7th Joint Student Seminaron Civil Infrastructure
19-20 August 2015
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
Sirindhorn International Institute of Technology (SIIT),
Thammasat University
Chulalongkorn University
Myongji University, Korea
andHanyang University, Korea
Edited by
Dr. Yudai Honma, Dr. Kunnawee Kanipong,
Dr. Hyunmyung Kim and Dr. Withit Pansuk

Organization Teams
Prof. Worsak Kanok-Nukulchai (AIT, Thailand)
Dr. Pennung Warnitchai (AIT, Thailand)
Dr. Kunnawee Kanipong (AIT, Thailand)
Dr. Pakawat Sancharoen (SIIT, Thailand)
Assoc. Prof. Thavivongse Sriburi (Chulalongkorn U,
Thailand)Dr. Withit Pansuk (Chulalongkorn U, Thailand)
Dr. Hyunmyung Kim (Myongji U, Korea)
Dr. Wonho Suh (Hanyang Univ., Korea)
Prof. Kimiro Meguro (UT, Japan)
Dr, Yudai Honma (UT, Japan)
Dr. Wataru Takeuchi (UT, Japan)
Dr. Akiyuki Kawasaki (UT, Japan)

7th JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURE
August 2015
PREFACEIn this era of rapid globalization, having an international sense and broad
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, 2nd 3rd, 4th, 5th, 6th and joint student seminar in
July 2008, July 2009, July 2010, August 2011, August 2012, and August 2013
respectively, and following this success the “7th Joint Student Seminar on Civil
Infrastructures” was held on 19 August, 2015.
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 40, consisting of 3 faculties and
15 students from Myong Ji University, Hanyang University, Chulalongkorn
University, Thammasat University (SIIT), Srinakharinwirot University and
The University of Tokyo.
We had a presentation session, having 3 faculties (Chulalongkorn
University and The University of Tokyo) lectures and 15 students’ presentation.
The topics covered wide range areas of 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 theend of the seminar, excellent presentation awards were given to the following 3
students.
1. Mr. Yuto Tokumitsu from University of Tokyo, Japan
2. Mr. Yuki Munemasa from University of Tokyo, Japan
3. Mr. Tanakorn Sritarapipat from University of Tokyo, Japan
Although the seminar was quite successful and fruitful: this seminargave 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.

YUDAI HONMA, KUNNAWEE KANIPONG,
HYUNMYUNG KIM AND WITHIT PANSUKICUS Report 79

August 2015, Thailand

Table of Contents
Page
Opening Session
SECTION 1

Invited Lectures

Introduction to mathematical analysis for next-generation vehicle
Yudai Honma

1

Water leak prevention at connections of precast concrete panels withbutyl rubber
Withit Pansuk

27

Near-real time meteorological drought monitoring and early warning
system for croplands in Asia
Wataru Takeuchi

55

SECTION 2

Student Presentations

Building classification in urban area in Yangon, Myanmar using stereo
high resolution images and night time light data
Tanakorn Sritarapipat

81

Experiment and simulation study on flexural behavior of reinforced
concrete slab before and after fire
Nguyen Thi Cao

91

The key index for rational bridge maintenance by municipalities inJapan
Riko SAKATA

101

Effect of free LIME content on mechanical properties of cement-fly ash
mixtures
Adnan Nawaz

109

SECTION 3

Student Presentations

The relationship between the rainfall intensity and the subway
passenger ridership in the Seoul metropolitan subway system
Kyoung-seon Park

117

Factors affecting the severity of motorcycles accidents and casualties inThailand by using probit and logit model
Santosh Baral

125

Optimum assignment of housings and jobs with constrained capacity
considering discomfort and travel costs
Yuki Munemasa

143

7th Joint Student Seminar on Civil InfrastructureAugust2015,
ThailandAugust2015,
Thailand

Factors influencing red light running behavior: a study of socioeconomic characteristics and geometry of intersections
Auearree Jensupakarn

155

The optimum traffic volume survey locations for the travel
demand analysis in Korea
Seong-min Kang

173

Site selection of potential railway route for supporting dawei
deep-sea port Case study : Tambon Ban Kao Amphoe Mueang
of Kanchanaburi province
Rattanawadee Dutsadeesuntornsakun

181

Numerical evaluation of road disconnection risk focusing on roadcollapsing after a large earthquake in Chigasaki city, Kanagawa
Yuto Tokumitsu

189

Image processing techniques for vehicle and pedestrian safety
evaluation
Jinwoo Park199

SECTION 4

Student Presentations

Ageing effect induced by particle movement
Koji Tanaka

205

An analysis of network throughputs for different origindestination patterns
Koki Satsukawa

213

Water retainability and basic properties of coal bottom ash usedas fine aggregate replacing material in concrete
Noulananh Lathsoulin

225

Experimental study on in-plane and out-of-plane behavior ofmasonry wallettes retrofitted with special fiber reinforced paintKenjiro Yamamoto

233

Knowledge based IT evacuation facility management system"COCOA"
Satoshi Takatsu

241

SECTION 5
Student Presentations
Report from Student Participation on 7th Joint Student Seminar on
Civil Infrastructure
Koki Satsukawa, Satoshi Takatsu, Kenjiro Yamamoto, Riko Sakata,
Yuto Tokumitsu, Yuki Munemasa, Tanakorn Sritarapipat, Koji Tanaka,
Cao Nguyên Thi, Noulananh Lathsolulin, Jinwoo Park253

7th Joint Student Seminar on Civil Infrastructure

ICUS Report 79

Seminar Venue

ICUS R

Opening
Yudaiceremony
Honma) (Dr. Yudai Hon
Seminar VenueOpening ceremony (Dr.

Committee (Dr. Wonho
Suh) (Dr. Wonho Suh)
Committee

Dr. Yudai Honma Dr. Yudai Honma

Dr. Withit Pansuk Dr. Withit Pansuk

Dr. Wataru TakeuchiDr. Wataru Takeuchi

Mr. Tanakorn Sritarapipat
Mr. Tanakorn Sritarapipat

Mr. Nguyen Thi CaoMr. Nguyen Thi Cao

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Ms. Riko Sakata

Mr. Adnan Nawaz

Ms. Kyoung Seon ParkMr. Yuki Munemasa

Mr. Seong Min Kang

Ms. Rattanawadee Dutsadeesuntornsakun

Mr. Yuto Tokumitsu

Mr. Jinwoo Park

7th Joint
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Seminaron
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CivilInfrastructures
Infrastructure
7thICUS Report 79

Mr. Koji Tanaka

Mr. Koki Satsukawa

Mr. Noulananh Lathsoulin

Mr. Kenjiro Yamamoto

Mr. Satoshi Takatsu

Closing ceremony (Dr. Hyunmyung Kim)

During Seminar

7th Joint7th
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August 2015, Thailand

During Welcome Dinner

Excellence Presenter Mr. Yuto Tokumitsu
Excellence Presenter Mr. Tanakorn Sritarapipat

Excellence Presenter Mr. Yuki Munemasa

Welcome Dinner & Close seminariviv

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Seminar: Group Photo at Chulalongkorn University

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Invited Lectures

INTRODUCTION TO MATHEMATICALANALYSIS FOR NEXT-GENERATIONVEHICLE
Yudai Honma
The University of Tokyo, Japan
yudai@iis.u-tokyo.ac.jp11August 2015, Thailand

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Introduction to mathematical analysis
analysis forfor next-generation
next-generationvehicle
vehicle3

August 2015, Thailand

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Introduction to mathematical analysis
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vehicle5August 2015, Thailand

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WATER LEAK PREVENTION AT
CONNECTIONS OF PRECAST CONCRETE
PANELS WITH BUTYL RUBBERWithit PANSUK1, Penpitcha KRITTAYANUPONG2 andPhongsak WIWATTANADATE3
1
Assistant Professor, Department of Civil Engineering,
Chulalongkorn University, Thailand
Withit.P@chula.ac.th2
Graduate Student, Department of Civil Engineering,
Chulalongkorn University, Thailand
3
Lecturer, Department of Materials Science,
Chulalongkorn University, Thailand

ABSTRACT
Construction technologies have been growing continuously. Many
projects are trying to develop technical construction with minimum time.
Precast concretes are defined as technologies that provide efficient strategy
to prepare products with time-competing approach. It includes
prefabricated methods, off-site productions, and offset manufacturing forbuildings. The most common applications which have been widely used as
members associated with building constructions are walls. Precast concrete
walls provide excellent functions of durability and desirability. However,
one of the biggest challenges of precast concrete walls is leakage between
precast connections due to loss of strain on the walls and strength ofreinforcing materials through connections. In Thailand, the most famous
prevention is application of backing rod with sealant and grouting with nonshrink grout cement. Backing rods are usually made from extruded
polyethylene foam with different diameters and lengths available. It is used
as a backup material for cold applied sealants to give the sealant a proper
thickness. Backing rod with sealant are a lot of advantages such ascompatibility with cold applied sealants (e.g. polyurethane and silicone)and highly flexible and compressible. However, installation of backing rod
must be done with care. During installation, the surface of the backing rod
shall not be cut or punctuated and excessive longitudinal stretching of therod is to be avoided. Also, it cannot work when the sealant is heated andapplied at a temperature above 70 °C. Joint width is affected by backing rod
diameter. The limitations of sealant are effects from environment and time
cyclic. If the sealant cannot work capably, it will be considered as a weak
point and leakage will occur. From previous researches about butyl rubber,its performance to prevent leakage has been widely reported. Therefore,
substitution of the previous prevention between precast connections is
promising by the possibility to substitute butyl rubber as backing rod withsealant. The common types of precast concrete connection in Thailand
which were modeled and tested had been studied and inferred. In this study,27

AugustAugust2015,
2015,Thailand
Thailandthe 3-dimentional finite element model of precast concrete connection andits interaction with two adjusted concrete panels were simulated.
Laboratory experiment of precast concrete panel connections was also
conducted. The studying parameter was the critical crack width at theconnections resulted from the differential loadings acting on the panels. The
suitable position of butyl rubber installed at the connections was also one of
the test parameter. The critical crack widths were induced on the real
specimens to ex-amine possible application of three proposed connection
types. Then, water permeability test was performed at specimens’
connections. The water pressure was adjusted to maximum of 5 kgf/cm2(water-head height: 50 m). The butyl rubber was found to have superiorwater leak prevention capability. The most suitable installation pattern wasshown.
Keywords: water leak, connections, precast concrete, butyl rubber,water permeability

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butyl rubber
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concrete panels
panels with
with butyl
butyl rubber
rubber33

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concrete panels
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butyl rubber
rubber35

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concrete panels
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butyl rubber
rubber37

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concrete panels
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butyl rubber
rubber39

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concrete panels
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with butyl
butyl rubber
rubber41

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concrete panels
panels with
with butyl
butyl rubber
rubber43

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concrete panels
panels with
with butyl
butyl rubber
rubber45

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concrete panels
panels with
with butyl
butyl rubber
rubber47

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concrete panels
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with butyl
butyl rubber
rubber49

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butyl rubber
rubber51

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concrete panels
panels with
with butyl
butyl rubber
rubber53

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NEAR-REAL TIME METEOROLOGICALDROUGHT MONITORING AND EARLYWARNING SYSTEM FOR CROPLANDSIN ASIA
Wataru Takeuchi1, Soni Darmawan1,2, Rizatus Shofiyati3, Mai Van
Khiem4, Kyaw San Oo5, Uday Pimple6 and Suthy Heng7

1Institute of Industrial Science, The University of Tokyo

6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPANE-mail:wataru@iis.u-tokyo.ac.jp

2Center for Remote Sensing, Institut Teknologi Bandung (ITB), Indonesia
3Indonesian Center for Agricultural Land Resources Research andDevelopment (ICALRD), Indonesia

4Institute of Meteorology, Hydrology and Environment (IMHEN), Vietnam
5Myanmar Geospatial and Resource Program (MGRP),

Myanmar Peace Center (MPC), Myanmar

6King Mongkut’s University of Technology (KMUTT), Thailand

7Mekong River Commission (MRC), Cambodia

ABSTRACTThis research focuses on a development of satellite-based droughtmonitoring warning system for croplands in Asian countries. Drought
condition of cropland is evaluated by using Keeth- Byram Drought Index(KBDI) computed from rainfall measurements with GSMaP product, land
surface temperature by MTSAT product and vegetation phenology by
MODIS NDVI product at daily basis. The derived information isdisseminated as a system for an application of space based technology(SBT) in the implementation of the Core Agriculture Support Program. The
benefit of this system are to develop satellite-based drought monitoring andearly warning system in Asian counties using freely available data, and todevelop capacity of policy makers in those countries to apply the developed
system in policy making. A series of training program has been carried out
in 2013 and 2014 to officers and researchers of ministry of agriculture andrelevant agencies in Greater Mekong Subregion countries including
Cambodia, China, Myanmar, Laos, Thailand, Vietnam and Indonesia. Thissystem is running as fully operational and can be accessed at
http://wtlab.iis.u-tokyo.ac.jp/DMEWS/.
Keywords: KBDI, yield estimation, land surface temperature, rainfall

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croplandsininAsia
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croplandsininAsia
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Student Presentation

BUILDING CLASSIFICATION IN
URBAN AREA IN YANGON, MYANMAR
USING STEREO HIGH RESOLUTIONIMAGES AND NIGHT TIME LIGHT DATA
Tanakorn SRITARAPIPAT1 and Wataru TAKEUCHI2
1
Ph.D. Student, IIS, The University of Tokyo, Japantanakorn@iis.u-tokyo.ac.jp
2
Associate Professor, IIS, The University of Tokyo, Japan

ABSTRACTThis research proposed a methodology to provide building type inurban area using remotely sensed data (that can observe in wide area) in
Yangon, Myanmar. Stereo high-resolution satellite images acquired by
GeoEye, medium-resolution images of Landsat 8 and low-resolution imagesof night time light of VIIRS (Visible Infrared Imaging Radiometer Suite)were used in this research. The high-resolution images was employed toprovide land cover and digital elevation model (DEM), and mediumresolution image was classified as land cover. The height of building weregenerated by using land cover and DEM. Combining height of building with
the night time light data, building type such as residential, commercial,industrial building were able to obtain. In our experiment, Yangon, former
capital of Myanmar, was concentrated to classify since it is the country's
largest city with five million people and is major economic country's center.
Building type map using our proposed method was compared with local
images in residential and commercial areas, and industrial zone map. The
experimental result indicated the relationship between building type map
and validate data is highly positive.
Keywords: residential building, commercial building, industrial
building
1. INTRODUCTION
Understanding type of building in each area is an important information for
urban planning and management in efficiency. Generally, building type
information is provided by surveying that takes a number of workers, longprocess time and high budget. In urban area, there are various human-built
features which is highly complicated. So it is quite difficult to collect thisinformation by surveying in vast area.

81AugustAugust2015,
2015,Thailand
ThailandSince remote sensing technology provides observation in large area.
Many researchers have widely used remotely sensed data to detect building
and classify type of building. For detecting building, Grigillo (2011)
introduced building detection method using GeoEye Images. They applied
segmentation method to extract building. Building extraction method was
proposed by Turker (2015). They used support vector machine and houghtransform also perception grouping to extract building as object-basedclassification. For building classification, Lu (2014) presented building typeclassification using LiDAR remote sensing data. They used LiDAR toprovide the height of building. Then, height of building was segmented asbuilding. Building classification method using very high resolution image
and GIS was introduced by Du (2015). They extract the building form theimage and related it to GIS data to provide building classification. Moreover,damage building detection by using high resolution oblique airborne images(Vetrivel, 2015). They detected anomaly segmentation of building.
We proposed a methodology to newly classify building types;
residential, commercial, and industrial buildings using GeoEye, Landsat
images and night time light data. In this research, Yangon (Morley, 2013),
Myanmar was concentrated since it is major center of country’s economics.
A methodology to classify building type was describe in section 2. In
section 3, experimental result was compared with validated data also
analyzed. Finally, Conclusion of this research was given in section 4.
2. METHODOLOGY
2.1 Factors to indicate building class
In this research, only three types of buildings classification wereconcentrated; commercial building, industrial building and residentialbuilding. Firstly, commercial building such as department store, shopping
mall, hotel etc. has characteristics of high height of building. Also in thenight time, this building has high consuming light energy due to various
activities and crowded area. As result, it can indicate that these building has
high night time light. Secondly, industrial building such as factory or plant
has feature of high height of building as well. However, in the night time,
this building has low consuming light energy. So, night time light data of
this building is low. Finally, residential building has aspect of low building
such as house and low night time light data.
2.2 Materials
We used three remotely sensed data sources; GeoEye, Landsat 8,
NPP (National Polar-orbiting Partnership) – VIIRS (Visible Infrared
Imaging Radiometer Suite). Stereo images of GeoEye Satellite were used toprovide height of building. Landsat-8 multispectral image was employed toclassify land cover area. NPP-VIIRS has capability to obtain day/night band
composite data (DNB). The radiance of DNB has a number in order of82
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magnitude of 10-9 and the unit of W/(cm2-sr). The details of these images
was displayed in table 1. Figure 1 shows satellite dataset of this research.

Satellite
GeoEye-1
Landsat-8
NPP-VIIRS
DNBTable 1: Details of satellite images
Resolution
0.5 m.
30 m.
460 m.

Bands
3
11
1

Acquisition Date
2013/11/16
2015/02/26
2012/04/18-2012/04/26
2012/10/11-2012/10/23(a) (Red: Red, Green: Green, Blue: Blue)(b) (Red:NIR, Green:Green, Blue:Blue)

Building Classificationin urbanUrbanarea
AreaininYangon,
Yangon,Myanmar
Myanmarusing
usingstereo
StereohighHighresolution
Resolution
Images and Night
Buildingclassification inimages
Time
Light
Data
83
and night time light dataAugustAugust2015,
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Thailand(c) (Red:High, Blue:Low)
Figure 1: (a) GeoEye Image, (b) Landsat Image, (c) NPP-VIIRS DNBFirstly, the stereo images of GeoEye were extracted to provide Digital
Surface Model (DSM), Digital Terrain Model (DTM) and Digital Building
Model (DBM). The multispectral images of Landsat 8 was classified as land
cover areas. Using urban area in land cover and DBM, Building class suchas residential building (low building) and commercial and industrial
buildings (high building) was obtain based on height of building. The nighttime light data from NPP-VIIRS DNB was taken exploit to separate
between commercial and industrial buildings. The mapping of buildingclassification was illustrated.
2.3 Classifications
The stereo images of GeoEye were used to provide DSM. Using
DSM, orthorectified images from GeoEye was obtain. Then DSM wasapplied by using morphological filtering to provide DTM. DBM was
generated by using the equation that DBM equals to DSM minus DTM.
Orthorectified images with RGB bands was classified by using Mahalanobis
distance method (supervised classification) into 2 classes; vegetation andnon-vegetation. Using non-vegetation area and DBM, the height of building
was able to obtain without effect of trees or forest areas. We separate heightof building using manual threshloding into two groups; low building from 2
to 10 meters height (1-2 floors) and high buildings more than 10 meters
height (more than 2 floors). In general, 1 floor of residential building is 3.1
meters and 1 floor of commercial building is 3.9 meters

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Figure 2: Building classification(Blue: High building, Green: Low building)
The Landsat image was classified by using Mahalanobis distance(Supervised Classification). Land cover classes were separated into 3 class;
Urban, Vegetation, Water. Training samples were selected more than 1000
samples per class.

Figure 3: Land cover classification(Red: Urban, Green: Vegetation, Blue: Water)
We separated night time light data into 3 classes by using K-means(Unsupervised classification). Three classes of night time light data are low
night time light class with mean of 1.83 (Digital number, DN), medium
night time light class with mean of 8.69 (DN) and high night time light classwith mean of 24.13 (DN).

Building Classificationin urbanUrbanarea
AreaininYangon,
Yangon,Myanmar
Myanmarusing
usingstereo
StereohighHighresolution
Resolution
Images and Night
Buildingclassification inimages
Time
Light
Data
85
and night time light dataAugustAugust2015,
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ThailandFigure 4: Night time light (NLT) classification(Red: High NTL, Green: Middle NTL, Blue: Low NTL )2.3 Hierarchy classification
After three resultant of each classification; height of building, land
cover, night time light, were already obtain. The hierarchy classification wasused to classify the building type; (1) residential, (2) commercial, (3)industrial buildings.
Firstly, height of building; low and high buildings, was provided
without any errors of tree. However, since the spectral resolution in GeoEye
image was poor (just RGB), some errors of height of building fromvegetation and water areas are still remain. Hence, integrating between
building areas and land cover classification; urban, vegetation, water wasapplied. Using only urban area from land cover with area of height ofbuilding, more accurate resultant of height of building was provides without
any errors from vegetation and water areas. Separating between low
building that indicate residential building and high building was provided.
However, high buildings were mixed between commercial and industrial
buildings. Combining height of building with night time light data,
commercial building that has high night time light intensity and industrial
building that has medium night time light intensity are separated. In thiscase, the group of low night time light generally indicates non-urban area.
Thus, it does not include in consideration for building classification.

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Figure 5: Result of classification building(Green: Residential Buildings, Blue: Commercial Buildings, Red: Industrial
Buildings)
2.4 Improving classification result
Since the spatial resolution of night time light data from NPP-VIIRSDNB is quite low (approximately 460 meters). The edge areas between twoclassifications are still errors. In order to solve such problem, spatial
relationship between commercial and industrial areas was introduced.
Commonly, industrial area such as factory and plant that probably makes
loud noise or some pollution should be located in isolation from commercial
area such as shopping center and hotel that intensive human’s activities. As
a result, the rule of distance between commercial and industrial areas were
defined that the building that located near the commercial buildings must be
residential or commercial building.

Figure 6: improved result of classification building(Green: Residential Buildings, Blue: Commercial Buildings, Red: Industrial
Buildings)
3. EXPERIMENTAL RESULT
Building Classificationin urbanUrbanarea
AreaininYangon,
Yangon,Myanmar
Myanmarusing
usingstereo
StereohighHighresolution
Resolution
Images and Night
Buildingclassification inimages
Time
Light
Data
87
and night time light dataAugustAugust2015,
2015,Thailand
ThailandIn our experiment, Yangon, former capital of Myanmar, was concentrated toclassify since it is the country's largest city with five million people and ismajor economic country's center. The statistical of building classification
area in urban area in Yangon with 238.48 km2 (focused area) using our
methodology was shown in table 2.
Table 2: The statistical of building classification area using our
method (total area is about 238.48 km2)

Type of Building

Area (km2)

Area (%)

Residential Buildings

45.85

84.19

Commercial Buildings

6.69

12.28

Industrial Buildings

1.92

3.53

Total Buildings

54.46

100.00In order to validate the result, the map of building classification
using our proposed method was compare with the local images with two
types; residential and commercial buildings in urban area in Yangon.

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(a)

(b)

(c)

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Figure 7: Comparison between the building type map (a) and local images;(b) residential building, (c and d) commercial building
The comparative result between the building class map and local
images is a very high relationship.
Also the resultant map was compared with industrial zone map from
Myanmar Industries Association (http://myanmarindustries.org).

(a)(b)
Figure 8: Comparison between (a) the building type map and (b) industrial
zone map
The resultant map is highly related to the industrial zone map.
However, the result of residential buildings looks overestimated.
Since some commercial and industrial buildings are low building especiallyindustrial buildings. Thus, low buildings of commercial and industrial wereclassified as residential buildings that are incorrect. To correct this problem,the spatial analysis is highly recommended.
4. CONCLUSION
We proposed a novel methodology to classify building type in urban area
using remotely sensed data in Yangon, Myanmar. Stereo images of GeoEye
was used to provide the height of building. Based on height of building,building class was obtain. Landsat-8 image was applied to provide land
cover area. Using land cover, result of building class was improved. We
combined building class with night time light data. Three building classes;
residential, commercial, industrial buildings, were obtain. The rule ofdistance between commercial and industrial areas were defined to improveclassification result. To validate the map of building classification, local
images in residential and commercial buildings were compared as well as
the industrial zone map in Yangon. The comparison between resultant map
and validated data has high relationship.
REFERENCES
Du, S., Zhang, F., Zhang, X., 2015. Semantic classification of urban
buildings combining VHR image and GIS data: An improved random forest
approach
Building Classificationin urbanUrbanarea
AreaininYangon,
Yangon,Myanmar
Myanmarusing
usingstereo
StereohighHighresolution
Resolution
Images and Night
Buildingclassification inimages
Time
Light
Data
89
and night time light dataAugustAugust2015,
2015,Thailand
Thailand

ISPRS Journal of Photogrammetry and Remote Sensing 105, 107–119.
Grigillo, D., Fras, M.K., 2011. Classification Based Building Detection
From GeoEye-1 Images. Joint Urban Remote Sensing Event, 381–
384, Munich, Germany.
Lu, Z., Im, J., Rhee, J., Hodgson, M., 2014. Building type classification
using spatial and landscape attributes derived from LiDAR remotesensing data. Landscape and Urban Planning 130, 134–148.
Morley, I., 2013. Rangoon, Cities 31, 601–614.
Turker, M., Koc-San, D., 2015. Building extraction from high-resolution
optical spaceborne imagesusing the integration of support vector
machine (SVM) classification, Hough transformation and perceptualgrouping. International Journal of Applied Earth Observation andGeoinformation 34, 58–69.
Vetrivel, A., Gerke, M., Kerle, N., Vosselman, G., 2015. Identification ofdamage in buildings based on gaps in 3D point clouds from very
high resolution oblique airborne images. ISPRS Journal of
Photogrammetry and Remote Sensing 105, 61–78

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EXPERIMENT AND SIMULATION STUDYON FLEXURAL BEHAVIOR OF
REINFORCED CONCRETE SLAB BEFORE
AND AFTER FIRE
Nguyen Thi CAO1, Withit PANSUK2 and Lluis TORRES3
1
PhD Student, Department of Civil Engineering, Faculty of Engin
eering, Bangkok, Thailand
caonguyenthi@tgu.edu.vn
2
Assistant Professor, Department of Civil Engineering, Faculty ofEngineering, Bangkok, Thailand
3
Professor, AMADE, Polytechnic School, University of Girona, Campus
Montilivi, Girona, Spain

ABSTRACT
After exposure to fire, the decrease in load capacity of reinforced
concrete (RC) structure occurs and the building can be damaged orcollapsed. In order to assess the state of fire-damaged structure for reusing
or replacing, it is essential to estimate the residual behavior of RC member
after exposure to fire. This paper will report an experiment to evaluate theload-deflection behavior of RC slabs before and after fire as a part of astudy in repairing and strengthening fire-damaged RC slabs. Besides, a
simple numerical investigation utilizing non-linear finite element (FE)analysis was carried out using ABAQUS software and verified withexperimental data. It is concluded that the developed FE model was suitableto predict the flexural behavior of unheated and heated RC slabs.
Keywords: Reinforced concrete slab, fire-damaged concrete,
ABAQUS
1. INTRODUCTION
As it is well known, the deterioration in the properties of reinforced concrete(RC) structure occurs when it is subjected to a fire. Of particular importanceare loss in compressive strength, loss of elastic modulus, cracking andspalling of the concrete; reduce yield strength, ductility and tensile strengthof the steel, and the loss of bond between them (Short et al. 2001). Although
the concrete structure is damaged during the fire, it may retain a residualload bearing capacity (Annerel & Taerwe 2013). Due to its low thermal
conductivity, concrete can be considered as a good fire resistance structural
material (fib 2007). It can protect reinforcing steel against the fire by theconcrete cover layers (Ünlüoğlu et al. 2007). Therefore it is possible to91AugustAugust2015,
2015,Thailand
Thailandreuse after repairing and retrofitting reinforced concrete structures that havebeen exposed to fire.
In order to supply the necessary data for retrofitting the structure, itis essential to estimate the residual behavior of RC member after exposure
to fire. The properties of the constituent materials of RC member, concreteand steel, in terms of strength and stiffness are progressively reduced by theincreasing temperature (Hsu & Lin 2006). The stress-strain curve of
concrete is strongly effected under fire and a number of stress-strain relation
models for concrete have been suggested such as Chang et al. 2006;
EN1994-1-2 2005; Nechnech et al. 2002; Youssef & Moftah 2007. After
exposure to elevated temperature, concrete cannot recover its initiallymechanical properties. With respect to steel rebar, the effect are alsosignificantly by the temperature increase, the yield strength and the modulus
of elasticity will be reduced. But it can recover almost the original
mechanical properties after cooling to room temperature (fib 2008).
However, in a report of stress-strain curves for steel bars after cooling fromvarious elevated temperatures to room temperature (Qiang et al. 2012), thereduction in yield and ultimate strengths become notable when heating
temperature are 500°C and above.
This paper carries out an experiment work as a part of a study forrepairing and retrofitting reinforced concrete slab after exposure to fire (Thiet al. 2015). The research aims to evaluate the flexural behavior of RC slabs
through of the bending test. The experiment conducts on the specimens
before and after fire whereby the load-displacement curves can be comparedfor both of cases. The experimental data will be useful for an evaluation to
propose the effective method for repairing fire-damaged RC slab. Besides, a
simple numerical investigation utilizing non-linear finite element (FE)analysis is carried out using ABAQUS software and verified withexperimental data. The model can be used to predict the state of RC slab
after fire and also will be possible to develop for making a simulation thatcan predict the flexural behavior of fire-damaged RC slab after repairing.

2. EPERIMENTAL SET-UP
Two RC slabs were designed under standard ACI 318-95 (ACI 1995) asshown in Fig. 1. The slabs were casted by using ready mixed concrete withthe concrete strength of 240 Kg/cm2. They had the dimension of
1000x900x150 mm and were reinforced longitudinally in flexure with 10
mm diameter steel bars. The specimens had been cured for 28 days before
conducting fire test in a furnace. The installation and process of fire test
were operated under ASTM E119 standard (ASTM 2000). These slabs had
been fired for 1 hour and 30 minutes to reach the peak point of the timetemperature curve of about 1000°C by using a furnace (Fig. 2). Moreover,
the thermocouples also were installed at 4 different depths (0, 25, 75 and125 mm) from the exposure surface to record the temperature during the test.
The details of slabs are reported in Table 1.

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Table 1: The details of specimens

Specimens
Concrete strength (MPa)Fire time (minute)Slab SC1
2400

Slab SF124
90

Figure 1: The design of reinforced concrete slab

Figure 2: The furnace used for the fire test

Figure 3: The set-up of flexural test on slab

Experiment and simulation study on flexural behavior of reinforced concrete slab before
Experimentandafter fireand simulation study on flexural behavior of reinforced concrete slab beforeand after fire93AugustAugust2015,
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ThailandAfter fire test, the slab would be cured in room temperature for one
month then move to the flexural test. The simply supported slabs were
tested under three-point bending test to determine the load-deflection curves(Fig. 3). The applied load was controlled by a hand-operated hydraulic jackwith a maximum capacity of 500 KN under a loading rate of 700 Kg/min
and was monitored by load cell. An LVDT was installed at the middle pointof the top of the slab and two other LVDTs were placed at 1/4 point of the
span to obtain the deflection profiles.
3. FINITE ELEMENT MODELLING
3.1 Element description
The developed three-dimensional (3D) FE models were createdusing the finite element code ABAQUS/Explicit (Simulia 2014). The
concrete was simulated using a solid element that was defined by eight
nodes (3D 8-Node) and each node had three translational degrees of
freedom in x, y and z directions. Besides, the 3D 2-Node truss element wasused to simulate the steel reinforcement bars. The steel reinforcement bars
were fully embedded in the concrete. The 3D 8-Node element also was used
to simulate the steel support and loading for slab.

3.2. Geometry of the developed FE model
As part of the research presented in this paper, 2 FE models of RC
slabs before and after fire were developed and the numerical solutions had
been correlated with the experimental data. The models had the same

geometry and boundary conditions of the tested simply supported slabs.
Two steel supports and one steel loading bars were modeled. The details ofmodel are shown in Figure 4.
(a)(b)
Figure 4: The model of RC slab(a) FE model with applied loading and boundary conditions.
(b) The reinforcement bars of slab model.

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3.3. Constitutive models for concrete
The Concrete Damaged Plasticity (CDP) model was used in FE code
ABAQUS to define the concrete behavior (Simulia 2014). It assumes thatthe main two failures mechanisms are tensile cracking and compressive
crushing of the concrete material. The evolution of the yield surface iscontrolled by two hardening variables, and (tensile and compressive
equivalent plastic strain), linked to failure mechanism under tension andcompression loading respectively. The model is based on the assumption ofscalar (isotropic) damage and is designed for applications in which theconcrete is subjected to arbitrary loading condition, including cyclic loading.
The model takes into consideration the degradation of the elastic stiffness
induced by plastic straining both in tension and compression.
In the present study, the Model Code (fib 2010) is adopted to define
the uniaxial compressive stress-strain relationship of concrete. The
compressive response of concrete is assumed to be linear elastic until
reaches the uniaxial yield stress and follows by a strain-hardening curve up
to the peak compressive stress, then a descending branch represents thepost-peak softening behavior of concrete.

Figure 5: Stress-strain diagram for uniaxial compression (fib 2010)
where σc = compression stress; εc = compression strain; fcm = maximum
compressive stress; Ec1 = secant modulus from the origin to the peakcompressive stress fcm; Eci = tangent modulus; εc1 = 0.0022; σc,lim = 0.5fcm;
εc,lim = the compression strain at the stress σc,lim.
Regarding to the tensile behavior of concrete, it is assumed to belinear elastic before cracking then follows by a descending branch. A
tension model proposed by Hsu is used in this study (Wang & Hsu 2001) asshown in Fig. 6 where the ascending and descending branches are given as: 1 EC 11   cr
(01)
0.4

 
 1 f cr  cr 
1   cr(02) 1 
where σ1 = tension stress; ε1 = tension strain; Ec = modulus of elasticity ofconcrete; fcr = cracking stress of concrete; εcr = cracking strain of concrete.

Experiment and simulation study on flexural behavior of reinforced concrete slab before
Experimentandafter fireand simulation study on flexural behavior of reinforced concrete slab beforeand after fire95

AugustAugust2015,
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ThailandFigure 6: Tensile stress-strain curve of concrete (Wang & Hsu 2001)
When concrete expose to elevated temperatures, its stress-strain behavior
will change due to the decrease of modulus of elasticity, compressive
strength and tensile strength. The tensile strength at temperature  is taken
as 0.1 of the compressive strength at temperature Dai et al. 2014 A
stress-strain diagram of concrete under compression at elevatedtemperatures can be obtained based on the Eurocode model (EN1994-1-2
2005):       3   c , f c , 3  c ,  / 2   c ,  (03)   cu ,     cu ,  
f c ,  kc , f c(04)
where σc, = compression stress at temperature fc, = compression
strength at temperature εccompression strain at temperatureεcuthe strain corresponding to fc,k = the reduction factor; fc =compression strength at ambient temperature.
With respect to simulate the fire-damaged slab, the temperature distribution
from the exposure surface is considered. The maximum temperatures
measured versus the cross section concrete depth during a fire test arereported in Table 2. In this paper, a simple method is used to model the slab
after fire by dividing the slab to be many layers. At each layers, the stressstrain relation of concrete will change corresponding to the temperature
distribution versus the depth of cross section. The detail is shown in Figure
7.

Table 2: The maximum temperatures measured versus the cross section
concrete depthDistance from exposure surface (mm)
0
25
75
125

96
96

Max. Temperature
949
358.4
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Figure 7: Stress-strain application range for each layers
3.4. Constitutive models for steel
The typical stress-strain relationships for steel bar is shown in Fig. 8.
The steel bar has approximately linear elastic behavior at low strain
magnitudes. At higher strain magnitudes, it begins to have nonlinear, whichis referred to as plasticity. The shift from elastic to plastic behavior occur ata yield point. Once the stress in the steel exceeds the yield stress, plastic
deformation begins to occur. The stiffness of the steel decreases once thematerial yields. After heating and cooling to the temperature, it may have anotable decrease in yield strength but the influence on the modulus ofelasticity is generally less significant (Tao et al. 2012). In general, the shapeof the stress-strain curve of steel remains the same as before heated.

Figure 8: Stress-strain curve for steel (Neale et al. 2005)
4. RESULTS AND DISCUSSION
4.1. Experimental results
After fire test, the crack and spalling could be observed on the fire
exposure surface but the damaged level was not much. Only 12% of thesurface area was spalling. Then the flexural test on control specimen andheated slab was conducted and the results are shown in Fig. 9.

Experiment and simulation study on flexural behavior of reinforced concrete slab before
Experimentandafter fireand simulation study on flexural behavior of reinforced concrete slab beforeand after fire97AugustAugust2015,
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ThailandBefore fire

After fire

Figure 9: The load-deflection curve of slabs SC1 and SF1
In regard to the control slab SC1 after bending test, its failure
patterns were typical bending cracks that located in the maximum momentzone. This type of failure can be observed with respect to any normal
flexural reinforced concreted members. In case of the fire-damaged slab SF1,
the bending cracks were also observed similar to the failure pattern ofspecimen SC1. However, the cracks appeared early due to the loss of load
capacity of the slab after fire.
4.2. Modelling results
The results from the FE analysis program are shown in Fig 10 in acomparison with experimental data. As it can be seen in the figure, the resultof modelling in case of unheated slab is in a good agreement with theexperimental curve. After cracking both modelling and experimental curves
have rather the same inclinations until yielding of steel bars and they show
the same cracking plateau after that. In order to the stress-strain relation offire-damaged slab, the curve of modelling shows an approximate shape withthe curve of experimental results. This indicates that the modelling method
as mentioned above can be acceptable with respect to simulate the firedamaged slab.

Figure 10: The results of modelling and experiment of RC slabs
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5. CONCLUSION
In this paper, the remaining strength of reinforced concrete slab after thethermal loading is evaluated at the room temperature and compare with theunheated slab. The simulation on flexural behavior of unheated and heatedslabs are performed by using 3D finite element analysis program ABAQUS.
The stress-strain-temperature relation for concrete is applied and thefollowing conclusions are made: Due to the effect of thermal loading, the loading capacity of firedamaged slab reduced and is lower the maximum value ofcontrol specimen. Besides, its stiffness was less than the value
obtained for unheated slab.
 The results of modelling indicated a good agreement with theexperimental curves in both of cases.
Therefore
thestress-strain-temperature
relationmodelscorresponding to the temperature distribution on cross section depth of slab
is suitable to develop a model for the prediction of flexural behavior on RC
slab exposure to fire.
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ASTM, 2000. "ASTM E119: Standard Test Methods for Fire Tests of
Building Construction and Materials." West Conshohocken,
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Dai J.-G., Gao W.-Y. and Teng J., (2014). "Finite Element Modeling ofInsulated FRP-Strengthened RC Beams Exposed to Fire." Journal of
Composites for Construction,
EN1994-1-2, 2005. "Eurocode 4: Design of composite steel and concrete
structures - Part 1-2: General Rules - Structural Fire Design."
London, UK: British Standards Institution.
fib, 2007. "Fire design of concrete structures - materials, structures andmodelling."
fib, 2008. "Fire design of concrete structures - structural behaviour andassessment." Switzerland: the International Federation for Structural
Concrete (fib).
fib, 2010. "Model Code 2010." Fédération Internationale du Béton
fib/International Federation for Structural Concrete.
Hsu J.H. and Lin C., 2006. "Residual bearing capabilities of fire-exposed
reinforced concrete beams." International Journal of Applied
Science and Engineering, 4, 151-163.
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Neale K., Ebead U., Abdel Baky H., Elsayed W. and Godat A., (2005).
"Modelling of debonding phenomena in FRP-strengthened concretebeams and slabs." International Institute for FRP in Construction,
Nechnech W., Meftah F. and Reynouard J., 2002. "An elasto-plastic damage
model for plain concrete subjected to high temperatures."Engineering structures, 24, 597-611.
Qiang X., Bijlaard F.S. and Kolstein H., 2012. "Post-fire mechanicalproperties of high strength structural steels S460 and S690."
Engineering structures, 35, 1-10.
Short N., Purkiss J. and Guise S., 2001. "Assessment of fire damaged
concrete using colour image analysis." Construction and Building
Materials, 15, 9-15.
Simulia, 2014. " ABAQUS Version 6.13-4 and ABAQUS user’s manual."
Hibbitt Karlsson & Sorensen Inc:
Tao Z., Wang X.-Q. and Uy B., 2012. "Stress-strain curves of structural andreinforcing steels after exposure to elevated temperatures." Journalof Materials in Civil Engineering, 25, 1306-1316.
Thi C.N., Pansuk W. and Torres L., 2015. "Flexural Behavior of FireDamaged Reinforced Concrete Slabs Repaired with Near-Surface
Mounted (NSM) Carbon Fiber Reinforced Polymer (CFRP) Rods."
Journal of Advanced Concrete Technology, 13, 15-29.
Ünlüoğlu E., Topcu I.B. and Yalaman B., 2007. "Concrete cover effect on
reinforced concrete bars exposed to high temperatures."
Construction and Building Materials, 21, 1155-1160.
Wang T. and Hsu T.T., 2001. "Nonlinear finite element analysis of concrete
structures using new constitutive models." Computers & structures,
79, 2781-2791.
Youssef M. and Moftah M., 2007. "General stress–strain relationship for
concrete at elevated temperatures." Engineering structures, 29,
2618-2634

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7th

THE KEY INDEX FOR RATIONALBRIDGE MAINTENANCE BY
MUNICIPALITIES IN JAPAN
Riko Sakata
Bachelor’s 4th grade student, Nagai lab, ICUS, IIS,The University of Tokyo, Japan
sakata@iis.u-tokyo.ac.jp

ABSTRACTJapan has almost 700,000 bridges over 2m spans. Many of themwere constructed in high economic growth period around 1970s, anddamaged due to severe environment, fatigue loading, inappropriate
maintenance etc. It becomes important to establish the rational and feasible
maintenance strategy considering the damaged condition of bridges, social
condition and capability of managers.
Most bridges are managed by municipalities in Japan. Thinking
about their efficient maintenance, there are 4 steps; specify the bridges weshould care, decide the management level we need, how we improve it, andconsider how we implement it in practice. The management level means
how well the municipalities responsible for their bridge maintenance, and it
was evaluated by the interview in rural municipalities in Niigata, Japan. It
was found that situation in each municipalities is totally different as well as
the population, area, number of brides so that the rational maintenancemethod should be established corresponding to the situation. For it,
indexation of the factors to be considered is useful to evaluate therationality of the maintenance framework.
This study focuses on 1st and 2nd steps above. For the first step,indexation of condition of bridges, basic data of bridges such as number,
length, bridge type, damage level etc. are selected because municipalities
usually have their data. Then, the number of important bridges to be caredis calculated. Through the comparison it with the management level, ideal
level of maintenance by referring some good municipalities are proposed.
Keywords: road bridge deterioration, maintenance, municipalities,
management level, important bridge101AugustAugust2015,
2015,Thailand
Thailand1. INTRODUCTION
We Japan have almost 700,000 bridges over 2m spans. Since many of them
are constructed during high economic growth by early 1970s, almost 70%
will be over 50 years old in 20 years, which is said to be the life span of thebridge. (Figures 1 and 2) So the cost of the bridge maintenance will increase
dramatically due to the damaged from chloride attach or long-time fatigue
loading, and other aren’t managed appropriately. In order to manage themeffectively and keep the bridge safety, we need to think about the rationaland feasible maintenance strategy.

Figure 1: Number and the age of bridges in Japan (MLIT, 2013)

Figure 2: Deterioration of bridge

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2. MANAGEMENT OF BREDGES BY SMALL MUNICIPALITIESAbout 70% bridges are managed by small municipalities in Japan as Figure
3 shows, but they don’t have enough experts. In many small municipalities,some staff deal with both technical work and deskwork, and no one haveenough technical knowledge or skill. It is necessary to consider thissituation in thinking about bridge maintenance.

Figure 3: Manager of Bridges in Japan (MLIT, 2013)

Figure 4: Manager of Bridges in Japan (MLIT, 2013)
In the near future, more and more bridges have the deterioration withaging, and much more cost will be needed to repair them. In order to deal
with this situation efficiently, each municipality has established the bridge
maintenance plan by 2013. And as of 2015 they have mostly finished theinspection of their bridges over 2m span and some of them start repairingthem based on the plan.

The key index for rationalrational bridge
bridge maintenancemaintenanceby
bymunicipalities
municipalitiesininJapan
Japan

103

AugustAugust2015,
2015,Thailand
ThailandIn previous research, Nakashima (2013) inquired the situation about
how small municipalities in Niigata prefecture manage bridges. Through theinvestigation, each municipality is scored the management level in terms of
how they can judge to meet the actual situation in the 4 steps shown in
Figure 5, although the system outputs some strategy automatically. Higherscore means that municipalities can judge by themselves in each step and it
was found the management level varies depending on the population and thenumber of bridge.

Figure 5: Flow of judgment (Nakashima, 2013)
Management
level

A
DamageB
Importance
Changeinspection
depending onimportanceC
PriorityD
How to repair
Examine/Arrange among
municipalities,
judgment○

2

Inspection by
staff

△

1

Inspection by
staff

Intensify thesystemArrange only
budget×

0

Don’t check theresults/Only
check

The level insystemOrder in system

Design by staff
Accept consultant’s
proposal（Some Action）
Accept consultant’s
proposal（No action）

Figure 6: Management level (Nakashima, 2013)

3. METHOD OF THIS STUDY
3.1 Introduction
There are many varieties of factors, such as physical condition,
potential manager and social condition, in thinking about the bridge
maintenance. 4 steps are proposed here for the rational and feasible strategy:
specify the bridges we should care, decide the management level we need,
how we improve it, and consider how we implement it in practice. In thisstudy the first 2 steps are treated. In Niigata prefecture, Niigata Construction
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Technology Center collects the inspection data from small municipalities
and supports them. The date is used to make a suggestion about the rational
strategy of bridge maintenance through the data.
3.2 Importance of the bridge
In the bridge maintenance plan, bridge importance is divided in
Division 1/2/3/4, based on physical and social condition, such as length,
bridge type and the volume of traffic. Division 1 is the most important
bridges, and municipalities should manage them with more cost.
Municipalities usually judge which bridges need repair by next inspection in
5 years with the matrix in Figure 7. Figure 8 shows the number of thesebridges in small municipalities in Niigata prefecture with the bridge need tobe repaired.

Division

Damage Level
E

C3

C2

C1

B2

1

①

⑤

⑨

⑫

⑭

2

②

⑥

⑩

⑬

3

③

⑦

⑪

4

④

⑧

B1

Figure 7: Matrix for repair priority with Damage Level and Division

The key index for rationalrational bridge
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AugustAugust2015,
2015,Thailand
ThailandFigure 8: Number of bridges(Total/Need repair by next inspection)
3.3 Management level and bridge in municipalities
Table 1 and Figure 9 show the relationship with the managementlevel investigated by Nakashima and the number of the bridges which needrepair by next inspection. It can be said that the more bridges municipalities
have, the higher management level they need for maintenance. Eachmunicipality has different situation, and smaller municipalities has limited
staff and budget. So smaller municipalities should rely on outsourcing
instead of gaining staff, while bigger ones should assign enough staff andimprove technical skill so that they can manage inhouse.
Number ofbridgesNagaoka
Sado
Murakami
Minamiuonuma
Tokamachi
Itoigawa
Shibata
TsubameKashiwazaki
Agano
Uonuma
Gosen

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Population

Total

Need
repair279,845
60,41564,73060,59357,35846,438100,035
81,18389,76844,72039,16353,316

2,037
755
862
601
325
533
888
609
538
490
559
415

457
163
143
113
106
102
100
95
93
91
87
76

Management level
DamageImportance

Priority

How toRepair

Total
Score

1
0
0
1
0
0
2
1
1
0
0
0

1
1
1
2
2
2
1
2
1
1
0

2
2
2
2
2
2
2
2
1
2

2
1
2
2
1
2
2
2
1
0
0

6
4
0
4
6
5
8
6
7
4
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Sanjo
Sekigawa
TainaiKamo
Aga
YahikoYusawaOjiya
Tagami
MitsukeIzumozaki
TsunanSeiro
KariwaNiigata
Joetsu
Myoko
Awashima

100,8446,244
30842
29,085
12,7238,404
8,351
37,712
12,577
41,294
4,759
10,57013,914
4,781
811,386
201,153
34,545
344

681
184
332
248
155
178
104
163
188
183
88
69
96
42
4,093
1,146
285
2

58
51
51
44
40
34
32
30
25
15
14
10
8
3
-

1
0
2
2
0
0
0
0
0
0
0
0
0
1
0
1
-

0
2
1
1
1
0
0
0
0
2
1
0
1
2
2
0
-

0
1
0
1
1
0
2
1
0
1
1
1
2
-

0
2
1
1
0
0
0
0
1
2
1
0
0
2
0
2
-

1
4
4
4
1
1
1
0
3
5
2
0
2
6
3
5
-

Table 1: Population/Number of bridges/Management level in small
municipalities in Niigata prefecture

The number of bridges(which needs repair by next inspection)
8

Shibata
Kashiwazaki

7

Management level

6

Nagaoka

Tokamachi

5

Izumozaki

Itoigawa

4

Sado

3
Mitsuke

2

Sanjo

1

Murakami
Tagami

0
0

50

100

150

200

250

300

350

400

450

The number of bridges(need repair)Figure 9: Management level with the number of bridges need to be repairedby next inspection
4. CONCLUSION
In municipalities, the management level is different depending on how
many important bridge they have, and this seems the key index for the

The key index for rationalrational bridge
bridge maintenancemaintenanceby
bymunicipalities
municipalitiesininJapan
Japan

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AugustAugust2015,
2015,Thailand
Thailandbridge maintenance by small municipalities. Each municipality has different
situation so they need the different way of the maintenance. They should
improve the management level as they can. And understanding theirposition compared with other municipalities helps them efficient bridge
maintenance.
REFERENCES
Nakashima Mari, 2013, The inquiry of the bridge maintenance bysmall municipalities and the analysis of the management level
Ministry of Land, Infrastructure and Transport, 2013, The
maintenance of the road infrastructure(Bridge)
http://www.mlit.go.jp/road/sisaku/yobohozen/yobo1_1.pdf

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7thEFFECT OF FREE LIME CONTENT ON
MECHANICAL PROPERTIES OFCEMENT-FLY ASH MIXTURES
Adnan NAWAZ a, Parnthep JULNIPITAWONG b,
Somnuk TANGTERMSIRIKUL caDoctoral student, School of Engineering and Technology,
Asian Institute of Technology
haveadnan@gmail.combResearch faculty, Construction and Maintenance Technology ResearchCenter, School of Civil Engineering and Technology,
Sirindhorn International Institute of Technology, Thammasat UniversitycProfessor, School of Civil Engineering and Technology,
Sirindhorn International Institute of Technology, Thammasat University

ABSTRACTThis study highlights the effect of free lime content in fly ash on themechanical properties of cement-fly ash. A detailed experimental program
was carried out in order to evaluate the effect of free lime content in fly
ashes with different SO3 contents on the mechanical properties including
initial and final setting times, water requirement and compressive strengthof cement-fly ash mixtures. Four distinct types of fly ashes were obtained
from two different sources, and free lime was added to obtain overall freelime contents of 5%, 7% and 10% for each type of fly ash. Tests wereconducted with two fly ash replacement percentages (20% and 40%).
Experimental results revealed that an increase in the free lime content
resulted in reduced water requirement. Higher free lime content also
resulted in faster setting times and improved compressive strength.
Keywords: Fly ash, Free lime, Mechanical Properties
1. INTRODUCTION
In Thailand, one of the main production source of fly ash is Mae Moh power
plant of the Electricity Generating Authority of Thailand (EGAT) which
contributes around 90% of total national production. Recently, fly ash
obtained from this source has shown increased content of free lime, which
may affect the mechanical and durability properties of cement-fly ash
mixtures. Kaewmanee et al. (2013) conducted a comprehensive study on
properties of fly ash mixes, with various free lime contents and reported a
set of experimental results to clarify the effect of free lime content on fly

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AugustAugust2015,
2015,Thailand
Thailandash mixes. Results showed that basic properties (normal consistency andwater demand) were not much affected by free lime added.
Moreover, fly ash obtained from the Mae Moh power plant has also
shown higher sulfur trioxide (SO3) content, which may ultimately result incompromised long-term properties. There are only a very few studies thatsimultaneously consider the effect of high free lime and high SO3 content in
fly ashes on mechanical properties of cement-fly ash mixtures. This study is
an attempt to further explore the knowledge of the effect of high free lime
content in different fly ashes of Thailand, particularly in the case of fly
ashes with different sulfur trioxide (SO3) contents, on the mechanical
Table 2 Chemical compositions of ordinary Portland cement type I, Mae Moh fly ashes A,
B, C and Rayong fly ash R.

properties of the cement-fly ash mixtures.

Table 1: Physical properties of ordinary Portland cement type I, Mae Moh
fly ashes A, B, C, Rayong fly ash R and free lime.

Physical properties

Specific gravity
Blainefineness(cm2/g)

OPC
type I
3.15
3100

Fly ash
A
2.21
2867

Fly ash
B
2.57
2820

Fly ash
C
2.57
2722

Fly ash
R
2.17
2723

Free
lime
2.96
3749

2. EXPERIMENTAL PROGRAM
2.1 Materials
Four primary fly ashes F(A), F(B), F(C), and F(R) from two
different sources and an ordinary Portland cement (OPC) type I, were usedin this study. F(A), F(B), and F(C) were obtained from the Mae Moh power
generating plant of the Electricity Generating Authority of Thailand(EGAT) in Lampang province (located in the north of Thailand), whereas
F(R) was obtained from the Rayong coal burning thermal power plant inRayong province (Eastern part of Thailand). Tables 1 and 2 show thephysical properties and chemical compositions of the materials utilized.
Based on chemical requirements of TIS 2135, F(R) was categorized
as Class 2a (low CaO fly ash) whereas F(A), F(B) and F(C) were classified
as Class 2b (high CaO fly ash). The amount of sulfur trioxide (SO3) in F(B)and F(C) exceeded the maximum allowable limit of 5%, as specified by TIS
2135.
Free lime was added in F(A), F(B) and F(C) to prepare fly ashes
having total free lime contents of 5%, 7% and 10%, respectively. River sandcompatible with ASTM C33-92a, with a specific gravity of 2.60 was used as
the fine aggregate.

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ICUS Report 79
Chemicalcompositions %
SiO2
Al2O3
Fe2O3
CaO
MgO
Na2O
K2O
SO3
LOI
Free lime
Equivalent sodium oxide(Na2O + 0.658 K2O)

OPC type
I
18.93
5.51
3.31
65.53
1.24
0.15
0.31
2.88
0.75
0.35

Fly ash
A
35.71
20.44
15.54
16.52
2.00
1.15
2.41
4.26
0.49
1.71
2.74

Fly ash B

Fly ash C

26.61
13.6
18.34
24.97
2.33
1.75
1.77
8.53
0.53
3.93
2.91

25.22
13.88
17.39
26.25
2.38
1.40
1.92
9.44
0.56
3.06
2.66

Fly ash
R
61.46
20.27
5.56
1.73
0.96
0.73
1.36
0.38
5.38
0.03
1.62

3. RESULTS AND DISCUSSION
3.1 Scanning electron microscope (SEM) images
It can be observed in Figure 1a that the particles of fly ash obtained
from the Mae Moh electricity generating power plant are round or sphericalshape while irregular particles of free lime can be seen, dispersed around fly
ash particles. Figure 1b shows that the Rayong fly ash particles are
relatively less spherical than those of the Mae Moh fly ash.

(a) Mae Moh fly ash(b) Rayong fly ash
Figure 1: SEM images
3.2 Water requirement
Water requirement results (Figure 3a) show that to attain same flow,
mixtures of primary fly ashes from Mae Moh, F(A), F(B), and F(C) required
a lower amount of water as compared to the cement-only mixtures, due tospherical particle shape. F(R) mixtures required more water than the
cement-only mixtures in order to achieve the same flow. The waterrequirement of fly ash mixtures further reduced as the replacement
percentage of fly ashes is increased from 20% to 40%, except for F(R)mixtures where 40% fly ash replacement showed higher water requirement
than that of 20% replacement. Figure 3b-d show the effect of addition offree lime in fly ashes on the water requirement of mixtures. Mixtures with ahigher amount of free lime required more water to attain the same flow.
Effect of free LIME content on mechanical properties of cement-fly ash mixtures
Effect of free LIME content on mechanical properties of cement-fly ash mixtures

111

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100.51100.51
100.20100
94.97 94.53
94.97
94.88
94.88 100.20
94.53
91.05 88.28
91.05
89.00
88.28 89.00

125
100
75
50
25
0

100125
100
75
50
25
0

Water requirement %100125
100
75
50
25
0

Water requirement %

125
100
75
50
25
0

Water requirement %Water requirement %August2015,
ThailandAugustAugust2015,
2015,
ThailandThailand

100
97.03
96.16
96.16 97.03
95.45
94.97 95.45
94.97
94.06
92.91
92.91 94.06
92.22
91.05 92.22
91.05

100
95.74
95.00
95.00 95.74
94.64
94.53 94.64
94.53
91.02
89.62
89.62 91.02
88.69
88.28 88.69
88.28

125
100
75
50
25
0

100125
100
75
50
25
0

Water requirement %100125
100
75
50
25
0

Water requirement %

125
100
75
50
25
0

Water requirement %Water requirement %(a) Mixtures(a) Mixtureswith withprimary
primary
fly ashes
fly ashes (b) Mixtures(b) Mixtureswith with
F(A)F(A)basedbased
fly ashes
fly ashes
100
95.55
95.08
95.08 95.55
94.88 94.91
94.88
94.91
91.18
89.87
89.87 91.18
89.17
89.00 89.17
89.00(c) Mixtures(c) Mixtureswith with
F(B) F(B)basedbased
fly ashes
fly ashes(d) Mixtures(d) Mixtureswith with
F(C) F(C)basedbased
fly ashes
fly ashes
FigureFigure3: Water
3: Waterrequirement
requirementof mortarof mortar
mixturesmixtures3.3 Initial
3.3 Initialand final
and finalsettingsettingtimestimes
FigureFigure4a shows
4a shows
that primarythat primary
fly ashflymixtures
ash mixturesof F(A),of F(A),
F(B),F(B),
F(C),F(C),and F(R)and F(R)exhibitedexhibited
longerlongersettingsettingtimestimes
than thanthe cement-only
the cement-onlymix. mix.
Mixtures
Mixturesof F(B)of F(B)as wellas wellas F(C),as F(C),tend tend
to settofasterset fasterthan than
F(A)F(A)mixturesmixturesbecausebecauseof theof thehigherhighercalcium
calciumoxideoxideand freeand lime
free lime
contents
contents
in F(B)in F(B)and F(C).
and F(C).
In contrast,
In contrast,
F(R) F(R)mixturesmixturesset slower
set slower
becausebecauseof itsoflower
its lower
calcium
calciumoxideoxide
(CaO)
(CaO)
and freeand freelime lime
contents
contents
and andhigherhigherwaterwaterrequirement.
requirement.
The Theincreaseincreaseof flyof ashfly ashpercentages
percentages
fromfrom
20% 20%to 40%
to 40%
resulted
resulted
in longerin longer
settingsettingtimes.times.
The Theinfluence
influenceof freeof limefree lime
addition
additionon setting
on setting
time time
is obvious
is obvious
in Figurein Figure4b-d.4b-d.
It It
can becanseen
be seenthat addition
that additionof freeof limefree lime
causedcausedfasterfastersettingsettingtimes.times.
109

C80F(R)20C80F(R)20

145

109

C80F(A10)20
145C80F(A10)20

70

70

108

108

C80F(B10)20
C80F(B10)20

68

108

68

108

C80F(C)20C80F(C)20

85

85
130

C80F(A7)20130 C80F(A7)20

77

77
123

C80F(B7)20123 C80F(B7)20

72

72
117

117

C80F(B)20C80F(B)20

80

80
127

127

C80F(A5)20
C80F(A5)2082

82

C80F(B5)20127 C80F(B5)20

76

76
123

123

C80F(A)20C80F(A)20

91

91
136

136 C80F(A)20C80F(A)20

91

91
136

136 C80F(B)20C80F(B)20

80

80
127

127

72

C100

72

C100

72

72

C100

C100
0

72

101

101

50 0 10050 150100 150
Time (minutes)
Time (minutes)

C100
0

72

127

101

101

50 0 10050 150100 150
Time (minutes)
Time (minutes)

C100
0

101

101

50 0 10050 150100 150
Time (minutes)
Time (minutes)(i) 20%(i) 20%fly ashflyreplacement
ash replacement
C60F(R)40C60F(R)40

131
131 C60F(A10)40
C60F(A10)40
164
164

74

114

C60F(C)40C60F(C)40

94

94
142

C60F(A7)40
142 C60F(A7)40

88

C60F(B)40C60F(B)40

88

88
140

C60F(A5)40
140 C60F(A5)40

95

104

C60F(A)40C60F(A)40
C100

C100
0

72

101

104
146
146 C60F(A)40C60F(A)4072

101

50 0 10050 150100 150
Time (minutes)
Time (minutes)

C100

C100
0

C60F(B10)40
114 C60F(B10)40

74

88

C60F(B7)40
127 C60F(B7)40

77

77
124

124

95

C60F(B5)40
137 C60F(B5)40

80

80
132

132

104

146 C60F(B)40C60F(B)40

88

88
140

140

101

C100

127

104
72

74

101

137

146
72

50 0 10050 150100 150
Time (minutes)
Time (minutes)

C100
0

72

114

101

74

72

114

101

50 0 10050 150100 150
Time (minutes)
Time (minutes)(ii) 40%(ii) 40%
fly ashflyreplacement
ash replacement(c) F(B)(c)based
F(B)flybased
ash fly
mixturesash mixtures(a) Primary(a) Primary
fly ashfly ash (b) F(A)(b) F(A)basedbased
fly ashfly ashmixturesmixturesmixturesmixtures
FigureFigure4: Initial
4: Initialand final
and finalsettingsettingtimestimesof differentof different
mixturesmixtures112 112112

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79Report 79

3.4 Compressive
strength strength
3.4 Compressive
Figure 5Figure
shows thecomparisonof compressive
strength (atdifferent
5 shows
the comparisonof compressive
strength(at differentages) of cement-only
mixtures mixturesand primary
ash mixtures.
Higher calciumages) of cement-only
and fly
primary
fly ash mixtures.
Higher calciumoxide (CaO)
free andlimefreecontents
and lowerandwaterrequirementin F(B) in F(B)oxideand(CaO)
lime contents
lower
water requirement
and F(C)andas comparedto F(A) contributed
to highertocompressive
strength strength
F(C) as comparedto F(A) contributed
higher compressive
than F(A)than
mixtures.
F(R) mixtures
showed lower
compressive
strengthsstrengths
at 1,
F(A) mixtures.
F(R) mixtures
showedlower compressive
at 1,
7, and 287,days
the thanmixtureswith Maewith
MohMaefly Mohashes.flyHoweverF(R)and than
28 days
the mixturesashes. HoweverF(R)mixtures mixtures
showed higher
because becauseof higherofSiO
showed91-day
higher strength
91-day strength
higher
SiO2 content
2 content(61.4%) leading(61.4%)toleading
more pozzolanic
to more pozzolanic
reaction inreaction
the longin term.
the long term.

C100

C80F20C100

C80F20C60F40C60F40(a) 1 day (a) 1 day(b) 7 days(b) 7 days (c) 28 days(c) 28 days (d) 91 days(d) 91 days
Figure :5Figure
Compressive:5 Compressive
strength ofstrengthmortarofspecimens
mortar specimens
with primarywith flyprimaryash mixes
fly ash mixes

18.3

13.6

10
0

C100

9.3

F(A)

40
30
14.0
20

10

70
60
50
40

38.8
31.4

30

18.3
14.5
14.0
13.614.8
2014.5
10.1
10.5
10.1
10.5
9.311.2

0F(A5) C100
F(A7) F(A)
F(A10) F(A5)

10

60
50
40
31.9
30

23.5
14.8
11.2

0
F(A7) C100
F(A10) F(A)(a) 1 day (a) 1 day

70

38.8
32.2
23.7

31.432.6

24.0

31.9

70
60
4032.223.730

24.4
23.5

20
10

0
F(A5) C100
F(A7) F(A)
F(A10) F(A5)

47.3

50

44.3
32.6

24.0

70
60
44.8
50

37.3

24.4

20
10
0
F(A7) C100
F(A10)F(A)4070Compressive strength (MPa)20

50Compressive strength (MPa)30

60Compressive strength (MPa)4070Compressive strength (MPa)50Compressive strength (MPa)60

Compressive strength (MPa)70Compressive strength (MPa)Compressive strength (MPa)Figure 6Figure
indicates
6 indicatesthat in thethat
case
in of
theF(A),case ofan F(A),
increase
an inincrease
free limein freecontent
lime content
resulted resulted
in improvedin improved
compressive
compressive
strength. strength.
The improvement
The improvement
is more is more
obvious at
obviousan early
at agean earlyof 1-day.
age of
In1-day.
the case
In oftheF(B),case anof increase
F(B), an inincrease
free limein free lime
content resulted
content inresulted
increasedin increased
compressive
compressive
strength atstrength
an early
at agean earlyof 1 ageday,of 1 day,as illustrated
as illustrated
in Figurein 7.
FigureAt 7 7.and
At 28
7 and
days,28thedays,
differences
the differencesin the in thecompressive
compressive
strengthsstrengthsof mixtures,of mixtures,with and withwithout
andadded
without
free
addedlime,free
are lime,
not are notsignificant.
significant.
At a laterAt agea laterof 91
agedays,of 91thedays,
compressive
the compressive
strengthsstrengths
are
arerelativelyrelatively
reduced. reduced.
For F(C),ForanF(C),increase
an increasein free limein freecontent
lime caused
content caused
improvedimproved
compressive
compressive
strength. strength.
HoweverHoweverat 28 andat 28
91 anddays,91F(C10)days, F(C10)mixture with
mixture40%withfly ash
40%showed
fly ash relatively
showed relatively
lower compressive
lower compressive
strength asstrength ascomparedcomparedto F(C) mixture,to F(C) as
mixture,shownasinshown
Figurein8.Figure 8.
60
53.7 51.6
51.7
47.345.2
45.9
44.8
44.345.95045.2
38.6
38.6
37.9
37.9
37.4
37.3

37.4

30
20
10

0
F(A5) C100
F(A7) F(A)
F(A10) F(A5)

40
30
20
10

0
F(A7) C100
F(A10)F(A)706053.8 51.851.754.0 51.853.7
54.3
51.6
51.9 53.8 51.8 54.0 51.8 54.3 51.9
50
40
30
20
10
0
F(A5) C100
F(A7) F(A)
F(A10) F(A5)F(A7)

F(A10)(b) 7 days(b) 7 days (c) 28 days(c) 28 days(d) 91 days(d) 91 days
C100

C80F20C100 C60F40
C80F20C60F40

18.3

10
0

C100

15.0

10.1

F(B)

40
30
16.7

20
10

70
60
50
40

38.8

(a) 1 day (a) 1 day

25.4

30

18.316.7
16.6
16.7
16.7
13.5
13.5 20
13.215.0
13.2
12.7
10.1

0
F(B5) C100
F(B7) F(B)
F(B10) F(B5)

34.6
16.6
12.7

10

0
F(B7) C100
F(B10) F(B)

70
60
50
34.2
40

30

38.8
34.2
25.7

34.634.3

25.7

34.2

25.6
25.4

20
10

0
F(B5) C100
F(B7) F(B)
F(B10) F(B5)

70
60

47.3

50
4034.225.730

25.7

45.3

40.8

34.3
25.6

20
10
0
F(B7) C100
F(B10)F(B)

70
60
45.1
50

40

70
60

51.7
47.345.0
45.344.9
45.1
50 45.0
40.8
40.4
40.4
40.4
40.4
40.4

30
20
10
0
F(B5) C100
F(B7)

F(B)
F(B10) F(B5)40Compressive strength (MPa)20

50Compressive strength (MPa)30

60Compressive strength (MPa)4070Compressive strength (MPa)50Compressive strength (MPa)60

Compressive strength (MPa)70Compressive strength (MPa)Compressive strength (MPa)Figure 6:Figure
Compressive
6: Compressive
strength ofstrengthmortarofspecimens
mortar specimens
with F with F(A) based(A)fly based
ash mixes
fly ash mixes
55.4 53.4
44.9
40.4

30
20
10
0
F(B7)C100
F(B10)F(B)70
60 52.9 52.6 52.855.4 53.4
53.6
52.2 52.553.6 52.9
51.7

C80F20C100 C60F40
C80F20

F(B7)

F(B10)

30
20
100
F(B5) C100
F(B7)

F(B)
F(B10) F(B5)

C60F40Figure 7:Figure
Compressive
7: Compressive
strength ofstrengthmortarofspecimens
mortar specimens
with F with F
Effect of freeEffectLIME
of freecontent
LIME
oncontentmechanicalon mechanicalpropertiespropertiesof cement-flyof cement-fly
ash mixtures
ash mixtures
Effect of free LIME content on mechanical properties of cement-fly ash mixtures

52.2 52.5

40(b) 7 days(b) 7 days(c) 28 days(c) 28 days(d) 91 days(d) 91 days
C100

52.6 52.8

50

113

113

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AugustThailand2015,Thailand
Thailand

9.8

10
0

14.7

C100

F(C)

30
20
10

40

(a) 1 day(a) 1 day

32.8

30

16.9 18.3 16.8 14.7 16.5 16.9
16.8
20
13.1
12.8
12.8
12.3 13.1
9.8

0
F(C5)C100F(C7) F(C)F(C10)F(C5)

38.8

10

50
4033.524.0 30

16.5
12.3

0
F(C7) C100
F(C10) F(C)38.8

33.4 32.8 33.3 33.5

25.4

25.4 24.0 25.5 25.4

20
100
F(C5)C100F(C7) F(C)F(C10)F(C5)

60

47.3

50
40
33.4

33.3

30 25.4
20
10

25.5

70
60

47.3
44.6 44.4 44.7 44.6
44.4
5044.642.3
40.8 40.6 38.6 42.3
40.6

0F(C7) C100
F(C10) F(C)

40
30
20
10

0
F(C5)C100F(C7) F(C)F(C10)F(C5)7060Compressive strength (MPa)

18.3

50

70

Compressive strength (MPa)

20

40

60

Compressive strength (MPa)

30

50

60

70

Compressive strength (MPa)

40

60

70

Compressive strength (MPa)5070Compressive strength (MPa)60

Compressive strength (MPa)Compressive strength (MPa)(B) based(B)flybased
ash mixes
fly ash mixes
70

70

55.5 55.1 52.3
54.5 52.0 6055.1 52.3 55.3 51.7
54.5 52.0
51.7
51.7
50.7
44.7
44.6
50
50
40.8
38.6

40
30
20
10

0
F(C7) C100
F(C10) F(C)55.3

C80F20C100

C60F40C80F2020
100F(C5)C100F(C7) F(C)F(C10)F(C5)F(C7)

C60F403.5 Strength
3.5 Strength
activity activity
index index
Figure Figure
9 shows9 thatshows
thethatstrength
the strength
activity activity
indices indicesof mixtures,of mixtures,containing
containing
Class 2aClassfly ash2a[F(R)]fly ashand[F(R)]
Class
and2bClass
fly ashes
2b fly[F(A),
ashesF(B),[F(A),
F(C)],
F(B), F(C)],are higher
arethanhigher
75%,
than
and
75%,
85%andof the85%strengthof the strengthof the referenceof the reference
cement-only
cement-only
mixture mixture
at 28, andat 28,
91 days,and 91
respectively.
days, respectively.
Thus, allThus,
testedallflytested
ashesflyfulfilledashes fulfilled
the strength
the strength
requirement
requirement
of TIS 2135.
of TIS 2135.

(c) 91 days(c) 91 days
Figure :9Figure
Strength:9 Strength
index of index
mortarofspecimens
mortar specimens

4. CONCLUSIONS
4. CONCLUSIONSThis is aThis
preliminary
is a preliminary
study tostudy
investigate
to investigate
and verify
andtheverifyeffecttheofeffectfree limeof free lime
and fly andash flyreplacement
ash replacement
content content
in fly ashes
in flywith
ashes
differentwith different
SO3 contents
SO3 contents
ratios only
ratioson only
the mechanicalon the mechanicalproperties
propertiesof cement-flyof cement-fly
ash mixtures.
ash mixtures.
Further Further
verification
verificationand durability
and durability
testing istestingneededisinneededthe future.
in theConclusions
future. Conclusions
drawn drawn
from thisfrom
study
this
arestudy
as follows:
are as follows:
i. Wateri. requirement
Water requirementof Mae Mohof MaeflyMohashesflyis ashes
loweristhan
lower
thethanRayongthe Rayong
fly ash fly
becauseash becauseof the highof theLOI
high
andLOI
relatively
and relatively
irregularirregularparticle particle
shape ofshape
the Rayongof the Rayong
fly ash. fly ash.
ii. Waterii. requirement
Water requirementof fly ashof flymixturesash mixtures
increasesincreases
as the freeas thelime
free lime
content iscontent
increased,
is increased,iii. Fly iii.
ash Fly
mixturesash mixturescontaining
containing
higher freehigherlime
freecontent
lime content
tend to tend
set to set
earlier. earlier.
iv. Higher
iv. freeHigherlimefreecontent
lime incontent
fly ashin results
fly ashinresults
improved
in improved
compressive
compressive
strength,strength,
particularly
particularly
at early ages.
at early ages.
v. In thev. caseIn theof flycaseashesof flywithashes
a naturallywith a naturallyhigh freehighlimefreecontent,
lime content,ananincreaseincreasein compressive
in compressive
strength strength
due to thedueaddition
to the additionof free limeof freeis lime isless significant
less significant
at a longatagea longof 91agedays.
of 91 days.

114

114

114

50.7

30

Figure 8Figure
Compressive
8 Compressive
strengthstrengthof mortarofspecimens
mortar specimens
with F with F(C) based(C)flybased
ash mixes
fly ash mixes(b) 28 days(b) 28 days

55.5

40

(b) 7 days(b) 7 days(c) 28 days(c) 28 (d)days
91 days(d) 91 days
C100

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vi. All the tested mixtures of fly ashes with free lime content up to 10%
fulfill the compressive strength requirements of TIS 2135.
ACKNOWLEDGEMENTS
The authors would like to thank the Electricity Generating Authority ofThailand for providing research support and fly ash samples for this study.
This research is also supported by Center of Excellence in Material Science,
Construction and Maintenance Technology, Thammasat University, theHigher Education Research Promotion and National Research University
Project, the Office of the Higher Education Commission of Thailand.
REFERENCES
Thai Industrial Standards. TIS 2135 standard Specification for coal
fly ash; 2002.
Kaewmanee K., Krammart P., Sumranwanich T., Choktaweekarn P.,
Tangtermsirikul S. Effect of free lime content on properties of cement–fly
ash mixtures. J Construction and Building Materials Res 2013;38:829-36.
Annual Book of ASTM Standards. ASTM C33 standard
specification for concrete aggregates; 2003.
Annual Book of ASTM Standards. ASTM C187 standard test
method for normal consistency of hydraulic cement; 1998.
Annual Book of ASTM Standards. ASTM C191 standard test
method for time of setting of hydraulic cement by Vicat needle; 1999.
Annual Book of ASTM Standards. ASTM C109/C109M standard
test method for compressive strength of hydraulic cement mortars (using 2in. or [50-mm] cube specimens)
Annual Book of ASTM Standards. ASTM C311 standard test
methods for sampling and testing fly ash or natural pozzolans for use inPortland-cement concrete; 2004

Effect of free LIME content on mechanical properties of cement-fly ash mixtures
Effect of free LIME content on mechanical properties of cement-fly ash mixtures

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THE RELATIONSHIP BETWEEN THE
RAINFALL INTENSITY AND THE SUBWAYPASSENGER RIDERSHIP IN THE SEOULMETROPOLITAN SUBWAY SYSTEM
Kyoung-seon PARK1, Sung-hi AN2, Chang-hyeon Joh3
and Hyun-myung KIM 4
1
Researcher/Transportation Planning Lab, Dept. of Transportation Eng.
College of Engineering, The MyongJi University, South Korea
seon3675@nate.com
2
Graduate Student School of Eng., The MyongJi University, South Korea
3
Ph.D., KyungHee University, South Korea
4
Ph.D., Director, The MyongJi University, South Korea

ABSTRACTThe in-vehicle congestion of the urban railway is the most important
indicator to reflect the operation state of the urban railway. To provide thegood service quality of urban railway, the crowdedness of the urban railway
should be managed appropriately. The weather is one of the critical factors
for the crowdedness. That is because even though the crowdedness of theurban railway is the same, passengers feel more uncomfortable in rainy
weather condition. Indeed if specific sections and stations suddenly areconcentrated excessive demand, it will lead far more serious problem.
Therefore, this study analysis the relationship between the number of urban
railway passenger and rainfall intensity in Seoul and then conducts thespace analysis to deduct passenger demand patterns.This study is expected
to be useful base study in order to manage the congestion at the urban
railway station effectively by considering the different rainfall intensity.
Keywords: urban railway, crowdedness, rainfall, GIS spatial
analysis, subway passenger ridership
1. INTRODUCTION
The crowdedness of the urban railway is the most important indicator toreflect the operation state of the urban railway. To provide the good service
quality of urban railway, the crowdedness of the urban railway should bemanaged appropriately. To evaluate the crowdedness in the urban railway,
quantitative factors and qualitative factors are needed. The weather is one of
the critical factors to influence the crowdedness. This is because eventhough the crowdedness of the urban railway is the same, passengers feel
more uncomfortable in rainy weather condition. Indeed if specific sections

117

AugustAugust2015,
2015,Thailand
Thailandand stations suddenly are concentrated excessive demand, it will lead far
more serious problem.
However, few literatures had been studied to analyze the relationshipbetween the weather and traffic demand. This is because collecting thetraffic demand and weather data was difficult. However, these data havebeen opened to the public; it is possible as get detailed information such as
Passenger data
Park et al. (2012) analyzed the passenger’s transfer pattern during
rainfall in Busan. This study showed that the ratio of the passenger’s mode
choice is different with the different amount of rain. This research also
showed that the ratio of mode choice is more influenced by the rainfall on
weekend.
Lee et al. (2014) conducted the relationship between the number ofpublic transportation passenger and the weather, especially for rain with thesmart card data. This study revealed that both the number of bus and urban
railway passengers were reduced in the rain. With these results, Lee et al.
(2014) pointed out that the public transportation passengers at Seodaemungu, Dongdaemun-gu, and Jung-gu were easily influenced by the rain
intensity.
Lee et al., (2011) analyzed the rain intensity and the bus travel timeto verify the quality of bus services. The study showed that the quality of thebus services was greatly influenced by rain start in the morning peak hours.
However, these researches focused on the specific regions so that it
could not show a detail analysis.
Therefore, this study constructs the database set about the urban
railway passenger with the rainfall intensity in Seoul and conducts thespatial analysis to deduct passenger demand patterns. This paper collects
rainfall data which are collected from July to September and urban railway
passenger data. With these data, this study analyzed the relationshipbetween the number of urban railway passenger and rainfall intensity.
2. THE RELATIONSHIP BETWEEN THE RAINFALL INTENSITY
AND THE SUBWAY PASSENGER RIDERSHIP
2.1 Data for analysis
Seoul is one of the biggest cities in the world and eight subway lines
are connected so people can reach every single place in Seoul. So many
people use subway for commuting and other travel purposes. According to
the Kang et al. (2014) study, up to 36 % of people responded that theychoose subway as a travel mode.
This paper analyzes the rainfall intensity to show its impact on
demand of the Seoul urban railway with the urban railway passenger data.
This study use railway passenger data which are collected from July to118
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September 2012 and 2013. Daily rainfall data which are used are collectedfrom the Automatic Weather Stations (AWS). To analyze the relationshipbetween the rainfall intensity and the subway passenger ridership, this study
selects in that same period rainfall data and then conducts a spatial analysis.
Based on the Meteorological Agency data, this study categorizes rainfall
intensity by 6 level (Table 1).

Table 1: Rainfall Analysis Database set
Level
1
2
3
4
5
6

Rainfall(mm)Less than 1
1~5
5~10
10~25
25~50
More than50
Sum (days)

Analysis days
2012(days)
2013(days)
36
36
6
8
5
6
7
5
2
5
9
6
65
66

Sum(days)
72
14
11
12
7
15
131

Table 2 describes in detail the urban railway passenger dataset fromSeoul metro (Line 1~4) and Seoul Metropolitan Rapid Transit Corporation(Line 5~8). Data are constructed total number of board passenger in July to
September 2012, 2013. The total number of stations on line 1 to 4 in the
2012-2013 is 119 stations. However, total number of stations on line 5 to 8
is different. In 2012, there are 148 stations on line 5 to 8. However, 9stations which are Gulpocheon station, Kkachiul station, Bucheon Cityhall
Station, Bucheon Stadium station, Samsan Gymnasium station, Sangdong
station, Sinjung-dong station, Chun-ui station data from October 2013 added.

Table 2: Urban Railway passengers Analysis Database set sample
Line
5
5
5
5

StationBanghwa
Banghwa
Banghwa
BanghwaDate
20120701
20120702…
20120930

06:00
139
670
…
124

07:00
250
1,239
…
178

08:00
347
591
…
178

…
…
…
…
…

(e.g. Passegner(person))

23:00
30
66
…
57

24:00
1
16
…
18

Sum
4,808
8,495
…
3.670

2.2 Urban railway demand analysis
This study set the number of passenger data in level 1 as a standard.
Based on this standard, this study compares each level’ passenger number
and standard and then calculates reduction ratio.
As a result, this study finds out that the number of passengers isdecreased during the rainfall. In level 2, total number of passengers isincreased by 0.53% than level 1. At level 3, total number of passengers isdecreased by 1.84% and level 4, total number of passengers is decreased by
3.17%. Demand was reduced by 2.65% at Level 5. At the level 6, it was
reduced by 5.39%. Detailed results are given in [Appendix A].
This study also conducted the passengers’ ridership on each station.
The results show that the ridership on Yeouinaru Station and Ttukseom
The relationshipTherelationship between
between thethe rainfall
rainfall intensity
intensityand
andthethesubway
subwaypassenger
passengerridership
ridershipin the Seoul metropolitan
subwaysystem119
in the Seoul metropolitan subway system

AugustAugust2015,
2015,Thailand
ThailandResort Station is highly influenced by amount of the rain. This is because
people visit these stations for leisure activities. On the other hand, theridership on Samsung station, Gasan Digital Complex Station, andGangnam Station does not have a significant difference by the rain. Thesestations are highly involved with work trip. Detailed results are given in[Appendix B].
Contrary to this pattern, Hangnyeoul station subway passenger rateis 47% increase from relationship. Regardless of the rainfall, It can imply
that this is because of the close Hangnyeoul station event.
3. SPATIAL ANALYSIS ON SUBWAY NETWORK IN SEOULMETROPOLITAN AREAA GIS software can be used to store, analyze and allows spatial data
layers. First, this study analyzes the high number of the board and alightpassenger station in Seoul.

Figure 1: The high number of the board and alight passenger station in
Seoul

120
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Figure 1 depicts the high total number of board passenger station
analysis data from top 1 to top 10; Gang-nam station: 37,181,419persons,Jam-sil station: 27,974,601 persons, Seoul station: 26,244,942 persons,Sillim station: 26,201,372 persons, etc. Figure 1 also shows the high total
number of alight passenger station: Gang-nam station: 38,498,150 persons,Jam-sil station: 25,588,647 persons, Sillim station: 25,491,673 persons,Hongik university station: 25,019,830 persons.
This study analyzes the high number of board and alight passenger
station by GIS, in which multiple layers of information can be integrated indifferent combinations ([Appendix B]). Figure 2 visualizes the spatial
analysis results on a map.

Figure 2: Stations which have high or low passenger reduction rate
4. CONCLUSION
The crowdedness of the urban railway is the most important indicator toreflect the operation state of the urban railway. The weather is one of thecritical factors to influence the crowdedness. That is because even though
the crowdedness of the urban railway is the same, passengers feel more
uncomfortable in rainy weather condition. Indeed if specific sections andstations suddenly are concentrated excessive demand, it will lead far more
serious problem.

The relationshipTherelationship between
between thethe rainfall
rainfall intensity
intensityand
andthethesubway
subwaypassenger
passengerridership
ridershipin the Seoul metropolitan
subwaysystem121
in the Seoul metropolitan subway system

AugustAugust2015,
2015,Thailand
ThailandThis study finds out that each station’s passenger is decreased by therainfall level, except for level 2. The number of passengers in level 6 isdecreased about 5.39%. The stations which are highly related to leisureactivity are sensitive to the rainfall.
Also, we analyzed the sensitivity area of the rainfall level. The
station was analyzed by using a high sensitivity for the station leisureactivities such as Yeouinaru Station and Ttukseom Resort Station. On theother hand, Samsung station, Gasan Digital Complex Station, Gangnam
Station was analyzed and the sensitivity is low.
With the GIS program, this study conducts the spatial analysis toshow the relationship between the rainfall intensity and the subway
passenger ridership in GIS map.
This study focuses on the entire date of data and passenger data. To
conduct more accurate analysis, the study should consider peak-hour andnon-peak hour data.
REFERENCES
Park Kun Young, Lee Si Bok, 2012. A study on the effect of adverse
weather conditions on public transportation mode choice, Journal of Civil
Engineering, Korean Society of Civil Engineers 32(1), 23-31.
Kwang Sub Lee, Jin Ki Eum, So Young You, Jae Hong Min, Keun Yul
Yang, 2014. The impact of rain on public transit ridership in Seoul, The
KoreanSocietyfor
Railway.
Yi Chang, Ko Joon Ho, Kang Young Eun, Lee Tae Kyung, 2011. The
impact of rainfall on public transport service in Seoul – Focusing on thechanges in punctuality and speed of bus service, Journal of Korea Planners
Association, 46(7), 73-87.
Kang Yeonggyeong, Yi Changhyo, Lee Seungil, 2014. A study on
the factors affecting crowding degree in the subway train considering theSeoul Metropolitan subway network and the land use of its catchment area,
Journal of Korea Planners Association, 203-218.

[Appendix A] Relationship of the reduction rate between the urban
railway passengers in accordance with the rainfall
StationYeouinaru
Ttukseom Resort
Stadium
Jegi-dong
DongmyoDobongsan
JangamChildren’s Grand Park
Magok
Myeongdong
Bokjeong
Hanyang Univ.

122
122

Less than 1mm
passenger
44,541.9
42,055.3
77,243.1
88,168.2
34,413.1
44,960.4
8,280.9
66,480.0
3,993.4
173,761.3
25,287.7
57,738.5

1mm~5mm
Reductionpassenger
ratio(%)
42,780.8-3.95
40,300.2-4.17
71,697.9-7.18
85,418.2-3.12
35,039.4
1.82
43,205.1-3.90
7,895.9-4.65
63,312.0-4.77
3,940.6
0.18
173,369.7-0.23
24,419.2-3.43
54,484.9-5.64

5 mm ~10 mm
Reductionpassenger
ratio(%)
42,050.5-5.59
39,574.1-5.90
69,869.9-9.55
82,734.3-6.16
33,319.5-3.18
42,102.7-6.36
7,860.5-5.08
58,590.3-11.873,643.0-7.38
168,717.4-2.90
22,643.6-10.46
46,208.0-19.97

7th Joint
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CivilInfrastructures
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7thICUS Report 79
Cheongyangni…
Hangnyeoul
Sum
StationYeouinaru
Ttukseom Resort
Stadium
Jegi-dong
DongmyoDobongsan
JangamChildren’s Grand Park
Magok
Myeongdong
Bokjeong
Hanyang Univ.
Cheongyangni…
HangnyeoulSum

136,757.4…
10,812.0

134,611.6…
10,618.7

-1.57
…
-1.79

21,118,821.4

21082416.3

0.53

10mm ~25mm
Reductio
passe
n
nger
ratio(%)
38,672.5-13.1837,105.7-11.7765,982.6-14.5881,311.1-7.78
31,464.8-8.57
41,823.5-6.98
7,457.6-9.94
61,769.9-7.09
3,646.4-7.30
163,398.3-5.96
23,933.5-5.36
51,641.0-10.56
128,715.3-5.88…
…
15,928.9
47.33
20,299,74
-3.17
8.3

25 mm~50mm
passenger

Reduction
ratio(%)

37,378.8
36,074.3
63,728.6
78,525.1
31,336.0
41,824.9
7,336.9
62,089.5
3,585.1
160,340.8
24,290.6
50,823.4
126,761.2…
10,460-16.08-14.22-17.50-10.94-8.94-6.97-11.40
-6.60
-8.86
-7.72
-3.94
-11.98
-7.31
…
-3.25

20,411,404.1-2.65

131,683.6-3.71…
…
13,006.0
20.29
20,589,430.
-1.84
4More than 50mm
passenger
34,958.1
34,158.7
64,305.6
74,327.4
29,403.9
38,630.8
7,166.3
57,770.8
3,447.0
152,588.4
22,446.2
51,317.4
121,660.8…
15,959.8
19,832,785.5

Reduction
ratio(%)-21.52-18.78-16.75-15.70-14.56-14.08-13.46-13.10-12.37-12.19-11.24-11.12-11.04
…
47.61
-5.39

[Appendix B] Sensitivity analysis between the urban railway passengers andrainfall intensity
Station[1] Yeouinaru[2] Ttukseom Resort[3] Stadium[4] Jegi-dong[5] Dongmyo[6] Dobongsan[7] Jangam[8] Children’s Grand Park
[9] Magok[10] Myeongdong
Average[1] Hangnyeoul[2] Sinseol-dong[3] Dorimcheon[4] Samsung[5] Sujin[6] Dogok[7] Songpa[8] Gasan Digital Complex[9] Gangnam[10] Yeoksam
Station[1] Yeouinaru[2] Ttukseom Resort[3] Stadium[4] Jegi-dong[5] Dongmyo[6] Dobongsan[7] Jangam[8] Children’s Grand Park
[9] Magok[10] Myeongdong
Average[1] HangnyeoulLess than 1mm

1mm~5mm
5 mm ~10 mm
Reduction
Reductionpassenger
ratio(%)
ratio(%)
44,541.9
42,780.8-3.95
42,050.5-5.59
42,055.3
40,300.2-4.17
39,574.1-5.90
77,243.1
71,697.9-7.18
69,869.9-9.55
88,168.2
85,418.2-3.12
82,734.3-6.16
34,413.1
35,039.4
1.82
33,319.5-3.18
44,960.4
43,205.1-3.90
42,102.7-6.36
8,280.9
7,895.9-4.65
7,860.5-5.08
66,480.0
63,312.0-4.77
58,590.3-11.87
3,993.4
3,940.6
0.18
3,643.0-7.38
173,761.3
173,369.7-0.23
168,717.4-2.90
21,118,821.4
21,082,416.2
0.53
20,589,430.4-1.84
10,812.0
10,618.7-1.79
13,006.0
20.29
14,660.1
15,678.9
6.95
14,605.0-0.38
4,661.9
4,877,875
4.63
4,750.6
1.90
292,916.8
299,579.7
2.27
294,401.8
0.51
21,597.3
22,128.79
2.46
22,281.4
3.17
37,286.0
37,933.0
1.74
37,020.9-0.71
28,086.5
28,762.2
2.41
28,388.9
1.08
184,319.8
186,244.2
1.04
181,779.6-1.38
482,931.1
500,013.7
3.54
493,907.8
2.27
247,416
248,602.0
0.48
248,142.5
0.29
10mm ~25mm
25 mm~50mm
More than 50mm
Reductionpasseng
Reductionpasseng
Reductionpassenger
ratio(%)
erratio(%)
erratio(%)
38,672.5-13.1837,378.8-16.0834,958.1-21.52
37,105.7-11.7736,074.3-14.22
34,158.7-18.7865,982.6-14.5863,728.6-17.5064,305.6-16.7581,311.1-7.78
78,525.1-10.9474,327.4-15.7031,464.8-8.57
31,336.0-8.94
29,403.9-14.56
41,823.5-6.98
41,824.9-6.97
38,630.8-14.087,457.6-9.94
7,336.9-11.407,166.3-13.46
61,769.9-7.09
62,089.5-6.60
57,770.8-13.103,646.4-7.30
3,585.1-8.86
3,447.0-12.37160,340.
152,588.
163,398.3
-5.96
-7.72
-12.198
420,299,748.
20,411,4
19,832,7
-3.17
-2.65
-5.39
3
04.1
85.5
15,928.9
47.33
10,460-3.25
15,959.8
47.61
passenger

passenger

The relationshipTherelationship between
between thethe rainfall
rainfall intensity
intensityand
andthethesubway
subwaypassenger
passengerridership
ridershipin the Seoul metropolitan
subwaysystem123
in the Seoul metropolitan subway system

AugustAugust2015,
2015,Thailand
Thailand124
124[2] Sinseol-dong[3] Dorimcheon

14,345.3
4,624.2

-2.15
-0.81

[4] Samsung

290,964.4-0.67[5] Sujin[6] Dogok[7] Songpa

21,692.9
36,558.4
27,931.9

0.44
-1.95
-0.55

[8] Gasan Digital Complex

184,758.5

0.24[9] Gangnam

463,939.4-3.93[10] Yeoksam

245,328.5-0.84

14,919.4
4,967.6
302,618.
3
21,235.7
37,056.2
28,459.6
186,835.
5
473,892.
0250,368.
7

1.77
6.56
3.31
-1.67
-0.62
1.33
1.36
-1.87
1.19

15,338.5
4,790.1
291,192.
3
21,429.1
36,779.2
27,643.1
180,528.
9
471.975.
9241,304.
7

4.63
2.75
-0.59
-0.78
-1.36
-1.58
-2.06
-2.27
-2.47

7th Joint
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CivilInfrastructures
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7th

FACTORS AFFECTING THE SEVERITY OF
MOTORCYCLES ACCIDENTS ANDCASUALTIES IN THAILAND BY USINGPROBIT AND LOGIT MODEL
Santosh BARAL 1, Kunnawee KANITPONG 2,
1,2
Thailand Accident Research Center, Asian Institute of Technology,
P.O. Box 4, Klong Luang, Bangkok, 12120, Thailand
a E-mail: baralsantosh5@gmail.com
b E-mail: kanitpon@ait.ac.th

ABSTRACTThis paper attempts to provide an additional insight into the factorsthat significantly influence the motorcycle accident severity in Thailand byusing ordered probit, binary orbit and binary logit model. We used the datafrom two sources: Department of Highways (HAIMS) and Road Accident
Victims Protection Co. Ltd (Eclaim), each of which depicts different factors
involved in the accident. This study reveals several factors that could lead tosevere motorcycle accidents and casualties: during late night and latemorning, during weekend and holiday season, collision with truck, driving
under influence, crash at curve, motorcycle to vehicle collision esp. head-on
types, age above 40 years old, pillion passengers and asphalt pavement.
Our findings reveal that the chances of being fatal in motorcycle accidents
are higher in motorcycle-truck collision than motorcycle-other vehicles
collision. Also, the likelihood of fatality of pillion passenger is higher thanrider.
Keywords: Motorcycle, accident severity, ordered probit, binary
probit/logit, Thailand
2. INTRODUCTION
Motorcycle has been a very popular vehicle type used in Thailand.
According to the statistical data from the Department of Land Transport(DLT), there are more than 18 million registered motorcycles in Thailand or
about 60% of all registered vehicles are motorcycles. During 2002-2011,
the number of motorcycles has been rapidly growing as reported by the
DLT that the number of new motorcycle registration is more than 1.8
million vehicles per year. The ratio of population to motorcycle is 4 to 1,
which is considered as the third highest rank among ASEAN countries,
following Malaysia and Vietnam.

125

125

AugustAugust2015,
2015,Thailand
Thailand

There are many reasons contributing to the popularity of motorcycles
such as convenience and agility, capability to access to destination, lowamount of fuel used, maintenance cost, and relatively low price of
motorcycle itself. According to the Royal Thai Police, motorcycles arefound to be the highest risk group of vehicle types to have accidents. During
2008 to 2010, the number of motorcycle injuries was about 70-80% of allaccidents, and this figure tends to increase each year. The statistical datafrom the Ministry of Public Health revealed that during such a period oftime, the number of fatalities from motorcycle accidents was as high as 80%of the total fatalities from road injuries in Thailand (Ichikawa et al., 2003;
Swaddiwudhipong et al., 1994). It was believed that the primary causes offatalities from motorcycle injuries were speed, drunkenness, violation of
traffic laws, careless ride, and lack of skill control during turning or
lowering speed.
2. LITERATURE REVIEW
Most previous research have identified the relationships between
motorcycle accident severity and explanatory variables of interest like age,gender, alcohol use, helmet use etc. Savolainen and Mannering (2007)
estimates the probabilistic models of motorcyclists’ injury severities insingle and multiple vehicle crash which shows that the increasing
motorcyclist age is associated with more severe injuries. Similar result was
found in other research that the likelihood of fatality and disabling injury insingle vehicle crash increases with increasing motorcycle rider’s age(Shankhar and Mannering, 1996; Quddus et al., 2002; Pai and Saleh, 2007;
Nunn, 2011). However, some studies show that the younger (less than 20
years old) and inexperienced motorcyclists are associated with highmotorcycle crash severity (Oluwadiya et al., 2009; Lin et al., 2003). Also,
Rutter and Quine (1996) examined the effect of the age, gender and driving
experience on motorcycle accident severity and found that there is a higher
casualty rate among middle-aged riders compared to those who areinexperienced. Youth drivers behave less safely than older drivers .The
motorcyclist less than 30 years old and more than 50 years old have greater
probabilities of injury whatever the level is (Lapparent, 2005). With respect
to gender, women are found to have a higher probability of more severe
injury relative to men (Quddus et al., 2002) and women motorcyclists
between 30 to 50 years old driving powerful motorcycle are most exposed
to risk of injury (Lapparent, 2005).In contrast, (Preusser et al., 1995;
NHTSA, 2007) found that male motorcyclist tend to be associated with ahigher degree of crash severity. Therefore, the contrasting findings of theprevious studies on the effect of age and gender in motorcycle accident
severity warrants further investigation on this matter.
Road type and geometry along with roadside installations, pavement
surface conditions, lighting and visibility conditions, and manner of crashes(such as run-off road, collision with stationary object or other) constituteanother group of risk factors (Vlahogianni et al., 2012). Intersection crashes
are more likely to result in no injury whereas crashes that occur on vertical
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ICUS Report 79

and horizontal curves are significantly more likely to result in serious injury
or fatal (Clarke et al., 2007; Savolainen and Mannering, 2007). Also,
research shows that motorcycle collision with roadside or fixed objects were
found to have higher fatality or serious injury risk (Quddus et al., 2002;
Savolainen and Mannering, 2007; Shaheed and Gkritza 2014; Keng, 2005;
Lin et al., 2003; Daniello, 2011).
Furthermore, extensive research has been conducted on investigating
the role of alcohol in motorcycle crash. The influence of alcohol was proved
to be most outstanding factor in Thailand (Kasantikul et al., 2005). The
study revealed that alcohol accidents were distinctly different from nonalcohol crashes and alcohol accidents are frequent on weekends. Lin andKrauss (2009) suggests that motorcycle riders are more likely to drink andride compared to other motor-vehicle drivers. Similarly, for single-vehicle
motorcycle crashes with riders under the influence of alcohol are more
likely to result fatal or major injury (Shaheed and Gkritza, 2014).
Only few previous research on motorcycle accident severity haveexamined the effect of time of day, days of week and season. Savolainen
and Mannering (2007) found that April and July have greater probability offatality in motorcycle accident. Furthermore, another study by Kasantikul etal. (2005) concluded that driving under the influence related crashes aremost likely to occur on weekends, at night in Thailand. Also, during thenight time and due to poor visibility, there is a significant increase in theprobabilities of severe or fatal crash (Lapparent, 2005; Savolainen andMannering, 2007; Walker, 2005).
The nature of motorcycle use and the engine size of the motorcycle
may be quite different in developed and developing countries. For e.g., indeveloping countries like Thailand, Vietnam, Phillipines, etc. motorcycle
riders use the vehicle every day for their regular work, while in the west
countries, the motorcycles are used more in weekend for recreational travel.
Hence, the result for time of the accident, day of the week and season fromprevious studies may not be significant predictor of accident in the contextof Thailand. In addition, a large proportion of motorcycles in developing
countries are scooters with a smaller engine capacity, and the characteristicsof the crash rate, crash type, and injury severity and pattern of suchmotorcycle seem to differ from those for big motorcycles (Salatka et al.,1990). Though several studies show that the motorcycle crash severity
depends upon the motorcycle size, unfortunately, very few studies haveattempted to investigate the small sized motorcycle to determine if thefactors affecting are same or not. Some other factors (e.g., seating position,other vehicle involved in the accident, collision types, pavement types andetc.) that may affect accident severity are often overlooked in the literature.

Factors affecting the severity of motorcycles accidents and casualties in Thailand by using probit and logit
127
model affecting the severity of motorcycles accidents and casualties in Thailand
Factors127
by using probit and logit modelAugustAugust2015,
2015,Thailand
Thailand3. METHODOLOGY
3.1 Data Set
Data from two sources: Department of Highways (HAIMS) andRoad Accident Victims Protection Co. Ltd (Eclaim) were used in theanalysis. Each of which depicts different factors involved in the accident.
The levels of injury categorized in both sources are also different as shownin Table 1. In HAIMS database, the typical ordinal variables are categorizedfrom least severe level to most severe level. Accidents data from the years
2008 to 2012 was obtained from HAIMS. A database of 18,463 accidents
involving motorcycle was extracted from the data and used for the study.
For these accidents, 5,526 (30%) are classified as PDO only, 7,836 (42%)
are classified as minor injury, 2,763 (15%) are classified as serious injury
and 2,338 (13%) are classified as fatality. This data also includes theaccidents considering the severity of pedestrian or other vehicle’s occupants.
The accident is classified in the most severe form of injury to the involved
person.
Similarly, in data set of Eclaim, the levels of injury are categorized
as either injured or fatal. Motorcycle accidents database contains a total
number of 586,809 motorcycle casualties from the years 2010 to 2012 wastaken for the analysis. The data includes the injury sustained by either
motorcycle rider or passenger. For these accidents, 571,376 (97.4%) areclassified as injury and 15,433 (2.6%) are classified as fatality.

Table 2 Definition of dependent variables with respect to injury level
Data
sources

HAIMS

EclaimLevel

Definition

0

PDO

1

Minor Injury

2

Serious Injury

3

Fatality0

Injury1

Fatality

Dependent Variable
There is no bodily harm to the person from thecrash- property damage only
Injury which is not of any threat to the life of theperson or his way of living.
Injury which is a threat to the life of the person andhis way of living.
An injury sustained in the crash that resulted indeath immediately or post-accident.
Injury which is either a threat or not to the life of
the person or his way of living.
An injury sustained in the crash that resulted indeath immediately or post-accident.

3.2 Model Selection
This paper attempts to provide an additional insight into the factorsthat significantly influence the motorcycle accident severity in Thailand byusing ordered probit model, binary orbit and binary logit model. The datafrom HAIMS is ordinal in nature and they are best analyzed using ordered
probit models because they require no assumptions regarding the ordinalityof the dependent variable, which in this case is the severity score. Quddus et
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al. (2002) utilized ordered probit models to analyze motorcycle damage andinjury severity resulting from crashes. Kockelman and Kweon (2002)
describes the use of ordered probit models to examine the risk of different
injury levels sustained under all crash types, two-vehicle crashes, andsingle-vehicle crashes. Likewise, the severity of driver at multiple locations
such as roadway sections, intersections, toll plazas are identified by AbdelAty (2003), using the ordered probit models. Due to a special property, i.e.
the ordered probit model discerns unequal differences between ordinal
categories in the dependent variable, it captures the qualitative differences
between injury severity levels, given a unit change in the explanatory
variables (Khattak and Targa, 2004; Duncan et al., 1998; O’Donnell andConnor, 1996). Some researcher used multinomial logit model in their study
to deal with injury severity levels (Chang and Mannering, 1999; Lee andMannering, 2002), however, these models require more number ofparameter estimates and neglects the data ordinarily. By reviewing theprevious studies, and because of its relative simple approach to recognize
the index of various response variables, the ordered probit model waschosen to analyze the HAIMS dataset. The general form of the model is:yi * = xi β + εi(1)where, yi* is a latent, unobservable and continuous dependent variable;
xi is a row vector of observed non-random explanatory variables;
β is a vector of unknown parameter;
εi is the random error term; which is assumed to be normally distributed.
The ordered probit model can be derived from a measurement modelin which a latent variable yi* ranging from −∞ to +∞ is mapped to an
observed ordinal variable y. The observed and coded discrete variable yi isdetermined from the model as follows:yi = m if Ԏm-1 ≤ y* < Ԏm for m = 1 to M
(2)
where, the threshold values Ԏ’ are unknown parameters to be estimated. The
extreme categories, 1 and M, are defined by open-ended intervals with Ԏ0 =- ∞ and τ M = ∞.
Accident severity (dependent variable) in Eclaim database has two
categories, fatal and injured. Therefore, binary logit and binary probit model
are applied in order to examine the contribution of several variables toaccident severity. Binary logit or probit model has been broadly used bymany researchers in their study. For instances, Simoncic (2001) appliedbinary logit model to overcome injury severity of collisions between apedestrian, bicycle or motorcycle and a car. Furthermore, (for e.g. Factor etal. 2008; Obeng, 2007; Pai, 2009; Simoncic, 2001) successfully used binary
logit/probit models to overcome the severity levels, which are categorized asless and high injury. Haque et al. (2009) identified time factors, roadfeatures and driver-vehicle characteristics that contribute to the fault ofmotorcyclist in crashes at specific locations by applying binary logit model.
In these models, which are applied for predicting the injury, the crashseverity is a binomial distribution. So, the response variable Yi for the ith
observation can take one of two values: Yi= 0 or 1, where Yi=1 presents thefirst state such as fatality and Yi= 0 presents the other state: injury. The
Factors affecting the severity of motorcycles accidents and casualties in Thailand by using probit and logit
129
model affecting the severity of motorcycles accidents and casualties in Thailand
Factors129
by using probit and logit modelAugustAugust2015,
2015,Thailand
Thailandprobability of Yi is denoted by πi = Pr(Yi=1) The logit transformation of theprobability of a πi crash being injured is given by
Logit (πi) = log [(3)And, the logit transformation to the predictor is
Logit (πi) = βXi(4)From (3) and (4), logit model can be presented as
Log [
= βXi
(5)
Hence, from (5), the probability of a πi crash being injured is obtained as
πi = Pr(Yi=1) = [where, Xi is a vector of explanatory variable such as time of day, day ofweek, types of collision, other vehicle involved and etc, which may haveinfluences on crash severity. Besides, β is the coefficient regression vectorof the independent variables, presenting how each independent variableaffects the fatality.
Binary probit models are similar to binary logit models except theerror distribution. In the binary logit models, the errors are assumed to havea standard logistic distribution with mean 0 and variance , while the errors
in binary probit models have an assumption that the error distribution hasmean 0 and variance 1. The probit transformation of the probability πi isgiven by
Probit (πi) = Ө-1(πi)(6)
And, the logit transformation to the predictor is,
Probit (πi) = βXi(7)From (6) and (7), probit models can be presented as(8)
Ө-1(πi) = βXi
Hence, from (8) the probability of a πi crash being injured is obtained as
πi = Pr(Yi=1) = Ө(πi)
where, Ө is the cumulative distribution function of standard normal
distribution.
The description of the study variables from both data sources areshown in Table 2 and Table 3.

Table 3 Description of the study variables from HAIMS data source
Variable
SUN
MON
TUE
WED
THU
FRI
SAT
JAN

130

130

Description of Variable
If accident on Sunday = 1, 0 otherwise
If accident on Monday = 1, 0 otherwise
If accident on Tuesday = 1, 0 otherwise
If accident on Wednesday = 1, 0 otherwise
If accident on Wednesday = 1, 0 otherwise
If accident on Friday = 1, 0 otherwise
If accident on Saturday = 1, 0 otherwise
If accident in January = 1, 0 otherwise

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ICUS Report 79
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
0-4
4-8
8 - 1212 - 16
16 - 20
20 - 24
STRGHT
CURVE
INTSC
U-TURNACCESSSPEED
NO_STOPVIOLATEDUIMCCARPICK UP
BUS
TRUCK
HEAD
RIGHT
REAR
SIDE
FIXED
SINGLE
OTHER
CONC
NIGHT

If accident in February = 1, 0 otherwise
If accident in March = 1, 0 otherwise
If accident in April = 1, 0 otherwise
If accident in May = 1, 0 otherwise
If accident in June= 1, 0 otherwise
If accident in July= 1, 0 otherwise
If accident in August = 1, 0 otherwise
If accident in September = 1, 0 otherwise
If accident in October = 1, 0 otherwise
If accident in November = 1, 0 otherwise
If accident in December = 1, 0 otherwise
If accident happened during 0:00am to 3 :59am = 1, 0 otherwise
If accident happened during 4 :00 am to7: 59am = 1, 0 otherwise
If accident happened during 8 :00am to 11:59am= 1, 0 otherwise
If accident happened during 12:00noon to3:59pm=1, 0 otherwise
If accident happened during 4 :00pm to 7 :59pm = 1, 0 otherwise
If accident happened during 8 :00pm to11 :59pm =1, 0 otherwise
if accident on straight section =1, 0 otherwise
if accident on normal curve = 1, 0 otherwise
if accident on intersection = 1, 0 otherwise
if accident on U-turn with no auxiliary lane = 1, 0 otherwise
if accident on access point = 1, 0 otherwise
If accident cause is due to speeding = 1, 0 otherwise
If accident cause is due to no stop at intersection = 1, 0 otherwise
If accident cause is due to violating traffic signal=1, 0 otherwise
If accident cause is due to driver under influence = 1, 0 otherwise
If other vehicle is motorcycle= 1, 0 otherwise
If other vehicle is passenger car = 1, 0 otherwise
If other vehicle is pick-up = 1, 0 otherwise
If other vehicle is bus/Coach = 1, 0 otherwise
If other vehicle is truck = 1, 0 otherwise
If the crash type is head on collision=1, 0 otherwise
If the crash type is right angle= 1, 0 otherwise
If the crash type is rear End = 1, 0 otherwise
If the crash type is side Swipe = 1, 0 otherwise
If the crash type is fixed object crash = 1, 0 otherwise
If the crash type is single vehicle crash=1, 0 otherwise
If the crash is others types = 1, 0 otherwise
If the road surface is concrete = 1, 0 otherwise
If accident occur at night = 1, 0 otherwise

Table 4 Description of the study variables from Eclaim data source
Variables

Description of variables

SUN
MON
TUE
WED
THU
FRI
SAT
JAN
FEB
MAR
APR
MAY
JUN
JUL

If accident on Sunday = 1, 0 otherwise
If accident on Monday = 1, 0 otherwise
If accident on Tuesday = 1, 0 otherwise
If accident on Wednesday = 1, 0 otherwise
If accident on Wednesday =1, 0 otherwise
If accident on Friday = 1, 0 otherwise
If accident on Saturday = 1, 0 otherwise
If accident in January = 1, 0 otherwise
If accident in February = 1, 0 otherwise
If accident in March = 1, 0 otherwise
If accident in April = 1, 0 otherwise
If accident in May = 1, 0 otherwise
If accident in June= 1, 0 otherwise
If accident in July= 1, 0 otherwise

Factors affecting the severity of motorcycles accidents and casualties in Thailand by using probit and logit
131
model affecting the severity of motorcycles accidents and casualties in Thailand
Factors131
by using probit and logit modelAugustAugust2015,
2015,Thailand
Thailand
AUG
SEP
OCT
NOV
DEC
0 TO 44 TO 88 TO 1212 TO 16
16 TO 20
20 TO 24SINGLEOVFIXEDDRIVER
MC
CAR
VAN
BUS
TRUCK
OTHERSMALE
AGE<20
AGE20-29
AGE30-39
AGE40-49
AGE50-59
AGE60-69
AGE70-79
AGE_C

If accident in August = 1, 0 otherwise
If accident in September = 1, 0 otherwise
If accident in October = 1, 0 otherwise
If accident in November = 1, 0 otherwise
If accident in December = 1, 0 otherwise
If accident happened during 0 :00 am to 3 :59 am =1, 0 otherwise
If accident happened during 4 :00 am to7: 59 am = 1, 0 otherwise
If accident happened during 8 :00 am to 11:59 am = 1, 0 otherwise
If accident happened during 12 :00 noon to 3:59 pm = 1, 0 otherwise
If accident happened during 4 :00 pm to 7 :59 pm = 1, 0 otherwise
If accident happened during 8 :00 pm to 11 :59 pm =1, 0 otherwise
If the crash type is single vehicle crash=1, 0 otherwise
If the collision with other vehicle=1, 0 otherwise
If the crash type is fixed object crash = 1, 0 otherwise
If the casualty is driver = 1, 0 otherwise
If other vehicle is motorcycle= 1, 0 otherwise
If other vehicle is passenger car/pick up = 1, 0 otherwise
If other vehicle is personal van= 1, 0 otherwise
If other vehicle is public bus/van = 1, 0 otherwise
If other vehicle is truck= 1, 0 otherwise
If vehicle is other type = 1, 0 otherwise
If gender is male = 1, 0 otherwise
If age is less than 20=1, 0 otherwise
If age is 20 -29 =1, 0 otherwise
If age is 30 -39 =1, 0 otherwise
If age is 40 -49 =1, 0 otherwise
If age is 50 -59 =1, 0 otherwise
If age is 60 -69 =1, 0 otherwise
If age is more than 70 = 1, 0 otherwise
Age is taken as continuous variable

4. ANALYSIS RESULTS
The estimation results are shown in Table 4 and Table 5 for all models fromHAIMS and Eclaim data sources respectively. In order to determine whether
the candidate variables are significant or not, several iterations were carriedout with changing the reference variables. Model 1 to 7 are the results of
HAIMS data analysis, whereas model 8 to model 16 are the results of
Eclaim data analysis. The findings from models are interpreted in thefollowing section.
Effects of time of day, days of week and season are presented in themodel 1 to 5 and model 8 to 15. Based on the results in Table 4, relative tothe reference time period, more severe accidents occur in the early morning(midnight to 3:59 am) followed by evening (4:00 pm to 7:59 pm). Result
from Table 5 also shows that the severity of motorcycle casualties is higher
during early morning and late night (8:00 pm to 11:59 pm) and lower duringlate morning time (8:00 am to 11:59 am). These findings are consistent to
the other studies (for e.g. Rifaat and Chin, 2005; Quddus et al., 2002). Low
visibility and late night drowsiness delay the driver’s reaction at theimpending collision with other vehicles or roadside objects. Therefore the

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collision impact is high due to high speed (Rifaat and Chin, 2005) resulting
severe injury or fatality.
Table 5 shows that the likelihood of fatality is high in weekend thanin weekdays, which also supports the findings from previous studies.
Motorcyclists who ride on the weekend are more likely to commit offenses
than those who ride during weekdays. Likewise, seasonal effects were
measured using monthly dummy variables. The result of the models fromboth data sources indicates that the seasonal effect is one of the significant
factors in increasing the severity of motorcycle crashes and severity of thecasualties. The accident severity is significantly high during holiday seasons(Songkran and New Year) in Thailand which are in the months of April,
December and January
Roadway type and geometry also constitute another risk factor andhence several types of motorcycle accident location were taken in the model.
The accident severity at the curve was found to be strongly significant insevere or fatal crash as seen in Model 1 to model 7. Results of past studies
summarized that insufficient sight distance, poor visibility due to horizontal
or vertical curvature or darkness have been associated with an increase inmotorcycle injury severity (Clarke et al, 2007; Savolainen and Mannering,
2007; Chang, 2005).
Our study also accounts for the second higher accident severity at
Junction (U-turn and access point). Seva et al. (2013) found that junction
type was significant in motorcycle accidents in the Philippines. Inattentionof the automobile driver and motorcycle being overlooked easily at junction
may attribute to this problem. Chang (2005) concluded that if the accident
occurs at an intersection, the likelihood of no injury is significantly
increased. Model 1 to 7 show that the severity of accidents associated withpavement types. Asphalt type accounts for more accident severity thanconcrete. The reason could be the better reflectivity of concrete pavement
than asphalt during night time.
As for the type of collision, the result of the model from HAIMS
shows that the severity of the motorcycle crash is high when collided withvehicle followed by fixed objects, and is low if it is single vehicle crash withno collision. This result is similar to the result of Eclaim, which explainsthat the likelihood of fatality is more in head-on collision than single vehicle
collision. These findings are also same with Savolainen and Mannering(2007), where the author made a conclusion that head-on and right angle
collision type has high severity in multiple crash. Moreover, research in pastshows that motorcycle collision with roadside or fixed objects were found tohave higher fatality or serious injury risk (Quddus et al., 2002; Shaheed andGkritza, 2014; Keng, 2005; Daniello, 2011). However, such studies have
only considered the single vehicle crash type to estimate the factors
associate with severity level.
Dummy variable for motorcyclist age and gender were included inmodel 8 to 13 and model 16 to 17 to estimate the severity of motorcycle
Factors affecting the severity of motorcycles accidents and casualties in Thailand by using probit and logit
133
model affecting the severity of motorcycles accidents and casualties in Thailand
Factors133
by using probit and logit modelAugustAugust2015,
2015,Thailand
Thailandcasualties. While young motorcyclists were usually expected to have an
increased involvement in severe or fatal crash, it is estimated that old
motorcyclist above 40 years appear to have likelihood of fatality and young
motorcyclist of age below 20 are more likely to have injury only. Similarly,
model 14 (probit) and model 15 (logit) were conducted taking the age as acontinuous variable. The age appeared to be a significant factor and inparticular, the estimated result indicates that increase in age could lead to anincrease in the likelihood of fatality. The reason for this may relate to thephysiological difference with older riders being susceptible to severe injury
or fatality. These findings are similar to the summary of previous studies
that the likelihood of fatality and disabling injury increases with increasing
motorcycle rider’s age (Shankhar and Mannering, 1996; Quddus et al.,
2002; Pai and Saleh, 2007; Nunn, 2011). A study by Savolainen andMannering (2006) found that a 1% increase in age increases the likelihoodof incapacitating injury by 4.2%. However, the findings may contradict thelong held belief that young and inexperienced driver are more likely to
involve in severe accident (for e.g. Ballesteros and Dischinger, 2002; Lin etal., 2003; Oluwadiya et al; 2009; Seva et al., 2013). With regards to thegender, our study reveals that male motorcyclists tend to have likelihood of
fatality than women, which is contrasting to the findings by Quddus et al.
(2002). But, the result is similar to (Preusser et al., 1995; NHTSA, 2007) inwhich they found that male motorcyclists are associated with a higher
degree of crash severity.
Understanding and quantifying the relationship between vehicle types
and severity of motorcycle crash has been often overlooked by the previous
studies. As expected, model 1 to model 7 show that motorcycle accidents
involving big trucks followed by bus results in higher injury severity as thesize and mass of the big truck are far greater than motorcycles and thecollision has a greater impact to the motorcyclist. The result also show thatthe severity of truck involved motorcycle accidents are greater thanpassenger car. This results appears to be same in the model 8 to 17 which
showing that the likelihood of fatality is high for both rider and passenger intruck involved accidents. The chance of being fatal in motorcycle- truck
collision is higher than motorcycle – other vehicle collision.
With respect to the severity of motorcycle crash related to the causeof accident, the estimated coefficients are presented in Model 1 to 7.
Driving under influence and no stop at intersection followed by violate thetraffic signal are the significant factors for the motorcycle accident. The
reason for the insignificance of speeding might be due to data
misinterpretation. For e.g. if the data collection officer doesn’t know theexact cause of accident, he could be tempted to judge speeding as a cause of
accident.
Another interesting finding in this study is the contribution ofseating position to the severity of motorcycle casualties, which are presented
in the Model 8 to 17. The coefficients of these models (model 8, 9, and 1215) indicates that pillion passengers are more vulnerable than riders since
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the likelihood of fatality of passenger is higher than rider. The findings issimilar from the study of Lei et al. (2007), where the author found that backseat occupant suffers fatal head injuries when he fall on the ground after
being thrown higher than the motorcycle driver over the top of the car. Also,
the reason can be attributed to the helmet wearing percentage of rider (53%)
and passenger (19%) in Thailand (thaiRoads foundation, 2013).
6. CONCLUSION
The research study has identified the main factors that affect the injury
severity of motorcycle accident and motorcycle casualties by analyzing theaccident data obtained from HAIMS and Eclaim using ordered probit,
binary probit and binary logit models. We determined the candidate
variables are significant for all models with changing the reference variable
that yielded a robust result. This study reveals several factors that could leadto severe motorcycle accident and casualties: late night and early mornings,
crash occurred during weekend and holiday season, collision with truck
followed by bus and van, driving under influence and no stop at intersection,crash at curve followed by crash at junctions, motorcycle to vehicle
collision esp. head-on types followed by right angle crash, age above 40
years old, pillion passengers and asphalt pavement. Crashes were found to
be less severe in weekdays, during late morning, collision with motorcycle,
single vehicle crash type and etc. There is also a disturbing findings esp.
speeding being significant in less severity, which might be due to data
misinterpretation during data collection process.
Most of our findings are consistent with previous studies, and more
importantly the significance of motorcyclist’s age above 40 years old being
likelihood of fatal, provides an additional evidence to the recent aggregate
data trends. It was believed that young and experienced motorcyclist would
engaged in severe motorcycle accident, however, we find the contrasting
result, young riders and passengers of age below 20 years old are less likely
to be fatal. It is estimated that increase in the age could lead to an increase in
the likelihood of fatality.
Unlike to the previous studies, this study has included additionalvariables like other vehicle types and seating position that could affect the
injury severity of the accident. Our findings reveals that the chances ofbeing fatal in motorcycle - truck collision is higher than motorcycle – othervehicle collision. Also, this study concludes that pillion passengers are more
vulnerable than riders since the likelihood of fatality of passenger is higher
than rider.
Although this study has contributed to the literature in identifying
the several factors that are significant in the severity of small sized
motorcycle accident, it is necessary to provide more research to verify thesefindings.

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Thailand

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Factors affecting the severity of motorcycles accidents and casualties in Thailand
by using probit and logit model

0.000
0.000
0.000

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.870
0.509
0.219
0.361
0.812

0.051

0.000
0.000
0.000
0.000

0.208
0.775
0.000
0.000
0.000

-0.161-0.351-0.427-0.276-0.340-0.337-0.325-0.373-0.303-0.318
0.005
0.024
0.036
-0.024-0.006-0.056

0.442
0.130
0.343
0.307

0.256
0.024
0.212
-0.525-0.696

0.000
0.011
0.000
0.002
0.246
0.000
0.000

0.260
0.186
-0.234-0.2700.053-0.569-0.734

0.269
0.151
0.215
0.623

0.039
0.042
-0.033-0.013
0.113
0.002
0.000

0.948
0.000
0.000
0.000
0.729
0.000
0.000
0.000
0.000
0.000
0.000
0.000

0.002
-0.162-0.351-0.419-0.011-0.276-0.339-0.336-0.324-0.364-0.303-0.320-0.046-0.100
0.361

0.399
0.677
0.704
0.875
0.123

P-value

0.026
0.013
0.012
-0.005-0.047

Coeff.

Model 2-0.2840.053-0.564-0.7310.103-0.231-0.046-0.197
0.270

0.038
0.044
-0.034-0.0160.001-0.162-0.351-0.417-0.012-0.274-0.336-0.332-0.324-0.364-0.302-0.317

0.024
0.012
0.011
-0.003-0.046

Coeff.

drop

0.001
0.251
0.000
0.000

0.147
0.000

0.110
0.000
0.000

0.290
0.129
0.195
0.546

0.962
0.000
0.000
0.000
0.686
0.000
0.000
0.000
0.000
0.000
0.000
0.000

0.428
0.683
0.728
0.921
0.127

P-value

Model 3

Image processing techniques for vehicles and pedestrian safety evaluation

0.355
0.724
0.718
0.931
0.140
0.905

0.029
0.011
0.011
0.003
-0.045
0.004

SUN
MON
TUE
WED
THU
FRI
SAT
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
0-4
4-8
8 - 1212 - 16
16 - 20
20 - 24
STRGT
CURVE
INTSC
U-TURNACCESSSPEED
NO_STOPVIOLATEDUIMCCAR

P-value

Coeff.

Variable

Model 1

0.053

0.228
-0.241
0.003-0.2680.020-0.0440.019
0.036-0.047-0.0260.005-0.148-0.369-0.4340.017-0.314-0.360-0.355-0.339-0.389-0.330-0.339

0.021
0.011
0.007
-0.006-0.046

Coeff.

drop

drop

0.037

0.001
0.000
0.989
0.002

0.515

0.127

0.595
0.211
0.073
0.309

0.876
0.000
0.000
0.000
0.582
0.000
0.000
0.000
0.000
0.000
0.000
0.000

0.503
0.718
0.819
0.830
0.134

P-value

Model 4

139

0.000
0.000

0.000
0.919
0.002

-0.241
0.021-0.269-0.279-0.446

0.131
0.000
0.055
0.004
0.609

0.362
0.140
0.166
0.460

0.879
0.000
0.000
0.000
0.765
0.000
0.000
0.000
0.000
0.000
0.000
0.000

0.339
0.672
0.749
0.924
0.126

P-value-0.044-0.3160.150-0.221
0.041

0.032
0.043
-0.037-0.0190.005-0.162-0.358-0.425-0.009-0.290-0.341-0.341-0.329-0.367-0.309-0.326

0.029
0.013
0.010
-0.003-0.047

Coeff.

Model 5

0.245
0.688
0.000
0.000

-0.307

0.000
0.000
0.000
0.000

0.036

0.824
0.695
0.287
0.852

P-value

Model 6

0.235
0.034
0.332

0.465
0.138
0.315
0.352

-0.0600.008
0.011-0.028-0.005

Coeff.

Table 5 Ordered probit model estimation results from HAIMS data source

0.144-0.369-0.537

0.001
0.000
0.000

0.000
0.803

0.000
0.003
0.000

-0.3440.143-0.237-0.249
0.051

0.058
0.105
0.965
0.440
0.097

drop

P-value

0.064
0.055
0.002
0.027
0.046

Coeff.

Model 7

ICUS Report 79

ICUS Report 79

139

140

0.000
0.000

0.836
0.214
0.019

0.215
0.935
0.960
0.001
0.008
0.005
0.011

0.003
0.017
0.032

-0.024-0.0020.001-0.062-0.050-0.052-0.048

140

0.095
0.929
0.699
0.121
0.016

0.054
-0.003
0.012
0.049
0.075

0.058
-0.004
0.022

SUN
MON
TUE
WED
THU
FRI
SAT
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG

0.000
0.000

0.000
0.000

-0.0530.169-0.0320.447-0.2360.000-0.2490.000-0.485
0.000
0.168
0.000
18463
-212444434.6
0.0945

0.575
0.216

-0.343-0.374

0.256
0.003
0.067
0.003
0.002
0.683
0.825
0.580

0.039
-0.0190.008-0.015-0.013
0.015
0.056
0.035
0.056
0.057
0.008
0.004
0.010

logit
P
value
0.003
0.167
0.569
0.289
0.361

Coeff.

probitModel 10

0.01
0.00
0.02

0.03
0.12
0.08
0.11
0.13

0.08
-0.04
0.01
-0.05
-0.03

Coeff.

probit

0.82
0.91
0.61

0.36
0.01
0.08
0.01
0.00

P
value
0.01
0.17
0.74
0.14
0.31

logit

Model 11

0.000
0.000
0.000
0.000
0.000
0.000
0.003
0.000
0.000

-0.534
0.000
0.171
0.000
18463
-21576.2
3770.450.0804

0.291
0.261
0.743
0.800
0.443
0.165
0.113
0.147
0.502

0.128
0.193
0.777
0.450
0.228
0.896
0.326
0.272
0.027
0.120
0.085
0.172

0.034
-0.026-0.021-0.018-0.0040.010
0.022-0.0020.017
0.020-0.040-0.028-0.030-0.024

logit
P
value
0.012
0.062

Coeff.

probitModel 12

7th Joint Student Seminar on Civil Infrastructures-0.045-0.0120.007-0.132-0.107-0.113-0.099

0.300
0.783
0.865
0.002
0.012
0.007
0.019

P
value
0.000

Coeff.

P
value
0.000
0.767
0.120

Coeff.

Variable

logit

probitlogit

probitModel 9

0.136

0.000
0.000

0.591
0.213
-0.0490.207-0.0340.420-0.2360.000-0.2490.000-0.483
0.000
0.169
0.000
18463
-21229.4
4463.88
0.0951

0.000
0.000

-0.344-0.379
0.202
0.000
0.000
0.026
0.000
0.001
0.004

-0.3320.000-0.477
0.000
0.172
0.000
18463
-21239.1
4444.72
0.0947-0.090
0.393
0.486
0.104
-0.121-0.152-0.135-0.057-0.039-0.0040.022
0.039-0.0100.019
0.038-0.093-0.069-0.077-0.0560.076-0.054

Coeff.

probit

0.076
0.224
0.900
0.483
0.344
0.811
0.637
0.355
0.027
0.091
0.057
0.165

P
value
0.014
0.089

logit

Model 13

0.000
0.020
0.000
0.000
0.008
0.128
0.771
0.048
0.421
0.000
0.002
0.004
0.006

-0.071-0.061-0.055-0.053

P
value

logit

-0.056-0.033-0.055-0.050-0.037-0.021-0.006-0.039-0.015

Coeff.

probitModel 14

0.000
0.675
0.000

-0.1140.010-0.3930.000-0.4190.000-0.334
0.000
0.013
0.654
-0.6100.000-0.519
0.000
0.188
0.000
18463
-21591.9
3738.850.0797

0.102
0.030
0.499

-0.160-0.140-0.126-0.119-0.120-0.073-0.132-0.112-0.079-0.050-0.020-0.089-0.052

Coeff.

probit

0.000
0.002
0.004
0.007

0.000
0.025
0.000
0.001
0.013
0.114
0.653
0.049
0.235

P
value

logit

Model 15

Table 6 Binary probit/logit model estimation result from Eclaim data source-0.0720.103-0.3500.000-0.3680.000-0.3120.000-0.3970.000-0.5580.000-0.511
0.000
0.172
0.000
18463
-21372.9
4176.87
0.089-0.296-0.349Model 8PICK UP
BUS
TRUCK
HEAD
RIGHT
REAR
SIDE
FIXED
SINGLE
OTHER
CONC
NIGHT
No. of obs.
Log likelihood
LR chi2
Pseudo R2

August 2015, Thailand

Coeff.

probit

P
value

logit

Model 16

Coeff.

probit

P
value

logit

Model 17

0.000
0.000
0.000
18463
-21394.1
4134.69
0.0881

0.000
0.000
0.002
0.017
0.008
0.099

0.285
0.525
0.154
-0.082-0.123-0.080-0.324-0.497
0.173

0.000

-0.136

August 2015, Thailand

7th Joint Student Seminar on Civil Infrastructure

Factors affecting the severity of motorcycles accidents and casualties in Thailand
by using probit and logit model

0.000
0.000
0.000
0.000
0.000
0.000
0.771

0.002

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

-0.332-0.555-0.505-0.457-0.251-0.346-0.005-0.032

0.333
0.497
0.701
1.080
0.348
0.308
-0.627-0.548-0.501-0.423-0.370-0.281586807-63434.503
15883.81
0.1113

0.002
0.207
0.642
0.369

-0.059-0.023-0.008
0.016

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

0.008

0.000
0.000
0.000
0.000
0.000
0.000
0.660

0.003
0.357
0.847
0.370

586807-63459.077
15834.66
0.1109

0.740
1.092
1.485
2.189
0.771
0.694
-1.393-1.217-1.111-0.926-0.806-0.600-0.062-0.746-1.236-1.129-0.998-0.530-0.843-0.017-0.126-0.038-0.008
0.035

0.000
0.000
0.772
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.018
0.000
0.000
0.000
0.000

0.517
0.387
0.003
-0.250
0.147
0.321
0.522
0.893
0.297
-0.071-0.027
0.050
0.103
0.194
0.307
586807-64304.136
14144.54
0.0991

0.859
0.053
0.004
0.000
0.000
0.000
0.000
0.000
0.000

-0.003
0.035
0.052
0.078
0.243
-0.071-0.287-0.248-0.201

0.00
0.01
0.00
0.00
0.00
0.00

0.00

0.00
0.00
0.42
0.00
0.00
0.00
0.00
0.00

0.82
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.00

586807-64346.026
14060.76
0.0985

-0.17
-0.07
0.11
0.23
0.44
0.69

0.67

1.23
0.95
0.02
-0.58
0.32
0.69
1.08
1.76

-0.01
0.08
0.12
0.17
0.51
-0.19
-0.67
-0.58
-0.46
0.399
0.023
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.499
0.000

0.014
0.037
0.448
0.122
-0.092-0.043
0.195-0.546-0.158-0.049-0.274
0.129
0.295
0.875
drop
0.316
-0.359-0.278-0.231-0.100-0.0120.094
586807-64025.76
14701.3
0.103

0.033

-0.038

Image processing techniques for vehicles and pedestrian safety evaluation

SEP
OCT
NOV
DEC
0 TO 44 TO 88 TO 1212 TO 16
16 TO 20
20 TO 24SINGLEOVFIXEDDRIVER
MC
CAR
VAN
BUS
TRUCK
OTHERSMALE
AGE <20
AGE20-29
AGE30-39
AGE40-49
AGE50-59
AGE60-69
AGE >70
AGE_C
No. of obs.
Log likelihood
LR chi2
Pseudo R2
0.000
0.509
0.000

0.000
0.000
0.000
0.000

0.000

0.000
0.000
0.000
0.000
0.000

0.000
0.000

0.419
0.052
0.000
0.000
0.000
0.000

0.027

141

586807-64055.207
14642.4
0.1026-0.231-0.026
0.206

1.723
drop
0.717
-0.824-0.643-0.534-0.352-0.104-0.631
0.279
0.635

0.445
-1.293

0.031
0.073
0.978
0.250
-0.226-0.115-0.090

0.000
0.000
0.000
0.000
0.000
0.000

0.007 0.000
586807-58129.574
12858.7
0.0996

0.312
0.451
0.679
1.045
0.329
0.307

0.000

0.000
0.719

-0.3440.006-0.060

0.001
0.079
0.422
0.627
0.000
0.000
0.000
0.000
0.000

-0.061-0.033-0.0150.009
0.254-0.075-0.288-0.239-0.194

0.000
0.000
0.000
0.000
0.000
0.000

0.000

0.000
0.710

0.002
0.102
0.497
0.759
0.000
0.000
0.000
0.000
0.000

0.016 0.000
586807-58178.239
12761.37
0.0988

0.696
0.997
1.447
2.131
0.733
0.701

-0.131-0.8470.015-0.138-0.070-0.0280.013
0.544-0.204-0.675-0.567-0.445

0.000
0.046
0.000
0.000
0.000
0.000

0.000
0.000
0.000
0.000
0.000
0.000

0.810

0.000
0.876

0.000
0.000
0.000
0.000
0.000

586807-64354.595
14043.62
0.0984-0.067-0.023
0.054
0.105
0.195
0.307

0.323
0.496
0.696
1.066
0.342
0.299

0.002

-0.345-0.0030.253-0.072-0.289-0.248-0.205

0.000
0.018
0.000
0.000
0.000
0.000

0.000
0.000
0.000
0.000
0.000
0.000

0.444

0.000
0.757

0.000
0.000
0.000
0.000
0.000

586807-64397.563
13957.69
0.0978-0.163-0.062
0.120
0.236
0.444
0.687

0.718
1.085
1.476
2.156
0.760
0.675

0.018

-0.847-0.0120.537-0.197-0.676-0.584-0.468ICUS Report 79

ICUS Report 79141August 2015, Thailand

142

7th Joint Student Seminar on Civil InfrastructureOPTIMUM ASSIGNMENT OF HOUSINGSAND JOBS WITH CONSTRAINEDCAPACITY CONSIDERINGDISCOMFORT AND TRAVEL COSTS
Yuki MUNEMASA1, Yudai HONMA2 and Kotaro IMAI3
1
Graduate Student, School of Eng., The University of Tokyo, Japan
munemasa@iis.u-tokyo.ac.jp
2
Lecturer, ICUS, IIS, The University of Tokyo, Japan
3
Professor, IIS, The University of Tokyo, Japan

ABSTRACTIn this paper, we propose a new calculation method of optimum
assignment and derive the optimum distribution of housings and jobs
theoretically in the urban models with constrained capacities. First, weshow that a problem which incorporates commuting travel cost, working
travel cost and discomfort cost caused by the density of the node will be a
non-convex quadratic programming problem. By using ReformulationLinearization Technique, we are able to derive analytically the optimum
location of housings and jobs.
Specically, we set the urban model with constrained capacity toeach nodes and show that the problem of optimum assignment of all costs isequal to mathematical programming problem. It is clear that the problem isa non-convex quadratic programming problem because the matrix we
introduce at the formulation does not meet semidenite programs. Therefore,
we formulate the problem to a linear programming problem with
Reformulation-Linearization Technique in order to derive the optimum
allocation of housings and jobs. For numerical examples, we show two
optimum assignments in linear and 2D urban models and describe themeanings of parameters, α: working travel frequency, and β: discomfort
parameter. This model will be a new standard model for the formation of
housings and jobs distribution.
Keywords: distribution of housings and jobs, travel cost, discomfort
cost, system optimum assignment, quadratic programming problem
3. INTRODUCTION
The purpose of this study is to derive the optimum arrangement structure ofresidence and jobs in theory considering working travel cost among working
region, constrained capacity of buildings and discomfort. We make a

143

AugustAugust2015,
2015,Thailand
Thailandprocess of optimum arrangement demanding a constant estimated result in
the range of the city scale using spatial interaction model and relaxation of
nonconvex quadratic programming problem. Moreover, after examination ofadvantages and disadvantages of the urban model, we recommend thepossibility to apply the optimum arrangement of residence and jobs in actualcity.
The population ratio in three metropolitan areas in Japan is alreadysurpassed 50%, and it has been estimated that the population of depopulatedareas will be decrease by 61% in the next 35 years (Ministry of Land,
Infrastructure, Transport and Tourism, 2011). And Japan is already an aging
society, sustainable regional development is a big problem to beinvestigated and they study the shift to compact city as one of the techniques
to solve those problems. However, the method to greatly change the urban
structure needs the planning process in short and mid-term and considering
the inuence to people going existing cities, the method is a high degree ofdifculty and the embodiment is still problem.
In this study, we handle two building types, working place (W) andhousing place (H), as elements in the city, and derive the optimum
arrangement which minimizes discomfort and travel cost in it. In urban
renovation or shrinking expected with depopulation, we have to consider not
only amenity and travel cost, but environmental conservation, otherelements of building type or unforeseen problems. However, we think that it
is conceivable to approach the actual city by formulating the distribution of
W-H.
The study of minimizing travel cost in urban cities has been carriedout in recent years. A series of the research stemmed from Hamilton et al.
(1983) has been achieved the development as follows: study of the urban
structure with population growth (Kondo and Yoshikawa, 2012), study of
optimal 3D urban form of energy saving city (Matsuhashi, 1996), study ofcongestion mitigation and surplus transportation (Suzuki, 2003; Ohtsuand
Koshizuka,1999;MaruyamaandHarada,2003, Yokota and Muromachi,2012),
study of determining joint distribution of homes and workplaces (Honmaand Kurita, 2004). However, the researches above mentioned derive
optimum arrangement by selecting or simulating distribution randomly inthe processofcalculatingtravelcostanditisnotthesolutioninastrictsense. Above
all, connection of working regions is the essential element in considering the
optimum arrangement of residence and jobs. Needless to say, Ohtsu et al.
(1999) and Honma et al. (2004) study the optimum arrangement considering
the element partially, but they simplify the process with mean distancetheory or numerical simulation. It is quite natural to come out mathematicalmodeling clearly considering commuting travel cost and working travel cost
simultaneously after Hamilton model. In this study, we have a shy atformulation of working travel cost as quadratic form and the solution.
In addition, we propose the formulation of discomfort and travel costof the urban model considering (a) commuting travel cost, (b) working
travel cost and (c) discomfort after setting constrained capacity in this study
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and simulate the optimum arrangement structure of residence and jobs
distribution which was not performed until now based on numericalsolutions. Constrained capacity we give here means to set the maximum of
the number of people in buildings. In the long term, urban city will bechanged to compact city is a likely supposition, however, so much energy isrequired to rearrange the urban structure in the sort and mid-term. In thatsense, the significant is that we are able to give existing buildings byconsidering constrained capacity and derive the optimum arrangement.
This paper is organized as follows. In Sec. 2, we organize the urban
model and show that minimizing problem of discomfort and travel cost is tobe quadratic programming problem, and we clear that it is nonconvex
quadratic programming problem because distance matrix we set does notsatisfy the condition of semidefinite. Then, we describe that we are able toapply nonconvex quadratic programming problem to linear programming
problem using reformulation-linearization technique. Moreover, weformulate relaxed linear programming problem in order to derive theoptimum solutions. Specifically, we derive the optimum solution according
to linearize executable area of linear programming problem by reasonable
quadratic inequalities. In Sec. 3, we analyze numerical solutions consideringof discomfort, constrained capacity and distribution of residence and jobs invirtual linear cities and 2D city. The urban model we set is much simple
structure model for clear understanding. At the last, we describe thepossibility how to apply discomfort and travel cost of the urban model inorder to derive the optimum arrangement of residence and jobs to actual city,and the significance (Sec. 4).
2. FORMULATIONFirst, we formulate discomfort and travel cost of urban model in this study
and disclose that minimizing problem of both of them is to be nonconvex
quadratic programming problem.
2.1 Urban Model
Let us consider an urban city which contains of working place (W)consisting of the building nodes I (1⩽i⩽I) and residence place (H) consistingof the building node J (1⩽j⩽J), and assume that only two building types exist
in the urban city in order to make formulation much more concise. Moreover,proposal urban model is independent; the total number of people moving W-H
is T (= constant number) and there is no in flow and out flow of people between
other cities.
We assume that there is two travel type in urban city, (i) commuting
travel of W-H and (ii) working travel of W-W’ and (iii) discomfort due todensity of the node. At this time, our purpose of this study is to derive
optimum arrangement which minimizing discomfort and travel cost in theurban city. On that account,
Optimum Assignmentof housings
Housings andand jobs
Jobswith
withconstrained
Constrainedcapacity
Capacityconsidering
Considering
Discomfort andOptimum
assignment of
discomfort
TravelCosts
and travel costs

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AugustAugust2015,
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where thij is a commuting number from i to j, wi is a working numberof people at node i and hj is a residence number of people at node j.
Furthermore, we assume the following inequalities to provide the idea of
constrained capacity mentioned in Sec. 1,

where Wi and Hj is each capacity at the node. When we derive
commuting travel cost, commuting travel is a regular trip and we set thecommuting distance between i and j as dhij,

In the next place, we derive working travel cost. We define twii′as
the total number of people moving among working regions and α asmovement frequency among them, so

In formula (7), we quote the way of spatial interaction model,
however twii′is free from decrement by the distance. We set dwii′as the
distance of node i−i′of working regions, working travel cost is,

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At the last, we introduce discomfort causing the density of
population. We set ρί and ρj is a density of each node,so, the formulation of discomfort is,where βw, βh is the discomfort value of each nodes i and j. A
conceptual diagram of the urban model and movement form is shown below(Figure1).

Figure 1: The urban model and movement form
2.2 Formulation as Quadratic Programming Problem
For the purpose of this study is to derive optimum arrangement which
minimizing discomfort, formula (12) and travel cost, formula (6) and (9)under constrain equations formula (1)–(5), we introduce (I+J+I×J)th variable
vector x, (I+J+I×J)th constant vector and (I+J+I×J)×(I+J+I×J) squarematrix Q. However, each factors are,And as a result, formula (6), (9) and (12) are to be,

therefore, minimizing problem f (x) is,
Optimum Assignmentof housings
Housings andand jobs
Jobswith
withconstrained
Constrainedcapacity
Capacityconsidering
Considering
Discomfort andOptimum
assignment of
discomfort
TravelCosts
and travel costs

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Formula (18) is clearly quadratic programming problem of linear
constraint. Here, square matrix Q was a symmetrical matrix and semidefinite,
formula (18) will be convex quadratic programming problem and the solutions
are well known such as quasi Newton method (Fujitaetal., 1998) or conjugate
gradient method (Togawa, 1997), however, matrix Q introducing formula (15)is distance matrix and it does not satisfy semidefinite condition generally, andminimizing problem of discomfort and travel cost of urban city in this study isnonconvex quadratic programming problem.

2.3 Derivation of Optimum Solution with RLT
We relax nonconvex quadratic programming problem using RLT
and propose the calculation method of derivation optimum solution in urban
model.
First of all, we think a quadratic programming problem including
nonconvex one;

and, we introduce new variable Yij=xixj (1⩽i⩽j⩽n), and set
executable area of problem (19) as D and its convex hull as coD. Moreover,
we define ⟨Q,Y⟩ as inner product of matrixes, so we can introduce newproblem as follows;

Here, Sherali (1995, 2012, 2013) prove that if the optimal solution ofproblem (20) exists and it is to be (Y*, x*), so objective function of the
problem will be linear. Asaresult, (Y*, x*), is end point of coD and objectivefunction is equal of problem (19) because optimal solution exists as Y=x*x*.
Furthermore, in the relaxation of nonconvex quadratic programming
problem,Ya-jima (1999) show the lemma as follow;

In brief, if (Y*, x*) is the optimal solution of the problem, g(Y*, x*) is
to be the lower bound value of quadratic programming problem (19), and at
the same time f(x*) has upper bound value because of x*∈ D, and more if x*
is the end of set D, then the value is the optimum solution of problem (19).

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2.4 Optimum Solution of Proposal Model
The preceding section demonstrates that we are able to get the
optimum solution in nonconvex quadratic programming problem. In thissubsection, we apply it to optimum arrangement of residence and jobs
which minimizing discomfort and travel cost, f(x), in formula (18).
Minimization problem of the urban model proposed in 2.2 is,

The problem (21) suits the condition of formula (19), so
reformulation-linearization technique introduced in 2.3 is realized.
Therefore, we are able to derive the optimum arrangement of residence andjobs in the urban model using the method of preceding section. In numericalexamples described later in Sec.3, we guarantee precision of the solution weget by calculating the gap, even if the case of f(x*) ≠ g(Y*,x*),

3. NUMERICAL EXAMPLESIn this section, we attempt to derive optimum arrangement of virtual urban
models with the proposal method in Sec.2. It includes (i)“H-W-H” linear
city and (ii)“H-W-H” 2D city.
To solve the problem, we use the following computer, CPU; Intel(R) Core (TM) i7-3770K (3.50GHz), RAM; 32.0GB and software,
IBMCPLEX12.6.
3.1 Optimum Arrangement of Residence and Jobs
We derive the distribution of residence and jobs while changing
the values of T, α and β in the urban model (i) and (ii).
(i)“H-W-H” linear city

Optimum Assignmentof housings
Housings andand jobs
Jobswith
withconstrained
Constrainedcapacity
Capacityconsidering
Considering
Discomfort andOptimum
assignment of
discomfort
TravelCosts
and travel costs

149

AugustAugust2015,
2015,Thailand
ThailandWe set the linear urban model as; I=10 in W, J=10 in H, constrained
capacity = 1.00, and T=5.5 (Figure2). When we change the parameter α(Figure2 (a)-(c)), working nodes get together in one and change theparameter β, working nodes spread and working place density is to beaverage due to rising of the discomfort cost. In spite of rising the cost ofworking travel and discomfort, housings nodes which close to working
place nodes have the number of people to constrained capacity due tocommuting travel cost.
(ii)“H-W-H”2D city We set the 2D urban model as; I=9 in W, J=40
in H, constrained capacity in W=1.00, constrained capacity in H=0.25, and
T=5.5 (Figure3). When we rise the parameter α, the center of W has thenumber of people of the capacity, however, percentage of discomfort cost in
total costs is high when we change β from 0.50 to 1.50 so nodes of W is to
be average. Moreover,we rise α and β together, the model shows final state
distribution of housings and jobs.
3.2 Specific Characters of T, α and β on Optimum Arrangement
In preceding section, we show that optimum arrangements fluctuate
while changing T, α and β in each linear city and 2D city. In this section, we
describe the specific character of them in the distribution of residence andjobs.
The formula (6) and (9) have distance element but informula (12),there is not. The unit of travel cost is “Number of People” × “distance” andso the unit of β is “distance”. The character of β introduced calculating
discomfort cost is “distance” shows that we can regard the cost of node
itself as distance, and it is reasonable by the distribution we show in 4.1.
In addition, it is expected that information society evolves andpopulation decline in near future, so α will rise and β will down in the urban
city in Japan. There is some possibility of recommending the policy ofreconstruction of the city with the method and numerical examples we
propose in this research.

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Figure 3: Optimum Assignment of Housing and Jobs in 2D City

Optimum Assignmentof housings
Housings andand jobs
Jobswith
withconstrained
Constrainedcapacity
Capacityconsidering
Considering
Discomfort andOptimum
assignment of
discomfort
TravelCosts
and travel costs

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2015,Thailand
Thailand

4. CONCLUSION
In this study, we clarify that minimizing problem of discomfort calculated
from population density and travel cost as a total of commuting travel andworking travel in urban city consisting or working place and residence is
non convex quadratic problem, and propose a new method of deriving
optimum solution using RLT. In particular, we relax nonconvex quadratic
problem as linear programming problem incorporated with spatial
interaction model and relaxation of nonconvex quadratic problem, and as aresult, we derive the optimum arrangement distribution of residence andjobs. Furthermore, we point out qualitative characteristics of T, α and β, we
get the knowledge that the method is effective to time development of
optimum arrangement of residence and jobs. Due to combine of linear urban
model and two dimensional urban model, we can construct complex urban
model, and we consider that it enables us to derive the optimum
arrangement conforming to more realistic urban city.
We get the optimum solutions without slight errors, and they show
the gap as follow, 0.128474-0.305086. We consider the reason why themethod makes gap is that the domain [D]L2 is evidently larger than the area
D at the time of using RLT. There are other ways of relaxation techniquelike Semidefinite Programming Problem (SDP) known as a type of convex
optimization (Kojima and Waki, 2004), so we consider that SDP relaxation
enable us to improve the formulation of minimizing problem of discomfort
and travel cost and adapt the virtual urban model to actual urban city. This is
the research task from now on.
REFERENCES
Ministry of Land, Infrastructure, Transport and Tourism, 2011. Long-Term
Prospects of the Country, Report of Ministry of Land, Infrastructure,
Transport and Tourism, p.7. (In Japanese)
Hamilton, B.W., 1982. Wasteful commuting, Journal of political Economy,
90, pp.1035-1053.
Kondo, T. and Yoshikawa, T., 2012. Study on Three-Dimensional Urban
Form which Minimizes Travel cost: Assuming introduction ofhierarchical districts and transportation, Journal of Architectural
Institute of Japan, Vol.77, No.675, pp.1087-1093. (In Japanese)
Matsuhashi, K., 1996. A Study on Optimal 3D Urban Form of EnergySaving City – From a Viewpoint of Trips -, Journal of the City
Planning Institute of Japan, 31,pp.43-48. (In Japanese)
Suzuki, T., 2003. A Commutation Flow Model and the Effect of Workers
Decentralization Using the Joint Distribution of Home andWorkplace Location, Journal of The Japan Society of Industrial andApplied Mathematics, Vol.13, No.3, pp.403-415. (In Japanese)
Ohtsu, S. and Koshizuka, T., 1999. An Optimum Distribution of Home andWorkplace Minimizing a Total Cost of Transportation in a City,
Journal of the City Planning Institute of Japan,34, pp. 775-780. (InJapanese)
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Maruyama, T. and Harada, N., 2003. Optimal Job-Housing Location Patter
Considering Congestion in Network and Its Empirical Study,
Journal of the City Planning Institute of Japan, 38 (3), pp. 517-522.
(In Japanese)
Yokota, Y. and Muromachi, Y., 2012. Reducing Railway Excess
Commuting in Tokyo Metropolitan Area by Residential Relocation,
Journal of the City Planning Institute of Japan, 47 (3). Pp. 853-858.
(In Japanese)
Honma, Y. and Kurita, O., 2004. Determining Joint Distributions of Homes
and Workplaces Based on the Random Utility Theory, Journal of theCity Planning Institute of Japan, 39, pp. 769-774. (In Japanese)
Fujita, H., Konno, H. and Tanabe, K., 1998. Optimization Problem,
Iwanami Shoten, Tokyo. (In Japanese)
Togawa, H., 1977. Conjugate Gradient Method, KYOIKU-SHUPPAN,Tokyo. (In Japanese)
Sherali, H. D. and Tuncbilek, C. H., 1995. A ReformulationConvexification Approach for Solving Nonconvex Quadratic
programming Problems, Journal of Global Optimization, 7(1), pp. 131.
Sherali, H. D. and Admas, W.P., 2013. A Reformulation-Linearization
Technique for Solving Discrete and Continuous Nonconvex
Problems, Springer US.
Sherali, H. D., Dalkiran, E. and Liberti, L., 2012. Reduced RLT
representations for nonconvex polynomial programming problems,
Journal of Global Optimization, Vol.52, Issue 3, pp. 447-469.
Yajima, Y., 1999. Nonconvex Quadratic Programming Problems andCombinatorial Optimization, Communications of the Operations
Research Society of Japan, Vol.44, No. 5, pp. 237-242. (In Japanese)
IBM Corporation, IBM ILOG CPLEX Optimizer 12.6, http://www01.ibm.com/software/commerce/optimization/cplex-optimizer/,
2014/11/7.
Kojima, M. and Waki, H., 2004. Semidefinite Programming Relaxation for
Polynomial Optimization Problem, Systems Control and Information,
48(12), pp.477-482. (In Japanese)

Optimum Assignmentof housings
Housings andand jobs
Jobswith
withconstrained
Constrainedcapacity
Capacityconsidering
Considering
Discomfort andOptimum
assignment of
discomfort
TravelCosts
and travel costs

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7th Joint Student Seminar on Civil InfrastructureFACTORS INFLUENCING RED LIGHTRUNNING BEHAVIOR: A STUDY OF
SOCIO-ECONOMIC CHARACTERISTICS
AND GEOMETRY OF INTERSECTIONS
Auearree JENSUPAKARN a, Kunnawee KANITPONG b,
Piyapong JIWATTANAKULPAISARN ca,bThailand Accident Research Center, Asian Institute of Technology,
P.O. Box 4, Klong Luang, Bangkok, 12120, ThailandaE-mail: a.jensupakarn@gmail.combE-mail: kanitpon@ait.ac.thcThaiRoads Foundation, Ladprao Soi 3, Jatujak, Bangkok, Thailand;E-mail: piyapong.j@gmail.com

ABSTRACTIn Thailand, red light running is considered as one of the mostdangerous behaviors at intersections. Red light running behavior is causedby the failure to obey the traffic control signal. However the drivers who arerunning through red lights constitute a major portion of intersection crashes,
little research study has been done to evaluate the contributing factors
influencing the red light violation behavior. This study aims to determine
the factors influencing the red light running behavior including road andtraffic condition, traffic control signal, and geometry of intersection.
Trained observers and video cameras were used to record the red lightrunning behavior at 33 intersections in Nakhon Ratchasima, the largest
province in the northeastern region of Thailand. In addition, the socioeconomic characteristics of red light runners were obtained from selfreported survey. The Binary Logistic Regression and the Multiple Linear
Regression model were used to determine the characteristics of red lightrunners and the factors influencing rates of red light running respectively.
The result from this study can help to understand the characteristics of red
light runners, and factors affecting them to run red lights.
Keywords: Red Light Running, Traffic Violation,
Characteristics, Road Safety, Injury Prevention, Thailand

Driver

1. INTRODUCTION
Red light violations are now a problem in many countries, especially indeveloping countries. A principal contributing factor in motor-vehicle
crashes in Thailand and other countries is failure of drivers to obey traffic
control devices. In Thailand, red light running is considered as one of themost dangerous behaviors on roadways. According to the Royal Thai
Police’s Database in 2011, there were 1,702 red light running crashes which

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AugustAugust2015,
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Thailandaccounted for 2.79% of all accidents caused by drivers. As illustrated in
Figure 1, though the number of accident caused by red light running is lessthan the others, it is still considered as a serious problem. Most of thesecrashes occur at intersection, where one or more drivers decide to disobey a
traffic signal, usually end up with the seriously crashes which resulting insevere injuries and fatalities.

0.05%
Vehicle overloading
Driving under the influence of controled… 0.05%
0.20%
Avoiding animals
0.21%
No light/signal for disabled vehicle
0.26%
No head lights
0.80%
Drowsiness
1.29%
Failure to keep left o 4-lane highways
1.73%
Disregarding stop sign
2.12%
Unskilled driver
2.40%
Driving in the wrong lane
2.40%
Failure to give signal whe parking/slowing…
2.45%
Failure to yield the right of way
2.79%
Disregarding traffic light
3.71%
Vehicle defects
3.79%
Illigal overtaking
8.76%
Driving too close the leading vehicle
9.83%
Drunkenness
13.73%
Suddenly cutting in
15.74%
Violating speed limit
27.68%

Others
0%

10%

20%

30%

40%

50%

Figure 1: Proportion of traffic accidents caused by the drivers (The Royal Thai
Police, 2011)
Thai Traffic Police Official reported more than 100,000 red lightrunners were issued a fine ticket over the past two months after the red lightcameras were operating (December 30, 2008). That means, on average, thenumber of red light runner is around 2,000 motorists per day. These
numbers obtained from red light camera at only 30 intersections from morethan 1,500 intersections around Bangkok; moreover, the red light camera ineach intersection was installed in one approach only. Though the number of
red light runners dropped to 800-900 motorists per day (Thai Traffic Police
Official, April 2012), it is better if the number of red light runners dropped
to zero.
Red light running is a complex problem that could result fromvarious causes. Some drivers run red lights because they are in a hurry or
they are sometimes too impatient to wait for the signal to turn green. Other
drivers are simply annoyed at red lights and they just do not want to wait. In

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many countries, especially Thailand, the yellow light has come to symbolize"hurry up" instead of "slow down". This driver’s behavior is now becoming
to a habit; if drivers have run red light at one time, it is likely that they will
do it again if they have a chance.
Running a red light can cause the adverse effects for everyone
involved. For instance, it can result in someone's death or injury, damaging
the vehicle, and getting a ticket from the police. One of the most dangerous
effects that running a red light could have is death. A car that runs a red
light is liable to come in the path of the traffic that has the green light. Many
drivers tend not to look right and left while crossing the intersection when
they have the green light. This means, that a person running a red light isdriving into a potential car that is coming violently at high speed from eitherside. A collision between the two cars could result in serious injuries andeven death.
However, the drivers who are running through red lights constitute amajor portion of intersection crashes, little is known about theircharacteristics or the other contributing factors influencing the number of
red light violations e.g. vehicle characteristics and geometry of intersections.
Therefore, this study aims to determine the factors those are influencing to
the number of red light running including: driver factors, vehicle factors,
and geometry of intersection factors. In addition, the importance of thisstudy is to provide the data that may help understand who the red lightrunners are and what factors affecting them to run red lights. Furthermore,this study could lead to the improvement of implemented policies to dealing
with running a red light through an intersection in order to reduce thenumber of red light runners.
The main objectives of this study are to:
1. Determine the characteristics of motorcycle drivers who run red
lights by observation.
2. Determine the socio-economic characteristics of red light runners
by using secondary data of self-reported survey.
3. Determine the intersection factors influencing rates of red lightrunning by observation.
2. LITERATURE REVIEW
Based on the findings from previous studies, the factors affecting to thenumber of red light running at an intersection can be divided into four
categories: driver characteristics, physical conditions of intersection, traffic
condition, and driving environment. These findings are presented in below
section.
Kraus and Quiroga (2004) reported on red light running trends in
Texas and found that older age groups accounted for a relatively small
portion of red light running crashes compared to the young age group. C.Y.
David Yang and Wassim G. Najm (2006) presented the results of the
Factors influencing
Factors
influencing red
red light
light runningrunning behavior:
behavior: aastudy
studyofofsocio-economic
socio-economiccharacteristics
characteristicsand geometry ofintersections
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AugustAugust2015,
2015,Thailand
Thailandrelationship between red light runners and their related factors from ananalysis of about 47,000 red light violation records collected from 11
intersections in the City of Sacramento, California, by red light photoenforcement cameras between May 1999 and June 2003. Three groups offactor were considered in the study: various driver, intersection, andenvironmental factors. They suggested that younger drivers under 30 yearsof age are more likely to run red lights than drivers in other age groups.
Another studies also reported in the same way that young drivers are more
likely to run the red lights compare to other age groups (Porter and Berry,2001) and red light runners tend to be drivers under 30 years old (Retting etal., 1999 and Retting and Williams, 1996). Gender of driver also found as acontributing factor. Retting et al. (1999) reported that the red light runners
are more likely than non-runners to be male.
Many research studies have considered the occupancy in a vehicle asa factor contributing to drivers’ behavior. Porter and Berry (2001)
concluded that passengers reduced drivers’ tendencies to run red lights. 25.6
percent of drivers reported being likely to run a red light however, thispercentage drops sharply when there are passengers in the vehicle.
Moreover, Porter (1999) has reported that passengers decrease red lightrunning probability especially child passengers.
Survey results from The Richmond Metro Poll (1999) also pointed
out that red light running was related to other risk behaviors. Drivers whoreported that they always wear seat belts were also more likely to havereported that they never ran red lights (54%). Driving record was proved to
be one factors influencing to running the red lights. The studies found thatthe drivers with poor driving records (Retting and Williams, 1996) such as
driving with suspended or revoked driver’s license (Retting et al., 1999) or
driver who have been ticketed (Porter, 1999) have a higher tendency to run
the red lights. There are another driver’s characteristics influencing to red
light running such as ethnicity, occupation, alcohol consumption, andfrustration.
In a study done by Elmitiny et al. (2010), a field data collection was
conducted at a high-speed signalized intersection, using video-based systemwith three cameras. The objective of their study was to know how the
probabilities of a stop or go decision and of red light running are associatedwith the traffic parameters. The results were obtained from the classification
analysis, as the distance from the intersection increase, the probability of astop decision increases while the probability of go decision decreases. In
1985, Chang et al. has concluded about the probability of a vehicle stopping
for a traffic light; probability decreases as the distance from vehicle to theintersection decreases. Moreover, Chang et al. (1985) was found that thewidth of intersection is another one factor affecting to stop and go decisionof drivers; most of drivers tend to stop for traffic lights more at wider
intersections than at narrower intersections.
Approaching speed was found as contributing factor to drivers’decision. The probability of a driver stopping for a traffic light decreases as
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the approach speed to the intersection increases (Chang et al., 1985).
Elmitiny et al. (2010) found that if operating speeds are lower than 50.55
mph, drivers are more likely to stop for a traffic light. Yang and Najm
(2006) also reported that about 56% of the red light runners were driving ator below the posted speed limit.
Furthermore, the signal timing, especially yellow timing, has someeffect on drivers’ decision. From the study done by Retting et al. (2008), theobjective was to evaluate the incremental effects on red light running of firstlengthening yellow signal timing, followed by the introduction of red lightcameras. Results showed that yellow timing changes reduced red lightviolations by 36%. The yellow signal timing should be adequate; otherwise,
it could increases probability of running the red lights. The result from astudy done by Brewer et al. (2002) shows that the frequency of red lightrunning increases when the yellow interval is less than 3.5 seconds. And theresult of Elmitiny et al. (2010) also shows that when the yellow time larger
than 4.3 seconds, the red light running violations can occur.
When considering the previously studies about factors affecting to
the red light running, the traffic conditions such as time pressure orapproach traffic volume can be affects to drivers’ behavior. A large numberof drivers tend to speed up to beat the yellow light which followed by the
red light because of wanting to save time (Porter and Berry, 2001). Whenconsidering the approach traffic volume factor, higher traffic volumes could
lead more red light running rates. The reason was reported by Elmitiny et al.
(2010); an intersection with higher traffic volumes, drivers would have morechances to be in a following position in the traffic flow at the onset ofyellow, therefore the red light running rate would increase.
Many studies found that time of day and day of week are affecting todrivers’ behavior to stop for the traffic lights. The number of red lightrunning tends to increase during the morning and evening peak hours
compared to other times of the day (Retting et al., 1998). At nighttime red
light running were more than at daytime due to high possibility of drivers tohave high blood alcohol concentrations (Harb et al., 2007). Whenconsidering the day of week factor, many researchers reported that there are
more red light violations during the weekdays compared to weekends.
3. METHODOLOGY
The study area is in Nakhon Ratchasima, the biggest province of Thailand.
It is located in the northeast of Thailand. The study was conducted at the 33
intersections in both rural and urban areas of the province. Potential
intersections were identified and selected according to the following criteria:
signalized, at-grade, four-legged, standard geometry layout (i.e.
approximately 90-degree angle for each approach), properly functioning
traffic signal, pre-timed signal operation, allow left-turning on red, not turninto flashing signal throughout the observation periods, and clearly visible
stop line and/or zebra crossing. The selected intersections were observed on
Factors influencing
Factors
influencing red
red light
light runningrunning behavior:
behavior: aastudy
studyofofsocio-economic
socio-economiccharacteristics
characteristicsand geometry ofintersections
159
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AugustAugust2015,
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Thailandweekdays and weekends and the data were collected for one hour atdifferent time periods: peak, off-peak, and nighttime.
3.1 Model Selection for the Analysis of Characteristics of Motorcycle
Drivers
The first objective is focused on the characteristics of motorcycle
drivers who ran the red lights. Their gender, estimated age, helmet wearing
status, number of pillion passenger, and type of motorcycle they used wererecorded. The data would be used for analyze the driver characteristics
factors influencing the red light running behavior by using Binary Logistic
Regression Method. The model is suit to the analysis that the alternative of aproblem are discrete. Discrete choice model is classified to several sub
models according to the assumption of the error term. Logit model, a formof discrete choice model, assumes the distribution of the error as the gumble
distribution. The model is widely used in several studies. Also, in this study,
the characteristics of motorcycle drivers who run the red light are described
via Binary Logistic Regression Model.
(1)“Y” is a dependent variable which represents the decision of
motorcycle driver when they when see the changing of traffic light fromgreen to yellow or from yellow to red at the intersection. A driver has two
choices: stop behind the stop line or violate the traffic light. “X1, X2, …, Xn”
are the independent variables include; gender, estimated age, helmet
wearing behavior, number of pillion passenger and type of motorcycle (asshown in Table 1). These variables would affect to the dependent variable“Y” which is the decision of motorcycle drivers.
Table :. Study variables from observation survey for Binary Logistic
Regression Model
Variable type

Explanatory variable

Description of Variable

Dependent
Independent

Driving behavior
gender
y_age
m_age
o_age
hm
psg0
mc_type

1 = run the red light, otherwise 0
1 = male driver, otherwise 0
1 = driver age below 20, otherwise 0
1 = driver age between 20-50, otherwise 0
1 = driver age more than 50, otherwise 0
1 = driver wears a helmet, otherwise 0
1 = drive with no pillion passenger, otherwise 0
1 = driver with automatic gear motorcycle, otherwise
0

Average
0.4
0.8
0.3
0.6
0.1
0.4
0.7
0.4

For both of red light and yellow light runners’ data, the highest
Pearson correlation was found between young aged drivers and middle aged
drivers with the coefficient of -0.888 and -0.844 respectively. Another one
was found between no passenger and one passenger with the coefficient of 0.977 for red light runners and -0.839 for yellow light runners. Therefore,
one of each variable for all of correlation will be excluded from the model.

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3.2 Model Selection for the Analysis of Socio-Economic Characteristics
of Red Light Runner
The second emphasizes is collecting the secondary data from selfreported survey in 2010 (thaiROADS Foundation, 2010). Questionnaire wasproviding the information about a frequency of violating behavior of each
respondent and their socio-economic characteristics. The data would be
used for analyze the socio-economic characteristics of drivers that affecting
the red light running behavior. Secondary information gathered from theprevious study— Self-Reported Survey on driver socio-economic
characteristics, driver behaviors, and driver attitudes. (ThaiROADSFoundation, 2010). The data about personal data and characteristics
collected are used for analyze the relationship between drivers’ violation
behavior and their socioeconomic characteristics by using Binary Logistic
Regression model.
“Y” is a dependent variable which represents the red light runningbehavior. “X1, X2, …, Xn” are independent variables include; gender, age,education level, occupation, vehicle types, driving license, helmet/safety
belt wearing, and attitudes on risk behavior as shown in Table 2 and Table 3
separated for the motorcycle users and personal car users respectively.
Table 2. Study variables from secondary data source (thaiROADS) of
motorcycle users for Binary Logistic Regression Model
Variable type
Dependent

Independent

Explanatory
variable

Description of Variable

Average

Driving behavior(scenario1)
Driving behavior(scenario2)

1 = run the red light, otherwise 0

0.4

1 = run the red light, otherwise 0

0.2

age
gender
edu_1
edu_2
edu_3
ocp_1
ocp_2
ocp_3
ocp_4
ocp_5
licen
helmetrisk

age of driver
1 = male driver, otherwise 0
1 = primary school or lower, otherwise 0
1 = high school/GED, otherwise 0
1 = bachelor degree or higher, otherwise 0
1 = agriculturist, otherwise 0
1 = employee, otherwise 0
1 = proprietor/trader, otherwise 0
1 = government/state enterprise officer, otherwise 0
1 = student, otherwise 0
1 = driver has driving license, otherwise 0
1 = driver wears the helmet, otherwise 0
1 (low) – 3 (high) the risk of being caught by police

29.3
0.5
0.2
0.5
0.4
0.1
0.3
0.2
0.1
0.4
0.7
0.6
2.2

Table 3: Study variables from secondary data source (thaiROADS) of
personal car users for Binary Logistic Regression Model

Factors influencing red light running behavior: a study of socio-economic characteristics and geometry of161intersections
Factors influencing red light running behavior: a study of socio-economic characteristics
161
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AugustAugust2015,
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ThailandVariable
type
Dependent

Independent

Explanatory variable

Description of Variable

Average

Driving behavior(scenario1)
Driving behavior(scenario2)

1 = run the red light, otherwise 0

0.4

1 = run the red light, otherwise 0

0.1

age
gender
edu_1
edu_2
edu_3
ocp_1
ocp_2
ocp_3
ocp_4
ocp_5
car_1
car_2
car_3
licen
belt
risk

age of driver
1 = male driver, otherwise 0
1 = primary school or lower, otherwise 0
1 = high school/GED, otherwise 0
1 = bachelor degree or higher, otherwise 0
1 = agriculturist, otherwise 0
1 = employee, otherwise 0
1 = proprietor/trader, otherwise 0
1 = government/state enterprise officer, otherwise 0
1 = student, otherwise 0
1 = driver usually drive a passenger car, otherwise 0
1 = driver usually drive a pick-up, otherwise 0
1 = driver usually drive a van, otherwise 0
1 = driver has driving license, otherwise 0
1 = driver fasten the seatbelt, otherwise 0
1 (low) – 3 (high) the risk of being caught by police

31.7
0.7
0.1
0.3
0.5
0.1
0.3
0.3
0.2
0.2
0.4
0.6
0.0
0.8
0.7
2.2

The highest Pearson correlation for motorcycle driver analysis wasfound between high school/GED and bachelor degree or higher which thecorrelation coefficient of -0.712. For passenger vehicle analysis was foundthe correlation between high school/GED and bachelor degree or higher
with the coefficient of -0.752 and another one was found between passenger
car and pickup with the coefficient of -0.952.
3.3 Model Selection for the Analysis of Intersection Factors Influencing
Rates of Red Light Running
The third one is the characteristics of intersection that may influence
the red light running rates. The physical conditions (i.e. geometry, traffic
signal conditions, etc.) of each intersection were recorded and used foranalyze the relationship between rate of red light running and theircontributing factors related to the physical conditions of intersection byusing the Multiple Linear Regression Method. Multiple Linear Regression(MLR) is a method used to model the linear relationship between adependent variable (Y) and one or more independent variables (Xi). The
model will explains how the independent variables affect to the dependent
variable.
(2)
where

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7thICUS Report 79“Y” is a dependent variable which is the rates of red light running.
“Xi” is predictors which are the general characteristics of intersection:
Cycle length, Yellow time, Signal phasing, Operating speed, Traffic volume,
Number of lane, Lane width, Median width, Available of auxiliary lane,
Presence of police booth and Illumination at intersection (in night time). as
shown in Table 4.
Table 4: Study variables from observation survey for Multiple Linear
Regression Model
Variable
type

Explanatory
variable

Description of Variable

Dependent

Rate of Red Light
Running

number of red light running per
analysis hourly volume

3.7

Independent

th
mi
wd
peak
night
vol
nol
lw
iw
med
mw
isd
sinst
sdisp
ph
cl
yt
locat
spd
ads
add
pave
lux
rn

1 = data collected in through direction, otherwise 0
1 = data collected on minor road, otherwise 0
1 = data collected from weekday, otherwise 0
1 = data collected in peak hour, otherwise 0
1 = data collected at nighttime, otherwise 0
1 = analysis hourly volume, otherwise 0
1 = number of travelling lane, otherwise 0
width of lane
width of intersection
1 = available of median, otherwise 0
width of medianintersection sight distance
1 = normal pole, otherwise 0
1 = normal (no countdown), otherwise 0
number of phase
length of cycle time
length of yellow interval
1 = intersection located in urban area, otherwise 0
approaching speed (kph)
1 = available of advanced warning sign, otherwise 0
distance of advanced warning sign to stop line
1 = asphalt, otherwise 0
illumination at nighttime
level of pavement roughness, 1(smooth) - 5(severe)

0.5
0.5
0.5
0.2
0.3
390.1
2.6
4.6
11.5
0.3
1.8
11.2
0.4
0.4
2.7
117.7
3.2
0.6
48.2
0.4
71.7
0.8
12.2
2.2

Average

The highest Pearson correlation was found between: number of lane
and auxiliary lane, number of lane and approaching speed, crossing lane
width and number of crossing lane, intersection sight distance and auxiliary
lane, intersection sight distance and location of intersection, location andtype of signal operation, location and number of phase, location and cycle
length, number of phase and cycle length, presence of police and cycle
length, advanced warning sign and advanced warning sign distance.

Factors influencing
Factors
influencing red
red light
light runningrunning behavior:
behavior: aastudy
studyofofsocio-economic
socio-economiccharacteristics
characteristicsand geometry ofintersections
163
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AugustAugust2015,
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Thailand4. MODEL RESULTS
4.1 Explanation of Statistical Test for the Red Light Runners’
Characteristics
In this section, the data were analyzed by using the Binary Logistic
Regression Model. This model will analyze the significance of independent
variables (drivers’ characteristics) that affecting to red light runningbehaviors. The statistical results are presented in Tables 5.
Table 5: Model estimation result of motorcycle drivers’ characteristics
affecting red light running behavior
Independent Variables
gendero_ageihm
psg0iimc_type
Constant
Summary of Statistics:
Observations
Log Likelihood

Model 1Model 2Model 3

Coef.

P>|Z|

Coef.

P>|Z|

Coef.

P>|Z|

1.113
-2.251-1.4810.958-1.120-0.940

***
***
***
***
***
0.000

1.235
-2.074-1.349

***
***
***

1.151
-2.401

***
***

1.100

***

-2.0910.000

4257-2272.8251-0.8230.000

4257-2493.8441

4257-2583.1661

i comparison group is young aged driverii comparison group is driver with two pillion passengers- Not relevant; *** Significant at 1% level; ** Significant at 5% level; * Significant at 10% level.

Gender of driver was found that it has a positive effect to thedependent variable. It means male drivers tend to run the red light if
compared with female drivers because males are generally feel confident
themselves and also like challenges. Especially for the critical time when
the light changes from yellow to red, males are more likely to speed up tobeat with the oncoming red light than females. Retting et al. (1999)
concluded that Red light runners are more likely than non-runners to bemale.
Age of driver is also the most significant to the model. The results
show that old aged drivers tend to stop than run a red light compared withthe young aged drivers. It might be because of their perception abilities (i.e.
visualization is not clear, take more times to perceive and react, etc.). Thus,
old aged drivers do not feel confident themselves enough to run a red light.
This finding is consistent with the previous studies done by Kraus andQuiroga (2004); Porter and Berry (2001); Retting et al. (1999); Retting andWilliams (1996).
Helmet use is one of driver behavior which was included in themodel. Drivers who wear the helmet tend to stop for a red light.

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Drivers with no pillion passenger are more likely to run the red lightthan those who travelling with their pillion passengers. Passengers decrease
red light running likelihood, particularly child passengers (Porter and Berry,
2001; Porter, 1999). Because when driver travelling alone, they feel like noneed to take care of someone. If they face the situation of oncoming red
light, the might decide to drive through the intersection with no otherconcerning. On the other hand, if drivers travelling with someone, they may
think more about the other lives that depending on their decisions and their
behaviors. So, driver could recognize that they have to take more
responsibility to their pillion passenger and they might decide to stop for a
red light.
A type of motorcycle has a negative effect to the model. It means thedrivers who drive the automatic gear motorcycle tend to stop more than thedrivers who drive the manual gear motorcycle. The reason is because of theaccelerating rate of each type. The manual transmission motorcycle isnormally has more power than the automatic one in terms of acceleratingrate. Drivers who drive the manual motorcycle might have more ability toaccelerate. So, they might be confident themselves to drive through theintersection before onset of red light.
4.2 Explanation of Statistical Test for Socio-Economic Characteristics
of Motorcycle Red Light Runners
As shown in Table 6 and Table 7, Binary Logistic Regression Model
was used to analyze the socio-economic characteristics affecting to behaviorof running the red lights of motorcycle drivers. Significant variables arecome in both directions; negative and positive which are described below.
Table 6: Model estimation result of motorcycle drivers who ran the red
lights and their socio-economic characteristics (Scenario 1)
Independent
Variablesage
gender
edu_1
ocp_1
ocp_3
ocp_4
ocp_5
licen
helmetrisk
Constant
Summary of
Statistics:
Observations
Log likelihood

Model 1Model 2Model 3Model 4

Coef.

P>|Z|

Coef.

P>|Z|

Coef.

P>|Z|

Coef.

P>|Z|

-0.005
0.518
0.002
0.050
0.099
-0.0540.127
0.162-0.123-0.073-0.514

***
***
0.956
0.224
***
0.143
***
***
***
***
0.000

-0.005
0.522

***
***

***
***

***

0.105

***

0.131
0.157
-0.125-0.073-0.522

***
***
***
***
0.000

***
***
0.542
0.276
***
0.204
***

-0.005
0.539

0.103

-0.0050.537-0.0180.045
0.102-0.047
0.113

0.119

***

-0.6500.000-0.6560.000

45,806-30139.396

45,806-30141.931

45,806-30195.403

45,806-30197.177- Not relevant; *** Significant at 1% level; ** Significant at 5% level; * Significant at 10% level.

Factors influencing
Factors
influencing red
red light
light runningrunning behavior:
behavior: aastudy
studyofofsocio-economic
socio-economiccharacteristics
characteristicsand geometry ofintersections
165
and geometry of intersections

AugustAugust2015,
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Thailand

Table 7: Model estimation result of motorcycle drivers who ran the red
lights and their socio-economic characteristics (Scenario 2)
Independent
Variables

Model 1
Coef.
P>|Z|
-0.008
***
0.438
***
0.164
***
-0.023
0.685
0.071
*
-0.087
*
0.087
**
0.106
***
-0.135***-0.079***-1.6110.000

Model 2
Coef.
P>|Z|
-0.009
***
0.437
***
0.161
***

Model 3
Coef.
P>|Z|
-0.009
***
0.452
***
0.154
***
-0.018
0.743
0.077
*
-0.084
0.107
0.086
**

Model 4
Coef.
P>|Z|
-0.009
***
0.455
***
0.166
***

age
gender
edu_1
ocp_1
ocp_3
0.076
**
0.098
***
ocp_4
-0.083
0.107
ocp_5
0.090
**
0.104
***
licen
0.107
***
helmet-0.135***risk-0.079***Constant-1.6130.000-1.7920.000-1.805 0.000
Summary of
Statistics:
Observations
45,80645,80645,80645,806
Log-19288.606-19288.688-19316.231-19317.543
likelihood- Not relevant; *** Significant at 1% level; ** Significant at 5% level; * Significant at 10%
level.

Gender of driver was found that it has a positive effect to the model.
It means male drivers tend to run the red lights than the females. Drivers
with low education level were found that they are more likely to run the red
light compared with drivers with bachelor degree or higher. For theoccupations, drivers who are a proprietor or trader and drivers who are a
student tend to run the red light in compared with drivers who are an
employee. The driving license also has the positive effect to the model
which means that drivers who have driving license are more likely to run the
red light. The reasons should be because of the drivers who have no driving
license do not want to being caught by doing something illegally because
they might get more fines due to their illegal behavior and also be charged
for drive with no driving license.
Age of driver has the negative effects to the model. It means thelower age of driver, the higher possibility to run the red lights. Drivers who
usually wear the helmet are more likely to stop for the red lights than those
who rarely wear the helmet. Drivers’ attitude towards risk of being caught
when they violate the traffic signal inversely affects to the possibility ofrunning the red light. It means if drivers define that they have less risk ofbeing caught; they will have less concerning about the police.
4.3 Explanation of Statistical Test for Socio-Economic Characteristics
of Passenger Vehicle Red Light Runners
Binary Logistic Regression Model was used to analyze the socioeconomic characteristics affecting to behavior of running the red lights ofpassenger vehicle drivers as shown in Table 8 and Table 9.

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Table 8: Model estimation result of personal car drivers who ran the red
lights and their socio-economic characteristics (Scenario 1)
Independent
Variablesage
gender
edu_1
ocp_1
ocp_3
ocp_4
ocp_5
car_1
licen
belt
risk
Constant
Summary of
Statistics:
Observations
Log likelihood

Model 1Model 2Model 3

Coef.

P>|Z|

Coef.

P>|Z|

Coef.

P>|Z|

-0.0130.351
0.178-0.293-0.129-0.281-0.1230.120
0.185-0.494-0.211
0.390

***
***
***
***
***
***
***
***
***
***
***
0.000

-0.0130.366
0.188-0.253-0.131-0.288-0.112
0.105

***
***
***
***
***
***
***
***

-0.015
0.359
0.182

***
***
***

0.001

0.976

-0.2710.000-0.0130.104
0.185-0.494-0.209
0.327

0.635
***
***
***
***
0.000

45,724-29519.633

45,724-29964.207

45,724-29570.84

Model 4
Coef.

P>|Z|

0.116
-0.304-0.193-0.388-0.013

***
***
***
***
0.625

-0.3770.000

45,724-30181.432- Not relevant; *** Significant at 1% level; ** Significant at 5% level; * Significant at 10% level.

Table 9: Model estimation result of personal car drivers who ran the red
lights and their socio-economic characteristics (Scenario 2)
Independent
Variablesage
gender
edu_1
ocp_1
ocp_3
ocp_4
ocp_5
car_1
licen
belt
risk
Constant
Summary of
Statistics:
Observations
Log likelihood

Model 1Model 2Model 3

Coef.

P>|Z|

Coef.

P>|Z|

Coef.

P>|Z|

-0.0160.244
0.607-0.444-0.217-0.277-0.1360.188-0.001-0.393-0.286-0.789

***
***
***
***
***
***
***
***
0.985
***
***
0.000

-0.0160.244
0.607-0.444-0.217-0.277-0.135
0.188

***
***
***
***
***
***
***
***

-0.0180.256
0.632-0.406-0.223-0.287-0.088
0.168

***
***
***
***
***
***
**
***

-0.393-0.286-0.790

***
***
0.000

-1.6130.000

45,724-16432.7

45,724-16432.7

45,724-16655.084

Model 4
Coef.

P>|Z|

0.503
-0.503-0.294-0.385
0.060

***
***
***
***
0.125

-1.9050.000

45,724-16758.328- Not relevant; *** Significant at 1% level; ** Significant at 5% level; * Significant at 10% level.

Gender of driver was found as a positive effect variable. It means
male drivers have more red light running frequency than females. Educationlevel also affecting to red light running behavior; drivers with low educationlevel (primary school or lower) are more likely to run the red light than the

Factors influencing
Factors
influencing red
red light
light runningrunning behavior:
behavior: aastudy
studyofofsocio-economic
socio-economiccharacteristics
characteristicsand geometry ofintersections
167
and geometry of intersections

AugustAugust2015,
2015,Thailand
Thailanddrivers with bachelor degree or higher. Drivers who drive the passenger car
tend to run the red light more than drivers who drive the van.
Negative effects of independent variables were found in the model.
Young drivers tend to run the red light than old drivers. For an occupation,
agriculturists, proprietor or traders, and government/state enterprise officers
are found to be more likely to stop for the red light compared with theemployees. The reason should be because most of employee drivers have
fixed-time working schedule so that they want to save time in a rush hour.
Drivers who often use the safety belt tend to stop for the red lights than thedrivers who never used. Red light runners are less likely to wear safety belts(Retting and Williams, 1996), same as our finding. The attitude towards therisk of being caught when they violate the traffic signal also has a negative
effect to the model. If driver think that there is less risk, they will have more
possibility to run the red lights.
4.4 Explanation of Statistical Test for Factors Influencing Rates of Red
Light Running
As shown in Table 10, Multiple Linear Regression Model was used
to analyze the factors influencing the rates of red light running. The resultsof analysis from four model show that nine variables are significant innegative and positive effects.
Table 10: Model estimation result of factors influencing rates of red lightrunning
Independent
Variables
wd
peak
night
mi
th
vol
nol
lw
mw
isd
sinst
sdisp
cl
yt
add
pave
lux
rn
Constant
Summary of
Statistics:
Observations
R-squared
Adjusted R-

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Model 1
Coef.
0.396
0.589
4.877
-0.2540.367-0.003-0.191
0.502-0.0040.040-1.2110.210
0.006-0.609-0.004-0.521
0.092-0.0242.814

Model 2

P>|Z|

***

Coef.

Model 3

P>|Z|

4.857

***

***

-0.002

***

**

0.558

***

**
**

0.034
-1.222

**
***

*
**

-0.716-0.003

**
*

***
*
0.152

0.079
-0.025
1.984

***
*
0.144

792
0.26210.2440792
0.25390.2463

Coef.

P>|Z|

0.383
0.450
4.974
-0.2890.367-0.0022.566

Model 4
Coef.

P>|Z|

***

***

0.000

792
0.20900.2030-0.621
0.367-0.006
0.064
0.491
0.028
0.034
-1.2770.211
0.005-0.652-0.005-0.3940.115-0.022
5.660

***
**
*
**

*
**
***
*
0.007

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squared- Not relevant; *** Significant at 1% level; ** Significant at 5% level; * Significant at 10% level.

Nighttime, illumination, traffic volume are the most significant.
Nighttime red light runners were more likely than daytime runners to havehigh blood alcohol concentrations. (Harb et al., 2007). In compared withdaytime, nighttime tend to have more red light running because traffic
volume is quite low and drivers do not want to wait for long time so thatthey might decide to run the red light. The significance of traffic volume
support that less traffic volume will lead to high rates of running the red
light. The intersections where there is high level of illumination at nighttime
tend to have more rates of red light running. It is because of driver can see
the objects including the other vehicles clearly at bright intersection, so they
can confident more.
Lane width was significant to the model; the wider width of lane, themore rates of red light running. If the intersection sight distance is so clear,
the rates of red light will tend to increase because driver can see the otherapproaching vehicles clearly, so they can ensure that no vehicle comes
before they reach the intersection and they might decide to violate the traffic
light. The intersection which there is an advanced warning sign to warn thedriver before they reach the intersection is more likely to have lower rates of
red light running. However, if the advance warning sign is located at too
close to the intersection, the rates tend to increase.
For traffic signal conditions, types of signal installation have a negative
effect to the rates of red light running; if the signal has installed as an
overhang pole. At intersection with short yellow interval, the rates of red
light running will increase. Normally, yellow time require to give an
opportunity for drivers to make a decision (i.e. stop or go), but if the yellow
time is too short, drivers might not be able to stop in time and resulting inrunning the red light. Similar findings from past research show that thefrequency of red light running increases when the yellow interval is lessthan 3.5 seconds (Brewer et al., 2002). In contrast, if the yellow timingchanges (increased by about 1 second), red light violation will reduce by
36%. (Retting et al., 2008).
5. CONCLUSION
This study identified factors that influencing to red light running behaviors.
The contributing factor for driver characteristics affecting to red lightrunning behavior is estimated in this study. Most of red light runners are
males. Old aged drivers tend to stop than run a red light compared with theyounger drivers. Drivers who wear the helmet tend to stop for a red light.
Drivers who travel alone are more likely to run the red light. Most of drivers
who ran the red light use a manual gear motorcycle.
The socio-economic characteristics of red light runners wereobtained from self-reported survey. The study revealed that, for motorcycle
drivers, most of red light runners are males and lower age of driver, higher
Factors influencing
Factors
influencing red
red light
light runningrunning behavior:
behavior: aastudy
studyofofsocio-economic
socio-economiccharacteristics
characteristicsand geometry ofintersections
169
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AugustAugust2015,
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Thailandpossibility to run the red lights. Compared to the drivers with high educationlevel (bachelor degree or higher), low education level drivers (primaryschool or lower) more likely to run the red light. Driver who is a proprietor
or trader and a student tend to run the red light in compared with driver who
is an employee. Most of red light runners has driving license. Driver who
usually wear the helmet are more likely to stop for the red lights than those
who rarely wear the helmet. Drivers’ attitude towards risk of being caught
when they violate the traffic signal has an inverse effect to the possibility ofrunning the red light; the lower risk, the higher probability to run the red
light. For passenger vehicle, most of red light runners are males and young
aged drivers. Driver with low education level has high possibility to run the
red light. Most of red light running is caused by passenger car drivers incompared with van. Agriculturists, proprietor or traders, andgovernment/state enterprise officers are found to be more likely to stop forthe red light compared with the employees. Drivers who often use the safety
belt tend to stop for the red lights than the drivers who never used. The
driver’s attitude towards the risk of being caught when they violate thetraffic signal is low, the probability to run red light is high.
Likewise, models were used to determine the factors influencing
rates of red light running, the estimated results shows that : for rates of red
light running, traffic volume is less, rates of red light running is high. Mostof red light running occurred during nighttime. Intersections with highillumination at nighttime have high rates of red light running. Wider lane
width, higher rates of red light running. Rates of red light running increase
when the intersection sight distance is so clear. The distance of advanced
warning sign decrease, the rates of red light running increase. For traffic
signal conditions, at intersection with short yellow interval, the rates of red
light running will increase.
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Federal Highway Administration (FHWA). (2003). Making Intersections
Safer: A Toolbox of Engineering Countermeasures to Reduce RedLight Running. Washington DC: U.S. Department of Transportation.
Harb, R., Radwan, E., and Yan, X. (2007). Larger Size Vehicles (LSVs)
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27.
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Kamyab, A., McDonald, T., Stribiak, J. J., and Storm, B. (December 2000).
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Kraus, E. and Quiroga, C. (2004). Red Light Running Trends in Texas.
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Liu, B. S. (2007). Association of Intersection Approach Speed with Driver
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Lum, K. M. and Wong, Y. D. (2003). Impacts of Red Light Camera on
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McGee, H. W. (2002). Safety Impact of Red Light Camera Enforcement
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Papaioannou, P. (2007). Driver Behaviour, Dilemma Zone and Safety
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Porter, B. E. and Berry, T. D. (2001). A Nationwide Survey of SelfReported Red Light Running: Measuring Prevalence, Predictors, andPerceived Consequences. Accident Analysis and Prevention, 33,
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THE OPTIMUM TRAFFIC VOLUMESURVEY LOCATIONS FOR THE TRAVELDEMAND ANALYSIS IN KOREA
Seong-min KANG1, Choong-shik LEE2, Cheol PARK3
and Hyun-myung KIM4
1
Researcher/Transportation Planning Lab, Dept. of Transportation Eng.
College of Engineering, The MyongJi University, South Korea
goodksm0508@gmail.com
2
Researcher, The MyongJi University, South Korea
3
Researcher, The MyongJi University, South Korea
4
Ph.D, Director, The MyongJi University, South Korea

ABSTRACTA traffic counts data has been widely used as baseline data for thetransportation planning analysis and the transportation engineering part.
Recently, it also is used for transportation planning to analyze traffic
demand and calibrate traffic network. However, the current traffic volume
survey locations had been located in based on the class of roads, which wasmore suitable for maintenance and management of roads. Because of this,
there are two problems to analyze the travel demand. First, the current
traffic volume survey locations have spatial bias on certain areas. This
makes hard to get traffic volume and its spatial distribution. Second, somesurvey locations have a huge number of intra-zonal traffic volumes. To
verify the travel demand between region, traffic survey locations should belocated in cordon line which is boundary between adjacent zones. Due toshort of average trip length at zone which has heavy intra-zonal traffics, thelocations are not appropriate to analyze traffic demand between zones.
For these reasons, this study proposed criteria which determine totraffic volume survey locations for travel demand analysis and identified theadequacy of the current traffic volume survey locations
Keywords: Traffic counts, Traffic volume survey location, Spatialbias, Intra-zonal traffic volume, Cordon line, Determination location theory,
GIS spatial analysis
1. INTRODUCTION
A traffic counts data have been widely used as baseline data for thetransportation planning analysis and the transportation engineering part. In
the field of transportation engineering, a traffic counts data can be used to173AugustAugust2015,
2015,Thailand
Thailandanalyze traffic flow or develop strategy for transportation management
throughout all processes. In the field of transportation planning, a traffic
counts data which check the total amount of demand play a key role thatvalidates and establishes the forecasting model. Also, these data can be used
to estimate and validate traffic volume of origin to destination. Especially,
traffic volume of cordon line and screen line is one of the most important
basic materials across transportation planning.
However, traffic volume survey is being done only part of location
because of limited budget. So, the current traffic volume survey locations
had been positioned based on the class of roads. The positions are not
optimal location in terms of traffic planning. As a result, Korea Institute of
Civil engineering and building Technology(KICT) was used to determine
traffic volume survey location through class of road. They didn’t include
necessary considerations to check the traffic pattern, but just they arefocused on collecting the total amount of traffic volume.
Intra-zonal traffic volume ratio of the current traffic volume survey
locations; 6,885 separated the grade of roads can be calculated. The highest
Intra-zonal traffic volume ratio means including location of lots of intrazonal traffic volumes that can’t be explained by traffic volume of origin todestination.
Therefore, traffic survey locations should be located in cordon lines,
which does not include intra-zonal traffic volume and screen line isnecessary for supporting traffic volume survey. This study proposed criteria
which determine to traffic volume survey locations for travel demand
analysis and identified the adequacy of the current traffic volume survey
locations.
2. LITERATURE REVIEW
Lee. & Lee.(2003) claimed to need a study about Appropriate positions andthe number, Because O-D estimation varies depending on location and thenumber of traffic volume survey location.
A study of Yang. & Zhou(1998) is representative for determining
traffic volume survey location on abroad. This paper is a leading role of therelated study. The study proposed 4 roles Related to link traffic volume
survey location based on maximum relative error and suggested integer
programming model and heuristic algorithm to determine link that meetthese 4 rules. Presented 4 rules are as follows by Yang et al.
Rule 1 (O-D covering rule): the traffic counting points on a road
network should be located so that a certain portion of trips between any O-Dpair will be observed.

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Rule 2 (maximal flow fraction rule): for a particular O-D pair, thetraffic counting points on a road network should be located at the links sothat the flow fraction between this O-D pair out of flows on these links is aslarge as possible.
Rule 3 (maximal flow-intercepting rule): under a certain number oflinks to be observed, The chosen links should intercept as many flows as
possible.
Rule 4 (link independence rule): the traffic counting points should belocated on the network so that the resultant traffic counts on all chosen links
are not linearly dependent.

Figure 1: 4 rules of determining traffic volume survey location (Yang et al, 1998)
Baek et al.(1998) applied the way that establish Surrounding each
unit zone and observe traffic volume at link across cordon line in order toestimate optimal location.

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ThailandAs study result, link traffic volume of cordon line satisfied 4 rules oflink observation location by Yang, and analyzed.
3. DETERMINATION TRAFFIC VOLUME SURVEY LOCATIONTHEORY
3.1 Establishing criteria of determination
A traffic volume survey location can be used to estimate about O-D
estimated value. The criteria deduct reasonable and reliable value so that it
can be admitted as true value. As shown we’ve just seen, various criteria
should be considered for travel demand analysis for determination. In thisstudy, the most important criteria are established as follow.
1) Traffic volume survey should be selected for link which passcordon and screen line.
In case of cordon line, it doesn’t need to consider passing traffic
volume because it doesn’t include Intra-zonal traffic volume. In case of(2+1) screen line in Korea, also it doesn’t include Intra-zonal traffic volume
because it is established according to boundary of zone. Therefore, if Traffic
volume survey can consist of link which pass cordon and screen line, anecessary data perfectly can be established for validation of Departure andarrival traffic volume.
2) Locations that pass through a lot of traffic volume of origin todestination should be selected.
If traffic volume of cordon and screen line can’t get caught, it isrequired to survey for traffic volume at position where a lot of flow volume
is captured.
3) Locations where pass through a lot of pairs of origin anddestination should be selected.
Locations where can verify the trip pattern should be selected fortravel demand analysis. From study of Yang et al.(1998) and Baek(1998),
locations where pass a lot of pairs of origin and destination should beselected.
4) At least one path about every pair of origin and destination
should be caught by a traffic volume survey location.
This criterion is one of requirements of traffic volume survey
location for verification of origin and destination’s traffic volume. If traffic
volume doesn’t completely captured anywhere in a traffic volume survey
locations, verification is not possible.
5) The last considerable is road grade.
Because regional traffic trip of origin and destination usually islikely to use higher road grade, if other conditions are similar, it should be
include a lot of high-grade road in the survey. Therefore this study will beapplied to the result to the secondary criteria of determination of location.

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3.2 The adequacy of the current traffic volume survey locations
Analyzing should be identified the adequacy of the current traffic
volume survey locations. First, cordon-screen line of traffic volume survey
location under the zone system can be checked as shown in Fig.2.

Figure 2: Cordon-screen line by system of road grade
The number of location passing through the cordon and screen line is5,193. The portion of urban is high among the entire survey locations. Afterverifying location of cordon-screen line, verifying adequacy of the current
traffic survey locations. The next step in the present analysis of spatial
distribution is whether the current traffic volume survey locations is how
much is located in cordon-screen line. To examine the spatial distribution,
the amount of locations and portion are considered as table 1.
As a result, urban road has a high percentage in case of cordonscreen line, but rural road has a high percentage in case of the current survey
location. (cordon-screen line : urban 76.22%, rural 23.78% / the current
survey location : urban 41.58%, rural 58.42%)

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Table 1: Cordon-screen line and current survey location
Cordon-screen
line

Current survey
location

Urban/
Rural

Count

Ratio

Count

Ratio

Urban
Rural
Urban
Rural

201
99
194
5

3.87%
1.91%
3.74%
0.10%

364
258
337
22

5.31%
3.76%
4.92%
0.32%

35

0.67%

2

0.03%

62

1.19%

0

0.00%

Road property
VDF
Road Type
1
2
3
4
5
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

Expressway

Urban
Expressway

Lanes
Below twolane
Abovethree-lane
Below twolane
Abovethree-lane

Urban

Urban
278
5.35%
297
4.33%
Rural
309
5.95%
674
9.83%
Above twoUrban
52
1.00%
73
1.06%
lane
Rural
20
0.39%
50
0.73%
Urban
394
7.59%
422
6.16%
one-lane
Rural
357
6.87%
1,082
15.78%
Above twoUrban
236
4.54%
297
4.33%
lane
Rural
69
1.33%
317
4.62%
Urban
128
2.46%
149
2.17%
one-lane
Rural
102
1.96%
422
6.16%
Above twoUrban
176
3.39%
164
2.39%
lane
Rural
34
0.65%
199
2.90%
Multi-lane
Highway
Urban
149
2.87%
151
2.20%
one-lane
Rural
86
1.66%
388
5.66%
Above twoUrban
480
9.24%
327
4.77%
lane
Rural
51
0.98%
342
4.99%
Urban
123
2.37%
36
0.53%
one-lane
Rural
28
0.54%
79
1.15%
Above twoUrban
679
13.08%
168
2.45%
lane
Rural
34
0.65%
136
1.98%
Urban
177
3.41%
18
0.26%
one-lane
Rural
23
0.44%
15
0.22%
Above twoUrban
594
11.44%
45
0.66%
lane
Rural
18
0.35%
21
0.31%
Urban
3,958
76.22%
2,850
41.58%
Total
Rural
1,235
23.78%
4,005
58.42%
Total
5,193
100%
6,855
100%
Second, this study compared to regional location of cordon-screen line and the current
survey location to examine the spatial bias of the current survey location.

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Figure 3: Estimated to the amount of matched locations
When comparing the traffic volume survey location and cordonscreen line, survey location id biased among regions. More importantanalysis is how much the current survey location consistent with cordonscreen line. Only 1,389 point is matched with cordon-screen line and current
location. This result is about 20% for current 6,855 points. Reviewing the
1,389 locations by region, total number of matched locations is small. Also
regional bias can be confirmed as Figure 3.

Figure 4: Estimated to portion of matched locations

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survey locations
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4. CONCLUSIONSThis study established the most important criteria for travel demand analysis
and identified the adequacy of the current traffic volume survey locations.
To identify the adequacy of the current traffic volume survey locations, thisstudy compared to cordon-screen line and the current survey location byregion and road grade. The current survey locations partly were covering thecordon-screen lines. However, Locations are selected on unnecessary roador biased among regions. A lack of location that total amount is smaller thanthe cordon-screen line need to complement to traffic volume survey location.
This study will rearrange to locations using navigation data. This
study may help to analyze travel demand by surveying on appropriate
location.

REFERENCES
Yang. H, Jing Zhou,1998. Optimal traffic counting locations for O/D
estimation, Transpn, Res, 25B, pp.315~363
Baek Seung-Kirl, Lim Kang-Won, Lee Seung-jae , 1998. Zone
Cordon line as Optimal Traffic Counting Locations for O-D Estimation,
Korea Planners Association, Vol.33 No.6[1998], pp.189~198
Lee Seung-Jae, Lee Hun-Ju, 2003. Selection of the Optimal Locationof Traffic Counting Points for the OD Travel Demand Estimation, Journalof Korean Society of Transportation, Vol.21 No.1[2003], pp.53~63

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SITE SELECTION OF POTENTIAL
RAILWAY ROUTE FOR SUPPORTINGDAWEI DEEP-SEA PORTCASE STUDY: TAMBON BAN KAOAMPHOE MUEANG OF KANCHANABURIPROVINCE
Rattanawadee DUTSADEESUNTORNSAKUN1, Sathaporn
MONPRAPUSSORN2 and Sutatip CHAVANAVESSKUL2
1
Department of Geography, Srinakharinwirot University, Thailand
rattanawadeeduts@gmail.com
2, Department of Geography, Srinakharinwirot University, Thailand

ABSTRACTThe objective of this study is finding the potential areas for building
railway route to support Dawei deep-sea port from the Ban Kao railway
station at amphoe Mueang, Kanchanaburi province, Thailand. The criteriaof site selection are road network, fault, soil, slope, land use, population
density, forests and water resources. Method analysis with rating scale,weighting scale and dividing potential area. Result of study was divided 3
potential areas as the low potential area 8,955 square kilometers at Ban
Huay Nam Khaw, Ban Pu Noi, Ban Tha Poh, Ban Tor Mai Dang, Ban NongBan Kao and Ban Takhian Ngam; the moderate potential area 45,913
square kilometers at Ban Tha Thung Na, Ban Huay Nam Khaw, Ban Pu
Nam Ron, Ban Kao, Ban Thung Sala and the high potential area 62,149
square kilometers at Ban Lum Thahan, Ban Pra Tu Dan, Ban Sam Nong and
Ban Lum Sai.
Keywords: decision support, site selection, railway route
2. INTRODUCTION
According the Asian Economic Communit is promoting, Thailand is locateda heat of South East Asian. The Thailand logistics is important for economic
in Asian region in term of supporting and employment creation. The deepsea port project is promoted to support the logistic system in Southeast Asia
region. Thailand is high potential to be gateway of the region,, be able theroute of goods pass through North India to Middle East via Dawei deep-sea
port of Myanmar. Ban Kao sub district (Tombon Ban Kao), located in
Kanchanaburi Province, is boarder area where the shorten route to the DeepSea Dawei part by train. The traditional transportation spends 16-17 day by

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Thailandtrain, pass Malacca Strait that facing with pirate hijack. Hence the newrailway is developed to get rid of those problems.

Figure 1: Traditional logistic system of Southeast Asian region.
The aim of research
The objective for this research is finding the potential area for
railway construction, routing from Ban Kao sub district to the border ofThailand (called Ban Phu Nam Ron).
The importance of research
The propose new railway is highly potential route which provides aneffectiveness of logistic, reduces the transport time period from 16-18 days
to 7-8 days.
Scope of ResearchThis research focusing in the potential area for railway construction
using spatial analysis and physical parameters. The construction area focus
only between Ban Kao train station and border of Thailand, that has theshortest route to the Dewai deep-sea project.
However, Ban Phu Nam Ron barrier near the Dawei deep-sea port
project possible. The distance from the border with Thailand to the Dawei
deep-sea port only 160 kilometers.
Literature review
According The 11th National Economic and Social Development
Plan (2012-2016) under the develop the country’s infrastructure andlogistics systems chapter;“… 1) Encourage development of multimodal transportation byadvancing energy efficient forms of transportation.

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Infrastructure and multimodal transportation management
systems should be developed that integrate roads, railways, andwater and air transport for both domestic and international uses.
This will improve efficiency and meet international standards for
speed, safety, and punctuality, and it will support regional
economic cooperation. Effective distribution systems for goods
should be built to reduce the overall cost of logistics.
2) Increase the efficiency of logistics management by developing
professional human resources. Business networks for transportand logistics should be created throughout the supply chain.
Laws and regulations should be amended to foster innovative
research and development in the logistics field. To make trade
more efficient and facilitate it, upgrade the National Single
Window system, distribution centers, and cross-border trade.
The efficiency of transport management systems should be
improved. The strategic role played by major airports andseaports should be defined in order to reduce transportation
costs and increase the nation’s competitiveness in the long run3) Improve railway transport by upgrading the present railway
system and the links between road and railway networks toincrease safety. Dual-track rail lines along major routes should
be added. Locomotives and rolling stock should be provided andrailway-signaling systems should be upgraded to increase
efficiency for transportation of passengers and goods. A highspeed railway should be developed to link major regional cities
and ASEAN countries. The management structure of the State
Railway of Thailand should be changed to improve efficiency
and promote better services and operation
This research used spatial technique called overlay analysis as themain analysis function. Overlay analysis is stated as“Geographic Information Systems (GIS) model selected aspects ofreality. Overlay analysis is one of the spatial GIS operations. Overlay
analysis integrates spatial data with attribute data. (Attributes are
information about each map feature.) Overlay analysis does this bycombining information from one GIS layer with another GIS layer to derive
or infer an attribute for one of the layers. At its simplest, overlay analysis
can be a visual operation, but analytical operations require one or moredata layers to be joined physically. This overlay, or spatial join, canintegrate data of different types, such as soils, vegetation, land ownership,jurisdictions, etc. with assessor's parcels. Results of overlay analysis rely on
the spatial accuracy of the GIS layers. If the layers don't line up well, thenthe attributes inferred by the overlay may be incorrect. That is, the results
are only as good as the GIS data used for the analysis.”
Related research
Artid Intara (2549). The development of rail transport in the
Northeast of Thailand, staring from Surin province to connect the lower part
Site selectionSiteselection ofof potential
potential railway
railway route
route forforsupportingdawei
supporting daweideep-sea
deep-seaport.
portCase study : Tambon Ban Kao
AmphoemueangofKanchanaburiprovince
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Case study : Tambon Ban Kao Amphoe Mueang of Kanchanaburi provinceAugustAugust2015,
2015,Thailand
Thailandof area with another part of area. Result in an increasing of the business.
Moreover the government is promoting a dual-track. and high-speed railway
transport to link those area to neighboring countries.
Farkas (2009). His research focused in the route selection andassessment of the impact of building a metro station that the route selected
for subway station construction in downtown area. His method used theassessment criteria technique with the GIS. Those criteria were used in his
study was 1) geological structure (ground) the appropriateness of theecosystem. 2) population density characteristics metro engineering.
3)construction costs. 4) mechanical characteristics and determine the cost of
construction of the metro. The effect on the outcome of the vote.
Jeong, et al (2012) The research focused in to determine the locationof the railway construction inthe countryside, using several spatial criteria
through decision-making process technique called Multi-criteria evaluation(MCA) with Simple additive weighting (SAW). Three criteria are only
physical environmental. . Result showed the satisfies classification which
the economy screening can find suitable areas with the highest total score on
four grounds
Malczewski (1999) Research focused in use of Multiple Criteria
Decision Analysis to assess the geographical conditions and created maps
using those criteria on the basis of the method. Multi-criterria Decision
Analysis (MCDA) and evaluation criteria to selected thesuitable area.
MCDA evaluate criteria weighting using Analytical Hierarchy Process(AHP). The identification and analysis based on hierarchy involving sevenfactors of land use gradient, industrial, forest, roads, hydrology and geology.
Safian (2012) Research focus in the evaluated the location of an
office building in Malaysia by applying analytic hierarchy process with GIS.
In a study using the five factors. The ability and ease of access to transport
and the distance from the parking location, the commercial area of trade andthe flow of vehicles. The results showed that the building suitable for mostoffice buildings.
Carver (2009) Research focus in geographic information systems(GIS) are effective in the decision-making process. To evaluate various
alternatives on the basis of several criteria for evaluation of multiple criteria
is a common way of finding the impact of external factors. While GIS canbring environmental and economic development.
This study selected a suitable location. Based on reviews, that themost research technique showed in the use of GIS with multi-decision
criteria. The decision by the Overly Analysis for the potential to lay the
tracks. As a way to accept the decision that has many options and applied
spatial well.

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Methodology
The criteria applied in this research came from government sectors.
All criteria are weighted by ranking technique, the rank came from previous
research and experts. For instance, the research of Napong Surongkarat andTonlavid Sathapanajaru found that road criteria have the difference priority
depend on the buffer distances as500 meter = 4 high potential area, and2000 meter = 1 lowest potential. For soil criteria, the research of Farkas
divided soil into 2 groups as group1: 33, 33B, 44B, 47B, 48B, 48C, 48D,
56B = 3 high potential area and group2: 17, 20, 29, 31B, 31C, 31D, 44C,
47C, 48C D, 55B, 56C, 62 C = 1 lowest potential area, population density
93.19 - 146.4 persons / sq km = 4 high potential area and 13.40 - 40.00
persons / sq km = 1 lowest potential area. Malczewski use factor land use
agricultural lands = 3 high potential area and Basin, ore mine and digging
wells = 1 lowest potential area. The state railway of Thailand use 2 factor
slop 0 – 5.3 % = 4 high potential area and 23.8 - 80% =1 lowest potentialarea, fault factor distance buffer from fault 9 kilometer = 4 high potential
area and 1 kilometer= 1 lowest potential area. The GIS data of Tambon Ban
Kao Amphoe Mueang of Kanchanaburi Province .This data 9 layers 1. Road
2. Soil 3. Fault Line 4. Slope 5. Land Use 6. Population Density 7. Water 8.
Woodland 9. Administrative district;
Factor
Road

Fault

Soil

Slop

stratification data
2000 meter
1500 meter1000 meter
500 meter
1 kilometer3 kilometer
6 kilometer
9 kilometer
Soil group; 17, 20, 29, 31B,
31C, 31D, 44C, 47C, 48C D,
55B, 56C, 62 C
Soil group; 52B, 52C
Soil group; 33, 33B, 44B, 47B,
48B, 48C, 48D, 56B
23.8 - 80%
13.8 – 23.8 %5.3 – 13.8 %0 – 5.3 %FactorLand use

Population Density

rank
1
2
3
4
1
2
3
4
1

2
3
1
2
3
4

stratification datarank

Basin, ore mine and digging
wells
Natural sources

1
2

Building

3

agricultural lands

4

13.40 - 40.00 persons / sq km
66.60 - 93.18 persons / sq km
93.19 - 146.4 persons / sq km

1
2
3

Site selectionSiteselection ofof potential
potential railway
railway route
route forforsupportingdawei
supporting daweideep-sea
deep-seaport.
portCase study : Tambon Ban Kao
AmphoemueangofKanchanaburiprovince
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Case study : Tambon Ban Kao Amphoe Mueang of Kanchanaburi provinceAugustAugust2015,
2015,Thailand
ThailandUnion analysis technique applied for analysis. Result from union
analysis, the potential area is come out as the potential area for railway
construction map. The following chart shows overall method for thisresearch.

Road
union

Fault
Soil
Slop

RatingLand use
Population
forestWater

Priority areas appropriate

The potential areas
for the railway
construction.

Figure 2: Chart

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Figure 3: Factor Map

Figure 4: Site Selection of Potential Railway Route for Supporting
Dawei Deep-Sea Port Case study: Tambon Ban Kao Amphoe Mueang
of Kanchanaburi Province Map

Site selectionSiteselection ofof potential
potential railway
railway route
route forforsupportingdawei
supporting daweideep-sea
deep-seaport.
portCase study : Tambon Ban Kao
AmphoemueangofKanchanaburiprovince
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2015,Thailand
ThailandResults
The results of the potential railway route construction, for
Supporting Dawei Deep-Sea Port, are divided into 3 classes as follows thelow potential area 8,955 square kilometers located at Ban Huay Nam Khaw,
Ban Pu Noi, Ban Tha Poh, Ban Tor Mai Dang, Ban Nong Ban Kao and Ban
Takhian Ngam. Moderate potential area 45,913 square kilometers located at
Ban Tha Thung Na, Ban Huay Nam Khaw, Ban Pu Nam Ron, Ban Kao,
Ban Thung Sala and the high potential area 62,149 square kilometers
located at Ban Lum Thahan, Ban Pra Tu Dan, Ban Sam Nong and Ban Lum
Sai.
Discussions
According this research is primitive research for potential area ofrailway construction, to more effective result the more criteria such as soil,
fault, landuse population, road, slop and Dawei railway station location will
be apply for further research.
REFERENCES
Andras, F., 2009. Route/Site Selection of Urban Transportation Facilities:
An Integrated GIS/MCDM Approach. Budapes, Hungary
Anzhelika, A., Fahui, W., and Chester, W., 2015. Urban land uses, sociodemographic attributes and commuting: A multilevel modelingapproach. Los Angeles, USA.
Edie, E., Mohd, S., and Abdul, H., 2012. Combining AHP with GIS in
the evaluation of locational characteristics quality for purpose-built offices
in Malaysia. aspen 2ndcampus. Selangor, Malaysia.
Jacek, M., 1999. GIS-based multi-criteria decision analysis: a survey of theliterature. John Wiley& Sons Inc., New York.
Stephen, J. C., 2007. International Journal of Geographical Information
Systems

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NUMERICAL EVALUATION OF ROAD
DISCONNECTION RISK FOCUSING ON RO
AD COLLAPSING AFTER A LARGE EARTHQUAKE IN CHIGASAKI CITY, KANAGAWA
Yuto TOKUMITSU1, Shoko KUDO2, Ryoko SERA3, Takaaki KATO4
1
Master Course Student, Dept. of Urban Eng.,The University of Tokyo, Japan
tokumity@iis.u-tokyo.ac.jp
2
Sewerage Works Department, Tokyo Metropolitan Government, Japan
3
Geosearch Co., Ltd, Research Development Center, Japan
4
Associate Professor, ICUS, IIS, The University of Tokyo, Japan

ABSTRACT
Securing a sufficient road network and its traffic functions after alarge earthquake is essential for handling post-earthquake situations andpreventing further damage. Although many causes can be involved in road
disconnection, road blockage by collapsed buildings is currently the only
cause backed with legal consideration. Road blockage may not be the only
significant cause, and it can be suggested that road collapsing is anothermajor cause for road disconnection other than blockage by collapsedbuildings. This study aims to numerically evaluate the risk of road
disconnection by focusing on road collapsing.
Numerical evaluations of the risk of road collapsing in Chigasaki
City, Kanagawa Prefecture, Japan, were done by developing a model that
expresses the behavior of underground cavities caused by aging city
infrastructure and applying it to the city’s road network. Comparing therisks of road collapsing to risks of blockage by collapsed buildings, current
calculations conclude that considering road collapsing is as important as
including collapsed buildings. Furthermore, it can be said that preventing
roads from collapsing is necessary to maintain traffic functions for daily use.
Therefore, this study suggests to create maintenance plans to secure traffic
functions after a large earthquake and to relate disaster mitigation plans
with city maintenance plans.
Keywords: earthquake, road collapsing, underground cavity, roadmaintenance, model analysis

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Thailand1. INTRODUCTION
Traffic functions provided by road networks are necessary for everyday life,
but are also essential when dealing with disasters and mitigating or
preventing further damage. Passage of fire trucks, transportation of the
injured is just examples of disaster handling actions that require a
sufficiently functioning road network. The absence of such network leads toan increase in the amount of damage and loss caused by earthquakes.
The Japanese government is fully aware of the importance of
securing a sufficient road network, and provides several laws aiding projects
to secure traffic functions. Yet, these laws only focus on preventing roadblockage by collapsed buildings, even though many causes of function loss
are involved. In this study, road collapsing caused by underground cavities
is suggested as another significant cause in the loss of traffic functions.
Through the construction of a model to express the behavior of undergroundcavities and application to an actual road network in Chigasaki City, thispaper aims to solidly suggest underground cavities are as significant as
collapsed buildings in the loss of traffic functions.
2. MODEL CONSTRUCTION
In order to compare the risks of collapsed buildings to the risks of roadcollapsing, models were created to numerically express the possibilities offunctional loss by the causes.
2.1 Model of Road Blockage by Collapsed Buildings
In the evaluation of risk of road blockage by collapsed buildings, theprocess shown in figure 1 below was used.

Figure 1: Evaluation process of Road Blockage Risk
The housing destruction probability is provided by the probability
curve included in “Damage Estimations of Tokyo caused by Nankai Trough

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Quake” by the Tokyo Disaster Prevention Council. In this paper, theprobabilities of seismic intensity 6.5 is assumed and used from figure 2.

Figure 2: Destruction Probability Curve(Left: Wooden, Right: Non-wooden)
Destruction in this case is defined as completely unusable for shelter,and in order to evaluate the risk of road blockage, probability of a structurecompletely collapsing into debris is needed. To do this, the following
equation is used to calculate the probability of collapsing from theprobability of destruction.
y = 1 – (1 – x) 1/4.5
y: probability of collapse
x: probability of destruction(1)

In this study, the spread of debris produced by a collapsed building
follows the Omnidirectional Debris Flow Model, but assumes the debris tospread over within a distance half of the building’s height and not equal.
Road blockage is defined as having less than 4 meters of road width that is
not occupied by this debris, and the probability of a road link becoming
blocked is as the following equation.
Probability of a Road becoming Blocked =n: Number of possible road blockage point
Pi: Possibility of road blockage at that point(2)

2.2 Model of Road Collapsing
To evaluate the risk of traffic function loss due to road collapsing, asimulation model expressing the behavior of underground cavities wasconstructed and used to calculate the probability of a road collapsing.
In order to simplify, all underground cavities are assumed to be
produced by cracks in underground sewage pipes. The model constructed inthis study assumes that an underground cavity grows according to theprocess illustrated in figure 3.

Numerical evaluation of road disconnection risk focusing on road collapsing
An analysis
network throughputs
forcity,different
origin-destination patterns
after
a largeofearthquake
in Chigasaki
Kanagawa

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AugustAugust2015,
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ThailandFigure 3: Steps in the growth of an underground cavity(Side view)
Observed from above, growth of the diameters of the cavity is expressed as
the diagram in figure 4.

Figure 4: Steps in the growth of an underground cavity (Above view)
When a crack occurs in a sewerage pipe, sediment continuously
flows. The amount of sediment flowing can be expressed as a constant Va
m3/i, in which i is one step in the simulation. The cavity grows in a coneshape until the small diameter reaches ϕ0 meters and the height hc meters,
and from there it grows upward in a cylindrical shape maintaining its
diameters. After reaching the pavement, the cavity grows horizontally until
the pavement gives way and collapses.
The height of the cavity and the time for growth in each stage can be written
as the following equations.
【Stage I】(3): time the cavity stays in stage I

β0: large diameter corresponding to ϕ0(4): height of cavity during stage I

ϕ: short diameter during stage I【Stage II】

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(6)
: time the cavity takes to reach the pavement
cavity: initial depth of the【Stage IV】(7): small diameter of cavity during horizontal growth
The behavior of the growth of underground cavities can be expressed
as a function of, but to simplify the above equations (3) through (7),variables ,, β0, , Va will be expressed as constants based on thefollowing assumptions.
: Since many sewerage pipes are placed 1.2 ~ 1.4 meters underground, theinitial depth for each cavity was set by a random number within that range.
: Based on observational data provided by Geosearch Co. Ltd., themaximum small diameter of the cone-shape, or the minimum small diameterof the cylindrical shape, is assumed to follow the distribution given as thefollowing table.
Percentage ofoccurrence (%)
0.901
2.703
16.216
16.21614.41419.820
9.910
4.505
8.108
5.405
1.802
Figure 5: Distribution of

Small diameter0.3-0.5
0.5-0.7
0.7-0.9
0.9-1.1
1.1-1.3
1.3-1.5
1.5-1.7
1.7-1.9
1.9-2.1
2.1-2.3
2.3-2.5

Numerical evaluation of road disconnection risk focusing on road collapsing
An analysis
network throughputs
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AugustAugust2015,
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Thailandβ0: Based on the observational data,and β0 were assumed to have alinear relationship. Therefore β0 can be given as the following expression
using
.
(8)
: The maximum height of the cone can be given by the following
equation(9)
In soil mechanics, the angle at which a pile of soil becomes stable is
at 30 degrees. Therefore, angle is assumed at this angle.
Va: From observational data by Geosearch, the average rate of soil flow ofunderground cavities was used. In this study it is assumed that this data issufficient enough for use. Further studies to increase the accuracy of thisnumber would lead to an increased ability of the model itself.
In addition to the growth of an underground cavity during daily
circumstances, the behavior of a cavity after an earthquake can be expressedas the following figure.

Figure 6: Behavior of underground cavity after an earthquake
It was experimentally determined that when shaken at a strong quake
level, the soil close to the surface collapses to create the stable angle statedearlier. This study assumes that soil within 0.5 meters from the surface will
collapse when shaken, and the small diameter of the cavity after the quake
can be given by the following equation.
(10)
To calculate the probability of a road collapsing due to a large
underground cavity, a probability function for the collapse of road pavement
at a given small diameter was calculated using observational data from
Geosearch. The function is as follows.
(when
(when
P: possibility of pavement collapsing

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)
(11)
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3. SIMULATION
After constructing the model to express the behavior of undergroundcavities, simulation was done to determine the probabilities of a road losing
functionality due to road collapsing. This was done by using observational
data of Chigasaki city. To simplify calculations, simulation to determine
probabilities was done using a representation model of the city road network.
This model assumes a 100m road with intersection points at both ends, andpossesses the average number of sewerage facilities such as manholes andsupply conduits.
First, the number of observed underground cavities in Chigasaki city
in a certain area that was target of analyzation by Geosearch was counted up
according to the year the sewerage was planted, and paired with causes foroccurrence using sewerage facility data. The categories for causes are asshown in figure 7.
Cause
ManholeSupply-conduit
Multiple causes
Pipe

Explanation of cause
Used for inspection, connections with adjacentpipes can become loose
Pipes that drain sewage out of buildings,
connections with adjacent pipes can become
loose.
Point where both manholes and supply-conduits
exist
Connections between pipes themselves canbecome loose.

Figure 7: Categories for the causes of underground cavities

Then, the numbers were averaged to fit the 100m simulation
model as expected numbers of cavities. Through 100, 000 times ofsimulation using the relationship that probability equals the expectedvalue divided by time to reach that value, probabilities for theoccurrence of cavities were calculated as follows.
year ofconstruction

1965 - 1969
1970 - 1974
1975 - 1979
1980 - 1984
1985 - 1989
after 1990

probability ofprobability ofcavity（road portion
without
conduits）

cavity（road portion
with only
conduits）

0.00970.00560.00220.00220.00130.00050.00180.00150.00070.003700.00080.00320.00140.00030

00

probability of cavity（intersection
points）

Figure 8: Probabilities of an underground cavity occurring

Numerical evaluation of road disconnection risk focusing on road collapsing
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Thailand4. CASE STUDY IN CHIGASAKI CITY
Using the calculated probabilities, the risk of the road network of Chigasaki
city losing function after a large earthquake was evaluated. This study
assumes that the occurrence of a single point of road collapsing renders aroad not functional. The evaluation also focuses on the main roads in thesouthern part of the city, where the sewerage infrastructure is relatively old.
The probability of a vehicle being able of passing through each roadlink was calculated using a function similar to that of (2). In this case, a roadlink is defined by a link connecting an intersection to the next adjacentintersection. The difference in probability of passage between road blockage
and road collapsing is as the following diagram. The construction year of thesewerage pipes were based on the Chigasaki city sewerage plans. Note thatthe calculated value is the probability of passage considering only collapsedbuildings minus the probability of passage considering only collapsed roads.

Figure 9: Difference in Probability of Passage
As it can be seen, most of the roads have a positive value, meaningthat the probability of a road being functional is less when considering roadcollapsing. This is because many of the sewerage pipes in this area wereconstructed more than 30years ago, and the aging of these facilities has
increased the risk of underground facilities.
To further evaluate the road network functions, several routes were
set connecting a fire station to a point in the network seen as important. 3
fire stations and 3 intersections were each paired and the probability ofarrival was calculated. The results are as figure 10.

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Figure 10: Probability of arrival for each route
Similar to the evaluation shown in figure 9, for most of the routes
road collapsing has a more significant effect on the probability of arrival
than that of collapsed buildings. This shows that road collapsing is as asignificant cause of loss of traffic function as collapsed buildings.
5. CONCLUSION
Looking at the given results from the case study in Chigasaki city, it can beconcluded that the risk of road blockage by collapsed roads are as
significant as the risk of road blockage by collapsed buildings. The
difference in handling the two risks are that while buildings may takeseveral decades until they can be fully renewed, infrastructure can be
renewed through regular maintenance plans.
Reinforcing buildings to withstand large quakes is a large project on
its own, but in the case of sewerage it is only necessary to include theobjectives for disaster mitigation in regular maintenance plans. By doing
this, renewing the facilities and improving the functionality of roads can bedone at the same time. However, because the renewal of pipes constructed
more than 30 years ago is an urgent matter, disaster mitigation plans that
include the improvement of sewerage should be made at first as emergency
procedures to ensure time to create the regular maintenance plans. After the
emergency procedures are done and the plans are made, it could be possible
to maintain resilient infrastructure at a higher level.
Yet, the accuracy of the evaluation method used in this study has
many areas in which it can be improved. Though this study assumed that aroad lost its function when at least one road collapsing occurred,
possibilities exist in which roads have enough width to maintain function
even after several road collapsing. Other assumptions such as the rate at
which soil flows should be improved, for it is an country wide average. Also,
when considering emergency maintenance plans, roads with old structures
built along them should have a priority on improving the buildings first
before starting on the sewerage.
Altogether, this study presents a method to analyze the risk of traffic
function loss considering factors other than collapsed buildings, andsuggests the inclusion of mitigation plans in regular maintenance plans.
REFERENCES
Many references made to internal documents provided by Geosearch.
Numerical evaluation of road disconnection risk focusing on road collapsing
An analysis
network throughputs
forcity,different
origin-destination patterns
after
a largeofearthquake
in Chigasaki
Kanagawa

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AugustAugust2015,
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Thailand(To be published) Ryoko Sera and Yuki Horiuchi. Model
Experimentation Report on the Effects of an Earthquake on the Expansionof Underground Cavities. To be presented at Geotechnical Engineering
Society 50th Geotechnical Engineering Research Presentation.
Chigasaki city. 2012. Chigasaki City Public Plumbing Pipes andFacilities Long-life Realization Plans

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IMAGE PROCESSING TECHNIQUESFOR VEHICLE AND PEDESTRIAN SAFETYEVALUATION
Jinwoo PARK1 and Wonho SUH2
Research Assistant, Hanyang University, Korea
2
Assistant Professor, Hanyang University, Korea
wonhosuh@hanyang.ac.kr
1

ABSTRACTWith the advancement of recent image processing technologies
numerous imaging applications have been developed to collect traffic
information. In this study, image processing technology is applied toroadway safety to detect and measure the conflict between pedestrians andvehicular traffic. By using an image processing technique, pedestrian andvehicular movements are detected and the distance between these two arecalculated to detect conflicts between them. In this study, Machine Learningtechniques within MATLAB program are utilized to facilitate more accurate
and efficient detection. It is assumed that the intersection is accident-prone
if conflicts between pedestrian and vehicular traffic appear more frequently
than other intersections. This approach is expected to identify accidentprone intersections without actually experiencing crashes; therefore, thiswould potentially reduce social costs associated with traffic accidents.
Keywords: image processing, vehicle detecting, traffic safety,
pedestrian safety
1. INTRODUCTION
Pedestrians are vulnerable road users. When they are involved in collisions
with vehicles, there are considerably higher chances of being severely or
fatally injured, while a large number of collisions between vehicles areproperty damage only. Surrogate safety measures has been developed as acomplementary method to improve pedestrian safety and offer more in
depth analysis than relying on historic accident data alone. There is a
growing effort to use recent image processing technologies for a conflict
analysis between vehicles and pedestrians. In this study, vehicle tracking
and pedestrian tracking technologies currently developed and used arereviewed.

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AugustAugust2015,
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Thailand2. VEHICLE TRACKING
2.1. 3D Model-based Tracking
Model-based tracking utilizes a priori knowledge of typical objects.
For a vehicle tracking, vehicles in an image captured from a video aretracked. These methods recognize vehicles. However, this method tends tofail when there is an occlusion for the target object. Also, this tracking
method is limited in recognizing all the vehicle types on the roadway.
2.2 Region-based Tracking
Region -based tracking method is to identify connected regions of
the image associated with each target vehicle. Regions are usually acquired
from background subtraction. Also, this method utilizes Kalman filters toprocess available information from the image including motion, size, color,shape, texture, and centroid. It is known that this tracking method iscomputationally efficient and the detection accuracy is generally good infree-flowing traffic conditions. However, if traffic is congested – whenbackground images do not change over a short period of time and vehicles
partially occlude one another, this method tends to have problems, since
separating individual vehicles are becoming challenging. Sometimes, suchvehicles are identified as one vehicle, resulting in low detection rates.
2.3 Contour-based Tracking
Contour-based tracking method uses the boundary curves of themoving object. For example, this method tracks pedestrians by identifying
the contour of a pedestrian’s head. This method is known to have more
efficient description of objects than region-based methods. However, thismethod still shares the weakness of region-based tracking when traffic
condition is congested.
2.4 Feature-based Tracking
Feature-based tracking does not track object as a whole in image
processing. Instead, this method tracks unique features. There features canbe points or lines of the object which are distinct from other objects. Whenthe target object is partially occluded, some of the distinctive features of themoving object remain visible and detectable, resulting in successful tracking.
The advantage of this method over other tracking methods is that the samealgorithm can be applied for tracking in different lighting or traffic
conditions. For example, this method utilizes window corners or bumper
edges during the day when the entire object is visible. During the night, thismethod uses tail lights, since other features are not detectable in night timelighting conditions. Currently, this method is advanced and computational
efficient allowing this method to be used in real time processing. An
example of vehicle tracking is provided in Figure 1.

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1. PEDESTRIAN TRACKING
To identify conflicts between vehicles and pedestrians, all movements ofvehicular and pedestrian traffic need to be tracked from video frames.
Especially, this is challenging since pedestrians sometimes move in groupand change directions, while vehicular movements are somewhat
predictable. To overcome the issues and increase the accuracy of tracking,there are three tracking methods which are currently popular; tracking bydetection, tracking using flow, and tracking with probability.
Tracking by detection method detects objects using backgroundmodeling and subtraction with the current image or deformable templates.
Tracking using flow is similar to feature based tracking. This method is
popular in traffic monitoring and pedestrian counting. Tracking withprobability uses a Bayesian tracking framework. This method also utilizes
other filers and methods, such as particle filters, Markov chain, and Monte
Carlo methods. An example of pedestrian tracking is provided in Figure 2.
Also, Figure 3 presents an example of vehicle and pedestrian tracking in anintersection.

Figure 1: Vehicle tracking (Ismail, Sayed, and Saunier, 2010)

Figure 2: Pedestrian tracking (Ismail, Sayed, and Saunier, 2010)

Image processing techniques forfor vehicles
vehicle andand pedestrianpedestrian safety
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AugustAugust2015,
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ThailandFigure 3: Vehicle and pedestrians tracking (Laureshyn, 2014)
3. CALCULATING CONFICTS
To calculate conflicts between vehicular and pedestrian traffic, potential
accident-prone conditions need to be identified. These conditions areillustrated in Figures 4, 5, and 6. Based on the calculated time gap between
the vehicle and pedestrian, conflict data can be obtained.

Figure 4: Left turning vehicle conflict example (Madsen, 2014)

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Figure 5: Thru moving vehicle conflict example (Madsen, 2014)
2. PRELIMINARY RESUTLS AND FUTURE WORKS
The research team is in a process of developing vehicle and pedestrian
tracking method. The team was successfully tracked vehicles in a video
collected from an intersection in Seoul, Korea (Figure 6). The team isplanning to continue to develop the tracking algorithm and calculate theconflicts between vehicular and pedestrian traffic. The team is utilizing
MATLAB Machine Learning techniques to reduce the detection error andincrease the efficiency of the program.

Figure 6: Vehicle tracking program snapshot

Image processing techniques forfor vehicles
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REFERENCES
Ismail, K., Sayed, T., and Saunier, N. 2010. Automated analysis ofpedestrian-vehicle conflicts: Context for before-and-after studies.
Transportation Research Record: Journal of the Transportation
Research Board, 2198, 52–64.
Laureshyn, A., 2014. Superpixel based road user tracker, Workshop on the
Comparison of Surrogate Measures of Safety Extracted from Video
Data, Transportation Research Board 93rd Annual Meeting.
Madsen T., 2014. Automatic Detection of Conflicts At Signalized
Intersections, Workshop on the Comparison of Surrogate Measuresof Safety Extracted from Video Data, Transportation Research
Board 93rd Annual Meeting

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7th

AGEING EFFECT INDUCED BY
PARTICLE MOVEMENT
Koji TANAKA1 , Ikuo TOWHATA2 ,and Shigeru GOTO3
1
Master Course Student, Department of Civil Engineering,
The University of Tokyo, Japank.tanaka@geot.t.u-tokyo.ac.jp
2
Visiting Professor, Kanto Gakuin University, Japan
3
Visiting Researcher, Waseda University, Japan

ABSTRACTDuring the 2011 Great East Japan earthquake, there were manyliquefaction damage sites around the Tokyo Bay. One of the typicaldamaged sites is Urayasu City, which is now suffering from land pricedecline and reduction of population. On the other hand, according to thedesign codes of each structure, they do not take "ageing effect" into
consideration. Ageing effect is a phenomenon by which the liquefaction
resistance of sandy ground increases with time. Thus, we cannot properly
evaluate the liquefaction resistance in current methods. By properly
evaluating the increment of liquefaction resistance by the ageing effect, therisk of future liquefaction will be more appropriately assessed and theabove-mentioned social problems will be mitigated.
Several phenomena, such as cementation or secondary consolidation,
are considered as the mechanism of ageing effect. In this paper, by
hypothesizing that particle movement within sandy ground contributes to theincrement of liquefaction resistance, particle movement of sand is observed
by a microscope under consolidation and shaking states. At the same time,liquefaction resistance of consolidated samples was measured by undrained
cyclic triaxial tests.
In conclusion, the relationship between the number of moving
particles and the liquefaction resistance was obtained. In addition, different
movement of sand particles was observed in consolidation and shaking
states.
Keywords: ageing effect, liquefaction
3. BACKGROUND
Evaluating the liquefaction resistance of subsoil is important to make anearthquake hazard map. Currently, FL method is often used in evaluation, by
taking into consideration SPT N-value, fines content, and so on1). However,205AugustAugust2015,
2015,Thailand
Thailandsignificant liquefaction damage was observed only in recently reclaimed
ground in contrast to lack of liquefaction in more aged alluvial soil2). Suchliquefaction resistance improvement by ageing effect is widely observed not
only in 2011 Great East Japan Earthquake, but also in previous earthquakes.
Thus, the risk of liquefaction is high in relatively young reclaimed land andabandoned river bed.
Ageing effect is not taken into consideration in FL method. This isprobably the main
Blue: No liquefaction
cause of the result that
all the area in Figure 1
was estimated to beliquefied before theearthquake in 2011.
Although this realizes
the safer design, it will
also lead to the land
pricedeclineandpopulation decrease intarget areas. Therefore,toestablish
more
accurate method forliquefaction resistanceestimation,themechanism of ageing
effect is discussed inthis paper.
Red: Liquefaction
Severalkinds
ofFigure 1: Liquefaction occurrence map inphenomena
are
Urayasu City after 2011 earthquake
considered to be themechanisms of ageing
effect3)4). Mitchel and Solymar (1984) stated that cementation strengthens
the connection between particles, which triggers the ageing effect. Goto andTowhata (2014) reported that secondary consolidation has something to dowith the ageing effect. As seen above, there is something unclear remaining
concerning the mechanisms of ageing effect.
Shintaku and Towhata (2014) pointed out the possibility that particle
movement within sandy ground contributes to the increment of liquefaction
resistance5), as shown in Figure 2. They conducted sand box observation test
and undrained cyclic triaxial tests, and finally concluded that there is arelationship between the frequency of particle movements and theliquefaction resistance. However, they could not clarify the effect of shaking
frequently happening in Japan on the particle movement and liquefaction
resistance.

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Figure 2: Image of particle movement

2. METHODOLOGY
2.1 Sand box observation
Two kinds of sand were prepared with the same mean diameter (D50)
and different uniformity coeffecients (Uc). Here are the table showing thephysical properties and the figure showing the grain size distribution curvesof the sands (Figure 3).

Table 1: Physical character of samples
Name
#6
S1

emax
1.023
0.907

emin
0.638
0.480

D50(mm)
356
365

Uc
1.5
4.0

FC(%)
0
5.4

These
samples are set in
the cuboid sandbox with 6cm ×20cm bottom and10cm
height.
They are fully
saturatedbypluviation
method,and
25kPa pressure isapplied.
The
cross section of
the samples isshotbymicroscope at the
Figure 3: Grain size distribution curves of samples
sameintervals
through one side of the sand box made by acrylic plate. Pictures obtained
each day are compared, and the number of moving particles are counted. In
addition, this sand box is set on the shaking table, and 20 sinusoidal shaking
waves were applied to it at 5 seconds intervals. The maximum acceleration

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AugustAugust2015,
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Thailandof one wave is 75 Gal, the frequency is 5 Hz, and the duration is 3 seconds,
which reproduces the relatively small but frequent earthquakes in Japan. In
a similar way as mentioned above, the pictures before and after shaking arecompared, and the number of moving particles was counted.

Figure 4: Sand box observation
2.2 Undrained cyclic triaxial test
S1 samples with 5cm diameter, 10 cm height, and about 60%
relative density are prepared with different consolidation terms (1day, 4day,
and 64 day). After the consolidation, undrained cyclic triaxial test is
conducted. It is reasonable to assume that the same kind of particle
movement as in the sand box observation occurs in the triaxial samples
during the consolidation period.
3. RESULTS AND DISCUSSION
3.1 Sand box observation
Figures 5 and 6 show the results of sand box observation in static
setting. The vertical axis stands for the accumulated number of moving
particles per 1 square centimeter within the observation area. The horizontal
axis indicates the elapsed time with logarithmic scale.
When the sand box is set statically, a larger number of particles
moved in S1 case, with a well graded sample. Besides, a greater number ofparticles moved in low relative density case in each material. Firstly, smaller
particles were more likely to move with well graded sample. Second, loosen
sand has more space for particles to move. These two would be the reasonof this result. Thus, particle movement is observed in local areas, not overall
movement. This phenomenon is called “void filling” in the following pages.

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Figure 5: The relationship between elapsed time andaccumulated number of moving particles (S1)

Figure 6: The relationship between elapsed time andaccumulated number of moving particles (#6)
On the other hand, when the sinusoidal wave is applied to the sandbox, overall structure of the sample changed in loose sand case. In place ofcounting the number, the pictures taken before and after shaking wereanalyzed by PTV (Particle Trace Velocimetry), and the arrow figures wereobtained as shown in Figure 7.

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AugustAugust2015,
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Thailand(μm) YX(μm)
Figure 7 : Result of PTV analysis (#6, Dr=24%)
Since one particle movement was judged in relation to surroundingones, it was difficult to say whether counting the number of moving
particles was valid. This phenomenon is called “structural change”, and isdistinguished from “void filling”. In denser sand, structural change was notobserved.
The actual observed distance is increased 10 times for better outlook.
Random movement of the particles is observed. The average element ofeach direction is +0.41(μm) for X direction and +0.69 (μm) for Y direction,and the average length of the arrows is 10.2 (μm).
Since there is no evidence of overall displacement or height change
during observation, structural change is not considered to be triggered by
the volume change caused by negative dilatancy. However, observation ofwhole cross section would be necessary since the target of this test is only
the one local point of cross section.
3.2 Undrained cyclic triaxial test
Figure 8 shows the result of undrained cyclic triaxial tests. The
relationship between the stress ratio and the number of cycles when the
double amplitude reached 5% is obtained. While the liquefaction resistanceof samples with 1 day and 4 days consolidation is almost the same, theincrement of the resistance was observed in 64 days consolidation case.
When the data are compared by the stress ratio, the resistance of 64 days
sample became 1.50 times greater than those of 1 day and 4 days sample.
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Figure 8: Liquefaction characteristic curve
Shintaku and Towhata (2014) 5) obtained the same kind of graph as
Figures 5 and 6 by using the same samples as this test with Dr around 60%.
Then, it was concluded that the frequency of void filling in case S1 is about
8 times greater than that in case #6. It is also reported by him that theliquefaction resistance of #6 sample in 64 days consolidation was 1.07 times
larger than that in 1 day case.
By combining these information and the result obtained in this test,
the relationship between the increment ratio of liquefaction resistance andthe frequency of void filling is obtained as shown in Figure 9.

Figure 9: Relationship between the increment ratio ofliquefaction resistance and the frequency of void filling

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4. CONCLUSION
In this study, to indicate the mechanism of ageing effect from theviewpoint of particle movement, sand box observation and undrained cyclic
triaxial tests were conducted. As a result, following two points were shown.



Different mode of particle movement was observed with and without
shaking. This might be because of the different effect of static loadand shaking on the sand ground.
There is a correlation between the increment ratio of liquefaction
resistance and the frequency of void filling.
Particle movement is more frequently observed in looser and better
graded sand.

REFERENCES
Japan Road Association 2012 : Specifications for highway bridges （V:seismic design part）
Ikuo Towhata, Yuichi Taguchi, Toshihiko Hayashida, Yuki Hamada, Shogo
Aoyama, Shigeru Goto, 2014. : About the phenomenon thatliquefaction resistance of sand ground increasese as time passes ,journal of geotechnical engineering, Vol. 10 (submitted)
Mitchell, J. K. and Solymar, Z. V., 1984: Time-Dependent Strength Gain in
Freshly Deposited or Densified Sand, Journal of ASCE, GT11,
Vol.110, pp.1559-1576
Shigeru Goto, Ikuo Towhata, 2014：the liquefaction characteristic of sandsample in ageing effect condition by repeatedly sheared in drained
condition or consolidation in high- temperature environment,journal of geotechnical engineering, Vol.9, No.4, 707-719
Yuki Shintaku, Towhata Ikuo, 2014： Test on the relationship between
particle movement and ageing effect from the viewpoint ofliquefaction resistance of sand, Proc. 49th Japan National
Conference on Geotechnical Engineering

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7thAN ANALYSIS OF NETWORKTHROUGHPUTS FOR DIFFERENTORIGIN-DESTINATION PATTERNS
Koki SATSUKAWA1, Kentaro WADA2
Graduate student, Dept. of Civil Engineering,
The University of Tokyo, Japankouki@iis.u-tokyo.ac.jp
2
Ph.D., Doctor of Information Science, Institute of Industrial Science,
The University of Tokyo, Japan
1

ABSTRACTA recent study revealed the existence of the Macroscopic
Fundamental Diagram (MFD) that links average density and average flow
within an area. MFD describes network performance and traffic state at an
aggregate level. It has also been shown that the spatial distribution of linkdensity in a network (congestion pattern)) is one of the key components indefining the shape of an MFD. However, the effect of congestion patterns on
MFD has not been clarified. In this paper, we analyze macroscopic network
performance by taking into consideration congestion patterns. Morespecifically, we derive the network throughput analytically by solving the
dynamic user equilibrium (DUE) problem, which describes the congestion
pattern based on the user’s route choice. And as an application of thistheory, we examine several example networks and clarify the effects of thedifference of the OD structure.
Keywords: macroscopic fundamental diagram, network throughput,
dynamic user equilibrium, many-to-one network, one-to-many network
4. INTRODUCTION
Recently, Daganzo (2007) has proposed a Macroscopic Fundamental
Diagram (MFD) that links average density (or accumulation) and average
flow (or throughput) within an area; it describes network performance andtraffic states at an aggregate level. This concept would be useful forestablishing a robust traffic control policy because we can simply
understand the traffic state of an area and control parameters can be flexibly
adjusted on the basis of this real time information.
Geroliminis and Sun (2010) have shown that the spatial distributionof link density in a network is one of the key components in defining the213AugustAugust2015,
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Thailandshape of an MFD. However, almost all empirical studies have only analyzed
the correlation between the frequency distribution of link density and MFD.
Therefore, the effect of spatial patterns of link density on MFD has not beenclarified. To understand this effect, Daganzo et al. (2011) theoreticallyanalyzed the dynamics of traffic flow on a simple network with twointerconnected rings. They revealed that network throughput decreases
because of the heterogeneity of densities between two rings. It is, however,difficult to extend the model to investigate the effect of congestion patterns
in more general networks.
The purpose of this study is to analyze macroscopic network
performance by taking into consideration general congestion patterns in a
network. To achieve this, we use a new method, proposed by Wada andSatsukawa (2015) and Satsukawa and Wada (2015), for deriving
throughputs based on a dynamic traffic assignment model with many-to-one
and one-to-many origin–destination (OD) demands. Specifically, for a given
congestion pattern, we consider the dynamic user equilibrium (DUE)
problem, which describes the congestion pattern based on the user’s route
choice. Therefore, by solving this problem, we can connect and clarify the
relationship between the macroscopic network performance and themicroscopic congestion pattern. This enables us to analyze the effects of the
topology of congested links and route choice on throughput.
The organization of the paper is as follows. In the next section, we
briefly review the DUE assignment. In section3, we derive the throughput
analytically by solving DUE problem. In section4, we examine several
example networks and clarify the effects of the difference of the OD
structure. Finally, we summarize the results.

2. PRELIMINARIES: DYNAMIC USER EQUILIBRIUM
2.1 Networks and Notation
This paper considers general networks with one-to-many and manyto-one OD (origin-destination) demands. A network consists of a set ofnodes N, a set of links L, and a set of OD pairs W. A node-link incidence
matrix A* (N × L matrix) represents the structure of a network. And we
define an irreducible incidence matrix A by eliminating an arbitrary row of
A*. We call the node corresponding to the elimination “reference node”. In
this paper, we choose the unique origin node in a network with one-to-many
pattern, and the unique destination node in a network with many-to-one
pattern as reference node. We also define a matrix A-, which is obtained byreplacing all the +1 elements with 0 of A. For each link, we employ a FirstIn-First-Out (FIFO) principle and model traffic flow dynamics by a point
queue model: it is assumed that each link (i, j) is consists of a free-flow
section with constant travel time mij and a bottleneck section with constant

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capacity ij . Then, the link travel time for a user entering the link at time t isgiven bycij (t)  mij 

xij (t  mij )

(1)

ij
where xij (t)  Aij (t)  Dij (t)(2)

where xij (t) is the amount of traffic in the queue at time t, Aij (t) , Dij (t) are thecumulative inflow and outflow of the link by time t.
2.2 Decomposed Property of Dynamic User Equilibrium assignment
The dynamic user equilibrium (DUE) is defined as the state that nouser can reduce his/her travel time by changing his/her route. Under the
DUE state, the users who arrive their destination at the same time have thesame arrival time at any node that is commonly passed through on the way
to their destination (Kuwahara and Akamatsu, 1993). Based on this property,
we can determine the equilibrium arrival time at each node uniquely forevery arrival time at the destination. This implies that the DUE assignment
with many-to-one OD pattern can be decomposed with respect to the arrival
time at the destination. Hence, we can obtain the equilibrium pattern forwhole time periods by successively applying the same procedure at theorder of the arrival-time.
2.2.1 Formulation and Equilibrium Solutions on Saturated Many-to-One
Networks
The DUE assignment on a many-to-one network can be decomposedwith respect to the destination arrival-time u. In the following, we formulate
the problem of obtaining equilibrium pattern for users, which arrive at
destination at time u. In the decomposed formulation of DUE withdestination arrival-time u, two types of variables, ( yiju ,  iu ) play a central roll.
 iu is the earliest arrival time at node i for users arriving at destination attime u; yiju is the link inflow rate with respect to u. In addition, we denotethe cumulative OD demands whose arriving at destination from origin ountil time u by Qod (u) . Using these variables, the decomposed DUE
assignment with respect to u is formulated as the following three conditions.
The first condition is the link travel time function given byuciju  ciju0   ciju du u .
(3)
0
(4)

Second is the flow conservation condition given by

.

.

(5)
(6)

Third is the user’s route choice conditionyiju{ciju   iu   uj }  0 u u uu cij   i   j  0, yij  0 ij  L, u .

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AugustAugust2015,
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ThailandThe DUE assignment is formulated as a complementarity orvariational inequality problem and we cannot solve analytically. Therefore,
to obtain analytical solution, we confine our analysis to “saturated
networks”, which satisfy the following two conditions: (1) all links in a
network have positive inflows (i.e., yiju  0 ), and (2) all links have queues(i.e., xij (t)  0 ). In a saturated network, the decomposed DUE assignment
results in a system of linear equations, and thus we can obtain an analyticalsolution (Akamats, 2000; Akamatsu and Heydecker, 2003b). In addition, we
can apply the same methodology to a non-saturated network by constructing
a saturated network, which is called “reduced network” (Akamatsu andHeydecker, 2003a). This network is a saturated network constructed byappropriately removing links, which have no flow or no queue (Figure 1).
Hereafter, we proceed to a discussion on a reduced network.
Now, let us derive an analytical solution of the DUE assignment
with respect to the destination arrival-time u. The complementarily
conditions of link travel time (4) and user’s route choice (7) result in thefollowing two equality conditions:Figure 1: Example of constructing reduced network, Dotted:
non saturated, Bold: saturated
.

(8)

.

(9)

By substituting equation (9) into equation (8), we derive
(10)

.

(11)

By substituting this equation into flow conservation condition (5), we seethat the DUE solution is governed by.
(12)
However, if there are links, which connect to a reference node,inflows of those links disappear because of eliminating an arbitrary row of
A* (i.e., a flow conservation condition is no longer satisfied in the saturated
network). We can solve this problem by treating these inflows as outflows

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from a network. Specifically, by using a column vector  , which
components are capacities of those links, equation (12) is revised by.
(13)
In addition, we can represent this equation as a block matrix by
distinguishing pure origin nodes, which has no incoming link (denoted by p),
origin nodes (denoted by o) and transient nodes (denoted by i).
.

(14)

2.2.2 Formulation and Equilibrium Solutions on Saturated One-to-Many
Networks
As with the many-to-one OD pattern, the DUE assignment on a oneto-many network can be decomposed with respect to the origin departuretime s. In the decomposed formulation with origin departure time s, twotypes of variables, ( yijs ,  is ) play a central roll.  is is the earliest arrival time atnode i for users departing from origin at time s; yijs is the link inflow ratewith respect to s. In addition, we denote the cumulative OD demands whose
departing from origin to destination d until time s by Qod (s) . By using thesevariables, we can derive the DUE solution,.
(15)
Similarly with many-to-one OD pattern, we can represent thisequation as a block matrix by distinguishing destination nodes (denoted byd) and transit nodes (denoted by i).
.

(16)

3. MACROSCOPIC NETWORK PERFORMANCEIn this section, we analyze the macroscopic network performance under the
DUE state. Firstly, we briefly review the concept of Macroscopic
Fundamental Diagram (MFD) and Network Exit Flow (NEF) proposed in
Daganzo(2007). Secondly, we mathematically describe the throughputs byusing the microscopic variables used in the formulation of DUE. Finally, wederive an analytical formula of the throughputs by solving an inverse
problem of the DUE formulation.
3.1 Macroscopic Fundamental Diagram and Network Exit Flow
Recently, Daganzo (2007) has proposed a macroscopic trafficmodeling approach. This approach is based on the following hypothesis: if
traffic demands to a network system change slowly compared with thetravel time across the network, then the system can be described as nearsteady-state. Under this state, the network throughput should be

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AugustAugust2015,
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Thailandapproximated by Network Exit Flow (NEF) G(n(t)) , which is a function of
the vehicle accumulation n(t) on the network.
However, it is difficult to measure the throughput practically.
Therefore, we employ the MFD that links average density k and average
flow q within an area as an alternative of NEF. We can derive therelationship between NEF and MFD when the average distance travelled byexiting vehicles is d and the total length of the network is l:
G(n(t))  (l / d )q(k) . Consequently, MFD is the result of a scale transformationof NEF. Hereafter, we deal with the NEF (throughput) to analyze themacroscopic network performance.
3.2 Formulations and Analytical Solution of the Throughputs
3.2.1 Throughput with Many-to-One OD pattern
Let us describe the throughput under the DUE state. First, we
describe the relationship between microscopic variables used in theformulation of DUE and macroscopic traffic state variables. Define Ao (t) ,Do (t) , no (t) as the cumulative number of vehicles to have arrived and left a
network distinguished by origin o. These macroscopic variables isrepresented by
Ao (t)  Qod (t  Co* (t)) o
Do (t)  Qod (t) ono (t)  Ao (t)  Do (t) o

(17)
(18)
(19)

where Co* (t) is the equilibrium trip time of vehicles which depart a origin oat time t. If these variables are differentiable, the dynamics of the vehicle
accumulation is given by.

(20)

Using these variables, we derive the analytical formulation ofthroughput under a steady state. We define this as the state, which satisfies
the following two conditions: (1) the vehicle accumulation in a network andthe each of the OD demand are constant, and (2) a congestion pattern isunchangeable. These conditions can be expressed as the following two
conditions. First condition is that the vehicle accumulation is constant. Fromequation (20), this condition is given by.

(21)

And second condition is that each OD demands is constant. This conditionis given by,.

(22)

Substituting (22) into (21), we can derive the following periodic boundary
condition given by,.
(23)This condition implies that
represent both of the inflow andoutflow rate, also the throughput itself. We denote this throughput in thecongestion pattern x as G x , to distinguish from the inflow and outflow rate.

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Therefore, the formulation of the throughput in a network with many-to-one
OD pattern can be described by,(24)

Define as a vector whose transit-node components are 0:.
Finally, by solving this formulation, we can derive following analyticalthroughput.
 p   V 1 + V (V )1[V 1  ]  
G
po
pi
ii
io
i
p 
.
Gx   x    G o   V 1 + V (V )1[V 1  ]   
x
oo
oi
ii
io
i
o

 


(25)

where Vab  A a MATb (a,b  { p,o,i})This implies that the macroscopic network performance ischaracterized by the structure of congested links. By using this equation, we
can derive macroscopic network performance, which takes into
consideration the network topology. In addition, we can represent thethroughput by elements from equation (24).
.

(26)

Equation (26) means the flow conservation condition in the reduced
network. Specifically, the first terms of these equations represent theoutflow, and the second term represents the inflow from the origin nodes in
the reduced network respectively. Note that this equation includes thevariable , which characterizes the equilibrium solution in the DUE state;e.g., the effects of the route choice. This variable represents the state of thecongested flow pattern in downstream of node l. It implies that thisthroughput has been influenced by the downstream states of a group oforigin nodes (the whole of network).
3.2.2 Throughput under One-to-Many OD pattern
As with the many-to-one OD pattern, we derive the analyticalformulation of throughput in a network with one-to-many OD pattern. We
can derive a periodic boundary condition in the same way.
.
(27)
This equation has the same meaning to many-to-one pattern. Then,
formulation of the throughput in a one-to-many network can be described by,.
(28)
From these equations, we can derive following analytical throughput.
G x  Vdd 1 Vdi (Vii )1[Vid 1 i ] d .
where Vab  A a MATb (a,b  {i,d})

To analyze this solution, let us describe the throughput by elements.
.

(29)
(30)

We can recognize that this equation is also the flow conservation
condition in the reduced network.
4. ANALYSIS OF EXAMPLE NETWORKS

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AugustAugust2015,
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shows the symmetrical networks. In these networks, each link has a capacityij (ij  L) .
4.1 Examples that Networks have the Same Throughputs
Consider the network shown in Figure 2(a), where nodes b, d, e and f
are origins; node a is the only destination. Links except link 4 are saturated
and link 4 is not saturated. The matrixes in equation (26) for this network
are given by
Vpo   0 6 7  .
(31)


 
0
 3

Voo  3 5  6

5
 0

  
 1 
o    2 .
 0 

0 
0 .

7 


(32)

(33)

By using these matrixes, the throughputs distinguished by origin
nodes and the network throughput become b  
1  3
 Gx   Gd          
356
 x  2 Ge  




5
7
x
 


f
6  7
 G x  


(34)

G x  1  2 .

(35)

Figure 2: Example networks, (a) One-to-many OD (b) Many-to-one OD
Double line: origin, Bold line: destination

We can easily find that the network throughput is consistent with thesum of inflow link capacities to the destination.

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Similarly, we can derive the throughput of the network in Figure2(b), where node a is the only origin; nodes b, d, e and f are destinations.
The matrixes in equation (30) for this network are given by
 

3
0
0
 1

 0    



2
3
5
6
.
Vdd  
0
5
7 
 0


0
0 6  7 
 0

(36)

Substituting these equations into (30), we derive b  
1  3
 Gx   Gd          
356
 x  2.
 Ge  




5
7
x
 


f
6  7
 G x  


(37)

G x  1  2 .

(38)

By comparing (35) and (38), we can easily find that the throughputs
are the same. This is because there are no route choice effects of the DUE
state in these networks, which do not have transit nodes. In other words, thesecond terms of the equation (25), (29) disappear. In this case, thethroughputs only depend on the structure of congested links to thedestination node(s). Therefore, the throughputs in these symmetric networks
become the sum of the inflow link capacities to the destination node(s).
4.2 Examples that Networks have the Different Throughputs
We now consider the networks where link 4 becomes congested. Let
us derive the throughputs with many-to-one OD pattern at first. The
matrixes in equation (26) for this network are given by
Vpo   0 6 7  , Vii  5  Vio   0 4 0 
(39)




 
0
 3
Voo   3  4  6

0
 0


  
 0 
0 
 1 


0  Voi   0  o   0  i   2   
7 
5 
 0 
(40)

Substituting these equations into equation (26), we can derive thethroughputs.
 b  
 G x   1  3

 Gd        46
 x  3.
 G e    2  4  7 
x

 

 G xf   6  7


(41)

G x  1  1 .

(42)

The network throughputs do not change at all regardless of thechanges of origin throughputs.
Then, we derive the throughput with one-to-many OD pattern in thesame way. The matrixes in equation (30) for this network are given by

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AugustAugust2015,
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
0
0
 0 
3
 1



 0 

0
6
  4 
3 Vdi  
Vdd  
.
0 5
7 
 0
0




 0 
00 6  7 
 0,
Vii  2  4 , Vid   0 5 0 0 



(43)

(44)

Substituting these equations into equation (30), we can derive thethroughputs.

1  3 b  G
 x  
 G d        4 5 
36
 x 
 2  4  .
 Ge  x5   7 f G x  




6
7



G x  1  5 

 4 5.
2  4

(45)

(46)

By comparing the equations (37) and (45), we can find that thethroughput from node d have changed and the network throughput may
decrease. This is because the second term of the equation (30) has changedby the route choice effects of the DUE state. The equilibrium DUE solution
at a node is affected by flows to destinations which are downstream of thenode. Therefore, at the upstream node d of node c, we derive the throughput
affected by the effect of route choice in the DUE state.
From these results, we find that the throughputs of the symmetricnetworks are not always symmetric and become different. This difference isinterpreted as the following. In the many-to-one network, all vehicles go to
the same destination regardless of the difference of their origins. Therefore,
the throughput results in just the sum of inflow link capacities to thedestination and not influenced by the other links strongly. On the other hand,in the one-to-many network, some vehicles may use an upstream destination
node as just a transit node to go to a downstream destination node. In thiscase, such vehicles interrupt vehicles, which go to upstream destination
nodes. Then, the throughput in one-to-many network becomes less than thesimple summation of inflow link capacities.
5. CONCLUSION
In this paper, we derived the network throughputs with taking into
consideration of the effect of network topology and route choice. In addition,
we analyzed the difference of the throughputs with respect to the OD
structure. Our simple analysis in example networks showed that even if the
networks have the symmetrical congestion patterns, the throughputs become
different due to the route choice effects. We can expect this kind ofasymmetry result in general. Therefore, we should extend our analysis to
more general sensitivity analysis: sensitivity to the changes of link capacityor the link state. In addition, in one-to-many network, we find the222
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throughputs may decrease with congestion by the effect of the route choice.
These results imply that network throughput in one-to-many network ismore sensitive to the user’s route choice than that in many-to-one network,and the key components for analyzing the characteristics of throughputs are
inherent in one-to-many network congestion pattern. Toward this direction,
we should explore the key congestion pattern with one-to-many OD pattern
for deciding the capacity of a network.
REFERENCES
Kuwahara, M. and Akamatsu, T., (1993) Dynamic equilibrium assignment
with queues for one-to-many OD pattern. Proceedings of The 12th
International Symposium on Transportation and Traffic Theory, pp.
185-204.
Akamatsu, T., (2000) A dynamic traffic equilibrium assignment paradox.
Transportation Research Part B: Methodological, 34, pp. 515-531.
Akamatsu, T. and Heydecker, B., (2003a) Detecting Dynamic Traffic
Assignment Capacity Paradoxes in Saturated Networks.
Transportation Science , 37, pp. 123-138
Akamatsu, T. and Heydecker, B., (2003b) Detecting Dynamic Traffic
Assignment Capacity Paradoxes: Analysis of non-saturated networks.
Working Paper, Tohoku University(Japan)
Daganzo, C. F., (2007). Urban gridlock: Macroscopic modeling andmitigation approaches, Transportation Research Part B:
Methodological 41, pp. 49 – 62
Daganzo, C. F., Gayah, V. V. and Gonzales, E. J., (2011). Macroscopic
relations of urban traffic variables: Bifurcations, multivaluedness
and instability, Transportation Research Part B: Methodological 45,
pp. 278 - 288
Geroliminis, N. and Sun, J., (2011). Properties of a well-definedmacroscopic fundamental diagram for urban traffic, Transportation
Research Part B: Methodological 45, pp. 605 – 617
Satsukawa, K. and Wada, K (2015) Macroscopic Traffic Performance under
Dynamic User Equilibrium with Many-to-One Origin-Destination
Patterns, Japan Society of Traffic Engineering, accepted.
Wada, K and Satsukawa, K. (2015) Network throughput under dynamic user
equilibrium, Working paper

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WATER RETAINABILITY AND BASIC
PROPERTIES OF COAL BOTTOM ASH
USED AS FINE AGGREGATE REPLACING
MATERIAL IN CONCRETE
Noulananh LATHSOULIN1,Pakawat SANCHAROEN2 andSomnuk TANGTERMSIRIKUL1,2
1
School of Civil Engineering and Technology,
Sirindhorn International Instituteof Technology, Thammasat University, Thailand.
2Construction and Maintenance Technology Research Center,
Sirindhorn International Institute of Technology,
Thammasat University, Thailand.
Khaek_noulananh@hotmail.com

ABSTRACT
Bottom ash from coal power plant is a porous material and its
particle size is comparable to that of fine aggregate used in concrete. So it
is considered to be used as porous fine aggregate. However, current
standard method for determining water absorption of fine aggregate cannot
be used effectively with porous fine aggregate due to it is difficult to justify
the saturated surface dry condition of it. Thus water retainability wasdetermined instead. Water retainability of bottom ash was tested byproposed method which was fully submerged a few days to reach the oversaturated condition before being vibrated-drained for 24 minutes. Other
basic properties such as specific gravity, gradation or chemicalcompositions were also studied and compared with natural fine aggregate.
The results showed possibility of coal bottom ash to be used as fineaggregate replacing material in concrete.
Keywords: porous aggregate; basic properties of bottom ash; waterretainability
1. INTRODUCTION
Presently, specific porous materials or water retaining materials have beenstudied for the concrete cases due to a large amount of waste materials has
been dumped or used as a land-filled material and amount of natural sand isinsufficient in some area. Bottom ash is used as partially replace sand, it cansaves a lot of money, solve some environmental problem and may enhancein mechanical properties and durability of concrete (Andrade et. al, 2009)
and (Kasemchaisiri and Tangtermsirikul 2007).

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Bottom ash is a porous material that has high water retainability,
coarse particle size and usually no pozzolanic effect (Andrade et. al, 2009),
However, determining water absorption of fine aggregate cannot be usedeffectively with porous fine aggregate due to it is difficult to justify thesaturated surface dry condition of it, so the water retainability wasdetermined instead.
This is the big problem for concrete mix design in order to achieve
the efficient concrete and also workability because these porous materials is
the moisture content of porous aggregate is always higher than normal
aggregate. Previous studies have reported that concrete mixed with bottom
ash and recycled concrete-crush was very nonworkable due to a significant
water demand from their porous properties (Bucholc and Ghafoori, 1996).
There was a certain difficulty in determining the exact moisture propertiesof bottom ash (Landgren, 1994).
Water retainability has been discovered by some researchers foranalysis and design of the fresh concrete properties (Kasemchaisiri andTangtermsirikul, 2007). For concrete incorporating porous aggregate, it isappropriate to apply water retainability instead of water absorption to adjust
amount of mixing water according to concrete mix proportion.
The purpose of this study is to develop of practical method todetermine water retainability of porous fine aggregate based on available
laboratory equipment and short testing time, In addition basic properties ofbottom ash were also studied.

2. METHODOLOGY
The study was tested by using 12 types of bottom ash were tested on basic
properties such as sieve analysis, specific gravity, chemical composition andwater retainability.
2.1 Materials
Bottom ash was collected from Mae Moh electricity generating plant,
Thailand during April to September 2014. Bottom ash on each month had
different properties such as physical and chemical properties of themselvesbecause of different percentage of coal. Main two bottom ashes withdifferent burning process had been tested in this study. The bottom ashes
with 150MW capacity and bottom ashes with 300MW capacity. Bottom ash
was sieved pass sieve (No.4) prior testing.
2.2 Gradation
Bottom ash particles had to be dried in the oven before testing. Sieve
analysis of bottom ash has investigated according to ASTM C136-06
standard (ASTM C136-06, 2004).

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2.3 Specific gravity
There are 2 sample conditions for testing specific gravity as shownin details below (ASTM C128, 2004).
(1) Water retainability sample condition: Determine the specific gravity
of bottom ash at the stage of water retainabilty, adjust the water ofdried-bottom ash until reach the water retainability condition. After
that, standard test method for specific gravity and absorption of fineaggregate (ASTM C128) can be applied.
(2) Dry sample condition: Standard test method for specific gravity andabsorption of fine aggregate (ASTM C128) can be applied but withthe use of oven-dried sample. Then bulk specific gravity at the stateof water retainability can be calculated from equation (1).
SGOD = SGOD/(1+WR/100)(1)Where SGOD and SGWR are the bulk specific gravity of bottom ash atoven-dried and water retainability conditions, respectively and WR is thewater retainability of bottom ash (% by oven-dried weight of bottom ash).
2.4 Chemical composition
For chemical analysis testing according to the (ASTM E1621-94,
2004), 12 samples of bottom ash have been analyzed for chemical analysis.
Bottom ashes were tested with the machine Bruker AXS, Germany, Model:
S4 Pioneer Wavelength dispersive, X-Ray Fluorescence (WDXRF)
Spectrometry. Voltage/Current : 60 kV / 50 mA. Conditions: Range 0.2 – 20
A (60 – 0.6 keV), total resolution 3 – 100 eV, typical measurement time 2 –10 s per element. Program used: SPECTRA Plus software of the Bruker
with the standardless Analysis.
2.5 Water retainability
Prepared the sample with the quantity required to fill the cylinder (56 kg) as showed in Figure 1(a). Mix the sample with the water in a container,
wrap the container and immerse the sample for 3 days as showed in Fig.
1(b). Before testing, sample should be drained around 30 minutes before
filling into the apparatus as showed in Figure 1(c).
Compacting sample into the cylinder pipe was separated into threelayers, then hammering 30 times for each layer. Vibrate the cylinder pipe byordinary fine aggregate sieving machine at 24 minutes. Collected the sample
from the top of container and obtained the moisture content of the sample
for 3 centimeter thickness. This method is called proposed test method. The
water retainability of sample is the value of the moisture content from the
top of the containers.

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(a)
(b)
(c)
Figure 1: (a) Sample with specified amount of water; (b) Sample with water(fully saturated); (c) Draining water from sample with cover.
3. RESULTS AND DISCUSSION
3.1 Gradation
Figure 2 and 3 showed the gradation of all types of bottom ash used in thisstudy. Bottom ash passing sieve no. 4 was used in this study to avoid thelarge particles and to maintain a gradation to be similar to that of the sand.
It can be observed that gradation and fineness modulus on each month in thesame burning process had nearly same values and same tendency as showed
in Figure 2 and 3. The gradation of bottom ash from 150MW capacity as
shown in Figure 2 had particles size bigger than bottom ash from 300MW
capacity as shown in Figure3. Fineness particle of bottom ashes from
300MW capacity showed finer than limit of standard ASTM C136-06,
especially No.50 and No.100 respectively. In addition, the fineness modulus
of bottom ashes from 150MW capacity are coarser than bottom ashes from
300MW capacity as shown in Figure 4 .

Figure 2: Gradation of bottom ash from 150MW capacity

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Figure 3: Gradation of bottom ash from 300MW capacity

Figure 4: Fineness Modulus of bottom ash from 150MW and 300MW
capacity
3.2 Specific gravity
In this study, specific gravity of bottom ashes were tested by waterretainability condition. Figure 5 showed that the specific gravity of all typesof bottom ash used in this study. It can be observed that specific gravity ofbottom ash on each month in the same burning process had nearly samevalues and same tendency as showed in Figure 5. It can be observed thatspecific gravity of bottom ash from 150MW capacity had the value higher
than bottom ash from 300MW capacity as shown in Figure 5.

Figure 5: Specific gravity of bottom ash from 150MW and 300MW capacity
3.3 Chemical composition
Table 1 and 2, showed that the chemical composition of all types ofbottom ash used in this study. It can be seen that chemical composition ofbottom ash on each month in the same burning process had nearly samevalues of chemical substances. All types of bottom ash from 300MW
capacity had Silicon dioxide higher than all types of bottom ash from
Water retainability and basic properties of coal bottom ash used as fine aggregate replacing
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150MW capacity. In contrast, all types of bottom ash with 300MW capacity
had Calcium Oxide and Sulfur trioxide lower than all types of bottom ashwith 150MW capacity.

Table 1: Chemical composition of all types of bottom ash of 150MW
capacity

Formula
Name
SiO2
CaO
Al2O3
Fe2O3
K2O
MgO
SO3
Na2O
TiO2
P2O5
MnO
BaO
LOI
Total

Concentration(%wt)
150MW150MW-May
April
30.13
29.50
27.78
29.50
17.84
16.43
15.99
15.66
2.27
2.25
2.19
1.90
1.92
1.85
0.90
0.79
0.50
0.49
0.24
0.22
0.00
0.00
0.00
0.00
0.20
1.41
99.96
100.00150MWJune
33.26
26.95
18.14
13.54
2.17
1.90
1.42
0.70
0.44
0.25
0.00
0.00
1.22
99.99

150MWJuly
30.12
28.11
14.58
15.56
2.18
1.69
2.93
0.65
0.51
0.23
0.15
0.14
3.15
100.00150MWAugust
29.61
28.03
14.68
15.60
2.16
1.74
2.86
0.66
0.50
0.23
0.15
0.16
3.6
100.00150MWSep
31.73
23.45
16.49
16.94
2.99
1.65
2.87
0.75
0.55
0.00
0.00
0.19
2.39
100.00

Table 2: Chemical composition of all types of bottom ash of 300MW
capacity

Formula
Name
SiO2
CaO
Al2O3
Fe2O3
K2O
MgO
SO3
Na2O
TiO2
P2O5
MnO
BaO
LOI
Total

Concentration(%wt)
300MW300MW-May
April
35.50
35.63
21.84
21.08
17.45
18.94
14.03
14.92
2.38
2.55
1.78
1.76
1.59
1.42
0.83
0.82
0.45
0.48
0.16
0.18
0.00
0.00
0.00
0.00
3.98
2.22
99.99
100

300MWJune
35.11
21.34
19.48
15.00
2.64
1.81
1.19
0.83
0.49
0.16
0.00
0.00
1.93
100

300MWJuly
37.17
17.68
21.46
10.08
2.46
2.38
2.45
1.21
0.4
0.17
0.00
0.00
4.52
100.00300MWAugust
33.23
21.73
17.28
15.26
3.01
1.72
2.58
0.78
0.56
0.17
0.00
0.16
3.52
100

300MW-Sep
32.61
20.28
17.46
14.05
2.58
1.69
2.35
0.73
0.48
0.17
0.14
0.15
7.30
100.00

3.4 Water retainability
In this study, water retainability of bottom ashes were tested byproposed test method. Fig. 6 showed that water retainability of all types ofbottom ash used in this study. It can be observed that water retainability ofbottom ash on each month in the same burning process had a little different
as showed in Fig.6. It can be also said that water retainability of bottom ashfrom 150MW capacity had the value lower than bottom ash from 300MW
capacity as shown in fig.6. The values of specific gravity showed highvalues, then the values of water retainability always showed low values.

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Figure 6: Water retainability of bottom ash from 150MW and 300MW
capacity
4. CONCLUSION AND RECOMMENDATION
According to the results above, it can showed that basic properties and waterretainability of porous aggregate was significant and necessary for concrete
mix design, especially water retainability because the amount of water wasvery important and effective on performance of concrete.
From the experiment results, it was concluded that basic propertiesof bottom ash such as gradation, specific gravity, chemical composition andwater retainability showed useful and appropriate values, it can also It also
showed that bottom ash has the possibility to be used as fine aggregate
replacing material due to its properties is comparable to that of natural fineaggregate. Moreover, high water retainabiltiy will show the benefit asinternal curing material for concrete.
REFERENCES
Andrade, L. B., Rocha, J. C., & Cheriaf, M., 2009. Influence of coalbottom ash as fine aggregate on fresh properties of concrete. Construction
and Building Materials, 23(2), 609-614.
Kasemchaisiri, Ratchayut, and Somnuk Tangtermsirikul., 2007. A
method to determine water retainability of porous fine aggregate for designand quality control of fresh concrete. Construction and Building Materials
21.6: 1322-1334.
Andrade, L. B., J. C. Rocha, and M. Cheriaf., 2009. Influence of coalbottom ash as fine aggregate on fresh properties of concrete. Construction
and Building Materials 23.2: 609-614.
Bucholc J, Ghafoori N. (1996). “Investigation of lignite-based
bottom ash for structural concrete” J Mater Civ Eng, American Society of
Civil Engineer; 8(3):128.
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AugustAugust2015,
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ThailandLandgren R. Unit weight, specific gravity, absorption, and surface
moisture. In: Klieger Paul, Larmond Joseph F., 1994, editors. Significanceof tests and properties of concrete-making materials, ASTM STP 169C.
Philadelphia: American Society for Testing and Materials; p. 421.
ASTM C136-06., 2004. Standard Test Method for Sieve Analysis ofFine and Coarse Aggregates. Annual Book of ASTM Standards 04.02.
ASTM C128., 2004. Standard Test Method for Density, Relative
Density (Specific Gravity), and Absorption of Fine Aggregate. Annual Book
of ASTM Standards 04.02
ASTM E1621-94., 2004. Standard Guide for X-Ray Emission
Spectrometric Analysis. Annual Book of ASTM Standards 04.02.
Kinaanath Hussain., (2011) “DEVELOPMENT OF A MINIMUM
CURING CONCRETE BY INTERNAL CURING PROCESS “Thesis CEMS. Sirindhorn International Institute of Technology, Thammasat
University

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EXPERIMENTAL STUDYON IN-PLANE AND OUT-OF-PLANEBEHAVIOR OF MASONRY WALLETTESRETROFITTED WITH SPECIAL FIBER
REINFORCED PAINT
Kenjiro YAMAMOTO1, Muneyoshi NUMADA2, and Kimiro MEGURO3
1
Graduate Student, Department of Civil Engineering,
the University of Tokyo, Japank-yama@iis.u-tokyo.ac.jp
2
Assistant Professor, ICUS, IIS, the University of Tokyo, Japan
3
Director/Professor, ICUS, IIS, the University of Tokyo, Japan

ABSTRACT
Unreinforced masonry buildings (adobe, rubble stone, burned brick,concrete block, etc.) can be damaged easily even by low intensities ofground shaking and be collapsed very rapidly by high intensities. Thesebuildings are common in seismic areas around the world, and have caused
heavy casualties in the past earthquakes (Coburn and Spence, 2002). To
improve seismic capacity of these weak structures, many methods have beendeveloped. However, most of them are time-consuming and labor-intensive
but not attractive as they don’t directly increase amenity of the life ofresidents which slowed down the spread of these methods. In our researchgroup, a much easier new method has been developed using SG-2000, a
kind of newly developed paint. (Yamamoto, 2014). In this study we haveconducted in-plane and out-of-plane tests to investigate the seismic capacityof this method using SG-2000. Based on the results, wallettes fully coatedwith SG-2000 have approximately 40 and 30 times larger deformation
capacities in cases of in-plane and out-of-plane tests, respectively, thanthose of the unreinforced masonry wallette. We have also found coating
only on the joints between bricks had almost the same efficacy as fullcoating.
Keywords: masonry, seismic retrofitting, fiber-reinforced paint
1. INTRODUCTION
In the last century there were more than 1,100 fatal earthquakes causing a
total loss of life exceeding 1.53 million people. From a classification ofearthquake deaths by cause during the 20th century, the collapse of masonrybuildings is the top cause of the deaths by earthquakes in the world. Besides,much of the increased populations in developing countries will continue tobe housed in this type of structure for foreseeable future (Coburn and233

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ThailandSpence, 2002). Therefore, retrofitting low earthquake-resistant structures is
the key issue for the reduction of casualties by earthquakes. Also, seismic
retrofitting ultimately reduces the costs of recuperating from an earthquake(reduces the cost of rescue and first aid activities, rubble removal, temporary
residence building, and permanent residence reconstruction to re-establish
normal daily life) (Yoshimura and Meguro, 2004). To retrofit thesestructures, many seismic retrofitting techniques have been developed so far(Bamboo band, Shotcrete, FRP and so on) (Amiraslanzadeh et al., 2012).
However, these techniques need much time and labor, but not attractive for
local people because they don’t improve the living standards, which hasdelayed these techniques’ spreading in developing countries. Considering
these problems, a new retrofitting technique has been suggested using glass
fiber reinforced paint (SG-2000). The material needed for this technique isonly SG-2000, which significantly reduces the amount of time and labor for
retrofitting. Also, paint is usually used to make houses look fine, and many
masonry structures are coated in paint. Therefore, SG-2000 can be easily
used by local people as the form of paint. The previous experiments in-planediagonal compression tests and out-of-plane bending tests shows thatwallettes partly coated with SG-2000 whose ratio of fiber is 1.5 % achievedlarger deformation capacities than unretrofitted wallettes did. Also, withcomparative studies, it has been suggested that the amount of paint could be
reduced by coating only on the joint of bricks (Yamamoto, 2014).
In this study, in-plane diagonal compression tests and out-of-planebending tests were conducted using wallettes fully coated with SG-2000.
Also, the efficacy of coating in two separate times was investigated. Finally,
the failure behavior of wallettes coated only on the joint of bricks wascompared with those fully coated to evaluate how much the amount of paintto use can be reduced.

2. AXIAL TENSILE TEST OF SG-2000
Tensile tests were conducted to check deformational capacities and strengthof dried SG-2000 whose fiber ratio is 1.5 %. To determine the change of its
elasitic modulus, ultimate strain and tensile strength corresponding to its
drying period, 3 specimens with drying period of 28days were tested under
uniaxial tensile condition with constant rate deformation (2 mm/min). The
nominal stress is determined using the cross section of each specimen
measured before the test.
From the result, the stress-strain curve is non-linear (Figure 1). Its
initial modulus of elasticity was 0.00389 GPa (Table 1). Considering its
large deformation capacity, SG-2000 can improve the structure’s ductility.

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Table 1: Tensile test results of SG-2000

Specimen
1
2
3
Average

Tensile strength
(MPa)
0.613
0.671
0.644
0.642

Elongation
3.71
3.63
2.79
3.38

Initial modulus ofelasticity (GPa)0.00330
0.00453
0.00384
0.00389

Figure 1: Stress-strain curve for SG-2000

3. IN-PLANE DIAGONAL COMPRESSION TEST
3.1 Test setup
To evaluate the seismic performance of the proposed SG-2000
retrofitting method in in-plane direction, in-plane diagonal compression
tests using unreinforced masonry wallettes and those retrofitted with SG2000 for burnt brick were conducted. The dimensions of wallettes were277.5 × 282 × 50 mm3, composed of 7 brick rows of 3.5 bricks each. The
mortar joint thickness was 5mm with the C/W ratio 0.14.
Unreinforced specimens (URM) were tested 56 days after theconstruction. For reinforced specimens were also tested after the same
period, but SG-2000 (1.5% ratio of fiber) was painted 28 days after theirconstruction with the thickness in 1mm, and dried for 28 days under thesame condition. To evaluate the efficiency of painting, the same amount ofpaint in two separate times, the specimens were also prepared that werepainted 28 days after their construction with half amount of SG-2000, andpainted the other half amount 14 days after that, and dried for 14 days again(Figure 2). Also, to evaluate how much paint can be reduced to make theretrofitting cost lower, the specimens that were coated only on the joints ofbricks and 5mm around them were prepared (Figure 3). These retrofitted
specimens were named in following convention: A-B. A indicates thenumber of times the specimens are painted; P1: the whole quantity of paint
was used at once, P2: half quantity was used and the other half was used 14
Experimental study
Experimental
study onon in-plane
in-plane andand out-of-plane
out-of-planebehavior
behaviorofofmasonry
masonrywallettes
wallettesretrofitted
retrofittedwith special fiber
reinforced
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Thailanddays after the first painting. B indicates the coated area; F: fully coated, J:
coated only on the joint and 5mm around the joint.
The loading rates were 0.15 mm/min for URM, and 0.25 mm/min
for retrofitted specimens to reduce the time of experiment. The previous
tests have shown that there is no difference in the results in between thesetwo different loading rates.
3.2 Behavior of retrofitted masonry

Figure 2: Curing period of each type of specimens

Figure 3: Coating on joints and around the joints
Figure 4 shows URM and P1-F at the end of the test, where thevertical deformations of URM and P1-F were 2mm and 80mm, respectively.
Figure 5 shows the diagonal compression strength with vertical deformation
for URM and P1-F. URM showed no residual strength after the specimen
with compressive strength of 0.55kN made the first cracking. On the other
hand, P1-F has connected bricks and produced the residual strength (3.5kN)
which is higher than the strength of mortar after the mortar joint had beenbroken. SG-2000 also changed its crack pattern, which made the strength
increase again by shifting the place to be loaded. This effect had been seen
until the SG-2000 itself was torn-off. Even after stopping producing residual
strength, SG-2000 barely connected bricks. From the failure pattern and thegraph, it has been shown that SG-2000 does not increase the masonry
structure’s initial stiffness, but improves its peak strength, and deformation
capacity.

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Figure 4: URM and P1-F after the tests

Figure 5: In-plane load-deformation curve
3.3 Efficiency of coating in two separate times
Figure 6 compares the compressive strength and the deformation
capacity of P1-F and P2-F. There is not so much difference in deformationand strength between these two.

Figure 6: Comparison between P1-F and P2-F

Experimental study
Experimental
study onon in-plane
in-plane andand out-of-plane
out-of-planebehavior
behaviorofofmasonry
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3.4 Seismic capacity of the specimen coated only around the joint ofbricksFigure 7 compares the compressive strength and the deformation
capacity of P2-F and P2-J. Some part of paint in P2-J has totally been
removed (Figure 8). However, the significant difference of the performancesof these two samples was not seen. This indicates that the suggested way inprevious research that painting on joints was effective enough to improve
the seismic capacity of masonry structure as much as full coating does, andthe amount of SG-2000 can be reduced.

Figure 7: Comparison between P2-J and P2-F

Figure 8: Total removal of SG-2000

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4. OUT-OF-PLANE TEST
4.1 Test setup
To investigate the seismic performance of the proposed SG-2000
retrofitting method in out-of-plane direction, out-of-plane tests wereconducted comparing unreinforced masonry wallettes and those retrofittedwith SG-2000 for burnt brick. The dimensions of wallettes were 475 × 241
× 50 mm3 , composed of 6 brick rows of 6 bricks each. The mortar joint
thickness was 5mm with the C/W ratio 0.25.
Unreinforced specimens (URM) were tested 56 days after theconstruction. For reinforced specimens were also tested after the same
period, but SG-2000 (1.5% ratio of fiber) was painted 28 days after theirconstruction so that they have a 0.5mm layer of SG-2000 coating, andpainted the same amount of paint 14 days after the first painting so that the
total thickness of SG-2000 layer becomes 1mm, and dried for 14 days again.
This type of retrofitted specimen was named like it has been named in thein-plane test: P2-F. The loading rate was 0.15 mm/min.
4.2 Behavior of retrofitted masonry
Figure 9 shows the out-of-plane strength with vertical deformation
for URM and P2-F. URM showed no residual strength after the specimen
with compressive strength of 0.34kN made the first cracking. On the other
hand, P2-F has connected bricks and produced the residual strength after its
initial crack has occurred on mortar joints. SG-2000 had been produced thespecimens residual strength until the SG-2000 itself was torn-off. Even after
stopping producing residual strength, SG-2000 barely connected bricks.
From the failure pattern and the graph, it has been shown that SG-2000 does
not increase the masonry structure’s initial strength, but improvesdeformation capacity.

Figure 9: Out-of-plane load-deformation curve

Experimental study
Experimental
study onon in-plane
in-plane andand out-of-plane
out-of-planebehavior
behaviorofofmasonry
masonrywallettes
wallettesretrofitted
retrofittedwith special fiber
reinforced
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Thailand5. CONCLUSIONS
SG-2000 retrofitting method proposed in this study is much easier to retrofit
than the previous retrofitting method. The following is the discussion of Tte
in-plane diagonal compression tests and out-of-plane tests that wereconducted using unreinforced wallettes (URM) and wallettes coated with
SG-2000.
The in-plane diagonal compression tests show that:(i) Retrofitted one showed no difference in initial stiffness, while theincrease of initial peak strength was shown. Furthermore, SG-2000
has changed its crack pattern from its initial crack, and generated
residual strength after the first crack had occurred only on mortar, andimproved the wallette’s deformation capacity.
(ii) The retrofitted ones showed approximately 3 times larger initialstrength, and produced 7 times larger residual strength than the initialstrength of URM. Also, the retrofitted ones showed about 40 times
larger deformation than URM.
The out-of-plane tests show that:(i) Retrofitted one showed no difference in initial stiffness, while theincrease of initial peak strength was shown. Furthermore, SG-2000
has generated residual strength after the first crack had occurred only
on mortar, and improved the wallette’s deformation capacity.
(ii) The retrofitted ones showed residual strength after the initial crack
occurred in mortar. Also, the retrofitted ones showed about 35 times
larger deformation than URM.
REFERENCES
Coburn, A., and Spence, R. 2002, Earthquake Protection, West
Sussex: John Wiley & Sons Ltd.
Yoshimura, M., and Meguro, K., 2004. Proposal of Retrofitting
Promotion System for Low Earthquake-Resistant Structures in Earthquake
Prone Countries. Proceedings on 13th World Conference on Earthquake
Engineering, Vancouver, Canada.
Amiraslanzadeh, R., Ikemoto, T., Miyajima, M., Fallahi, A., 2012. A
Comparative Study on Seismic Retrofitting Methods for Unreinforced
Masonry Brick Walls. Proceedings on 15th World Conference on
Earthquake Engineering, Lisboa, Portugal.
Yamamoto, K., Numada, M., Kimiro, M., 2014, Experimental Studyon Seismic Retrofitting of Masonry with Special Fiber Reinforced Paint.
Proceedings on 13th International Symposium on New Technologies for
Urban Safety of Mega Cities in Asia

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7thKNOWLEDGE BASED IT EVACUATIONFACILITY MANAGEMENT SYSTEM"COCOA"
1

Satoshi TAKATSU1 and Muneyoshi NUMADA2
Graduate Student, the University of Tokyo, Japans-taka@iis.u-tokyo.ac.jp
2
Lecturer, ICUS, Institute of Industrial Science,
the University of Tokyo, Japan

ABSTRACT
It was difficult to obtain and share the disaster information for theeffective initial responses during the 2011 Great East Japan Earthquake
disaster.
For examples, creating a refugee roster was difficult under theconditions of the concentration of a lot of evacuees. And, a lot of inefficient
operations to share the information such as the total number of evacuees ineach facilities and those personal information, the needs and demands ofrefugees, etc. were seen in damaged areas.
It is necessary to achieve effective and smooth operations by sharing
the information in the disaster headquarters and related administrations.
In this study, we have developed the IT evacuation facility system“COCOA” as one of information systems that can manage and support inaccordance with conditions of each evacuation facilities by sharing theinformation such as the needs and demands, the number of evacuees andthose personal information. “COCOA” can serve and collect the various
personal conditions and manage the information of all evacuation facilities.
“COCOA” can be used in many cases such as staying theevacuation facility, moving to the temporary house, condition ofreceived administrative services and etc.
For the next huge disaster, IT system such as “COCOA” canprovide the environments for sharing and managing the personal
information and conditions of each evacuation facilities for effective initial
responses.
Keywords: information sharing, administration of the person,
evacuation facility, refugee, development of IT system
2. INTRODUCTION

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It is very important that it is performed information sharing at the time of adisaster smoothly in performing management effectively.
For example, if Information sharing is practiced smoothly, it can doclarification and unification of the action, equalization of the supplies
supply, effective placement of people, and efficiency of the work, todecrease in mistake and what not. There will be many people saved about
these
Therefore I researched an information sharing method of theevacuation facility management in this study. It was difficult to obtain andshare the disaster information for the effective initial responses during the2011 Great East Japan Earthquake disaster.
The reason is no time to read manuals of the evacuation facility
management. (Originally manuals did not exist.) , the placement of avolunteer and the staff was difficult, a labor shortage, the setting of theevacuation facility took a long time, the supply and demand of goods did
not accord and the situation grasp of each facility is difficult because atransfer with the notebooks.
The new method that is unprecedented to improve these situation isnecessary. So, I recommend the introduction of the IT system to perform theinformation sharing of the evacuation facility smoothly.

2. THE IT SYSTEM TO MANAGEMENT A EVACUATIONFACILITY
2.1 IT system
The system which can prepare a thing for refuge beforehand isnecessary.
So, I recommended the introduction of the IT system to perform evacuation
facility management by using the lesson that I got by East Japan greatearthquake disaster smoothly. It can improve the regime not to understandthe situation if we do not go to the evacuation facility. And, it can be madeby effective and average supply by performing the management of people
and the management of supplies with a system. Furthermore, it can makeuse of a past lesson next. Therefore, it can lighten the burden on city officers
and refugees.
There are the following things in the IT evacuation facility system now.
 ORANGE（Ishinomaki City）- Safety confirmation system at the time of the disaster Victim support system（Nishinomiya City）

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-






System for local governments made after The Great
Hanshin Earthquake
IT support system (ISE Ltd.)- The system that the doctor of the evacuation facility andthe doctor who are in the distant place take thecommunication using video call application
Disaster needing people refuge support system (MJC Inc.)- Support system is that needing people evacuate smoothlyand safely at the time of disaster outbreak
Refuge guides of the whole country (first media Inc.)- Navigation application for disaster that we search the
refuge around the present location, and a route guides theroute

But, it cannot be said that these systems have utility. The reason is
1. From a problem such as a certain system having many input
items, and how to use being difficult, personnel is not a usable
thing now. (As for the public employee, a duty place changes inapproximately three years.)
2. There are many things which are not updated in nothing more if
it was made once. (The situation of the evacuation facility andthe environment of the circumference change.)
3. The administration of the person is possible, but the carefulmanagement of the evacuation facility is not possible. (It is toolate in the situation to think about an administration method after
going to each refuge.)
4. There is the system which does not include knowledge of theevacuation facility management and does not know it whether it
is utility, and we can really apply it at the time of a disaster.
So, a system with the utility that include knowledge of the disasterprevention and let you really reflect the opinion of a person concerned withthe management of the evacuation facility is necessary.
2.2 IT evacuation facility management system "COCOA"
2.2.1 What is “COCOA”
In this study, I developed evacuation facility management system"COCOA" to improve a current problem. The lower image is an illustrationof system constitution

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ThailandThe language that I used for this development is php and it isstructure exchanging the information at five tables. There are thegovernment and the page of the nation each. But, the ID of the evacuation
facility uses the same thing to attach correlation. The system is such that I
receive the highly confidential thing by a POST.
System in itself made anyone one that it was simple and was plain to make ausable system. For example, the item to tell on page 1 made one.
The data such as the date or the time were in the table automatically to omit
trouble of the input.
The lower image is a figure of image of the utilization.

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The user uses this system with a PC and a smart phone. It is
performed the information sharing through a database. The user can obtainthe latest information easily without moving from the place.
The headquarters can know the situation of the current evacuation facility(The number of the refugees, the demand of evacuation facility or necessary
supplies) quickly and can take measures.
The manager of the evacuation facility can input refugee information
quickly by bringing the information of the person in the database by ID or
the name. Then, administration and the management of the person arepromoted efficiency. And,The refugees can send a demand to a refuge manager and see abulletin board by a system. In this way of supplies are available surely, anda demand becomes easy to be reflected. The manager of the evacuation
facility can do the smooth information sharing with the headquarters and therefugee by using the note of a bulletin board and the demand.
It can do the measures before the earthquake disaster are enabled byentering the facilities information (map) of the evacuation facilities andmeasures based on experience and it which are past into a system.

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AugustAugust2015,
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Thailand2.2.2 Real screen of “COCOA”
The lower 3 images are real screen of “COCOA”.

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A basic A
screenbasicisscreenthese is3 screens.
these 3 The
screens.
userTheusesuserthe uses
top page
the top page
dependingdependingon each viewpoint.
on each viewpoint.
Because the
Becausebasicsthearebasics
simpleareandsimpleare made,and areeach
made,itemeachbecomes
item able
becomes able
to look bytodouble
look byclick.
double click.
Items in Items
top pagein topof headquarters
page of headquarters
are Addition
are Additionof the evacuationof the evacuation
facility, Deletion
facility, ofDeletion
the evacuationof the evacuation
facility, The
facility,
number
Theofnumber
currentofrefugees,
current refugees,
The latestThedemandlatestfrom
demandan evacuationfrom an evacuation
facility andfacility
Evacuationand Evacuation
facility name.
facility name.
Items in top
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page
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topanpageevacuationof an evacuation
facility are
facility
Evacuation
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facility name,facility
Thename, The
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Input of Input
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bulletin
to a bulletinboard, Watchingboard, Watching
the demand
the from
demanda refugee
from aandrefugeeCommunication
and Communication
matter to matter tothe disastertheheadquarters.
disaster headquarters.
Items in Items
top pagein topof pagerefugeesof refugees
are Evacuation
are Evacuation
facility name,facility name,WatchingWatchinga bulletin aboard
bulletinandboard
Inputandof theInput
demandof thetodemandan evacuation
to an evacuation
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on. now on.

facility management
management
system"COCOA""COCOA"
KnowledgeKnowledge
based IT evacuation
facility management
system "COCOA"
based IT evacuation
facility
system247

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AugustAugust2015,
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Thailand2.2.3 Effect of “COCOA”
The lower 2 images are things which is necessary for evacuation
facility management. Red letter is it can manage or can anticipate
improvement by using “COCOA”.

It cannot cover all when you watch an image. But, can cover about
the 80% of the things which is necessary for evacuation facility
management in COCOA.
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The lower image is the calculation result of system effect.

I suppose that it takes refugee alone for 2 minutes a day tocorrespond. And, I assigned Grasp and correspondence of the refugee
information, Management and administration of the evacuation facility andDistribution of supplies each with by 40 seconds.
I can anticipate the improvement of 3 items (Grasp andcorrespondence of the refugee information, Management and administrationof the evacuation facility and Distribution of supplies) by using cocoa.
When I think that there are many parts which a person must manage
directly, I suppose the time that evacuation facility management takes
becomes about half by using “COCOA”.

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AugustAugust2015,
2015,Thailand
ThailandThe lower image is the graph of calculation result.

I suppose ten refugees to correspond with one staff. When we lookin three items (The Great Hanshin Earthquake, The Great East Japan
Earthquake, The capital directly under earthquake (supposition)), there is
the effect of the cocoa by all earthquakes. And, there is it in 1/2 with themean number of the necessary staff in evacuation facility management (evil- two months) per day.
When we look the limit number of public employees whom we candispatch, we understand that the number of staffs is still insufficient.
So, it is revealed that it must make up for a labor shortage by
volunteers.
But, “COCOA” can improve the current situation. And, the situation thatmust use the IT system even immediately includes it.

3. CONCLUSION
It is necessary to achieve smooth information sharing at the time of thedisaster. When I have researched to refuge administration, there are thesudden things, and information sharing did not go well by the East Japan
great earthquake disaster.

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So, I recommend the information sharing method by the IT. But, acertain system cannot cover all important points of the refuge administration
by it now. Therefore, I have developed knowledge based IT evacuation
facility management system "COCOA". Using it to be able to share theinformation more smoothly. This system are characterized by a humanbeing studying disaster prevention is concerned from the development of the
program. So, we can develop a system with the utility.
But, the development such as the new systems of the volunteer isnecessary because we cannot manage all "only COCOA". So, we want todevelop such a system too.
In the end, the coming times need management of the information by
the IT as a new way.
REFERENCES
Ishinomaki ward office, 2011. Facility management document, City
officer, Ishinomaki, Japan.
Ishinomaki City General Affairs Department Crisis Measures
Section, ORANGE, Ishinomaki City, Japan.
Nishinomiya City Center for Information Technology, 1997, Victim
support system, Nishinomiya City, Japan.
ISE Ltd., Effective working papers of the IT system in the refuges at
the time of the disaster, ISE Ltd, Japan.
MJC Inc., 2015, Disaster needing people refuge support system,
MJC Inc., Japan.
President-director Kaichi Yamazaki, 2015, It supports Apple Watch!
Disaster prevention information "whole country refuge guide" Ver.5.3, first
media Inc., Japan.
Cabinet Office, 2011, about a change of the number of the refuge
dwellers of the East Japan great earthquake disaster, Japan.
Orient economic online editorial department, 2014, Latest "public
employee yearly income ranking" top 500, Japan.
Local public entity capacity management meeting for the study,
2012, a present conditions, analyses sheet such as the number of the staffs
by the reference index, Japan

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Student ReportImpressions from the Student Participants
The
Joint
Seminaron
Civil
Infrastructures was the first international
conference in my life. This seminar gave
me a precious opportunity to discusswith people who have different
background.
In this seminar, I experienced and leaned
many things that I had not done before
going and taking this seminar. And, thecommunication with students who camefrom different countries was valuable for me and increased my motivation
to learn. I hope to proceed with my research based on the things which
I obtain in this seminar. Lastly, I want to express my gratitude to people
related to this seminar. (by Koki Satsukawa)
The Student Seminar was the firstinternational conference in my life that I
participated in as a presenter. I learned
how difficult it was to tell one's thing that
I wanted to convey to audience by thisseminar clearly briefly. It was different in
how to make and how to convey
PowerPoint and watched it by thedifference of a country and the school
and was very interesting for me. It wasvery stimulating for myself with a little
overseas experience that I was able to give presentation while learning thecharacteristic of the country in Thailand. And, what was able to interchangewith a student of Thai and Korea which could not usually interchange wasvery valuable and I was glad. I’m looking forward to joining Student
Seminar again. (by Satoshi Takatsu)
Thisseminarwasthesecondinternational conference for me, after
USMCA 2014. The place we havepresented was not as large as that of
USMCA, and the amount of participants
was very small. Therefore, I think thisseminar was the best place for students
who have joined international conference
for the first time to practice having apresentation there. Also, it was niceplace to become friends with students from other countries. For me, thisexperience was vital for the next USMCA 2015, held in Nepal. (by Kenjiro
Yamamoto)

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AugustAugust2015,
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ThailandThis was the first time for me to joinsuch an international conference.
Making presentation in English is a little
tough for me, but this seminar was avery good chance to practice it. Throughseminar, my professor and my senior
students advised me about my research,and I had some chances to talk withforeign students from Thailand, Korea,
and Laos. From them, I learned not only
how to research and how to express thestudy but also foreign cultures and values. My research is on the way now,
so I’ll try to brush it up to achieve better results. Unfortunately, we couldn’t
join the field trip, but I had a very good time there. I appreciate the effort
and work by everyone who organized and supported this great seminar.
(by Riko Sakata)
Participating in the Student Seminar
held at the Chulalongkorn University
was a great opportunity to share myresearch to other students and topractice my presentation skills. Throughpresenting to an audience from a wide
variety of fields, I was able to learn how
to clearly state the contents of myresearch in an easy to understand flow.
It was also an opportunity to learn
research from a different department;
being from the urban engineering department, it was especially interestingfor me to learn about researches related to civil engineering. I was also able
to receive valuable advice from other professors, enabling me to further
improve my research. Unfortunately, due to unexpected events, we Japanese
students were not able to be part of the tour. Yet, it was a valuable
experience visiting Thailand and comparing the different layout of the city
between Japan and Thailand. (by Yuto Tokumitsu)
I am honored to be able to participate
The 7th International Joint Student
Seminar on Civil Infrastructure in
Bangkok, Thailand. It was the first time
I have been and I could have many
precious experiences there. All ofpresentations gave me so many stimulus
and I was happy that I was able toexchange with participants. Needless tosay, there is a sense to continue like thisseminar, so I hope the seminar will beannual event. (by Yuki Munemasa)

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Attending the join student seminar on
Civil Infrastructure in Bangkok,
Thailand is a great opportunity for me topropose my research and get feedbackfor improving my research. In thisseminar, there are various kinds ofresearches related to civil works. Since
participants with students and professors
were gathered from many countries andmany backgrounds as well, it was very
useful for researchers who areinteresting to know other researches including problems and solutions, andexchange the ideals. In this opportunity, I would like to sincerely thankorganizers and attendees to meet successful seminar among the unexpected
accident in Bangkok. Also I would like to thank all committees to give mefor excellent presentation award. Hopefully, this activity will be
continuously held as a great chance for assembling among students andprofessors. (by Tanakorn Sritarapipat)
Firstly, I would like to express mygratitude to everyone who are involvedin this seminar. Thanks to thoseconcerned, the seminar was finishedsuccessfully in spite of the bad security
due to bomb explosion. It was precious
experience for me to make an English
presentation because I’ve never tried
before. Through this seminar I was able
to get to know and learn a lot of things,
not only how to prepare forinternational conferences, but also various kinds of fields in civilengineering, or the culture like food traditionally eaten in Thailand. In
addition, communication with other students motivated me to study more,
and made me think that I’d like to be the one who can make good impact onothers. I would like to join this seminar again if I have chance, and hopenext seminar will be held in a safe place. Again, thank you for all to manageand organize this great seminar. (by Koji Tanaka)
The Joint Student Seminar on Civil
Infrastructures provided me with agood opportunity to share my research
works
and
practicemakingpresentations. The other presentations
were from a wide variety of fields,
which gave me the chance to learn
about different areas in civilengineering. I was fortunate to have theopportunity to discuss with Korean andJapanese
professorsabout
mypresentation, also is a part of my thesis, and received many useful advices.

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AugustAugust2015,
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ThailandThanks to several discussions at this seminar, I again realize the importanceof communication with researchers. Lastly, I would like to express myprofound gratitude and appreciation to the professors and students from theother universities as well as the staff related to this seminar.
(by Cao Nguyên Thi)
The 7th Joint Student Seminar on Civil
Infrastructures was held at the Sasa
international house, Chulalongkorn
University, Thailand on the 19th ofAugust, 2015. It gave me a good
opportunity and new experience in theseminar stage. In this seminar, I waspresenter and shared my research
project to the other presenters. All
presenters had difference of interested
topic and I got a lot of new knowledge
from them. In addition, I have known many kindly friends and we talked
about changing our culture. Finally, I am so thank you to the seminar toorganize the good seminar like this. I am looking forward to join thisseminar again. (by Noulananh Lathsolulin)
I had a great time when I attended the
Joint Student Seminar on Civil
Infrastructures held at the Sasa
international house in Thailand. I had
my presentation in English in front ofmany foreigners for the first time andalso I was able to listen to various
presentations of other foreign student.
Until I am bachelor now, but it was
great experience to me. Maybe thisexperience can be make me morestrong. And, I want to thank all of people attended this seminar for giving
me a chance to have wonderful memories in Thailand. I hope this Joint
Student Seminar will be activated and held continuously. (by Jinwoo
PARK)
This is my first international seminarand first visit to first presentation topeople who belong to otheruniversities. The topic of mypresentation was Site Selection of
Potential
Railway
RouteforSupporting Dawei Deep-Sea Port.
Case study : tambon Ban Kao Amphoe
Mueang of Kanchanaburi Province inThailand. I learned that the mostimportant thing when presenting is to make listeners understand what you

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want to say. I want to make good use of these experiences and keep
studying. I wish to express my gratitude to those people related to thisseminar. I got a lot of stimulation from this seminar and would like to use
this experience towards my future activities. (by Rattanawadee
Dutsadeesuntornsakun)

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7th Joint Student Seminar on Civil Infrastructures
Venue: Sasa International House (Room: Dipak C. Jain), Chulalongkorn University
Date: 19-20 August 2015
Time
08.00-08.30
08.30-08.40
08.40-09.00
09.30-10.00
10.00-10.30
10.30-11.00

11.00-11.15
11.15-11.30
11.30-11.45
11.45-12.00

13.00-13.15
13.15-13.30
13.30-13.45
13.45-14.00
14.00-14.15
14.15-14.30
14.30-14.45
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Topic

Page 1Name of speaker
Dr. Yudai Honma, ICUS

Registration
Opening remarks
Group photo & Coffee break
Invited Professor Section(chair: Dr. Hyunmyung Kim, Myong Ji University)
Introduction to mathematical analysis for nextKeynote Japangeneration vehicle
Dr. Yudai Honma
Water leak prevention at connections of precast
Keynote Thai
concrete panels with butyl rubber
Dr. Withit Pansuk
Near-real time meteorological drought monitoring andKeynote Japan
early warning system for croplands in Asia
Dr. Wataru Takeuchi
Student Presentation 1: Civil Engineering Section(chairs: Dr. Yudai Honma, The University of Tokyo)
Building classification in urban area in Yangon,
Tanakorn SRITARAPIPAT
Myanmar using stereo high resolution images and(Japan, UTokyo)night time light data
Experiment and simulation study on flexural behavior
Nguyen Thi CAOof reinforced concrete slab before and after fire
(Thai, CU)
The key index for rational bridge maintenance by
Riko SAKATA
municipalities in Japan(Japan, UTokyo)
Effect of free LIME content on mechanical properties
Adnan Nawazof cement-fly ash mixtures(Thai, TU)
Lunch
Student Presentation 2: Transportation Section(chairs: Dr. Wonho Suh, Hanyang University)
The relationship between the rainfall intensity and the
Kyoung-seon PARK
subway passenger ridership in the Seoul metropolitan(Korea, Myong-Ji U)
subway system
Factors affecting the severity of motorcycles accidents
Santosh BARAL
and casualties in Thailand by using probit and logit(Thai, AIT)
model
Optimum assignment of housings and jobs with
Yuki MUNEMASA
constrained capacity considering discomfort and travel(Japan, UTokyo)
costs
Factors influencing red light running behavior: a study
Auearree JENSUPAKARN
of socio-economic characteristics and geometry of(Thai, AIT)intersections
The optimum traffic volume survey locations for the
Seong-min KANG
travel demand analysis in Korea(Korea, Myong-Ji U)
Site selection of potential railway route for
Rattanawadee
supportingdawei deep-sea port. Case study : Tambon
DUTSADEESUNTORNSAKUNBan Kao Amphoe mueang of Kanchanaburi province(Thai, SWU)
Numerical evaluation of road disconnection risk
Yuto TOKUMITSU
focusing on road collapsing after a large earthquake in(Japan, UTokyo)
Chigasaki city, Kanagawa
Image processing techniques for vehicles andJinwoo PARK
pedestrian safety evaluation(Korea, Hanyang U)
Coffee Break

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Venue: Sasa International House (Room: Dipak C. Jain), Chulalongkorn University
Date: 19-20 August 2015

Page 2Student Presentation 3: Technical Section(chairs: Dr. Wataru Takeuchi, The University of Tokyo)
15.30-15.45
Ageing effect induced by particle movement
Koji TANAKA(Japan, UTokyo)
15.45-16.00
An analysis of network throughputs for different
Koki SATSUKAWA
origin-destination patterns(Japan, UTokyo)
16.00-16.15
Water retainability and basic properties of coal bottom
Noulananh LATHSOULIN
ash used as fine aggregate replacing material(Thai, TU)
16.15-16.30
Experimental study on in-plane and out-of-plane
Kenjiro YAMAMOTO
behavior of masonry wallettes retrofitted with special(Japan, UTokyo)
fiber reinforced paint16.30-16.45
Knowledge based IT evacuation facility management
Satoshi TAKATSU
system "COCOA"(Japan, UTokyo)
16.45-17.15
Review comment
Closing remarks
18.00
Dinner at Sasa International House,
Presentation Award Excellence Presenter Ceremony
Thai 6 speakers (AIT 2, Chula 1, Thammasart 2, Srinakharinwirot 1)
Korea 3 speakers (Myong Ji Univ. 2, HanYang Univ 1)
Japan 8 speakers (University of Tokyo 8)
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REPORT FROM STUDENT PARTICIPATION ON
7th JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURE
DATE: August 19, 2015
The 7th Joint Student Seminar on Civil Infrastructure was scheduled to beconsisted of an in-house seminar and a tour of several sites, but because of
unexpected events, this year’s tour was unfortunately cancelled. The in-house
Seminar was held at Dipak C. Jain Room, SASA International House,
Chulalongkorn University, on August 19, 2015. The seminar was mainly divided
into 3 parts; the lectures by invited professors, student presentations, and dinnercombined with the Presentation Award Excellent Presenter Ceremony.

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PRESENTATION SESSIONOPENING REMARKS
Before the lectures by the invited professors, Dr. Yudai Honma from the
University of Tokyo gave the opening remarks to start up the seminar. He firstexpressed his gratitude towards all who were involved in arranging, and thenoffered his special thanks to each of the other professors. He then went on toemphasize that the seminar was an opportunity to share research and help eachother, which will improve each of the participants’ works. He ended his remarksby also mentioning that becoming accompanied with other students was asimportant.
INVITED PROFESSORS
Three lectures were given in the invited professors section, chaired by Dr.
Hyunmyung Kim Myon from the Ji University.
First, Dr. Yudai Honma from the University of Tokyo gave his lecture titled“Introduction to mathematical analysis for next-generation vehicle”. Focusing on
fuel-cell powered cars as the next-generation vehicle, he introduced a method toefficiently plan the number of hydrogen stations and gasoline-hydrogen combined
stations to smoothly shift from using conventional cars to using fuel-cell powered
cars.
Next, Dr. Withit Pansuk from the Chulalongkorn University gave his lecture on“Water leak prevention at connections of precast concrete panels with butyl
rubber”. After explaining the issue on the difficulty of connecting rubber toconcrete and the seeping of water into the cracks between the materials, heintroduced butyl rubber, a material easy to supply which chemically bonds toconcrete creating a strong connection, as a possible solution.
Last, Dr. Wataru Takeuchi from the University of Tokyo lectured on “Near-realtime meteorological drought monitoring and early warning system for croplands
in Asia”. He lectured that by using remote sensing technology, meteorologicalpatterns can be monitored to estimate oncoming draughts and issue warnings
beforehand.
STUDENT PRESENTATIONS
15 speakers from Chulalongkorn University (Thai), Thammasart University (Thai),
Srinakharinwirot University (Thai), Myonig Ji University (Korea), Han Yang
University (Korea), and the University of Tokyo (Japan) presented and shared on
various topics. Presentations were divided in to the Civil Engineering Section, theReport from Student Participation on 7th Joint Student
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Transportation Section, and the Technical Section, each chaired by Dr. Yudai
Honma, Dr. Wonho Suh, and Dr. Wataru Takeuchi, respectively.
CLOSING REMARKS
To close the seminar, closing remarks were given by Dr. Hyunmyung Kim from
the Myong Ji University. After explaining the history of the joint seminar, hecommented that the seminar has increasingly become better and better by eachseminar, and that the student presentations were well prepared and well presented.
He also commented to take the experience and use it to further develop academic
and international abilities.
PRESENTATION AWARD CEREMONY
In the evening after the seminar, a dinner banquet was held in which students
further discussed their research as well as becoming acquainted with one another.
During the banquet, 3 were announced and awarded with the Excellent
Presentation Award with the professors as judges. The presenters were chosen
according to criterion such as research quality, presentation abilities, and question
answering. The following students were awarded with the Excellent Presentation
Awards.
1. Mr. Yuto Tokumitsu, University of Tokyo (Numerical evaluation of road
disconnection risk focusing on road collapsing after a large earthquake in
Chigasaki city, Kanagawa)
2. Mr. Tanakorn Sritarapipat, University of Tokyo (Building classification in urban
area in Yangon, Myanmar using stereo high resolution images and night time lightdata)
3. Mr. Yuki Munemasa, University of Tokyo (Optimum assignment of housingsand jobs with constrained capacity considering discomfort and travel costs)

Excellent Presenters: (from left to right) Yuto Tokumitsu, Tanakorn Sritarapipat,
Yuki Munemasa

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