the flood depth shown on the flood danger maps. It leads to88
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securing the function of disaster-prevention facilities and refuges in case offlood. In regards to ordinary buildings, though the target is limited, it isimportant that the guidance based on the ordinance gives the owner the
information on flood risk and an opportunity to make a decision whether heor she employs flood mitigation measure in the same way as Setagaya,
Suginami and Shinjuku City.
2.4. The cases of flood disaster mitigation by municipal subsidy
2.4.1 The cases of subsidy to high-floored building
In 1970’s, floods of rivers running through urban area occurred
frequently in the west part of Tokyo because river improvement work did
not catch up with the speed of urbanization. For this reason, Nakano and
Suginami City started the subsidy device to build houses with the floor
elevated above ground level. Afterward, the subsidy device was demolishedonce because the number of subsidies decreased. However, in September
2005, heavy rain caused the flood damage in the Myoshoji River and
Zenpukuji River basins. Nakano and Suginami City restarted the subsidy
each in 2005 and 2008.
Nakano City gives the subsidy to the owner who will construct orreconstruct a building in the areas which have flooded since 1981 and whichis still danger of being flooded because of the delay of river improvement
work. Additionally, Nakano City eased the height regulation on the subsidy
object area. Nakano City will downscale the subsidy object area following
the progress of the river improvement work.
Suginami City gives the subsidy to the areas which is danger of being
flooded higher than 0.5m in Suginami City Hazard Map in addition to theareas which have flooded. Suginami City will continue the subsidy
regardless of the progress of river improvement work.
Table 3: The standard price of subsidy to high-floored building
Construction method, structure type
Standard price(yen/m2)
Elevation of existing building
78,000Wooden construction
55,000New
Steel construction
42,000construction
Reinforced concrete structure
29,000(Source: Nakano and Suginami City)

In both of Nakano and Suginami City, the given subsidy is half theamount multiplying the high-floored space (m2) by the standard price(yen/m2) shown in table 3. However, the limit is 2,000,000 yen in both ofthem.
Nakano City had given 101 cases by May 2009, but Suginami City
had never given subsidy at the time of December 2009. Comparing thenumber of subsidy with the flood damage in Nakano City, it is found that

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the number of subsidy increases soon after flood damage, but afterward
gradually decreases.
2.4.2. The cases of subsidy to flood protection shield
Shinagawa City, in Tokyo, started the subsidy to mobile flood
protection shields since 1989 because floods of rivers running through thecity frequently caused damages in 1980’s. Shinagawa City gives the subsidy
to the owner or user who will establish the shields on house, store or office.
This subsidy aims to cover the delay of flood control measures. According
to the officer of Shinagawa City, it will be demolished at the time whenflood control measures are improved and flood damages are solved.
Suginami City started the subsidy to mobile flood protection shields in2008 in addition to the subsidy to high-floored houses. Suginami City gives
the subsidy to the person who will establish the shields. Suginami City will
also continue the subsidy regardless of the river improvement work.
Table 4: The subsidy to flood protection shield
Subsidy content
To person: 3/4 of the expense for flood protection shield
To company: 1/2 of the expense for flood protection
Shinagawa City
shieldHowever, the limit is 1,000,000 yen.
1/2 of the expense for flood protection shield
Suginami City
However, the limit is 500,000 yen per one building.

(Source: Shinagawa and Suginami City)

By May 2009, Shinagawa City had given 119 cases, and Suginami
City had given 3 cases. Comparing the number of subsidy with the flood
damage in Shinagawa City, it is found that the number of subsidy increases
soon after flood damage, but afterward gradually decreases in the same wayof the subsidy to high-floored houses in Nakano City.
2.4.3. The case of subsidy to flood protection wall
Komaki City, in Aichi Prefecture, have offered subsidy to flood
protection wall since 2006. It is impossible to preventing from flood damage
by river or sewage improvement in regard with the buildings which arelocated lower than roads or water channels. The subsidy aims to mitigate
flood damage on those buildings.
2.4.4. Effect and problem
The effects of subsidies for flood disaster mitigation are as follows: 1)
citizen’s self-motivated flood damage mitigations are promoted, and 2) theflood damages on small spots which are lower than the surrounding roads orwater channel can be individually managed.
Generally, the number of subsidy gets increased soon after flood
damages, but the flood damage mitigation by subsidy is not developed after
a certain period of time. It is pointed out that the people who employ flood

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mitigation measure getting subsidy are almost those who got damaged byflood.
Additionally, the flood damage mitigation by subsidy is apt to be
conducted as municipal measure which covers the delay of river
improvement work. For developing flood mitigation measures in city fromlong-term viewpoint, municipalities have to reconsider the role of subsidy
for flood damage mitigation and put forward flood risk information withsubsidy.
3. DISCUSSION
From the results of case studies, the condition for developing flood risk
control in urban planning will be discussed below.
Flood disaster mitigation employing the designation of disaster riskzones like Nagoya City Building Ordinance raises a safety level of whole
the designated area because the targets of regulation include all buildings.
Additionally, it is effective from a viewpoint of implementation because theordinance is based on Building Code. However, except Nagoya City, no
municipality introduces the designate of disaster risk zones into wide urban
area. As a result of interviews to the municipal officers, the municipalities
are negative to introduce the regulation flood disaster mitigation because of
the following reasons: 1) they hesitate to impose a burden to citizens, and 2)
it is difficult to establish the standard for regulation.
The reason why Nagoya City could introduce the designation ofdisaster risk zones is considered that Isewan typhoon convinced bothgovernment and citizens the necessity of flood disaster mitigation. At thesame time, it is found that the standard of flood disaster mitigation wasdetermined from the damage investigation of Isewan typhoon.
Currently, flood hazard map is published, but citizens are not familiar
with the maps because flood risk itself is not enough shared in society. In
this current situation, like Kusatsu, Setagaya, Suginami and Shinjuku City,
informing flood risk in the urban planning process is effective. In thosecities, however, the target is limited to part of buildings. Flood risk
information should be informed to more persons concerned like building
owners, constructors and designers. For example, the system that the floodrisk and mitigation measure on building must be informed to customer isconceivable. Like Kusatsu City, flood mitigation measures on disasterprevention facilities and refuges should be also implemented by regulation.
Up to now, in Japan, the responsibility for countermeasure to largescale flood beyond flood control facility has never been discussed. For
developing flood risk control in urban planning, flood risk should be sharedin society, and the role-sharing among river administrators, municipalities
and citizens needs to be discussed and clarified.

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NOTES
1. N.P. ±0m is the water level of Nagoya Port at the time of low tide.
REFERENCES
Katada, T., Kimura, S., and Kodama, M., 2007. Desirable utilization offlood hazard maps for risk communication. Doboku Gakkai
Ronbunshu D, Vol.63, No.4, p.p.498-589 (Japanese)
Panel on Infrastructure Development, 2008. Climate change adaptation
strategies to cope with water-related disasters due to global warming,
Ministry of Land, Infrastructure, Transport and Tourism
Building Research Institute, Ministry of Construction, 1960. The
investigation of urban area and building damage in Nagaya City by
Isewan Typhoon and the disaster prevention plan, Nagoya City
(Japanese)

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Preeda CHATURABONG1, Kunnawee KANITPONG2and Piyapong JIWATTANAKULPAISARN3
1
Graduate Student School of Eng., Asian Institute of Technology, Thailand
joauin_sanchez@hotmail.com
2

3

Assistant Professor, TRE, SET, Asian Institute of Technology, Thailand
Evaluation Group, Bureau of Planning, Department of Highways, Thailand

ABSTRACT
It is well known that the accident costs need to be estimated tounderstand the existing problem and to perceive a major economic impactof road accident. In addition, the accident cost can be used in the processof road planning and the development of road safety policy. In many
developing countries including Thailand, the road accident cost has been
traditionally evaluated by the Human Capital Method. This approach,however, has a shortcoming of underestimating the accident cost by the factthat it focuses only on the economic effects of the loss of life and does notaccount for the value of enjoyment of life forgone. In this study, anotheralternative method which is the Willingness-To-Pay method (WTP) was
selected to evaluate the accident cost. WTP method or the value of riskchange is used to estimate the value that individuals would pay for reducing
the risk of loss of life. The Contingent Valuation (CV) method is adopted inthis study to determine the WTP. In this paper, the cost due to motorcycle
accident was focused because the motorcycle crash is the biggest portion
among all type of vehicle crashes in Thailand. The questionnaire survey
was designed to determine the amount of money that each motorcycle user
would pay to reduce the risk of loss of life from motorcycle accident. In thisstudy, a total of 1,015 motorcycle users in Bangkok and surrounding areas
were interviewed. The results show that the Value of Statistical Life (VOSL)
and the Value of Statistical Injury (VOSI) are in the range of 5.5 to 7 million
baht and 2.6-3.4 million baht, respectively. The age, gender, occupation,
income, and behavior of helmet use are significant factors affecting thewillingness to pay of motorcycle users to reduce the fatality risk. The age,gender, income, household income, frequency of using motorcycle, accident
experience, and the behavior of alcohol-impaired riding are significant
factors affecting the willingness to pay of motorcycle riders to reduce thesevere injury risk.
Keywords: willingness-to-pay,regression; accident cost; value of life.

contingent

valuation,

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1. INTRODUCTION
The economic growth in Thailand has led to an expanding network of roads
and increasing number of the vehicles. The growing number of vehicles on
the roads has resulted to the significant increase of road crashes in recent
years. A total estimated national economic loss due to the road accidents isabout 204,000 million Thai baht or 3.1 percent of the GDP in 2005. To
address the road safety problem, it is essential to understand accident costs
which is related to motivation and awareness. The awareness must be
perceived that the problem does exist and also has a major economic impact.
It means that if the cost could be visualized in some ways, it would provide
motivation to handle the question in an adequate manner, that is, by theprovision of the necessary legislation, organization, funding, etc.
In many developing countries, the road accident cost has been
traditionally evaluated by the Human Capital Method. This approach,however, has a weak point of underestimating the accident cost by the factthat it focuses only on the economic effects of the loss of life and does notaccount for the value of enjoyment of life forgone. Therefore, this method
underestimates the actual value of road crashes even though a “pain, griefand suffering” component is sometimes included to represent “human cost”,
and increases the value derived.
Another alternative method for theevaluation of accident cost is the Willingness-To-Pay method (WTP). Thisapproach has been widely used in many developed countries. WTP methodor the value of risk change is used to estimate the value that individuals
would pay for reducing the risk of loss of life. This approach is principally
based on survey design to determine the amount of money that individuals
would pay to reduce the risk of loss of life.
In Thailand, the statistics shows that motorcycle crash has taken the
biggest portion among all type of vehicle crashes as shown in the number of
56% from total of 1,003,890 crashed vehicles. Due to this high proportionof the motorcycle accidents, this study selects the motorcyclists as a target
group. Currently, the analysis of the value of life of motorcyclists is notavailable in Thailand. This study is therefore to estimate the costs ofmotorcycle accidents in Thailand by using the WTP method. In addition,
this study includes the estimation of economic costs of motorcycle crashes
by considering the differences in socio-economic characteristics, the attitude
towards risk on the value of life, the trip purpose of motorcycle riding, thefrequency of trips making by motorcycles. The study also evaluates how
these factors affecting the willing to pay of motorcyclists to avoid crash
involvement and risk of death.
2. PREVIOUS STUDIES ON ACCIDENT COST ANALYSIS
A number of research studies on the analysis of economic losses due to theroad accidents have been conducted in Thailand. Most of these studies haveadopted the human capital approach in estimating the accident losses.
Research studies in the national level include Patamasiriwat (1994), Tosutho

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ICUS Report 45(1997), Boontam (2001), Suwanrada (2005), and Luathep and Tanaboriboon(2005). Studies which are limited to the calculation of road accident losses
in Bangkok are Komnamoon (1979), Promglam (1998), Kradang-Nga(1999), and Sumiratana (1996). Other studies, such as Chanchareon et. al.
(1993) and Kunaratanapruek et. al. (1995), focus on the calculation of
medical costs due to the road accidents. The most recent study conducted toestimate the road accidental loss by using the human capital method was led
by Thongchim et al. (2007). In this study, the total losses due to roadaccidents were reported to be 204,000 million baht throughout 2005. It isreported in this study that the accident cost of individual fatality of Thai
people is 4-4.7 million baht, and the accident costs of individual disability
and severe injury are 5 million and 0.12 million, respectively.
3. METHODOLOGY
In this study, the CV questions were designed in two basic forms,
open-ended and closed-ended. For the open-ended CV question, therespondent was asked to state the maximum amount at which he or she iswilling to pay for the good that is being valued which is the reduction orchange of road accidental risk in this study. For the closed-ended CVquestion, the respondent was asked whether he or she is willing to pay a
specified amount presented as the value of the service. For the closed-ended
questions, the respondent is supposed to reply “yes” or “no.” Closed-ended
questions have been the preferred form of elicitation question since it was
introduced by Bishop and Heberlein (1979). In contrast, open-ended
questions provide more information than closed-ended questions; and do not
require econometric modeling to analyze, as the mean WTP values of
respondents can be readily estimated by simple arithmetic calculation.
3.1

Questionnaire Design

The designed questionnaires include the questions which were separated
into three sections. The questions in the first section are related to thesocioeconomic characteristics and household characteristics of the
respondents. The second section includes the questions related to the riding
behavior and the risk-taking behaviors of motorcycle users such as
frequency of using motorcycle, helmet use, riding against traffic (counterflow riding), speeding, experience of involving in the accident, and alcoholimpaired riding. The last section of the questionnaire includes the valuationquestions which were designed to ask the respondents to provide theestimates of the relevant rates that they are willing to pay for a reduction in
the probability of fatality and injury due to the motorcycle accidents.
The valuation questions were presented in two scenarios. Eachscenario was filled out as two elicitations which are the open-ended andclosed-ended method. The first scenario is to evaluate the WTP of
respondents in their own risk of death, and the second scenario is to evaluate
the WTP to reduce the risk of severe injury. For the closed-ended question(also referred to as a “dichotomous choice” or “referendum” question), theAnalysisbyby
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respondent is asked whether he or she is willing to pay a specified amountpresented as the value of the risk reduction. The respondent is expected toanswer “yes” or “no.” The closed-ended questions have been the preferred
form of elicitation question since it was introduced by Bishop and Heberlein(1979). The example of the valuation question in the questionnaire design is“Are you willing to pay 400 baht per trip to take Bus B to Khonkaen which
can reduce the fatality risk from 16 to 8 deaths in every 100,000 people or
50% reduction in fatality risk?” this is for the fatality questionnaire only.
For the severe injury questionnaire would be quite similar to this but it isdisplayed assumption another situation. For the opened-ended question, therespondents were asked to state the maximum amount that he or she iswilling to pay for the good that is being valued.
3.2 Data Collection
A total of 1,015 respondents were interviewed for the data collectionin this study. The respondents are the motorcycle users who live in Bangkok
and suburb areas such as Nonthaburi, Pathumtani, Samutprakarn, andNakornpathom. The interview was conducted at several locations such as
government offices, private companies, schools and universities, markets,and on public streets, and etc. Respondents were randomly selected fromdifferent days of week, time of the day, places in each province andcharacteristics of drivers (gender, age, family status, occupation, education,monthly income, and household income).
3.3 Methodology for accident cost analysis
For the open-ended questions, the mean WTP values of respondents
can be estimated by simple arithmetic. For the closed-ended questions, themean WTP values can be estimated by using econometric models which isthe probit regression technique (Hanemann 1984, Cameron and James 1987,
Cameron 1988). In this study, the concept of statistical life is applied todetermine the Value of Statistical Life (VOSL) and the Value of Statistical
Injury (VOSI) (Kochi et al., 2001). VOSL and VOSI are the total
willingness to pay to avoid an expected occurrence of one fatality or injury,
if each person in the population of 1 million is willing to pay a specified
amount of money to avoid a risk of 10-6. VOSL or VOSI is usually
expressed as willingness to pay for a change in risk divided by the change inrisk.
VOSL or VOSI = mean WTP / change in risk for fatality or injury
To calculate the risk of fatality or injury due to motorcycle crash, the risk
proportion was analyzed from the probability of motorcycle crashes in
Bangkok and surrounding areas. The fatality risk or injury risk can becalculated by using the following equation.
Fatality risk = Prob. Crashes Prob. Injury/Killed Prob. Fatal
Severe Injury risk = Prob. Crashes
Prob. Injury/Killed Prob.

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Severe Injury
The change in risk was then calculated by taking the percentage ofrisk reduction multiplied by each type of risk
4. ANALYSIS OF ACCIDENT COST
4.1 WTP to reduce the risk
The mean values of WTP were determined from the open-ended andclosed-ended methods by using the methodology as described in Section 3.
The mean values of WTP from both methods were summarized in Table 1.

Risk Type
Fatality
Severe Injury

Table 1: Mean Values of WTP
Willingness To Pay (baht)
Closed-Ended
815
945

Note Starting point for fatality
Starting point for severe injury

Open-Ended
695
841= 250 baht
= 500 baht

4.2 Mean WTP for Risk Reduction
Table 2 shows the expected value which is the net willingness to pay
to avoid any fatality and injury risk due to the motorcycle accident.
Table 2: WTP for Risk Reduction
Willingness To Pay for Risk Reduction (baht)
Injury Type
Closed-Ended
Open-Ended
Fatality
565
445
Severe Injury
445
341
4.3 Value of Statistical Life and Injury (VOSL and VOSI)
Table 3 shows VOSL and VOSI calculated based on the WTP. The
VOSL and VOSI were calculated by dividing the mean WTP for risk
reduction by the change in risk. The VOSL and VOSI are the net
willingness to pay by the individual to avoid one occurrence of the indicatedinjury type.
Table 3: Value of Statistical Life and Injury (VOSL and VOSI)VOSL and VOSI (baht)
Injury Type
Closed-Ended
Open-EndedVOSL(Fatality)
7,063,625
5,560,739
VOSI(Severe Injury)
3,425,077
2,622,205

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5. ANALYSIS OF FACTORS INFLUENCING WTP
This section is to evaluate the factors affecting the willingness to pay of
motorcyclists to avoid crash involvement and risk of death. In this study,
the logistic regression model was applied to analyze the significant factors
influencing willingness to pay of respondents in the closed-ended method,while the linear regression model was applied in the open-ended method.
The independent variables considered in the analysis include socioeconomic characteristics of the respondents, such as gender, age, status,
occupation, education, income, household income, and number ofhouseholds, and riding behavior such as frequency of using motorcycle,
helmet use, riding against traffic, speeding, experience of involving in theaccident, and alcohol-impaired riding. Table 4 shows the definitions of the
independent variables remaining in the analysis.
Table 4:Definitions of Independent Variables
Variables
Definition
Age (Continuous variable)
AGE
Gender (1 if male, 0 otherwise)
GENDER
Family Status (1 if single, 0 otherwise)
FAMILY
Education (1 if college level, 0 otherwise)
EDUCATE
Government Officer (1 if government officer, 0 otherwise)
OCCUP1
Private Employee (1 if private employee, 0 otherwise)
OCCUP2Student (1 if student, 0 otherwise)
OCCUP3
Income level 1 (1 if income < 10,000 baht, 0 otherwise)
INCOME1
Income level 2 (1 if income = 10,001-20,000 baht, 0
INCOME2
otherwise)
INCOME3
Income level 3 (1 if income > 20,000 baht, 0 otherwise)
INCOMEh1
Household Income 1(1 if household income < 20,000 baht,
INCOMEh20 otherwise)
INCOMEh3
HOUSEHOLD Household Income 2(1 if household income = 20,00140,000 baht, 0 otherwise)NO.
Household Income 1(1 if household income > 40,000 baht,
RIDING0 otherwise)
FREQ
Number of household (Continuous variable)
HELMET
Frequency of Using Motorcycle (1 if often riding
AGAINST
motorcycle, 0 otherwise)
TRAFF
Helmet Use (1 if often using helmet, 0 otherwise)
SPEEDING
Riding Against Traffic (1 if often riding against traffic, 0
ACCI EXP
otherwise)
ALCOHOL
Speeding (1 if riding > 70 km/h, 0 otherwise)
Accident Experience (1 if having accident at least 1 or
more, 0 otherwise)
Alcohol-Impaired Riding (1 if often alcohol-impaired
riding, 0 otherwise)
Table 5 and Table 6 present estimation results from the logistic
models for WTP to reduce fatality and injury risks based on the closedended method, and the linear regression models for WTP to reduce fatality
and injury risks based on the open-ended method. The relative magnitude of98
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estimated coefficients indicates the extent to which socio-economic
characteristics and riding behaviors affect the WTP for the risk reduction
due to the motorcycle accident.
Model 1 and Model 2 were used to evaluate the significant factors
influencing the WTP to reduce fatality risk of motorcycle accident. The
dependent variable in Model 1 is the answer made by the respondents
whether he or she is willing to pay a specified amount presented as the valueof the risk reduction. The dependent variable in Model 2 is the amount ofmoney that the respondents are willing to pay to reduce the fatality risk ofmotorcycle accident. The results of regression analysis of Model 1 and 2
were presented in Table 8. It is evident from the model that the significant
factors influencing WTP to reduce fatality risk of motorcycle accident areage, occupation, income, and helmet use for the closed-ended method, andgender and helmet use for the opened-end method. The coefficients of thesevariables are statistically significant at 1-10% level; however, the signs arevaried depending on the effect of each variable.
Table 5:Regression Models for WTP to Reduce Fatality Risk
WTP to Reduce Fatality Risk
Variables
Model 1: Closed-End
Model 2: Opened-End
Coeff.
tP>tCoeff.
t
P>t
AGE-0.0119** -1.97 0.049 -1.659 -1.21 0.225
GENDER-0.148** -1.96 0.049-1.87 0.062
34.123*
FAMILY-0.057-1.29 0.199 -2.385 -0.29 0.771
EDUCATE
0.009
0.11
0.913 21.413
1.2 0.229
OCCUP12.09
0.036 16.370 0.39 0.699
0.397**
OCCUP2
0.023
0.17
0.867
6.512
0.21 0.836
INCOME1-0.512*** -2.66 0.008 -63.460 -1.51 0.132
INCOME2-0.361** -2.01 0.044 -36.147 -0.92 0.358
INCOMEh1
0.038
0.21
0.836 -21.098 -0.51 0.607
INCOMEh2-0.118-0.72 0.469 -37.044 -1.02 0.309
HOUSEHOLD NO.
0.029
1.01
0.311 -3.188 -0.51 0.611
RIDING FREQ
0.016
0.13
0.894 -21.108 -0.76 0.445
HELMET
1.87
0.062 45.472* 1.72 0.085
0.217***AGAINST TRAFF-0.110-0.87 0.383
6.349
0.22 0.824
SPEEDING
0.124
1.01
0.314 36.616 1.33 0.183
ACCI EXP-0.128-1.16 0.245 -25.912 -1.05 0.294
ALCOHOL-0.096-0.82 0.414 -2.984 -0.11 0.91
Constant
3.857
11.87
0
821.201 11.96
0
No. of observation
1015
1015
Note: *** indicates significance at the 1% level.
** indicates significance at the 5% level.
* indicates significance at the 10% level

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Model 3 and Model 4 were analyzed to evaluate the significant factors
influencing the WTP to reduce severe injury risk of motorcycle accident.
The regression analysis results of Model 3 and 4 were presented in Table 7.
Model 3 and 4 are the results of analysis of the closed-ended and openended methods, respectively.
Table 6:Regression Models for WTP to Reduce Severe Injury Risk
WTP to Reduce Severe Injury Risk
Model 3: Closed-End
Model 4: Opened-End
Variables
Coeff.
tP>tCoeff.
t
P>t
AGE-0.003-0.4 0.688-1.8 0.073-1.965*GENDER-0.179** -2.07 0.038 -28.737** -1.98 0.048
FAMILY-0.028-0.59 0.557-5.842-0.7 0.484
EDUCATE
0.075
0.89 0.374
22.4391.58 0.114
OCCUP10.293
1.43 0.152
14.0970.42 0.675
OCCUP2-0.234-1.57 0.116-39.780-1.58 0.114
INCOME1-2.98 0.003-0.607*** -2.91 0.004
102.046***
INCOME2-0.496** -2.55 0.011 -59.944* -1.88 0.06
INCOMEh1-0.195-0.97 0.33
-60.549* -1.84 0.066
INCOMEh2-0.451** -2.52 0.012 -83.172*** -2.85 0.004
HOUSEHOLD NO.
-0.012-0.41 0.683
3.225
0.63 0.528
RIDING FREQ
-1.74
0.082
-11.486-0.52
0.603-0.230*HELMET-0.048-0.38 0.702-4.819-0.23 0.819
AGAINST TRAFF
0.038
0.28 0.777
9.472
0.41 0.68
SPEEDING
0.187
1.42 0.154 44.484**
2
0.046
ACCI EXP-2
0.046 -32.663* -1.65 0.1-0.236**
ALCOHOL-0.330*** -2.61 0.009 -38.795* -1.82 0.069
Constant
3.923
10.84
0
1086.552
19.7
0
No. of observation
1015
1015
Note: *** indicates significance at the 1% level.
** indicates significance at the 5% level.
* indicates significance at the 10% level.

6. SUMMARY OF FINDINGSThis paper is to estimate the costs of motorcycle accidents in Thailand byusing the Willingness-to-Pay method. This study also evaluate how thedifferences in socio-economic characteristics and riding and risk-taking
behaviors affecting the willingness to pay of motorcycle users to avoid crash
involvement and risk of death. The study was separated into two parts, themotorcycle accident cost analysis and the analysis of factors affecting theWTP. The questionnaire survey was conducted in Bangkok and surrounding
areas such as Nonthaburi, Pathumtani, Samutprakarn, and Nakornpathom.
A total of 1,015 motorcycle users were interviewed by using thequestionnaires designed based on the Contingent Valuation (CV) method.

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The designed questionnaires include the questions which were separated
into three sections, the socioeconomic characteristic questions, the riding
behavior questions, and the valuation questions. The valuation questions
were designed in two basic forms, open-ended and closed-ended questions.
In the first part, the motorcycle accident cost was analyzed by using theconcept of statistical life. The Value of Statistical Life (VOSL) and theValue of Statistical Injury (VOSI) were determined for the motorcycle users.
It was found that the VOSL calculated from this study is in the range of 5.5
to 7 million baht, and the VOSI is in the range of 2.6-3.4 million baht. The
estimated value of life in this study is considerably higher than the value
calculated in previous studies conducted by Thongchim (2007) and
Suwanrada (2005). The result can be explained that different methods wereapplied in the calculation of the accident cost, and the target group was
differently focused in this study.
In the second part, the significant factors affecting the willingness to
pay of motorcycle users to reduce the risk of death and injury were
evaluated. It seems that the respondents with different socioeconomic
characteristics and different riding behaviors express their willingness topay for the risk reduction differently. The regression model reveals thatolder people and male riders show less willingness to pay to reduce thefatality risk. Government officers tend to pay more to reduce their risk toface the fatal motorcycle accident. It is apparent to see that the motorcycle
riders with lower income have less willingness to pay to save their lives
from the accident. This is what has been expected since lower income
people may have the financial constraint in paying for some money values
to reduce the accidental risk. However, it is interesting to find that people
who often use the helmet show more willingness to pay for the fatal risk
reduction.
To reduce the risk of injury, age, gender, income, household income,frequency of using motorcycle, accident experience, and alcohol-impaired
riding significantly affect the willingness to pay. The trends of age, gender,
and income are similar to the results found from the analysis of WTO to
reduce the fatality risk. In addition, it was found that people who always
use the motorcycle, those who have had accident experience, and those who
often impaired by alcohol consumption while riding are not willing to pay
more to reduce their severe injury risk. Surprisingly, the motorcycle users
who always ride motorcycle with higher speed tend to have more
willingness to pay for saving their lives from the accident.
REFERENCES
Alberini, A., Cropper, M., Krupnick, A., Simon, N., B. (2004). Willingness
to Pay for Mortality Risk Reductions: Does Latency Matter?
Resources for the Future, Washington, D.C., USA, April 2004.
Andersson, H. (2007). Willingness to pay for road safety and estimates of
the risk of death: Evidence from a Swedish contingent valuation study.
Accident Analysis and Prevention 39 (2007) 853–865.
Analysisbyby
Willingness-to-Pay
MethodAnalysis ofof Motorcycle
MotorcycleofofAccident
AccidentCost
CostininThailand
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Method101
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July 2010, Thailand

Bishop, R. and Heberlein T. (1979), ‘Measuring Values of Extramarket
Goods: Are Direct Measures Biased?’ American Journal of
Agricultural Economics, vol. 61,
926-930.
Bhattacharya, S., Alberini, A., Cropper, M., L. (2007). The value ofmortality risk reductions in Delhi, India. J Risk Uncertainty (2007)
34: 21-47, USA, 10 January 2007.
Boontam, A. (2001). The Number of Lost Years and the Loss of National
Income due to Deaths in Road Accidents in Thailand, 1995 – 1999,
in Public Health Journal, 31(2),126 – 135. (in Thai)
Islam, S., Loehman, E., T., Sinha, K., C. Willingness to Pay to Avoid Risk of
Injuries due to Motor Vehicle Crashes: Implications for Benefit-Cost
Analysis and Finance for Road Safety Improvement Programs. USA,2003.
Komnamoon, P. (1979). The Economic Loss of Road Accidents: A Case
Study of Bangkok, Master Thesis in Economics, Chulalongkorn
University. (in Thai)
Luathep P. and Tanaboriboon Y. (2005). Determination of Economic Losses
due to Road Crashes in Thailand, in Journal of the Eastern Asia
Society for Transportation Studies, Vol. 6, 3413 – 3425. .
Md. Nor, N., G., Mohd Yosoff, M., F., Radin Sohadi, R., U. The value oflife and accident costing: a Willingness To Pay study amongst young
motorcyclists in Malaysia. Malaysia.
Patamasiriwat, D. (1994). The Economic Cost of Road Accidents, TDRIWhite Paper, No. 9, July, Thailand Development Research Institute.
(in Thai)
Promglam, C. (1998): Estimation of Economic Loss from Accidents on
Expressways, Master Thesis in Economics, Kasetsart University. (inThai)
Suwanrada, W. (2005). The Cost of Road Accidents in Thailand- An
Analysis of the Efficiency of Road Safety Budget and Risk Behavior byRoad Users, submitted to the Office of the Thai Health Promotion
Foundation. (in Thai)Thongchim, P., Taneerananon, P., Luathep, P., Prapongsena, P.(2007).
Traffic Accident Costing for Thailand. Journal of the Eastern Asia
Society for Transportation Studies, Vol.5, December 2007.
Tosutho, R. (1997). Economic Loss and Road Accident Related Factors,
Thesis submitted to Department of Economics, Graduate School,
Chulalongkorn University.

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3rdÌÎ×ßÔ ÌÛÍÌ ÑÚ Ó×ÝÎÑÞß×ßÔ
ÝÛÓÛÒÌßÌ×ÑÒ ÑÒ ÌÑÇÑËÎß ÍßÒÜÚ×ÔÔÛÜ ×Ò ß ÍÇÎ×ÒÙÛ
Makoto HOSOO
Graduate Student
Department of Civil Engineering, The University of Tokyo, Japanhosoo@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
ABSTRACTA new technique of ground improvement using microbial function has
been recently proposed, as more eco-friendly technique than conventional
one. Metabolic function of microbes produces carbon dioxide, which helps
to generate calcium carbonate in soil. In this study, a series of trial test was
conducted in order to evaluate the degree of soil cementation generated bythose function in Toyoura sand specimens filled in a syringe. It was foundthat the concentration of nutrient affected the amount of precipitatedcalcium carbonate in sand.
1. INTRODUCTION
Some researchers reported that urease enzyme from Bacillus Pasteurii
could be used to add cementation in sand (Dejong et al., 2006; Whiffin et al.,2007).
In this study, a series of trial test was conducted in order toevaluate the degree of soil cementation generated by those microbial
functions in Toyoura sand.
Bacillus Pasteurii helps to precipitate calcium carbonate through thechemical process (Eq.1, 2, 3). Precipitated calcium carbonate on a soil grain
and/or between soil particles seem to contribute to stiffen and strengthen the
ground.
CO(NH2)2 +2H2O

2NH4+ +CO32-(1)

CO32-+2H+(2)

Ca2++CO32- CaCO3(3)

CO2+H2O

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2. MATERILS AND METHODS
Toyoura sand was filled in a 50ml plastic syringe to prepare a specimen of
29.5mm in diameter and 48mm in height. 17ml Nutrient solution whichcontains Calcium and carbon source, and 3ml Pasteurii (2x106 cells/ml) was
put in a syringe in advance. Toyoura sand was pluviated through thesolution to relative density of approximately 90 %.
In test series 1, two types of nutrient were used, as Case A and B as
shown in Table 1. 20cc nutrient was poured into a specimen from top of asyringe and the same amount (20cc) was drained from the bottom at one day
and two days later.
In test series 2, three kinds nutrient were used as shown in Table 2.
Concentration of Case C is same as Case A (Table 1), Case D is twice, Case
E is three times of that of Case C. Nutrient solution was supplied one day
and two days after for only Case C.
For the evaluation of degree of cementation in a sand specimen,
penetration tests were conducted. The penetration needle of 3mm diameteris shown in Figure 2. Penetration test was conducted at a speed of 10mm per
minute up to 45mm in depth. The calcium concentration and the pH of thepore liquid were also measured. After the test, enough amount of distilledwater was flushed through the specimen in order to remove the remained
nutrient and dry weight of specimen was measured. For all test cases, two
specimens were tested for the same condition in order to check therepeatability of the test. In addition to that measurement, the strongly
cemented portion from one of Case A specimen was observed by scanningelectron microscopy (SEM) and energy dispersive spectroscopy (EDS) was
performed using JEOL JSM-5600 .
Injection
direction

Pinch cockFigure 1: Syringe

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3mm

Figure 2: Penetration needle and tip shape
Table1: composition of nutrient solution, Test1 (g/L)
Case
A
B
Bacto nutrient broth
3.5
0.35
Urea
35
3.5
82
8.2
CaCl2 2H2O
NH4Cl
12
1.2
NaHCO3
2.4
0.24
Table1: composition of nutrient solution, Test2 (g/L)
Case
C
D
E
Bacto nutrient broth
3
6
9
Urea
30
60
90
70
140
210
CaCl2 2H2O
NH4Cl
10
20
30
NaHCO32
4
6
3. RESULTS AND DISCUSSION
3.1 Test 1
Penetration resistance is shown in Figure 3 and 4. Comparing with
dense/loose saturated Toyoura sand, it is clear that microbial cemented soil
indicated larger resistance. The effectiveness of Pasteurii was therefore
confirmed.
0

0

30

Penetration Resistance (N)
60
90
120
150

180

CaseA 2day

15

CaseB,
Toyoura sand

CaseA 1day

30

45

CaseA 3dayFigure 3: Penetration resistance in Case A
Trial
Sand
Filledin in
a Syringe
Trial Test
TestsofofMicrobaial
MicrobialCementation
CementationononToyoura
Toyoura
Sand
Filleda Syringe

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0

0

1

Penetration Resistance (N)
2
3
4

5

CaseB 1day

15

CaseB 2day

CaseB 3day

30
Dense Toyoura sand
Loose Toyoura sand45

Pore liquid within dashed line of Case B 3day was drained during penetration

Figure 4: Penetration resistance in Case B

Distinctive difference was shown between Case A and Case B due todifferent nutrient concentration. When three days passed, 3N was measured
in Case B while 130  150N was measured in Case A.
Variation of Calcium concentration and pH is shown in Figure 5.
Values of pH were 7.3 to 8.0 for both case A and B, It is almost neutral thatis appropriate for the calcium carbonate precipitation. Calcium
concentration decreases greatly in Case A day by day. Case B was
decreased from 1st day to the 2nd day. Afterwards, it was similar value.
30

9

25

Case A Ca2+

CaseB pH

20

8

15
CaseA pH

10
5
0

7

Case B Ca2+012elapsed time (day)3

6

Figure 5: Calcium concentration and pH
The increment ratio of dry weight of the specimen before and after thetest is plotted in Figure 6. Assuming that all remained nutrient were flushed
in the washing process after experiment, weight increment is thought to be
the calcium carbonate precipitation. The maximum value drawn in the shortdashed line indicates the case in which all the added calcium ion precipitate
as calcium carbonate. Plotted open symbol indicates calculated value basedon the measured calcium ion in the pore liquid, assuming that a consumed
calcium ion was precipitated as calcium carbonate. Weight increases day by
day in Case A, and its increment is about 5% in three days. Case B was
increased 0.5%. It almost corresponds to the ratio of the amount of calcium
source given to the specimen.

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7
6
5

Maximum value
Measured value
Calculated value

4

CaseA

3
2

CaseB

1
0

0

1
2
elapsed time (day)3

Figure 6: Increment ratio of dry weight of the specimen before and after the
test
Images of SEM and compositional mapping by EDS were provided
in Figure 7. It is obvious that precipitated stuff adhere to the particle
surface and pore in the part that has cemented strongly (Figure 7(a))
Furthermore, the result of compositional mapping shown in Figure 7 (band c) indicates that precipitated stuffs mostly contains Calcium.
Remarkable increase of penetration resistance in Test 1 was thought to becaused by particle bonding or friction increase due to precipitated calcium
carbonate.
Level
2

Level
4
3

1

2
1

0
0

20 m

20µm

20µm

(a)
(b) Ca(c) SiFigure 7: Images of SEM and compositional mapping by EDS
3.2 Test 2
Penetration resistance is shown in Figure 8. Distinctive difference wasalso shown between Case C, B Case B due to nutrient concentration. When
three days passed, 10N was measured in Case B, 30N was measured in CaseD while 150  200N was measured in Case A.

Trial
Sand
Filledin in
a Syringe
Trial Test
TestsofofMicrobaial
MicrobialCementation
CementationononToyoura
Toyoura
Sand
Filleda Syringe

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0

0

50

Penetration Resistance (N)100
150

Case C 2day

200

Case C 3day

15
Case D , E
Toyoura sand

30

45
0

Case C 1day

0

10

20

30

40

Case D 1day
Case D 2day

15

Case D 3day

30

Dense Toyoura sand
LooseToyoura sand

45
0

0

5

10

Case E 1day
Case E 2day

15

Case E 3day

30
Dense Toyoura sand
Loose toyoura sand45

Figure.8: Penetration resistance in Case C(a), D(b), E(c)Specimen that has the higher initial nutrient concentration indicateslower penetration resistance.
Variation of Calcium concentration and pH is shown in Figure 9.
Lower values of pH were shown in case with higher concentration ofnutrient. Substantial calcium ion was consumed day by day in Case C. In
contrast, it was consumed 1/2 , 1/3 of initial calcium ion in Case D ,E.
60

8Case C pH

50
Case D pH

40

7
Case D Ca2+

30
Case E pH

20

6
Case C Ca2+

100Case E Ca2+012elapsed time (day)3

5

Figure 9: Calcium concentration and pH

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The increment ratio of dry weight of the specimen before and after thetest is plotted in Figure 10. Maximum value was calculated by same method
as shown in Figure 4. A weight in Case C increases steadily. It reached 6%
in three days, and got close to maximum value. Case D was increased 2%,
Case E was increased 1%. Specimen with a larger resistance exhibitedheavier tendency. From the result of Test 2, it is concluded that inject
nutrient by suitable concentration frequently is more reasonable.
Maximum value
Measured value
Calculated value

7
6

Case C

5

Case E

Case D

4
3
2
1
0

0

1
2
elapsed time (day)3

Figure 10: Increment ratio of dry weight of the specimen before andafter the test
3.3 Relationship between calcium carbonate content and degree of
cementation
Relationship between maximum penetration resistance and increment
ratio of dry weight is presented in Figure 11. Amount of calcium carbonate
and penetration resistance shows good agreement, which implies that
consumption of calcium ion in the pore liquid can be reasonable indication
to evaluate the degree of cementation.
200

150

100

Case A
Case B
Case C
Case D
Case E

50

0

0

2
4
6
increased dry weight / initial dry weight (%)

8

Figure 11: Relationship between maximum penetration resistance andincrement ratio of dry weight

Trial
Sand
Filledin in
a Syringe
Trial Test
TestsofofMicrobaial
MicrobialCementation
CementationononToyoura
Toyoura
Sand
Filleda Syringe

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4. SUMMARY
The microbially cemented soil using urease enzyme from Bacillus Pasteurii
exhibited success of cementation corresponding to nutrient concentration.
The exsistence of a large amount of calcium carbonate on the Toyoura sandwas also confirmed by the observation using SEM-EDS.
When total amount of added calcium and nutrient is same, it is more
effective that nutrient is injected separately and frequently.
However, specimens in this test series show partial cementation. Moreeffective nutrient concentration, injection interval and method for uniform
cementation should be examined in the future.
REFERENCES
Jason T. DeJong, Fritzges, M.B. and Nüsslein, K.(2006), Microbiallyinduced cementation to control sand response to undrained shear.
Journal of Geotechnical and Geoenvironmental Engineering, Vol.132,
No.11, pp.1381-1392.
Marien P. Harkes, Van Paassen L.A., Booster J.L., Whiffin V.S., Van
Loosdrecht M.C.M. (2010), Fixation and distribution of bacterial
activity in sand to induce carbonate
precipitation for groundreinforcement. Ecological Engineering, Vol.36, Issue2, pp.112-117.

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3rdÚÑËÒÜßÌ×ÑÒ ÍÇÍÌÛÓ ßÐÐÔ×ÛÜÌÑ ÝÑÒÍÌÎËÝÌ ÌØÛ Ý×Ê×Ô
×ÒÚÎßÍÌÎËÝÌËÎÛÍ ×Ò ÌØÛ ÍÑËÌØó
ÉÛÍÌ ÎÛÙ×ÑÒ ÑÚ ÞßÒÙÔßÜÛÍØMD JULFIKAR HOSSAIN
Assistant Engineer
Khulna University of Engineering & Technology (KUET)
Khulna, Bangladesh
kuetjewel@yahoo.com
MD ABDUR RAZZAQUE
Deputy Chief Engineer, Khulna University
Ph.D. Student
Khulna University of Engineering & Technology (KUET)
Khulna, Bangladeshrazzaque63@yahoo.com
MUHAMMED ALAMGIR
Professor
Department of Civil Engineering
Khulna University of Engineering and Technology (KUET)
Khulna, Bangladesh
alamgir63dr@yahoo.com
ABSTRACTThis paper describes the case studies of different foundation systems
applied to construct civil infrastructures in the south-West of Bangladesh.
The sub-soil of South-West region of Bangladesh, specially in Khulna
region, consists of very soft fine grained soil with significant percentage oforganics up to great depth. The buildings in this region are experienced
huge amount of total and differential settlements, and also titled, 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. The foundation systems used to construct
Academic, Commercial and Residential Buildings have been studied in fieldlevel and hence presented in this paper. The findings depict the inherent
constraints of the use of conventional foundation system in such sub-soil
conditions and also will provide a guideline to the geotechnical engineers
while constructing civil infrastructures in the south-west region of
Bangladesh.

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1. INTRODUCTION
South-Western part of Bangladesh situated in the lower reaches of Bengal
Basin where the three mighty rivers - the Ganges, the Brahmaputra and theMeghna and their associated tributaries contributed very much in theformation of sub-soil profiles. 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 thesecoast regions were part of the Sundarbans, the world largest mangrove
forests which extends over an area of 577,285 hectares as recorded (Vishnu& Gupta 1972, Sen & Banerjee 1984, Das & Siddiqui 1985, Naskar &Bakshi 1987, Mukharjee 1992). During the geological changes in the past,some parts of the Sundarbans were submerged by the weathered andsedimentary deposits resulting in the present peat deposits in these regions.
In this region peat soils is found in different layers and in different depth.
Also this soil exists below the peat layer has very low bearing capacity. For
that purpose all infrastructures are settled by large amount. Due to these
inherent limitations, the foundation system for the construction of civil
infrastructures is designed with special consideration and employed very
carefully, which leads to high cost for the preparation of sub-structures inthis region of Bangladesh.
Khulna University of Engineering and Technology (KUET), Khulna
University (KU) located in the south-western region of Bangladesh. In thisregion many Government Office, commercial and Private residential
structures are already situated. More over place of structure is situated in a
low-lying topography with a very thick soft and organic soil deposits up to agreat depth. Academic (Khulna University of Engineering and Technology,
Khulna University Infrastructures site), Commercial and Residential
Infrastructures were suffered very large settlement, which leaded to rethink
about the types of foundations. In a series of construction, in the recent
years, several new buildings have been and/or are being constructed such as
Academic, Commercials and residential in which different types offoundation systems are considered based on the sub-soil profiles, building
types, purpose and the consultant preference. The performance of theconstructed buildings are evaluated and discussed here.
2. LOCATION AND SUB-SOIL CONDITIONS
The investigated sites of the infrastructures are Khulna University ofEngineering and Technology (KUET), Khulna University (KU), Adams
Integrated Training Institute (AITI), Sonadanga Commercial area, and few
residential area in Khulna city. KUET is situated northern side of Khulna
city corporation area It covers 101 acres of land. It is near Dhaka –Khulna
highway. KU is situated in the north side of Khulna-Satkhira Highways. The
University Campus is located in the western side of Khulna City
Corporation (KCC) area. It covers 105 acres of land. It is beside KhulnaSatkhira highway. AITI is situated northern side of Khulna city corporation
area. Building of Delta Life Insurance Co. Ltd. beside of M.A. Bari Sarak,

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Sonadanga, and few residential and commercial site in Khulna. Many years
ago Khulna was the part of the biggest

Figure 1: Location map of South-West region in Bangladesh and
KUET, KU campus and study site of Khulna city
To investigate the sub-soil condition of the sites at Khulna Universityof Engineering & Technology, Khulna University, ADAMS Integrated
Training Institute(AITI) campus, others Residential & Commercial places,
sub-soil exploration program was conducted by different geotechnicalengineering consulting firms, soil samples were collected and tested throughconventional laboratory tests (BRTC 1998, SAAR 1992 and NSE 1995).
Depth
(m)
0-3

Table 1: Typical sub-soil profile at South-West region
Thickness
StrataClassification of Soil
N(m)
Value
Soft clay soil with silt
3-4
3

3-6

3.0

6-19.5

13.5

19.523.5
23.530.5

4.0
7.0

Dark grey organic soil
Silty clay with dark
Gray
Clayey silt gray.
(medium stiff)
Silty fine sand

30.534.5

4.0

Gray medium dense siltwith some fine sand

34.539.5

5.0

Gray medium dense todense fine sand and silt

1-4
4-8
8-11
11-21
21-27
27-38

Despite the differences of sub-soil profiles and the values of soil
parameters in different locations within the Khulna region, typical sub-soilFoundation
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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 3m is soft clay. Next layer depth exists from 3 to 6m i.e. 3m
or 4.5m thick layer of dark grey organic soil with decomposed woods andvegetations. At the depth of 6 to 19.5m, grey medium silty clay having N
values vary from 4 to 8 exists. Next to this, a layer of light grey medium
compressible silt, trace fine sand is encountered at 19.5 to 23.5m depth with
N value varies from 8 to 11. Here soil consistency is medium to stiff. In thebottom of the sub-soil profiles as executed, 23.5 to 30.5m depth, soil of lightgrey of non plastic silt, trace fine sand having N value varies from 11 to 21,
is countered.
3. APPLIED FOUNDATION SYSTEM
3.1 Academic Infrastructures
In South-West region there is no typical or standard or uniquefoundation system that have been applied for the construction of any civil
infrastructures in Khulna region as well as in Khulna University ofEngineering and Technology(KUET) Campus, Khulna University Campus
and others places in Khulna city. Moreover, different consultants were also
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.
3.1.1 Sand cushion with mat foundation
In construction of new academic building of KUET, mat foundation
with sand cushion was used. The building is planned for seven stories but
primarily two stories are constructed shown in Figure 2. The advantage ofmat foundation was evident as there is no differential settlement. In a soft
soil region like Khulna where excessive settlement is a problem; suchfoundation can save the structures from failure due to differential settlement.

Figure 2: New Academic Building of KUET

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Figure 3: Pictorial view of Academic Building-I,KU
In Khulna University campus, the first major construction works wasstarted in 1992 with the adoptation of mat foundation system with sandcushion 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.

PL

FGL

FGL

4.25m Sand Cushion

Beam
Mat

Figure 4: Schematic diagram of sand cushion with mat foundation
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. Herepeat 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 aratio of 1:1. The filling sands were compacted in layers and the required
degree of compaction was achieved. At the top of the filled and compacted
sand layer, several mat foundations were provided. The thickness of the matfoundation varies from 300 to 450mm. At the top of the mat, a foundation
beam was provided to reduce mat thickness. Despite the consideration ofsuch special precaution in the foundation, the building suffered from verylarge 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
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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 in KU.
3.1.2 Sand cushion with column footing
In construction of five storied new student dormitory (Amar EkusheHall) building at KUET, single column footing is used, after 2.20m soil isreplaced by sand which schematic diagram shown in Figure 5.
Load Bearing
ColumnFGL

PL

Footing
2.2m Sand Cushion

Figure-5: Sand Cushion with single column footing

Figure 6: A Pictorial view of Student Dormitory of KUET (Amar Ekushe Hall)

Figure 7: A Pictorial view of Academic Building.-II,of KU

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3.1.3

Floating foundation
Academic Building–I of KU showed huge settlement due to theimplementation of mat foundation with a sand cushion. As a result theuniversity authority and the consultant looked for alternative solution tominimize the settlement while constructing the Academic Building-II. Afterlooking on the various possible alternatives, finally consultant decided touse floating (compensated) foundation, hence designed the foundation andthe building was constructed accordingly as shown in Figure 7. The
schematic diagram of the foundation system used for the construction of thisbuilding is also presented in Figure 8.
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). The settlement of this building was observed as insignificant, which
is about 20mm. Similar type of foundation system was also used in theconstruction of the Female student’s dormitory named as Aparajita Hall.

PL
FGL

FGL

Tie
Bracing
Bottom SlabFigure 8: Schematic diagram of floating foundation- Academic Bldg-II
3.2 Commercial & Residential Infrastructures
There is no typical or standard or unique foundation system that canbe applied for the construction of commercial buildings in Khulna region as
well as few residential and commercial sites in Khulna.
3.2.1 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 buildings.

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FGL

PILE CAP

PILE

Figure 9: Schematic diagram of pile foundation used for the construction of
Nargis Memorial Clinic Building
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 it’s impacts on the reduction of pile
capacity was considered. The piles are configured in a group with RCC pile
cap based on the layout of the locations of different columns of thebuildings and loads as shown in Figure 9. Later, additional nine buildings
named as Administrative Building, Academic Building-III, Central Library,
Central Mosque, Professor’s, Assistant Professor’s, Lecturer Quarters, Staff
Quarter-I, Staff Quarter-II and Delta Life Insurance Building wereconstructed and planned to construct using pile foundation. Table 2 shows
the comparison of various aspects such as dimension and design load of thepile foundation used and/or planned for the construction of different
buildings, till now ten buildings, in the Khulna University campus
constructed on pile foundation.
Table 2: Information of pile specification used in different buildings

118
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Sl
No

Name of the
Buildings

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

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

Plinth
Area
(m2)
581
1,488
242
223
316
214
218
1,115
2,611
1,390
450

Storied
4
4
4
4
4
4
4
3
4
4
22

Pile
length
(m)
26
24
30
30
30
30
30
26
26
27
36

Pile
DesignDiameter load(mm)
(Tons)
450
450
500
450
500
500
500
500
450
500
600

55
40
53
46
50
44
46
38
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Figure 10: Construction picture of Nargis memorial clinic Building

Figure 11: Future completed view of Nargis memorial clinic Building
Few commercial buildings as well as an eight storied Nurgis
Memorial Clinic Building at south central road in Khulna which is now
under-construction. Pictorial views of these buildings are shown in Figures
10 and 11.
3.2.2

Pile with mat foundation
Construction work of 22(Twenty two) storied building of Delta Life
Insurance Co. Ltd. beside of M.A. Bari Sarak, Sonadanga, is going on. Mat
slab and .60m dia and 36.0m length pile were used as foundation of thisbuildings. In this structural design load of foundation was considered as 80
tone. Bearing capacity of Soil in upper layer is not good, so Raft over thepile foundations is used as shown in Figures 12 and 13.

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Figure 12: Construction picture of Delta Life Insurance Building

Figure 13: Schematic diagram of pile with mat foundation of Delta Life
Insurance building
3.2.3 Sand Compaction Pile
Sand Compaction Pile was used in a four Storied Residential Building
at the Fulbarigate, Khulna. This is normal that different building provides
different depth and diameter of pile. Although in this 4(four) storiedbuilding the pile length is 20 feet depth and dia 0.20m for low bearing
capacity for end bearing and skin friction. In peat layer negative skinfriction is consider for the design of pile.

Figure 14: Scematic diagram of sand compaction pile

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Figure 15: Pictorial view of the building
3.2.4

Timber Pile Foundation

Timber Pile foundation is used for the construction of a four Storiedcommercial Building, ADAMS Integrated Training Institute located as
Raligate, Khulna. Only two storied of this building was completed as shownin Figure 16 and 17. Timber pile of 150mm dia and 5.0m length are used
under isolated footing foundation for the construction of AITI
Administration Building. Bearing capacity for end bearing and skin friction
are considered for design. Here Timber pile is constructed manually which
are shown in Figures 16 and 17.

Figure 16: Picture of Timber Pile driving.

Figure 17: Pictorial view of AITI Administration Building

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3.2.5 Strip Footing Using Continuous Brick Wall
Some buildings were also constructed using spread footing i.e.
Continuous type Reinforced Cement Concrete (RCC) footing resting on
0.30 to1.0m compacted filling sand cushion. One of such buildings is shownin Figure 9, the Vice Chancellor’s Quarter of KU, Teachers Dormitory
Building and Engineering Office at KUET. The building was constructed
through load bearing wall resting on the footing.
FGL

PL

CC

Brick Soling

Sand filling

Figure 18: Strip footing constructed by continuous brick wall andcontinuous footing
Some buildings are constructed just using simple brick foundation. For low
bearing capacity width of wall is higher. Bottom width of the foundation is
60 inch. Gradually decrease the width of foundation from the bottom to top
shown in Figure18. Above Plinth, width of the wall is 10 inch. Strip Footing
Using Continues Brick Wall provide in Suktara, Building behind Khan
Jahan Ali Hall at KUET. Continuous RCC foundation with load bearing
wall is used for Construction of Teachers Dormitory Building of KUET asshown in figure 19 and 20. The performance of such foundation system isfound satisfactory.

Figure 19:Teachers Dormitory at KUET

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Figure 20: Suktara Building Beside KUET
4. RESULTS AND DISCUSSIONSIn South-west regions different types of building was constructed withvarious 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.
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 KUET Campus, KU Campus, its surrounding area and few places
in South-West region as an alternative of conventional foundation system(e.g. pile foundation), ground improvement technique may be adopted to
examine the applicability in such compressible ground condition. In thisregion, sand compaction piles with a diameter of 200 to 300mm and thedepth of 6 to 8m were constructed successfully in several projects and theperformance are satisfactory. In some cases, geosynthetics was also usedsuccessfully 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 for the sub-soilFoundation
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conditions exist in this region may be considered to overcome the inherent
limitations of conventional foundation.
5. CONCLUSION AND RECOMMENDATIONSSpecial 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 south-west region. It is not proper toimplement a unique system considering general sub-soil profile, appropriate
foundation system might be chosen based on the soil conditions in thespecific location, type of superstructures and relevant requirements. Field
investigation shows that the rate and amount of settlement is insignificant
while Pile foundation such end bearing and Floating types 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.
REFERENCES
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 repostof 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 forests of
Bangladesh, Mangrove silviculture division Bullatin No. 2.
Haque, M.A., Alamgir, M., Salim, M. and Kabir, M.H. 2001. Performanceof Geotextile-Reinforced Shallow Foundations used in Bangladesh.
Proc. International Seminar on Earth Reinforcement (IS Kyushu
2001), 16-18 Nov. 2001, Fukuoka, Japan, pp.565-570.
Hoque, A. M., Islam, M. M. and Alamgir, M. 2010. Foundation system
adopted to construct building in and around KUET Campus, CIVIL
Engineering Department at Khulna University of Engineering andTechnology, Khulna, Bangladesh
Hossain, M.M., and Razzaque, M. A., 1999. Structural Advantages of
Floating foundation over traditional mat foundation for soft clay: A
case study. Proc. Pre-conference symposium or Ground improvement
and Geosynthetics. 5 Nov, 1999, Bangkok. pp. 201-210.
Mukherjee, B.B 1992. Quaternary pollen Analysis as a possible indicator ofprehistoric 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.

124
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onCivil
CivilInfrastructures
Infrastructures
3rdICUS Report 45
ICUS Report 45

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 observationof 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.
Razzaque, M. A. and Alamgir, M. 2009. Foundation system adopted toconstruct the Civil Infrastructures in Khulna University of Bangladesh,
2nd Joint student seminar on civil infrastructures, AIT, Thailand.
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 inthe 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 andAssociates Limited, Dhaka.
Vishnu M. and Gupta, H.P. 1972. Pollen Analysis study of Quaternary
Deposit in the Bangla Basin, Paleobotanist, vol, 19: 297-306.

Foundation
in inthetheSouth-West
Regionof Bangladesh
Foundation System
SystemApplied
AppliedtotoConstruct
Constructthe
theCivil
CivilInfrastructures
Infrastructures
South-West
Regionof Bangladesh125
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July 2010, Thailand

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3rdÜÛÊÛÔÑÐÓÛÒÌ ÑÒ ÐÎÛÝßÍÌ ØÇÞÎ×Ü
ÚÎßÓÛóÉßÔÔ ÞË×ÔÜ×ÒÙÍ ÚÑÎ ÍÛ×ÍÓ×ÝÎÛÍ×ÍÌßÒÝÛ
MAY PHYO AUNG
School of Engineering and Technology
Asian Institute of Technology, Thailand
mayphyoa@gmail.com

Development of Precast Hybrid
FrameWall Buildings for Seismic
ResistanceBy:

May Phyo Aung

Examination Committee:
Dr. Pennung Warnitchai (Chairperson)
Dr. Naveed Anwar
Dr. Park Kyung-Ho
Dr. Sun Sayamipuk

BackgroundNowadays, the use of precast concrete members arevery popular in many countries.
Significant benefitsÝ±­¬ »ºº»½¬·ª»²»­­¸·¹¸ ¯«¿´·¬§ °®±¼«½¬·±²»¿­» ±º ½±²­¬®«½¬·±²

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July 2010, Thailand

Background
However, the precast concrete system is not extensively
used in high seismic zones.
Failuresnon-ductile behavior
poor connection details
poor design concepts.

Ú¿·´«®» ±º Ð®»½¿­¬ Ý±²½®»¬» Í§­¬»³

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3rdICUS Report 45
ICUS Report 45Ú¿·´«®» ±º Ð®»½¿­¬ Ý±²½®»¬» Í§­¬»³­

Partially collapse during
Northridge Earthquake [Iverson,
1994]

BackgroundÌ©± ½±³³±² ¬§°»­ ±º °®»½¿­¬ ­§­¬»³­

Ó±²±´·¬¸·½ »³«´¿¬·±²­§­¬»³

Ò±²ó³±²±´·¬¸·½ »³«´¿¬·±²­§­¬»³Û³«´¿¬» ¾»¸¿ª·±® ±º½±²ª»²¬·±²¿´ ½¿­¬ó·²ó°´¿½»­¬®«½¬«®»­

Ë­» °±­¬ó¬»²­·±²»¼ ­¬»»´­ ±®
¬»²¼±²­ ¬± ½±²²»½¬ ¬¸» °®»½¿­¬³»³¾»®­Development
forforSeismic
Resistance
Development on
onPrecast
PrecastHybrid
HybridFrame-Wall
Frame-WallBuildings
Buildings
Seismic
Resistance

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July 2010, Thailand
July 2010, Thailand

Ó±²±´·¬¸·½ Û³«´¿¬·±² Ð®»½¿­¬ Í§­¬»³
×² Ò»© Æ»¿´¿²¼ ¿²¼ Ö¿°¿²

Ó±²±´·¬¸·½ Û³«´¿¬·±² Ð®»½¿­¬ Í§­¬»³
Holden[2003]

Park [1995]+ Good energy dissipationóLarge stiffness degradation & Pinching ----observedó Difficult to be repaired << not concentrated onconnection only

Lots of cracks --- formed

Wall

Severe damage occurred at the
precast beam, column and wall panel
Frame

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Ò±²óÓ±²±´·¬¸·½ Û³«´¿¬·±² Ð®»½¿­¬ Í§­¬»³

Rocking
Rocking
Behavior
SystemË²¾±²¼»¼
Ð±­¬ó¬»²­·±²»¼¬»²¼±²Use PT steels only
Self-centering characteristics
Reduce damage to structure
Low energy dissipation

Ë²¾±²¼»¼ Ð±­¬ó
¬»²­·±²·²¹ ¬»²¼±²

Ò±²óÓ±²±´·¬¸·½ Û³«´¿¬·±² Ð®»½¿­¬ Í§­¬»³
What is Hybrid Precast System?

Ë­» Ð±­¬ó¬»²­·±²·²¹ ­¬»»´ õ Û²»®¹§ ¼·­­·°¿¬»®
Í»´ºó½»²¬»®·²¹ ½¸¿®¿½¬»®·­¬·½
Þ»¬¬»® »²»®¹§ ¼·­­·°¿¬·±² ½±³°¿®»¼ ¬± ¬¸»­¬®«½¬«®» ©·¬¸ ÐÌ ±²´§

Þ±²¼»¼
Ó·´¼ Í¬»»´

HybridPrecastSystemÞ±²¼»¼
Ó·´¼ ­¬»»´

Ë²¾±²¼»¼
Ð±­¬ó¬»²­·±²»¼¬»²¼±²Development
forforSeismic
Resistance
Development on
onPrecast
PrecastHybrid
HybridFrame-Wall
Frame-WallBuildings
Buildings
Seismic
ResistanceË²¾±²¼»¼
Ð±­¬ó¬»²­·±²·²¹¬»²¼±²

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July 2010, Thailand
July 2010, ThailandÐ®±¾´»³ Í¬¿¬»³»²¬
Hybrid precast beam-column frames
can be implemented as the main lateralforce resistance for low-rise and mid-risebuilding without any major designdifficulties..
However, when No. of stories increase, it
is becoming more and more difficult todesign the hybrid B-C frames to satisfy theseismic design requirements.
Frames – Become too soft (Low stiffness)- Increase lateral deformation demands- Increase strength demands

Ð®±¾´»³ Í¬¿¬»³»²¬
This problem can be effectively
solved by adding hybrid walls beyond
the hybrid B-C frames.
This hybrid dual system has neverbeen studied before. So, it is stillrequired to confirm the practicalfeasibility and efficiency to use thedual system in high seismic regions.

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Ñ¾¶»½¬·ª»­ ±º Í¬«¼§
To develop the post-tensioned precast building byusing hybrid precast dual system.
To conduct an experiment to investigate the seismic
performance of a hybrid precast dual system building.
To approach a simple analysis to predict the behavior of
hybrid dual system and compare with experimental results.

Connection in Hybrid Dual System Building
PT steelïò Ë²¾±²¼»¼ ÐÌîò Ó·´¼ Í¬»»´­íò Ì®±«¹¸ ±® Í³¿´´¹«¬¬»®­
ìò É¿´´óóó×ó­¸¿°»¼ëò Ó«´¬·ó°¿²»´ É¿´´êò Ù¿° ±°»²·²¹
éò Ü±©»´ ¾¿®­èò Í¬»»´ ß²¹´»­

Dowel bars

Precast concrete shear wall

Fiber reinforced grout
Mild steels

Precast concrete column
Precast concrete column
TroughMild steels

PVC duct

PT steeSteel angle
PT steel

Corrugated duct

Development
forforSeismic
Resistance
Development on
onPrecast
PrecastHybrid
HybridFrame-Wall
Frame-WallBuildings
Buildings
Seismic
Resistance

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July 2010, Thailand
July 2010, ThailandÜ»­·¹² Ý±¼»­ ¿²¼ ß½½»°¬¿²½» Ý®·¬»®·¿
ACI 318-05
ACI T1.1-01 for moment frames
ACI ITG5.1-07 for structural wall
ACI T1.2-03, the design guideline

Design Consideration for Beam

M1

M2

Ú®»» Þ±¼§ Ü·¿¹®¿³ ±º Þ»¿³

M1

V1

V2

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Design Consideration for Column
Þ»¿³ Í¸»¿®
Î±±º Ô»ª»´ÛÏ õ Þ»¿³ß¨·¿´ Ú±®½»

Ð®±¾¿¾´» Ó±³»²¬ Ý¿°¿½·¬§±º Ø§¾®·¼ Ý±²²»½¬·±²

Ú±«²¼¿¬·±² Ô»ª»´Þ¿­» Í¸»¿®

Ð®±¾¿¾´» Ó±³»²¬ Ý¿°¿½·¬§ ±º
Ý±´«³²óÚ±«²¼¿¬·±² Ý±²²»½¬·±²
Ý±´«³² ß¨·¿´ Ú±®½»

Ú®»» Þ±¼§ Ü·¿¹®¿³ ±º ×²¬»®·±® Ý±´«³²

Design Consideration for WallÞ»¿³ Í¸»¿®
Î±±º Ô»ª»´ÛÏ õ Þ»¿³ß¨·¿´ Ú±®½»

Ð®±¾¿¾´» Ó±³»²¬ Ý¿°¿½·¬§±º Ø§¾®·¼ Ý±²²»½¬·±²

Ú±«²¼¿¬·±² Ô»ª»´Þ¿­» Í¸»¿®

Ð®±¾¿¾´» Ó±³»²¬ Ý¿°¿½·¬§ ±ºÉ¿´´óÚ±«²¼¿¬·±² Ý±²²»½¬·±²É¿´´ ß¨·¿´ Ú±®½»

Ú®»» Þ±¼§ Ü·¿¹®¿³ ±º Í¸»¿® É¿´´Development
forforSeismic
Resistance
Development on
onPrecast
PrecastHybrid
HybridFrame-Wall
Frame-WallBuildings
Buildings
Seismic
Resistance

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July 2010, Thailand
July 2010, Thailand

Design Consideration for Wall

ÚÞÜ

ÍÚÜ

Test SpecimenÞÓÜPT steel
Dowel bars

PT steel

Precast wall

Precast column

Precast beam
3300mmTroughCorrugated ducts
PVC pipe

Steel angle
Interface grou

Mild steels

PT steel

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Column dowel bar

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Fabrication of steel cages and steel angles
Corrugated ducts
for Dowel Bars

PVC pipes for
PT tendonsÍ¬»»´ ½¿¹»­ ±º É¿´´­
PVC pipes for
PT tendonsÍ¬»»´ ½¿¹»­ ±º Þ»¿³­

Corrugated ducts
for Mild steelÍ¬»»´ ½¿¹» ±º Ú±±¬·²¹

Corrugated ducts
for dowel barsÍ¬»»´ ½¿¹» ±º Ý±´«³²

Construction
Sequence
Interface
Grout

Grout in DuctPT Tendons
TroughDuct

TemporarySupportMild steel

Interface
Grout

Foundation

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Resistance
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July 2010, Thailand
July 2010, Thailand

Actual Construction In LabFill the interface Grout
Erecting the BeamStressing
Inserting theMild Steel inthe
the PT
Ducts at connection

3.Grouting andInstalling Temporary
Support

Erecting
1.Liftingthe 1stColumn
Wall
Panel
Grouting at the interface andin Corrugated
Erecting
2ndDucts
Wall
Pnel
2. Inserting PT into
wall
Erecting
3rd WallPanel
Grouting 2nd Wall Panel

Test Specimen
After Erection

Temporary Supports

Temporary LateralSupports

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Load History
3.00

2.50%

2.50

2.00%

2.00

1.50%

1.50
1.00

ï

0.50

0.30%

0.00

0.20%
0.10% 0.15%

î

1.00%

0.75%

0.50%

-0.50
-1.00

ì

í

-1.50
-2.00
-2.50
-3.00

Cycle Numberó Í¬»° ï ¿²¼ í óóó ¿ ³±ª»³»²¬ ¬± ¿ °»¿µ º±®©¿®¼ ¿²¼ ¾¿½µ©¿®¼ °±­·¬·±²òó Í¬»° î ¿²¼ ì óóó ¿² ±®·¹·²¿´ °±­·¬·±²òó Ì¿®¹»¬ ­¬±®§ ¼®·º¬ óóó îòë ûÚ±®½»óÜ·­°´¿½»³»²¬ Î»´¿¬·±²­¸·° ±º Ü«¿´ Í§­¬»³
250

õ Í»´ºó½»²¬»®·²¹ Ý¿°¿½·¬§
õ Í¬¿¾´» Ø§­¬»®»¬·½ Ô±±°­200

õ Ò± Í¬®»²¹¬¸ Ü»¹®¿¼¿¬·±²
õ Ô±© Í¬·ºº²»­­ Ü»¹®¿¼¿¬·±²

150
100

õ Ò± Ð·²½¸·²¹ ·² ¸§­¬»®»¬·½ ´±±°­ 50
0
-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

-50

0.00

0.50

-100
-150
-200
-250

1.00

1.50

2.00

2.50

3.00

0.10% Drift
0.15% Drift
0.20% Drift
0.30% Drift
0.50% Drift
0.75% Drift
1.00% Drift
1.50% Drift
2.00% Drift
2.50% Drift

Story Drift (%)

Development
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Resistance
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Resistance

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July 2010, Thailand
July 2010, ThailandÐ®»¼·½¬»¼ Û²ª»´±°» Ý«®ª» ±º Ø§¾®·¼ Ü«¿´ Í§­¬»³
Predicted Envelope Curve of Dual System
250.00
Dual System

200.00150.00Wall

100.00

Frame

50.00
0.00
-3.00

-2.00

-1.00

0.00
-50.001.00

2.00-100.00

Predicted Shear Force of Wall

3.00

Predicted Shear Force of Frame-150.00

Predicted Shear Force of Dual System-200.00-250.00

Story Drift (%)Ý±³°¿®·­±² ±º ÍòÚ Þ»¬©»»² Û¨°»®·³»²¬ ú Ð®»¼·½¬·±²
p

p

y

250

Yield State

200

Softening State

150

Target Drift Limit State100

Decompression State

50
0
-3.00

-2.00

-1.00

-50

0.00

-100
-150

1.00

2.00

3.00

Experimental Shear Force of Dual System
Predicted Shear Force of Dual System

-200
-250

Story Drift (%)

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According to ACI Acceptance Criteria,
Normalized Stiffness Degradation
120
100
80

Minimun Stiffness Degradation
following acceptance criteria (5%)
for frame and (10 %) for wall.

60
40

14.37

20
0
0.00

0.50

1.00

1.50

2.00

2.50

3.00

Story Drift (%)Ú±® Ð«´´·²¹ Ü·®»½¬·±²

ß¬ îòëðû

14.37 % â ïð û º±® É¿´´[OK]ß¬ îòëðû

14.37% â ë û º±® Ú®¿³» [OK]Û²»®¹§ Ü·­­·°¿¬·±²
According to ACI Acceptance Criteria,
Relative Energy Dissipation Ratio
60
50
40
30
18.61

20
10

Minimum Relative Energy Dissipation
Ra tio according to ACI T1.1-01= 12.5%

0
0.00

0.50

1.00

1.50

2.00

2.50

3.00

Story Drift (%)×º ¬¸» ®»´¿¬·ª» »²»®¹§ ¼·­­·°¿¬·±² ®¿¬·± ·­ ´»­­ ¬¸¿² ïñèô ¬¸»®» ³¿§¾» ·²¿¼»¯«¿¬» ¼¿³°·²¹ º±® ¬¸» ¾«·´¼·²¹ ¿­ ¿ ©¸±´»ò Ñ­½·´´¿¬·±²­ ³¿§½±²¬·²«» º±® ­±³» ¬·³» ¿º¬»® ¿² »¿®¬¸¯«¿µ»ô °®±¼«½·²¹ ´±©ó½§½´»º¿¬·¹«» »ºº»½¬­ô ¿²¼ ¼·­°´¿½»³»²¬­ ³¿§ ¾»½±³» »¨½»­­·ª»òß¬ îòëðû Ì¿®¹»¬ Í¬±®§ Ü®·º¬18.61 % â ïîòë û ±® ïñè [OK]Development
forforSeismic
Resistance
Development on
onPrecast
PrecastHybrid
HybridFrame-Wall
Frame-WallBuildings
Buildings
Seismic
Resistance

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Shear Slip Displacement

2 (+)

1(-)

W-F Joint
C-F Joint
1st B-W Joint
2nd B-W Joint
Interior B-C Joint
Exterior B-C Joint

0.42 < 1.5 mm [OK]0.42 <1.5 mm [OK]0.68 < 1.5 mm [OK]0.59 < 1.5 mm [OK]0.88 < 1.5 mm [OK]0.40 < 1.5mm [OK]Í¸»¿® Í´·° Ü·­°´¿½»³»²¬

12

Ó¿¨ò Í´·° Ü·­°´¿½»³»²¬ óóóóóóó²±¬ ¾» »¨½»»¼ ïòë³³ò
[Acceptance Criteria ACI ITG5.1]
×º ¬¸» ³¿¨·³«³ ®»´¿¬·ª» ­´·° ¾»¬©»»² ¬¸» ¬©± º¿½»­ ±² »·¬¸»® ­·¼» ±º ¬¸» ¶±·²¬»¨½»»¼­ ¿°°®±¨·³¿¬»´§ ïòë³³ô ¬¸» ¿¹¹®»¹¿¬» ·²¬»®´±½µ ½¿°¿½·¬§ ¿´±²¹ ¿ ¸±®·¦±²¬¿´¶±·²¬ ­¬¿®¬­ ¼»½®»¿­·²¹ ¿²¼ ¬¸» ½±²½®»¬» ½±ª»® ·² ©¿´´ ¬±» ®»¹·±²­ ­¸¿´´ ¾» ­°¿´´·²¹ò
[Acceptance Criteria ACI ITG5.1]

Ü¿³¿¹» ¿¬ ß´´ ×²¬»®º¿½»­ ø ß¬ îòëðû ­¬±®§ ¼®·º¬ ÷Û¨¬»®·±® Þ»¿³óÝ±´«³² Ö±·²¬

Ó·²±® ¼¿³¿¹» ¿¬ ¹®±«¬ ú Ò±

Flexural½®¿½µ ·²Ö±·²¬©¿´´ °¿²»´Þ»¿³óÉ¿´´
CrackÉ¿´´óÚ±«²¼¿¬·±² Ö±·²¬
óÓ·²±® ¼¿³¿¹» ±²´§ ¿¬ ·²¬»®º¿½» ¹®±«¬×²¬»®·±® Þ»¿³óÝ±´«³² Ö±·²¬
Í³¿´´ Ý®¿½µóÒ± ½®¿½µ ·² ©¿´´ °¿²»´
ó ×² Ð®»½¿­¬Ý±´«³²óÚ±«²¼¿¬·±²½±´«³² ¿²¼ ¾»¿³­Ö±·²¬ô Ñ²´§¸¿·®´·²» ½®¿½µ »¨°»®·»²½»¼òÉ¿´´ Ð¿²»´ó¬±óÐ¿²»´ Ö±·²¬

142
142

ó ß­ »¨°»½¬»¼ô ¿¬ É¿´´ Ð¿²»´ó¬±ó Ð¿²»´¶±·²¬­ ¹¿° ±°»²·²¹ óóóóó°®»ª»²¬»¼

3rdJoint
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3rdICUS Report 45
ICUS Report 45ß¬ ¬¸» Û²¼ ±º ¬¸» Ì»­¬Crack Pattern and Damage of Test Specimen

Conclusion
Based on overall results, Hybrid Precast FrameWall buildings can be effectively used for seismic
resistance.
Behavior of hybrid dual system is very simple todesign. So, only a simple calculation can be used
to predict well the behavior of dual system.

Development
forforSeismic
Resistance
Development on
onPrecast
PrecastHybrid
HybridFrame-Wall
Frame-WallBuildings
Buildings
Seismic
Resistance

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3rdÛÍÌ×ÓßÌ×ÒÙ ÊÛØ×ÝÔÛ ÌÎßÖÛÝÌÑÎ×ÛÍÑÒ ËÎÞßÒ ßÎÌÛÎ×ßÔÍ ÞÇ ÜßÌßÚËÍ×ÑÒ
Farhana NAZNIN1, Babak MEHRAN2 and Masao KUWAHARA3
1
Master course student, Institute of industrial science,
The University of Tokyo, Japanneatly60@iis.u-tokyo.ac.jp
2
Researcher, Institute of industrial science, The University of Tokyo, Japan
3
Professor, Institute of industrial science, The University of Tokyo, Japan
ABSTRACT
Fusion of probe and fixed sensor data has been widely used toestimate travel time on urban arterials. Considering different accuracies
and limitations of traffic data from various sources, data fusion is applied toextend the spatial and temporal coverage of data. Majority of existing
fusion techniques to estimate travel time are merely based on statisticalmethods without considerations for traffic engineering concepts. In addition,probe trajectories include much richer information of traffic conditions
rather than only travel times, which could be used more effectively. In thisresearch a data fusion technique is proposed to estimate vehicle trajectories
using multi-sensor traffic data and concepts of traffic engineering which
can fully utilize probe trajectory information. Proposed method is based onmodeling vehicle trajectories by applying the simplified theory of 3D
kinematic waves. Assuming a triangular shaped flow-density curve,
variational formulation of kinematic waves is considered to estimate
cumulative number of vehicles in time and space. Given cumulative numberof vehicles as heights, a three dimensional surface is created over timespace plane and vehicle trajectories are estimated as contours of the surface.
Once vehicle trajectories are estimated, they can be used for several
purposes including travel time estimation, travel time prediction, emission
monitoring and signal timing optimization. Performance of the methodology
regarding travel time estimation is evaluated and it is shown that it iscapable of estimating reliable travel times on urban arterials.
Keywords: probe data, vehicle trajectories, data fusion, kinematicwave theory

1. INTRODUCTION
Availability of travel time information is of great importance for congestion
management on urban streets. In addition, it enables traffic engineers todynamically evaluate efficiency of different signal timing plans in order to

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minimize overall delay and emission. Travel time on urban streets can be
estimated directly using probe sensors (e.g. probe vehicles) or indirectlyfrom fixed sensor data (e.g. loop detectors). Considering different
accuracies and limitations of traffic data from various sources, data fusion
techniques are applied to extend the spatial and temporal coverage of data.
Majority of fusion techniques to estimate travel time are merely basedon statistical methods without considerations for traffic engineering
concepts. In addition, existing methods do not use rich probe trajectory data
which represent traffic conditions in time and space. Instead probe
trajectories are used simply to extract travel times. Probe data could be
combined more effectively with fixed sensor data to reproduce trajectoriesof all vehicles.
This paper is proposing an original fusion technique to reproduce
vehicle trajectories by effectively using multi sensor data and signal timing
parameters. Proposed methodology is based on kinematic wave theory of
traffic flow and can be used for several purposes including travel time
estimation, prediction, signal timing optimization and emission monitoring.
2. LITERATURE REVIEW
Existing data fusion techniques for travel time estimation can be categorized
as follows (EL Faouzi, 2004): i) Statistical based, ii) Probabilistic based and,iii) Artificial cognition based. Among statistical techniques, weighted
combination of travel times from different sources is the most common
approach. Weights are generally derived from variance-covarianceestimation errors by applying methods such as “voting technique” (Choi and
Chung, 2002). Probabilistic approaches such as Bayesian approach (ELFaouzi, 2006), Dempster-Shafer inference (EL Faouzi et al., 2009) wereused to tackle the problem of data fusion for the purpose of travel time
estimation. El Faouzi (EL Faouzi, 2006) describes application of Bayesian
approach to combine travel times estimated from conventional loop
detectors and probe vehicles on urban routes. Mathematical basis for
applying Dempster-Shafer inference to improve travel time estimation byfusing estimated travel times from toll collection stations and conventional
loop detectors is demonstrated by El Faouzi et al (EL Faouzi et al., 2009).
Ivan et al. (Ivan et al., 1995) applied artificial neural networks (ANN) to
develop arterial incident detection models that fuse probe vehicle and fixed
detector data. Existing data fusion methodologies basically relay on
statistical concepts and do not benefit from principles of traffic engineering
to fill the gaps in data. Recently Berkow et al. (Berkow et al., 2009)
proposed a fusion method to combine the data derived from loop detectors
and probe buses to improve travel time estimations on urban routes. Theydeveloped an algorithm to identify congested time periods using detector
data which were used later to reproduce bus trajectories. However, they did
not consider the delay at signalized intersections within their study area.

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Proposed methodology in the current research considers delay at signalizedintersections and implements principals of traffic engineering to combine
probe and detector data to reproduce vehicle trajectories on urban arterials.
The next section introduces study area, available data and analysis period.
3. STUDY AREAAs shown in Figure 1, a 3.3km stretch of National Highway 1 in Osaka,
Japan, was considered to conduct a case study. Selected section is a two lanefacility and includes 19 signalized intersections. Analysis period is chosen
during the evening peak period from 17:00 to 18:00 on 22 July 2009.Traffic
volumes (pulse data) for each lane were collected using ultrasonic detectors
installed in the upstream and downstream of each intersection. Signal timing
data including detailed phase schemes and cycle length were also available.
Probe data including time and position at 1 second intervals were collectedusing GPS equipped vehicles. A time-space diagram which shows thelocation of intersections, signal timing patterns, detector positions and probe
trajectories is presented in Figure 1.
4. METHODOLOGY
Proposed methodology to estimate vehicle trajectories is based on the
kinematic wave theory originally developed by Lighthill, Whitham andRichards (LWR) (Lighthill and Whitham, 1995). They described a theory of
one-dimensional wave motion which could be applied to certain types of
fluid motion or to highway traffic flow. The key feature of the LWR theory
was that there is some functional relation between the flow q and the density
k which might vary with location x but not with time t. Assuming noentering or exiting traffic, the conservation equation implies:k ( x, t )tq ( x, t )
x

0

(1)

Equation (1) can be written as:w( q ( x, t ), x )where w(q, x)

q ( x, t )tq ( x, t )
x

0

(2)

k (q, x) / q and the 1/w is called the “wave speed”.

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Downstrea m
Detector

SignalTimingProbe
Trajectory

Upstrea m
Detector

Figure 1: Signal timings, detector positions and probe trajectories
Newell (Newell, 1993) combined the concept of cumulative curves
with LWR theory and extended it to the 3D kinematic wave theory in 1993.
Given N(x, t) as cumulative number of vehicles at location x and time t,
Newell suggested evaluating N(x, t) rather than q(x, t):dN( N / x ) dx

( N / t ) dt

kdx

qdt

( k

qw) dx(3)

According to equation (3), if one knows N(x0, t0) and q(x0, t0) at someboundary point, one can determine N(x, t) at all points along the same
cumulative curve. In the special case of a homogeneous road section, k, q
and w are all constant and, as a result of equation (3), so is dN/dx. If weinterpret N(x, t) as a surface in a three-dimensional (N, x, t) space, thesurface N(x, t) is a surface generated by a family of straight lines. The valueof N(x0, t0) and q(x0, t0) at any boundary point would determine a line in thesurface N(x, t). From appropriate boundary data one could easily construct
such a surface and, thereby, determine N(x, t) everywhere.
The 3D kinematic wave theory has been used for several applications
such as dynamic network assignment, traffic simulation, etc. Recently
Daganzo (Daganzo, 2005) proposed an efficient calculation method basedon variational formulation of the 3D kinematic wave theory to evaluate
forward and backward waves within the traffic flow. We employed
Daganzo’s method to estimate vehicle trajectories and validated the
proposed methodology on the test bed of this study. Given N(x0, t0) and q(x0,t0) at some boundary point, variational theory provides a solution to estimate
N(x, t) by treating the problem as capacity constrained optimization problem.
Flow at any point in time-space is bounded from above by qmax, the capacity.
A similar capacity constraint should also hold if the road is viewed from arigid frame of reference that moves with speed x'. In this case the capacity
relative to the frame (the “relative capacity”) is the maximum rate at which

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traffic can pass an observer attached to the frame. An observer that moves
with speed x next to a traffic stream with density k and flow q is passed bytraffic at a rate q-kx'. In simplified variational theory, fundamental diagramis triangular and the relative capacity function (cost function) is linear. In
this case the maximum cost is:r(1 w / u)qmax(4)

where u and w are forward wave speed and backward wave speed,
respectively. In variational theory the problem is solved approximately byoverlaying a dense but discrete, network with short straight valid paths aslinks, and the following two properties: i) the slopes of links branching fromeach node which represent range of wave speeds with sufficient resolution
and ii) link costs.

Figure 2: Solution network
As shown in Figure 2, the solution domain can be modeled with a
network at which the mesh resembles triangular fundamental diagrams withidentical steps in time-space plane. Each node in the network represents theheight of the three dimensional cumulative surface at a specific point. Cost
rate along forward wave is 0, which means there is no change in cumulative
number of vehicles along forward wave. Cost rate along backward wave isr (equation (8)) which is the maximum allowable change in cumulative
number of vehicles. If there is any red interval in the solution domain, thecost rate of that path becomes 0. In this case, red interval creates a shortcut
in the network along which cumulative number of vehicles is constant.
4.1 Calculation steps
4.1.1 Constructing the network
The process begins with defining the network. Considering a
triangular fundamental diagram, given the forward wave speed u, jam
density kj and saturation flow rate qmax, backward wave speed w is estimatedusing equation (5). The horizontal distance between nodes, tstep, is an input
variable. Considering computational straightforwardness, time step of 1
second is recommended. The vertical distance between network nodes, sstep,
can be estimated using equation (6).
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w

sstep

q max
q max
kj
u

(5)

u w tstepu w(6)

4.1.2 Setting boundary conditions
Each node in the network presents the height of the cumulative
surface. To set initial conditions, the height along the first column isassumed to be 1. The next step is assigning appropriate heights for the
network boundaries. Considering the passing times of the vehicles recorded
by detectors, cumulative traffic counts at the upstream and downstream areassigned as the height to the nodes along the lower and upper boundaries of
the network.
4.1.3 Reference probe trajectories
When probe trajectories are used as reference to estimate othertrajectories, additional treatments are necessary. A probe trajectory can beinterpreted as a contour on a three dimensional surface of cumulative curves.
Therefore a constant height should be assigned to the nodes in the vicinityof the probe trajectory. Since cumulative vehicle counts at upstream are
known a constant height can be estimated once intersection point of theprobe trajectory with the lower boundary of the network is known.
4.1.4 Signal timing constrains
Red intervals create shortcuts in the network as shown in Figure 2. Inother words, during red intervals, it would be possible to move from node (i,j-1) to node (i, j) without incurring any cost. Therefore it is necessary to
distinguish all the nodes in the network that represent red intervals. Since
positions of signalized intersections are known, considering signal timing
data, such a process can be accomplished.
4.1.5 Optimization
Optimization is performed to find the height of each node in the
network. Considering equations (4) and (7) the link cost can be estimatedusing equation (8). Optimization process begins from upstream node andcontinues along each column until the last node in downstream.
u
tstep
u w
(7)

link cost

r

qmax

tstep

(8)

In general, height of node (i, j) is estimated from equation (9). If node(i, j) represents red intervals in the time-space plane, then considering theshortcut effect of red intervals, height is estimated from equation (10).

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Implementation of the rules declared in equation (9) and equation (10)culminates in shortest path between every pair of nodes in the network.

N(i, j)N(i, j)Min[N(i 1, j), N(i 1, j 1) link cost](9)

Min[N(i 1, j), N(i, j - 1), N(i 1, j 1) link cost](10)

4.1.6 Trajectory estimation
Considering the three dimensional shape of the cumulative surface,
trajectory of vehicle i would be the contour level i on the three dimensionalsurface where i is an integer number. For the test bed of this study, forward
wave speed was estimated from probe data during free-flow conditions
while saturation flow rate was estimated using detector data. It is assumed
that there are no vehicles entering or leaving the study area from midway
intersections. The test bed of this study is a two lane facility. However,proposed methodology is applicable when there is no passing allowed (firstin, first-out conditions). As a result, only one lane of the study area isconsidered for further evaluations. Saturation flow rate, jam density, freeflow-speed and passing times of the vehicles were estimated for the medianlane. For complete analysis, same procedures can be applied for theshoulder lane.
5. ANALYSIS RESULTS
5.1 Quantitative evaluation
Figure 3 shows estimated trajectories when none of probe trajectories
were used as reference. Although forward and backward waves are modeled
correctly, estimated trajectories still do not match with probe trajectories.
Since height of the first column in the network was set to 1, impact of traffic
conditions from earlier time intervals is not reflected in estimatedtrajectories. Since a probe trajectory reflects real traffic conditions, using aprobe trajectory as a reference to set up initial boundary conditions would
improve accuracy of estimations. Figure 4 shows estimated trajectories
when only first probe trajectory is used as reference.
The agreement between estimated trajectories and corresponding
probe trajectories were evaluated when only the first probe trajectory is used
as a reference. Figure 5 shows the Mean Absolute Error (MAE) relative totravel time of corresponding probe for each case. As expected, thosetrajectories which are closer to the reference probe trajectory show better
agreement and the MAE increases by time.

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To evaluate applicability of the proposed methodology for short term
travel time prediction, each probe trajectory was used as a reference toestimate trajectories of the proceeding vehicles. Afterwards the estimatedtrajectory corresponding to the next probe trajectory was considered and its
travel time was compared with the travel time of the respective probevehicle. Figure 6 shows the comparison results. While proposed method
underestimates travel times without using probe trajectories as reference,
when probe trajectories are used as reference, estimated travel times
significantly improve and estimation error becomes smaller.

Estimated
Trajectory

SignalTimingProbe
Trajectory

Figure 3: Estimated trajectories without considering probe trajectory as
reference
Reference
Probe
Trajectory

Estimated
Trajectory

SignalTimingProbe
Trajectory

Figure 4: Estimated trajectories considering first probe trajectory as reference

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12

) 10
(%
T
T8
ot
e
vi 6
ta
le
R4
E
A
M2
0
17:00:00

17:15:00

17:30:00

17:45:00

18:00:00

Entrance Time

Figure 5: Agreement between estimated and probe trajectories using first probe
trajectory as reference
1200

1200
Average TT of Probe= 890 sec
RMSE = 474.14 sec(54.31%))cse( 1000
e
m
i
T 800
le
av
r
T
600
d
et
a
m
tis 400
E

Average TT of Probe= 890 sec
RMSE = 71.89sec(8.08%))ces1000(e
imT
le 800v
arTdet 600
a
m
it
s 400
E

200

200
200

400

600

800

1000

1200

200

Measured Travel Time (sec)a) Without probe as reference

400

600

800

1000

1200

Measured Travel Time (sec)b) With all probes as reference

Figure 6: Travel time estimation improves after using probe trajectories asreference
6. CONCLUSION AND FUTURE WORK
Most of existing data fusion techniques use probe trajectories to simply
estimate travel time and neglect the rich trajectory data which represent
traffic conditions in time and space. In contrast, proposed methodology isbased on 3D kinematic wave theory of traffic flow and can fully employ
probe trajectory data to estimate vehicle trajectories. Such features
culminate in reliable travel time estimation with minimal input datarequirements. In addition, estimated trajectories can be used for various
applications such as travel time prediction, dynamic signal optimization andemission monitoring and optimization.
The methodology can still be improved and generalized in several
ways. In this study vehicles coming in or going out in the middle of thesection were not considered. Neglecting incoming or outgoing vehicle may
result in significant over or underestimation. It was also assumed that first
in-first out (FIFO) condition holds throughout the study area. Such anassumption might not be true for multilane facilities where passing is

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allowed. Tackling these issues require adjustment of the fundamental theory
as well as introducing extensions to the proposed methodology which areconsidered in the future directions of this research.
REFERENCES
Berkow, M., Monsere, C. M., Koonce, P., Bertini, R. L., and Wolfe, M.,
2009. Prototype for Data Fusion Using Stationary and Mobile Data
Sources for Improved Arterial Performance Measurement. TRB 88th
Annual Meeting, Washington D.C.
Choi, K. and Chung, Y., 2002. A data Fusion Algorithm for Estimating Link
Travel Time. ITS Journal: Intelligent Transportation Systems Journal,
Vol. 7, pp.235~260.
Daganzo, C.F., 2005. A variational formulation of kinematic waves:
Solution methods. Transportation Research Part B, Vol. 39, pp.
934~950.
Daganzo, C.F., 2005. A variational formulation of kinematic waves: basic
theory and complex boundary conditions. Transportation ResearchPart B, Vol. 39 (2005), pp.187~196.
EL Faouzi, N. E., 2004. Data Fusion in Road Traffic Engineering: An
Overview. Proceedings of Multisensor. Multisource Information
Fusion: Architectures, Algorithms, and Applications, Vol. 5434,
pp.360~371, Orlando.
EL Faouzi, N. E., 2006. Bayesian and Evidential Approaches for Traffic
Data Fusion: Methodological Issues and Case Study. TRB 85th Annual
Meeting, Washington D.C.
El Faouzi, N. E., Klein, L. A., De Mouzon, O., 2009. Improving Travel
Time Estimates from Loop and Toll Collection Data with DempsterShafer Data Fusion. TRB 88th Annual Meeting, Washington D.C.
Ivan, J. N., Schofer, J. L., Koppelman, F. S., and Massone, L. L. E., 1995.
Real-Time Data Fusion for Arterial Street Incident Detection Using
Neural Networks. Transportation Research Record, Vol. 1497, pp.
27~35.
Kuwahara, M., and Akamatsu, T., 1993. Dynamic equilibrium assignment
with queues for a one-to-many OD pattern. Proceedings 12th
International Symposium on Transportation and Traffic Theory, pp.
185–204 Berkeley.
Lighthill, M. J., and Whitham, G. B., 1955. On kinematic waves. I: Floodmovement in long rivers. II: A theory of traffic flow on long crowded
roads. Proceedings Royal Society, London, Vol. A229, pp.281~345.
Newell, G.F., 1993. A simplified theory on kinematic wave in highway
traffic, part I: general theory. Transportation Research Part B, Vol.
27(4), pp.281~287.
Newell, G.F., 1993. A simplified theory on kinematic wave in highway
traffic, part II: queuing at freeway bottlenecks. Transportation
Research Part B, Vol. 27 (4), pp.289~303.
Newell, G.F., 1993. A simplified theory on kinematic wave in highway
traffic, part III: multi-destination flows. Transportation Research Part
B, Vol. 27 (4), pp.305~313

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3rdÛÍÌ×ÓßÌ×ÒÙ ×ÒÌÛÎÓÑÜßÔ ÚÎÛ×ÙØÌÑóÜ ÓßÌÎ×È ÚÎÑÓ ÑÞÍÛÎÊßÌ×ÑÒÍ
Parichart PATTANAMEKAR1, Dongjoo PARK2,
Kangdae LEE3 and Chansung KIM4
1
Ph.D. Candidate, University of Seoul, Korea
pim_pattana@yahoo.com
2

Associate Professor, University of Seoul, Korea
Research Associate, Korean Standard Association, Korea
4
Research Associate, Center for Transport Database, The Korea Transport
Institute, Korea
3

ABSTRACTIn most cases, freight flow data from shipper-based surveys areaggregated and released in terms of mode-specific origin-destination (OD)
matrices. This makes them less useful for intermodal transportation
planning. Existing methods of estimating OD matrices cannot consider
intermodal transport information, which is mostly obtained from shipperbased surveys. The objective of this paper is to propose a new approach to
the estimation of an intermodal OD matrix from mode-specific OD matrices.
The proposed formulation is based on entropy maximization. An entropy
formula for the intermodal transportation problem was derived and used as
an objective function, which was maximized subject to the set of linear
constraints that constituted the observed mode-specific ODs. The problem
became a non-linear optimization and was solved using LINGO. The
proposed model was tested using a hypothetical network. Two cases ofassumed true OD matrix structures (gravity- and non-gravity-based flows)
were used in the numerical study. The results showed that the proposedmodel can estimate an intermodal OD matrix with acceptable accuracy
when the true OD matrix has a gravity-based structure..
Keywords: intermodal OD, OD estimation, mode-specific OD, freight
demand estimation, entropy maximization

1. INTRODUCTION
Intermodalism has become an essential component of national freighttransportation planning and has attracted the attention of many researchers
in the past decade. It can be defined as the transport of a load from its origin
to its destination by a sequence of at least two transportation modes, withthe transfer from one mode to the next being performed at an intermodal
terminal. In freight modeling, the outputs of demand models are mostly a

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series of origin-destination (OD) matrices. Customary freight OD matrices
have been estimated using the following four distinct methodologies:(a) conduct of a survey such as a commodity flow survey or ashipping-based flow survey;(b) use of secondary data and application of the Input/Output model;(c) application of a trip distribution model such as the gravity modeland the spatial interaction model; and(d) use of link flow counts to update the existing OD matrix.
The first approach yields the most accurate results but requires a large
amount of resources, due to which it tend to be very expensive and timeconsuming. The second approach requires nationwide information on goods
production and consumption. Factors for converting monetary trade into
goods quantities are necessary in this approach. The third approach ismainly applied for rough calculations since it is based on assumptions of
distribution behavior. The last approach has been mostly used in the past 25
years, and many related modeling techniques are documented in passenger
transport literature. Authors such as Crainic et al. (2001), Florian (1984),
and Gedeon et al. (1993) agree that the last approach can estimate an
acceptable OD matrix at a low cost. It is limited, however, to the estimationof the OD matrix in urban areas, where intermodal transport is not being
considered.
Yet there is a little to be found in published literature on theestimation of the intermodal OD matrix. In practice, OD flows from survey
or estimation methods are aggregated and released in terms of modespecific OD matrices such as truck and rail tables. With only mode-specific
OD flows, it is impossible to conduct an intermodal network assignment in astraightforward manner. This requires an additional modeling approach. As
far as what is known, previous studies have not proposed an explicit methodof estimating the intermodal OD matrix from available mode-specific OD
matrices.
The objective of this paper is to propose a new approach for theestimation of intermodal OD flows from mode-specific OD matrices. The
proposed formulation is based on the objective of entropy maximization
with respect to the knowledge information generated from mode-specific
OD flows. An entropy formula for the intermodal transportation problem
was first derived, with all observed flows classified into direct and indirectflows. The derived entropy function was then used as the objective functionthat was maximized subject to a set of linear constraints that constituted theobserved mode-specific OD flows. The problem became non-linear
optimization. It is expected that the proposed approach will give rise toother researches in the field of OD estimation. While existing approaches inthis area synthesize the OD matrix using the link traffic count and the initial
single-mode matrix, the proposed model formulated the estimation problem
based on the given mode-specific OD matrices and took into account theintermodal transport. The model outputs included not only single-mode, i.e.,
direct, OD flows but also intermodal, i.e., indirect, flows. Note here that inthis paper, intermodal OD flows are defined as those that are carried by

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multiple modes wherein transshipment can be explicitly considered. In thissense, they allow mode shifts among OD pairs during path assignments.
The paper first reviews literature in the field of demand matrix
estimation in freight modeling. Next, the problem description is introduced
and an entropy maximization formula for the intermodal transportation
problem is derived. In Section 4, the proposed model is tested on ahypothetical network. Two cases of assumed true OD matrix structures(gravity- and non-gravity-based flows) are used in the numerical study.
Finally, Section 5 presents the concluding remarks.
2. DEMAND MATRIX ESTIMATION FOR FREIGHT MODELING
2.1 Estimation of the Mode-specific OD
There are many publications on modeling passenger transportation,
but there have been limited studies in the freight area (Al-Battaineh andKaysi, 2005; Crainic et al., 2007; Fernandez et al., 2003; Gedeon et al.,
1993; Holguin-Vegas and Patil, 2008a and 2008b; List and Turnquist, 1994;
Tamin and Willumsen, 1988; and Tavasszy et al., 1994). Most formulations
that are proposed in freight literature have been developed for estimating asingle-mode OD matrix from link traffic counts (Holguin-Vegas and Patil,
2008a and 2008b; List and Turnquist, 1994; and Tamin and Willumsen,1988) or the initial matrix (Al-Battaineh and Kaysi, 2005 and Tavasszy etal., 1994). Several modeling techniques have been proposed, such as thegravity-opportunity-based formulations (Tamin and Willumsen, 1988),
linear programming (List and Turnquist, 1994), and the genetic algorithm
technique (Al-Battaineh and Kaysi, 2005; Holguin-Vegas and Patil, 2008aand 2008b; and Tamin and Willumsen, 1988).
2.2 Estimation of the Intermodal OD
Apparently, intermodal OD flows can be derived from survey datawhen the information on successive intermediate shipment legs is available.
Otherwise, it should be estimated from other sources of data or requires acomplex solution. Gedeon et al. (1993) estimated the intermodal OD matrix
by solving an optimization problem in which the freight distribution is
combined into the mode and route choice processes. Over the intermodal
network, the OD estimation problem was formulated as a single-level
optimization problem, so the intermodal OD matrix can be simultaneously
obtained, assuming optimal link flow patterns. The only demand inputs totheir model are the total production and consumption. To extend the studyof Gedeon et al. (1993), Crainic et.al. (2001) proposed bi-level optimization
to modify the estimated intermodal OD so that the link and transfer volumes
would be as close as possible to the observed volumes.

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3. MODEL FORMULATION
3.1 Notations
The notations used throughout this paper are as follows:= set of linksA= set of nodesN
cijt , cklr

= link cost of truck and rail transport, respectively

ciklj
Tijt

= total cost of intermodal transport; i.e., cikt

r
kl

T
xij

xiklj

y

c klr

cljt

= observed truck OD flow for pair i-j= observed rail OD flow for pair k-l= direct flow of commodity from origin i to destination j= indirect flow from origin i to destination j via depots k and l, in that order= assigned link flow

kj

= binary variable i.e. = 1 if k belongs to j, and 0 otherwiseli= binary variable i.e. = 1 if l belong to i, and 0 otherwise

iklj= binary variable i.e. = 1 if ciklj

cijt , and 0 otherwise

3.2 Problem Description
Consider a transportation network, abstracted into a direct graph,
G ( N , A) , wherein a node pair joined by a link is associated with a linkcost. A node from which a homogeneous commodity that is beingtransported originated and/or terminated is called a centroid. Let Trs denotethe amount of commodities carried from centroid r to centroid s , whereinr, s N , within a given time period. Let T be a demand matrix with thecomponent Trs . T is usually referred to as the OD matrix. In transportmodeling, each OD flow Trs is subdivided on the network into path flows
followed by traffic assignment. In the single-mode modeling approach, themode split model is applied to estimate mode-specific OD matrices, i.e.,truck and rail tables.
Consider a problem wherein intermodal transport is considered for
freight demand modeling. The commodity flow of a particular OD pair is
assigned onto a multimodal network wherein intermodal transport, such as
the truck-rail mode, is allowed. In this case, the mode can be shifted atintermediate points along the path. To clarify the problem, consider a simple
network, such as that shown in Figure 1, wherein the figures in parentheses
represent the cost of travel between the two nodes. Two modes (i.e., thetruck and rail modes) exist in this sample network. Rail transport isavailable for traversing along 3-4 nodes. Assuming a freight demand from
Origin 1 to Destination 5 of T15 100 , and from Origin 2 to Destination 6

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of T26 50 , the assigned paths can be simply determined. As a result, theintermodal paths 1-3-4-5 and 2-3-4-6 are obviously selected for transporting
T15 and T26 , respectively.
Once OD flows in the problem are observed, mode-specific OD
matrices, i.e., for the truck and the rail, can be obtained. Let the set of nodes
A be partitioned into the following three subsets: O , the set oforigin/production nodes; D , the set of destination/final consumer nodes;
and H , the set of intermodal depot nodes. (Note that sets O, D , and H are
not necessarily disjointed.) An intermodal depot can be a rail station, freight
hub, or consolidation terminal where the transshipments can be carried out.
Denote T t with components Tijt is the observed truck matrix and T r withcomponents Tklr is the observed rail matrix. Therefore, in this problem, theobserved mode-specific OD flows are T13t T45t 100 , T23t T46t 50 , andT34r 150 .

Figure 1: Sample problem that represents intermodal transportation
In common practice, suppose that there is a collection of observationsof mode-specific OD flows that were obtained via several types of surveys.
These could be shipping-based surveys, commodity flow surveys, road-side
interviews, or secondary reports from railroad authorities. Based on theseobserved mode-specific OD information, the intermodal OD can be
estimated. The problem that this paper tries to solve is how to generate theinitial OD matrix that corresponds to the observed mode-specific OD
matrices when the mode-specific OD matrices in an intermodaltransportation network are given. In this paper, the initial OD matrix iscalled the intermodal OD matrix. The problem to be solved is similar to thedemand matrix estimation problem in literature (principally Van Zuylen andWillumsen, 1980, and Brenninger-Gother et al., 1989). The difference isthat in the existing methods, the developed formulations are proposed toestimate OD flows from traffic counts (and the initial or target matrix in thecase of updating). The generic forms of estimation models have beendeveloped in a straightforward manner and are sufficient for a single mode
as a passenger transportation problem. A useful review of literature on OD
estimation modeling approaches for passengers can be seen in the study ofDoblas and Benitez (2005).
3.3 Entropy of the Intermodal Transportation Problem
The maximum entropy estimation of the OD matrix has become aclassical transportation engineering tool. First proposed by Wilson (1967) toEstimating
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intermodalFreightfreight O-D
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determine the matrix with the highest probability of satisfying a set oforigin- or destination-based constraints, it has been successfully extended tomake use of traffic counts (e.g., Van Zuylen and Willumsen, 1980). In thetransportation model, entropy is used as a measure of dispersal of trips
between origins and destinations. An interesting alternative interpretation ofentropy in transportation modeling can be seen in the study of Wilson(1970). In this section, the principal concept of entropy maximization intransportation problems is first introduced. Then the entropy for intermodaltransportation based on the observed direct and indirect flows is derived.
In the principle of entropy, an abstract definition of the (Shannon)
entropy of a variable X is defined as:En( X )p ( x) ln p ( x) ,(1)xwherein p(x) is the probability that X is in the state x .
In general problems, normalizing the trip number Tij by dividing it by
the total number of transported commodities, T , yields the bivariate
Tijprobability distribution pij. The objective is to define the most likelyTmatrix, i.e., that with the greatest number of micro-states associated with it.
The number of ways of selecting an OD matrix, Tij , with the total numberof transported commodities T can be obtained as follows:
T!

W (T)
j

Tij !

i

.

(2)

Based on the aforementioned formula, two different entropy
maximization functions were derived from published literature. See Wilson(1970) and Islam and Roy (2006).
In the intermodal transportation problem, the flow of a particular OD
pair Tij can be classified as both a direct and indirect flow, as follows:
Tij

xij

k

l

xiklj .

(3)

Once the OD flows of the rail and the truck are observed, the total
number of transported commodities in the study area is also given and canbe expressed as:
T
i

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i O j DTijt

2k Hl HTklr

k Hl H

Tklr(4)

Let T 1 and T 2 replace the terms in the first and second parentheses,
respectively. Thus, the number of ways of selecting an OD matrix Tij with a
T 1 T 2 ) can be obtained

total number of transported commodities T ( T
as follows:
T 1!

W (T)
i

j

T 2!
xij !

i

j

k

(5)

xiklj !

lor

ln T 1!

ln W (T)iln xij ! ln T 2 !

j

i

j

k

ln xiklj ! .

l(6)Using Stirlings formula, the function becomes:
ln W (T)T 1 ln T 1 T 1i

T 2 ln T 2

xij

j

j

T2
i

(T 1 ln T 1ij

T 2 ln T 2 )
i

j

k

l

xij ln xij

xiklj

xij ln xij

i

i

j

j

k

k

l

l

xiklj ln xiklj

xiklj ln xiklj .

(7)

Since T 1 and T 2 are constant and given, maximizing
ln W (T)( En(T)) is equivalent to maximizing the entropy, as defined
above.
3.4 Estimation of the intermodal OD matrix

Based on the entropy maximization of the intermodal transportation
problem derived in the preceding section, the non-linear optimization
problem for the estimation of the intermodal OD flows from mode-specific
OD flows can be formulated as follows:
minimize
i o j Dxij ln xij

i o j Dk Hl H

xiklj ln xiklj(8)subject to

xij

kj

l H j D

iklj

xiklj

li

i Ok H

Estimating
Estimating Intermodal
intermodalFreightfreight O-D
O-D matrix
matrixfrom
fromObservations
observations

iklj

xiklj

Tijt , i

O, j

D

(9)

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i O j D

iklj

Tklr ,

xiklj

xij , xiklj

0k,li O, jH(10)

D, k , lH(11)

The objective function (8) maximizes the entropy of the intermodaltransportation problem. The constrained equations to be fulfilled are as
follows. For an observed truck, OD flows where the trucks origin or
destination can be joined to an intermodal depot can be classified into direct
and indirect transport, as they are part of the intermodal transportation chain
and belong either to the incoming or outgoing leg. These conditions are
enforced by Equation (9), wherein kj and li are binary variables. kj is 1
if k geographically belongs to j, and 0 otherwise. Also, li is 1 if lgeographically belongs to i, and 0 otherwise. Equation (10) ensures that theestimated interdepot flows are satisfied by the observed rail ODs, wherein
iklj stands for the proportional assignment computed as a fraction of thetrips from origin i to destination j for which the intermodal route through theintermediate nodes k and l, in that order, were used. Note that thesevariables have the same implication as the existing OD estimation models(see Fish, 1988; Van Zuylen and Willumsen, 1980; and Brenninger-Gothe etal., 1989). To be realistic, though, the type of assignment is a crucial
assumption in the determination of iklj . Where the link costs are
independent of the flows, the value ofiklj

is a given constant that depends

on the assumed path choice. For the shortest path assignment, this predefined variable can be determined as follows:

iklj

1 if ciklj cij
0 otherwisei, k , l , j .

(12)Finally, the non-negativity of the flows is qualified by Equation (11).
4. NUMERICAL RESULTS
4.1 Test Network
To illustrate model performance in a simple way when the true OD
matrix can be determined, tests were carried out on a hypothetical network,
as shown in Figure 2. The test network had eight zones, eight nodes, and 17
bi-directional links, including 15 road links and two rail links. Threeintermediates that were located in Zones 4, 5, and 6 allowed transshipments
between the two modes. Only a homogenous product of 10.5 million tons
was considered transported in the network. To investigate the performanceof the proposed model, two cases of assumed true OD matrix structures

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ICUS Report 45(gravity- and non gravity-based flows), were created and used in the
numerical study. In the first case, true OD matrix was created using agravity-based structure. Based on a non-gravity-based structure, the second
true OD matrix was manually assigned by the researchers. To apply theestimation method, each case required the corresponding unimodal OD
flows as inputs to the proposed model. To provide each of them, a pathanalysis was conducted to trace the user mode. For this, the study used amultimodal network assignment (Guelat et al., 1990) to do the network
assignment. Since the modal links were coded, the mode-specific OD
matrices, which were the truck and the rail tables, were determined simply
from the results of the path analysis.

Zone centroid
Zone boundary
NodeCentroid connector

Road link
Rail link
Distance (km.)

Figure 2: Test network.
4.2 Performance Measurement
To test the model, the proposed formulation was coded and solved
using an optimization package namely LINGO. The proposed model
successfully estimated the intermodal OD flows from the mode-specific OD
matrices. The results included direct as well as indirect flows. Then theestimated intermodal OD flows were assigned onto the intermodal network,and the flows on the links were observed. Since the true OD flows and link
flows were known, the mean absolute percent errors (MAPEs) in themeasurement of the performance of the proposed model in terms of the OD
flows and the assigned link flows was calculated.
Figure 3 shows the relationships between the true and estimated flows
for each OD pair. Figure 4 compares the assigned link flows from theestimated intermodal OD flows with those from the true OD flows. Table 1
shows the accuracy of the proposed model in several aspects. It can be seenthat in the case of the gravity-based OD matrix structure, the proposedmodel was able to estimate the intermodal OD flows and provide theassigned link flows with acceptable accuracy. The proposed model, however,
did not provide good results when the true OD matrix had a non-gravitybased structure.

Estimating
Estimating Intermodal
intermodalFreightfreight O-D
O-D matrix
matrixfrom
fromObservations
observations

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140,000

140,000

Case 1: Gravity-based distribution

Case 2: Non gravity-based distribution

120,000

120,000

100,000

100,000

80,00080,00060,00060,00040,00040,00020,00020,000----20,00040,00060,00080,000Real intermodal O-Ds

100,000

120,000

20,00040,000140,000

60,00080,000100,000

120,000

140,000

Real intermodal O-Ds

Figure 3 Scatter plot of real and estimated intermodal OD
80,000120,000

Case 1: Gravity-based distribution

Case 2: Non gravity-based distribution

70,000100,000

60,00080,00050,00040,00060,00030,00040,00020,00010,00020,000--20,00040,00060,00080,000Assigned link flows using real O-Ds

100,000

10,00020,000120,000

30,00040,00050,00060,00070,00080,000Assigned link flows using real O-Ds

Figure 4: Scatter plot of assigned link flows from real OD and fromestimated OD
Table 1: Percent error (MAPE)
Comparison aspects
Case 1(Gravity-based)
1) O-D flows for all each pairs- Overall
14.62%- Direct flows
5.56%- Indirect flows
9.05%2) Assigned link flows- Overall
9.50%- Truck links
10.32%- Rail links
3.37%3) No. of iterations
5,847 iter.

Case 2(Non Gravity-based)
58.47%
20.11%
38.36%
38.06%
42.20%
7.00%
7,096 iter.

5. CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCHThis paper discussed the issues in intermodal freight transportation planning.
A review of literature showed that intermodal freight transportation
planning requires not only an intermodal transportation network but also anintermodal OD matrix. In common practice, when national data sources are

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unavailable, freight demand data from shipping-based surveys areaggregated and released in terms of mode-specific OD matrices. With onlymode-specific OD flows, intermodal transportation network assignment
cannot be conducted simply. This paper proposed a new approach toestimating intermodal OD flows from available unimodal OD matrices. The
proposed model, which is based on the entropy maximization of intermodaltransportation, was formulated. The proposed model was tested on a test
network. Two cases of the true OD matrix structure were created and used
to evaluate the model performance. The results show that in the case of thegravity-based OD matrix structure, the proposed model estimated theintermodal OD flows with acceptable accuracy. The assigned link flows
from the estimated OD matrix were also much closer to those from the true
one especially in terms of the rail links.
The model proposed in this paper is very useful for freight transportmodelers, since it uses a new approach to the estimation of an intermodal
OD matrix from mode-specific OD matrices without requiring additional
surveys. As a result of the proposed model, the estimated intermodal OD
matrix was inputted to freight network equilibrium models and tointermodal network assignment. The proposed model, however, had lessaccuracy in the case of the non-gravity-based OD matrix structure. It is
expected that the next generalization of this paper will be of particularinterest especially for solving these issues. Two extensions may be
developed as further studies. One might be the development of a newalgorithm to combine the estimation method that was proposed in this paper
to the intermodal network assignment problem for consistent accuracy. The
other extension may be the estimation of the intermodal OD matrix not only
from mode-specific OD matrices but also from the initial OD matrix, suchas an outdated OD matrix.
REFERENCES
Al-Battaineh, O., Kaysi, I.A. (2005). Commodity-based truck matrix
estimation using input-output data and genetic algorithms. Transp Res
Rec 1923:37-45.
Brenninger-Gothe,M., Jornsten, K.O., and Lundgren, J.T. (1989).
Estimation of origin-destination matrices from traffic counts using
multiobjective programming formulations, Transp Res-B, vol.23B(4),pp.257-269.
Crainic, T. G., Kim, K. H. (2007). Intermodal Transportation, Handbooksin Operations Research and Management Science, C. Barnhart and G.
Laporte (Eds.), Elsevier Science, Amsterdam.
Crainic, T.G., Dufour, G., Florian, M. et al. (2001). Demand matrix adjustment
for multimodal freight networks, Transportation Research Record (1771),
pp. 140-147.
Doblas, J., and Benitez, F.G. (2005). An approach to estimating andupdating origin-destination matrices based upon traffic counts
Estimating
Estimating Intermodal
intermodalFreightfreight O-D
O-D matrix
matrixfrom
fromObservations
observations

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preserving the prior structure of a survey matrix, Transportation
Research Part B, 39, pp. 565-591.
Fernandez, J.E., de Cea, J., Soto, A. (2003). A multi-modal supply-demandequilibrium model for predicting intercity freight flows. Transportation
Research 37B, 595614.
Fish, C.S., (1988). On combining maximum entropy trip matrix estimation
with user optimal assignment, Transp Res Part B, 22B(1), pp. 6979.
Florian, M. (1984). An introduction to network models used in transport
planning. In: Transport Planning Models, Amsterdam, North-Holland, pp.
137-152.
Gedeon, C., Florian, M., Crainic, T. (1993). Determining origindestination
matrices and optimal multiproduct flows for freight transportation
over multimodal networks. Transp Res Part B, 27B(5), pp. 351368.
Guelat, J., Florian,M., Crainic,B.C. (1990). A multimode multiproductnetwork assignment model for strategic planning of freight flows.
Transp. Sci. 24 (1), 2539.
Holguín-Veras, J., Patil, G. (2008a). An integrated commodity based/empty
trip freight origindestination synthesis model. Transp Res Rec (inpress)
Holguin-Veras, J., Patil, G. (2008b). A Multicommodity Integrated Freight
Origin-destination Synthesis Model, Networks and Spatial Economics,
vol. 8, pp. 309-326.
Islam, S., and Roy, T.K. (2006). A new fuzzy multi-objective
programming: Entropy based geometric programming and its
application of transportation problems, European Journal Research,
vol.173, pp. 387-404.
Tamin, O.Z., and Willumsen, L.G. (1988). Freight demand model
estimation from traffic counts. PTRC Annual Meeting, University ofBath, England.
Tavasszy, L.A., Stada, J.E., Hamerslag, R. (1994). The impact of
decreasing border barriers in Europe on freight transport flows byroad. Proceedings of the 36th Annual Conference of theTransportation Research Forum, Florida, USA.
Van Zuylen, H.J., and Willumsen, L.G. (1980). The most likely trip matrix
estimated from traffic counts, Transp Res-B, vol.14B, pp. 281-293.
Wilson, A.G. (1970). Entropy in Urban and Regional Modelling, Pion,
London.

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3rdÐÛÜÛÍÌÎ×ßÒ ÚÔÑÉ Í×ÓËÔßÌ×ÑÒÑÒ ÐÛßÕ Ì×ÓÛChalat TIPAKORNKIAT1, Hyunmyung KIM2
1
Ph.d candidate, Field of Transportation Engineering,
School of Engineering andTechnology, Asian Institute of Technology, Thailand
ninechalat14@hotmail.com
2
Assistant Professor, Myong Ji University, Kyunggi Do, Korea
ABSTRACT
Pedestrian flow in peak time in real world is investigated by video
camera. The pedestrian flow model is performed for the walkway with open
boundaries. Two types of walkers, going to the right and to the left are
taken into account. The video recordings and measurements of individual
density, speed and arrival time are evaluated. The pedestrian flow model isdepended on density of walkway and the position of condensed areas. It
shown that separation walkway in several sections can predict the speed
and travel time better than one section.
Keywords: pedestrian, pedestrian flow simulation, cellular automata

1. INTRODUCTION
Many observed self-organization phenomena in traffic and pedestrian flows
have been successfully reproduced with physical methods. It has also
encouraged physicists to study evacuation processes. Pedestrian flow is akind of many-body system of strongly interacting persons. The pedestrianflow dynamics is closely connected with the driven many particles system.
To know the properties of pedestrian flow is important in our life. It isnecessary to know the flow rate of pedestrian for rush hour and panic escape.
(Isobe et al, 2004).
The typical pedestrian flows have been simulated by the use of a few
models: the cellular automata, the lattice-gas model of biased-random
walkers (Muramatsu et al, 1999. Nagatani, 2001. Tajima et al, 2001).
Helbing (1995) has shown that human trail formation is interpreted as selforganization effect due to nonlinear interactions among persons that call thesocial force model. Henderson (Handerson, 1971) has conjectured thatpedestrian crowds behave similarly to gases or fluid-dynamic model. The
magnetic model, agent-based model, game theoretic model and approaches
based on experiments with animals.

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In this paper, we investigate and study the pedestrian flow on walkway andsimulation. We would like to address the characteristic properties of thepedestrian flow. We carry out the investigation of pedestrian flow on
walkway in CBD on rush hour. The investigations are recorded by video
cameras. The recordings are evaluated to derive the pedestrian flow
properties. For comparison with the investigation, we will conduct thecellular automata model to simulate the pedestrian flow model by extending
the biased random walker model in the future.
2. DATA COLLECTION
We would like to address the characteristic properties of the pedestrian flow.
We carry out the data collection of pedestrian flow on the real situation, thedata collection are recorded by video cameras. The recordings are evaluatedto derive the pedestrian flow properties such as density-speed-travel time
relationships.
2.1 Scope of data collection in Bangkok
Researcher collects data by video recorder and pedestrian traffic count.
The peak time such as morning and lunch time were selected to record
pedestrian walking on the Silom walkway. Time period is 60 minutes startfrom 8.00 – 9.00 and 12.00-13.00. Dimension of walkway were measured
to investigate the travel speed, density and flow. Walkway is separated in 7
lines to investigated density-speed-travel time relationships.
2.2 Data collection method
The data collections are recorded by video cameras. The recordings
are evaluated to derive the pedestrian flow properties. Researcher set
positions for each camera at front, middle and end of walkway which allcameras can record cover the all area of study. We carry out the datacollections for the pedestrian flow on the real walkway in Silom Bangkok.
Figure 1, shows the schematic illustration of the experimental setup. Figure
2, show details of exact width of the walkway is 2.45 m and its length 25 m.
There are no obstacles on the walkway. Three video cameras are located at
the left, center and right boundaries over the walkway. The cameramen areable to observe all the walkers who exist within the walkway by video
cameras.

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Figure 1: A sample of a pedestrian walking through the measurement
section
1

2

3

ë

4

ë
êòïí

ïòëë

5

6

ë
ðòêî

ïòéî

ïòéî

ë

ïòéî

ðòèð

ïòéî

ë

ïòîî

ïòîî

ðòçî

ðòêî
ðòèé

×²º±
­·¹²

ïòîð

ðòçð

Ô·¹
¸¬
Ð±´
»í

îòîë

îòìð

Ì®»
»í

ß¼ª
­·¹²

ïòïð

íòêð

ïòîð

ëòðð

Ô·¹
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Ð±´
»î

ðòçð

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ðòíê

ðòéè

ðòêî

ðòèð

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Í·¹²¿´ ¾±¨

7

ïòîð

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ìòîë

ðòêð

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»ï

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ï

ïòîð

ïòîð

îðòðð

Figure 2: Illustration of study area on Silom walkway, Bangkok
Next step, researcher transfer data to VDO files in computer andinvestigate pedestrian travel time, density and speed.
3. FLOW PROPAGATION MODEL
3.1 Pedestrian flow models: Parameter relationships
Pedestrian density is defined as the average number of pedestrians perunit of area within a walkway expressed as pedestrians per square meter(p/m2). In this study, the walkway in Central Business District (Silom,
Bangkok, Thailand) is chosen as the observation entities to study the
relations of pedestrian density, speed and travel time. All parameters aredefined as follow:p: Number of pedestrians: Average density ( p/m 2)
Kavg
v : A pedestrian speed (m/s)
k1: Density while a pedestrian passing the line number 1( p/m2)

Pedestrian
Pedestrian flow
flowSimulation
simulationon
onPeakpeakTime
time

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( p/m 2)k4
( p/m 2)k5
( p/m 2)k6
( p/m 2)k7
( p/m 2)

: Density while a pedestrian passing the line number 2: Density while a pedestrian passing the line number 3: Density while a pedestrian passing the line number 4: Density while a pedestrian passing the line number 5: Density while a pedestrian passing the line number 6: Density while a pedestrian passing the line number 7

The density in traffic flow is defined as the number of vehicles perunit area of the roadway and the inverse of density is spacing which is the
distance between two vehicles. The speed is the inverse function of density.
Density is high, the walking speed has to slow down and travel time isincreasing but the characteristics of pedestrian quite different from motor
vehicle. Pedestrian require less space for accelerate the walking speed.
In whole section, the density is random fluctuant density, and thecondensed area may occur in many points as shown in figure 1. A
pedestrian have to change to speed depend on degree of density for eachcondensed sections and general sections.

Figure 3: Illustration of the density.

Figure 4: Example of pedestrian walking for density 0.14 p/m2 at 1st line

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Figure 5: Example of pedestrian walking for density 0.28 p/m2 at 2nd line

Figure 6: Example of pedestrian walking for density 0.44 p/m2 at 3rd line

Figure 7: Example of pedestrian walking for density 0.44 p/m2 at 4th line
1

2

3

4

5

10.00 mFigure 8: Example of pedestrian walking for density 0.32 p/m2 at 5th line
From figure 4-8, if we use the density at an origin line (1st line) to
calculate the travel time and travel speed to destination only, a pedestrian(yellow) arrives a 1st line at the density is 0.14 p/m2. There is predicted thata pedestrian (yellow) can walk with high speed and less travel time becauseof a low density. However, in fact the density of pedestrian is a fluctuant
density along the walkway such as in figure 5, a density at a 2nd line is 0.28
Pedestrian
Pedestrian flow
flowSimulation
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p/m2, in figure 6 a density at 3rd line is 0.44 p/m2, in figure 7 a density at 4thline is 0.44 p/m2 and in figure 8 a density at 5th line is 0.32 p/m2. So, whena pedestrian (yellow) walks to a center line (3rd line) a pedestrian have to
reduce speed then a speed have to reduce and travel time have to increase
because of high density of walkway.
Therefore, the density is an important factor to forecast the speed andtravel time of pedestrian walking. To predict whole travel speed and travel
time of a pedestrian, a walkway has separated in several sections for an
average of a list of density. The average density for a pedestrian is
calculated by combining the values of density at each line when a pedestrianis passing a line. A single number of an average density is calculated thetravel speed and travel time.
We calculate the mean velocity of walkers from the experimental data.
The velocity of the individual is defined as the length between the initial
position and the boundary divided by the arrival time.
Authors study the density effect of the 5 cases when the local
condition of a huge system is investigated or the short-term transition if the
focus of interest. The density boundaries can affect the speed and traveltime; the nearest region of density may highly affect the speed and travel
time more than the far region. So we need to analysis the parameters or
ratio of density region. Currently, we separate walkway in 7 lines and use
heuristic method to find the optimal solution of parameter; however, wefound that we consumed calculation time too much for 7 lines.
Currently, author uses 51 examples to determines density –speedtravel time relationships and author uses five models to find the appropriate
average density.
Authors checked each pedestrian by an example pedestrian’s position isover the define line then author check the arrival time and counts density forwhole walkway until an example pedestrian leaves the walkway.
Case 1, the average density equation (1) is whole density of walkway
when an example pedestrian arrives at the 1st line.
Kavg = k1(1)

Case 2, the average density in equation (2) is the average wholedensity of walkway when an example pedestrian arrives at 1st, 4th line and7th line.
K avg =

k1 + k 4 + k 7
3(2)

Case 3, the average density in equation (3) is the average wholedensity of walkway when an example pedestrian arrives at 1st, 4th and 6th
line.

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K avg =

k1 + k 4 + k 6
3(3)

Case 4, the average density in equation (4) is the average wholedensity of walkway when an example pedestrian arrives at 1st, 2nd, 3rd, 5th
and 6th line.

K avg =

k1 + k 2 + k 3 + k 5 + k 6
5(4)Case 5, the average density in equation (5) is the average wholedensity of walkway when an example pedestrian arrives at 1st, 2nd, 3rd, 5th,
6th and 7th line.
K avg =

k1 + k 2 + k 3 + k 5 + k 6 + k 7
6(5)

4. MODEL RESULTS
4.1 Case 1: Kavg = k1
Figure 9 and 10 show the average density is whole density of walkway
when an example pedestrian arrives at the 1st line related with speed andtravel time respectively.

Figure 9: Density and speed relationship of case 1

Pedestrian
Pedestrian flow
flowSimulation
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time

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Figure 10: Density and travel time relationship of case 1
4.2 Case 2: K avg =k1 + k 4 + k 7
3

Figure 11 and 12 show the average density is the average wholedensity of walkway when an example pedestrian arrives at 1st, 4th line and7th line related with speed and travel time respectively.

Figure 11: Density and speed relationship of case 2

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Figure 12: Density and travel time relationship of case 2
4.3 Case 3: K avg =k1 + k 4 + k 6
3

Figure 13 and 14 show the average density is the average wholedensity of walkway when an example pedestrian arrives at 1st, 4th and 6thline related with speed and travel time respectively.

Figure 13: Density and speed relationship of case 3

Pedestrian
Pedestrian flow
flowSimulation
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Figure 14: Density and travel time relationship of case 3
4.4 Case 4: K avg =k1 + k 2 + k 3 + k 5 + k 6
5

Figure 15 and 16 show the average density is the average wholedensity of walkway when an example pedestrian arrives at 1st, 2nd, 3rd, 5th
and 6th line related with speed and travel time respectively.

Figure 15: Density and speed relationship of case 4

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Figure 16: Density and travel time relationship of case 4
4.5 Case 5: K avg =k1 + k 2 + k 3 + k 5 + k 6 + k 7
6

Figure 17 and 18 show the average density is the average wholedensity of walkway when an example pedestrian arrives at 1st, 2nd, 3rd, 5th,
6th and 7th line related with speed and travel time respectively.

Figure 17: Density and speed relationship of case 5

Pedestrian
Pedestrian flow
flowSimulation
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Figure 18: Density and travel time relationship of case 5
The density (k) was counted in every line from 7 lines and author
calculates the average of density for each methods
Table 1: R2 comparison of speed-density relationships
Case
Equations = -0.7143k + 1.3793
1. Kavg =   k1s = -1.0851k + 1.4752
2. Kavg = k + k + k
1

3. Kavg =4. Kavg =5. Kavg =473
k1 + k 4 + k 6
3
k1 + k 2 + k 3 + k 5 + k 6
5
k1 + k 2 + k 3 + k 5 + k 6 + k 7
6

R2
0.28080.3555s = -0.9908k + 1.4527

0.3667s = -0.9874k + 1.4551

0.3775s = -1.0203k + 1.4629

0.3645

2

Where s is pedestrian speed (m/s) and k is density (p/m )Table 2: R2 comparison of travel time-density relationships
Case
EquationR2
t = 13.517k + 17.831
0.2988
1. Kavg =   k1k +k +kt = 21.171k + 15.85
0.4021
2. Kavg =1

3. Kavg =4. Kavg =5. Kavg =473
k1 + k 4 + k 6
3
k1 + k 2 + k 3 + k 5 + k 6
5
k1 + k 2 + k 3 + k 5 + k 6 + k 7
6

t = 19.203k + 16.321

0.4092

t = 19.124k + 16.279

0.4208t = 19.961k + 16.074

0.4146

Where t is travel time (second) and k is density (p/m2)

From table, speed-density-travel time relationship of case 5 is selected
because goodness of fitness is 3.76 for speed-density relationship and 0.419
for travel time-density relationship are better among other cases. So, theauthor will use case 5 to determine speed-speed- travel time relationship inflow model.

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6. SUMMARY
We have investigated the pedestrian flow in peak time by video recording.
We have discussed the pedestrian flow which depends on density. The
condensed area of platoon may occur in several points along the walkway.
The variety of density affects the speed and travel time of pedestrian. We
have compared the pedestrian flow in 5 models that depend on the numberof sections for each model. It shown that separation walkway in several
sections can predict the speed and travel time better than one section. The
4th model is selected to simulate the pedestrian flow in the next task, the 4thmodel has highest R2 among other models however the R2 is low value but
the linear trend line is good to predict the speed and travel time and one
reason is variance of data that authors do not classify socioeconomic factor
such as gender. The next task is pedestrian simulation with cellular
automata based on this study.
REFERENCES
Isobe, Motoshige., Adachi, Taku and Nagatani, Takashi.,
2004.
Experiment and simulation of pedestrian counter flow. Physica A 336,
638-650.
Maramatsu, Masakuni., Irie, Tunemasa and Nagatani, Takashi., 1999.
Jamming transition in pedestrian counter flow. Phycisa A 267, 487498.
Nagatani, Takashi., 2001. Dynamical transition and scaling in a mean-fieldmodel of pedestrian flow at a bottleneck. Phycica A 300, 558-566.
Tajima, Yusuke and Nagatani, Takashi., 2001. Scaling behavior of crowd
flow outside a hall. Physica A 292, 545-554.
Helbing, Dirk and Molnár, Péter., 1995. Social force model for pedestrian
dynamics. Phys. Rev. E 51, 4282–4286.
Henderson, L.F., 1971. The Statistics of Crowd Fluids. Nature 229, 381 –383.

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SK. ARIFUL ISLAM
Assistant Engineer
Local Government Engineering Department(LGED), Bhola, Bangladesh
M.Sc. Student
Khulna University of Engineering & Technology(KUET)
Khulna, Bangladesh
MD. MAHMUDUR RAHAMAN
M.Sc. Student
Khulna University of Engineering & Technology(KUET)
Khulna, Bangladesh
MD. SAIFUL ISLAM
Professor
Department of Civil Engineering
Khulna University of Engineering & Technology(KUET)
Khulna, Bangladesh
MD. ABDUR. RAZZAQUE
Deputy Chief Engineer, Khulma University
Ph.D Student
Khulna University of Engineering & Technology
Khulna, Bangladesh
ABSTRACT
Paying attention to link-up the present scenario of sanitation system in
Khulna city, this study was conducted in different wards of Khulna City
Corporation (KCC), Bangladesh. A total of 575 selected spots including
Residentialbuilding,
Commercial
building,
Primary
School,Dormitory/Hostel, Hat/Bazar and Slum area were investigated during thestudy period in the year of 2009-10. It is observed that direct discharge ofdomestic sewage and solid wastes into the surface drain is a common
practice which are creating unwanted environmental problems in therelevant communities and aquatic life, especially the surrounding rivers
namely the Rupsha, the Bhairab and nearest Khal and Ghar. High fecal coli
form contaminations were detected in the sewage which is very harmful forenvironment. About 74% of generated sewage and 57% of solid wastes aredischarge direct to the surface drain by residential, commercial/office,dormitory/hostel, primary school and market/bazar. In slum area about
76% of the generated sewage and 60% of solid wastes are discharged direct
to the drain.

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1. INTRODUCTION
Sanitation is necessary to overcome the effects of man’s activities on his
environment. The increase in population and the movement of population
into urban and metropolitan areas have intensified environmental-control
difficulties in those areas. The provision of safe water; the collection anddisposal of human, domestic and industrial wastes; the prevention of
atmospheric pollution and stream pollution and the control of ionizing
radiation are becoming more difficult from year to year.
A major challenge faced by the developing countries is that of humanwaste management and disposal. Human wastes or excreta are the terms
used to mean human faces and urine, excluding other types of domestic
waste such as household solid waste or garbage. In developing countries
nearly half the urban populations lack adequate waste disposal facilities.
In Bangladesh, about 16% of the 90 million rural population use
sanitary latrines and 22% use the so-called home-made pit latrines. About
61% of the total populations have access to some form of latrine. Of about
30 million dwellers, sanitation coverage is only about 42%. In urban areas
septic tanks, single and double pit pour-flush latrines are used. Conventional
sewerage systems are used only in parts of Dhaka. The sanitary condition ofurban slums is deplorable. Most of the slum dwellers have literally no
latrines [1].
Khulna obtained its status as a formal town after the establishment of
the municipality in 1884 during the British colonial regime. During the late
1950s and 1960s Khulna became an important centre for industrial
development. Many industries such as newsprint mills, shipyard, jute mills,
match factories, jute bailing presses, hardboard mills, etc. were establishedand associated commercial activities also increased manifold. Khulna
attained the status of a City Corporation in 1990. Administratively, it isdivided into 31 Wards. Over the period, Khulna city experienced continuous
population growth accompanied by periodic changes to its territorial area.
Again, due to the establishment of Khulna University, Khulna Medical
College and Khulna University of Engineering & Technology and Teacher’s
Training College, Agricultural Training Institute, etc. along with increased
activities resulting from the expanding shrimp export, Khulna has gained
tremendous potential for further socio-economic activities and physicaldevelopment. All these developments will have a far reaching impact on theoverall environment of Khulna city. Most of the manufacturing industries
are located along the Rupsha River that acts as industrial pollutant sink. At
the same time sewage are also dispose into the rivers because of improper
management causes a direct threat to the urban environment, human healthand ecology.
Khulna is plagued with various challenges that exacerbate many areasof the people’s lives. The city, for instance, has a poor drainage system
resulting in water-logging in the slums. This puts the lives of the poor in
peril. This study was conducted and their adverse to find out the present
sanitation condition and their adverse environmental impact on local
community as well as aquatic environment.

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2. STUDY AREA
Khulna is the third largest city in Bangladesh has a total area of 59.57 km²
out of 4394.46 km² district area. It is located in the south–western part of thecountry between 22047’16” to 22052’north latitude and 89031’36” to89034’35” east latitude. The city is located along the rivers the Rupsha andthe Bhairab. Khulna is the divisional headquarters of Khulna Division and amajor industrial and commercial center. Mongla sea port is located 38 km
outskirts from Khulna City along the Pasur River to the Bay of Bengal. It ispart of the largest delta in the world. In the southern part of the delta lies
the Sundarban, the world's largest mangrove forest.
The population of the city, under the jurisdiction of the City
Corporation, was 855,650 in 2007. The wider Statistical Metropolitan Area
had at the same time an estimated population of 1,388,425. Figure 1 shows
the location of Khulna City in Bangladesh map and its area map.

Figure 1: Location of Khulna City in Bangladesh map and the detailed mapof Study Area.
3. FIELD INVESTIGATIONThe study was conducted to find out the present sanitation status of Khulna
City. For this reason a survey was conducted which would represent sewage
discharge pattern in Khulna City. The survey covered almost 31 wards ofKhulna City.

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4. METHODOLOGY
The data and information have been taken from the field survey and local
authorities. The field survey was conducted by group-wise. They worked inseveral groups each of three members. The study was carried out onquestionnaire survey basis. The samples were selected on random basis
from different aged residential building, commercial building, primaryschool, dormitory/hostel, and bazaar & slum area. A total of 575 survey
spots were interviewed in different Wards of the KCC which are represents
about 70-80% of the study area. Table 1 show the no. of survey spots at
different locations in the study area.
Table 1: No. of survey spots at different locations in the study area.
No. ofNo. ofNo. ofNo. ofNo. ofNo.
Total
Residential Commercial/ Primary Market Dormitoryof
no. ofBuilding
OfficeSchool /Bazar/Hostelslum Survey
Building
spots
247
53
50
16
9
200
575

5. EXISTING CONDITION
About 25 % of the attached drains connected to homesteads are not paved
and 51 % of the drains are paved but open. About 15 % of the houses haveno outlet to drain out waste water. Such practice creates public nuisance anda dirty and unpleasant environment.
Khulna city does not have a sewer network of any kind. Lack ofaffordability forces the large low-income and poorer sections of the people
to use unhygienic latrines which are not only a threat to household healthbut also to the local environment. About 5% of the households have hanging
latrines and 3% of households have no latrines. There are concentrations ofhanging latrines in some areas of the city.
In the past, the storm water of Khulna city had been drained out
through some natural drainage (e.g., creek and canals). Due to rapid
urbanization and increasing development over the years, these naturaldrainage and other water retention areas have gradually been converted into
built-up areas. Some of them are replaced by narrow surface drains. As aresult, some parts of the city are flood regularly during intense rainfall
particularly in the late monsoon. Most of the water logged areas are locatedin Ward No. 30, 10, 14, 27 and 31. In these Wards, percentages ofhouseholds affected by annual water logging are 94 %, 100 %, 93% and
98% respectively. In Khulna city, 38% of households regularly experienceshort-term water logging (e.g., 1 day). Victims of longer term duration water
logging live in Ward no. 31, 21, 20 and in 22 [3]. Figure 2 shows thepresent scenario of different category sanitation system of Khulna City.

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ICUS Report 45(a) Inactive soak well & direct discharge (b) Newly constructed buildingof Sewage into KCC drain
without soak well(c )Human waste discharge direct
to the drain discharge(d) Domestic Sewage
direct to the drain(e) Inactive Soak well due to over flow
during rainy season(f) Open latrine in slum area

Figure 2: Present Scenario of different category sanitation system ofKhulna City

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6. RESULT
6.1 Technical Issues
Figure 3 and 4 shows the present scenario of sock well and sewage
disposal status in the study area. Figure 5 shows present scenario of waste
disposal. From Figure 3 it was seen that commercial/office building had
highest 34% of soak well and residential building had lowest 13% of soak
well. In residential building the percentage of soak well was foundsignificantly low. In most of the cases it was observed that old aged(agemore than 50 years) residential buildings had soak well but now they areinactive due to the existence of high water table in rainy season. Anotherobservation was that newly constructed residential building does not haveany soak well.

S oak well

87%

13%

W ithout s oak well

66%

34%

80%

72%

67%

20%

28%

33%

Figure 3: Present scenario of sock well used
From Figure 4 it was seen that about 88% of generated sewage fromresidential building are directly discharged into the surface drain. From
Figure 5 it was seen that 64% solid wastes from residential building aredirectly discharged into the surface drain, 21% into mud hole and 15%
carried by KCC van. From Table 2 it was seen that only simple pit latrines
are using slum area instead of septic tank. The % of direct discharge ofsewage and solid wastes from slum area are 76% and 60% respectively.

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Figure 4: Present scenario of sewage disposal

Figure 5: Present scenario of waste disposal

Component
Slum

Table 2: Present scenario of slum areas
Sewage disposed in
Solid waste disposed in(%)(%)
Drain Mud River/Low Drain Mud River/Low
hole
land
hole
land
76%
18%
6%
60%
38%
2%

Most of the septic tank users are not interested to provide soak well
due to existence of high water table during rainy season. During theseseasons most of the septic tank effluent over flows from the soak well.

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6.2 Social Issues
Lack of awareness is the main constraint for effective disposal of
sewage and solid wastes. It is found that users have lack of technicalknowledge about the adverse environmental impact of sewage and solid
wastes.
7. SEWAGE EFFLUENT CHARACTERISTICS
Several samples from different location were tested to evaluate the sewage
characteristics.
Table 3: Characteristics of Sewage effluent
Parameter
Unit
Sewage effluent
BOD5
mg/L
54
COD
mg O2/L
3763.2pH
7.0 ~ 8.3
DO
mg/L
6.3
Fecal Coli form
N/100ml
94400
Total Coli form
N/100ml
112800From the Table 3 it is seen that the discharged sewage contain a large
number of total and fecal coli form which mixed with the drain water andultimately pollute river, canal,ghar etc. by the transmission of pathogenic
agent. It also contains a large amount of BOD & COD, which is favorable
for bacteria growth.
8. CONCLUSION
Based on the field investigation Conclusion was drawn:
About 74% of generated sewage and 57% of solid wastes weredischarge direct to the surface drain by residential,commercial/office, dormitory/hostel, primary school andmarket/bazar.
In the slum area about 76% of sewage and 60% of solid wastes
are discharged direct to the drain.
Septic tank users are not interested to provide soak well due toexistence of high water table during rainy season
Lack of appropriate training among the septic tank users also
responsible for improper management.
Local authority may provide some restriction about domestic
sewage and may imposed to the septic tank users.
Low cost small bore sewage system can be provided by the local
authority.
Motivation Campaign for safe disposal of solid wastes andsewage should be ensured within the community through

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awareness raising programmed, group meeting, leaf-lets and othermeans.
Hygiene promotion is essential.
Promotional activities such as restriction of the uses of drain for
any other purposes.
Replacing hanging Latrine over the drain by sanitary latrine in thehousehold should be ensured
ACKNOWLEDGEMENT
The authors express their deep gratitude to Prof. Dr. Muhammed Alamgir,
Environmental lab of Civil Engineering Department, Khulna University ofEngineering & Technology, to provide information and logistic support forthe paper.
REFERENCES
Ahmed, M. F. and Rahman, M. M. (2000). Water Supply & Sanitation:
Rural and Low Income urban Communities. ITN-Bangladesh, Center
for Water Supply and Waste management, ISBN 984-31-0936-8,
BUET, Dhaka, Bangladesh.
Ehlers, V.M. and Steel, E.W. (1965). Municipal and Rural Sanitation.
McGraw-Hill. ISBN 07-0019089-5, USA
Murtaza, G. (2001). Environmental Problems in Khulna City, Bangladesh: a
Spatio-Household Level Study. GBER Vol. 1 No. 2, pp 32-37.

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Sattrawut PONBOON, Kunnawee KANITPONG, Nuttapong BOONTOB,
Pakorn ANIWATTAKULCHAI, and Berlian KUSHARI
Thailand Accident Research Center, Asian Institute of Technology,
Thailandsattrawut@gmail.com
ABSTRACTThe vehicle run-off-road (ROR) accidents are reported as the main
accident type occurred on the national highways. Between 2003 and 2007,
the ROR shared 47% of all accidents and 40% of killed or serious injured(KSI). It is obvious that this accident type is mainly contributed by driver
behaviors, either from the performance errors, i.e. speeding, unsafe
maneuver, or from the recognition errors by drowsy driving. However, theinjury outcomes occurred when the vehicle hit the roadside objects orvehicle rollover. The Thailand Accident Research Center performed theaccident in-depth study through the accident investigation andreconstruction for this type of crash under the supports by the Departmentof Highways and the World Bank. This paper presents the methodology ofscene documentation and accident reconstruction, and the evaluation ofroadside safety features.
barrier

Keywords: accident investigation, roadside hazard, clear zone, pole,

1. INTRODUCTION
Road traffic accidents are inflicting heavy burden on Thai economy as
majority of the victims are from the economically active segments of the
population. Every year road crashes take around 13,000 lives and cost theeconomy a loss of more than 232,000 million Baht (DOH, 2007). In 1998,
supported by the World Bank loan assistance, the Ministry of Transportation
developed a comprehensive road safety master plan. The document clearly
identifies that the lack of knowledge regarding road accidents is one of the
major constrains for road safety improvement in Thailand. The
establishment of Thailand Accident Research Center (TARC) in 2003 wastherefore one of the recommendations and finally an outcome of this masterplan.
The main achievements of TARC during the first three years after its
establishment have been the development of methodology and knowledge
for an in-depth accident investigation and reconstruction. As for theoutcomes, TARC has identified five major types of crashes in Thailand that

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require further in-depth studies in order to determine effective
countermeasures. They include motorcycle accidents, alcohol impaired
drivers, rollover crashes, behavior of young drivers and crashes withroadside hazards.
2. STATEMENT OF PROBLEMS
Apart from the deterioration of the carriageway (e.g. roadway defects,
potholes or surface damages), people might consider run-off road accidents
are mainly contributed to by driver errors which might sound partially true.
However, this fact should not lead to idleness. As inspired by the principlesof Vision Zero based on the Ethical Rules, necessary steps must be
taken to prevent the occurrence of similar events that could kill or seriously
injure people. In this regards, changes in roads and vehicles are known to
influence the drivers behavior, more or less.
The roadside hazard accidents may cause both fatalities and serious
injuries. Many countries face problems with this type of accidents. Figure 1
shows the proportion of fatality due to roadside accidents in different
countries.

Source: ETSC (1998) and Kloeden (1999)

Figure 1: Fatalities from Roadside Accidents
Statistics from the Bureau of Highway Safety show that one of the
major types of traffic accidents in Thailand occurring on the highways under
the jurisdiction of the Department of Highways (DOH) is the roadside crash.
This type of accident occurs as a consequence when a vehicle runs off theroad and ends up with a crash in roadside area. During the years 2001 
2005, 40% of the total traffic accidents on the national highways wereclassified as roadside accidents. In 2005, roadside accidents accounted
7,219 cases or 44%, caused 703 deaths. This figure accounts to about onethird of the total fatalities caused by all types of accidents.
However, one study from the Australian Transport Council states in
the 10 years National Road Safety Strategies that more than 700 lives would
be saved annually by the actions of improving roads, making safer cars, andchanging drivers behavior. Among these actions, improving the safety levelof the road system is considered the most important measure that could

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significantly reduce road trauma and result in saving more than 50% lives
from traffic collisions (Australian Transport Council, 2000).
Based on the international and national statistics shown above, it ishighly recommended by the Department of Highways and the World Bank
that TARC should conduct a research on roadside hazards towards creating
a forgiving highway system. This project is hence a realization of therecommendation.
3. PURPOSE OF STUDY
The main purpose of this study is to analyze roadside hazard accidents in
Thailand, by means of an in-depth analysis. This study also aims at
providing an evaluation on the extent of problems caused by roadsideaccidents and giving recommendations on how to improve the safety levelwith respect to roadside protection design. The specific objectives of thisstudy include;- To determine the accident contributing factors by conducting an
accident investigation and reconstruction- To assess the performance of the current roadside protection systemand provide recommendations in order to achieve a more forgiving
highway system.
4. ACCIDENT INVESTIGATION AND RECONSTRUCTION
The main purpose of the accident investigation and reconstruction is todetermine the causes of an accident and to answer What happened and how
to prevent the recurrence of this type of accident? The investigation
normally started with an inspection of the accident site and the gathering ofevidences. Then, the investigator monitors back the events to figure out theroot cause of the accident.
To investigate an accident, the main information required are thoserelated to vehicles, victims, road & environment as well as some specificinformation. The modified TARC Accident Form is used to facilitate theinformation gathering. The form is also designed as a check list to makesure that no important information is lost or is not collected during the field
investigation.
Most of the crashes investigated in this study were single vehicle
crashes, marked by the vehicles leaving the roadway and hitting roadsideobjects. The in-depth analysis therefore employed the accident
reconstruction process, requiring the theory of physics on work and energy.
It could estimate the speed of the vehicles, and/or their relative positions at
different times during the accident sequences. Information such as directionof travel, skid marks, distances travelled point of impact, impact angles, andRoadside
Roadside Hazards
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vehicle weight were important inputs for the simulation model used toreconstruct the accident. A computerized system was used in both stages.
5. RESULTS OF STUDY
From a total of 17 cases, there were 10 fatality cases, 4 serious injury cases,
and 3 slight injury cases. Regarding to crash consequences or severity level,
61 fatalities, 101 serious injuries, 132 slight injuries, and 1 no occupant
injuries were recorded for all cases. The potential causes of accidents werecategorized into three main crash contributing factors, i.e. human, vehicle,
and road and environment. It is clearly seen human errors played a majorrole in most of the crashes. These include over speeding, drowsy driving, or
unsafe driving maneuvers. Five cases of drowsy driving occurred on the
widened-long road section where the interrupting conflicts were not present.
In addition to human errors, driving on a wet surface was found tocontribute to losing control in single vehicle accidents. Wet surface mainly
reduces the capability of friction force between rotated tires and roadsurface. Two cases affected by hydroplaning occurred when the waterpressure lift up the tires while driving in high speed.
The subsequent sections evaluate roadside features at the accident
scenes, classified by related roadside hazard issues found at the scene.
Principles and examples of strategies that can be implemented to improveroadside safety are also discussed.
5.1 Clear Zone – Need of Traffic Barrier
Provision of a Clear Zone at roadside area is one of the means toprevent such events. The Roadside Design Guide by AASHTO (2002)
defines a clear zone as the total roadside border area, starting at the edge ofthe traveled way, available for safe use by errant vehicles. This area can be
simply interpreted as the unobstructed roadside that allows for vehicle
running off the roadway to stop or to regain control and turn back to theroad safely without having to suffer from harmful events.
Generally, the extent of desirable clear zone is dependent on the
roadway and traffic situations, which include speed, volume, and horizontal
geometry. Sometimes, lateral slope can dictate the requirement for the clear
zone since the roads are often constructed either higher or lower than thesurrounding terrain. This side slope contributed to higher consequences ofaccidents by increases a likelihood of vehicles rollover. Figure 2 shows
example of accidents dissatisfying the clear zone concept and rolled over
into the side slope.

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Figure 2: Example of Vehicles Rollover on Side Slope Accidents
In addition, a collision with a pole or tree is considered as the mostharmful event in fatal crash as shown examples in Figure 3. It is generally
more severe than car-to-car crashes. In car-to-car crashes, vehicle freely
moves on the ground. Besides, it has been design to absorb the impact
energy with typically distributed spread over the wide area. On the other
hand, since pole or tree size is narrow and solidly fixed on the ground, thevehicle therefore absorb all of impact energy from relatively small area. The
occupant injuries received from this type of crash are more severe thanothers. Eleven crashes involving poles and trees were investigated in thisstudy, causing 17 fatalities, 23 serious injuries, and 85 slight injuries.

Figure 3: Example of Vehicles Hit Pole or Tree Accidents
Roadside
Roadside Hazards
HazardsthroughthroughAccident
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For further improvement, the height of filled embankment androadside slope are the main factors to determine barrier need, based on crashanalysis which related to embankment, as shown in Figure 4. The
embankment where the control value lowers than the chart, the barrier might
not be required. This value is based to 1:3 fill section slopes. The traffic
volume is needed to consider as the input value. Low traffic volumes reduce
the possibility of crash, and require less warrant of barrier.

Source: AASHTO (2002)

Figure 4: Comparative Risk Warrants for Embankments
In cases where the obstacles located in the clear zones, three principles
can be considered for improving the roadside safety at locations where poles
and trees cause safety deficiency. These include (1) reducing the hazard of
specific poles and trees in high-crash and high- risk locations, (2) preventing
the placement of specific poles and trees in high-risk locations, and (3)minimizing the likelihood of crashing into a pole or tree when vehicles run
off the road.
5.2 Installation of Traffic Barrier
A longitudinal roadside barrier is used to protect errant vehicle fromhitting roadside obstacles. It can be installed in the areas where the conceptof clear zone is undesirable. Sometime, in the area where it is likely to havemore pedestrian crashes, the barrier can be used to separate the nonmotorized road users from vehicular traffic. As previously mentioned, thewarrant used to install a barrier is mainly based on the consideration if theroadside at particular location is a hazard. The length of barrier is thenneeded to determine the location of installation from the obstacles to protect
the errant vehicle hitting them.

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However, TARC investigated three crashes where the vehicles run off
the road before the beginning of barrier and hitting poles, at the places
where the 20-m longitudinal barriers are equipped (Figure 5). Those crashes
caused 5 fatalities, 17 serious injuries, and 18 slight injuries.

Figure 5: Crash Vehicles left the road before Barriers
In order to solve this problem, a desire length of need need to beproperly calculated. A primary factor to determine the length of protection
barrier is run out length (LR). It is the distance required for a vehicle tostop after left the roadway, measured along the roadway from the upstream
to the point where vehicle started to leave the road (AASHTO, 2002).
Figure 6 illustrates the important parameter to calculate length of barrier.

Source: AASHTO (2002)

Figure 6: Parameters to Determine Barrier Length
Length of need (X) where the flared is applied can be calculated fromthe following equation;
L A + b (L1 ) − L 2
a
(1)
X=
L
⎛
⎞
b + A
a ⎜⎝ L R ⎟⎠
where
X
=
Length of needY=
Lateral offset()()

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LR
LA
LC
L1
L2
b/a

=
=
=
=
=
=

Run out length
Lateral extent of the area of concern
Lateral extent to the outside edge of the clear zone
Length of barrier upstream from the area of concern
Lateral distance from the edge of the traveled way
Flare rate

Since run off length is the function of traffic volume and traffic speed.
The 2005 average annual daily traffic on highway from the Bureau of
Highway Safety, Department of Highways reported 51,552 and 38,642
vehicles per day for inbound and outbound directions on Highway No.1,
respectively, were used. Considering the design speed of 90 km/h, run out
length along this section is 110 m. Assuming the flare rate is zero because of
the limitation of space, therefore a length of need is equal to 93 m. Thisvalue reflects the real situation of three experiences crash. Vehicles left the
roadway 35 m, 81 m, and 170 m, respectively. However, the exiting lengthof the approach concrete barrier and w-beam guardrail are only 20 m.
5.3 End Treatment
End treatment, or end terminal, is the term of the manner to protect theend (normally refers to the beginning) of traffic barriers in order to provide
a safe conditions and reduce harmful to vehicular occupant that may impact
to this area. In the crashes involving vehicles hit the unprotected barrierends result in relative high severity since it acts the same with other strong
fixed object, i.e. post or tree. Good treatment should dissipate the chance ofvehicle redirect in unpredictable movement, i.e. pitch or roll, as same withthe longitudinal traffic barrier.
However, the results from accident investigation represent an
insufficient safety protection in the real situation. The situations become
worst when the end treatments return negatively adverse in fatal crashes.
Lack of providing safe end treatments can increase the level of crashseverities in four crashes as shown examples in Figure 7.

Figure 7: Untreated Barrier Ends
Another issue found on the treated barrier end. It called turned-down
guardrail terminal or sloped concrete end treatment (Figure 8). It caused
vehicles rollover in two crashes, caused 9 fatalities. AASHTO has reviewed

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their performance in 1996 Roadside Design Guide and mentioned in theformer part that (AASHTO, 1996);“As a result of the recent tests, the turned-down end terminal
should no longer be used for new installations on the approachend of guardrail installations on high-speed, high-volume
roadways. Where turned-down guardrails currently existing inthese conditions, they should be upgraded as required by thestate’s policies and practices during rehabilitation projects or asneeded during maintenance operations.”
The approach end of concrete barrier is also recommended to be usedin the locations where the traffic speed is 60 km/h or lower and where thespace is limited.

Figure 8: Sloped Concrete End Treatment
To select the type of end treatments used in Thailand, several factors
are need to be take into account in order to provide most appropriate devices.
The designer should consider all characteristics including speed
environment, cost of installation and maintenance, potential crash frequency,
space available, and some special devices might require backup structure as
well.
5.4 Barriers and Heavy Vehicles
The experiences learned from two major bus crashes were strongly
used to evaluate the efficiency of existing concrete barrier installed along
the curve. From Figure 9, in 2007, the bus crash in Doi Saket, Chiang Mai,
killed 17 passengers and another 35 passengers suffered serious injured.
One year after, the same scenario happened with another crash in
Sangklaburi. In this crash, 13 passengers were killed while 13 passengers
and 23 passengers were serious and slight injured, respectively. The buses
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left the roadside while entering to the curve on the mountainous terrain andthen hit the concrete barrier. They flipped over immediately after collided
on its front left, turned over and stopped behind the barriers. The angle ofbus impact with the concrete barrier in Doi Saket crash was estimated fromthe tire marks found on the pavement. It was about 18.8 to 19.8 and theimpact speed was 103 km/h. It was much lower for Sangklaburi crash. The
debris marks printed on the concrete barrier illustrated small impact angle,
estimated 5 with the impact speed of 70 km/h.

Figure 9: Two Bus Accidents
Regarding to the New Jersey Barrier installed in two-bus crashes,considering the unique design on front shape of the barrier, it intends to lift
up the vehicle in order to reduce the impact energy and recover vehicle low
into the roadway. However, if the lifting energy is exceeded especially forheavy vehicle, the vehicle can probably turnover and fall into the opposite
side. Sometime, for small vehicle, it might cause yaw, pitch, or roll when it
contacts the barrier, and possibly rollover after tires contact the roadbecause of the deformation on suspension (Main Roads, 2007).
To reduce to possibility of heavy vehicle overturning on particularroad section, i.e. mountainous terrain, the existing concrete barrier should be
replaced by the one that either higher design or more flexible. It must be
certified by NCHRP Report 350 for longitudinal barriers test (Ross et al,1993). Test level 5 and test level 6 should be a meaningful for barrier along
the curve where crash statistic on heavy vehicle crashes is significant. Table
1 reviews the list of concrete barrier below obtained from Federal Highway
Administration, U.S. Department of Transport certified by TL-5 and TL-6.

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No.
1
2
3
4
5

Table 1: TL-5 and TL-6 Concrete Barriers
Test Height
Name
Subsection
Level (cm)42 Single Slope
New Jersey Concrete Barrier
TL-5
107
Concrete Barrier
Texas TT Rail
TL-6
228
Type 742 Concrete
Vertical Concrete Parapet with
TL-5
107
Barrier
Aluminum Tube Bridge Rail
Texas Type HT
TL-5
127
New Jersey Barrier with Rail42 F-Shape
TL-5
107
F-Shape Concrete Barrier

Source: FHWA 2005

It is clearly seen that the Type 2 is the highest. It passed Test Level 6
in the crash test conducted by Texas Transportation Institute by a 36,374 kg
tractor trailer tank, running with speed 83 km/h hit the barrier 15 degrees.
The result was shown positively redirected. Experience in US showing thatit used to be installed on a loop ramp in U.S where a high crash history waspresented. It contained all impacting heavy vehicles. However, the reactionenergy is not significantly reduced, causing vehicle rollover inside the curve
instead.
A rigid system together with a flexible system (Type 4) might be agood choice to contain heavy vehicle crash especially along the curve since
the semi-rigid bar is more likely to limit vehicle rollover over the barrier andminimize rebound force for heavy vehicle.
6. CONCLUSION AN RECOMMENDATION
Statistics from the Bureau of Highway Safety indicated roadside accident is
one of the major types of traffic accidents occurring on the national
highways. It happens as a consequence when a vehicle runs off the road andends up with a crash in roadside area. From a total of 118 accident cases
investigated by TARC, 17 accident cases were single vehicle crashing into
roadside objects or roll over. In all of these cases, the errant vehicles ended
up at their respective point of rest after hitting a road side object, such as
barrier, utility pole, trees, and raised median. The 17 cases investigated haveproduced a total of 61 fatalities, 101 seriously injured persons, and 132
slightly injured. The vehicles involved include pick-up trucks, passenger
cars, buses, and trucks. The contributing factors identified include driving
while drowsy, speeding, and unsafe driving maneuvers, mechanical failures,
tire bursts, slippery road surface, steep grade, and hydroplaning effects.
Severity of the impact of the crashes was aggravated by the errant
vehicles hitting roadside objects or road furniture which were located wellwithin the clear zone or unprotected. Overall, the roadside safety issues
identified from on-site investigations can be classified as: side slope, poleand tree, traffic barrier, and curb issues.

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In order to prevent the roadside crash and reduce its severity, the concept of
the strategies should be applied at the design stage. Some specific examples
highlighted throughout the investigation will make this clear.
- Pay particular attention to the embankment height and its side slope
during the design stage, if it exceeds the safety criteria, thenprovision of proper barrier must be specified.
- The importance of Clear Zone concept which has been used in the
US, Australia, and Europe for more than two decades should be
adopted as a basic concept for DOH safety standard andimplemented proactively at the design stage and retrospectively at
post-construction stage.
- It was obvious from the cases investigated that the Clear Zone
requirement was not met. Under this circumstance, a practicalsolution is to install flexible or rigid barrier as appropriate, at thesesites to shield off the hazardous trees and poles.
- The length of need, or length of the barrier required for proper
protection of errant vehicles should be carefully computed using theexpression given in AASHTOs Roadside Design Guide. A typicalcalculation of the length of need required for design speed of 90km/h for AADT greater than 6,000 gives a value of 93 m. This is far
greater than the current practice of DOH which specifies the lengthof 20 m.
- The use of turned-down guardrail terminal and sloped concreteend treatment on high-speed high-volume highways should be
discontinued.
- The concrete barriers currently installed in areas where the risk of
having heavy vehicle accident is high should be upgraded. At thisstage of development in Thailand, a rigid system together withflexible system with the total height of 127.0 cm is recommended.
The height of this barrier is significantly higher than the commonlyused 81.0 cm. New Jersey barrier on Thai national highways.
REFERENCES
AASHTO (1996) Roadside Design Guide, American Association of State
Highway and Transportation Officials, Washington D. C.
AASHTO (2002) Roadside Design Guide, American Association of State
Highway and Transportation Officials, Washington D. C.
Australian Transport Council (2001) The National Road Safety Strategies
2001 – 2010, Australian Transportation Research Bureau.
DOH (2007) The Study of Traffic Accident Cost in Thailand, Department ofHighways, Faculty of Engineering, Prince of Songkla University.
ETSC (1998) Forgiving Roadsides, European Transport Safety Council,
Brussels. Accessed from http://www.etsc.eu/documents/bri_road5.pdf
FHWA (2005) Bridge Rail Guide, Federal Highway Administration,accessed on http://www.fhwa.dot.gov/bridge/bridgerail/index.cfm
Kloeden, C.N., McLean, A.J., Baldock, M.R.J, Cockington, A.T. (1999),
Severe and fatal car crashes due to roadside hazards: a report to the

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Motor Accident Commission / by C. N. Kloeden ... [et al.] NHMRC
Road Accident Research Unit, University of Adelaide, Adelaide,
South Australia.
Main Roads (2007) Assessment of Roadside Hazards, Road and Traffic
Engineering, Western Australia.
Ross, H.E., Sicking, D.L., Zimmer, R.A., and Michie, J.D. (1993) Report
350 Recommended Procedures for the Safety Performance Evaluationof Highway Feature, National Academy Press, Washington D. C.

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ÑÚ ×ÒÍÌßÔÔßÌ×ÑÒ ÑÚ ÌØÛ ÐÑ×ÒÌ
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×Ò ÚÎÛÛÉßÇ
Sanghwa LEE1, Youngtae SON2, Dr. Hyunmyung KIM3, Dahee KIM4
1
Ph.D. Candidate, Myong Ji University, Korea
soboru2@hotmaile.com
2
Professor, Myong Ji University, Korea
3
Assistant Professor, Ph.D.&P.E., Myong Ji University, Korea
4
1Master’s Student, Myong Ji University, Korea
ABSTRACTThe objective of this study is to estimate the optimal installation
distance of traffic detectors. Previously the detectors have been installed inequal distance based on the traffic characteristics (length of congestion andspeed of Travel) in congested area and non congested area. The optimal
installation distance of detector shall be estimated to minimize travel timeerror to provide the exact road information to the drivers and theadministrators of the road.
For the purpose, the error in travel time in the section was derived inrelational formulae of total length of the section, queue length, travel speed
and quantity of detector installation in congested and non congested
sections.
In addition, the 5% converged error in travel time was defined as the
optimal installation section of the detector and the optimal installation
section of detector depending on the speed changes in congested area wassuggested. If the result of this study is applied to the field, the enhancement
on accuracy of the information on the traffic travel is anticipatederror

Keywords: Optimal point for installation, congestion, travel time

1. INTRODUCTION
The FTMS (Freeway Traffic Management System) which was introduced in1991 employed the detector as the main source of the traffic informationcollection. The traffic information collected from detector is traffic flow,
speed and occupancy ratio (density). The traffic data such as the point speed,
section speed and the travel time across the section can be utilized forvarious purposes. They are able to enhance the convenience of the driver via
the real time provision of the travel condition in freeway, unexpected
situation and the operation status of the road track. In addition, the traffic

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data might be used for traffic control and operation such as the exclusive
track for bus, toll gate operation and variable track. However, theconventional point detectors are generally installed on the freeway with thesame distance of 1 km. Therefore, it is difficult to get the information on thestatus happened in between the installation points. Besides that, considering
the geometric structure, detectors installed with the same distance are hard
to collect high quality traffic information. In order to achieve detail traffic
flow characteristics, detector installation distance should be decided withconsideration of traffic congestion. In addition, the detectors installed withthe same gap might be cost-inefficient when traffic flow is low.
The aim of this study is to find the optimal installation distance ofdetectors in consideration of the traffic characteristics in congested area andnon congested area to enhance the accuracy of the data collection of point
detector in a cost-efficient way.
2. ANALYSIS ON THE CHARACTERISTICS OF THE COLLECTEDDATA IN POINT DETECTOR
2.1 Selection of the target section
The target section for traffic data collection is generally a location inwhich there is a significant traffic speed change due to the mixture of thecongested and non-congested traffics. Moreover, it shall be the location to
identify the accuracy of the information provision on each track where theoperation methods on each track are different. In this context, Yangjae IC ~Osan IC section (37.44km) in the Kyungbu freeway was selected.

Figure 1: Status of the target section location

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2.2 Status of detector installation in target section
According to a field investigation, previously inductive loop detectors
had been used as a conventional point detector, but recently image-based
detectors have been increasingly deployed in the field. The reason is to
avoid the re-installation of the detector and to improve the convenience of
maintenance when there are the geometrical changes due to the Pangyo IC
renovation for the installation of variable lanes. 23 image detectors and total
46 image detectors area installed between Yangjae IC and Osan IC. In thesection, traffic information is collected and analyzed for installing image
detectors. For each direction, the detectors will be installed on the four-laneroad for each lane. The detectors installed at 23 points over the total 37.44
km length, so on average the gap between detectors is 1.62 km. It exceeds 1
km which is the conventional criterion of installation distance, but it seemed
to be affected by the non-installation of detector in between Seoul Business
Office ~ Pangyo JC where the geometrical structure improvement is being
under construction.
2.3 Type and time of the data collection
The data collected from the target section for analysis are classified
mainly according to the travel time survey data and detector data. The
measured travel time survey data are classified into operating vehicle survey
data which is the general vehicle travel time survey data and bus riding
survey data which is the bus travel survey data. The detector data areclassified into the data used the point detector.
Table 1: Type and time of the data collection
Classification Data description
Data collection
methodReallyData for travel
Survey on themeasured datatime and speed
operating vehicle
survey datafor bus andSurvey on thegeneral vehicle
bus riding

Point detector
data

FTMS datafrom Korea
Expressway
Corporation

FTMS processeddata (5minutes,
15minutes)

Scope of datacollection
Spatial range :Banpo IC ~ Osan
IC
Time range : 7th,
9th and 12th ofOct, 2009(6 AM~
10 PM)
Spatial range :
Banpo
IC
~
Cheonan ICTime range: 7th,
9th and 12th ofOct, 2009(0 AM~
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They were analyzed in timely and spatially unification because the datafrom the survey travel time (general vehicle and bus) and point detector
show the difference in time and spatial differences.
2.4 Result of data analysis for point detector
2.4.1 Result of analysis on non peak time and peak time
The data of travel time in peak time and non peak time were compared andanalyzed to find the pattern of the collected data from the measured travel
time collected data. Figure 2 shows the cumulated travel time on eachsection in non-peak time based on the travel time in each section of themeasured vehicle driving to Seoul on Kyungbu freeway starting from Osan
IC at 9:20AM on 7th of October, 2009. It is considered that the point
detector provides the reliable information in non-peak time because thedifference in the travel in each section did not show big difference in travel
time as it is shown in Fig. 2. Fig 3 shows the measured (unified) and point
detector (unified) cumulative travel time in each section based on the travel
time in each section of measured vehicle driving to Seoul on Kyungbu
freeway starting from Osan IC at 5:20 PM on 7th of October, 2009.
The measured data (unified) shows higher value comparing to thepoint detector (unified) as it is shown in Figure 3. It was identified that thedifferences after Shingal JCT was distinctive. It means the confirmation of
the big error from the detector data as the congestion gets severe. It meansthat the application of same installation section in the non-congested area
and congested area affect the accuracy.

Figure 2: Result from non peak time analysis

Figure 3: Result from peak time analysis

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2.4.2 Result of analysis on travel time error rate (%) in each section
In this section, two types of data are compared in order to analyze thetravel time estimation error over road sections. The first data type is theunified data between actual travel time data and the estimated data frompoint detectors. The second data type is field data which are segregated byeach lane. The aims of this analysis are two; 1) To find the accuracy ofdetecting method with respect to detecting time (i.e., peak time vs. non-peaktime), 2) To investigate the accuracy of detecting method with respect toroad lanes and sections. For the purpose, the error in estimated travel time
are calculated with comparison of actual surveyed travel time. In thecomparison, all travel time averages on the both directions of studied roadsection over all surveillance time are used. The actual travel time data foundby probe vehicles for bus were compared with point detector in the 1st lane
and point detector (unified) in classification of each track to identify theaccuracy of the collected data. The actual travel time data were comparedwith the point detector in other track and point detector (unified). The result
revealed that the calculation of the travel time in classification of the track
showed smaller errors.

Figure 4: Analysis on travel time error rate in bus track

Figure 5: Analysis on travel time error in other track
The summary of the analysis result for the collected data from point
detector is as followings.
- The analysis result of non peak time did not show significant errors
in travel time from the collected data, however, the actual travel time data
and the point detector data showed the big error in peak time.
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July 2010, Thailand- The comparison of the travel time in each section showed that theerror can be reduced greatly comparing to the unified data when the data
which are provided in unification currently are processed in classification
into the data of the 1st line (bus) and other line.
Consequently, this study is to suggest the optimal section of detector
installation to reduce the error in travel time for point detector and to
propose the installation of the detector on each track in application in the
future.
3. CALCULATION OF THE OPTIMAL SECTION FOR DETECTORINSTALLATION
3.1 Derivation of the relational formulae using the travel time error andtraffic characteristics
The accuracy of the traffic information collection differs from whether
the detector is installed to reflect the traffic characteristics of the road.
Travel time error in traffic information in case of traffic situation changed
because the conventional detector installation section is classified dependingon the geometric structure characteristics. In this context, the aim of thisstudy is to develop a methodilogy for deciding the optimal gap amongdetectors with consideration of the characteristics of traffic flows in thecongested and uncongested conditions.
3.1.1 Examining the relation between the travel time error and queue
length when 1 detector is installed
Figure 6 is the definition on the necessary factor for calculation of thepoint detector installation section.

Figure 6: Definition on the necessary factors for installation on 1 detector
The derivation of the travel time in congested area (TQ) and travel
time (TD) in non congested area using basic factors suggested in Figure 6 asabove.
Travel time in congested area (TQ) : TQ =

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Travel time in non congested area (TD) : TD =

S −Q
V1

(2)

Travel time error with a single detector is the subtraction of travel
time of the non-congested section from the travel time in the congested area.
It can be defined as follows.
travel time error = travel time in congested area (TQ) - travel time in noncongested area (TD)(3)
The travel time in the non-congested area can be derived as following
formula when the queue length (Q) is reached to detector installation section(Q < S ) .
2
TD = S
V1(4)

Travel time error in this moment is as follows.
TQ − TD =V 2 − V1
( −Q )V1V 2(5)Travel time in non congested section is as follows when the queue length(Q) gets longer than the detector installation section ( Q ≥ S ).
2
TD = S
V2(6)

travel time error in this moment is as follows.
TQ − TD =V 2 − V1
(S − Q)
V1V 2(7)

3.1.2 Examining the relation between the travel time error and queue
length when 2 detectors are installed
The essential factors as shown in Fig. 7 are designed for finding therelation of travel time error and length of congested section when 2
detectors are installed.

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Figure 7: Definition on the necessary factor for installation of 2 detectors
Travel time error of congested section (TQ) and non congested section(TD) is the same in the previous case of 3.1.1. The relational formuladepending on the queue length and detector installation section can bediffered depending on whether the queue length reached to the detector
position.
Travel time in non congested area can be derived as following formula
when the queue length (Q) is reached to the first detector (Q < S ).
3
TD = S
V1(8)

Travel time error in this moment is as follows.
TQ − TD =V 2 − V1
( −Q )V1V 2(9)Travel time over the non-congested section when the queue length (Q)exists in between the 1st detector and the 2nd detector installation section( S ≤ Q < 2 S ) is calculate as Eq. (10).
33TD = S + S
2V1 2V 2(10)

Travel time error in this moment is as follows.
TQ − TD =V 2 − V1 S
( − Q)
V1V 2 2

(11)
Travel time in non congested section is as follows when the queue length(Q) exists in between the 2nd detector installation section ( Q ≥ 2 S ).
3

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TD = S
V2(12)

Travel time in this moment is as follows.
TQ − TD =V 2 − V1
(S − Q)
V1V 2

(13)

3.1.3 Establishment of the relational formula with travel time error andqueue length depending on the quantity of detector installation
With the change of the number of detectors, the relationship between
the error in travel time estimation and traffic flow charicteristics undervarious traffic conditions is found.
This was generalized as the Figure 8.

Figure 8: The generalization of installation section during detection
considering the queue length
3.2 Examining on the travel time error rate change depending on the
installation quantity of point detector
The graph for the error in travel time estimation is drawn with respect
to the number of detectors when traffic flow are under the realistic condition
with the mixture of congested and uncongested conditions. The conditions
for travel time error graph were defined as follows.
- Total length (S) : 10km/h- Travel speed in non congested section (V1) : 100km/h- Travel speed in congested section (V2) : 50km/h- Queue length (Q) : 50m ~ 9,950m
The result calculated from the travel time error depending on the
installation quantity of point detector based on this showed the graph patternin vertical symmetry on X axis as in Figure 9. It shows the error increases
with traffic queu length and decreases with the number of detectors.
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Figure 9: The changes in travel time error depending on the installation
quantity of point detector
3.3 Calculation of the maximum installation section of point detector
The error of travel time was investigated through changing the speed
and queue length in the congested and non-congested section in order toderive the maximum installation section of point detector. With increasing
the number of detectors, a threshold point at which the error in travel time
estimation becomes insensitive to the increase of detector numbers can befound. The travel time error rate was calculated by increasing the total
length in 5km, queue length in 50m to identify this.
Table 2: The pre-conditions to calculate the travel time error ratedepending on the speed change in congested area
Basic pre conditions
Condition for change
Total length (S) : 5km
Speed in non congested section :100km/h ~ 80km/h
Queue length (Q) : 50m ~Speed in congested section :4,950m
10km/h ~ 90km/h
Detector installation section :100m
The calculation of the error rate in travel time showed the reduction in
detector installation quantity as the speed in congested area increases. It was
known that travel time error rate converges to 0 when the quantity ofdetector is more than a certain quantity.
The identification of the installation quantity within travel time error
of 5% revealed the 16 detector installations when the speed in congested
area is 10km/h and 1 detector installation when the speed in congested area
is 90km/h.

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Figure 10: The quantity of detector installation and change of travel timeerror rate depending on the speed change in congested area
The following Table 3 was derived for maximum installation section
through detector installation quantity.
Table 3: The maximum installation section of detector depending on the
speed change in congested area
Speed in congest
Quantity of
Maximum
area (km/h)
maximum detector
installation section
10
16
0.31
20
11
0.45
30
9
0.55
40
7
0.71
50
5
1
60
4
1.25
70
3
1.67
80
2
2.5
90
1
5
3.4 The optimal detector installation section depending on the speed
change in congested area
Based on the above finding, the optimal gap for detector installation is
proposed. The optimal gap accounts for a traffic condition and theoperational aspect of highway. The criteria for freeway traffic condition areclassified into to 3 categories as very slow (under 30km/h), moving slowly(30km/h ~ 70km/h) and moving well (beyond 70km/h).
The optimal installation gap of freeway detector been suggested as thefollowing Table 4 depending on the basic speed. The detector shall be
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installed in consideration of the surrounded conditions when the tunnel,
bridge and accident prone areas are included in the main line.
Table 4: The optimal detector installation section depending on the speed
change in congested area
Classification
Speed inMaximum
OptimalVery Slow
MovingSlowlyMoving Wellcongested area
(km/h)

installation
section of

10
20
30
40
50
60
70
80
90

0.31
0.45
0.55
0.71
1
1.25
1.67
2.5
5

installation
section (km)0.31 ~0.55
0.71 ~ 1.25
1.67 ~ 5

4. CONCLUSION AND FUTURE SUBJECT OF STUDYThis study calculated the optimal installation section of detector inconsideration of the traffic characteristics of the congested area to amend
the travel time error information due to the detector installation section
considered the characteristics of geometric structure.
As a result, the application of the detector installation sections was derived
in section of 0.31 ~ 0.55km in case of Very Slow, 0.71 ~ 1.25km in case ofMoving Slowly 1.67 ~ 5km for Moving Well. The methodology developed
in in this study will be employed for using various types of data collected by
Korea Expressway Corporation. It is also expected that the provision of
more reliable travel information will be possible with minimization of theerror in the travel time estimation.
However, in the developed methodology, it is impossible to consider
the detail levels of traffic congestion. In addition, it is assumed that themain source of the error in travel time estimation is the deployment ofdetectors in this study. However, the estimation algorithm embeded in thedetectors also can be another error source. Therefore, an advanced method
which is alble to account for the error should be developed in the future
study.
REFERENCES
Korea Research Institute For Human Settlements (2005), vFinal report on
the calculation and performance evaluation (II) on the travel
information in each section for detectorw

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Road Traffic Authority (2004), vA study on the reliability certification andimprovement method on each collection system of traffic informationw
Chung Hun Yang. Young Tae Sohn, vA study on the estimation on the
optimal installation point of detector at the merging point of freewaysw,
Journal of thesis from Korea Road Association, the 1st edition in the10th volume pp. 221~227
So Young Yoo, Jung Hyun Noh, Dong Ju Park (2004), vDecision on the
optimal data collection section for loop detector data for estimationand expectation of travel timew, Academic Journal of Korea
Transportation Academy Association, the 6th edition in 22nd volume,pp. 109~119
Korea Expressway Corporation (2006), vA study on the effective collection
and maintenance and management system of vehicle detector data.

A Study on the Optimal Selection of Installation of the Point Detector Considered the Characteristics in theAstudy on the
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3rdÜÛÊÛÔÑÐÓÛÒÌ ÑÚ ÝØÛÓ×ÝßÔÔÇ
ÐÎÛÍÌÎÛÍÍÛÜ ÚÛÎÎÑÝÛÓÛÒÌ
AKIRA KODAKA1 and RAKTIPONG SAHAMITMONGKOL2
1
Regional Network Office for Urban Safety (RNUS)
Asian Institute of Technology, Thailand
st107173@ait.ac.th2

Researcher, Construction and Maintenance Technology Research Center
Sirindhorn International Institute of Technology (SIIT)
Thammasat University, Thailand
sahamit@siit.tu.ac.th

ABSTRACTA theoretical and experimental study was conducted to observe
effectiveness of chemically prestressed ferrocement (CPFC) which combines
chemical prestress (CP) and ferrocement (FC). CP is produced in concrete
or mortar by proper amount of expansive additive and restraints.
Chemically prestressed concrete or mortar has ductility and high potential
to control crack width and number of cracks. FC, which has better crackarresting mechanism with smaller crack width and spacing, consists of wire
mesh and/or skeletal bars which should act as restraints against chemical
expansion. Therefore, FC has a high possibility to be developed by CP on
crack resistance especially higher first cracking strength, and lessdeflection.
Based on the concept, tests namely, direct tensile test of ferrocement
and flexural test of strengthened beams, were conducted with four varying
parameters, (i) type of wire mesh, (ii) number of mesh layers, (iii) amount ofexpansive additive, and (iv) age. CPFC was applied as strengthening
material attaching bottom surface of reinforced concrete beam by installing
U-shaped mechanical shear connectors on flexural test. One control beamand seven strengthened beams were tested under two monotonicallyincreasing load at twenty-eight days after installing ferrocement.
CPFC increased first cracking strength significantly compared to thatof FC with less deflection. Theoretical value of ultimate tensile strength had
good agreement with experimental results and showed that CPFC did notincrease the ultimate tensile strength. CPFC strengthened RC beams
increased ultimate moment capacity compared to that of control beam andthose strengthened with FC. Furthermore, more mesh layers increases
strength and stiffness of the CPFC and showed higher first cracking
strength for both types of wire mesh.

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1. INTRODUCTION
Reinforced concrete (RC) has been widely used as one of major materials
which comprise social infrastructures. However, in-situ RC structures havebeen deteriorated by various factors such as corrosion of steel, chemical
attack, etc. In addition, in terms of economic and social situation, thebearing capacity of RC structures has been required to be enlarged to meet
updated code of practice. Therefore, necessity of maintenance including
strengthening of existing RC structures has been widely actualized.
Here, ferrocement (FC) is a one of the useful materials to strengthen
and repair existing RC structures. FC has better mechanical properties anddurability with its homogeneous orthotropic elastic properties. Infiltrated
wire meshes and skeletal bar with mortar are distributed uniformly results inbetter crack arresting mechanism with smaller crack width and spacing[1, 2].
On the other hand, normal concrete has inherent drawback, shrinkage,
during hardening due to drying. To solve the problem, expansive additive isused to expand the member during hardening with proper restraints, i.e.
reinforcements. Compressive stress named chemical prestress (CP) can be
induced to the member by the restrained expansion. Chemically prestressed
member has comparatively ductile and high potential to control crack widthand number of cracks [3].
This study aims to improve performance of FC by introducing CP, inother words, to develop chemically prestressed ferrocement (CPFC).
Flexural behavior of RC beam strengthened by FC and CPFC on its bottom
surface is investigated. Tensile properties of FC and CPFC are also
compared. In order to observe CPFC performances, ordinary RC beam wascompared to beam strengthened with FC and CPFC in terms of ultimate
moment capacity, deformation and crack formation.
2. EXPERIMENTAL PROGRAM
2.1 Direct Tensile Test
The test was conducted to observe tensile properties of FC and CPFC,
such as ultimate tensile capacity, first cracking strength against
monotonically increasing load. To investigate the effect of CP in different
conditions, fifteen types of specimens were designed by varying fourparameters, (i) type of wire mesh: welded and hexagonal wire mesh (ii)number of mesh layers: two and four (iii) amount of expansive additive: fiveand ten percent replacement of expansive additive to total binder in weight,and (iv) age: three, seven, and twenty-eight days.

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2.1.1 Details of the specimens
Details of the specimens were summarized in Table 1. In the Table 1, thefirst letter "T" of each specimen denotes "Tensile". The second letter
denotes the amount of expansive additive in percentage of total binder
weight. The third letter denotes the type of wire mesh: "W" for welded wire
mesh and "H" for hexagonal wire mesh, followed by number of layers as theforth letter.
Table 1: List of specimen in direct tensile test
NumberSeries
EA
Type
Age
of
No.
(%)
of mesh(days)
layers
T0W4
0
4
T5W2
5
2
Welded3, 7, 28
T5W4
5
4
T10W4
10
4
T0H4
0
4
T5H2
5
2
Hexagonal
28
T5H4
5
4

Figure 1: Details of direct tensile test specimen
Specimens with thickness of 25mm, width of 75mm, and length of
500mm as shown in Figure 1 were prepared. Linear Variable Displacement
Transducer (LVDT) was set to measure elongation of a central portion with
150mm length. The specimen was designed to be cracked at the end of thiscentral portion by installing two grooves with approximately 1.5mm depth
at each end of the section. All specimens were demolded twenty-four hours
after casting and covered by wet sacks until loading. Temperature controlled
room with twenty-eight degrees Celsius with ninety-five percent of
humidity was used for curing of the specimens.
2.1.2 Materials Properties
EXPAN K, CaO2 based powder, manufactured by Taiheiyo Materials
Corporation was used as expansive additive. Normal weight mortar withcube compressive strength of 60N/mm2 at 28 days was used. Ordinary

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Portland cement and natural river sand passing sieve No.30 were mixed in
the proportion of 1.0:1.85 by weight with water/cement ratio of 0.5. Six
millimeter hot rolled mild steel with strength of 430 N/mm2 was used as
skeletal bar. Galvanized welded and hexagonal wire mesh with Birmingham
Wire Gauge (B.W.G.) 19 and 21 with their ultimate strength of 725
N/mm2and 410 N/mm2 respectively were used.
2.1.3 Initial Expansion Measurement
Initial expansion of the specimens was measured by Demountable
Mechanical (DEMEC) strain gauge. The measurement was conducted
continually until loading. Initial CP can be obtained by eq. (1) assuming
strain of mortar m delivered from measurement equals to that of wire meshw and skeletal bar sk.
CP =

εm

Am

( E sk Ask + E wηV r Ac )(1)

Where Am, Ask and Ac denote cross-sectional area of mortar, skeletalbar, and composite (mm2), respectively. Esk and Ew is elastic modulus ofskeletal bar and wire mesh (N/mm2) respectively. Here, is effective factorof wire mesh which is depended on geometrical properties of the mesh typeand its orientation to loading direction. Vr is the volume fraction of wire
mesh to composite section.
2.2 Flexural Test of Strengthened RC Beams
The test was carried out to investigate efficiency of flexuralstrengthening by FC and CPFC, in terms of first cracking load, mid-span
deflection, and ultimate moment capacity. There were one control beam andseven types of strengthened beams with same parameters as direct tensile
test except age which was only at twenty-eight days. Two monotonicallyincreasing load were symmetrically applied with shear span of 725mm.
2.2.1 Details of the Specimens
Details of the specimens were summarized in Table 2 and shown in
Figure 2. In Table 2, the first letter "F" of each specimen denotes "Flexure" other letters are the same as that of direct tensile test (Table 1). Eightrectangular RC beams with 2400mm in length and 150 × 250mm crosssection were fabricated initially. Sixteen millimeter diameter deformed bar
was put as longitudinal reinforcements. Nine millimeter diameter hot rolled
mild steel was used as stirrups with 125 mm spacing. Seven beams were
strengthened by FC or CPFC at the bottom surface with their thickness of
25mm. To achieve the composite behavior between RC beams and FC or
CPFC layer, mechanical U-shaped shear connectors made from six
millimeter diameter hot rolled mild steel, were installed with 185 mm
spacing.

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2.2.2 Materials Properties
Normal weight concrete designed for 28 days cylindrical compressive
strength of 30N/mm2 was used. Ordinary Portland cement, natural riversand passing sieve No.4 and crushed limestone with its maximum size of
10mm were mixed in the proportion of 1.0 : 2.0 : 2.83 by weight with water
cement ratio of 0.55. Sixteen millimeter deformed steel with strength of 600
N/mm2 was used as longitudinal reinforcements. Nine millimeter hot rolled
mild steel with strength of 430 N/mm2
was used as stirrups. Materials for ferrocement production were the same asthat of direct tensile test. Hot rolled mild steel with same properties ofskeletal bar was used as U-shaped shear connector. Two parts type epoxy
adhesive named Sikadur-30 manufactured by Sika Ltd. was used to fix theconnectors.
Table 2: Summary of flexural test specimens
NumberSeries
EA
Type
of
No.
(%)
of meshlayersControl
F0W4
0
4
F5W2
5
2
Welded
F5W4
5
4
F10W4
10
4
F0H4
0
4
F5H2
5
Hexagonal
2
F5H4
5
4

Figure 2: Details of flexural test specimen
2.2.3 Strengthening of RC beam
Holes with diameter of 6.5mm at required spacing were drilled in thebottom surface to a depth of 70mm by electrical hand drill. Subsequently,
the surface was roughened uniformly through the surface by the drill andcleaned out by air-jet machine to remove dirt and chips of concrete. Skeletalbars and wire meshes were placed onto the bottom surface. Then,mechanical U-shaped shear connectors were inserted to RC beam andsecured therein with epoxy resin adhesive. After installing the connector,
specimens were left one day to ensure the hardening of epoxy resin adhesive
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and then mortar was cast with a mold. One day after plastering, thespecimens were demolded and covered by wet sacks for twenty-eight days.
Since the specimens were kept outside, water was sprayed three times per
day to maintain the wet condition.
3. RESULTS AND DISCUSSION
In this section, remarkable result from direct tensile test and flexural test is
discussed individually by experimental and theoretical approaches. Material
properties obtained from material tests such as yield strength ofreinforcements, compressive strength of concrete and mortar were used intheoretical approach.
3.1. Initial Chemical PrestressInitial expansion of the tensile specimens at different ages is shown in
Figure 3 which were measured by DEMEC strain gage. In Figure 3, it shallbe noted that the amount of initial expansion on four layers in both weldedand hexagonal wire meshes resulted in smaller than that of two layers
among specimens with five percent of expansive additive. This is becausethat the more number of wire meshes, the more restraint against chemical
expansion -.CP was efficiently introduced into matrix. Figure 4 shows initial
CP induced by the expansion calculated by eq. (1). It is clear that the more
expansion, the more initial CP in a linear manner.

Figure 3: Initial expansion at different age

Figure 4: Initial chemical prestress induced by expansion

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3.2. Ultimate Tensile Strength (Tu)
Theoretical and experimental ultimate tensile capacity of a specimen(Tu) was summarized in Table 3. Theoretical values were calculated by eq.
(3) assuming that skeletal bar yields and wire mesh broke at the ultimate
phase,
N

Tu = ∑ Fi 2 f tiVi Ac + f sky Ask

(2)

i =1

where Fi refer to the direction cosine of ith wire mesh to the loading
direction. fti, Vi , and fsky denote respectively the ultimate strength of ith wire
mesh (N/mm2), with its volume fraction and yield strength of skeletal bar(N/mm2). Ac and Ask denote cross-sectional area of composite and skeletalbar (mm2) respectively. Theoretical values and experimental values havegood agreement through all specimens. Tensile capacity of FC and CPFC
were almost same. Therefore, even chemical prestress installed into
ferrocement effectively and express better tension stiffening, tensile
capacity is governed by mechanical properties of reinforcements.

Series
No.
T0W4
T5W2
T5W4
T10W4
T0H4
T5H2
T5H4

Table 3: Theoretical and experimental ultimate tensile capacity
3days
7days
28daysThe. Exp.
(N/mm2)

7.87
6.53
8.06
8.24
-

7.36
6.45
7.82
7.95
-

Diff.
(%)

6.5
1.2
3.0
3.5
-

The. Exp.
(N/mm2)

8.18
6.56
8.11
7.63
-

8.04
6.58
7.84
7.36
-

Diff.
(%)

1.7
0.3
3.3
3.5
-

The. Exp.
(N/mm2)

8.26
6.86
8.24
8.18
6.78
5.92
6.50

7.93
6.91
8.10
8.11
6.50
6.16
6.29

Diff.
(%)

4.0
0.7
1.7
0.9
4.1
4.1
3.2

Figure 5: Load - Average strain curve with welded wire mesh

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Figure 6: Composite stress - Average strain curve with welded wire mesh3.3. Additional Cracking Stress
Figure 5 shows Load - Average strain curve of the specimens withwelded wire mesh at twenty-eight days. For reference, a curve of skeletalbar is also plotted in the figure. Figure 6 is the result converted toComposite Stress - Average strain curve from Figure 5 subtracting theload which skeletal bar carries from total applied load and dividing by areaof mortar and wire mesh so that tension stiffening effect can be seen clearly.
First cracking strength of CPFC (series of T5W4 and T10W4) was larger
than FC (series of T0W4) with same type and number of layers. Here, theincrement of first cracking strength is called additional cracking strength.
Theoretically, additional cracking strength should be the same as the amountof initial CP which is calculated by eq. (1).
Additional cracking strength and initial CP of each specimen wascompared in Figure 7. In the figure, specimens with four layers of wire
mesh, T5W4, at each age marked approximately two times greater
additional cracking strength than initial CP. On the other hand, additional
cracking strength of T10W4 and T5H4 resulted in the almost same amountof additional cracking strength and initial CP. It might be said that initial CP
or initial prestrain was declined due to insufficient bond strength between
skeletal bar and mortar matrix against expansion on T10W4, and due todeformation of hexagonal wire mesh on T5H4. On the other hand, bothwelded wire mesh and hexagonal wire mesh with two mesh layers, T5W2
and T5H2, showed smaller additional cracking strength to initial CP. In
other words, experimental results of the stress increment were smaller thanthat of calculated expectation even the specimens had almost same amountof chemical prestress as the specimens with four layers. Specimens withfour layers of wire mesh possibly have better restraining effect against
chemical expansion and create initial CP effectively than that of two layers
which may have loss of initial CP.

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Figure 7: Relationship between initial CP and Additional cracking strength
3.4. Failure Modes Under Flexure
All specimens were failed by flexural (yielding of rebar followed bycrushing of concrete) failure except the F5W4 which failed by debonding of
ferrocement. At flexural failure, major crack went through ferrocement into
concrete continuously (see Photo 1), while at debonding, major crackdeveloped from interface between FC and concrete (see Photo 2).
Debonding failure decrease ultimate moment capacity of the specimen as
described in following subsection 3.7.

Photo 1: Flexural failure on F5H4

Photo 2: Debonding failure on F5W4

3.5. Load-Deflection Curve at Mid-Span
Figure 8 and Figure 9 show the mid-span load-deflection curve ofspecimens with welded and hexagonal wire mesh respectively. All
strengthened beams except F0H4 showed decrement of applied load when
the specimen became close to failure. This behavior was due to debonding
between RC beam and ferrocement- effective depth of the beam wasdecreased to that of control beam. The debonding occurred because shear

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force which acts on the interface increased by large deflection and exceeded
the bond strength of ferrocement.
Strengthened beam with CPFC showed higher stiffness than the beamwith NF even F5W2 and F5H2 to F0W4 and F0H4 respectively.
Reinforcements, wire mesh and skeletal bar, were initially stretched withchemical pre-strain. Thus, when the same load is applied to CPFC an FC,
tensile strain of CPFC becomes smaller than that of FC by the amount ofchemical pre-strain.

Figure 8: Mid-span deflection with welded wire mesh

Figure 9: Mid-span deflection with hexagonal wire mesh3.6. First Cracking Moment
In terms of deflection at first crack, unlike the control beam whose
stiffness was changed clearly at the load of approximately 20 kN, stiffnessof strengthened specimens changed non-linearly (Figure 8 and 9). Well
distributed wire mesh which relatively close to the tension fiber carried theload which transferred from mortar when crack occurred. Therefore, first
cracking moment was measured by visual observation (Table 4). CPFC
showed better crack resistance to FC due to deformation capability of CPFC.
CPFC deformed without cracking and the load was carried by tensile
reinforcements that resulted in higher cracking moment.

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3.7. Ultimate Moment Capacity
Ultimate moment capacity of specimens is shown in Table 4. It was
obtained on welded wire mesh that the more CP the larger ultimate moment
capacity. However, due to debonding, F5W4 did not increase the capacity
unlike direct tensile test. On the other hand, hexagonal wire mesh, F0W4
showed better ultimate moment capacity compared with F5W4. It can besaid that when CP is installed to ferrocement effectively, it will increase
ultimate moment capacity of the strengthened beam with better bonding
property to reinforcement including shear connectors, which will provide
better composite action against applied load.
Table 4: First cracking moment and ultimate moment capacity of eachspecimen
First
Ultimatecraking
Increment
momentIncrement
Specimen
moment(%)
capacity(%)(kN·m)(kN·m)
Control
7.11
39.11
F0W4
7.24
0.31
48.24
13.94
F5W2
8.63
3.52
47.67
9.99
F5W4
8.59
3.43
54.33
18.44
F10W4
12.10
11.53
55.7
28.52
F0H4
6.96
-0.33
49.55
26.88
F5H2
7.17
0.15
47.49
15.09
F5H4
8.77
3.84
54.10
24.83
4. CONCLUSION
CPFC with any type of wire mesh, (hexagonal or welded) has
higher stiffness and crack resistance to FC in direct tension andflexural tests.
CPFC increased first cracking strength of direct tensile specimens
although CP did not increase that of ultimate tensile capacity ofstrengthened beams regardless the amount of CP. Theoretical
approach had good agreement with ultimate tensile capacity ofexperiment.
In terms of efficiency with welded wire mesh, five percent
replacement of expansive additive to binder weight performed
better than that of ten percent on crack resistance under direct
tensile condition.
On flexural test, the more CP the higher ultimate moment capacity
was observed when RC beam and ferrocement have fully
composite action- debonding of ferrocement resulted in low
ultimate moment capacity.

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REFERENCES
B.K.Paul and R.P.Pama. FERROCEMENT. International Ferrocement
Information Center, 1995.
M.Jamal Shannag and Tareq Bin Ziyyad. Flexural response of ferrocementwith fibrous cementitions matrices. Construction and Building
Materials. Vol. 21, pp. 1198-1125, 2007.
Raktipong Sahamitmongkol. Microscopic Mechanism and Influencing
Factors on Cracking Resistance of Chemically Prestressed Concrete
Member. Ph.D. dissertation, University of Tokyo, 2005

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3rdÛÈÐÛÎ×ÓÛÒÌßÔ ÍÌËÜÇ ÑÚÍÛ×ÍÓ×Ý ÞÛØßÊ×ÑÎ ÑÚ ÍÝßÔÛÜ
ÒÑÒóÛÒÙ×ÒÛÛÎÛÜ ÓßÍÑÒÎÇ
ÍÌÎËÝÌËÎÛÍ ÎÛÌÎÑÚ×ÌÌÛÜ ÞÇÐÐóÞßÒÜ ÓÛÍØKimiro MEGURO1, Navaratnarajah SATHIPARAN2, Shahid NAZIR3
1
Professor, Director, ICUS, IIS, The University of Tokyo, Japanmeguro@iis.u-tokyo.ac.jp
2
Researcher, ICUS, IIS, The University of Tokyo, Japan
3
Graduate Student, The University of Tokyo, Japan
ABSTRACT
Most residential structures in developing countries are masonry
structures which are highly vulnerable to earthquake and increase thestructural damage and the death toll. This paper discusses the seismic
capacity of non-engineered common masonry structures in Pakistan, whichhaving more tendency to collapse due large weight of slab, by experimental
approach using 1/12 miniature models. These models were constructedusing acryl blocks which can be used several times. This paper also includes
the comparison of retrofitted masonry model by PP-band, a method
proposed by Meguro Laboratory, IIS, the University of Tokyo, with nonretrofitted one. The PP-band retrofitted masonry model showed almost 4
and 16 times greater seismic capacity than the non-retrofitted one in termsof ground acceleration and arias intensity, respectively.
Keywords: Masonry structure, seismic retrofitting, PP-band meshes,
shake table test
1. INTRODUCTION
Pakistan is one of the developing countries in South Asia where mostresidential structures are non-engineered masonry structures. The 2005
Kashmir Earthquake of magnitude 7.6 (Mw) (US Geological Survey)
caused a significant failure of these masonry structures which yielded adeath toll of 79,000 people [1]. These masonry structures are highly
vulnerable to earthquake. This vulnerability can be reduced by retrofitting
using some locally available, affordable, and easily applicable method like
PP-band mesh retrofit proposed by Meguro Lab, IIS, the University ofTokyo [2, 3, 4, 5, 6].
These structures are usually built up by fully or semi burned bricks
with RCC (reinforced cement concrete) or BRCC (brick RCC) slab as roof.
The clay soil and brick tile are also placed on RCC or BRCC slab for heat

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isolation and floor finishes, respectively. This concrete slab is usually castin-situ without any shear connection with walls. The excessive weight ofslab and absence of shear connection increase the probability of collapse of
these structures during seismic events.
These masonry structures were modeled using 1/12 scaled specimen
with acrylic blocks, cement lime sand mortar for experimental studies [5].
These models were prepared under the different scenarios, i) non-retrofitted,ii) retrofitted without shear connection between slab and walls, and iii)
retrofitted with shear connection between slab and walls as shown in Figure
1. All three models were constructed under the same conditions of materials,
strength and dimensions. The input motion applied to all three structures
was also the same. The both retrofitted and non-retrofitted models were
tested using shake table to asses their seismic performance. The results
obtained from shake table tests were compared with each other and an
excellent improvement of seismic capacity of PP-Band retrofitted masonry
structure was observed. It was also observed that the shear connection
between the slab and walls increased the seismic capacity of structure.
2. MODEL CHARACTERISTICS
Three 1/12 scaled single room models were constructed for shake table test
as shown in Figure 1. The characteristics of prototype were incorporated asmuch as possible in the models as shown Table 1. The size of the model was
determined on the basis of permissible payload of shake table [i-e ~ 18 Kg].
The plan size of the models was approximately 290mm x 290mm. The
model height and wall thickness were 240mm and 20mm, respectively. The
door was placed on the one side of the model and window was placed
opposite to it. The dimension of the door opening was 84mm x 156mm inhorizontal and vertical direction, respectively. The dimension of the window
opening was 84mm x 84mm.

(a): S-NR-XSC(b):S-R-XSC(c):S-R-SC

Figure 1: Structure models used
The concrete slab of prototype structure was modeled by using woodplate and additional steel plates were attached to make the weight of slab
proportional to 1/12 scaled concrete slab weight. The slab was placed on thewalls without any connection between wall and slab in case of S-NR-XSC

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and S-R-XSC models as shown in Figures 1(a) and 1(b), while, in case of SR-SC model, the slab was connected to walls by the PP-band as shown in
Figure 1(c). The model was placed on the shaking table and input motion
was applied to the model parallel to the walls having openings.
Table 1: Characteristics of prototype and corresponding values of model
Quantity

Equation

Length
(L)

SL

LP
LM

Stress ( )SP

Weight(W)

SW

M

S = Scale Factor,

2

SL S

Theoretical
FactorPrototype

Model

ActualFactor

12

228 mm

20 mm

11.40

12

0.2400MPa0.0192
MPa

12.50

1,728

7,010 Kg

4.05 Kg

1,728= Shear Stress

3. MATERIALSFor the construction of 1/12 scaled masonry house model, the two sizes of
the acryl blocks, 40 x 20 x 10 mm3 and 20 x 20 x 10 mm3, were used asshown in Figures 2(a) and 2(b). The half blocks were used for break ofjoints among the successive layers. The major reason of the use of acryl
blocks was their reusability [5].

(a): Full size acryl block(b): Half size acryl block(c): Acryl block with sandpapers(d): PP-band mesh

Figure 2: Material used for the models preparation
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The surface of acryl blocks was very smooth and inefficient to provide
required friction; sandpapers were used to solve this problem. Two basic
tests, Direct Shear Tests and Friction Coefficient Tests were conducted
as shown in Figures 3(a) and 3(b), by using acryl blocks with sandpapers
pasted on the both sides by double sided tape as shown in Figure 2(c). For
the selection of appropriate sandpaper, the more importance was given toshear test. The shear test demonstrated the sandpaper of Grit# 80 as the mostappropriate sandpaper to achieve the require shear strength as shown Figure3(c).
After the various trials with different mixture ratios of mortars, themortar with the mixture ratios of cement: lime: sand: water = 1:8:30:10 [byweight] were finally selected based upon the shear strength. The above
described mortar gave the shear strength nearest to the target shear strengthof prototype proportional to 1/12 scaled model as shown in Figure 3(c).
The cross sectional area of the PP-band was approximately 1mm x 0.3mm.
These PP- bands were placed with a pitch of 20mm center to center to makemeshes as shown in Figure 2(d). The pitch of 20mm was selected so thateach acryl block was covered by two PP-bands. The PP-Band meshes wereconnected across the wall by using threads at a grid of 40 and 20mm inhorizontal and vertical direction, respectively.

ðòðì
Þ®·½µ

ðòðí

Ù®·¬ ý êðÙ®·¬ ý èððòðî(a): Direct shear TestÙ®·¬ ý ïððÙ®·¬ ý ïèð

ðòðï
ð
ð

ðòë

ï

ïòë
(b): Friction Coefficient TestÚ®·½¬·±² Ý±»ºº·½·»²¬(c): Shear test and friction coefficient test results

Figure 3: Selection of appropriate sandpaper
4. EXPERIMENTAL SETUP AND INPUT MOTION
The models were constructed on the rectangular wooden frames which wereplaced on the shake table and connected by shear keys. The first layer ofacryl blocks was permanently fixed with wooden frame. The one
dimensional sinusoidal wave motion was applied to shake the specimen.
The models were placed on the shake table and the input motion wasapplied parallel to walls having openings.

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The shake table used can apply sinusoidal motion of two amplitudes, 5
and 15mm, and the frequencies from 2 to 66 Hz. In the experiment,
sinusoidal motions of frequencies from 2 to 6Hz with 5 and 15mm
amplitudes were applied to the specimen in the pattern shown in Table 2.
The duration of motion of each frequency applied was 30 Sec. Arias
Intensity, IA, was also calculated from the input motion by Equation. 1 [7].
Acceleration and displacement at the base and slab of the structure were
measured by using accelerometer and laser displacement measuring system
as shown in Figure 4.
Table 2: Input motion used in experiment
Amplitude(mm)

Frequency
(Hz)

5

2

3

4

5

6

15

2

3

4

5

6

Acceleration
Displacement
Figure 4: Location of sensors
IA
(1)

2g 0

a 2 (t )dt

5. RESULTS AND DISCUSSIONS
The first model tested by the shake table was S-NR-XSC. The model
performed well and it did not show any considerable damage until the input
acceleration was below 141 gal [IJMA < 5.0]. During this stage, theacceleration observed at the base was almost same as that of slab
acceleration. When the base acceleration was increased to 200 gal [IJMA ~ 5],
the slab displacement was amplified and became much larger than basedisplacement. But meanwhile the slab acceleration observed became smaller
than the base acceleration due to cracks in masonry walls as shown in
Figure 5(a). But when the acceleration was further increased, the collapse ofstructure occurred suddenly at an acceleration of 300 gal [IJMA > 5] asshown in Figure 5(b).
Experimental Study of Seismic Behavior of Scaled Non-Engineered Masonry Structures Retrofitted by PPExperimental
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On the other hand, the retrofitted model, S-R-XSC, showed higher
seismic capacity. The PP-band effect became more prominent after
appearance of large cracks. Due to appearance of cracks and holding effectby PP-Band mesh, the energy dissipation capacity was improved which
prevented the complete collapse. But when the amplitude of input motion
was changed to 15mm [at which acceleration was 903 gal, IJMA ~ 6], the slabof model started sliding because slab was not connected with walls asshown in Figure 5(c).
The retrofitted masonry model, S-R-SC, whose slab was connectedwith walls, was tested by shake table under the same motion used for S-NRXSC and S-R-XSC. In this model, as the slab was covered by PP-bandmeshes to prevent its movement, the slab did not move during the shake
table test as shown in Figure 5(d). The collapse point of each type of model
was recorded in terms of JMA Intensity [8], Arias Intensity and time except
S-R-SC which did not collapse completely even an input motion of morethan 1,000 gal was applied [IJMA > 6]. These observations are shown ingraphical form in Figure 6. It is also important to note that the S-R-SC did
not fail completely like S-NR-XSC and also slab did not slide like S-R-XSC.
The lateral sway displacements recorded at slab level of all models areshown in Figure 7.

(a): Cracks in case of S-NR-XSC(b): Collapse of S-NR-XSC model(c): Movement of slab in S-R-XSC(d): S-R-SC after shake table test

Figure 5: Results of shake table test

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The European Macro-seismic Scale (EMS) Grades were used fordamage level definition as shown in Table 3. The EMS Grades were
carefully observed and corresponding time was recorded during the shake
table test. The damage level of each model, EMS Grades and corresponding
Arias Intensity are shown in Figure 8(b). The EMS Grade 1 and Grade 2
occurred at almost same Arias Intensity [time] for all three models.
However after EMS Grade 2 [considerable cracks], the PP-band became
effective and a considerable seismic capacity was observed in retrofitted
model as compare to non-retrofitted one. The retrofitted model (S-R-SC)
didnt collapse completely and maximum EMS grade obtained was 4 evenat the peak acceleration of over 1,000 gal [IJMA > 6].
600
6

500

øìïèòçìîôóóó÷

400

5øííîòçéëô ïèêòíì÷

4300

ÍóÒÎóÈÍÝ

Label (Arias Intensity, collapse time)3200ÍóÎóÈÍÝ

100ÍóÎóÍÝ

øîëòçììô êêòïî÷

2
0

50

100

0
150

200

250

Time [Sec]Figure 6: Comparison of collapse point
6. CONCLUSIONS
It was clearly observed that PP-Band retrofitted masonry house could havemuch higher seismic capacity as compare to non-retrofitted one. The
seismic capacity improved by PP-band retrofitting was almost 4 and 16
times higher than non-retrofitted model in terms of ground acceleration andArias Intensity, respectively. The retrofitted model can tolerate 8 times
larger lateral sway displacement than non-retrofitted one.

Experimental Study of Seismic Behavior of Scaled Non-Engineered Masonry Structures Retrofitted by PPExperimental
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Label (Displacement (mm), Time (sec), EMS Grade)(30.88, 213.91, 4)(21.788, 186.34, 3)

S-NR-XSCÍóÎóÍÝ
øìòíéëô êêòïîô ë÷

S-R-XSCÍóÎóÈÍÝ

S-R-SC
ÍóÒÎó
ð

ïð

îð

íð

ìð

Ô¿¬»®¿´ Í©¿§ Å³³ÃFigure 7: Lateral sway displacement (in case of S-R-XSC
Model: just before slab sliding)

The heavy slab of structure accelerated the crack generation in walls
but if the structure retrofitted properly by PP-band, the energy dissipation
capacity could be improved and complete collapse can be prevented.
Furthermore, the shear connection between slab and walls of the structure
further could increase seismic capacity.
Table 3: EMS grades of damage
Grade

Description

1

Hair-line cracks
in very few walls

2

Cracks in many
walls

3

Large cracks inmost walls

4

Very heavy
damage5

Total or near
total collapse

PictureëìÍóÒÎóÈÍÝ

í

Sliding of slab

ÍóÎóÈÍÝ

îÍóÎóÍÝ

ï
ð
ð

ïðð

îðð

íðð

ìðð

ëðð

ß®·¿­ ×²¬»²­·¬§ Å³ñ­»½Ã

Figure 8: EMS grades and corresponding Arias

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REFERENCES
http://en.wikipedia.org/wiki/2005_Kashmir_earthquake
Mayorca P. and Meguro K. (2001), Strengthening of Masonry Structures –An ongoing research, Proceedings of EQTAP Workshop, Lima-, Peru.
Mayorca P. and Meguro K. (2003), Efficiency of Polypropylene Bands for
the Strengthening of Masonry Strructures in Developing Countries,
Proceedings of 5th International summer symposium.
Sathiparan, N. and Meguro, K. 2004, Shaking Table Experiment of Masonry
Buildings and Effectiveness of PP-band Retrofitting Technique,
Seisan-kenkyu, Institute of Industrial Science, University of Tokyo,
Japan.
Fujieda, T., Mayorca, P., Sathiparan, N. and Meguro, K., (2008),
Experimental Study of the Behavior of PP-Band Mesh Retrofitted
Masonry Houses Using Miniature Models, University of Tokyo, Japan.
Neshli, K., Sathiparan, N., Guragain, R., Mayorca, P., Ito, F. and Meguro,K., (2006), Full Scale Shaking Table Tests on Masonry Buildings
Retrofitted By PP-Band Meshes, Proceeding of the 5th International
Symposium on New Technologies for Urban safety of Megacities in
Asia, Phuket, Thailand.
Arias, A. (1970). "A Measure of Earthquake Intensity," R.J. Hansen, ed.
Seismic Design forNuclear Power Plants, MIT Press, Cambridge,
Massachusetts, pp. 438-483.
J. R. Murphy and L. J. Brien (1977) The Correlation of Peak GroundAcceleration Amplitude with Seismic Intensity and Other PhysicalParameters Bulletin of the Seismological Society of America, Vol 67,
No. 3, pp. 877-915.

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3rdStudent ReportÎÛÐÑÎÌ ÚÎÑÓ ÍÌËÜÛÒÌ ÐßÎÌ×Ý×ÐßÒÌÍÑÒ ÌØÛ í®¼ ÖÑ×ÒÌ ÍÌËÜÛÒÌ ÍÛÓ×ÒßÎÑÒ Ý×Ê×Ô ×ÒÚÎßÍÌÎËÝÌËÎÛ
Venue: Asian Institute of Technology, Pathumthani, Thailand
Date: July 29-30, 2010

All participants at AIT Center

Registration Desk

Presentations

Participants

SEMINAR SESSION
On July 29, the 3rd Joint Student
Seminar on Civil Infrastructure washeld at Asian Institute ofTechnology, Bangkok, Thailand.
The seminar was held, under theumbrella of ICUS and RNUS, toprovide the facility to students topresent their research in aninternational environment. The
seminar was attended by many
speaker students from various nationalities like Japan, Thailand, Pakistan,
Korea, and Bangladesh along with other students from Sri Lanka andAfghanistan. The student of The University of Tokyo, Japan, joined theseminar as team consisted of five students, out of which three were Japanese

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national, one was from Pakistan and one from Bangladesh. This was myfirst time to present my research among the international students and I felt
it as very important and successful step of my life.
After opening speech in seminar,
three professors delivered lectures about
their research which was really
informative and were showering light onthefuturedemandsforcivil
infrastructures. After that, student session
started. All speaker presented theirresearch and answered every question indetail. Presentations were mainly about
mitigation
Engineering,disasterTransportation
Engineering,
Traffic
Engineering, Floods and Hydrology,
Geotechnical Engineering etc.
At the end and in between the eachsession of seminar, the delicious Thai foodwas served with a touch of international
standard for the guest participants.
LIFE IN THAILAND
Life in Thailand is full of culture and customs. Local people are very
friendly especially with foreigners. The local facilities in Thailand alreadyvery developed and are further developing. For awareness about current
development and future development, one day trip was arranged by theorganizing committee.
First of all, we went to a gas
company responsible for providing gasin Thailand. The company name was
PTT Public Company Limited. The
representative of that company present
the current and future plane of thatcompany to enhance the life standardof Thai people.
Second of all, we went to thehead office of Royal Thai Navy. The
landscape was really beautiful andeverybody enjoyed on the beach. I
always thought that armed forces meant
for war and fights but I saw first time in
my life that Royal Thai Navy wasworking hard to save the different kinds
of Turtles in Thailand which is also
helpful for the restoration of sea

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ecosystem of Thai coastal line. The representative of Thai Navy also present
the procedure by which they were making efforts for collecting thehistorical symbols of nearby areas.
At the end, we went to Thai Floating Market, where every shop wason the water. The commodities werevery cheap and food was delicious
there. We also went to enjoy theinterior portion of Bangkok City,
where we ride a train and drunkorange juice with its natural flavor.
I am really thankful to ICUS for
providing me an opportunity topresent my research, to gain
knowledge and to have fun.

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July 2010, Thailand
July 2010, ThailandÍ¬«¼»²¬ Î»°±®¬ º®±³ Õ±®»¿Hyungwoo Lim (Yonsei University, Korea)
It is my great pleasure to report the activities in AIT. I’ve got to say that I
really enjoyed participating the conference. From visiting Thailand to thefield trip, it was full of precious memories.

Figure1: Night view of AITFirst, I want to thank the people who organized this conference. From
hotel to field trip, those people saved my time and energy. Moreover, thisconference brought me very precious interests. As a person who is notworking or studying in the field of civil engineering, I didn’t have muchchance to learn or think over the subject until now. However, thanks to theconference I had to, whether by my own will or others will, study and learn
about the subject. This was the first time I wrote a professional paper, noneed to say in English. I thought of giving it up several times during thework, and unpleasant of my outcome. But now, I am very proud of my workand I’m glad I finished the work successfully and had a chance to show it toothers.

Figure 2: Presentation of the paper

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In case you don’t remember, our work is about the comparison
between node-based and link-based shortest path algorithm. In this research,
we wanted to prove that node-based algorithm is inferior to link-basedalgorithm in case of efficiency and accuracy. Until now, manytransportation networks are based on node-based algorithms. However, we
think that it is the time to move on to superior and better algorithm. Back to
my impression, although I said I’m proud of my work, compared to otherfabulous presentations, our work looks coarse and rough. I pledge that I will
come up with more fascinating and well-embellished work next time (if I
have next time).
Anyway, I personally liked and was interested in disaster warning
system presented by Tokyo University. Similar to Japan, Korea is mostly
consists of highlands and therefore, fragile to landslide and downpours.
I thought if Korea could introduce disaster warning program to highland
districts, Korea could save many lives and money by anticipating the effectof natural disaster. Also, I enjoyed the study about roadside hazards.
Especially the part researchers suggested changing the shape of guardrails.
I was surprised that the shape can make accidents worse and I wondered
whether Korea has the same problem or not. I think it could be aninteresting topic to compare the difference of safety protectors between
Korea and Thailand.
Probably the thing I enjoyed the most is making friends fromdifferent nationalities. I only have few friends in other countries and I didn’t
even have a chance to make one these days. At first, I felt very awkward anduneasy to get close. However, through the conference, dinner and field trip I
could feel new relationship growing up. I was surprised that I can actuallyconverse with people from different nationalities and enjoyed conversing
with them. Even though we had only two days together, I felt sorrow todepart with them.
The field trip is worth mentioning as well. As I couldn’t stay long in
Bangkok, it was the best way to go sightseeing and look around.

Figure 3: Beach of Pattaya
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It was a bit pity to miss temples and famous scenic spots in downtown
but I enjoyed Pattaya so much. There we saw sea turtles, and visited
museums as well. I was amazed by the beauty of sea turtles and people’s
effort to protect them and their habitats.

Figure 4: Sea sight of Pattaya
Moreover, the scene was so beautiful in the museum. As the museum
is built by the hill (it felt like a mountain to me in the hot sun), once wereached the last and highest museum, we could see whole town and sea
twinkling in hot day.

Figure 5: View of Pattaya
I’ve got to say I really enjoyed the conference and trip. Thanks tothe conference, I could gain more interests in other fields and enjoy thesummer. Not to mention making new friends as well. Thank you for inviting
me to the conference and I believe it will keep going on for long time. It
would be my pleasure to visit Thailand and join the conference again.

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ICUS Report 45ÐÎÛÍÛÒÌßÌ×ÑÒ ÍÛÍÍ×ÑÒ
On 29 July, 2010, 3rd Joint Student Seminar on Civil Infrastructures
was held at Asian Institute of Technology (AIT) Organized by International
Center for Urban Safety Engineering (ICUS) of The University of Tokyo
with great success. The Japan team consisted of 5 presenters from various
nationalities: 1 Pakistani, 1 Bangladeshi and 3 Japanese. Although this was
first time to meet each other, we shared all the time together in goodfriendship.

All participants at AIT

Joint Student Session

In the presentation session 3
professors gave us lectures andthen 16 students presented theirresearches with various fields such
as Transportation Engineering,GeotechniqueEngineering,
Concrete Structure Engineering,
Disaster Mitigation Engineeringand Applied Remote Sensing.
Each of us presented our research
theme successfully with great
efforts.
After presentation session
all members joined the party held
by ICUS. The party was so
enjoyable and gave us goodchance for cultural exchange andfriendship with other university
students. At the end of party 4
students awarded as best
presenter voted by all staffs.

From this conference wegot precious idea of various
interesting topics related to civil engineering field which is quite importantfor our future. Furthermore, toward this conference, we had good
opportunity to present our research topics to all. Although the session was
only for one day, we spent valuable and memorable day.
Student award for best presentation

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Ú·»´¼ Ì®·° ·² Ö±·²¬ Í¬«¼»²¬ Í»³·²¿® îðïðæÌ¸» ×²¼«­¬®§ ¿²¼ Ò¿¬«®» ·² Ì¸¿·´¿²¼
FIELD TRIP
At the seminar, we had a field trip around Thailand all day long. We visited
to PTT, Sea Turtle Conservation Center, Thai Island and Sea Natural
History Museum and Pattaya Floating Market. Through this field trip, we
studied the natural gas industry, enjoyed the nature of Thailand and got toknow students from Asian counties.

1. PTT (PETROLEUM AUTHORITY OF THAILAND)
The first place was a PTT opperation office. We took a lecture on natural
gas pipeline business and watched some samples of pipelines. Mr. Chatchai
Subin, PTT staff, gave us an understandable explanation on natural gas
transmission history, demand & supply, pipeline network in Thailand, safetymeasure and maintenance activity.

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2. SEA TURTLE CONSERVATION CENTER
Secondly, we went to Sea Turtle Conservation Center managed by Royal
Thai Navy. The Thai Navy conserves the beach of sea turtle’s breeding area.
They nurture and release 20,000 turtles a year. We watched the museum of
sea turtle and the turtles nurtured in aquariums. We found patient effort wasneeded to conserve natural environment.

3. THAI ISLAND AND SEA NATURAL HISTORY MUSEUM
Thirdly, we went to Thai Island and Sea Natural History Museum which
was located on a hill facing to sea. We learned Thailand natural history inthe museum. It was hard work to climb the hill, but the view of sea from thehilltop was so wonderful!! We enjoyed the nature of Thailand.

4. PATTAYA FLOATING MARKETThe final place was floating market in Pattaya. Literally, it was a shopping
mall on the water, but the design of building was Thai traditional style. We
could feel that Thai people’s life was closely connected to water.

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×³°®»­­·±²­ º®±³ ¬¸» Í¬«¼»²¬ Ð¿®¬·½·°¿²¬­
The 3rd Joint Student Seminar on Civil Infrastructure
was a valuable experience. Firstly, it was first time forme to have an English presentation in an academic
event. I learned a lot on presentation from thisexperience. Next time I am in an academic conference,
I think I can give a better presentation. Secondly, thisseminar was a good opportunity to know various
research issues and approaches. As a matter of fact,some presentations were difficult to understandbecause I was not familiar with them. However, theirattitudes toward researches were good stimuli for me. Additionally, field
trip was a good chance to know the industry and natural environment inThailand. It also helped me make friends from Asian countries. Finally, I
want to express my gratitude to the organizers of this seminar.
(by Mr. Yuto Shiozaki)
The 3rd joint student seminar on civil infrastructures
provided me a precious experience. Firstly,
presentation session was so exciting that we could
have a good discussion for our research topic andinterchange communication with students andresearchers. Secondly, the field tour was
unforgettable trip. We had full of new experiencesuch as gas transmit company, turtle preservation
center and floating market. Finally, the best point inthis seminar is to get many friends of civil
infrastructure students from Thailand, Korea and Bangladesh. Fortunately, it
was my great pleasure to have received an excellent presentation award in
the student session. I really appreciate to the organizer and participants inthis seminar and hope for having good 4th joint student seminar next year.
(by Mr. Hirotoshi Kishi)
On July 29, the 3rd Joint Student Seminar on Civil
Infrastructure was held at Asian Institute ofTechnology, Bangkok, Thailand. The seminar was
held, under the umbrella of ICUS and RNUS, toprovide the facility to students to present theirresearch in an international environment. The
seminar was attended by many speaker students
from various nationalities like Japan, Thailand,
Pakistan, Korea, and Bangladesh along with otherstudents from Sri Lanka and Afghanistan. The
student of The University of Tokyo, Japan, joined the seminar as teamconsisted of five students, out of which three were Japanese national; one
was from Pakistan and one from Bangladesh. This was my first time topresent my research among the international students and I felt it as very
important and successful step of my life. (by Mr. Shahid Nazir)

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ICUS Report 45

The 3rd Joint Student Seminar in AIT and trip in
Bangkok were so great, enjoyable and unforgettable
for me. It was my first opportunity to attend aninternational seminar and to be a presenter. It was also
my first chance to go to Thailand. The seminar gave
me a chance to meet, talk and know many friends
from other universities and various nationalities. At
the presentation session, I have learnt a lot from theprofessor’s lectures and other friend’s presentations,and I am also happy to share my research idea withall. Visiting Gas transmission pipeline factory, Royal Thai Navy, Natural
history museum, and Pattaya floating market gave me some good memories.
This visiting trip on second day in Thailand had made our team members
get to know each other better. I had a very good time in Thailand! Finally, I
would like to thank ICUS for giving us this great opportunity.
(by Ms. Farhana Naznin)
I was very glad to participate in the 3rd Joint Student
Seminar on Civil Infrastructures at the Asian Instituteof Technology. It was my first time to visit Southeast
Asia, Thailand. The seminar provided me a lot ofinteresting subject for research. I got some useful
information from the other speakers who talked about
different topics. Speaking in English is very tough forme. It gave me motivation for English. Field trip also
gave me a great experience to talk with many friends
from other countries. I would like to keep in touch withthem. And, the Sea around Pattaya was so beautiful! I was impressed totouch the sea turtle. My roommate in AIT conference center, Mr. Shahidfrom Pakistan, always amused me to say his humorous joke. I have to tell
him thanks a lot. Finally, I want to express my gratitude to all organizers
who held this event and all my friends.
(by Mr. Makoto Hosoo)
The 3rd Student Seminar was a great experience to me
not only as development of presentation skill or civilengineering knowledge but also as making aninternational friendly relationship among student
and/or faculties participants. On the 1st day of student
presentations, various types of research regarding
civil engineering inspired me greatly. I realized all
fields of study such as geotechnical, transportation,
structural
engineering
have
significant
interrelationship each other when we think about
construction and management of infrastructures. So, the international
friendship would be the first step to accomplish such cooperative works inthe future. At Petroleum Authority of Thailand (PTT) where we visited as
field trip on 2nd day, we could learn the gas transmission pipeline networks
in Thailand that how the gas is distributed to customers from place of origin
with proper maintenance and management. Second place, named Sea Turtle
Conservation Center maintained by Royal Thai Navy, was a good place for
Report fromonThethe 3rdfrom Student
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July 2010, Thailand

engineers to remind us that we have to mitigate negative impact toenvironments induced by civil engineering works as much as possible.
Finally, I would like to express my gratitude to all of organizers andparticipants.
(by Mr. Akira Kodaka)

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