ISSN 1880-8468
Technical Report ofInternational Development Engineering
国際開発工学研究報告

TRIDE-2023-01

March 31, 2023

Abstracts of Master Theses Presented
from September 2016 to March 2019

Department of International Development Engineering,
Graduate School of Science and Engineering,Tokyo Institute of Technology
http://www.ide.titech.ac.jp/TR

Preface
This technical report consists of the abstracts of Master theses presented in the
Department of International Development Engineering, Tokyo Institute of Technology,
on August 2, 2016, October 19, 2016, February 14, 2017, Augusut 1, 2017, February 15,
2018, and February 14, 2019.

i

Technical Report of International Development Engineering
TRIDE-2023-01
Table of Contents(Completeng in September 2016)
Accelerated Composting by Inoculation of Acid Degrading Microorganism………………………………………………………………………….. Hapsari PUSPITALOKA 1(Completeng in December 2016)
Effect of Land Use on Crime Types in Dhaka, Bangladesh………………………………………...………………….… Mashroof Tahsin Mashfi HOSSAIN

5(Completeng in March 2017)
Competitive Dynamics among Cross Regional Hub Ports for the Container Transshipments: Case
Study for the Port of Colombo……...…………………………………………………………..…… Chathumi KAVIRATHNA

9

Impact of High-Speed Rail Entry on Airport Choice Behavior in China……………………….……………………………………….…………...…… Jiawen JIANG 13
Influence of Defects on Steel Corrosion of Reinforced Concrete Exposed to Marine Environment forLong-Term…………………………………………………………………………..… Daisuke MIZUSHIRI 17
Study on Cement-Free Concrete using Fly Ash……..…………………………………………………………………………. Hikaru TANAKA

21

A Study on Residential Relocation Focusing on Life Stage Changes for Compact Cities………………………………………………………………………...…… Kosuke KIRIYAMA

25

Clarification of Supply Chain in Tokyo Metropolitan Area……………………………………………………………………………. Yutaro WATANABE 29
Characteristic of Non-Specular Scattering on Outside Surface of Buildings at 25 GHz………………………………………………………………………………………….. Hao LIU 33
Web Applications for Multilingual Semi-Machine Translation Based on Collective Intelligence…………………………………………………………………………………... Wuning LI 37
Wavelet Based Image Coding via Linear Prediction and Improved Clustering……….……………………………………………………………………………... Dan WANG 41
Regression Based Image Super Resolution through Decision Tree Learning…………………………………………………………………………………….. Zhengqi ZHU 45ii

Energy Dissipation in Adhesion Contact between an Elastic Beam and a Flat Rigid Surface…………………………………………………………………………..…. Takeru KUSAKARI 49
A Condition-Based Maintenance Policy for Electric Vehicles (EVs)……………………………………………………………………………………... Toshiro ABE 53
A Study of Leasing Scheme and its Perception for the Diffusion of Residential Electricity Storage
System……………………………………………………………………………………. Junya ISHIDA 57
Estimation of Environmental and Economic Impacts Induced by the Promotion of Accommodation
Sharing Services Based on Input-Output Table……………………………………………………………………………….. Yosuke SOEJIMA 61
Effects of ICT Based Urban Mobility as a Case of Collaborative Economy in Indonesia………………………………………………………………..…………………. Benny LEONO 65
Current Status of Subsidized Taxi Operations by NPOs in Tokyo for the Elderly and Disabled
Mobility………………….……………………………………….…………… Kazuhiko MATSUMURA

69

Factors Affecting Mongolian Primary School Leaders' Attitudes toward ICT in Education…………………………………………………………………………………….Yusuke OHYA 73
Application of Drone for Riverbank Landscape Monitoring in the World Heritage Site of Luang
Prabang, LAO P.D.R.
……………………………………………………………………………………… Hironari YU 77
Estimating Near-Surface Wind Field by Aerial Thermal Image Velocimetry……………………………………………………………………………. Motoyuki HIJIKATA 81
Projection of High Tide Inundation under Rapid Land Subsidence and Sea Level Rise
―Effectiveness and Limitation of Coastal Dykes in Jakarta………………………………………………………………………………….… Daisuke FUJII 85
Photocatalytic Degradation of Methylene Blue using TiO2 Supported on Natural Zeolite andActivated Carbon…………………………………………………………………...…… Takahiro TAKAMATSU 89
Estimation of CO2 Emission from Chinese Manufacturing Industry using Hybrid Method……………………………………………………………………………...……. Jianxin GUAN 93
Production of High Concentration Bioethanol from Cassava Stem………………………………………………………….…………………. Kazumasa TANAKA

97

Liquid-Liquid Equilibrium in Concentration and Purification of Bioethanol by Solvent Extraction...……………………………………………………………………...……. Tomonori MASUDA 101
Forecasting Extreme Storm Surges in Manila Bay - an Adverse Combination of Unusual Tropical
Cyclone Tracks and Southwest Monsooniii

…………………………………………………………………………………….. Jerome SILLA 105
Utilization of Indonesia Natural Bentonite for Dye Removal……………………………………...………………………... Harish Reza Septiano WARSONO 109(Completeng in September 2017)
Synthesis of N-Doped Mesoporous TiO2 by Facile One-Step Solvothermal Process for Visible Light
Photocatalysis………………………………………………………………………...… Erlandy Dwinanto TOE 113
Development of GPSDO-Synchronized Channel Sounder using Software Defined Radio…………………………………………………………………………………... Gautam Deepak 117
Estimation of National Logistics Cost using Macro Economic Data……………………………………………………………………………………. Shidong FENG 121
Passenger’s Airport and Airline Choice in a Multiple Airport Region: The Case of Greater Jakarta……………………………………………………………………….... Fajar Fauzie RAKHMAN 125
Operational Sustainability of Rural Electrification Program through an Effective Fee Collection
Strategy: Case Study of the Philippines…………………………………………………………………………... Keshav Raj POKHREL 129
Adsorption Equilibrium of Metal Cations of Acid Mine Drainage Treatment using Indonesian
Natural Zeolite……………………………………………...…………………………... Hironobu HASEGAWA 133(Completeng in March 2018)
A Mars Rolling-Rover Integrated with Entry, Descent and Landing System…………………………………………………………….……………..… Masaaki ICHIMURA 137
Development of a Multi-Beam Structure by Stacking Comb-Shaped Rubbers…………………………………………………………………………….. Mikhail SALYUKOV 141
A Huge and High Resolution Large Eddy Simulation Coupling with Energy Balance Model in Urban
Area……………………………………………………………………………….. Takafumi SUEISHI 145(Completeng in March 2019)
Studies on Human Motion Recognition through Wireless Sensing with Communication Devices…………………………………………………………………………………...… Haitao PANG 149

iv

ACCELERATED COMPOSTING BY INOCULATIONOF ACID DEGRADING MICROORGANISM
Student Number: 14M51687 Name: Hapsari PUSPITALOKA Supervisor: Kiyohiko NAKASAKI
酸分解微生物の接種による高速コンポスト化
In this study, the effect of inoculating thermophilic acid degrading bacteria in composting was investigated inorder to accelerate the composting by overcoming the low pH problem. Base on the detailed analyses of theprevious work of accelerated composting by Pichia kudriavzevii RB1, three candidates of thermophilic
bacteria which has potential to degrade organic acids were found, namely Bacillus coagulans, Geobacillus
thermantarcticus and Bacillus subterraneus. From this three candidates, one of the bacteria Bacillus
coagulans IP1 was successfully isolated. Solid culture fermentation of IP1 exhibited that IP1 degraded acetic
acid, also they produced acetic acid. IP1 was also found to be able to degrade the organic acids in thecomposting. To conclude, it was suggested that inoculation of IP1 can accelerate the composting during
thermophilic condition by the vigorous organic acid degradation.
This research investigated a composting run(Run 3A) from previous study with inoculation of
RB1 and controlling temperature (at 40 °C for 1 day)[4]. The rabbit food and rice (ratio 7:3) with theadditional of organic acids were used as arepresentative model of food waste. This experiment
was conducted in bench scale reactor (300 mm x 400
mm).

1. Introduction
Composting is one of best method to recycle themunicipal solid waste due to its eco-compatibility
and easy operational procedures. Food waste is afraction of the organic waste which can be recycled
as compost for the agricultural purpose [1]. One of
the common problems in food waste composting is
the low pH [2]. Accumulation of organic acids in thefood waste prior to composting has contributed to theacidic condition of the raw material. Furthermore,
the organic acids concentration increases during theinitial phase of composting, with the degradation oforganic matter [2,3]. The low pH condition of raw
material can lead to the failure of composting.
A Previous study found that by the inoculationof Pichia kudriavzevii RB1 (a mesophilic yeast)
into the raw material and temperature controlling,
the composting can be accelerated [4]. RB1 wasactive and degrade organic acid in mesophilic
condition. However, with the temperature rise, RB1
died off. Interestingly, degradation of organic acidwas observed at the later thermophilic period,
although RB1 is absent. Therefore, it could be
hypothesized there were other bacteria can degrade
the organic acid in the composting and this bacteria
can tolerate to thermophilic and acidic condition. In
this study, the aim is to find out the acid degrading
bacteria which can tolerate to thermophilic condition
and investigate the effect of inoculating the isolatedacid degrading bacteria in the food waste composting,in order to accelerate the composting of acidified
food waste.

Temperature (ºC)

80
70
60
50
40
30
20
12
Log cell density(CFU/g-ds)

11
10
9
8
MF
MB
TB

7
6
5

Conc. of organic acids(mg/g-ds)

4
28
AA
LA
PA
BA

24
20
16
12
8
4
0
0

2

4

Time (d)6810Fig. 1 Time course of temperature, cell density andorganic acids concentration in Run 3A [4]. In Log
cell density graph MF, MB and TB representative
mesophilic fungi, mesophilic bacteria andthermophilic bacteria, respectively. In conc. oforganic acids graphs AA, LA, PA and BA
representative acetic acid, lactic acid, propionic acidand butyric acid, respectively.

2. Material and methods
2.1 Proliferation of acid degrading
microorganism in accelerated composting1The detailed analyses of accelerated composting wasstarted by observed the physicochemical factor ofcomposting. Fig. 1 shows the time course of
temperature, cell density and organic acids
concentration from previous study. When thetemperature reached to 40 ºC by self-heating it wasmaintained for one day. After that, the temperature
increased until 60 ºC and RB1 was not detectedduring this period. Meanwhile, the concentration ofacetic acid started to increase from day 2 and reached
its peak on day 6. The concentration of acetic acidwas then decreased and diminished on day 10,
though RB1 died off during this period. Therefore,
we suggest that there were bacteria which actively
degraded the organic acids during the thermophilic
stage of composting. PCR-DGGE analyses ofaccelerated composting by Pichia kudriavzevii RB1
was then observed in this study. The analyses wasstarted by DNA extraction of the compost samples.
PCR amplification targeting the 16S rRNA generegion was then conducted using the extracted DNA
as template. Afterwards, PCR product was subjected
to Denaturing Gradient Gel Electrophoresis (DGGE).
Using the DGGE image as a reference, the bands ofinterest were excised from the gel and DNA
sequence was determined. The sequencing results
was then compared to the 16s rRNA genes available
in GenBank databases.

solid culture was cultivated with temperature at
50 °C until three days. The compost samples were
measured for pH, concentration of organic acids andcell density of IP1.

2.2 The ability of Bacillus coagulans IP1 indegrading organic acids in solid culture
fermentation

3. Results & Discussions

2.3 Effect of inoculating Bacillus coagulans IP1
towards thermophilic composting
The mini reactor which was used in thisexperiments similar to the solid culture fermentation
experiment (Section 2.2). About 15 gram of rabbit
food, rice (ratio 7:3), seeding material (Alles GTM)
and saw dust (the bulking agent) were mixed, themixture of rabbit food and rice was represent asimulated food waste in the composting experiment.
Four kinds of organic acids (acetic acid, lactic acid,propionic acid and butyric acid) were added to raw
compost mixture. The moisture content of mixed
composting material was maintained around 60%
throughout the experiment. Two experiment runs
were conducted, namely Run 5A and 5B. Run 5A
was inoculated with IP1, meanwhile in Run 5B(control) the composting was started without theinoculation of IP1. For both Run 5A and 5B, theincubator was kept at 50 °C throughout thecomposting process (10 days). The compostingmaterials in mini reactor were mixed daily. The
compost samples were measured for pH,
concentration of organic acids and cell density ofthermophilic bacteria.

3.1
Proliferationofacid
degradingmicroorganism in accelerated composting1

A thermophilic bacteria was isolated from Run
3A compost sample (day 10), namely as Bacillus
coagulans IP1. The ability of this bacteria indegrading organic acids were tested in solid culture
fermentation experiments. The experiments werecarried out in a mini-reactor comprised of a Pyrex
glass cylinder (diameter: 45 mm, depth: 100 mm).
The mini reactor consisted 15 gram of the mixture of
rabbit food and sawdust (as bulking agent). Prior to
the inoculation of IP1, the solid substrate wassterilized by autoclaving, then add with filter
sterilized of acetic acid with the concentration of 3
mg/g-ds and inoculum of IP1. Throughout thecultivation period, the reactor was shaken daily, theaeration rate was fixed at 5.5 mL/min and moisture
content was maintained around 60%. A 10 L
polyvinyl fluoride plastic bag was used for collection
the exhaust gas. The plastic bag was changed once
daily, and CO2 concentration in the exhaust gas wasmeasured using Kitagawa gas detector tubes. The

3

4

6

8 10

3
2
1

Fig. 2 The dynamic changes of bacterial community
in DGGE patterns in Run 3A. The number over eachlane shows the age in days after the start ofcomposting. These bands represented by number
were excised and sequenced.

2

Fig. 2 shows the dynamic changes of bacterial
community in DGGE patterns in Run 3A. From the
DGGE image results, three bands were suggested
have ability in degrading the organic acids during
thermophilic condition. Band 1, band 2 and band 3
appeared on or after 3 days of composting, whereas
during this period the temperature of composting
started to increase to 60 ºC. The concentration ofacetic acid at this period increased from day 3 to day
6 then decreased after reached its peak on day 6. The
sequence of band 2 and band 3 were closely related
to Bacillus subterraneus and Geobacillus
thermantarcticus. Interestingly, the sequence band 1
which was closely related to Bacillus coagulans
might have ability to produce as well as to degrade
the acetic acid since this bacteria appeared during theperiod when acetic acid concentration increased anddecreased.

stage of fermentation the concentration of acetic
acid, propionic acid and butyric acid were around 3.7
mg/g-ds, 1.58 mg/g-ds and 0.96 mg/g-ds,
respectively. Meanwhile, lactic acid was not detectedin the initial condition of culture fermentation. After
one day of fermentation, the concentration of acetic
acid increased until 7.5 mg/g-ds. Moreover, pH after
one day fermentation also decreased from pH 5.5 to
5.2. From this results it seems that the IP1 could
produce the acetic acid. However, after 2 days offermentation, the organic acids including the acetic
acid were totally degraded. The pH also increased toalmost neutral condition at the end of fermentation.
In conclusion, the IP1 has ability to produce theacetic acid and degrade the organic acids. Therefore,
the IP1 has the potential to be used as inoculum incomposting in order to overcome the acidic condition
in food waste composting.
3.3 Effect of inoculating Bacillus coagulans IP1
towards thermophilic composting

3.2 The ability of Bacillus coagulans IP1 indegrading organic acids in solid culture
fermentation

Fig. 4 shows time course of CO2 evolution rate,organic acids concentration, pH and concentration ofcell density of thermophilic bacteria in Run 5A and
5B. The CO2 evolution rate in Run 5A (with IP1
inoculation) kept increase until reached its peakvalue at 16.1 x 10-3 mol/day on day 4 of composting.
Afterwards, the CO2 evolution rate in Run 5A
decreased until the end of composting process. In
Run 5B (without IP1 inoculation), there wereincrement of CO2 evolution rate until day 4 ofcomposting. However, the increment of CO2
evolution rate in Run 5B was only until 0.7 x 10-3
mol/day. For the concentration of organic acids in
Run 5A, the acetic acid increased at day 1 ofcomposting yet all the organic acids were totallydegraded at day 2 of composting. Meanwhile, theorganic acids for the control run (Run 5B) were nottotally degraded. Especially for the concentration ofacetic acid, it became higher until reached to 18.85
mg/g-ds. Furthermore, the pH for Run 5A increased
after 2 days of composting time, and finally the pHreached to pH 8.7 at the end of composting time. On
the contrary, the pH in Run 5B could not elevate toneutral condition. From the results above, it was
concluded that the composting in Run 5A succeeded
while Run 5B failed. The successful of Run 5A wasmarked by rapid of organic matter degradation
during the composting and the pH reached until
alkaline condition at the end of composting. In Run
5A, IP1 can degrade the accumulation of organic
acids in the composting. Thus, the condition incomposting
becomeneutralandothermicroorganisms can grow. Finally, the composting
was accelerated. In contrary, the composting failedin Run 5B because the organic acids cannot be
degraded thus it inhibited the activity ofLog. cell density of IP1(CFU/g-ds)

12
11
10
9
8
7
6

Conc. of organic acid(mg/g-ds)

25
20

AA
LA

PA
BA

15
10
5
0
10

9

pH (-)

8
7
6
5
4
0

1

Time (d)2

3

Fig.3 The concentration of cell density, organic acids
concentration and pH in solid culture fermentation of
IP1. In conc. of organic acids graphs AA, LA, PA
and BA representative acetic acid, lactic acid,propionic acid and butyric acid, respectively.
Fig.3 shows the concentration of cell density,organic acids concentration and pH in solid culture
fermentation of IP1. There was increment in celldensity of IP1 during three days solid culture
fermentation. The figure shows that in the initial3

day 3 of composting until 107 CFU/g-ds. In Run 5B,composting failed because the high concentration oforganic acids inhibited the activity of
microorganisms to degrade the organic matter of the
raw material. Meanwhile, the organic acids in Run
5A were degraded after two days of composting thus
the degradation of organic matter was not inhibited.
Therefore, the composting in Run 5A succeeded.
From the results above it can be concluded, by
inoculating IP1 in the thermophilic composting at
50 ºC throughout the composting could enhance theproliferation other thermophilic bacteria. The IP1
could degrade the high concentration of organic
acids in the initial stage of composting and has madethe environment of composting become favorable toother microorganisms to grow and thus the organic
matter was vigorously degraded.

25
20
15
Run 5A10

Run 5B

5

Conc. of organic acid (mg/g-ds)

Conc. of organic acid (mg/g-ds)

2

3

CO evolution rate x 10 (mol/d)

microorganism. As the results, the microorganisms
cannot degrade the organic matter in the composting.
Hence, the acidic condition in Run 5B still remain
until the end of composting.

0
30
25

AA
LA

PA
BA

Run 5A

AA
LA

PA
BA

Run 5B

20
15
10
5
0
30
25

4. Conclusions
Base on detailed analyses of accelerated compost, it
was suggested three bands of DGGE gel related tothe thermophilic bacteria might have ability todegrade the acetic acids in the composting. The
sequence band were closely related to Bacillus
coagulans, Geobacillus thermantarcticus andBacillus subterraneus. One of the bacteria namely as
Bacillus coagulans IP1 was found able to degrade theorganic acids in solid culture fermentation atthermophilic condition. Furthermore, the inoculationof IP1 into the raw material of composting has
decreased the organic acids concentration incomposting and elevated the pH, thus it can
proliferate other thermophilic bacteria and accelerate
the thermophilic composting.

20
15
10
5
0

10
9

pH (-)

8
7

Run 5A6

Run 5B

Log Cell density of TB (CFU/g-ds)

54
12Time (d)

11
10
9
8

Run 5A7

Run 5B

6

References[1]
K. Nakasaki, S. Araya, H. Mimoto,
Inoculation of Pichia kudriavzevii RB1
degrades the organic acids present in raw
compost material and accelerates
composting, Bioresour. Technol. 144 (2013)[2]
C. Sundberg, S. Smars, H. Jonsson, Low pH
as an inhibiting factor in the transition frommesophilic to thermophilic phase incomposting, Bioresour. Technol. 95 (2004)
145–150.
[3]
K. Nakasaki, Y. Sasaki, H. Kubota, Effectsof pH control on composting of garbage,
Waste, Manag. Res. 11 (1993) 117–125.
[4]
H. Hirai, Acceleration of the composting byinoculating strain RB1 and controllingcomposting temperature, Tokyo Institute ofTechnology, 2015.

5
4
0

2

4

6

8

10

Fig.4 Time course of CO2 evolution rate, organic
acids concentration, pH and concentration of celldensity of thermophilic bacteria in Run 5A and 5B.
In concentration of organic acids graphs AA, LA, PA
and BA representative acetic acid, lactic acid,propionic acid and butyric acid, respectively.
The cell density of thermophilic bacteria in Run
5A decreased until around 108 CFU/g-ds on the firstday of composting. It might be due to the fact there
were increment of organic acids concentration incomposting which negatively affected to the growthof thermophilic bacteria. However, after two days ofcomposting the cell density of thermophilic bacteria
increased and it achieved to 1010.6 CFU/g-ds at theend of composting. In Run 5B, the cell density ofthermophilic bacteria started with 105 CFU/g-ds,
then it decreased to 104.5 CFU/g-ds and increased on

4EFFECT OF LAND USE ON CRIME TYPES IN DHAKA, BANGLADESH
Student Number: 14M51658

Name: Mashroof Hossain Supervisor: Shinya HANAOKA

The research takes Uttara, Dhaka, the Northern zone of the capital city of Bangladesh, as the study area and aims at
finding solutions to its high crime rate and low resource to countervail it. For that, it collects crime records for the year
2013-2014 and uses GIS to evaluate its association with various land use, e.g., residential area, commercial area,
educational institutes, mosques, open space and highways. Notable findings suggest that drug related crimes are positively
related to mosques and commercial areas; murders take place near mosques but away from highways, grievously hurttakes place closer to mosques and negatively related to open space and residential areas; robbery takes place near mosques,
and theft takes place near mosques and highways.
1. Background- identifies various land use and road network attributes

Crime is a very severe problem in Bangladesh andand develops a logistic regression based model toaccording to Bangladesh Police website, total number ofinvestigate the underlying relationships between thesecrimes has crossed the total of 150 thousand every single

variables and crime types.

year since 2007. In Bangladesh, Police is the federal
2. Study Area and the Data

agency solely responsible for law enforcement and crime
prevention. There are major resource constraints

Uttara is one of the eight crime divisions in Dhaka,prevailing in the law enforcement sector that are clearly

Bangladesh. It is one of the newest parts of the city andvisible. Police to people ratio in Bangladesh is around

was initially designed to be a model residential town.

96:100,000 which is the lowest in the region and just one-

However, due to unplanned land use, it is now house tothird of the proposed UN ratio1 (300:100000). The

numerous educational institutions, mosques, community

existing law through which police department operates

center, street markets, and commercial areas along withgoes back to 1861, and there has been very little change

empty plots coexisting with residential settlements. Being

since then. The possibility of sudden, revolutionary

at the outskirt of the city, it connected to highways as wellchange in the existing bureaucratic system is small. Underas semi-urban slums within close proximity. The study

these circumstances, it is imperative to investigate if thearea covers an area of 36.91 square kilometers with asituation can be improved through alternative measures.

population of 179,907. There are 180 police officers

Existing literature suggests that there is a relationshipresponsible for the law and order situation of Uttara.

between crime pattern and land use. Greenberg et.al2

Detailed crime data for two years (2013 - 2014) havestated that the differences in physical characteristics ofbeen collected from First Information Report (FIR) books

various neighborhoods significantly affect their crimeof Uttara East and Uttara West Police stations. The data

scenario. The role of commercial land use mix in crime

contained information on crime types (theft, robbery, drug,

has been investigated in some literature. The role of urban

grievous hurt and murder), location, time, time for police

circumstances in occurrence of crime is also well

to arrive along with a brief description of the crime. The

discussed. Spatial patterns of crime and shortest pathother data, i.e., land use data, was collected by identifying

distance from an area to some facilities as a factor to bevarious landmarks, e.g., commercial areas, educational

exposed to crime has also been investigated. Some studies

institutes, mosques, community centers, open spaces andfound significant relationship between violent assault,

residential areas, and geocoding them using GPS. Apartmurder and land use. This research takes Uttara, thefrom that, GIS map of Uttara along with its road network

Northern part of Dhaka city as study area and investigates

was collected.

if certain types of crimes have association with certain
land-use or special arrangement of the road network. In

3. Methodology

order to do so, the study

The study uses ArcGIS to digitize the crime locations
along with various land uses – commercial area,- collects historical crime data and investigates its patterns,

5residential area, schools and open spaces. Then, the study

4. Analysis and Resultsarea is divided into three different mesh sizes – 50 m X 50
m, 100 m X 100 m and 150 m X 150 m.

Figure 2 presents the number of different types of

Afterwards,

crimes taking place in Uttara between 2013 and 2014.

each cell of the mesh was assigned an appropriate land use.

300
250
200
150
100
50
0

The attributes of each cell was also updated with crime
types, i.e., if a crime of certain type took place in thatparticular cell. Figure 1 represents the land use andmosque locations on GIS map for 50m X 50 m mesh.

260
189
85

55

67

Figure 2: Frequency of crime in Uttara

Among the five types of crimes, drug is unique for the
study area. Uttara is the entry point of the capital city,which makes it a preferred place for criminals in bringing
drugs and distributing it throughout the city. As a category,different types of thefts (car theft, fraudulent activity etc.
all put under theft category) show the highest frequency.
Uttara is also a densely populated area with a high number

Figure 1: Land Use and location of mosquesof both residents and businesses, which gives the thievesa fairly high number of possible victims to choose from.

Next, for each cell, shortest route distances from its

There are 85 murders in two years, which is very highcentroid to the closest mosque and the Dhaka-

compared to the population density and overall murder

Mymensingh highway was calculated. Finally, logistic

rate. However, given the urban-commercial nature of

regression based models were constructed for each of the

Uttara this is not unusual. The number of grievous hurt

five aforementioned crime types where presence of crime

crimes and robberies seem to be low compared to the otherwas marked as ‘1’ and its absence as ‘0’ for each cell. The

areas. Well-planned and neatly structured residential

independent variables for each cell were the dichotomous
dummy

variables

representing

commercial

sectors with collapsible gates might lead to this.

area

From the results of logistic regression (Table1) we can(DUMMY_COMMERCIAL), dichotomous dummy variable

get a very clear picture of the crime pattern and its relation

representing residential area (DUMMY_RESIDENTIAL),

with land use. The table shows the results found for all

dichotomous dummy variable representing open space

five crime categories for 50X50 mesh. The patterns of(DUMMY_OPENSPACE), dichotomous dummy variable

continuous and dummy variables reveal how each

representing educational institutes (DUMMY_SCHOOL),

separate type of crimes takes place in different pattern. For

continuous distance variable from the centroid of the celltonearestmosque(MOSQUE_DISTANCE)

continuous

distance variable from the centroid of the

cell

Dhaka

to

(HIGHWAY_DISTANCE).

-

Mymensingh

theft, areas near to mosque and highway more vulnerable

andcompared to far areas. This result is intuitively meaningful
because highway gives a better chance of escape for a thief.

highway

DUMMY_SCHOOL

However, in case of mosque this is significant becauseisBangladesh is a Muslim oriented country, it is surprisingconsidered as the base variable. Apart from logisticthat theft takes place closer to mosque areas. The reason

regression that tested the likelihood of different crime

behind this might be that criminals’ take advantage of thetypes, Poisson regression was also used in the end tomyth of safety where in reality security measures near

examine the crime count data in each cell.

mosques are less than other areas.

6

Table 1: Results of the Logistic Regression for 50X50m
Estimate

Odds

Table 2: Results for overall crime in all three mesh sizeMeshp-value

Estimate

Ratio

Theft

Odds

P-Value

Ratio

INTERCEPT-0.967***

0.380

0.000

DUMMY_RESIDENTIAL-0.764***

0.465

0.000

DUMMY_COMMERCIAL

0.209

1.232

0.488

DUMMY_OPENSPACE-2.574***

0.076

0.000

HIGHWAY_DISTANCE-5.348**

0.004

0.003

MOSQUE_DISTANCE-1.183**

0.306

0.001

INTERCEPT-2.914***

0.054

0.000

DUMMY_RESIDENTIAL

0.141

1.151

0.792

DUMMY_COMMERCIAL

0.674

1.962

0.305

DUMMY_OPENSPACE-0.344

0.708

0.658

HIGHWAY_DISTANCE-4.213

0.014

0.198

MOSQUE_DISTANCE-1.967**

0.139

0.007

INTERCEPT-1.727***

0.177

0.000

DUMMY_RESIDENTIAL-0.451

0.636

0.126

DUMMY_COMMERCIAL

1.086**

2.962

0.002

DUMMY_OPENSPACE-0.481

0.618

0.248

HIGHWAY_DISTANCE-2.736

0.064

0.176

MOSQUE_DISTANCE-1.577***

0.206

0.000

Grievous

INTERCEPT-2.484***

0.083

0.000

Hurt

DUMMY_RESIDENTIAL-1.193**

0.303

0.001

DUMMY_COMMERCIAL

0.109

1.115

0.838

DUMMY_OPENSPACE-1.443*

0.236

0.030

HIGHWAY_DISTANCE

4.257

70.59

0.117

sense intuitively. In case of grievous hurts, mosque area is

MOSQUE_DISTANCE-1.947**

0.142

0.006

again very prone to this crime, whereas open spaces and

INTERCEPT-2.850***

0.057

0.000

residential areas are less prone to such crimes compared

DUMMY_RESIDENTIAL-0.407

0.665

0.273

to school areas. In case of murder we can see that murder

DUMMY_COMMERCIAL

0.779

2.179

0.117

DUMMY_OPENSPACE-2.419*

0.089

0.022

HIGHWAY_DISTANCE

4.313

74.6@

0.060

Apart from specific crime results, logistic regression

MOSQUE_DISTANCE-1.457**

0.232

0.007

was also used to find out the overall crime scenario (any

50M

Robbery

100M

Drug

150Mr

INTERCEPT-0.3410.711

0.084.

DUMMY_RESIDENTIAL-0.560**

0.571

0.001

DUMMY_COMMERCIAL

0.742**

2.100

0.002

DUMMY_OPENSPACE-1.303***

0.271

0.000

HIGHWAY_DISTANCE-1.680

0.186

0.153

MOSQUE_DISTANCE-1.592***

0.203

0.000

INTERCEPT

1.098***

2.998

0.000

DUMMY_RESIDENTIAL-0.507*

0.602

0.027

DUMMY_COMMERCIAL

0.949*

2.583

0.029

DUMMY_OPENSPACE-1.908***

0.148

0.000

HIGHWAY_DISTANCE-0.031

0.969

0.850

MOSQUE_DISTANCE-1.700***

0.182

0.000

INTERCEPT

2.708***

14.9990.000

DUMMY_RESIDENTIAL-1.265*

0.282

0.012

DUMMY_COMMERCIAL-0.396

0.673

0.593

DUMMY_OPENSPACE-2.457***

0.086

0.000

HIGHWAY_DISTANCE

0.186

1.204

0.497

MOSQUE_DISTANCE-2.113***

0.121

0.000

(***0.1%, **1%,*5% Significance level)

We can also see that drug crimes happen more in

Murdercommercial areas compared to school areas, which makes

is more prone to mosque areas and less prone near
highways. Open spaces are less prone to murder comparedto school areas.

of the five crimes taking place). In all three mesh sizes,(***0.1%, **1%,*5%,@10% Significance level)mosque areas were found crime prone (table 2).
In case of robbery we can see that areas closer toPoisson regression was applied to the count data for fivemosques are more prone to robberies. In case of drug

crime categories on each cell of all three mesh sizes which

crimes we also find that mosque areas are very highly

brought out similar results. For example, expected number

prone to drug crimes.

of drug occurrences decreased with an increase of distancefrom the mosque. A representative result for drug crime(50 meter mesh) is showed below.

7

approach in spatial analysis to evaluate any result at
Drug

Estimate

INTERCEPT-1.210***0.298

DUMMY_RESIDENTIAL-0.503*0.605

DUMMY_COMMERCIAL-0.960*0.383

DUMMY_OPENSPACE-0.853*

0.426

HIGHWAY_DISTANCE-6.008**0.002

MOSQUE_DISTANCE-0.989*0.372

multiple spatial resolutions, three different mesh sizes

Exp.(Estimate)

were chosen. This was done to avoid Modifiable Areal
Unit Problem (MAUP). We can see that in most of thecases our findings remain same.

Table 3: Poisson Regression on Drug (50m)(***0.1%, **1%,*5%, Significance level)

However, results for dummy (commercial) in Poisson
regression shows opposite direction from the result of
logistic regression. In logistic regression, predicted
likelihood of drug crime is higher in commercial area
compared to schools (base) but in Poisson regression

Figure 3: Predicted Probabilities VS mosque distance inpredicted number of drug crimes is lower in commercial

50m, 100m and 150m mesh (from left to right)

area (compared to base). As dummy variables are

Note: Dummy1-4 representing school, residential, commercial

compared against base variable, the estimate signs can be

and open space areas consecutively

completely opposite depending on how the occurrences
5. Conclusion

are spread in the area. If few occurrences are spread in
more cells, likelihood of crime will be higher in the

The most significant finding from this study is thedummy. On the other hand, when frequency is considered,vulnerability of areas near mosques. This finding

higher number of densely occurred crime in the base willcontradicts the common belief that mosque areas are safer

predict lower number of occurrence in the dummy. Thisdue to the religious beliefs of majority population. Local

explains the opposite signs in commercial dummy in two

police administrations often do not emphasize mosquedifferent regressions.

areas for security. An important policy implication for

In figure 3, predicted probabilities of all four types ofpolice administration can be dispatching more resources

land use have been plotted against distance from mosque(police patrol, guards etc.) near mosque areas to improvein case of overall crime. The plot shows that all four landsafety and security. Drug is the most notorious crime intypes show decreasing crime probability with an increase

Uttara and we find evidence that commercial areas areof distance from mosque, making the mosque area more

highly prone to it. If a special anti-drug drive is taken,

crime prone. Since mosques are spread throughout Uttara

commercial areas should be the main target for the police

irrespective of specific business districts (figure-1), thisto secure. In this way, every specific crime can be studied

supports our findings that crimes happen more nearby

and necessary measures can be taken. If similar method is

mosque areas.

used in another police jurisdiction, we produce
customized strategies that would help Police inIn smaller mesh sizes commercial areas are more

Bangladesh to fight crime more efficiently.
Reference:

crime infested compared to other areas. This changes with
150m mesh size; school areas become the most crime

1. United Nations Office on Drugs, & Crime. (2005). Crime and Development inprone. This happens because smaller mesh size helps toAfrica. United Nations Publications. 67-87.

locate the crimes more accurately at a finer resolution.

2. Greenberg, Stephanie W., William M. Rohe, and Jay R. Williams. (1982) "Safety

Larger mesh size, with a coarser resolution, aggregatesin urban neighborhoods: A comparison of physical characteristics and informal

multiple crimes in one cell and thereby compromises theterritorial control in high and low crime neighborhoods." Population andlocational accuracy of the crime points. As a common

Environment 5.3: 141-165.

8

COMPETITIVE DYNAMICS AMONG CROSS REGIONAL HUB PORTS FOR THE
CONTAINER TRANSSHIPMENTS: CASE STUDY FOR THE PORT OF COLOMBO
Student Number: 15M51756

Name: Chathumi Kavirathna

Supervisor: Shinya HANAOKA

Hub port competition has complicated with numerous decisions making criteria and multiple liner network structures
while accelerating beyond the regional boundaries. Despite location advantages, the port of Colombo has serious exposurein losing market share due to competitive hub ports located in surrounding regions established to cater transhipment
market. The study aims to analyse the transhipment hub role of port of Colombo for both “Hub and Spoke” and “Relay”
networks considering competition with Ports of Singapore, Kelang and TanjungPelepas. The hub port selection criteria
categorized under Monetary, Time, Location, Operational and Liner related categories are analysed by shipping lines
while evaluating the performances of competitive hub ports. The Port Performance Index is generated and generalized
cost approach is used for assessing the port choice behaviour via estimating the transhipment market share of hub ports
while discussing potential implications of liner shipping industry with several scenarios analysis.
competitiveness compared to other competitive hub ports
located in surrounding regions. Further transhipment
status of port of Colombo is highly vulnerable due toserious dependency on Indian sub-continent feeder
market as development of Indian port infrastructures
could eventually reduce the transhipment volume
handled in port of Colombo. Further majority oftranshipment cargo handled in port of Colombo is related
to hub and spoke network while currently port doesn`tplay an important role in terms of relay networks.
Therefore, the study is designed to achieve four different
research objectives as follows,
1. To develop a contextualized framework of the factors
influencing on the hub ports selections from the practical
viewpoints of decision makers
2. To identify significant criteria for the hub port’s
selection by shipping lines for both “Hub and Spoke” and“Relay” networks separately
3. To analyse the competition among Port of Colombo
and other competitive hub ports to act as the transhipment
hub for different maritime markets
4. To identify future role of port of Colombo as atranshipment hub in the region

1. Introduction
Involvement of multiple players with strategic alliancesand enlargement of vessel’s size accelerated the hub port
competition beyond the regional boundaries (Veldman,
and Buckmann, 2003). Meanwhile multiple network
structures and numerous decision making criteria haveintensified the complexity of hub port section decision.
Transhipment refers to the shipment of goods/containers
to an intermediate destination before being taken to thefinal destination while transhipment hub port facilitates
the efficient logistic platform to carry out transhipment
operation between vessels. According to Ducruet andNotteboom (2012, p.2), ‘A container on average washandled 3.5 times between the first port of loading andthe last port of discharge in 2008” which indicates thesignificance of transhipment operation. Further theoverlapping origin/destination markets which are being
served simultaneously by cross regional multiple hub
ports created many alternative choices for shippinglines.Even though there is no scarcity of studies related
to port choice or port competition analysis, none of theprevious studies focused on cross regional hub ports
considering different types of liner service networks
alone with range of decision making criteria. Thus
current study fulfils the research gap by analysing the hub
port competition through hub port choice behaviour forboth hub and spoke and relay networks while considering
range of qualitative and quantitative criteria rather thanconventional approach of port competition analysis.

Thus study focuses on Port of Colombo located in South
Asian region while Port of Singapore, TanjungPelapas
and Kelang located in Southeast Asian region are
considered as competitive hub ports. Competition isanalysed for nine feeder ports in two feeder markets
related to hub and spoke network individually and relay
network separately.

Hub and Spoke network is characterized by service
optimization effort with both mainlines and feeder
services operated in an integrated manner to realize thetransportation between ultimate origin and destination
ports regardless of the infrastructure constrains
associated with minor ports while enabling transhipment
between mainline vessels and feeder vessels. Secondly
Relay network is designed to optimize the network
configuration by integrating multiple mainline services toenhance the geographical coverage while undertaking
transhipment between two mainline vessels withdifferent port of calling’s patterns.
The port of Colombo is strategically located with EastWest main sea route for being developed as atranshipment hub port. But currently the port has serious
exposure in losing market share due to lack ofFigure 11: Hubports andFeederports
considered
Figure1-1: Locationsof ports
consideredin study
in study9Turn Around Time, Waiting Time, Deviation Time of Hubport and JT (A-X) represents the Journey time in feeder
link between hub port and feeder port. ‘i (Average)’ reflects
the average value of ith time related criteria among four hub
ports.

2. Methodology
Qualitative analysis based on grounded theory approach isapplied for achieving first research objective with the datacollected through interview survey. Rest of the objectives
are achieved using sequential mathematical modelingapproach based on generalized cost model and logit model
as indicated in Figure 2-1. Initially hub port selection
criteria identified through literature reviews and interviews
are grouped in to Monetary, Time, Location, Operationaland Liner Related categories while the significance of
criteria and the performances of hub ports related to nonquantitative criteria are evaluated based on questionnaire
survey conducted with sample set of shipping lines withthirteen respondents.

𝑇𝐶(𝐴) = [𝑉𝑂𝑇(𝑉𝑇𝑇) ∗ 𝑉𝑇𝑇(𝐴)] + [𝑉𝑂𝑇(𝑊𝑇) ∗ 𝑊𝑇(𝐴)]+ [𝑉𝑂𝑇(𝐽𝑇) ∗ 𝑁𝑖 ∗ 𝐽𝑇 (𝐴 − 𝑋)] + [𝑉𝑂𝑇(𝐷𝑇)(6)∗ 𝐷𝑇 (𝐴)]
𝑉𝑂𝑇(𝑖, 𝐴) = [𝑃𝐶 (𝐴) ∗ (𝐴𝑆𝑆(𝑖) / 𝐴𝑆𝑆(𝑃𝐶) )]/𝑖(𝐴𝑣𝑒𝑟𝑎𝑔𝑒)(7)

As the next step, the calculation of Journey cost (JC) is
done using equations (8) and (9) as mentioned below.
𝐽𝐶(𝐴) = [𝑁𝑖 ∗ 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝐴 − 𝑋) ∗ 𝑈𝑛𝑖𝑡 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑉𝑎𝑙𝑢𝑒(𝐹𝐶)]+ [𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛)(8)∗ 𝑈𝑛𝑖𝑡 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑉𝑎𝑙𝑢𝑒(𝐷𝐶)𝑈𝑛𝑖𝑡 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑉𝑎𝑙𝑢𝑒(𝑖)= [𝑃𝐶 (𝐴) ∗ (𝐴𝑆𝑆(𝑖) ⁄ 𝐴𝑆𝑆(𝑃𝐶) ) ]⁄𝑖 (𝐴𝑣𝑒𝑟𝑎𝑔𝑒).

(9)

Distance (A-X), Distance (Deviation) represent the
distance of feeder link and deviation distance from East
West trunk sea route respectively. For the equations of bothtime and journey cost, Ni is a dummy variable which Ni=1
for hub and spoke and Ni=0 for relay. After calculating all
cost values, Generalized cost (GC) related to hub port
choices are measured with following GC function.
𝐺𝐶 (𝐴) = 𝑃𝐶 (𝐴) + 𝑀𝑖 ∗ 𝐽𝐶(𝐴) + 𝑀𝑖 ∗ 𝑇𝐶 (𝐴)− 𝑃𝑃[𝐷/𝐶] (𝐴)PC represents the “Port Monetary Cost” and Mi is adummy variable which Mi=1 for hub and spoke, Mi=2 forrelay. Then calculated GC is converted in to utility withfollowing approach as proposed by Koi Yu (2006), which
GC(Min) and Utility (A) represent the GC of best
performing port and utility of hub port A respectively.

Figure 2-1: Methodology flow for achieving Objective 2, 3 and 4

Port Performance Index (PPI) is developed for each hub
ports considering non quantitative criteria in terms of hub
and spoke and relay network separately as follows.
𝑃𝑃𝐼 (𝐴) = 𝐿𝐸𝐼 (𝐴) + 𝑂𝐸𝐼 (𝐴) + 𝐿𝑅𝐸𝐼(𝐴)(1)

𝑈𝑡𝑖𝑙𝑖𝑡𝑦(𝐴) =𝑖=𝑛

𝐿𝐸𝐼(𝐴) = [∑ 𝐴𝐴𝑆(𝐴, 𝐿𝐸) ∗ 𝐴𝑆𝑆(𝐿𝐸)] /𝑛

(2)

𝑖=𝑛

(3)

𝐸𝑀𝑆(𝐴) =𝑖=1
𝑖=𝑛

𝐿𝑅𝐸𝐼(𝐴) = [∑ 𝐴𝐴𝑆(𝐴, 𝐿𝑅𝐹) ∗ 𝐴𝑆𝑆(𝐿𝑅𝐹)] /𝑛

(4)

(11)

𝑒 𝑈(𝐴)
𝑈(𝑖)
∑𝑖=𝑘
𝑖=1 𝑒

(12)

After estimating market share, model market share iscompared with predicted market share from alternative
approach based on simple linear regression. Further model
dynamics are analysed to understand the relationshipbetween each components of GC functions. Finally four
practical scenarios are analysed considering various
potential implication of liner shipping industry.

𝑖=1

LEI(A) , OFI(A) and LRFI(A) indicate the “Location
Efficiency Index”, “Operational Efficiency Index” and“Liner Related Efficiency Index” of Port A respectively.
AAS and ASS refer to the “Average Appreciation Score”
and “Average Significance Score” while “n” indicates thenumbers of criteria under each category. Next in order tocalculate the monetary values of non-quantitative criteria,“Port Performance Discount/Cost” (PPD/C) is calculatedusing equation (5). The positive and negative PPD/C
indicate discounts and cost respectively.
𝑃𝑃[𝐷/𝐶] (𝐴) = 𝑃𝐶(𝐴) ∗ 𝐿𝐸𝐼(𝐴 )⁄𝐴 𝑆𝑆(𝑃𝐶) + 𝑃𝐶(𝐴)∗ 𝑂𝐸𝐼(𝐴 )⁄𝐴 𝑆𝑆(𝑃𝐶) + 𝑃𝐶(𝐴)∗ 𝐿𝑅𝐸𝐼(𝐴 )⁄𝐴 𝑆𝑆(𝑃𝐶)1
𝐺𝐶 (𝐴)
𝐺𝐶(𝑀𝑖𝑛)Next logit model approach is used to estimate the market
share (EMS) of hub ports with equation (12) using theutility values calculated from previous steps.

𝑖=1

𝑂𝐸𝐼(𝐴) = [∑ 𝐴𝐴𝑆(𝐴, 𝑂𝐸) ∗ 𝐴𝑆𝑆(𝑂𝐸)] /𝑛(10)

3. Analysis and Findings
The contextual framework is developed as the result of firstresearch objective while identifying five main influencesand various sub influences for the hub port selection
decision from the practical aspect as illustrated in Figure 31. Then as the results of the questionnaire survey, ASS is
calculated for both networks separately as mentioned inTable 3-1. According to that, related to majority of criteria,
Hub and Spoke network obtains high ASS than relay
network even though “Berth availability” is considered as
the most significant criteria for both networks.

(5)

As the next step, the time related cost is calculated using
following equations. VTT, WT, DT represent the Vessel

10Hundreds ($)

It is important to mention that CMB has lesser PPI for relay
case than hub and poke case while all other three hub ports
have higher PPI for relay case than hub and spoke case.
Further SIG has the highest while TPP has the lowest PPI
for all three cases.
8.00

6.77

6.00
2.46
2.13

2.00
0.00

4.68

3.56

4.00

7.11

6.53

Indian East Coast
Feeder Market

4.54
2.27

2.44
2.05

2.26

Indian West Coast
Feeder Market
SIG

Relay Network
Case
CMB

KLG

TPP

Figure 3-1: Contextualized framework of factors on hub ports

Figure 3-3: PP D/C of competitive hub ports

Selection
Criteria“Hub port Accessibility”, “Frequency of Delays”,“Efficiency of Husbandry Services” and “Availability of
Dedicated/Own Terminal” obtained higher ASS for relay
case than hub and spoke case while “Port Superstructures”,“Port‘s Flexibility” and “Financial Clearance Capability”
are obtained equal ASS for both networks.

Further monetary values of non-quantitative criteria arecalculated as PPD/C and results are indicated in Figure 33. Next market shares are calculated for all nine feeder
ports related to hub and spoke case as mentioned in Figure
3-4 which indicates dominant market share of SIG.
CMB
Feeder Ports

Table 3-1: ASS of Hub port Selection Criteria

23.86
31.37
23.22
23.86
19.30
20.80
25.62
25.05
24.01

Kolkata
MundraChittagong
HaldiaVisakhapatnam
Chennai
JNPT
Pipavav
Karachi

SIG

PKG

40.76
35.32
41.48
40.77
30.24
26.83
29.25
26.55
26.80
25.40
27.06
25.60
27.60
25.91

0%

TPP

35.38
33.31
35.30
35.37
23.62
23.40
22.18
22.29
22.48

50%

100%

Figure 3-4: Estimated market share for Hub and Spoke Case

Besides that market share related to relay network case arecalculated for each hub ports as mentioned in Figure 3-5
which indicates least market share for CMB.
SIG
Market Share

0%

CMB

PKG

34.08

17.95

23.88

20%

40%

60%

TPP

24.09
80%

100%

Figure 3-5: Estimated market share for Relay Case

Next estimated model market share is compared withpredicted market share from regression model. For theprediction, the “No of Vessels deployed in services” is usedas the explanatory variable as it derived the best fitting
model to estimate the transhipment volumes. Thenestimated model market share and predicted market sharefrom regression method is compared and two examples ofresults related CMB and SIG ports are illustrated infollowing figures.
.

PPI is developed for two feeder markets in hub and spoke
network and relay network separately as indicated in
Figure 3-2. SIG, CMB, PKG and TPP indicate Singapore,
Colombo, Kelang and TanjungPelepas ports respectively

20
10
0

23.45

24.13

22.13

16.12

CMB (Predicted)

15.65

14.81

13.31
11.58

12.07
10.95

Indian East Coast
Feeder Market

Indian West Coast
Feeder Market
SIG

Market Share %

30

12.72
11.94
Relay Network
Case
CMB

KLG

CMB (Model)

40
30
20
10
0
1

2

3

4

5

6

7

8

9

1-Mundra, 2-Kolkata, 3-Pipavav, 4–JNPT, 5–Chennai, 6 –Vishakhapatnam,
7 –Haldia, 8 –Chittagong, 9-Karachi

TPP

Figure 3-6: Model VS Predicted market shares of CMB

Figure 3-2: PPI of competitive hub ports

11

SIG (Model)

12% Shift of Market Share

Market Share %SIG (Predicted)
60
40
20
0

1
2
3
4
5
6
7
8
9
1-Mundra, 2-Kolkata, 3-Pipavav, 4–JNPT, 5–Chennai, 6 –Vishakhapatnam,
7 –Haldia, 8 –Chittagong, 9-Karachi

Figure 3-7: Model VS Predicted market shares of SIG

PKG
CMB
SIG

-4
-6

% Shift of Market Share

Figure 3-8: Positive/Negative shifts of Market share from Scenario-1
6

2

SIG

-8

15.00
% Shift of Market Share% Shift of Market Share

TPP

4

CMB

-3

Next Figure 3-12 illustrates the positive and negative shiftsof market share related to scenario analysis in Relay
network case which indicates clear difference between first
three scenario and forth scenario. Further related to first
three scenarios, highest negative shifts is observed from
SIG while PKG and TPP has nearly equal negative shift ofmarket share. As indicated CMB has the least negative
impact from SINGA project development in SIG.

4

-2

PKG

Figure 3-11: Positive/Negative shifts of Market share from Scenario - 4

6

0

TPP
2

-13

As the next step, four different scenarios focusing on “Pricechange effect”, “Colombo port expansion project impact”,“Best performing port effect” and “SINGA project impact”
respectively named as scenario 1- 4 are analysed forachieving forth research objective and results are indicatedin following figures.

2

7

TPP

SIG
CMB

10.00

PKG
TPP

5.00
0.00
Scenario 1

Scenario 2

Scenario 3

Scenario 4

-5.00
PKG

0
-2
-4

CMB

-10.00SIG

Figure 3-12: Positive /Negative shifts of market Share of
Scenario in Relay case-6

% Shift of Market Share

Figure 3-9: Positive/Negative shifts of Market share from Scenario-2

4. Conclusion
The study develops a mathematical modelling approach toevaluate the competition among four competitive hub ports,
Colombo, Singapore, Kelang and TanjungPelepas
considering “Hub and Spoke” and “Relay” networks
separately. Further significant decision making criteria are
identified for the selection of transhipment hub port while
the results indicate a visible differences in significance of
selection criteria between two networks. Dominant
performance of Singapore port is highlighted for all
markets while performances of other three hub ports arevaried based on different markets. Recommendations are
made for the Port of Colombo based on the analysis ofcurrent competition and several future scenarios.

15
10
TPP
5

PKG

0

CMB

-5

SIG

-10
-15
Figure 3-10: Positive/Negative shifts of Market share from Scenario-3

First three scenarios has been analysed based on CMB
while Scenario 4 is based on SIG. According to the resultsof scenario analysis, first two scenarios show
comparatively lesser market share gain than 3rd scenario
while there is significantly higher impacts related to feeder
ports located in Indian West coast feeder market thanIndian East coast feeder market. Further SIG has thehighest negative shift while TPP has the least negative shift
from all three scenarios. Then results of Scenario 4 isillustrated in Figure 3-11 indicating the significant market
share gain of SIG as the results of SINGA project while
highest negative impact is showed by PKG.

References
Ducruet, C. and Notteboom, T. (2012) ‘Developing Liner
Service Networks in Container Shipping, Maritime
Logistics: A complete guide to effective shipping and port
management’, pp. 77-100.
Koi-Yu, A. (2006) ‘Theory and structure of port
competition’, University of Oxford.
Veldman, S.J .and Buckmann, E. H. (2003) ‘A Model on
Container Port Competition: An Application for the West
European Container Hub-Ports’, Maritime Economics andLogistics, 2003, 5, pp.3–22.

12

Impact of high-speed rail entry on airport choice behavior in China
Student Number: 15M18210

Name: Jiawen JIANG

Supervisor: Shinya HANAOKA

中国の高速鉄道の開通が空港選択行動に与える影響
姜佳文
本研究は、都市間が高速鉄道で繋がっている上海＝南京(301km)及び成都＝重慶(350km)路線を対象に選好意識調査を行い、非集計選択モデルを用いて、国際線利用時における対象地域の人々の空港とアクセス交通機関の選択要因を明らかにした。分析の結果、航空運賃、アクセス費用、アクセス時間、公共交通の運行頻度、アクセス時の乗換回数、路線距離ダミーが有意な変数となった。また、回答間の誤差を考慮した MXML と MXNL モデルの推定結果が優れており、高速鉄道のアクセス交通機関選択率が最も高い結果となった。

1. Introduction

China has the world’s longest high speed rail
(HSR) network as 20,000km by Sep.2016, and the number
is going to reach 45,000km by 2030.
In short to middle-haul domestic routes, HSR had
taken over the market of air transport and coach , however,
the impact of HSR’s feeder effect on international routes
at China’s airports have not been discussed. Terpstra(2015) mentions that HSR connection between two
airports will increase both of their catchment area, and as
the catchment area increases, competition between
airports will be enhanced. However, it lies in theassumption that HSR is used as airport access mode.
Despite the fact that even the average price on the routes
with higher speed (For example: Guangzhou-Wuhan,Zhengzhou-Xi’an) is less than half of those in Japan andEurope, HSR price is still expensive compared to otherairport access mode. Furthermore, while the share oftransfer passengers has been shown as an important
determinant for the scope of cooperation, according toreport by CAMIC (2010) only one fourth of Chinese
passengers showed an interest in HSR service.
Considering facts discussed above, potential of HSR’s
feeder effect is still vague.
This research sets two objectives as follows:(1) To identify significant factors in passenger’s airport
and access mode choice behavior in China.
(2) To identify whether HSR is chosen as airport accessmode by international flight passengers in China.
As far as our knowledge, there is no study on
HSR’s feeder effect on international travel from China.
When impact of HSR on airport choice behavior isclarified in this study, the concept of multiple airport
region in China will be stretched as the service hinterlandthat is accessible via ground transport mode within one
and two hours of each international airport expands. This
provides insights for alleviating congestion at majorinternational hub airports as they can divert traffic to otherairports by HSR. Furthermore, new roles can be assigned
to each airport in the stretched multi-airport region. Figure
1 is the illustration of the change of roles after concept ofmultiple airport region is stretched. Airport A and B are
two international airports, yellow and grey arrows
represent domestic and international flights, separately.

13

Figure 1 Illustration of airport’s role change under different scaleof multi-airport region.

As in airport A, slots that are previously used for domestic
flights can be used for international flights instead, which
will enhance connectivity of the multi-airport region, as
well as its competitiveness among world hub airports.

2. Methodology
Discrete choice model is widely used in marketingand transportation field since the model analysis areusually based on the random utility maximization
framework. It is used in order to measure the significanceof factors of goods or services by observing respondents’choice behavior when choosing their favorite alternative
among choice sets.
Index the number of respondents as n = 1,2,3,…N and thenumber of alternatives as i = 1,2,3,…I. The utility of
respondent n for alternative i is:

U ni   ni  i X ni   ni  Vni(1)

Where as, 𝑈𝑛𝑖 is the utility of alternative i asperceived by respondent n, 𝑉𝑛𝑖 represents characteristics
of alternative I and respondent n, 𝑋𝑛𝑖 is a vector ofparameters to be estimated and 𝜀𝑛𝑖 is a random
unobserved component of utility, which is independentand identically distributed (i.i.d). The rest of this chapter
introduces choice model conducted in this study, all themodels are estimated using Biogeme (Bierlaire, 2013)

2.1 Multinomial Logit Model (MNL)
MNL model is the simplest and most popular
practical discrete choice model. It is based on theassumption that the random components 𝜖𝑛𝑖 are
independently and identically distributed. With thisassumption the choice probabilities are:exp(Vni )P   exp(V )
ni

jI

nj

(2)

Despite the fact that the MNL model is the mostcommonly used model in transport research field, MNL
model has the property of independence from irrelevantalternatives (IIA). The advantage of IIA property is that it
makes the subset of the full choice set available for model
estimation when the total number of alternatives in achoice set is large. The disadvantage, however, if somealternatives are “correlated”, MNL calculates choiceprobabilities inconsistent with our intuition.

(1997). Probability of individual n choosing alternative i isshown as below:

Pni   Lni ( ) f ( )dL𝑛𝑖 (𝜃) is the probability that individual n chooses
alternative i. 𝑓(𝜃) is the density function of parameter 𝜃,which is known as mixing distribution. In this study,
multiple observations were acquired from each respondent.
Therefore, panel effect was added to the utility function,in the consideration of accounting for intra-respondent
homogeneity of tastes.

2.2 Nested Logit Model (NL)
NL model relaxed the i.i.d. assumption in MNL
when alternatives are not independent, in which similaralternatives are grouped together (the group is called nest)
and each alternative belongs to exactly one nest. A
structural parameter (also can be called as nest parameteror scale parameter) is assigned to each nest to measure thedegree of independence between alternatives in therespective nest. Assuming a nest of two levels, with M
number of lower nests. Scale parameter of upper andlower nest are μ and ϑ respectively. Choice probabilityof individual n choosing alternative i among A𝑔alternatives in nest g is shown as below:

exp( ln 

P ni  P[ g ]* P[i | g ] 



aAg

Va



V
exp( i )

)

Vk



*

3. SP Survey
3.1 Survey OD Selection
Nanjing ~ Shanghai and Chengdu ~ Chongqing
are selected as target route. Details of the two routes are
given in Table 1. Five origin cities are selected based on
criteria below: (1) Located in between airports on target
route; (2) Presence of HSR station; (3) Residents are able
to access both airport by coach, HSR and private car; (4)significant distance difference to either airport on thetarget route.

(3)

Table 1Vl exp(  ln   )  exp(  )k A j

(4)

Route

lAg

Nanjing~ShanghaiChengdu~Chongqing

Ratio of structure parameters (upper and lowernest) can be estimated together with utility parameters,with a value being in the range of [0,1] to hold the
hypothesis of random utility maximization.

Details of Target Routes

Designed
Speed(km/h)

Distance(km)

Service
LaunchYear

En-route
time

250

301

2011

2h4min

200

350

2012

1h49minFor destination, Seoul and Singapore wereselected as short and middle haul destination for theirhigh popularity among Chinese travelers and highmonthly frequency of direct service from all target
airports.

2.3 Cross Nested Logit Model (CNL)
CNL can be seen as an extension of the NL model. Its
model structure allows to simulate multiple correlations
between alternatives hypothesizing partially overlappinggroups. CNL was conducted because the choice process indesigned SP scenarios is two dimensional. Therefore, we
assume if a respondent’s current airport-access mode
combination becomes unavailable, he or she will be more
likely to switch to another option that keeps one of thedimensions of choice constant, rather than changing both
airport and access mode. That’s why we aimed to allow
for correlation along both dimensions of choice. However,estimation result turns out that decision making of
respondents in this study do not correspond with thismodel structure.

3.2 Survey Design
The designed survey is consisted of three parts.
Part one asks about personal information and trip
experience of respondents, such as gender, age,occupation, average monthly income, ownership of private
car in the household, number of domestic andinternational round trip by air in 2016 and most frequent
trip purpose among international trips. In part two,
respondents will be asked about details of the lastinternational trip under their most frequent trip purpose.
Questions include trip destination, number of accompanies,
departure airport (last domestic departure airport if that
was a transit flight), access mode to the airport and choose
three important factor for choosing this departure flightthen rank them in order.
Part three starts with questions on the departureand arrival time in respondent’s last international trip
under their most frequent trip purpose. Then respondents
are asked to imagine they live in a city without airport,
and they are planning whether to go for an international
trip to Seoul and Singapore. Respondents are required tomake airport and access mode choice based on given
situation in different scenarios that follow. Scenarios aredifferent in terms of whether it is a departure or arrival
flight, haul length, presence of accompany, number of

2.4 Mixed Logit Model (ML)ML model was developed to relax the MNL
assumption that the error term is independently andidentically distributed across individual alternatives. It ishighly flexible because it can approximate any random
utility model. In this study, mixed logit model in MNL,NL and CNL structure was conducted. By change the
distribution of parameters and error terms, ML models all
for random taste variation, unrestricted substitution
patterns and correlation in unobserved factors over time.
Choice probabilities of ML model is defined in Train

14

baggage, flight schedule, airfare, total access time, accesscost, number of transit from starting point to airport andadditional waiting time if respondent miss the designatedbus or train. Information on two airports (Airport A andAirport B) is provided, therefore respondent can choose
which airport for departure or arrival, access mode to that
airport, or give up the trip if they are not satisfied with theprovided scenario. Airports name is unlabeled because
revealed airport name might introduce biases if
respondents don’t have experience of both airports andsimply choose the airport that they are more familiar with.
Access mode attributes include access time, accesscost, service interval of coach, HSR and private car, as
well as total number of transit from start point to airport.
Base information of each mode is acquired from widelyused map and ticket booking website Gaode.map andctrip.com.
Schedule delay is the time difference between
desired and actual departure and arrival time, which iscommonly used as an alternative for flight frequency. We
assume the departure and arrival time of the last trip ofeach respondent is their desired flight schedule. Departureor arrival time might be two hours earlier, two hours later
than, or the same with desired flight time in different
scenarios. Base airfare from the same airline (China
Eastern) is collected from ctrip.com.
To lessen the burden of respondents while
preserving orthogonality between each scenario, Ngene

is under the condition of travelling with someone else).
Estimation result on MNL, NL, MXML, MXNL
and MXCNL model for observation type (O), adjusted
rho-square ( 𝜌̂2 ), coefficient of parameter (β), nest
parameter (μ) and diagnostic are summarized in Table 2.
Abbreviations are concluded in the bracket after the table.
Table 2 Summary of Model Estimation Results
No
O
𝛽
𝜇
𝜌̂2
MNL

Figure 2

Wt

0.053

○

2

Wo

0.091

×

D

-

○

3

Wc

0.061

○

NL(A)

4

Wc

0.064

○

○(W)

△

NL(M)

5

Wc

0.063

×

○

△

6

Wt

0.150

○

7

Wo

0.146

×

8

Wc

0.148

○

-

○

MXML

MXNL
(A)
MXNL
(M)
MXNL
(A)

(Ⓒ2014ChoiceMetrics) is used as a tool for orthogonal
design. After a thorough correlation and logic check,
eventually 12 designs with HSR alternative and 8 designs
without HSR alternative are selected. Figure 2 is the
scenario example.
SP survey was conducted through Internet duringDec.29, 2016 ~ Jan. 3rd, 2017, 569 effective samples weregathered during survey period. To ensure the reliability ofsurvey result, we selected respondents resident in thetarget area who experienced at least one international
travel in 2016.

1

MXCNL

9

Wc &B

0.167

×

10

Wc &N

0.140

○

11

Wc

0.153

○

○(W)

○

12

Wc

0.168

○

×

○

13

Wc &B

-

-

-

×

14

Wc &N

0.143

○

○(W)

○

15

Wc &F

0.162

○

×

△

16

Wc &F&B

-

-

-

×

17

Wc &F&N

0.162

○

×

△

18

Wc &F&C

0.163

○

×

○

(Wt : Choice data with HSR alternative [2276 samples]
Wo : Choice data without HSR alternative [1138 samples]
Wc: Combined choice data with and without HSR alternatives
B: Choice data made under business trip purpose [714 samples]
N: Choice data made under non-business trip purpose [2700 samples]
F: Allocation parameters are fixed to a value
C: Constraints are added to nest parameters○: Satisfying level,△: Not satisfying but acceptable level
×: Rejected)

Significant improvement in adjusted rho-square
can be observed in Model 6 ~ Model 18 because panel
effect was taken into consideration, which accounts for
intra-respondent homogeneity of tastes since multiple
observations were acquired from each respondent. Models
that are estimated only on choice data in scenario without
HSR alternative are rejected due to low significance levelof 𝛽 which results from the lack of scenario pattern in the
choice set. Discussions mainly focus on models that are
estimated using a combination of choice data with andwithout HSR alternatives, so that marginal utilities ofvariables for scenarios without HSR alternative can becorrectly estimated and discussed. A scale parameter for
choice data with HSR alternative was estimated, which isinversely proportional to the variance of the error term.
Among all 569 respondents, only less than 120
were familiar with international business trip, therefore,
convergence in Model 9, Model 13 and Model 16 werehardly reached because of the small sample size. Model 12,
in which access mode is on the upper nest, was rejected

Scenario Example in SP Survey

4. Model Estimation
All models were calibrated using both continuousand dummy variables. Continuous variables include
airfare, access cost, access time, interval of public
transport service, number of transit and income. Dummy
variables are trip haul length (set to 1.0 for short haul trip),and number of accompanies (set to 1.0 when the scenario

15because the ratio of nest parameters indicates the structure
failed to hold the hypothesis of random utility
maximization. MXCNL structure was rejected because in
Model 15 ~ Model 18, all nest parameters were smaller
than 1.0, implying decision making of survey respondents
do not fit the model structure. Therefore, in the following
part, we will discuss on Model 8 and Model 11. Details
are given in Table 3.

to zero as reference. As other ASCs are estimated relative
to the fixed ASC, there is no loss of generality by doing so.
For access mode, we can see that all else being equal,
coach (the base) has the highest utility and the lowest
utility is obtained for car in both models. Regarding thefact that in both models the scale parameter for choiceswith HSR alternative exceeds 1.0, it can be indicated thata somewhat reduced error variance for responses in thosescenarios.
In Model 11, nest parameter on the upper nest isfixed to be 1.0 in order to estimate the nest parameters on
the lower part. For both airports, the ratios of nest
parameters in scenarios without HSR alternative are
greater than 1.0, which indicates the structure failed tohold the hypothesis of random utility maximization. This
result can be interpreted as in scenarios without HSR
alternative respondents do not decide departure or arrival
airport in the 1st place. Improvement in 𝜌̂2 can beobserved in Model 11 because correlation between
unobserved utilities of airports in scenarios with HSR
alternative are captured.
Access mode share of Model 11 calculated using
logit model is shown in Figure 3. We can see that coachhas the largest share when without HSR alternative and
HSR dominants over coach and private car. There are
more respondents switched from coach to HSR than thatof private car, and the share of respondents that are notsatisfied with provided scenario remains the same.

Table 3 Estimation Results of Model 8 and Model 11
Models

MXML-8MXNL(A)-11𝛽𝑡-value𝛽𝑡-value

Airfare-0.164-9.69**-0.171-7.28**

Access Cost

-0.203-4.17**-0.193-4.21**

Access Time

-0.111-3.88**-0.0775-2.96**

Interval of PT-0.0763-4.19**-0.0544-3.5**

No. of Transit-0.0805-4.83**-0.0524-3.82**

Haul Length

0.35

2.42**

0.28

1.88*

No. of Accompany

0.0738

0.52

0.03

0.2

Income-0.0141-0.76-0.0103-0.57

ASC (Ap2)-0.517-6.52**-0.835-5.93**ASC (Car)-0.554-4.89**-0.416-3.22**ASC (HSR)-0.232-2.71**-0.172-2.47**ASC (No)-4.72-10.84**-4.68-8.28**

2.45

6.21**

0.702

6.41**

Wt

1.27

8.00**

Wo

0.495

7.38**

1.44**

Nest Parameter
Wt
Ap1
Wo

Figure 3 Access Mode Share of Model 11

Ap2

Scale Parameter

𝜌̂2

1.56

3.36
0.148

( : 5% significance level
*

**

**

2.32

5. Conclusion
In this study, to clarify impact of HSR entry on
airport and access mode choice behavior in China, SPsurvey was conducted on two target routes. MNL, NL,
CNL, ML are estimated, major findings are: (1) Airfare,access cost, access time, interval between public transport
service, number of transit in public access mode as well as
the length of international flight were significant inrespondent’s airport and access mode choice; (2) Modeshare of HSR dominants over coach and private car; (3)MXNL model with panel effect outperformed othermodels for its consideration of intra-respondent
homogeneity of tastes.

0.153: 1% significance level)

Results show that negative marginal utilities forincreases in airfare, access cost, access time, interval of
public transport service, number of transit in both models
are at 1% significance level. Haul length is significant at
5% level in Model 8 and 1% level in Model 11, thepositive sign can be interpreted as respondents have higher
utility when taking short haul trip. Number ofaccompanies and income were insignificant in bothmodels. Airport and mode alternative specific constants(ASC) were introduced to represent the part of individual
utility that is not captured by proposed variables but wellperceived in the respondent’s mind. Due to how logit
model works, it is not possible to specify an ASC for
every alternative, ASCs for airport 1 and coach are fixed

Reference[1] Mid-Longterm Railway Development Plan (2016), National Development and Reform
Commission (NDRC).
[2] Domestic Passenger Characteristics Report (2010), Civil Aviation Management Institute of
China (CAMIC)[3] Terpstra. I and Lijesen. M. (2015), The impact of high speed rail on airport competition,
Tijdschrift voor Economische en Sociale Geografie, 106, (3), 263-275[4] Johnson, D., Hess, S. and Matthews, B.(2014), Understanding air travelers’ trade-offs
between connecting flights and surface access characteristics, Journal of Air Transport
Management, 34, 70-77.

16INFLUENCE OF DEFECTS ON STEEL CORROSION OF REINFORCED
CONCRETE EXPOSED TO MARINE ENVIRONMENT FOR LONG-TERM
Student ID: 15M18173

Name: Daisuke MIZUSHIRISupervisor: Mitsuyasu IWANAMI

長期間海洋環境に暴露された鉄筋コンクリートの鉄筋腐食に及ぼす初期欠陥の影響
水尻大輔

本研究では、既存の研究例がほとんどない長期間実海洋環境に暴露された欠陥を有する鉄筋コンクリート
供試体を用いて、打継目やひび割れといった初期欠陥が海洋環境下で鉄筋腐食に及ぼす影響を明らかにすることを目的とした。実験結果から、初期ひび割れを有する海水練りの供試体では、初期ひび割れ幅が大きいと腐食による鉄筋の質量減少率が大きくなる傾向が確認された。打継目を有する供試体では、打継ぎ材料を用いた供試体では、レイタンスの除去により腐食による鉄筋の質量減少率が増加していた。また、実験結果
および既往の研究結果を用いて、実環境下における鉄筋腐食の経時変化や腐食速度の経験式の妥当性について検討を行った。
1. Introduction
There are many reinforced concrete (RC) structures undermarine environment in Japan because Japan is an island
country and surrounded by the ocean. Then deterioration of
RC structures by chloride attack is one of the major social
problems.
Chloride attack causes corrosion of steel bar in concrete,
which deteriorate the durability of RC. Deterioration factors
go into concrete through defects, such as crack andconstruction joint. Influence of defects on the durability ofconcrete have been investigated by laboratory tests.
However there are few studies on corrosion of steel inconcrete exposed to real marine environment. Therefore inthis study, the following objectives have been set.
1. To clarify the influence of crack and construction joint
on steel corrosion in the marine environment
2. To examine the process of steel corrosion under the realenvironment and the validity of empirical equations ofcorrosion rate

Table 1 Types of Specimens(a) RC with Initial Crack
Size Cover M ixing Water Cement(mm) (mm) Water
Ratio (%)
55
W
65
20
55
S
65
100×
100
×600

W

65
45

40
S

150×
150
×600

2. Experimental Overview
2.1. Specimens and Exposure Condition
Two types of concrete specimens after long-term exposure
to marine environment were used in this study.
One was prism concrete specimens with a steel bar made in
1981, and shown in Table 1 (a). These specimens were madewith flexural cracks at the center by loading as initial cracks.
The initial crack width was 0.1, 0.2 and 0.3 mm. They were
exposed to a seawater tank which emulates dry and wet cycles
by 6 hours for 32 years.
The other was cylinder concrete specimens made in 1973,
and shown in Table 1 (b). These specimens were made with 3
steel bars and construction joint. Three steel bars of 9 mmdiameter and 18 cm length were set at cover depths of 2, 4 and7cm. Seven types of joint treatments were conducted on theconstruction joint. Those specimens were submerged in thesame seawater tank continuously for 40 years.
2.2. Experimental Methods(1) Corrosion Current Density
Polarization resistance was measured by using a corrosion
monitor. It was measured at 3 points in specimens (the centerand both edges). Then, corrosion current density was given by
Eq. (1).
K
Icorr =
Eq. (1)

70

55

55

Initial Crack
Width (mm)
0.2
0.2
0.1
0.2
0.3
0.2
0.2
0.1
0.2
0.3
0.2

65
55
W
65
0.2
55
S
65
W: Tap Water S: Sea water

Name
W20_55_0.2
W20_65_0.2
S20_55_0.2
S20_55_0.2
W40_55_0.1
W40_55_0.2
W40_55_0.3
W40_65_0.2
S40_45_0.2
S40_55_0.1
S40_55_0.2
S40_55_0.3
S40_65_0.2
W70_55_0.2
W70_65_0.2
S70_55_0.2
S70_65_0.2(b) RC with Construction Joint
No.
A
B
C
D
E
F
G

Joint Treatment
Removal of Laitance
Joint Material
Nothing
Not Removed
MortarEpoxy Resin
Nothing
MortarRemovedEpoxy Resin
Latex Paste

Where Icorr is the corrosion current density and K is a constant(0.0209 V). Rct and S are polarization resistance and surface
area of a steel bar respectively.
(2) Corrosion Area Ratio
Steel bars in specimens were removed after measurement ofcorrosion current density. Corroded points on the steel surface
were sketched on a film wrapped around the steel bar.
Corrosion area sketched on the film was calculated by animage analysis software.
(3) Mass Loss Ratio and Corrosion Amount
After measuring of corrosion area ratio, steel bars were
soaked in 10% diammonium hydrogen citrate solution for two

Rct ×S17

Corrosion Current
Density (μA/cm2)

3

Table 2 Results of Measurement for Specimens
with initial Crack

0.1mm
0.2mm
0.3mm

2

Name

1

W20_55_0.2
W20_65_0.2
S20_55_0.2
S20_55_0.2
W40_55_0.1
W40_55_0.2
W40_55_0.3
W40_65_0.2
S40_45_0.2
S40_55_0.1
S40_55_0.2
S40_55_0.3
S40_65_0.2
W70_55_0.2
W70_65_0.2
S70_55_0.2
S70_65_0.2

0
0

100
200
300
400
500
Distance from Left Edge (mm)600(a) Tap Water, Cover: 4cm, W/C: 55%
Corrosion Current
Density (μA/cm2)

3

0.1mm
0.2mm
0.3mm

2
1
0
0

100
200
300
400
500
Distance from Left Edge (mm)Corrosion
Corrosion
Corrosion
M ass
Current Density Area Ratio
Amount
Loss
2
(%)
Ratio
(%)
(μA/cm )(mg/cm2)
2.84
100.0
10.8
262.0
2.49
100.0
12.0
293.3
2.29
96.9
12.7
308.5
0.66
100.0
12.1
295.3
0.25
100.0
8.4
203.7
1.36
93.0
8.3
203.2
1.04
100.0
8.8
214.7
2.03
100.0
7.7
187.8
0.11
100.0
5.2
125.6
0.14
100.0
8.1
196.4
0.58
49.0
8.3
201.8
1.46
93.6
9.4
229.2
2.42
97.1
7.8
190.3
0.05
99.8
20.9
333.3
1.40
98.9
25.9
483.0
0.07
81.7
9.0
220.4
0.34
-

600

Table 3 Results of Corrosion Current Density forSpecimens with Construction Joint
Corrosion Current Density (μA/cm2 )Cover:20mm
Cover: 40mm
Cover: 70mmNo.
60 150 240 60 150 240 60 150 240
mm mm mm mm mm mm mm mm mm
A 0.21 0.85 0.77 0.18 0.48 0.54 0.33 0.43 0.45
B 0.10 0.93 1.63 0.09 0.16 0.77 0.18 0.16 0.16
C 1.02 0.18 1.72 0.14 2.08 1.11 0.19 0.21 0.13
D 0.18 0.26 0.57 0.24 1.04 0.96 0.14 1.11 0.22
E 0.99 0.44 0.51 1.54 1.78 1.88 0.58 0.29 0.44
F 0.51 0.68 0.57 0.35 1.32 0.37 0.19 1.79 1.33
G 0.81 4.17 1.88 0.57 0.99 1.26 0.38 0.52 0.91(b) Sea Water, Cover: 4cm, W/C: 55%
Fig.1 Corrosion Current Density of Specimens
with Initial Crackdays to remove rust on steel bars. A mass of the steel bar after
removing rust was measured.
3. Steel Corrosion of RC with Initial Crack Exposed for
32 years(1) Corrosion Current Density
Fig.1 shows one of the results of corrosion current density ofprism specimens. When the corrosion current density ishigher than 0.2 μA/cm2 (Red line in Fig.1), the steel inconcrete is corroded according to evaluation method of CEB.
In the case of tap water mixing, there is not a clear tendency
by difference in initial crack width in results of corrosion
current density. On the other hand, specimens mixed with sea
water showed that corrosion current density was larger as
increase in initial crack width. Differences in corrosion
current density by initial crack did not show a clear trend
between the center (30cm) and the edge of specimen (15cm,
45cm).
(2) Corrosion Area Ratio
Results of corrosion area ratio are shown in Table 2. Steelcorrosion occurred on the whole of steel surface because
corrosion area ratio was 100% or near 100%. But corrosion
area ratio of S40_45_0.2 was 49.0%, which was the smallest
value of all. It was a half of values of other specimens. In thecase of S40_55, corrosion current density became larger as
initial crack width became larger. The tendency of S40_55
was not confirmed in W40_55.
(3) Mass Loss Ratio and Corrosion Amount
Mass loss ratio of W70_65_0.2 was the largest, which was
25.9% from Table 2. When initial crack width increased from
0.1mm to 0.3mm for W/C 55% specimens, mass loss ratio
increased by 0.4% in the case of tap water mixing. The valueof specimens mixed with sea water also increased by 1.3%.
Therefore mass loss ratio showed a tendency to increase asincrease in initial crack width.
As well as mass loss, corrosion amount of W70_65_0.2 was
the largest, which was 483.0mg/cm2. The same tendency as

the mass loss ratio relative to initial crack width was
confirmed on the corrosion amount.

4. Steel Corrosion of RC with Construction Joint
Exposed to 40 years(1) Corrosion Current Density
Corrosion current density at construction joint (150mm) was
generally higher than that at the edge of specimen (60mm,
240mm) in case of A, D and F type as shown in Table 3.
Corrosion current density did not have a big difference byremoving laitance. Also, a clear tendency was not observed indifferent joint materials after exposure to submerged zone for40 years.
(2) Corrosion Area Ratio
Fig.2 shows corrosion area ratio of specimens withconstruction joint. Corrosion area ratio was 100% or near
100%, and steel corrosion occurred on the whole of steel
surface. A difference in corrosion area ratio by joint treatment
and cover was small at 40 years after exposure to submergedzone.
(3) Mass Loss Ratio and Corrosion AmountResult of mass loss ratio is shown in Fig.3. In the cases
without removing laitance, mass loss ratio of B and C typedecreased by 0.1-2.1% and 2.0-3.6% than A type, respectively.
In the other cases with removing laitance, mass loss ratio of E,
F, G type increased by 0.5-2.8% than D type, which was the
opposite tendency.

18

40mm

70mm

Corrosion Amount(mg/cm2)

20mm

Corrosion Area
Ratio (%)

100
80
60
40
20

200

40mm

C

D

70mm

160

120
80
40

0

0
A

B

C

D

E

F

G

A

Fig.2 Corrosion Area Ratio of specimens
with Construction Joint
12

20mm

40mm

B

E

F

G

Fig.4 Corrosion Amount of Specimens
with Construction Joint
100

70mm

Corrosion Area
Ratio (%)

Mass Loss Ratio (%)

20mm

9
6
3

80
5 years
15 years
32 years, W40_55
32 years, S40_55

60
40
20
0

0

0

A

B

C

D

E

F

0.1
0.2
0.3
Crack width (mm)G

Fig.3 Mass Loss Ratio of Specimens
with Construction Joint

0.4

Fig.5 Time History of Corrosion Area Ratio ofSpecimens with Initial Crack

Mass loss Ratio of D type decreased by 2.1-3.0% comparedwith A type, which is an effect of removing laitance. Whenthe joint materials were used, a clear difference by removing
laitance was not observed.
Result of corrosion amount is shown in Fig. 4. Corrosion
amount of A type is the highest value, which was 177.4, 185.2,
and 182.2 mg/cm2 at 20, 40, and 70 mm of cover, respectively.
Differences by joint treatment were the same trend as result ofmass loss ratio.

1.5 years

Corrosion Area
Ratio (%)

100

5 years

10 years

40 years

80
60

40
20
0
A

B

C

D

E

F

G

Fig.6 Time History of Corrosion Area Ratio ofSpecimens with Construction Joint

5. Time history of Steel Corrosion of RC with Initial
Defects Exposed to Marine Environment
Examination of the process of steel corrosion under the realenvironment was conducted by use of experimental andprevious results in this chapter.

and 10 years3), 4). Fig.6 shows corrosion area ratio of 20mm ofcover after 5, 15, and 32 years’ exposure to submerged zone.
Corrosion area ratio of A and F type were 0.2% and 0.6%
respectively at 1.5 years’ exposure. After 5 and 10 years,
corrosion area ratio of all types increased up to about 1-11%.
Steel bars in specimens exposed for 40 years were corrodedon the whole of steel surface. Therefore steel corrosion
developed during 30 years after the first 10 years. Thesetendencies were also observed in the cases of 40 and 70 mm
cover.

5.1. RC with Initial Crack Exposed to Tidal Zone forLong-Term
Previous Studies have investigated steel corrosion of RC
with initial crack exposed to tidal zone for 5 and 15 years1), 2).
Fig.5 shows corrosion area ratio after 5, 15 and 32 years’
exposure to tidal zone. Corrosion area ratio was about 1% at
5 years’ exposure, but it increased about 30% after 15 years.
After that, corrosion area ratio after 32 years became 100%.
Steel corrosion occurred on undersurface of the steel aroundthe initial crack after 5 years. Corrosion was confirmed on thesteel surface other than initial crack at 15 years. Also,
corrosion area was large around initial crack unlessautogenous healing was observed at 15 years.
Therefore, the following is considered. Steel corrosion starts
from around initial crack during the first 5 years’ exposure. It
develops on steel surface other than initial crack in the next 10
years, the whole steel bar is corroded by 32 years.
5.2. RC with Construction Joint Exposed to Submerged
Zone for Long-TermPrevious Studies have investigated steel corrosion of RC
with construction joint exposed to submerged zone for 1.5, 5,

6. Consideration of Empirical Equations andExperimental Results
6.1. Empirical Equations in Previous Studies
Some empirical equations were suggested in previous
studies so far. Then, experimental results were compared with
two following empirical equations with regard to the
corrosion amount.
(1) Empirical Equation 1
・W/C: 50~55%
1
q50-55 =0.092∙exp {(0.0423∙(W/C)-1.5091)∙Cl- }∙√cEq. (2)19

・W/C: 55~65%-

Table 4 Calculation Results of Specimen with
Initial Crack Exposed to Tidal Zone1q55-65 =0.092∙exp{(-0.0003∙(W/C)+0.8390)∙Cl }∙√c
Eq. (3)∆W∆W
W0
q= r ; qs = ⁄
=
∙q
⁄
t

=

(S 100 )∙t 100∙(S 100)(d⁄10)∙(1000ρ)400∙q=1.95∙d∙q

Experimental EmpiricalEmpirical
Water
Cover M ixing
Initial Crack
Results
Equation 1
Equation 2
Cement(mm) Water
Width (mm)Ratio (%)
(mg/cm2/year) (mg/cm2/year) (mg/cm2/year)

20

Where, qs：Corrosion Rate (mg/cm2/year), q:Corrosion Rate(%/year), Cl-：Chloride Ion Concentration (kg/m3), W/C：
Water Cement Ratio (%), c：Cover (mm), d：Diameter of a
Steel Bar(mm), ρ：Density of Steel (=7.8)(mg/mm3), L：
Length of a Steel Bar (mm), W0 ：Mass of a Steel Bar(=π(d/2)2Lρ)(mg), S：Steel Surface (=πdL)(mm2), ΔW：
Mass Loss (mg), ΔWr：Mass Loss Ratio (=ΔW/W0×100)(%), t：Period (year)(2) Empirical Equation 2
0.78
V3tn =(7.70∙Cltn +0.503(W⁄C)-40.6
Eq. (5)

S

65

40

55

S

65

0.2
0.2
0.1
0.2
0.3
0.2
0.1
0.2
0.3
0.2

55
W
65
70
0.2
55
S
65
W: Tap Water S: Sea water

Where, c: Cover (mm), Cltn: Chloride Ion Concentration(kg/m3), W/C: Water Cement Ratio (%)
Chloride ion concentration of specimens was assumed as
15.0 kg/m3 from the previous studies4).
6.2. Comparison of Empirical Equation andExperimental Results
Calculation results of specimen with initial crack wereshown in Table 4. Corrosion rate given from empiricalequation 1 was much higher than that of experimental results.
The values given from empirical equation 2 were close to theexperimental results. Those were 0.4-1.6 times as large as theexperimental results.
Table 5 is calculation results of specimen with construction
joint. Corrosion rate given from empirical equation 1 wasmuch higher than that of experimental results as well as thecalculation results of specimens with initial crack. Corrosion
rate given from empirical equation 2 was 1.9-3.3 times aslarge as that of experimental results.
In two empirical equations, empirical equation 1 describesthat corrosion rate increase exponentially. On the other hand,Corrosion rate increased linearly in empirical equation 2.
Chloride ion concentration used in this calculation was aslarge as 15.0kg/m3 because the specimens were exposed tomarine environment for long-term. Then, calculation results
became excessively higher in empirical equation 1 due tochloride ion concentration in the exponent part. Therefore
empirical equation 2 may by good for the case of highchloride ion concentration
Chloride ion concentration and water cement ratio are usedas parameters in the above empirical equations. However, theamount or permeability of oxygen are not considered in theequations even though oxygen is also one of important factors
on steel corrosion. It may be necessary to consider a highchloride ion concentration and the influence of exposure
condition like amount of oxygen when corrosion rate isconsidered after long-term exposure

8.19
9.17
9.64
9.23
6.37
6.35
6.71
5.87
6.14
6.30
7.16
5.95

110188113714110188113714

13.70
18.94
5.47
-

58898
60783
58898
60783

13.1

77915
80408

9.3

77915
80408

7.0

Table 5 Calculation Results of Specimens with Construction
Joint Exposed to Submerged Zone
Empirical
Empirical
Water Experimental
Cover
Results
Equation 1
Equation 2
Cement(mm)
Ratio (%) (mg/cm2/year) (mg/cm2/year) (mg/cm2/year)
20
40
70





50

4.0

4617

13.1

3.8
3.6

3265
2468

9.3
7.0

With laitance removal, mass loss ratio of E, F and G typeincreased by 0.5-2.8% than D type, which was the
opposite tendency.
Steel corrosion of specimen with initial crack developed
after 5 years’ exposure. Steel corrosion of specimen withconstruction joint developed during 30 years after 10
years’ exposure
Calculation results became higher in empirical equation
1 due to chloride ion concentration in the exponent part.
Empirical equation 2 may by good for the case of highchloride ion concentration

References
1) H. SEKI, N. OTSUKI, Y. HORII: Corrosion of Reinforcing
Bars due to Concrete Mixed with Sea Water and Residual
Crack (Five Years’ Test), TECHNICAL NOTE OF THE
PORT HARBOUR RESEARCH INSTITUTE, No. 237,
pp.3-25, 1976.6
2) T. U. Mohammed, N. Otsuki, H. Hamada：Corrosion of
Steel Bars in Cracked Concrete under Marine Environment,
Journal of Materials in Civil Engineering , No. 15, pp.460469, 2003.10
3) S. NAGATAKI, N. OTSUKI, M. HISADA: Influence of
Joints and Cracks on Macro-Cell Corrosion in Reinforced
Concrete, Proceeding of the 4th International Conference on
Structural Failure, Durability and Retrofitting, pp.327-334,
1993
4) F. UKISHIMA: Experimental investigation on Corrosionof Steel Bars in Concrete with Horizontal Construction Joint
under marine environment, a doctoral thesis, Tokyo Instituteof Technology, 2008

7. Conclusions



55

W

√c



55
65
55
65

W

Eq. (4)

Corrosion area ratio, mass loss ratio and corrosion
amount increased as increase in initial crack width inspecimens mixed with sea water with initial crack
Without laitance removal, mass loss ratio for samples
with joint covered with mortar and epoxy resin (B, Ctype) decrease by 0.1-3.6% than not covered sample A.

20

STUDY ON CEMENT-FREE CONCRETE USING FLY ASH
Student Number: 15M18121

Name: Hikaru TANAKA Supervisor: Hirofumi HINODE,
Keisuke MATSUKAWA

フライアッシュ使用セメントフリーコンクリートに関する研究
田中光

本研究では，セメント使用量の削減と廃棄物の有効利用の観点から，セメントの代わりにフライアッ
シュと水酸化カルシウムを混合した粉体を使用し，練り混ぜ水として水道水の他に海水やアルカリ溶液
を用いてコンクリートを作製することを検討した．結果として，練り混ぜ水にケイ酸ナトリウムと水酸
化ナトリウム水溶液を用いることで，材齢 28 日において 2.7MPa の強度が得られ，外観上通常のコンクリートと遜色ない硬化体を作製することができた．

1 Introduction

2 Materials

1.1 Background

2.1

Concrete, which is usually made from cement, is one of
the most common structural materials. It is used forbuildings, dams, roads, and many other infrastructures.
Nowadays, the cement usage in the world is increasing
with the rapid development such as in China and India. It
leads to an environmental problem because the productionof cement is energy-intensive and emits a great deal of
CO2. On the other hand, the fly ash discharged from coal
fired power plants is also increasing. The amount ofdischarged fly ash is already beyond the utilization
capacity because coal fired power plants owe a part of theelectricity supply which is originally assigned to nuclear
power plant. Therefore, from the environmental viewpoint,
the cement usage in concrete should be decreased and therecycling of fly ash should be further promoted.

The fly ash used in this experiment is classified as “classF” by ASTM C618 which contains CaO less than 10 wt%.
Chemicals are all regent grade. The chemical compositionof the artificial seawater is in accordance with ASTM D
1141-98.

1.2

Specifications

Existing methods

There are two major methods which can decrease thecement usage and increase the fly ash utilization. One is
fly ash concrete, and the other is geopolymer concrete. As
shown in Fig. 1, fly ash concrete can reduce the cement
usage by 5-30 wt% and geopolymer concrete can be madewithout using cement. Instead of the use of cement,
geopolymer concrete needs alkaline solution as anactivating solution.

Figure 1

Comparison of Existing Methods

1.3 Objectives
The general objective of this research is to make cementfree concrete by utilizing coal fly ash. In order to achieve
this objective, the specific objectives are as follows:
1) To propose new mixtures which can be alternatives tocement concrete and check their feasibility in terms ofstrength and workability.
2) To conduct analysis study in the hardening mechanismin the proposed mixtures.

Figure 2

Flow of Study

Table 1Mix Proportions

Binder Composition （wt％）
Lot.

Cement（OPC)

Fly Ash
（FA)

OPC

100

0

1.4 Flow of the study

FA70_SW30

The flow of the study is shown in Fig. 2. The investigationon the proposed mixtures conducted in two ways. One is
to check the feasibility by making concretes and measure
their strength. The other is to investigate the mechanismof hardening in the proposed mixtures by some test
analyses.

GP

0

CH
CH_SW
CH_NS
CH_NS_NH

21

SodiumMixingNaOH
Silicate
Slag
Water(mol/L)
Ca(OH)2
（g/L)
（BFS)
0

0

Tap

0

0

70

0

0

Sea

0

0

88

12

0

Tap

130

0.5

0

70

0

30

Tap

0

0

0

70

0

30

Sea

0

0

0

70

0

30

Tap

130

0

0

70

0

30

Tap

130

0.5

2.2

total Ca content of Ca(OH)2, CaCO3, and C-S-H is

Mixtures16.2 wt% (i.e. initial Ca content).

The mix proportions used in this study are listed in Table
1. OPC, FA70_SW, and GP means ordinary Portland
cement, fly ash concrete mixed with seawater with the fly
ash replacement ratio of 70 wt%, and geopolymer,
respectively. The other mixtures are newly proposed inthis study. The common binder components of the new
mixtures are fly ash and Ca(OH)2, of which mass ratio is7:3. CH, CH_SW, CH_NS, and CH_NS_NH means thebinder is mixed with tap water, seawater, sodium silicate(NS) solution, and sodium silicate and NaOH (NH)solution, respectively.

Figure 3

2.3 Preparation of specimens

3.4

Paste specimens were made of water and binder. The
water-binder ratio (W/B) is 0.5. Mortar specimens weremade of water, binder, and sand. The mass ratio between
binder and sand is 1:2. The W/B is 0.5. Concrete
specimens were made of water, binder, sand, and gravel.
The ratio of sand to aggregates (sand and gravel) is 44
wt%. The W/B was 0.5 for OPC, FA70_SW and GP, and
0.6 for CH_SW and CH_NS_NH. All specimens were
cured in air for 7 days. Afterwards, specimens weredemolded and cured under tap water until the testing day.

4 Results and Discussion
4.1 Strength and feasibility
4.1.1

Compressive strength

The results of strength test are shown in Table 2. The
tendency of the strength development of mortar andconcrete were almost the same as paste. The 28-day
strength and 91-day strength were almost the same for all
samples. Thus, by taking the paste samples asrepresentative, the strength development between the 7
and 28-day was compared in Fig. 4. “xO.O” in the figuremeans the strength ratio of 28-day to 7-day and that of
OPC was x1.8. The acceleration of pozzolanic reaction by
seawater was indicated in the previous study [1]. In thisresearch, the acceleration was confirmed in thecomparison of CH and CH_SW. Compared with OPC,
FA70_SW, and GP, the strength ratios of 28-day to 7-day
samples for the proposed mixtures were larger. As there isno cement hydration in the new mixtures and strength
development in this period is known to be attributed topozzolanic reaction for fly ash concrete, the main
hardening reaction of proposed mixture is considered to
be pozzolanic reaction.

Compressive strength

For the paste test, 40x40x40 mm cube specimens wereprepared and testing age were 7, 28, and 91-day. For the
mortar test, φ50x100 mm cylinder specimens wereprepared and testing age were 7 and 28-day. For theconcrete test, φ100x200 mm cylinder specimens wereprepared and testing age were 7 and 28-day.

3.2

Infrared spectroscopy (FTIR)

Samples for Fourier-transform infrared spectroscopy weretaken from the paste cube specimen after 28-day strength
test. Measured range is 400-4000 cm-1. Scan speed is 2
mm/sec. Cumulated number is 16 times.

3 Experimental Methods
3.1

Estimation of C-S-H by TG-DTA

X-ray diffraction (XRD) analysis

Samples for analysis were taken from the paste cube
specimen after 28-day strength test. Type of x-ray is Cu
Kα and measured range is 2θ=5-60°.

3.3 Thermal analysis (TG-DTA)
Samples for thermogravimetry-differential thermal
analysis were taken from the paste cube specimen after 7
and 28-day strength test. The reference material used wasγ-alumina, and the sample was analyzed under air.
Analyzed temperature was RT-900°C. Heating rate was
10°C/min.
Calcium silicate hydrate (C-S-H) is one of the major
products of hydration of CaO-based cementitious
materials. Especially in the reaction between fly ash(amorphous-SiO2) and Ca(OH)2 , that is called pozzolanic
reaction, C-S-H is produced by the reaction described asformula (1).
a-SiO2 + Ca(OH)2 + H2O → C-S-H(1)
Thus, the amount of C-S-H reflects the intensity of
pozzolanic reaction for proposed mixtures (FA +Ca(OH)2). In this study, the amount of C-S-H wasestimated by the procedure shown in Fig. 3. The initial
content of Ca is only Ca(OH)2 (16.2 wt% as Ca). After thereaction, Ca(OH)2 changes into three calcium compounds,
Ca(OH)2, CaCO3, and C-S-H. The content of Ca(OH)2 andCaCO3 can be measured with TG-DTA, so the content of
C-S-H can be calculated. In order to compare the amountsof the three different calcium compounds, the weights ofall of the calcium compounds were converted into theweight ratios of Ca to the total weights. Therefore, theTable 2

Results of Compressive Strength Test
Compressive Strength (MPa)

Lot.

PASTE

MORTAR

7-day

28-day91-day7-day

28-day7-day

28-day

OPC

27.7

50.3

47.9

21.1

35.7

26.5

37.2

FA70_SW

5.8

10.0

9.6

2.8

6.2

3.1

6.1

GP

3.9

5.1

5.2

1.9

3.4

1.9

3.6

CH

0.2

0.7

-

-

-

-

-

CH_SW

0.7

2.1

2.1

0.3

1.0

0

1.1

CH_NS

1.6

4.8

4.8

-

-

-

-

CH_NS_NH

1.8

4.2

4.3

0.8

3.2

0.6

2.7

Table 3

22

CONCRETE

Slump Test

OPC

FA70
_SW

GP

CH_SW

CH_NS
_NH

W/B

0.5

0.5

0.5

0.6

0.6

Slump
(cm)

13

6

14

9

8

Figure 4

4.1.2

28-day/7-day Compressive Strength

Feasibility

The results of slump test (JIS A 1101) are shown in Table
3. For the proposed mixtures, sufficient slumps wereobtained by increasing the water content up to 0.6 in W/Bratio. Fig. 5 shows the appearance of concrete. The
appearance of CH_NS_NH was as good as OPC concrete.
The compressive strength of the concrete as shown in
Table 2 are of strength level sufficient for the ground
improvement [2]. In terms of workability, strength, andappearance, the proposed mixtures are considered feasible.

Figure 5

Appearance of Concrete (28-day)

4.2 X-ray diffraction (XRD)
The results of XRD analysis are shown in Fig. 6. The word
“powder” means binder which is not mixed with mixing
solution. The raw fly ash consists of quartz, mullite, andamorphous-SiO2 (“FA powder” in (a)). Regarding GP (b),there is no significant change by mixing with solutions.
For the proposed mixtures (c), the peak around 2θ=11-12°
appeared after the reaction, which is considered to becalcium aluminate hydrate [3]. Regarding the proposed
mixtures, the broad peak around 2θ=20-45° shifted
upward by the mixing. The reason of this shift is thoughtthat the amount of amorphous increased by the generationof C-S-H substances.

4.3 Thermal analysis (TG-DTA)
Compositions of calcium compounds in the samples andtheir comparison are shown in Fig. 8. The tendency of CS-H content at age 7-day is similar to that of the strength
on the same age. Thus the reason of strength development
is considered to be C-S-H generation. Comparing the CS-H content of CH and CH_SW with that of CH_NS and
CH_NS_NH, the latter is larger. The clear difference
between the former and the latter is the alkaline solution.
Using alkaline solution, it is considered that fly ash
dissolution is accelerated and the pozzolanic reaction was
promoted. Graph (c) shows the comparison of C-S-H
content between 7-day and 28-day. Addition of alkali (NSor NH) leads to smaller C-S-H content ratio of 28-day to
7-day. That means addition of alkali accelerates thereaction in early age.

Figure 7

Figure 6

Pozzolanic Reaction (XRD)

Figure 8

23

Results of XRD analysis

Results of TG-DTA

4.4

Infrared spectroscopy (FTIR)

The results of FTIR are shown in Fig. 9. The peaks whichcorrespond to CO2, Ca(OH)2, CaCO3, sodium silicate(NS), NaOH (NH), and O-H stretching are indicated in thegraphs. The peak of 1080 cm-1 which corresponds to Si-O
symmetric vibration is the peak of raw fly ash (“FApowder” in (a)). The other peaks, 961 cm-1, 1022 cm-1, and1058 cm-1 are discussed as follows.
(1) Peak of 1022 cm-1
The peak of 1022 cm-1 appears in GP result (b). As shownin graph (b), the peak of 1080 cm-1, which corresponds to
the Si-O symmetric vibration peak of the raw fly ash,shifted to 1022 cm-1 after the reaction. One of thecharacteristic peaks related to geopolymer is the peak of1008 cm-1, which corresponds to Si-O and Al-O
asymmetric vibration [4]. Geopolymerization is thereaction in which Al dissolved from fly ash are inserted
into Si-O-Si-O- chain and polymerize. Therefore, the peakshift from 1080 cm-1 to 1022 cm-1 is considered to be theincrease of Al-O bond caused by geopolymerization. The
peak of 1022 cm-1 is the result of 1080 cm-1 peak
disappearance and 1008 cm-1 peak appearance.
(2) Peaks of 961 cm-1 and 1058 cm-1
First, the peak of 961 cm-1 can be observed in the all
proposed mixtures, OPC, and FA70_SW after reaction.
Possible chemical compounds contained in those samples
are Ca(OH)2 or C-S-H. Ca(OH)2 does not have a peak of
961 cm-1. Thus, the peak is considered to be the peak of
C-S-H. This fact is confirmed in another research [5]. The
peaks of 1058 cm-1 were observed in CH and CH_SW
clearly, and CH_NS slightly. It seems to have a relation to
the alkalinity. As alkalinity becomes higher, the peak of1080 cm-1 and 1058 cm-1 disappears. Therefore, it isthought that there is a tendency that the peak of the raw
fly ash disappears and shifts to the peaks of smaller
wavenumber as the hardening process.

5 Conclusions
In this study, in order to make cement-free concrete withutilizing coal fly ash, proposed mixtures are proposed andinvestigated from two aspects: one is from feasibility
viewpoint, the other is by analysis of chemical reaction.
1) Feasibility of proposed mixtures
By increasing the water content up to 0.6 in W/B,
proposed mixture concrete can be casted with sufficient
workability as OPC concrete. The appearance of the
CH_NS_NH concrete was as good as OPC concrete. Its
compressive strength was 2.7 MPa on 28-day, which is
sufficient for actual uses such as ground improvement.
2) Analysis of hardening mechanism
Both pozzolanic reaction and geopolymerization occurs inproposed mixtures. Especially, by using FTIR, those tworeactions are separated to some extent. Geopolymerization
is observed as peak shift from 1080 cm-1 (Si-O symmetricvibration) to smaller wavenumber. Pozzolanic reaction isobserved as a peak shift to 961 cm-1 (C-S-H).

Figure 9

Results of FTIR Analysis[3] H. E. Schwiete and U. Ludwig (1968), “Crystal
Structures and Properties of Cement Hydration Products”,
Fifth International Symposium on the Chemistry of
Cement.
[4] J. Davidvitz, “Geopolymer: Chemistry &Applications”, 4th edition, 2015.
[5] G. Igarashi and I. Maruyama (2013) , “Modeling of
Water Adsorption of Calcium Silicate Hydrate”, AIJ Tokai
Chapter Architectual Research Meeting.

References[1] C. Yi (2016), “Lifetime Prediction of Reinforced
Seawater Concrete Mixed with Fly Ash Which Were
Produced in Japan or Philippines Against Chloride
Attack”, Master thesis of Tokyo Institute of Technology.

[2] K. Matsukawa, “Treatment of Solid Waste
Contaminated with Heavy Metals”, 2013 Proc. SeminarWorkshop “Utilization of Waste Material”, 2013.
24

A study on residential relocation focusing on life stage changes for compact cities
Student Number: 14M18046

Name: Kosuke Kiriyama

Supervisor: Shinya Hanaoka

集約型都市構造実現のためのライフステージ変化を考慮した転居ターゲット層の分析
桐山

弘有助

集約型都市構造実現のためには、世帯の居住誘導が必要となる。本研究では、世帯をライフステージに分類
した上で、転居傾向や交通行動、転居意向から集約型都市構造に向けたターゲット層を把握する。分析の結果、若年の単身世帯は短期的な視点において、若年の夫婦のみ世帯は長期的な視点においてターゲットにな
りえ、一方、退職や子の独立後の世帯を居住誘導するためには、意識の変容が求められるべきと分かった。

1.

Background2.

Compact city and transit-oriented development havebeen introduced in many cities in the world. The Japanese
government has also announced to promote compact andconsolidated urban layouts with multipolar networks as apolicy. Location optimization plans was established in2014 to proceed compact city inducing the residential andurban function to the city center or the area along withrailway as well. Each local government selects “areas that
attract urban functions” and resident-attracting district are.
Resident-attracting district is the area where residents canaccess public transportation easily. And people are induced
to change residence to those area to stabilize the population
density. Then, it is necessary to clarify the appropriate
target to live in resident-attracting districts and use theservice of areas that attract urban functions.
To achieve such urban form, residential relocation fromsuburban area to surrounding area of public transportation
corridors are necessary. Decisions to change residence
affect households, and are affected by life stage changes
such as education, marriage, having children and retirement.
It is important to induce residential relocation withconsidering transition of life stage. Simultaneously, trip
character change such as shift from automobile to public
transport is required to realize low carbon society andsustainability. Residential relocation can also act as a
crucial point for a household to decide if it will own and use
a family car or not.
The objectives of this study are 1) to classify household
data into type of life stage and analyzing the preference toselect living area of each life stage type, 2) to analyze theresidential relocation pattern and the trip character after
residential relocation in each life stage, and 3) to clarify thestratified groups which may be most suited to compact
urban layouts and give recommendation for policy ofresident-inducing.

Data

The data of trip character of each family member andhistorical data of residential relocation is needed to clarify
the stratified groups which may be most suited to compact
urban layouts. In Japan, the National Person Trip Surveys(NPTS) has been conducted in major cities andmetropolitan areas 6 times. The survey used in this researchis fifth one. This Survey can be divided into “individual /household sheet” and “awareness/ experience questionnaire
survey sheets”. The outline of this survey is shown in Table
1.
Table 1. Outline of the 5th Nationwide Person Trip Survey.

3.

IndexesContents

TargetpeopleThe responses from approximately 500 households percity (all member over age of 5 )Targetarea

Urbanization Promotion Area

Surveydate

A weekday and a holiday in November or October,
2010

SurveymethodReceiving sheet by mailType ofsheetsIndividual survey sheets (trip character on weekday
and holiday), household survey sheets and awareness/experience questionnaire survey sheets

Methodology

The stratified groups which may be most suited to compact urban layouts are clarified by using following two
approach.
1. Clarifying the normal targets for compact city from trip
character data and historical data of residence
relocation. :The normal targets mean households which tend to change
residence from outside of resident-attracting districts toresident-attracting districts and change trip character suchas shift from automobile to public transport.
2．Clarifying the potential targets for compact city fromtheir determining factor to choose the location. :The characteristics of households such as the number of
persons per household and car ownership are considered as
one of the factors of individual-unit analysis by using
person trip data in many previous studies [1]. There are somethesis which analyze the relationship between residential
relocation and the characteristics of households as well [2].
However, they do not treat with the relationship between
residential relocation, trip character and life stage.

The potential targets mean those who changed residence
to outside of resident-attracting districts, although they had
residential relocation awareness to change residence toresident-attracting districts when they change residence.
Steps of classifying the target is following;25

each family composition when households change
residence” are set.

Step (1): Classifying household into each segmentation(1-1): Classifying the household data into segmentation oflife stage when thy change residence.

(1) Estimating the present family composition
Each household are classified into segmentation of present
family composition, using the data of “relationship between
household members” and “the age of household members”.
Table 2 show these criteria and result.

(1-2): Classifying the districts before and after residential
relocation into the residential zone types.
(1-3):Classifying the residential zone types into
segmentation of Resident-attracting district, referring
the location optimization plans written by each local
government.

Table 2. Family composition and its criteria

Step (2): Clarifying the normal targets(2-1): Analyzing the pattern of residential relocation
Using the data of residence before and after residential
relocation of each household, “the type of residence after
residential relocation” and “residential relocation pattern”
is analyzed in each life stage.

Criteria
The number of household is 1.

After child’s
independence

1. The number of household is 2.
2. The relationship between
household members are husband
and wife.
3. Present age of householder ismore than 50 years old.

Beforegiving birth

1. The number of household is 2.
2. The relationship between
household members are husband
and wife.
3. Present age of householder is lessthan 50 years old.

Marriedcoupleonly(2-2): Analyzing the trip character“The frequency to use public transportation a year”, “Thevariability of trip character” and ”The situation of usingcars” of those who change their residence to residentattracting district are analyzed in each life stage.
(2-3): Discussion of the normal target
The normal target are and residential relocation inducing
policy are discussed by using the result of (2-1) and (2-2).

Married couple andchild(ren)Step (3): Clarifying the potential targets(3-1): Striking an average of the residential change
awareness.
Striking the average of the consideration degrees as theattractive area when the respondents selected the present
house in two residential relocation pattern, “to resident
attracting district” and “not to resident-attracting district”

1. The number of household is morethan 3.
2. The relationship between
household members are husband,wife and child.

(2) Estimating the life event which household experience ineach family composition.
Firstly, the life events which household experience in eachfamily composition are set. Secondary, the criteria to judge
which life event households belong to when they change
residence. This life event of each family composition is thesegmentation of life stage in this research. Finally, thehouseholds are classified into segmentation of life stage
when they changed residence based on criteria of life event.
Table2 shows the each segmentation of life stage, its
criteria and its household number of sample.

(3-2): Discriminant analysis
Calculating the function to discriminate “the households
which change residence to resident-attracting districts” and“the households which change residence to outside ofresident-attracting districts”, using their average score ofresidential change awareness.
(3-3): Clarifying the potential target by the score of
discriminant function
Calculating the score of the discriminant function of “thehouseholds which change residence to outside of residentattracting districts”, applying the average score of theresidential change awareness in each life stage.

Table 3. Life stage and its criteria
Life
stage

LS1
LS2
LS3

4.

Family composition
One-person households

Classifying data into each segmentation

LS4

4-1.Classifying the household into segmentationof life stage
It is necessary to estimate the life stage of households
when they change residence, because there is no data
regarding it in NPTS. In previous studies, “relationshipbetween household members” and “the age of household
members” are used to estimate the life stage. In addition tothem, “the period of living present residence” is used toestimate the life stage when household change residence inthis research. Using these 3 factors, segmentations of life
stage which are the combination of “present family
composition” and “life event supposed to experience in

LS5
LS6
LS7
Life
stage

LS8
LS9

Present family composition

Life event
when changing
address

Enrollment/ Empolyment
One-person households
OthersRetirement/ Independence
Marriage
After/ Childbirth
Marriedchild's
Retirement
couple only
dependence/ Independence
Education
Before giving birth
Marriage
Present family composition

Life event
when changing
address

Age of householder Number
when changing
ofaddress
sampleless than 22622from 22 to 60

7725

more than 60

499

less than 30

313

from 30 to 60

3125

more than 60
less than 30

391
741

Age of eldest child Number
when changing
ofaddress
sampleMarriageless than -1
Childbirthfrom -1 to 4
Education
LS10
from 4 to 12
for infants
Married couple with child(ren)
Education
LS11
from 12 to 22
for students
Retirement
LS12more than 22/ Independence

26

565
934
1289
425
230

4-2.Classifying districts into residential zone types.
In order to define some districts as Resident-attracting
district, it is necessary to classify the districts into
residential zone types firstly, because there is no data
regarding characteristics of residential area in NPTS data.
Residential zone types is the classification which reflects
the characteristics of residential area such as land use,
distance from station, and distance from city center. This
segmentation is classified in Nakamichi(2008), and the
updated one in Lu(2016) is used in this research [3][4].

important factor to induce residential relocation. Oneperson households tend to live in rental apartment and not
to have car. After getting married, people tend to startowning their houses and car gradually. And One-personhouseholds also have characteristics to change residence toresident-attracting districts and use public transportation.
The same may be said of the households after retirement orchild’s independence. And it can be said that thehouseholds after retirement or child’s birth can adapt thecharacteristics of compact city because they tend toincrease the frequency to use public transportation and walkafter changing residence to District (ii)

4-3.Classifying the residential zone types into
segmentation of resident-attracting district
125 residential zone types are classified into segmentation
for resident-attracting districts. Based on 3 issues: “thepolicy issue response-based urban planning procedural
guidelines drawn by Ministry of Land, Infrastructure,
Transport and Tourism”, “laws which is relevant to location
optimization plans” and “location optimization plans drawn
by each local government”. This research refers 4 local
governments published their official location optimizationplans. And it is identified that 2 characteristics, “land use
regulation” and “distance from station”, have large impact
to decide where resident-attracting districts are.
Using these factors, the following 3 categories are defined.

Table 5. Result of residential relocation pattern and trip character
1.Average 2. Type ofLife

Age of

stage

house

LS1

LS2

Outside of urban planning area
Exclusive industrial zone
Urbanization control area (50% ~)-LS3

Semi-industrial zone
Urbanization control area (~ 50%)
Mixed zone (mainly industrial zone)

Other land use regulation

5.

1km or more
Less than 1km
1km or more
Less than 1km
1km or more
Less than 1km
1.5km or more
1km or more /less than 1.5km
Less than 1km

District (iii)
District (i)District (iii)
District (i)District (iii)
District (i)District (iii)30

District (ii)

47

use

transportaion

districts

in Disrict (ii)Times/year Variability

2-a 19% 3-a 36% 4-a 27% 5-a

298 5-a 2.18

2-b

461 5-b 0.93

4% 3-b

2% 4-b 47% 5-b

54.0

74.6

LS4

59.7

66.3

2-b 11% 3-b

322 5-b 2.07

2% 4-b 40% 5-b

72.6

48.2

2-b 15% 3-b

421 5-b 0.73

1% 4-b 36% 5-b

47.6

45.0

2-b

5% 3-b 16% 4-b 26% 5-b

349 5-b 1.03

8% 3-c166 5-c

5% 4-c 26% 5-c

LS10

46.3

451 5-a 2.29

2-b 12% 3-b 19% 4-b 28% 5-b

325 5-b 2.02

8% 3-c 14% 4-c 33% 5-c

LS11

50.4

68.5

2-b 25% 3-b 16% 4-b 36% 5-b

345 5-b 2.63
130 5-c0.87

2-a 52% 3-a 64% 4-a 38% 5-a

444 5-a 3.67

2-b

296 5-b 4.11

7% 3-b 23% 4-b 34% 5-b

135 5-c

9.17

2-a 62% 3-a 69% 4-a 41% 5-a

583 5-a 1.59

2-b 16% 3-b 24% 4-b 33% 5-b

198 5-b 4.51

7% 4-c 26% 5-c

210 5-c

1.67

2-a 66% 3-a 64% 4-a 34% 5-a

470 5-a 1.36

2-b 15% 3-b 32% 4-b 36% 5-b

187 5-b 2.05

4% 4-c 30% 5-c

210 5-c

3.78

2-a 68% 3-a 66% 4-a 36% 5-a

463 5-a 1.41

2-b 13% 3-b 29% 4-b 30% 5-b

163 5-b 2.76

4% 4-c 34% 5-c

203 5-c

4.52

2-a 62% 3-a 62% 4-a 42% 5-a

407 5-a 1.31

2-b 18% 3-b 29% 4-b 33% 5-b

198 5-b 1.43

8% 4-c 26% 5-c

240 5-c

1.65

2-a 72% 3-a 56% 4-a 30% 5-a

387 5-a 1.45

2-b 16% 3-b 21% 4-b 41% 5-b

338 5-b 10.4

(years old) 2-c

8% 3-c 22% 4-c 30% 5-c

184 5-c2-a: Owned house, 2-b: Rental house,
2-c: Rental apartment
3-a: Owned car, 3-b: Shared car, 3-c: No
4-a: District (i), 4-b: District (ii), 4-c: District (iii)
5-a: car, 5-b: on foot, 5-c: train/bus

27

2.79

373 5-a 1.14

(years old) 2-c 13% 3-cLS12

162 5-c2-a 52% 3-a 52% 4-a 30% 5-a(years old) 2-c 15% 3-c

In order to clarify the normal targets, residential
relocation pattern and trip character before and after
residential relocation is analyzed. Residential relocation
pattern and trip character are analyzed in each life stage,“1.age of householder”, “2.type of residence”, “3.situationof car used”, “4.ratio of households changing address toeach district”, “5.The number of times to use public
transportation a year and the variability of trip character ofpeople who change residence to resident-attracting districts“are calculated. (Table 5)
Discussion on the normal target is following;
The type of residence and whether to have car are

1.47

2-a 78% 3-a 66% 4-a 38% 5-a(years old) 2-c 14% 3-cClarifying the normal target

1.59

513 5-a 2.24

(years old) 2-c 17% 3-cLS9

226 5-c2-a 87% 3-a 78% 4-a 48% 5-a(years old) 2-c 38% 3-c 13% 4-c 28% 5-c
LS8

2.59

297 5-a 3.30

(years old) 2-c 19% 3-c 29% 4-c 34% 5-cLS7

187 5-c2-a 28% 3-a 25% 4-a 31% 5-a(years old) 2-c
LS6

2.27

357 5-a 3.02

(years old) 2-cLS5

249 5-c2-a 39% 3-a 54% 4-a 30% 5-a(years old) 2-c 51% 3-c 70% 4-c 32% 5-c

# of
zones

7
4
14
3
1
6
3
2
2
4

District (i)5. Using frequency of(years old) 2-c 46% 3-c 43% 4-c 30% 5-c

Table 4. Criteria of segmentation of resident attracting district
Category

32.4

4.Resident(years old) 2-c 74% 3-c 61% 4-c 26% 5-c

Table 4 shows the criteria of each segmentations forresident-attracting districts. (RZT: residential zone types)
Distance fromstation

3. Carthey can attracting

holderDistrict (i): The districts which cannot be
resident-attracting districts.
District (ii): The districts which may be resident-attracting
districts.
Districts (iii): The districts which must be
resident-attracting districts.

Land use regulation of RZTResidence

1.34

6.

positive number. This result means that almost households
could change residence as desired. On the other hand, thescores are negative number at LS1, LS2 and LS7.
Especially it is really low at LS1. It can be said that they are
the potential targets.

Clarifying the potential target

6-1. Striking an average of the residential relocation
awareness.
Residential relocation awareness is analyzed by using theresults of awareness/experience questionnaire survey
sheets in the fifth NPTS. The questionnaire No. 9-(5) is theconsideration degrees of eight perspectives as the attractive
area when the respondents selected the present house. The
perspectives are (i) City center or sub-center or the area
which is close to them, (ii) The shopping district or the area
which is close to it, (iii) The area where it is easy to go to
shopping center in suburb, (iv) The area where there ismuch green, (v) The area where it is easy to get parking
lots, (vi) The area where it is useful to use public
transportation, (vii) The area where it is enable to use only
bicycle or take a foot in daily life and (viii) The area where
it is useful to drive. The average values of eachconsideration degrees are calculated for each life stage.

7.

Conclusion

The discussion on targets for compact city and resident
inducing policy through 3 steps is following;
One-person household, especially LS1 (life event:enrollment/employment) tend to change residence toresident-attracting districts and tend to use public
transportation in Resident-attracting districts. In addition,
LS1 which change residence to outside of residentattracting districts had residential relocation awareness tochange residence to resident-attracting districts. Therefore,
in order to let LS1 change residence to resident-attracting
districts, it is necessary to improve the environment such as
money to change residence and the apartment for LS1.
However, the household which people experience early,
tend to staying in rental apartment and tend to change
residence in short term. It can be said that they are target
from the view point of short term.

6-2. Discriminant analysis
In order to clarify the potential targets, discriminant
analysis is performed, using these results of 2 residential
relocation pattern. Firstly, the discriminant function arecalculated, which discriminate the households whichchange residence to District (i) and households whichchange residence to District (iii). Secondary, the averagescore of consideration degrees of those who change
residence to District (i) are applied into this discriminantfunction in each life stage. Finally, the potential targets areclarified by the score of discriminant function in each life
stage.

On the other hand, LS7 tend to change residence to outsideof resident-attracting districts. But, they also had residential
relocation awareness to change residence to residentattracting districts actually. Therefore, it can be said thatthey are potential targets. LS7 also tend to own cars after
residential relocation, and tend to increase frequency to use
car. However, they tend to adjust to the character ofresidential districts, such as using the public transportation
often in resident-attracting districts. And the LS7 tend tochange residence to districts whose character is the same as
districts before residential relocation. Therefore, it isnecessary to improve the environment for the households
of LS7, in order to sustain compact city from the view pointof long term. Especially residential relocation inducing
policy such as providing a housing loan is appropriate to letthem live for long time, because people tend to stay in theowned house from this life stage.

(1) Calculating the discriminant function
The discriminant function (eq.1) are calculated. In thisfunction, the score of discriminant function are calculated,which is the sum of score which is the consideration
degrees of eight perspectives are multiplied by thecoefficients of discriminant function of each perspective. If
it is positive number, the household is estimated as one
which change residence to District (i), if it is negative
number, the household is estimated as one which change
residence to District (iii).

The households which change residence with opportunityof retirement or child’s independence, such as LS6 andLS12, tend to change residence to resident-attracting
districts. However, they do not tend to use public
transportation and have residential relocation awareness tochange residence to resident-attracting districts. Therefore
it is necessary to change their mind of residential relocation,in order to let them change residence to resident-attracting
districts.

(eq.1)(2) Applying the score into discriminant function
Figure 1 shows the average score of consideration degreesof those who change residence to District (i) are applied
into this discriminant function in each life stage.(Accuracyof discriminating is 61.8%.)

References[1] Taniguchi, M., Murakawa, T., Morita, T., 1999. Designation of common segmentation fortrip maker and their influence on urban transport. Infrastructure Planning Review 16, 601607. (in Japanese)[2] Nakamichi, K., Taniguchi, M., Matsunaka, R., 2008. The possibility for reduction of car
dependence from the perspective of relocation for compact city - A study on change of trip
character before and after relocation with a focus on metropolitan area -Journal of the City
Planning Institute of Japan 43-3, 889-894. (in Japanese).
[3] Kumiko NAKAMICHI, Mamoru TANIGUCHI and Ryoji MATSUNAKA (2006)
Development and Application of “a Pictorial Encyclopedia of Japanese Residential Zones”
for sustainable urban planning, 9th Pacific Regional Science Conference Organization(PRSCO) Summer Institute, Kuala Lumpur, Malaysia.
[4]中道久美子・呂田子・花岡伸也: 大都市圏及び地方圏都市における住宅地タイプと交通行動特性の変化, 第 53 回土木計画学研究発表会, 土木計画学研究・講演集,
Vol.53, CD-ROM, 2016.

Figure 1. The score of discriminant function.

Overall, the score of discriminant function tend to be28

Clarification of Supply Chain in Tokyo Metropolitan Area
Student Number: 15M18196 Name: Yutaro WATANABE
Supervisor: Shinya HANAOKA, Kumiko NAKAMICHI, Tomoya KAWASAKI

東京都市圏におけるサプライチェーンの解明
渡邉 雄太郎本研究では、第 5 回東京都市圏物資流動調査のデータを用いて、調査対象施設を含む物資の搬出搬入に関する OD を
サプライチェーンの「上流」｢中流｣「下流」として捉え、品目ごとにサプライチェーンの観点から東京都市圏の物流を明らかにする。分析の結果、品目ごとにサプライチェーン上の施設の立地数や立地場所に関する物流の傾向が異なることがわかった。また、サプライチェーンの観点から物流を分析することにデータの限界があることがわかったため、次に物資流動調査を行う上での改善点を提案した。

1. Introduction"business office", and information on each "Surveyed Facility",such as items to be handled, industry type, facility type andlocation. In "Inflow/Outflow Sheet", information on carry-in /carry-out of "Surveyed Facility" to which survey form of "Office
Function Survey" was distributed is obtained. In other words, it
is OD data of "Destination" and "Origin" of "Surveyed Facility"
mainly used in this research. Figure 1 summarizes theinformation obtained from "Establishment sheet" and"Inflow/Outflow sheet".

Tokyo Metropolitan Area (TMA) is the center of Japan’s
industry and economy, and it is a large consuming area withmany populations [1]. In such a region, it is important to grasp theflow of goods closely related to industry/economy and people’s
livelihood. In addition, the main stakeholder in the logistics
business is a private company, and they propound the importanceof supply chain management from the viewpoint of cost
reduction due to intensified competition among companies andregions with recent diversification of economic activities. There
are several researches on location choice problem[2] and locationof each logistics facility in TMA, while there is no research fromthe viewpoint of the supply chain (SC) by linking the OD (origin
and destination) among facilities to capture the flow of goods as
a SC. Also, since the upstream part of the SC is generally the raw
material procurement and production stages, excavation areas
and factories correspond. Whilst in the middle stage there arewarehouses and logistics facilities in wholesale andtransportation. In the downstream stage, restaurants and retailers
correspond [3]. It is important to identify the "upstream","middle" and "downstream" facilities and to clarify the SC foreach item.
The objectives of this research are (1) to understand a clarified
distribution in TMA by quantitatively analyzing thecharacteristics of the logistics of each item, from the viewpointof "upstream", "middle" and "downstream" of the SC, (2) tovisualize the facility of "upstream", "midstream", "downstream"
per each item in the map within TMA, in order to understand thecharacteristics of the facility location for each item, (3) to show
the limitation of data on Tokyo Metropolitan Area Urban Freight
Survey (TMAUFS) and make proposals for future survey in thecase of making SC.

Figure 1: Contents of “Establishment Sheet” and“Inflow/Outflow Sheet”
The number of establishments surveyed in "Facility function
survey" is approximately 140,000, and effective responses wereobtained from approximately 44,000. (Effective answer rate is32%) The number of OD pairs in "Inflow/Outflow Sheet" was227,633. In this research, we excluded data that eachestablishment answered "unknown" or "no answer" for"municipality code", "industry type", "facility type" and "item".
In addition, deleted the case where the transport company lends
private cars for delivery, because there is a possibility of
duplication of "Inflow/Outflow Sheet" in each between transportcompany and shipper, we decided not to include in this research.
There are 73,471 total exclusion OD pairs, and the number of
ODs used in this study is 154,162.

2. Methodology
2.1 Data
In this research, survey result of the 5th TMAUFS conducted
in 2014 is used. TMA covered in this study is Tokyo Prefecture,
Saitama Prefecture, Kanagawa Prefecture, Chiba Prefecture,
central and southern part of Ibaraki Prefecture, southern part ofTochigi Prefecture and southern part of Gunma Prefecture,
which is the survey scope of TMAUFS. In this research, we use
two questionnaires, "Establishment Sheet" and "Inflow/Outflow
Sheet" obtained from the result of the "business establishment
function survey" of TMAUFS. "Establishment Sheet" recognizeof the facilities related to logistics in TMA on the basis of

2.2 Clarification of goods flow in TMA in term of SC
2.2.1 Outline of Methodology
As a method of clarifying the SC, (i) downstream facility
analysis of the SC, (ii) upstream facility analysis of the SC, (iii)
middle class facility analysis by aggregating the "type ofindustry" and "facility type" of each item and (iv) The logistics
analysis with out of TMA. Figure 2 shows an image of theapplication point of each method on the SC. With regard to29

contain the OD of what “facility type” of “Origin” in“Inflow/Outflow Sheet” is “Factory”. Because OD with logistics
from factory to factory is more likely to be "upstream".

method (iv), since the logistics are completed within only TMA
by applying the method of (i) - (iii), from the method (iv), it ispossible to grasp the tendency to how much items are exchanged
in/out bound shipment from TMA.

2.2.4 Facility Analysis on Middle part of SC
By analyzing the combination of "industry type" and "facility
type" of all the OD data of “Inflow/Outflow Sheet”, the numberof combinations in each item is grasped. From the results andexclusion of the OD including the "downstream" and "upstream"
facilities found from the methods (i) and (ii), it is possible toidentify the facility having the combination "industry type" and"facility type" as the "middle" of the SC. We analyzed to howmuch "wholesaler" existed in the SC compared to otherindustries by examining the proportion of OD including"Surveyed Facility" of "Raw material wholesaler" and "Productwholesaler" to the number of total OD among wholesaler thatare mostly in the middle of the SC. On the other hand, for items
that contain the largest number of "industries" other than"wholesaler" as a percentage of "industry type" in the total OD
number of each item, we select the "middle" facilities of the SC
from the combination of the most "industry type" and "facility
type", and it can be defined.

Figure 2: Range in application of each method in SC structure
2.2.2 Facility Analysis on Downstream side of SC
First, "Last destination" which is the most downstream part of
the SC is specified. The definition of "Last destination" is"destination" which apply the facility that "19 industry type" are"various goods retailers", "retailer (except for various goods
retailers), restaurants", "retailer, restaurants (unknown details)" ,and "12 facility type" are "Office", "Store", "Restaurant, Hotel","House, educational institution, etc. ". Next, connecting OD
having "destination" matching "origin" of OD containing "lastdestination" and "origin" of OD containing "last destination".
The procedure of this method is shown in Figure.3.

2.2.5 Goods Flow Analysis with out of TMA
By classifying the OD of “Inflow/Outflow Sheet” into OD
related to logistics within TMA, OD relate to outbound shipment
including overseas from TMA and OD relate to inboundshipment from outside of TMA including overseas per items, it
can be measure its “the number of OD” and “weight (ton/day)”.
2.2.6 Visualization of each facility per items on TMA
We visualized the SC of each item by mapping the latitude andlongitude information of "core facility" of OD "upstream","middle" and "downstream" of each item defined by the previous
paragraph.

Figure 3: How to connect each OD
In Step1, when integrating "Inflow/Outflow Sheet" and"Establishment Sheet", one company has one "Establishment
Number", and in order to specify one base of the company whichhave many bases because it is impossible to identify with just"Establishment Number". Therefore it is necessary to combine"industry type" and "436 municipality code", and to match 3
codes of "Establishment Sheet" and "Inflow/Outflow Sheet".
In Step2, it is fact that two ODs connect if “Origin” of one OD
and “Destination” of other ODs have same “19 industry code”,“12 facility code” and “436 municipality code”. These twoprocedures can identify the logistics that is closest to theconsumer as the most downstream part of the SC.

3. Result and Discussion
3.1 Facility Analysis on Downstream Side of SC
Figure 4 shows the number of OD including "Last destination"and the ratio of its OD to total number of ODs.
Figure 5 shows the number of ODs that have "Destination" thatcoincide with the "Origin" of the OD including the "Lastdestination" and the ratio of the number of its OD to the total
number of OD. The numbers 1 to 20 shown in Figure 4 and 5 are
the codes of each item. Table 2 shows the correspondence tablebetween codes and item names.
6000

2.2.3 Facility Analysis on Upstream side of SC
In this method, we tried the two approaches of (a) and (b) on
20 items and analyzed the tendency of facilities in the upstream
part of the SC.
(a) Measure the number of ODs for the combination of "19
industry type" and "12 facility type" for "Origin" of"Inflow/Outflow Sheet". From the result, we thought that we cangrasp the trend of logistics in the upstream part for each item canbe grasped by comparing the difference in trends by item.
(b) From (a), we considered we can say that the OD exist
upstream part by investigating “industry type” and “facility type”
of “Surveyed Facility” as the next facility of “Origin” which

The number of OD including [Last destination」
The ratio of this to total OD

35.0%
30.0%

5000

25.0%

4000

20.0%
3000
15.0%

2000

10.0%

1000

5.0%

0

0.0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Figure 4: Number of OD including “Last destination”

30

200

The number of OD have 「destination」 which match with [origin」 of
OD including [Last destination」
1.40%
The ratio of this to total OD

180

Figure.6, there is a large proportion of “Others” of “Facility type”
in "Agricultural product (thermal)", "Agricultural product (nothermal)", "mineral product" and "Specialty". However, since"Specialty" is scrap and construction waste, it is hard to say that
it is a facility located upstream of the SC among "others". In"Agricultural product (thermal)", "Agricultural product (nothermal)", "mineral product", Table 1 shows the ratio of OD
including the facility have a combination of “Industry type” and“Facility type” to the number of total OD per items.

1.20%

160
1.00%

140
120

0.80%

100
0.60%

80
60

0.40%

40
0.20%

20
0

Table 1: Combination of “Industry type” and “Facility type”,
and its ratio

0.00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Figure 5: Number of OD have “destination” match “Origin” of
OD including “Last destination”

Item

From Figure 4, the proportion of the number of OD cases that
apply to the "downstream" of the items related to daily life to thetotal number of ODs are that “Agricultural product (thermal)” is
30%, “Agricultural product (no thermal)” is 22.5%, “Foodproduct (freezing)” is 32.6%, “Food product (thermal)” is 32.4%,“Food product (no thermal)” is 25%, “Daily necessity” is 33.1%,“Print product” is 21.8%. Considering that other items are
between 3% and 14%, it can be said that items related to daily
life have many ODs in "downstream". In addition, "Precision
instrument" also has 15%, which is more than othermanufacturing items. This can be inferred that the detailed items
included in "Precision Equipment" are products such as watches
and cameras, so that there will be many logistics close to
consumers. On the other hand, from Figure 5, we also found thatthe number of ODs connected as a SC is considerably small. For
example, even "food product (thermal)", which has the largest
number of ODs connected by "the most downstream" among all
items, was found to be only 1.3% of the total number of OD
cases. Therefore, we concluded that it is difficult to concretely
connect SC.

Agricultural
product(no thermal)
Mineralproduct

Mining industy

The ratio of a
combination of"Industry tyoe" and"Facility type"

Others5.70%

Others

14.20%

Others10.50%

Therefore, in these items, a combination of Table 1 contains thedefinition of “upstream” facility of SC.
Also, analyze the contents of the "facility type" of the "corefacility" when "facility type" of "origin" is "factory" fordistribution at the "upstream" of the SC. From this result, OD
with logistics from factory to factory can be said to be"upstream" in all items because there are the largest proportionof factory to factory in all items.
Also, items with many OD in 'upstream' were raw material
items and manufacturing items. The proportion of the number of
OD that apply to "upstream" to the total OD is 77% for "mineralproducts" which are raw material items, 74% for "light industrial
product" which are manufacturing items, 82% for "metalindustry products", "chemical industry products" is 78%. It canbe said that these ratios are higher than other items.
3.3
Facility Analysis on Middle stream side of SC
Since the facilities that apply to the "middle" of the SC are"wholesaler" or "transport company", the contents of the number
of ODs including facilities of those industries to all industries isshown in Figure 7. The numbers 1 to 20 of Figure 7 are the codesof each item. Table 2 shows the correspondence table between
codes and item names.

20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
40%
50%
60%
70%
80%
Store
Restaurant,Hotel
Transfer facility
House, School

Agriculturalforestryindustry andfisheries
Agriculturalforestryindustry andfisheries

Agricultural
product(thermal)

3.2 Facility Analysis on Upstream Side of SC
The contents of "Facility type" in "Origin" of "Inflow/Outflow
Sheet" is shown in Figure 6 for each item. From Figure 6, there
are many "factory" in the "upstream" facilities in the SC. The
numbers 1 to 20 of Figure 6 are the codes of each item. Table 2
shows the correspondence table between codes and item names.

0%
10%
20%
30%
OfficeFactory
Distribution center
Truck terminal

Facility
type

Industry type

20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1

90%
100%
Warehouse
OthersFigure 6: Share of “Facility type” in “Origin”
Meanwhile, facilities of "agricultural work place" and"mining site" in the contents of that "Facility type" is "Others"
are likely to be the facility in "upstream" of SC. As shown in

0%

10%

20%

Light disarray industry
Product wholesaler

30%

40%

50%

Warehouse
Others

60%

70%

80%

90%

Figure 7: Share of “industry type” in “Core facility”
31

100%

Raw material wholesaler

necessity” from items related to daily life and “Metal industry
products” from manufacturing items.

Items with many "product wholesaler" can be said that many
OD occur in places close to consumers. Based on the above
analysis, we defined the "middle" facility of SC in each items
from the combination of "industry type" and "facility type".
These are shown in Table 2. As a result, it turned out that there
were variations in the combination of "industry type" and"facility type" depending on the item for "middle" facilities.
Table 2: Definition of “middle” facility in each items
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

Item
Industry type
Facility type
Agricultural product (Thermal ） Product wholesaler
Distribution facility
Agricultural product (No thermal) Product wholesaler
Distribution facility
Food product(Freezing)
Warehouse
Warehouse
Food product(Thermal)
Product wholesaler
Distribution facility
Food product(No thermal)
Product wholesaler
Distribution facility
Light industrial productProduct wholesaler
Distribution facility
Print productWarehouse
Warehouse
Daily necessity
Product wholesaler
Distribution facility
Miscellaneous goods
Product wholesaler
Distribution facility
Forest products
Raw material wholesaler
Distribution facility
Mineral products
Raw material wholesaler
Distribution facility
Metal industry products
Raw material wholesaler
Distribution facility
General instrument
Product wholesaler
OfficeElectric instrument
Product wholesaler
OfficeTransport instrument
Product wholesaler
Distribution facility
Precision instrument
Product wholesaler
OfficeCeramic industry products
Raw material wholesaler
OfficeChemical industry products
Raw material wholesaler
OfficeSpecialty
Raw material wholesaler
Factory
Mixed
Land transportation freighter Distribution center

Figure 9: Map of “Daily necessity”

3.4 Goods Flow Analysis with out of TMA
Distribution within TMA was many in all items on the basisof the number of OD, but in weight-based logistics, there weremore distribution with outside of TMA than those within TMA.
The results of those items are shown in Figure 8.
100

90
80
70

60
50

40

(%)

Domestic

Overseas

Agricultural
product(no thermal)
Agricultural
Mineralproduct(thermal)
product

Figure 10: Map of “Metal industry products”

Chemicalindustry
product

30

4.

20

From the viewpoint of "downstream", "middle" and "upstream"of the SC, it was found that there are many "downstream"
facilities in items relate to daily living and many “upstream”
facilities in manufacturing items and instruments. We also foundthere is a tendency for logistics within TMA depending on theitem.
Also, since there was a limit in using TMAUFS from theviewpoint SC, we will list the improvement points of the next
survey. (1) It is necessary to unify the categories of "industry
type" and "facility type" of "Inflow/Outflow Sheet" and"Establishment sheet". (2) The number of samples is increased
by sending a survey form to the facility of "retailer" or"restaurant" which is highly likely to be located "downstream"of SC. (3) It is necessary to provide a field for writing the item
names of both "Origin" and "Destination" of "Inflow/Outflow
Sheet".
As a possibility of future research, by dividing the divisionaccording to the characteristics of the land such as the supercentral area, suburbs and the coastal area where logistics
warehouses are accumulated because TMA is quite huge area, sothat it can be grasped a more accurate supply chain structure.

100Figure 8: Distribution with in/out TMA
The means of transportation when there was a large amount ofgoods to be delivered was a ship. "Agriculture products(thermal)" has been carried in large quantities from overseas tofactory facilities with thermal management, and has beendelivered to warehouses of transport companies in Kamisu City,and food arrangement factory in Funabashi City. "Agricultural
products (no thermal)" is carried in large quantities fromoverseas to warehouses of transport companies in Kamisu City.
"Mineral products" are carried in large amounts in Kashima City.
This is the Kashima coastal industrial area, which is considered
to be because the steel industry is flourishing. "Chemicalindustry products" were imported from overseas to factory in
Ichihara. This is because there is a huge Chiba port where cargohandling volume is 7% of the whole of Japan.
3.5 Visualization of each facility per items on TMA
We found that many of the facilities related to “Daily necessity”
and “Precision instruments” were in "downstream" and many of
the facilities in the manufacturing industry and instruments werein "upstream". Figure 9 and 10 show facility location of “Daily

Conclusion

References[1]東京都市圏交通計画協議会：東京都市圏の望ましい物流の実現に向けて, 2015[2]兵藤哲朗, 首都圏における物流施設の立地とその方向性, 産業立地, vol.51, No.5, pp9-14, 2012.
[3]David Simchi-Levi: Designing and Managing the Supply Chain, 200232

C HARACTERISTIC OF N ON -S PECULAR S CATTERING ON O UTSIDE S URFACE OF
B UILDINGS AT 25 GH Z
Student Number: 15M18256

Name: Hao Liu

Supervisor: Jun-ichi TAKADA

25 GH Z 帯における建物外壁面の非正規散乱の特性劉コウ

第 5 世代移動通信ネットワークシステムでは，準ミリ波もしくはミリ波帯を利用した高速伝送の実現が検討されている．高周波化に伴い波長が短くなり，建物外壁面の不均質性や凹凸が無視できなくなるため，平面による正規反射の仮定が成立せず，非正規散乱の影響も大きくなると予想される．本研究では，実際の建造物を対象とし，
正規反射点付近の構造の違いに注目し，正規反射だけでなく非正規散乱の強度を実測した結果について分析した．

1 Introduction

Many researches have been done in outdoor environment and indoor environment [2] - [8]. For example,
Takada, et al,(2002) [2] measured propagation channel in a
suburban residential area at 8.45 GHz. By comparing withthe measurement result and ray-tracing simulation result,the LOS component and specular reflection are correctlypredicted. on the other hand, the recieved diffuse component could not be calculated correctly by ray-tracing simulation. And H.Budiarto, et al, (2004) [3] focused on one
building surface and measure the scattering position. By using array antenna and estimation of signal parameters using
rotational in-variance technique(ESPRIT) method, angularparameters could be estimated for each discrete position onbuilding surface. All the result show that non-specular component will affect the propagation channel. In those literatures, frequency bands smaller than 10 GHz are analyzed,
however the 5th generation is going to use the frequency
above 24 GHz in order to achieve high data rate. So it isnecessary to confirm the characteristic of the channel due
to buildings at 24 GHz.
In this research, The final objective is to design the position and coverage of base station. Therefore, the modelthat can predict the received power is necessary. The propagation mechanism of the predicted model do not include
the detailed scattering effect which is due to rough building
surface or different building surface material. Hence, thisresearch objective is to measure the scattering coefficientfrom buildings surface. In the future, by comparing withthe simulation result, the final objective which is to design
the position and coverage of base station could be achieved.

Recently, wireless data traffic has increased rapidly[1].For the next generation of mobile network, the data
rate above 10 Gbps is planned to be achieved. In order toachieve that goal, a large number of small cells, which could
only cover several meters but with wide bandwidth, need to
be set at the street. Normally, optical cables are used to connect between base stations(connection is called backhaul).
However, there are a lot of places where it is not possible
to set the optical cable, such as river and highway. Hence,
the wireless backhaul is considered for the 5th generation
mobile network.
In order to increase the data rate when using the wirelessbackhaul, one of the solution is to increase the bandwidth.
This solution is based on Shannon’s channel capacity theorem, which is shown in Eq. (1).
()
S
C = Blog2 1 +N(1)Where C is the channel capacity, B is the bandwidth ofthe channel, S is the average of received signal power overbandwidth, N is the average power of the noise over the
bandwidth.
The Ultra High Frequency(UHF) band and micro-wave
frequency band, which has been used for non-Line-of-Sight
wireless communication, could not provide such wide bandwidth. Hence, recently quasi-millimeter wave and millimeter wave bands are considered.
Even though it is reasonable to use high frequency toachieve high data rate, however, there are lots of challenges
need to be solved from the viewpoint of wireless propagation. Firstly, when the frequency is high, the first Fresnelzone, which is the area that most part of energy will passthrough, will become small. Especially for quasi-millimeterwave and millimeter wave, the first Fresnel zone could beshaded even by human body. In that case, to guarantee thatthe receiver could receive enough power, the non-line-ofsight path such as reflection or scattering from buildings
surface will become important. Secondly, because of thehigh frequency, the wavelength is shorter. When the wavelength is roughly the same as the roughness of the buildingsor the difference materials on building surface, the assumption that the building surface is flat becomes invalid. Thisimplies that the scattering effect will become more significant.

2

Analysis of non-specular scattering

2.1

Friis transmission equation

Friis transmission equation is used to show the relationship between transmitted power and received power at freespace. Free space means there is no obstacle between transmitter and receiver. received power Pr is denoted by

Pr = SAi = (Pt)(λ2 /4π) = Pt (λ/4πd)2
4πd2

(2)

where, S is specific power, A is effective area of receive
antenna, d is the distance between transmitter antenna andreceiver antenna, λ is wavelength, Pt is transmitted power.
here λ = c/f , c is speed of light, f is frequency of electric33where, RTE = Er /Ei ，TTE = Et /Ei for TE wave, and
RTM = Er /Ei ，TTM = Et /Ei for TM wave.
When the boundary is not flat, not only the specular reflection exist, but also scattering could be observed(see Fig.
(3) 2). Whether the surface is flat or rough is determined byRayleigh standards g as expressed by Eq. (11). If g < 1,
the surface roughness could be consider as smooth surface,on the other hand, if g >1, the surface roughness could be(4) consider as rough surface.

wave. Free space path loss Lp (d)is defined as the reciprocalof the power gain given by Eq. (3).
(Lp (d)

=

4πdf
c

)2

Finally, Pr could be express by Eq. (4),Pr=

Pt Gt GrLp (d)

where, Gt is the gain of transmitter antenna, Gr is the gainof receiver antenna. By using Eq. (4), receiver power could
be calculated when any antenna is used.
2.2 Reflection and transmission
Figure 2: Relationship between roughness and scattering
At free space, Friis transmission equation could calculate
the received power. However, in reality especially in urban
area, antennas are surrounded by buildings so the Friis
4πδhtransmission equation does not meet the conditions of freeg=(11)
λsin(θ)space. At this situation, the loss due to the buildings should
be counted. Depending on the characteristic of material,
In case of rough surface,
the scattering
electric wave(TE wave or TM wave), the reflection
coefficientLs (ψt , ψr ) will be the function of incidence ancoefficient will be different. TE wave is the wave thatgle and departure angle as Eq. (12).
electric field is parallel with boundary. TM wave is thewave that magnetic field is parallel with boundary(see Fig.
Gt (0)Gr (0)(12)
Pr = Pt
Lp (d)Lc Ls (ψt , ψr )2.4

Radius of Fresnel zone

Fresnel zone is the area that phase shifting is within π, due
to the difference of path length. When the incident angle isθ, the radius of the n-th Fresnel zone is given by Eq. (13)and Eq. (14)1).
Figure 1: Reflection and transmission of TE and TM wave√
Due to Snell’s law, incidence angle θi is equal to θr .
ndλ
The relationship between θi and θt is given by Eq. (5), and(13)
R1,n =
2complex permittivity δ is given by Eq. (6).
√
2 (R2 + d2 )
√
R1,n
1,n
sin(θt )
δ1
R
=
(14)
2,n
=√
(5)
2 + d2 cos2 θR1,nsin(θi )δ2σδ =ϵ−j(6) where, d is the distance between transmitter antenna to scat2πfc
tering point,R1,n is the smaller radius of Fresnel zone, andwhere, j is imaginary unit, ϵ is permittivity, σ is electrical R is the larger radius of Fresnel zone. Depending on the2,nconductivity, fc is the carrier frequency.
percentage that first Fresnel zone is sheltered by obstacle. If
2.3 Scattering and transmission coefficient
the sheltered rate is lower than 60 % it can be considered asafree space environment. If half of the first Fresnel zone is
The reflection coefficient R and transmission coefficient T
sheltered,
the loss will be 6 dB larger than free space. And
of TE wave and TM wave are different. transmission coefif
thehole
first Fresnel zone is sheltered, the loss will be 16
ficient for TE wave is denoted bydB
losslargerthan free space.
√√δ1 cos(θi ) − δ2 cos(θt )√(7) 3 Measurement theory
RTE = √
δ1 cos(θi ) + δ2 cos(θt )√2 δ1 cos(θi )3.1 Scattering modeling√
TTE = √
(8)
δ1 cos(θi ) + δ2 cos(θt )To transmit the signal, antenna is an indispensable part.
Comparing with the isotropic antenna, directional antenna
Transmission coefficient for TM wave is denoted by√√could make signal transmit to certain direction and antennaδ2 cos(θi ) − δ1 cos(θt )gain could be higher than isotropic antenna in that√(9)
RTM = √
δ2 cos(θi ) + δ1 cos(θt )direction.
The Tx antenna used in this research is a√broadband mobile access communication
2 δ1 cos(θi )√TTM = √
(10)
system(HiSWANb). The ration pattern of the antenna isδ2 cos(θi ) + δ1 cos(θt )34

given by Fig. 3.

3.3

In this research, 2 kinds of receiver signal analyzer andspectrum analyzer are used. For signal analyzer, IQ data
was recorded on time axis. Since the duty cycle is 1:1, thedata without signal transmitted was removed to calculate
the received power by take the average of the remaining
data. For spectrum analyzer, power data was recorded on
frequency axis. By summing up all power within the bandwidth, received power could be calculated. For both cases,
we could use the power to calculate the scattering coefficient by using Eq. (15).

Relative Amplitude[dB]0Radiational Pattern
Noise Level

-5
-10
-15
-20
-25
-30
-35
-30

-20

-10

0

10

20

Data analysis

30

Theta[deg]Figure 3: antenna pattern of Tx antenna

In this research, every point on the wall surface have
Gt (0)Gr (0)different non-specular reflection coefficient is assumed. As
Ls (ψt , ψr ) = Pt
(15)
Lp (d)Lc Pr
Fig. 4 shows, the blue line is the path of non-specular reflection and red line is specular reflection. Scattering coeffi3.4 Method of calculate theoretical value
cient Ls (ψt , ψr ) is defined as the sum of specular reflection
In order to know whether non-specular reflection affectand non-specular reflection.
the received power, it is necessary to compare the measured value to the ”theoretical value” which assumes that
only specular reflection exist. If spread in x axis could befound, that means not only specular reflection, but also nonspecular reflection affect the scattering reflection.

4

Measurement and Result

4.1

Measurement at NTT Yokosuka R&D Center

This measurement was held at NTT Yokosuka R&D Center.
the surface of target building are mainly made from glassand metal. Environment and building surface are shown in
Fig. 5 and Fig. 6.

Figure 4: Parameters for calculate the scattering coefficient

3.2 Measurement system
In this research, by taking antenna position(xT , xR ), received power(Pr ) and angle of Tx antenna(ψt ) into Eq.
(12), scattering coefficient could be calculated and analysed. The specification of antenna is shown in Table 1.
Other parameters are shown at Table 2 in this measurement.
Table 1: Transmitter specification

Frequency
Sample rate
Communication
Modulation
Transmitted power
TDD ratio

25GHz
25.0MHz/40.0MHz
TDD
64QAM/16QAM/QPSK
3dBm（Max）
AUTO/FIXED

Figure 5: Measurement environment of NTT R&D Center

Table 2: Measurement parameter

Frequency
Bandwidth
Transmitted power
Modulation
I/Q sampling frequency
Measurement time

Figure 6: Building surface

25GHz
25MHz
3dBm
QPSK
50MHz
20ms

The result is shown in Fig. 7 - Fig. 9. By analyzing
the result, four results could be found. First, the spread inangle could be found at all results. That means the nonspecular reflection affect the scattering coefficient. Second,material of building surface will affect the scattering coefficient. Comparing with the glass, scattering coefficient ofmetal has 10 dB larger than glass. Moreover, both glassand metal have 5-8 dB difference between measurement
value and theoretical value around specular reflection point.
Third, even for the same material, the difference is still 24 dB when antennas are shifted for 0.75 m. That might
because the beam spot is larger than the size of window.

Before the measurement, loss due to cable and system must
be measured. In calibration, Tx and Rx are set at certain
distance and pointing to each other. The difference between
measured received power and theoretical received power isthe loss due to cable or other system loss.
35

Thus, scattering from fence above the measurement point
and window frames may lead to scattering. Four, when theincident angle is increased, the scattering coefficient is increased. This might because the increased incident angle
make the beam spot became larger on building surface, thus
the scattering coefficient became stronger.
00

Scattering Coefficient [dB]

-10

-40

-60
-4

-15

-1

0
Scattering Position [m]

1

2

3

4

0

-20
-10

-25
Scattering Coefficient [dB]

-30
-35
-40
-45

-55
30

↓
32

↓
34

36

38

40

42

44

46

48

-20

-30

-40

50

Scattering Position [m]

-50

-60
-4

Figure 7: Result on metal
↓

-10

-2

-1

0
Scattering Position [m]

1

2

3

4

As the result, first, effect from scattering could also befound due to the scattering coefficient is spread on angular axis. Second, the scattering coefficient is different due
to the building material, metal have higher coefficient thantail, and coefficient of tail is higher than glass. Third, when
the incident angle is increased, the scattering coefficient isincreased. This might because the increased incident angle
make the beam spot became larger on building surface, thus
the scattering coefficient became stronger.

←
←

-15
-20
-25
-30
-35
-40
-45
↓

-50
-55
10

-3

Figure 13: Result on tail and metal

0-5

Scattering Coefficient [dB]

-2

-10

-50

12

14

16

18

20

22

24

26

28

30

Scattering Position [m]Figure 8: Result on glass
0-5

Scattering Coefficient [dB]-3

Figure 12: Result on tail and glass←←

Scattering Coefficient [dB]

-30

-50

↑

-5

-20

-10
-15
-20

5

-25
-30

Conclusion

-35
-40

To solve the demand of data rate, The wireless backhaul utilizing more access points and high frequency was proposed.
In this research, characteristic of non-specular scattering on
outside surface of buildings at 25 GHz was analyzed.
The effect of scattering could be found at all measurements. when building material is different, the scattering
coefficient could be different upto 10 dB. Even the material
is similar there still could have 2-4 dB difference due to thedifferent scattering object. About the effect of incident angle, when the incident angle become larger, it can find thatthe scattering coefficient will also become larger.

-45
↓

-50
-55
10

15

20

25

30

35

40

Scattering Position [m]Figure 9: Result on glass(large distance)

4.2 Measurement at Tokyo Institute of Technology

The measurement held in Tokyo Institute of Technology issimilar with NTT R&D center, but the distance between
building surface and building material are different. the environment is shown by Fig. 10. The wall has 7 parts withsame shape, the scattering coefficient was measured separately and compared with each other. The result that build- References
ing surface material is tail, tail & glass, and tail & metal is [1] 総務省, ”我が国の移動通信トラヒックの現状,”, Jun. 2016.
[2] J. Takada, J. Fu, H. Zhu, and T.Kobayashi, “Spatio-Temporal Chanshown by Fig.11 - Fig. 12.
nel Characterization in a Suburban Non Line-of-Sight Microcellular

Environment”, IEEE, J.Select. Areas in Coom., vol. 20, no.3, pp.532538, Apr. 2002
[3] H. Budiarto, “Prediction Model and Measurement of the Electromagnetic Wave Scattering from Building Surface in Mobile Propagation Modeling”, Doctoral Thesis, Tokyo Institute of Technology,
Mar. 2004[4] J. S. Lu, V. Degli-Esposti, E. Vitucci and H. L. Bertoni, “Studyof back-scattering from buildings using a 60 GHz scaled model,”The 8th European Conference on Antennas and Propagation (EuCAP
2014), pp. 814-816, The Hague, 2014.
[5] H. Budiarto, K. Horihata, K.Haneda, and J. Takada, “Experimental
study of non-specular wave scattering from building surface roughness for themobile propagation modeling,” IEICE Trans. Comun.,vol.E87-B, no.4 pp. 958-966, April. 2004.
[6] V. Degli-Esposti, “A diffuse scattering model for urban propagation
prediction,” in IEEE Transactions on Antennas and Propagation, vol.
49, no. 7, pp. 1111-1113, Jul 2001.
[7] V. Degli-Esposti, D. Guiducci, A. de’Marsi, P. Azzi and F. Fuschini,“An advanced field prediction model including diffuse scattering,” inIEEE Transactions on Antennas and Propagation,” vol. 52, no. 7, pp.
1717-1728, July 2004.
[8] E. M. Vitucci, F. Mani, V. Degli-Esposti and C. Oestges, “Polarimetric Properties of Diffuse Scattering From Building Walls: Experimental Parameterization of a Ray-Tracing Model,” in IEEE Transactions
on Antennas and Propagation, vol. 60, no. 6, pp. 2961-2969, June2012.

Map Data ©2017 Google

Figure 10: Measurement environment of Tokyo Institute of Technology
0

Scattering Coefficient [dB]

-10

-20

-30

-40

-50

-60
-4

-3

-2

-1

0
Scattering Position [m]

1

2

3

4

Figure 11: Result on tail36

Web Applications for Multilingual Semi-Machine Translation based on CollectiveIntelligence
Student Number: 15M18240 Name: Wuning LI Supervisor: Yukihiko YAMASHITA

集合知に基づく高精度多言語半機械翻訳のウェブアプリケーション化李 呉寧
本論文では，インターネット上の一つの文書ファイルから高精度な翻訳で異なる母語の人々が情報を共有し合うことを実現するための集合知に基づく多言語半機械翻訳において，既存の研究では Java で実装されていた翻訳機を新たに JavaScript と PHP などを使って実装し，ウェブアプリケーション化する
共に，現存の原文書情報付加ツールを改良する。さらに開発したシステムを評価する実験を行い，それらの有効性を確認する。

1.

Introduction

to make it as a web application, I develop a new

Wiki-based online encyclopedia Wikipedia provides

LILITH system implemented in JavaScript, PHP, etc.

useful information continuously in a wide area fromAnd I also improve the support tool to make ADAM

arts and sciences to life by collaboration. However,documents.

there is a language barrier to prepare document files forexperiments to verify their effectiveness.

Further,

I conduct two evaluation

wiki in every language at the present time. Machinetranslation (MT) is a solution to cross the barrier andacquire the information in various languages, but it has
a problem that incorrect recognition occurs in syntactic
analysis or semantic selection.
In order to enable high-accuracy translation, atechniquecalledMultilingualSemi-MachineTranslation based on Collective Intelligence (Fig.1.)

Figure 1: Multilingual Semi-machine Translation based

was proposed by Ishibashi [1]. By this technique, an

on Collective IntelligenceXML document is described using a support tool to add

2.

Description to Attach Meaning) document. A translator

Multilingual semi-machine translation
based on collective intelligence2.1. Overview

called LILITH (LInguaL Interpreter To Human) system

Multilingual Semi-Machine Translation based on

was proposed by Negishi [2] to construct a multilingual

Collective Intelligence enables that a user describes aninformation service system. It translates an ADAMADAM document and uses the LILITH system todocument to one in various languages.

translate it into plural languages based on thethe information such as syntax, grammar and meaning.
Such a document is called ADAM (Auxiliary

The existing LILITH system was written in Java so

information on tags. Because mistakes during syntactic

that it is difficult to work as a web application. In orderanalysis and vocabulary interpretation which are the37

3.1. Web Application for LILITH Systemlargest factors of the mistranslation in MT disappears,
highly accurate translation is enabled by information of

In order to make LILITH system easier to use on thetags. All of these can be done on the Internet which

Internet to realize CI, I developed LILITH system andmeans based on Collective Intelligence (CI).

made it as a web application with HTML, CSS,

CI is group intelligence which is treated as a synonym

JavaScript and PHP. In addition, I also used jQuery andof “the wisdom of the crowd” [3]. In the world of the

Bootstrap.

Internet, costs of collecting knowledge are extremely

Its main functions are shown below.

low and system architecture is enabled to gather a lotof participants so that CI can be realized effectively.

The contents of the ADAM document can be checked.
Open an ADAM document

Translate an ADAM document

2.2. Problems of the existing system

Japanese and Chinese can be chosen as the target

The existing LILITH system is developed using thelanguage at the present time.

Java language, and it is inconvenient and incomplete

LILITH system, a user should install it on his/her

3.2. Improvement on ADAM documentsmaking support toolcomputer and use a software such as Eclipse to work

In order to make the support tool to describe thethe program. Then the user can find the code to assign

ADAM documents easier to use, I improved the toola path for an ADAM document.

and remade it as a web application with HTML, CSS,

A support tool [4] is developed to describe AdamJavaScript and PHP. In addition, I also used jQuery,documents. However, the following challenges remain.

jQuery UI, Bootstrap, and other techniques such asAjax and modal window.

since it does not have user interface (UI). To use the

It is impossible to save data of the halfway
description of an ADAM document so that once a

Its main functions are shown below.

user starts describing an ADAM document, he/she

has to complete the work to the end.

Only English is available at the present time.

The UI of this tool is written only in English. Itmakes difficult for users who are not familiar with

Information of tags includes syntax, grammar andEnglish or knowledge such as grammar and syntax

meaning.

of linguistics to describe an ADAM document.



Since there is no manual on how to use this tool, aSave the data into a txt file which can be used to restartuser needs to intuitively learn the way to use it.

describing the ADAM document.
3.

Input an original document

Add tags to make an ADAM document

Save data of the halfway description

Support users who are not familiar with English

Web Application for LILITH system andimprovement on ADAM documents-

In addition to English, UI language can be selectedmaking support tool

automatic input of prototype, explanation of tag andfrom Japanese and Chinese. And functions such as

In order to solve the problems mentioned in 2.2, inpresentation of example are added.

this study, I newly develop LILITH system and make itas a web application with JavaScript, PHP, etc. And I

A manual on how to use this tool is added and it isalso improve the support tool to describe ADAM

possible to turn on/off to display it. And when some of

documents.

buttons are hovered over, an explanation will be

38

Improve operabilityimproved tool.

displayed as "balloon".

Table 1: Results of experiment 1

4.

Experiments

Items

In order to evaluate the effectiveness of the newly

Mean.
Min.
Max.
Mean.
Min.
Max.
Mean.
Min.
Max.

Time
(s/word)developed LILITH system and the improved tool, I
carried out the following two experiments.

Operability

4.1. Experiment1:
effectivenessof
Improved ADAM document-making
support tool

Experiencetime

Group AGroup B
Existing Proposed Existing Proposed1136(63.1) 723(40.2) 933(51.8) 735(40.8)519(28.8) 407(22.6) 810(58.7) 655(36.4)
1510(83.9) 883(49.1) 1056(45.2) 814(45.2)
2.3
4.7
2.3
4.3
2
4
2
4
3
5
3
5
2.7
4.3
3.0
5.0
2
4
3
5
3
5
3
5

and the improved tool to make ADAM documents of

4.2. Experiment 2: effectiveness of LILITH
systemdoc. 1 (I bought a book yesterday.) and doc. 2 (I think

In this experiment, subjects used the existingthat that that that that writer used is wrong.) based on

LILITH system and the proposed LILITH system tothe given information. The time of making abovetranslate the ADAM documents made in Exp.1, and

ADAM documents is measured, and the operation

evaluated the operation burden on a user subjectively.

burden on a user is evaluated subjectively.

Further, I compared the translation results of 4

In this experiment, subjects used the existing tool

6 subjects (2 Japanese and 4 Chinese) were divided

documents by the LILITH system and other MT

into two groups of Group A and Group B. For Group A,

services.

first made the ADAM documents with the existing tool

The result of Exp. 2 is shown in Table 2. ‘Operability’and then made the same ADAM documents with thewas used as the five-grade evaluation index to evaluate

improved tool. For Group B, first made the ADAM

the operation burden on a user.

documents with the improved tool and then made thesame ADAM documents with the existing tool.

Table 2: Results of experiment 2

Operability
Mean.
Min.
Max.

The result of Exp. 1 is shown in Table 1. ‘Time’ isthe time (in seconds) of describing the ADAM
documents. ‘Operability’ and ‘Experience time’ wereExisting Proposed
1.8
4.7
1
4
2
5

used as the five-grade evaluation index (higher isbetter) to evaluate the operation burden on a user.

The following are examples of the translation results

Regarding the ‘Time’, it shows that using theof LILITH system and other MT services.


improved tool can make description of ADAM

Doc. 1: I bought a book yesterday.

documents faster than using the existing tool. It means(LILITH system) 私は昨日 1 冊の本を買った。

that the improved tool is more practical. And as for the(Google Translate) 私は昨日本を買った。

‘Operability’ and “Experience time”, the improved tool

(Weblio Translate) 本を昨日買った。

generally gained a better rating than the existing tool.

(Excite Translate) 私は昨日本を買った。
In addition, all subjects had a better impression on the
proposed one.

Doc. 2: I think that that that that that writer used iswrong.
(LILITH system) 私はあの作家が使ったあのTherefore, although there is still room forthat は間違っていると思う。

improvement, it has showed the effectiveness of the39

(Google Translate) 私が使っている作家は間Weblio Translate and Excite Translate have an error

違っていると思います。

occurred in vocabulary interpretation for “rice”. On the(Weblio Translate) 使われるその作家が間違っother hand, the LILITH system translated theているために，私はそれをそれと考えます。

documents using the information of tags, so it obtained(Excite Translate) 私は考える that それ that 作

translations with higher accuracy than the others.

家が使ったことが悪い。
Therefore, the above results showed the practicality

Doc. 3: Are you still playing Pokemon Go?

and effectiveness of the proposed LILITH system.

(LILITH system) あなたはまだポケモン Go をしていますか？

5.

(Google Translate) あなたはまだポケモンをIn this study, I developed a new LILITH system as a

していますか？

web application with JavaScript, PHP, etc. And I also

(Weblio Translate) あなたは，まだポケモン試

improved the support tool to describe ADAM

みをしていますか？

documents. Further, I conducted two evaluation(Excite Translate) あなたはまだ遊びポケモン

experiments and the results verified their effectiveness.

行きであるか？
Conclusions

As future works, it is necessary to further improveDoc. 4: I want to eat rice.

the tool and LILITH system, and it is important to(LILITH system) 私はご飯を食べたい。

increase target languages of LILITH system and(Google Translate) 私はコメを食べたい。

vocabularies of the multilingual dictionary in order to(Weblio Translate) 私は，米を食べたい。

construct a multilingual information service system. In(Excite Translate) 私は米を食べたい。

addition, in order to be able to provide as a service
based on CI, it is necessary to make an attractive web

As for the ‘Operability’, the proposed one gained a

information system for users without causing abetter rating than the existing one. And all subjects had

problem of security.

a better impression on the proposed one.

References

Regarding the comparison of the translation results,
all of them obtained a correct translation of Doc. 1[1] Takuya ISHIBASHI. Study of multilingual semi-

which has a simple constitution. However, as for Doc.

machine2 which has a complicated constitution, in Google

Intelligence, Master's thesis, Tokyo Institute ofTranslate, an error occurred in parsing that '私' which

Technology, 2008. (in Japanese)is the subject of the whole sentence was recognized astranslation

based

on

Collective[2] Yuhei NEGISHI, Study of multilingual semi-

the subject of the adjective phrase modifying '作家'. In

machineWeblio Translate and Excite Translate, an errorIntelligence. Bachelor’s thesis, Tokyo Institute ofoccurred in vocabulary interpretation for 'that'. As forTechnology, 2008. (in Japanese)Doc. 3 including a proper noun, Google Translate andtranslation

based

on

Collective[3] James Surowiecki, THE WISDOM OF CROWDS,

Weblio Translate have an error occurred in vocabulary

Anchor, New York, Aug. 2005.

interpretation for “Pokemon Go”, and Excite Translate[4] Wuning LI, Study of multilingual semi-machine

has an error occurred in parsing where “playing

translation based on Collective Intelligence.

Pokemon Go” was recognized as a noun phrase. As for

Bachelor’s thesis, Tokyo Institute of Technology,

Doc. 4 including a multisense word, Google Translate,2015. (in Japanese)40

Wavelet Based Image Coding via Linear Prediction and Improved
Clustering
Student Number: 15M18049

Name: Dan WANG Supervisor: Yukihiko YAMASHITA

線形予測と改良されたクラスタリングに基づくウェーブレット画像符号化
王丹

本論文では, 線形予測を用いたウェーブレット画像符号化の計算時間を削減するため, その予測器の学習に使われるクラスタリングの部分を改良することを提案する. この中で, 予測に使うブロックサイズの変更と予測誤差の大きいクラスタの削除を行う. この改良によって, 符号時にクラスタを探す計算回数を減らすことが可能であり, 予測効率と符号化効率に影響を与えずにエンコード時間を減らすことができる。
また, 計算機実験によって, 提案手法の有効性を確認する.

1

Introduction

extracted block and removing the vector data belonging to the clusters which generate larger error
The rapid development of multimedia product is rate than the conventional ones. I show expercausing the insuﬃcient bandwidth of network and imental results to demonstrate the advantage ofstorage of memory device. Therefore, the image the proposed method.
coding methods have been invented to reduce or
eliminate the data redundancies for saving storage 2 Linear Prediction with Cluscosts or transmission time [1].
tering
The discrete wavelet transform (DWT) is a toolthat cuts up data, functions, or operators into dif- 2.1 Linear Prediction
ferent frequency components. Wavelet-based image coding is an image coding method using the Linear prediction is a mathematical operation
DWT. By encoding the coeﬃcients, it reduces the where future values of a discrete time signal aredata size [1][2]. Predictive coding is another tech- estimated as a linear function of previous samnique for eﬃcient transmission or storage by send- ples. The most common representation of linear
ing only the diﬀerence between the original and its predictive coding isppredicted values to reduce the data size [3]. How∑ŝ(n)=ai s(n − i),(1)ever, it is diﬃcult to apply predictive coding to thei=1
two-dimensional DWT that used in JPEG2000 directly because the distance of decoded pixels and where ŝ(n) is the predicted signal value of the acpixels that have to be predicted is quite far. To tual signal value s(n) from the linear combination
solve this problem, a predictive coding method of p signal values with ai as the coeﬃcients. The
that applies prediction to one-dimensional trans- error generated by estimating the diﬀerence beform part in 2D-DWT decomposition was pro- tween the actual signal value s(n) and the preposed [2]. According to the experimental results, dicted signal value ŝ(n). In error form in onethe predictive method that applies prediction to dimensional linear prediction can be expressed as
1D-DWT transform part in 2D-DWT decompo- follows:
sition shows better prediction eﬃciency and code(n) = s(n) − ŝ(n),(2)
ing eﬃciency than the conventional one. K-means In order to obtain the linear prediction coeﬃcientsclustering is applied in this method to partition a , the most common choice is to minimize the sumithe vectorized data of extracted blocks into k clus- of squares of the errors defined in the form
ters and to generate the coeﬃcients of the lineare(n) = s(n) − ŝ(n)predictor. However, due to the calculation of prep
diction, it takes more time for coding process than∑= s(n) −ai s(n − i),(3)the method without prediction.
i=1
In this paper, in order to save encoding time∑
without influencing coding eﬃciency, I propose toE=e2 (n).
(4)
reduce the calculation time by changing the size ofn41

where E is the short-time average prediction error.

2.2

K-means Clustering

K-means clustering is a method of vector quantization, it aims to partition n observations into
k clusters to which each observation belongs with
the nearest mean. It is a prototype-based, partitional clustering technique.
Here, let (x1 , x2 , ..., xn ) be a set of observation,and each observation is a d-dimensional real vector. K-means clustering aims to partition then observations into k sets S = {S1 , S2 , ..., Sk }by minimizing the within-cluster sum of squares(WCSS)

arg mink ∑∑∥x − µi ∥2 .

Figure 1: Coeﬃcients of 1D-DWT decomposition

3

Prediction is a key technology which can enhance
the coding eﬃciency of image coding. If we apply
predictive coding to 2D-DWT directly, it should
be based on the decoded values. However, in 2DDWT decomposition, the target pixel we want topredict is apart form the decoded pixels. Therefore, the prediction with the 2D-DWT decomposition is considered to be diﬃcult.
To solve this problem, prediction is applied into
the 1D-DWT part in the 2D-DWT decomposition[2]. Fig. 1 shows the process of 1D-DWT decomposition. Diﬀerent with 2D-DWT decomposition, in 1D-DWT decomposition, we firstly decompose the input image (I) vertically into LI and HI(Fig. 1(a)) and then we respectively decompose
LI and HI horizontally into LL, LH and HL, HH(Fig. 1(c)). Here L represents for low and H forhigh frequency coeﬃcients. After these two steps,
one stage of 2D-DWT is completed. Alternatively,
horizontal 1D-DWT can also be done firstly (Fig.
1(b)), followed by vertical decomposition of IL and
IH into LL, LH and HL, HH.
To predict a target pixel of the target row, ablock around the target pixel is extracted andshown as Fig. 2. In 1D-DWT part, the decomposition is done row by row from top to bottom.
Since the target pixels we want to predict and thereference pixels we can use are adjacent, the predictive coding can successfully be applied to 1DDWT part .

(5)

S1 ,S2 ,...,Sk i=1 x∈Siwhere µi (i = 1, 2, ..., k) is a centroid that is themean of points in Si .
Algorithm 1 K-means algorithm.
Select K points as initial centroids.
repeatFrom K clusters by assigning each point to
its closest centroid.
Recompute the centroid of each cluster.
until Centroids do not change.
K-means clustering is formally described by Algorithm 1. The algorithm proceeds by repeating
between two steps until only 1% of the observations change clusters.

2.3

Linear Prediction with Clustering

In this paper, K-means clustering is implemented
for the blocks extracted from the input image according to their features to make the categories,Ωc (c = 1, 2, ..., C). The prediction model in Ωc isgiven byq = ⟨ac , v⟩ ,Wavelet Based Image Codingwith Prediction

4(6)

Improved Clustering Methodwith Linear Prediction

where ac is constant for linear prediction. For
training data vi and qi (i = 1, 2, ..., N ) in Ωc , ac The predictive method that applies prediction to
1D-DWT transform part in 2D-DWT decompois decided to minimize the following criterion:
sition shows better prediction eﬃciency and codN
ing eﬃciency than the conventional one. However,c∑|qi − ⟨ac , vi ⟩ |2 .
(7) due to the calculation of prediction, it takes more
i=1time for coding process than the approach with42

Figure 2: Extraction of blocks with size of 5 × 3

Figure 3: Extraction of blocks with size of 6 × 3

Table 1: Encoding time for diﬀerent test data
Test Data
5×3
Conventional
Intersection 5.5412
0.1037
Lenna
4.21570.0777
Barbara4.19460.0772

Table 2: RMSE for diﬀerent test data
Test Data
6 × 3E 5×3E 6×3O 5×3O
Intersection 2.907 2.921 6.084 6.086
Lenna
1.947 1.969 3.843
3.796
Barbara7.830 6.751 15.647 14.757

out prediction. Table 1 shows the encoding time
for diﬀerent test data with the same learning data.
Here, the number of clusters is 128 (nCls = 128).
To solve this problem, I propose to reduce thetimes of calculation for prediction by choosing two
pixels as a target set. In order to choose two pixels
in the middle as a target pixel pair, the numbersof pixels from extracted block in the target row
should be even. Fig. 3 shows an example of theextracted block with size of 6 × 3. With K-meansclustering, we can get two parameters of liner predictor from each cluster for the target pixel pairat even and odd coordinates at the same time.
In other words, instead of calculating even andodd coordinates separately, the proposed method
makes it possible to calculate those at the sametime. Therefore, the times of calculation for selecting the best clustering can be reduced by half.
Furthermore, the experimental results showsthat there exists some clusters which generatelarge prediction error rate compared with the conventional one. I propose to remove the vector data
belonging to the clusters which generate larger error rate than the conventional ones to improve prediction and coding eﬃciency. After that, I propose
to reduce the number of the remaining clusters tobe half of the previous one. Therefore, the times
for finding the best cluster will be also reduced byhalf.
The estimation of the real value of target pixel
y can be given by:

estimation of the vector of prediction parameters.
1 is the vector of which all elements are 1.

ŷ = ⟨â, x − m · 1⟩ + m.

5

Experimental
Results

Methods

andIn the experiment, I used 8 standard images aslearning data and 3 standard images as test data.
I changed the numbers of clusters (nCls) and thesize of blocks (bx × by ) and quantization factor q
to obtain the best results for prediction eﬃciency
and coding eﬃciency.

5.1

Evaluation Methods

In this research, root mean square error (RMSE)
and peak signal-to-noise ratio (PSNR) are used toevaluate the prediction eﬃciency and coding efficiency respectively. The smaller RMSE is, themore eﬃciency prediction has been done. The
high PSNR value denotes the reconstructed image
quality is high, and the low PSNR value denotes
the reconstructed image quality is low.

5.2

Results and Discussion

In order to compare with the conventional one, I
will show the results that image n2 was used aslearning image. Furthermore, I set the numbersof clusters as 128 and changed the block size as 5
× 3 and 6 × 3.
• Prediction Eﬃciency(8)

where x represents for the vectors made from exTable 2 shows the results of RMSE of test data
tracted blocks, m is the average of x. â is the ”Intersection”, ”Lenna” and ”Barbara” respec43

Intersection (n2)36Table 3: Encoding time for diﬀerent test data by
diﬀerent methods
Test Data
6×3
5×3
Conventional
Intersection 3.3435 5.5412
0.1037
Lenna
2.5291 4.2157
0.0777
Barbara2.5321 4.1946
0.0772

34

PSNR

32

30

28

26
0.2

the proposed method outperformed the previous
one in prediction accuracy.

prediction(666)conventional;
prediction(555)

0.4

0.6

0.8

1

BR Rate(a) Intersection

6

Lenna (n2)40

In this paper, the background of image coding has
been introduced. Followed by the basis of linear
prediction, K-means clustering and the approachthat applies linear prediction to 1D-DWT in 2DDWT decomposition. Then the problem in encoding time has been figured out. To solve thisproblem, I introduced my proposed method to reduce the calculation time for prediction to savethe coding time. Experiments are conducted toshow the prediction and coding eﬃciencies.
According to the experimental results of the
proposed method, encoding time has been reduced
by appropriately 40 % without influencing coding
eﬃciency.
For future work, it is possible to improve coding eﬃciency by developing a more precise prediction algorithm. For example, instead of using
K-means clustering, we can apply neural network
in prediction step to improve coding eﬃciency.
Furthermore, we can apply EBCOT (Embedded
Block Coding with Optimized Truncation) in coding method to improve coding eﬃciency.

39

PSNR

38

37

36

35

34
0.2

prediction(666)conventional;
prediction(555)

0.4

0.6

0.8

1

BR Rate(b) Lenna
Barbara (n2)
36

34

PSNR

32

30

28

26
0.2

prediction(666)conventional;
prediction(555)

0.4

0.6

0.8

Conclusions

1

BR Rate(c) Barbara
Figure 4: Rate distortion curve

References

tively. According to Table 2, it is obviously that [1] Marc Antonini, Michel Barlaud, Pierre Mathieu and Ingrid Daubechies, ”Image Coding Usthere is almost no diﬀerence between the RMSEs
ing Wavelet Transform”, IEEE Transactionsof the proposed method (6 × 3) and the previous
on Image Processing, Vol. 1, No. 2, April 1992.
one (5 × 3).
[2] Shiyu Zhou, Yukihiko Yamashita, ”Wavelet
Based Image Coding via Linear Prediction and
Clustering”, Master Thesis, Jan 2014.

• Coding Eﬃciency

Fig.4 shows the results of coding eﬃciency fortest images Intersection, Lenna and Barbara re- [3] Rajesh P.N. Rao and Dana H. Ballard, Prespectively. Table 3 shows encoding times for difdictive coding in the visual cortex: a funcferent test data by diﬀerent methods.
tional interpretation of some extra-classicalAll of the three cases shows that the proposed
receptive-field eﬀects , Nature Neuroscience
method reduced the encoding time by appropri2, 79-87(1999), dol: 10.1038/4580
ately 40% successfully without influencing coding
eﬃciency. Especially in the case of Intersection,
44

Regression based Image Super Resolution through Decision Tree
Learning
決定木学習を用いる画像回帰超解像
Number: 15M18262

Student Name : Zhengqi Zhu

Supervisor: Yukihiko Yamashita

本論文では，決定木を用いる新しい画像超解像手法を提案する。これは，先に一連の回帰予測器を用意し，その中から，最適な予測器を決定木によって選択する方法である。この決定木の学習方法を具体的に与えると共に，計算機の実験によって低解像度画像から質の高い高解像度画像が復元できることを示す。

1

Introduction

Image super resolution (SR) is a technique for image resolution enhancement. It has various applications such as medical imaging, satellite imaging andsurveillance camera. The fundamental task of image
SR is to construct high resolution (HR) images by using low resolution (LR) images. It is technically easy
to down-sample HR images into LR images. However,
the reverse process is quite diﬃcult, since it is necessary to create more pixels by using less pixels andthe solution is not unique. Figure 1 is an example ofthe the general problem of SR. In this case, a desired
4×4 HR image patch is predicted by a 2×2 LR image
patch. In recent years, many SR algorithms have been
proposed. Interpolation-based methods utilise filterssuch as cubic convolution filter [1] to estimate HR
pixel values. Although interpolation-based methods
are widely used for their low computational cost, theestimated HR images are always with blurry edges.
Reconstruction-based method use prior knowledges
like Gradient Profile Prior [2] to estimate the HR
images. However, the quality of estimated HR images are still lackluster. As a result, learning-based
methods are being developed. Learning-based methods estimate the HR image by learning from example
HR and LR image patch pairs from external datasets.
Timofte et al. proposed adjusted anchored neighbourhood regression (A+) [6] which produces the state of
the art result for image SR.

Figure 1: Super Resolution Problem

trained regressors for each LR patch. The regressors
are pre-trained by using a method similar to a previous study on image intra-prediction [8]. Hyper-planes
are generated based on maximum information gain toform binary tests for the decision tree. Experimental
results showed that this method has the potential toreconstruct high quality HR images.

2

Related previous work

As it was mentioned in the previous section, the goalof this research is to select an optimal regressor froma set of pre-trained regressors to predict HR images.
An advanced regressor training technology [8] fromthe image intra-prediction field is adopted to accomplish this goal for its ability to produce high quality
regressors. Ochotorena and Yamashita proposed aneﬀective method for regressor training [8]. Firstly,
they classified image patches into small groups by using k-means clustering. They trained a regressor foreach group by obtaining the least square solution ofpatches belong to each group. Then they started an
iterative refining process to continuously regroup image patches and retrain the regressors based on errorsof the predictions. This method would be explainedin the next section in detail.

With all these methods being developed, a method to
obtain more accurate HR images is still desired. Most
recent learning based image SR methods, such as [5]and [7], classify image patches as groups first and thentrain regression models (a mapping between LR and
HR image patches) for each group. However, there is
not yet a study doing the opposite approach, which
is training regression models first and then classifying
image patches to select the best regression model foreach patch. Training regression model before classification could be advantageous firstly because it canfully utilise the regressor training technology developed on other fields such as intra-prediction and secondly because the remaining problem can be simplified as a selection problem. In this study, I will
demonstrate a method that utilise a binary decision
tree to select optimal regressors from a set of pre-45

3Proposed method

3.1

Regression model

As we can see in Figure 1, image SR is about predicting HR images by using LR images with less pixels.
A 2 × 2 LR image patch can be represented as a 4 × 1
column vector ⃗l. Similarly, a 4 × 4 HR image patch
can be represented as a 16 × 1 column vector ⃗h. Converting a LR patch to a HR patch is⃗h = R⃗l,

Figure 2: Hyper-plane Generation(1)

where R (our regressor) is a 16 × 4 matrix.
As the LR image patches inherently contain less pixel
information, in this method I decided to use middle
image patches, of which dimension is the same as oneof the HR image patches, m⃗ to predict the HR image
patches. Middle image patches are created by themost commonly used bi-cubic interpolation.
m⃗ = B[⃗l],(2)

where B is the operator of bi-cubic interpolation.
Then the regression model can be as follows:⃗h = Rm,⃗

Figure 3: Decision Tree(3)

where R is the regressor in the form of a square matrix.

Rj = arg min
R

3.2||h⃗i − Rm
⃗ i ||2 .

(7)⃗i ∈HjHm⃗ i ∈Mj

By adding a Tikhonov regularization term, the newregressor is given by:Regressor training

Rj = Hj Mj T (Mj Mj T + λI)−1 .

At first, by downsampling the images from the example HR image database PH , LR image database PL
can be created. Middle image database PM can becreated as well by using the bi-cubic interpolation.
A set of regressors were trained by using an approachsimilar to the clustering and iterative refining processof the previous research [8]. Firstly, for each HR image patch ⃗h, an error image patch ⃗e were created bysubtract the original HR image patch ⃗h and the Middle image patch m⃗ .
⃗e = ⃗h − m.
⃗∑

This refining process will be repeated over several
times to obtain the final regressors R1 , R2 ...... and Rn .

3.3

k-means clustering is used to divide the set of error patches E into n clusters. Corresponding regressors R1 , R2 ......Rn for each cluster are obtained afterwards. Then, the members of each cluster Hj , Lj
where j ∈ {1, 2, ..., n} will be updated through a refining process by finding the minimum predicting error
for each regressor.
(5)

Mj = {m⃗ i | arg min ||h⃗i − Rp m
⃗ i ||2 = j}.

(6)

Decision tree learning

The remaining problem is simplified as a selection
problem, i.e. how to select the optimal regressor from
R1 , R2 ...... and Rn for each individual middle image
patch.
Hyper-planes were created to form a binary decision tree to do the selection. 500 random vectors
w⃗1 , w⃗2 , ......w⃗500 with the same dimension as the middle image patches with the norm of 1 are created. a setof 50 scalars b1 , b2 , ....., b50 is created with a designatedrange of value. The hyper-planes can be generated by
calculating the projection of the middle patch data
onto the random vector subtract a certain parameterw⃗i T m⃗ − bj . Figure 2 is a demonstration of a hyperplane generation. Each hyper-plane divides the original dataset into 2 subsets. Therefore, ’Which subset
does the data belong to?’ can be a binary test for thedecision tree. The overall decision tree is illustrated
in Figure 3.
The data belong to the first subset fulfils:(4)

Hj = {h⃗i | arg min ||h⃗i − Rp m
⃗ i ||2 = j},

(8)

p

p

Then the regressors will be updated too by using
these updated clusters. The new regressor can be calculated by using least square methodw⃗i T m⃗a − bj ≥ 0,

46

(9)

The data belong to the the second subset fulfils:w⃗i T m⃗a − bj < 0.

Finally the HR image is obtained.

(10)

4

Information gain is calculated for each generated
hyper-plane.
IG = N∑i− Nb−Pi log Pi − Na∑∑

Two test image sets were used. I down-sampled theoriginal test images and created LR images as the
input. 4 × 4 LR image patches were collected in orderto predict 8 × 8 HR image patches.

−P ai log P ai

i

−P bi log P bi ,

(11)

4.1

i

(12)

4.2

w,b
⃗

Results of The Proposed Method

The decision tree were trained from an image databaseconsists of 240000 LR and HR image patch pairs. On
each node of the decision tree 500 random vectors w⃗
are generated and the value range of b is between 0
to 0.3 with 0.006 as interval. When the total numberof patches in a node is below 10, the node will bedeclared as an end-node. If there are still nodes thatare not end-nodes, the decision tree will grow until the
total hight of decision tree exceeds log2 1000000 ≈ 20.
Image SR was conducted by using 10, 20, 30, 40, 50,
64 as the number of regressors. Table 1 shows the
PSNR results of this method with comparison to the
bi-cubic interpolation where the number of regressorn = 30.

After selecting the optimal (w,⃗ˆ b̂) for one node,
the same calculation will continue at the next node.
When the number of patches in a node is lower than
10 or when the total number of nodes exceeds a certain parameter, the node will be declared as an end
node (node without child node). For each end node,
the most used regressor will be declared as the resultof that node. The binary test for each node and theresult for each end node are memorised and a decision
tree is constructed.

3.4Best Case Scenario

The best case scenario where I assumed all regressors
are chosen correctly were created. This best case scenario is an ideal situation that can show both the quality of the regressors and the potential of this method.
Table 1 shows the PSNR comparison between the bestcase scenario and the most common Bi-cubic interpolation on test image set 1. There is a significant improvement of PSNR. This suggests that this method
has a good potential if the decision tree can select the
optimal regressor correctly. This also suggests thatthe pre-trained regressors and the regression model inthis method can properly conduct image SR.

where N is the number of data in the parent node
before divided by a hyper-plane. Na and Nb represent
the number of data in both child nodes respectively.
i is the index for the regressor. Subsequently, Pi =Ni /N where Ni is the number of patches which use
the ith regressor. P ai and P bi is calculated similarly
for both child nodes.
The optimal pair of (w,⃗ˆ b̂) that generates the hyperplane with the maximum information gain is selected
to formulate the binary test of each node for the decision tree.
(w,⃗ˆ b̂) = arg max IG,

Experimental results

Test phase

Test phase is the real application of this method. In
real application the only available input informationis the LR image. We also have a set of regressors
and a constructed decision tree for HR image prediction. We take the following steps to conduct image
SR under this method.

Table 1: PSNR (dB) Comparison 30 Regressors
Best Case Decision
Name
Bi-cubic
Scenario
Tree
Arctichare 40.5895
46.885243.7192
Airplane
31.3801
37.5875
33.8867
Barbara25.3533
27.939325.8367
Boat
29.9539
33.6441
31.2945
Fruits32.2379
37.0949
34.6799
Lena
34.1440
38.216736.0167
Baboon23.6435
26.2082
24.5553
Goldhill
31.230234.3052
32.1606
Monarch31.6676
36.7382
34.1039
Mountain
19.4424
21.2262
20.1788
Peppers
31.8690
36.2276
33.6440
Sails
28.311432.370930.1149
Average29.9852
34.0370
31.6826

1: Make the middle image by using LR image
through bi-cubic interpolation.
2: Collect middle image patches by using a sliding
window.
3: For each middle image patch mi , ask binary questions on the first node and proceed to the next
node until reaching the end node.
4: The result of the end node R̂i is obtained.
5: Predict the HR image patch hi by Hi = R̂i × mi
and move on to the next patch until all the patch
are predicted.
6: Construct the HR image by calculating the average of the overlapping sliding patches.

There is a fair amount PSNR gain compared withthe bi-cubic interpolation. We see an average of 1.7

47result of the proposed method is still inferior to A+
method by 0.78 dB, the best case scenario can outperform A+ by 1.62 dB. Therefore as long as the decision
tree selection accuracy can be improved in the future,
this method has a potential to outperform the state of
the art methods. Instead of using bi-cubic interpolation, I also want to use A+ to produce middle patch.
This also makes it possible to outperform A+ in the
future.

References

Figure 4: Barbara: bicubic (Left), proposed method(Right)[1] R.G. keys,”Cubic convolution interpolation
for digital image processing,” IEEE Trans.
Acoust.,Speech, Signal Process., vol. 29, no. 6,
pp. 1153-1160, Dec. 1981.

dB gain on set 1 with 30 regressors used. We see anaverage of 2.1 dB gain on set 2 with 20 regressors
used. Table 2 shows the PSNR gain on both test sets.
As we can see in Figure 4, the visual quality has been
improved particularly at the area with edge.

[2] J. Sun, J. Sun, Z. Xu, H.Y. Shum, Gradient Profile Prior and Its Applications in Image SuperResolution and Enhancement. IEEE Trans. Image
Process., vol.20, no.6, pp.1529-1542, 2011.

Table 2: Average PSNR Gain
Number of PSNR (dB) gain PSNR (dB) gain
Regressors
Set 1
Set 2
10
1.5493
1.894120
1.67082.066430
1.69742.064340
1.55571.998350
1.6622
1.983764
1.54481.9846[3] H. Chang, D.T. Yeung, and Y. Xiong, Superresolution through neighbor embedding, Proceedings, IEEE International Conference on Computer
Vision and Pattern Recognition (CVPR 2004),
vol. 1., 2004.
[4] R. Zeyde, M. Elad, and M. Protter, On single
image scale-up using sparse-representations, In
Curves and Surfaces, Springer Berlin Heidelberg,
pp. 711-730, 2012.
[5] R. Timofte, V. De Smet, and L. Van Gool, Anchored neighborhood regression for fast examplebased super-resolution, Proceedings, IEEE International Conference on Computer Vision (ICCV2013), pp. 1920-1927, 2013.

I compared the result of this method to some of thestate of the art methods mentioned in the introduction by using test image set 2. Table 3 shows the resultcomparison with 30 regressors used for this method
and for the best case scenario. As we can see, theproposed method can produce similar PSNR resultscompared to other major methods.

5[6] R. Timofte, V. De Smet, and L. Van Gool, A+:
Adjusted Anchored Neighborhood Regression for
Fast Super-Resolution, IEEE Asian Conference on
Computer Vision (ACCV 2014), 2014.

Conclusion and future work

[7] Jun-Jie Huang and Wan-Chi Siu, ”Learning Hierarchical Decision Trees for Single Image SuperResolution,” IEEE Transactions on Circuits andSystems for Video Technology, 2015

In this paper, I proposed a new regression based image
SR framework. A set of regressors were trained anda binary decision tree was created to select the optimal regressor. This method produces similar PSNR
results compared with the state of the art methods. I
also tested a best case scenario where I assume every
regressor is selected correctly. Although the current[8] Carlo Noel Ochotorena and Yukihiko Yamashita”Regression-based Intra-prediction for Image andVideo Coding” (Preparing to submit)

Table 3: PSNR (dB) Results Comparison
Test Images
Baby
Bird
Butterfly
Head
Women
Average

Bi-cubic
37.1
36.8
27.4
34.9
32.1
33.66

Zeyde [4]
38.2
39.9
30.6
35.6
34.5
35.78

NE+LLE[3]
38.3
40.0
30.0
35.5
34.5
35.77

GR[6]
38.3
39.0
29.1
35.6
33.7
35.13

48

ANR[6]
38.4
40.0
30.5
35.7
34.5
35.83

A+[7]
38.5
41.1
32.0
35.8
35.3
36.55

Proposed Method
38.5
40.0
30.1
35.7
34.6
35.77

Best Case
40.5
43.8
32.6
36.9
37.1
38.17

Energy dissipation in adhesion contact between an elastic beam and a flat rigid surface
Student number: 15M18084 Name: Takeru KUSAKARI Supervisor: Kunio TAKAHASHI

弾性梁と剛体平面間の凝着・脱離プロセスにおけるエネルギー散逸
草苅 太建

本論文では、弾性梁と剛体平面を凝着・脱離させたときに生じるエネルギー散逸のメカニズムをより詳細に議論するべく、梁幅が変化する台形梁と、梁の自重の影響を取り入れることで、従来の弾性梁と剛体平面間の凝着モデルを改善している。また、改善を加えた梁モデルにおけるエネルギー散逸を解析的に算出し、実験にて測定している。測定結果より、接触端部の長さがエネルギ
ー散逸に対して影響を及ぼすことが示唆される。また、弾性梁が伸び縮みやせん断変形を起こしている可能性が示唆されるため、今後はそれらの効果を梁モデルに加える必要があることを示している。

1. Introduction
Force curve in adhesion phenomena is very useful for designing applications using adhesive force, such as Gecko-inspired adhesive [1-3]. In the force curve, there are differences between loading
process and unloading process and it is called adhesion hysteresis. In
order to predict force curve more precisely, it is significant to understand the mechanism of adhesion hysteresis.
Recent studies say that energy dissipation in adhesion con-Fig.1 Comparison between Johnson’s theory and Baek’s theory

tact causes adhesion hysteresis. Energy dissipation can be calculated

related to energy release rate

as the difference between work of adhesion ∆𝛾𝛾 and energy release
rate 𝐺𝐺 like

∂𝑈𝑈dissipation
� = −(𝐺𝐺 − ∆𝛾𝛾)∂𝑆𝑆
𝑑𝑑

In this study, in order to clarify the mechanism of adhesion(1)

hysteresis, existing beam model is modified in two points. Firstly, trapezoidal beam model is introduced to discuss the effect of contact width

𝑆𝑆 is contact area, i.e. it expresses the energy dissipation per unit con-on adhesion hysteresis. Secondly, the weight of the beam is considered.

tact area. Because work of adhesion ∆𝛾𝛾 is physical property, in order

And then, energy dissipation is measured and discussed based on theto predict energy dissipation, energy release rate 𝐺𝐺 is necessary tomodified model.

calculate. Johnson suggests a formula to calculate energy release rate

𝐺𝐺 in a point contact model based on JKR theory [4]. It is expressed as

2. Modified Beam Contact Model to Calculate Energy

𝛼𝛼 is dissipation factor and it is a fixed value defined by property [5].

Fig.2 shows schematic illustration of modified beam con-

𝐺𝐺 = ∆𝛾𝛾/(1±𝛼𝛼)(2)Dissipation

Meanwhile, Baek suggests a different formula expressed as
𝐺𝐺 = ∆𝛾𝛾 + 𝑓𝑓/(2𝜋𝜋𝜋𝜋)tact model. In trapezoidal beam, the width changes linearly. Change(3)

rate of the width 𝑚𝑚 is expressed as𝑎𝑎 is contact radius in a point contact model and 𝑓𝑓 is dissipation force

𝑚𝑚 =[6]. But these two theories have contradiction expressed as Fig.1. So,

in point contact model, the mechanism of adhesion hysteresis hasn’t

𝑊𝑊1 − 𝑊𝑊0𝑊𝑊0(4)

By using change rate of the width 𝑚𝑚, trapezoidal beam can be consid-

been clarified yet.

ered theoretically. And also when 𝑚𝑚 = 0, it expresses a rectangular

As the different model, there is adhesion contact model be-beam.

tween an elastic beam and a flat rigid surface [7]. Kusakari measured
the energy release rate in adhesion contact between a rectangular elastic beam and a flat rigid surface. He suggests that energy release rate
in beam model can be adapted to both Johnson’s and Baek’s. But thisbeam model has two problems. Firstly, because of rectangular beam,
contact width is fixed and the effect of contact width can’t be discussed.
Secondly, the weight of the beam is ignored. Therefore, in order tounderstand the mechanism more precisely, this beam model needs toFig.2 Schematic illustration of modification of beam contact model

be modified.

49

Based on the assumption that the density of the beam isTab.1 Parameters to calculate energy dissipation

uniform, the weight of the beam is considered as
𝑔𝑔(𝑥𝑥) =

1.

𝑓𝑓𝑔𝑔
𝑥𝑥
(1 + 𝑚𝑚 )
𝐿𝐿
𝐿𝐿

(6)

2.
3.

𝑓𝑓𝑔𝑔 is the total weight of the beam and 𝐿𝐿 is the length of the beam.

4.

In modified beam model, this is considered in calculation of the force
𝑓𝑓𝑧𝑧 and energy dissipation 𝑈𝑈dissipation .

5.

face can be calculated based on linear beam theory. In this model, it is7.

assumed that the friction force can be neglected. The force 𝑓𝑓𝑧𝑧 in the

8.

The force 𝑓𝑓𝑧𝑧 between an elastic beam and a flat rigid sur-

6.

line contact is expressed as

1 𝐸𝐸𝐼𝐼0𝑑𝑑
𝛼𝛼2
𝑓𝑓𝑧𝑧 =
� + sin 𝜃𝜃� + 𝑓𝑓𝑔𝑔
𝛼𝛼1 𝐿𝐿2 cos2 𝜃𝜃 𝐿𝐿
𝛼𝛼1

9.

(7)

10.

𝐸𝐸𝐼𝐼0 is the bending stiffness at the root of the beam. 𝑑𝑑 is the displace-ment of the root of the beam. 𝜃𝜃 is the contact angle. And 𝛼𝛼𝑖𝑖 is the

𝐿𝐿

Length of a beam

𝐼𝐼0

Moment of inertia at the root of a beam𝜃𝜃Contact angle

𝑚𝑚

Change rate of width

𝑓𝑓𝑔𝑔

Weight of whole beam

𝑑𝑑

Displacement of the root of a beam𝑓𝑓𝑧𝑧

Force between a beam and a surface

𝐸𝐸

Yong’s Modulus𝑙𝑙Length of non-adhesion area∆𝛾𝛾Work of adhesion

Therefore, in order to measure displacement of the root ofparameter related to change rate of the width 𝑚𝑚. In area contact, thea beam 𝑑𝑑, force between a beam and a surface 𝑓𝑓𝑧𝑧 , and length of

1 𝐸𝐸𝐼𝐼0
𝑑𝑑𝛽𝛽3
� + (1 − 𝛽𝛽2 ) sin 𝜃𝜃� + 𝑓𝑓𝑔𝑔
𝛽𝛽1 𝑙𝑙 2 cos2 𝜃𝜃 𝑙𝑙
𝛽𝛽1

stepping motor(KZL06075-C1-GA, SURUGA SEIKI). Force be-force 𝑓𝑓𝑧𝑧 can be calculated as
𝑓𝑓𝑧𝑧 =

non-adhesion area 𝑙𝑙, the experiment system are prepared as shownat Fig.3. Displacement of the root of a beam 𝑑𝑑 is controlled by(8)

tween a beam and a surface 𝑓𝑓𝑧𝑧 is measured by electronic𝑙𝑙 is the length of non-adhesion area. 𝛽𝛽𝑖𝑖 is the parameter related toscale(Practum🄬🄬612-1S, Sartorius). Length of non-adhesion area 𝑙𝑙change rate of the width 𝑚𝑚 and the length of non-adhesion area 𝑙𝑙.

is observed by microscope(SKM-3000B-PC, SAITOH KOU-

When change rate of the width 𝑚𝑚 → 0, the force 𝑓𝑓𝑧𝑧 equals to the

GAKU). And the elastic beam used in this experiment isforce 𝑓𝑓𝑧𝑧 in a rectangular beam. Whether this theoretical model is

PDMS(SYLGARD🄬🄬184 SILICORN ELASTORMER KIT, Dow

valid is discussed based on experimental result later.

Corning). As a flat rigid surface, slide glass(MICRO SLIDEAs for the calculation energy dissipation, the energy re-

GLASS S9213, MATSUNAMI) is used. And also, in order to re-lease rate should be calculated. Energy release rate is defined asduce the effect of friction force, still balls are set between the sur-

∂𝑈𝑈elastic
𝐺𝐺 =
�
∂𝑆𝑆
𝑑𝑑

(9)

face and the electronic scale.

Therefore, in order to calculate energy release rate, elastic energy must
be calculated. In this model, because the tangential force is assumed
as ignorable value, it can be considered that bending deformation only
occurs. Accordingly, elastic energy in this model can be calculated as
𝑈𝑈elastic =2

𝑓𝑓𝑔𝑔 𝑙𝑙 3 cos2 𝜃𝜃
𝛽𝛽1 𝑓𝑓𝑧𝑧 2 𝑙𝑙 3 cos2 𝜃𝜃
𝐸𝐸𝐼𝐼0
+ 𝛽𝛽4
tan2 𝜃𝜃 + 𝛽𝛽5
2
𝐸𝐸𝐼𝐼0
𝑙𝑙
𝐸𝐸𝐼𝐼0
𝑓𝑓𝑧𝑧 𝑓𝑓𝑔𝑔 𝑙𝑙 3 cos2 𝜃𝜃
− 𝛽𝛽3
𝐸𝐸𝐼𝐼0

(10)

After all, by using formulas (1), (9), (10), energy dissipation in adhesion contact between an elastic beam and a flat rigid surface can be
Fig.3 Schematic illustration of experimental system

calculated.

In this study, viscoelasticity is not considered. Therefore

3. Experiment to Measure Energy Dissipation

the experiment need to be performed as static state as possible. Ac-

In order to calculate energy dissipation, the parameters

cordingly, the displacement is controlled by a step of 50μm and wait-shown at Tab.1 need to be known.

ing duration between steps is 30 seconds. And the maximum displace-

The parameters from 1 to 5 are fixed by initial condition.

ment is chosen 3500μm.

The parameters from 6 to 8 are measured experimentally. 9 and 10

After all, force curves are measured as shown at Fig.4-5.

are estimated based on experimental result later.

50

Fig.4 Force curve between a rectangular elastic beam and a flat rigid
surfaceFig.7 Energy release rate between a rectangular elastic beam and a flat
rigid surface

Fig.5 Force curve between a trapezoidal elastic beam and a flat rigid
surfaceFig.8 Energy release rate between a trapezoidal elastic beam and a flat
rigid surface
Comparing theoretical force given by formula (7) and (8)with the measured force curves, Yong’s Modulus is estimated. One ex-

In the loading process, all the results show the same tendency and theample of the comparison is shown at Fig.6.

same fixed value. Therefore the energy release rate in loading process
can be considered as physical property and it is assumed as work ofadhesion ∆𝛾𝛾 . Accordingly, ∆𝛾𝛾 is estimated as 0.031J/m2 and thisvalue almost equals to the ∆𝛾𝛾 of a point contact model [6].

After all, all the parameters to calculate energy dissipation

have been decided. The results of the energy dissipation are shown at
Fig.9-10.

Fig.6 Comparison between measured force and calculated force

From Fig.6, in the trapezoidal beam whose edge is wide, the weight of
the whole beam 𝑓𝑓𝑔𝑔 is very influential on the force curve. And by using modified model which considers the weight of the whole beam 𝑓𝑓𝑔𝑔 ,fitting of force curve can be performed better. From this result, Yong’s
Fig.9 Dissipation energy between a rectangular elastic beam and a flat

modulus 𝐸𝐸 is estimated as 1.15MPa.

Because Yong’s modulus 𝐸𝐸 has been estimated, energy

rigid surface

release rate 𝐺𝐺 can be calculated using formula (9) and (10) as shownat Fig7-8.

51

the contradiction between the result in this study and the existing theories. So ,based on the result, it can be suggested that the shear deformation induced by the friction force should be considered in order tocalculate energy dissipation more correctly in the future works.

Fig.10 Dissipation energy between a trapezoidal elastic beam and aflat rigid surface
Fig.11 Comparison between 𝛿𝛿 calculated theoretically and 𝛿𝛿 meas-

ured experimentally

4. Discussion
In using rectangular beam, the energy release rate in un-

5. Conclusion

loading process tends to increase if the width of the beam gets wide asshown at Fig.7. On the other hand, in using trapezoidal beam, the beam

Introducing trapezoidal beam model and the effect of the[4] whose contact width increase in unloading process has much moreweight of a beam, the existing beam model is modified theoretically.

energy release rate than another one, i.e. the beam [4] needs more en-

Also, it is shown experimentally that this model can fit to the measured

ergy to separate from the surface as shown as Fig.8.

force curves and the weight of the beam effects on the force curve. It

And also, in energy dissipation, wider the width of the rec-can be the indication to design applications using adhesive force. Re-tangular beam is, more energy is dissipated as shown at Fig.9. In thegarding the energy release rate, in loading process, it is fixed value

trapezoidal beams, the beam [4] has much more energy dissipation as

regardless of the shape of the beam. And considering this value asshown at Fig.10.

work of adhesion, energy dissipation can be calculated. However the

From the results of energy release rate and energy dissipa-

result contradicts to the existing theories in some points. One of thetion, it can be suggested that the width of contact edge effects on thereasons is considered as the deformation induced by the friction force

energy dissipation, i.e. adhesion hysteresis. However, in using the rectangular beam, the energy release rate increases as the beam is sepa-

based on the result of the displacement of the edge 𝛿𝛿. Therefore, it israted from the surface. Based on existing theories such as Johnson’s

should be considered in order to calculate energy dissipation more cor-

one and Baek’s one, when width of contact edge is fixed, the energy

rectly in the future works.

suggested that the shear deformation induced by the friction force

release rate is also fixed. Therefore these results contradicts to the exiting studies. The reason why is that there are some factors which are

References

not considered in this model.

[1] Geim A. K., Dubonos S. V., Grigorieva I. V., Novoselov K. S.,
Zhukov A. A. and Shapoval S. Y., ”Microfabricated adhesive

In fact the shear deformation induced by the friction force

mimicking gecko foot-hair”, Nature Materials 2, 461-463, 2003

can be thought as one of the factors causing the contradiction, although[2] Santos, D., Spenko, M., Parness., A., Kim, S. & Cutkosky,

steel balls are set to reduce the effect of the friction force. In order toestimate the effect of the friction force, the displacement of the edge

M., ”Directional adhesion for climbing: theoretical and practicalof the beam 𝛿𝛿 is measured by micro scope. And based on geometricconsiderations”, Journal of Adhesion Science and Technology,

conditions, the displacement of the edge 𝛿𝛿 is calculated theoretically21, pp. 1317-1341, 2007.
[3] Jeonga, H. E., Leeb, J. K., Kima, H. N., Moonb, S. H., Suha, K.

as,δ = − tan 𝜃𝜃 (𝑑𝑑 + 𝑙𝑙 sin 𝜃𝜃)Y., ”A nontransferring dry adhesive with hierarchical polymer(11)

nanohairs”, PNAS vol.106 no.14, 5639-5644, 2009In Fig.11 the relation between the δ calculated theoreti-

cally and the measured δ is shown. Around the start point of the load-[4] K. L. Johnson, K. Kendall and A. D. Roberts: Proc. R. Soc. London

the loading is getting advanced, the difference is increasing. Accord-[6] D. Beak, et al., a master thesis of Tokyo Institute of Technology

A 324 (1971), pp. 301-313.

ing process and the end of the unloading process, both the measured[5] K. L. Johnson, et al.: Tribol. Int., 31., 8, p.413-418 (1998)

δ and the calculated δ are almost same value. On the other hand, as(2013)

ingly, there is a possibility of the friction force working on the beam.

[7] T. Kusakari, et al., a bachelor thesis of Tokyo Institute of Technol-

Therefore the deformation induced by the friction force could effectogy (2015)

on the energy release rate and energy dissipation. And it could causes52

A condition-based maintenance policy for Electric Vehicles (EVs)Student Number: 15M18010

Name: Toshiro Abe

Supervisor: Naoya Abe

電気自動車のための状態に基づく最適なメンテナンス制度
阿部 敏朗
本論文は、今後の更なる普及が見込まれる電気自動車に対し、車両を構成するコンポーネントの劣化
過程を過去のデータを用いて予測し、適切なタイミングで故障を未然に防ぐメンテナンスを行う戦略を提案した。分析結果から、 従来の車検制度のタイミングでメンテナンスを行う場合と本手法を比較した場合、車両の信頼性を同等に保った上で、最大で 40%程度の整備費削減が見込まれることが示唆された。
economically more viable to consumers. The wide
diffusion of EVs makes it important to consider new
inspection policy mainly designed for EVs.

1. Introduction
1.1 Inspection and Electric Vehicles (EVs)
Mandatory inspection and the maintenance being carriedout for vehicles are assumed to play an important role toensure road safety by reducing breakdowns as well as tomitigate environmental problem such as air pollution
created by the emission from poorly maintained automobile
traffic. In Japan, despite the fact that vehicles haveexperienced dramatic technological development, theperiodical mandatory inspection system was revised only
twice since it was firstly introduced in 1953.1
According to the Ministry of Land, Infrastructure,
Transport and Tourism, a vehicle owner typically pays JPY
91,000 to JPY166,000 on every inspection.2 In fact, it wassuggested that the vast majority of current vehicle owners
feel maintenance/inspection expenditure as a criticalfinancial burden.3 This expenditure also refrains potential
owners (i.e. people who possess a driving license butchoosing not to own a vehicle) from purchasing a vehicle.4
Even though the target of these surveys was associated withconventional vehicle owners, this tendency may apply to
EV owners.
EVs and Internal Combustion Vehicles (ICVs) are
different in some respects. Firstly, EVs emit no tailpipe
pollutants while they are driven. Secondly, an EV has
relatively non-complex mechanical structure compared to
an internal combustion vehicle especially in the number ofmoving parts. For example, a combustion engine consistsof 20,000 to 30,000 parts, on the other hand, an electricmotor has only 30 to 100 parts.5 In accordance with thecomplexity of the structure, it is logical to speculate thatEVs may have higher reliability.
When it comes to the future perspective of the diffusion
and adoption of EVs in Japan, the Japanese government
released its strategy in 2010, which suggests that EVs willaccount for 20-30% of passenger vehicle sale in 2030.6 In
order to meet the target, the price of an EV, and charging
infrastructure have been considered as major obstacles.
However, as of 2016, there are more than 40,000 places
nationwide where electric car owners could recharge theirvehicles.7 This is more than the number of existing petrol
stations in Japan. In addition, battery cost which has been areason for EV’s high price is decreasing. Björn Nykvist etal. (2015) reported EV manufacturers lowered battery pricefrom US$1,000 per kWh to around US$300 per kWh,during 2007 to 2014.8 They also suggested that the cost of
Li-ion batteries continue to decline which makes EVs

1.2 Design of a maintenance strategy
As the inspection was initially intended to reduce themechanical breakdown on a vehicle to ensure the roadsafety, an ideal maintenance strategy for EVs should also
maintain vehicles mechanical reliability and preferably
financially efficiency. The trade-off here is that the more
frequent maintenance actions to keep a vehicle in a goodcondition, the higher the resulting cost can be. In thisrespect, condition-based maintenance (CBM) which has
been used in many industries such as Information
Technologies or Energy, is a promising approach.9 Thisapproach gives a strategy to perform the minimal numberof preventive maintenance actions that can restore thereliability of aged components to avoid costly breakdowns
based on the components failure rates in the system.
The CBM approach mainly contains of two steps: asystem modeling based on failure rates and an optimizationof maintenance decisions according the system modeling,which often involves Monte Carlo simulation, which is thecalculation that generates random objects or processes withusing a computer.10 This method enables one to perform asimulation to see how a quasireal-life system which
consists of many components with non-linear deterioration
mode behaves. However, the difficulty of using the Monte
Carlo approach is the computing time, because one has torun several hundred or thousands of simulations for every
single step of optimization of maintenance decision.
In order to run the Monte Carlo practically, goodsolutions which meet demand upon evaluation function
must be chosen from the solution space with using an
optimization algorithm. Since the function being optimized
is a decision of preventive maintenance actions which isexpressed as a discrete problem where information of thefunction is expressed as an independent bit, typically {0,1},
Genetic Algorithms (GAs) which employs binary strings issuitable as an optimization approach.

2. Objectives and Contribution
2.1 Objectives
A CBM approach with flexible interval maintenance policy
based on the vehicle condition is proposed. This aims toidentify: (i) the times in which preventive maintenanceactions should be performed to minimize the necessary total53

maintenance cost, including the recovery cost for
breakdowns, and (ii) how CBM is effective in reducing the
lifetime maintenance cost compared to existing inspection
policy.

3.2 Optimization
3.2.1 Genetic Algorithms
In this problem, an individual must encode all possible
scheduling combinations for all system components. Since
we want to know when and how many actions must be
carried out, in this sense, a binary string so-called
individual is employed. Each individual has a length of Nc
bits (TM×12×number of component), and its decoding issuch that a ‘1’ means that component is selected to have
preventive maintenance, while ‘0’ means no action iscarried out on that time-unit. Multiple numbers ofindividuals are the population. After initialization of thefirst population, the fitness of each individual in population
is evaluated by objective function. Then, two individuals
are chosen and mated to produce new individuals for the
next generation. These new individuals can be modified
again by mutation. This continues until a termination
condition is met. (See Figure 3-2 right)

2.2 Significance of the study
EVs have been adopted to the Japanese market at
unprecedented speed. Despite the penetration, there havenot been proactive discussions for the reform of theirinspection policy. This study is proposing a financiallyfavorable maintenance strategy for EVs through aquantitative analysis, and the outcome of the study is
expected to provide policy-makers insights for the
introduction of new inspection policy specifically for EVs.

3. Methodology
3.1 System modeling
The system resembling an EV consists of eight criticalcomponents connected in series, and the system shows abreakdown when single component causes a malfunction.
(See Figure 3-1). Due to the similarity of mechanicalstructure between an EV and a conventional vehicle, theselection of critical components follows current inspection
practice which is speculated to be designed upon theconsideration of road safety. On top of that, components
unique to an EV (electric motor and battery) are added in
the system.
The deterioration process of each component is assumed
to follow a Weibull distribution, which is commonly usedwithin reliability engineering due to its versatility andrelative simplicity. The most general expression of the
Weibull distribution is given by the two parameters: β as ashape parameter and η as a scale parameter. The values ofthese parameters used in the study can be found in Figure
3-1.
Figure 3-2 describes the transition of failure rate on eachcomponent. It can be seen that a component with larger η
generally shows higher reliability. The failure data for thecomponents were retrieved from a report from the Japanese
government and a non-profit organization called Plug in
America.10 11 In addition, in this study, it is assumed that a
preventive maintenance action returns the components toan “as good as new” condition.

Figure 3-2 A string representation of maintenance strategy andan outline of GAs

3.2.2 Objective function
The objective function for evaluating a given set ofindividuals is to reduce the sum of preventive maintenancecost 𝐶𝑀 and corrective maintenance cost 𝐶𝑅 and repair
cost within the mission time 𝑇𝑀 .
G = min (𝐶𝑀 + 𝐶𝑅 )(eq.4.1)

Preventive maintenance cost 𝐶𝑀 can be written as
eq.4.2 𝑁𝑐 is the number of components, 𝑛𝑀 is the number ofmaintenance activities in total through the mission
time. 𝑛𝑀𝑖 is the number of preventive maintenance on
component i. 𝐶𝑀𝐵 is the base cost per single maintenanceintervention and 𝐶𝑖 is the component specific cost.
𝐶𝑀 = 𝑛𝑀 𝐶𝑀𝐵 + ∑𝑁𝑐
𝑖=1 𝑛𝑀𝑖 𝐶𝑖(eq.4.2)

𝐶𝑀 consists of the component dependent cost 𝐶𝑖 andthe base cost 𝐶𝑀𝐵 , which represents the minimum cost to
carry out preventive maintenance actions including agency
fee, and basic labor cost. This setting encourages one toperform preventive maintenance on several components atthe same timing to save 𝐶𝑀𝐵 .

Figure 3-1 Components of the system and their Weibull
parameter settings

𝑇𝑀
𝐶𝑅 = ∑𝑁𝑐
𝑖=1 ∑𝑡=1 𝐶𝑅𝑖 ∙ 𝐼𝑅𝑖 (𝑡)𝐶𝑅𝑖 = 𝜇𝑅𝑖 + 𝐿𝑐

(eq.4.3)(eq.4.4)

𝐶𝑅 is the total corrective maintenance cost which occurs
after a breakdown of a component. 𝐶𝑅𝑖 is the repairing
cost per time on component i which consists of labor cost
𝐿𝑐 and base cost 𝜇𝑅𝑖 𝐼𝑅𝑖 (𝑡) is a characteristic function
equal to 1 if the component i is under repair at time-unit t
and 0 otherwise.

Figure 3-2 Transition of unreliability on components over time54

3.2.3 Cost parameters and simulation settings

Figure 4-2 indicates that the optimization of the CBM
strategy has decreased both maintenance cost and numbersof breakdown on all components. In the end of the
optimization (generation=10,000), for the average of 8components, it showed only 11% of breakdowns comparedto the no maintenance strategy before the optimization.

Cost parameters are the crucial factors that determine thelifetime maintenance cost of an EV. Since theseparameters heavily depend on a variety of factors such as
the type of a vehicle, the situation of breakdown, the typeof maintenance garage and so forth. However, this study is
focusing on the comparative analysis of flexible
maintenance strategies; therefore, considerably
appropriate cost parameters are estimated based on theavailable data. The mission time here is set as 13 years
with the consideration of the average ownership period forpassenger cars in Japan as well as the battery lifetime. In
order to save computational time, one month is theminimal time unit.
Table 3-1 Cost parameters for preventive maintenance andcorrective maintenance
C

C

C

(JPY)

(JPY)

l
(JPY)

Steering

70,00010,0008,000 165,000 Yourmechanic.com

Breaks

70,00010,0008,000 58,000

Costhelper.com

Tires

70,00010,0008,000 19,000

Costhelper.com

Shocks

70,00010,0008,000 85,000

Costhelper.com

70,00010,0008,000 56,000

Nationwide
Inspections

70,00010,0008,000 400,000 Rakuten insurance

70,00010,0008,000 200,000

Alibaba

70,00010,0008,000 620,000

Inside EVs

Comp.

Electric
parts
ChassisElectric
Motor
Batteries

MB

MBi

SourceR(JPY)

Figure 4-2 Number of expected breakdowns on components by
GAs generation

Figure 4-3 suggests the lifetime maintenance cost of
an EV with different maintenance intervals. The lifetime
maintenance cost of the 3-2-2-year strategy was JPY
815,240. This was approximately 40 % more expensive
than the CBM strategy. Among all fixed interval strategies,
the 5-year interval showed the highest cost efficiency ofmaintenance, which was JPY 567,700. In the figure, when
the intervals are relatively shorter or longer, themaintenance costs were high. In the former case, this isbecause excessive numbers of preventive maintenanceactions were performed, and in the latter case, the lack ofpreventive maintenance actions resulting in higher chancesof getting breakdowns.

4. Results
This study mainly focuses on whether a CBM strategy
could reduce the lifetime maintenance cost compared to the3-2-2-year fixed maintenance interval where inspection is
performed after three years from the purchase of an EV andthen every two years. This corresponds to the current
vehicle inspection policy.
Three types of maintenance strategies are employed; azero-maintenance with no preventive maintenance actions,
fixed interval strategy, and flexible interval CBM strategy.
Table 4-1 shows the result of the CBM strategy. CBM
suggests the four times of preventive maintenance activities
on a vehicle within 𝑇𝑀 (=13 years) and the resulting
lifetime maintenance cost was JPY 490,000, which isapproximately 48% less than the zero maintenance strategy.
Table 4-1 Simulation result of CBM and zero maintenance
Component
1.Breaks
2.Electricmotor
3.Electric parts

1st(Month)3rd(Month)563Figure 4-3 Lifetime maintenance cost as a function on
maintenance interval

4th(Month)96

Figure 4-4 illustrates the comparison of reliabilities
among three different strategies: CBM strategy, 3-2-2-year
fixed interval strategy and zero maintenance strategy
respectively. CBM and fixed interval maintenancestrategies showed dramatically higher reliabilities
compared to the zero-maintenance strategy. It could be
inferred that the right number of maintenance actions have
reduced failures. However, it is also reasonable to point out
that every preventive maintenance action leaps the system
reliability to as good as the initial condition in thesimulation.

5
21

96

21

4.Steering
5.Chassis

2nd(Month)

-

-

96
-

21

6. Tires7.Shocks

5

8.Batteries

-

96

63
-

-

-

Cost for flexible interval strategy (JPY)

490,000

Cost for zero maintenance strategy (JPY)

1,011,100

55

Figure 4-6 Sensitivity of reliability and maintenance cost todifferent preventive maintenance cost parameters
Figure 4-4 Comparison of system reliabilities with different
strategies

5.Conclusion
The result is confirming that not only CBM can possibly
cut down the maintenance cost within vehicle lifetime, butalso it enables to operate a vehicle with high reliability
comparable to the maintenance actions under the current
inspection policy. Several remarks are summarized asfollows: (i) the lifetime maintenance cost for the CBM
strategy was JPY 490,000 and the preventive maintenanceactions were performed for four times within TM, and (ii)
CBM strategy can potentially decrease approx. 40% of themaintenance cost compared to the current 3-2-2-year fixedinterval strategy which was JPY 815,240.
In addition, in the fixed-interval case, the 5-year interval
was optimal and it showed the highest cost efficiency withmaintenance cost being 38% smaller compared to the one
with the 3-2-2-year strategy, however, it was still 15% morecostly than the CBM strategy. Furthermore, the sensitivity
analysis revealed that the amount of lifetime maintenanceexpenditure is more sensitive to preventive maintenancecost parameters than corrective maintenance cost
parameters, and therefore it is logical to assume that
decreasing the cost per single preventive maintenanceaction possibly has a strong impact on the reduction of anEV’s lifetime maintenance cost.
Our result is encouraging the introduction of the CBM
maintenance strategy for EVs. However, several works
remain to be done for future studies as listed following:(i)identifying the effect of imperfectness of the preventive
maintenance by conducting several different scenarios andevaluate the effect both on reliability and lifetime
maintenance cost,(ii) increasing the number of components
with more precise component structure (e.g., try a different
kind of model such as parallel logic),and (iii) designing asimulation program for the run on TSUBAME (a largescale supercomputer) which enables us to obtain a more
precise result with a more complex system.

Figure 4-5 and Figure 4-6 report the reliability of thesystem and lifetime maintenance cost obtained by flexibleinterval CBM when the cost parameters 𝐶𝑅 (corrective
maintenance) and 𝐶𝑀 (preventive maintenance) are
multiplied by the factors 𝐶𝑇 as well as 𝑃𝑇 that vary from
0.2 to 5.0.
In Figure 4-5, when 𝐶𝑇 is small, fewer preventive
maintenance actions were preferred. This is because when
the corrective maintenance cost was small, the incentive todecrease the number of breakdowns also became small.
However, the increase of the factor 𝐶𝑇 encouraged
frequent preventive maintenance actions which result insmaller number of breakdowns at 𝐶𝑇 =2.0 and 𝐶𝑇 =5.0
in the figure.
In Figure 4-6, when the cost for preventive maintenanceactivities was as small as 𝑃𝑇 =0.2, a strategy with frequent
preventive maintenance actions was performed since one
can hold a high system reliability and a low lifetime
maintenance cost at the same time. On the other hand, when
𝑃𝑇 is larger than 2.0, it was inevitable to experience higher
maintenance expenditure since the replacement cost
generally is high, and preventive maintenance, that candecrease the number of breakdowns, became expensive.
It is worthwhile noticing that the cost parameter
regarding the cost of preventive maintenance actions
greatly affected the lifetime maintenance cost. By changing
𝐶𝑇 , the cost varies from JPY 430,600 to JPY 670,000 so thatthe maximum cost with this setting has increased byapproximately 150%. However, the change in cost
parameter 𝑃𝑇 from 0.2 to 5.0 resulting in the increase of
lifetime cost of JPY 430,510 to JPY 1,201,900 that isequivalent of 258 % of increase.

References[1] Japan Automobile Manufacturers Association. (2014). History of automobile inspection
system and significance and role of automobile inspection.vol.48. pp. 2-8.
[2] Ministry of Land, Infrastructure and Transport Vehicle Transportation Bureau. (2005). Basic
Survey Results Report on Automobile Inspection, Inspection and Maintenance Report[3] Life media. (2014). Survey on vehicle owners. Retrieved January 12, 2017.
[4] Sony Assurance. (2014). National Car Life Survey, Retrieved January 12, 2017.
[5] NIKKEI, Inc. (2010). Disappearance of parts on a vehicle -The identity of an electric car (4).
Nikkei Shimbun. Retrieved January 12, 2017.
[6] METI (2010) Next Generation Vehicle Strategy. http://www.meti.go.jp/english/press
Retrieved January 12, 2017[7] Coutler,T.(2015). Japan Now Has More EV Chargers Than Gas Stations. Bloomberg
Retrieved January 12, 2017[8] Nykvist,B & Nilsson,M.(2015). Rapidly falling costs of battery packs for electric vehicles,
Nature Climate Change 5, 329–332[9] Gray,C- Watson,S.(2009).Physics of Failure approach to wind turbine condition based
maintenance. Wind Energy.vol. 13(5) ,pp. 395–405.
[10] Marseguerra,M. E,Zio.(2000).Genetic algorithms and Monte Carlo simulation for optimalplant design, Reliability Engineering & System Safety Volume 68, Issue 1, pp 29–38.

Figure 4-5 Sensitivity of reliability and maintenance cost todifferent corrective maintenance cost parameters56

A study of leasing scheme and its perception
for the diffusion of residential electricity storage systemStudent Number: 15M18026

Name: Junya ISHIDASupervisor: Naoya ABE

一般家庭用蓄電池システム普及におけるリース制度と利用者意識に関する研究
石田純也
本論文では、家庭レベルの蓄電池システムの普及を促す方策を検討するため、日本における蓄電池リース
事業の契約者を対象にアンケート調査を行った。調査では、導入に至る経緯や使用実態を把握するとともに、システム導入に伴う満足度や要望と経済的便益の関係を明らかにした。その結果、蓄電池システムの満足度に対して、経済的便益や蓄電システムに対する理解度、技術的に新しい製品を使用することに対する満足感が統計的に有意に影響を及ぼし、また当該リース制度により経済的便益を享受していない利用者の存在を推定した。

1. Introduction
1.1 Background

Table 2 Example of ESS lease by ONE energy [5]Contract period
Monthly cost
Total of monthly costs
Charging capacity
Other services

Electricity storage system (ESS) is becoming one of
the key options for grid stabilization and distributed powersystem, under the given state of the rapid growth ofrenewable energy sources (RES) in Japan, partially due tothe Great East Japan earthquake in 2011. Because of
feed-in tariff started in 2012, photovoltaic (PV) system is
rapidly being diffused in household sector. One of the
expected uses and roles of ESS is to be installed inhouseholds and help to cope with those issues in addition
to bringing to the benefits to the ESS users.
There are two ESS’s representative benefits forhouseholds. ESS can provide them with opportunities tosave electricity bill and can be used as an emergency
power source. Since ESS literally stores electricity, users
can shift the timing of purchasing electricity, allowing
ESS users to buy cheaper night-time power for theelectricity consumption in daytime when the tariff ishigher. Also, ESS users with PV systems can charge
surplus electricity produced by a peak difference between
the generated electricity from a PV systems and household
electricity demand. Currently, due to feed-in tariff scheme,
selling such surplus electricity to utilities is more
profitable than self-consumption. However, theprofitability in the future is uncertain as FITs is becoming
less financially attractive, implying that self-consumption
will become financially more important. The role of ESS
can become more significant in the future and thus its
further diffusion is expected.
ESS has, however, several issues to be overcome
such as expensive introduction cost and recovering thecost. [1] [2] Hence, this study focused on residential ESS
leasing as it is an affordable introduction form of ESS to
its users. Lease scheme is a financial contract to introduce
new equipment with paying fixed monthly costs. Its main
advantage for consumers is to mitigate the burden of
initial payment. For companies, one of the advantages is
to capture more customers, which in turn contributes tomodestly stable revenue streams, even though the sale perTable 1 Pros and Cons of lease scheme [3][4]
Pros
Cons

Consumer•Mitigating initial cost
•Avoiding obsolescence•Tax saving•Losing salvage value•Unsatisfaction due to noownership•Fixed financial burden

10 years
9,396 JPY (Tax included)
1,127,520 JPY (Tax included)
5.53kWh
Monitoring, Performance guarantee

contact is relatively lower.
In Japan, ONE Energy Corporation (ONE Energy)
is a leading company for ESS leasing business since 2013,
and this study was conducted in partnership with this firm.

1.2 Previous studies
1.2.1 Profitability of ESS

For profitability of ESS, there are several studies,
which focus on a combination of PV system and ESS.
Researchers generally mentioned the significance ofelectricity consumption pattern in addition to technicalcharacteristics and the introduction cost of them. [6][7]Conformity of that pattern to PV’s electricity generation
profile is important in terms of economics. Thus, life styleis one of the significant factors for profitability of ESS.
Also, some researchers pointed out the economic
importance of self-consumption in the future where
electricity selling price is very low after feed-in tariff is
expired. [8][9] To achieve more economically effective
PV system use in the future, ESS is expected to play asignificant role since it can stock surplus electricity andincrease self-consumption.

1.2.2 Consumer preference for ESS

In 2013, Sekisui Chemical Company, Limited andEnvironmental Research Institute, Inc. conducted aquestionnaire survey targeting actual ESS users in Japan.
[10] So far, there are no similar surveys. It revealed thatmany users are satisfied with ESS because of saving
electricity bill and the potential use as an emergency
power source. In addition, this study found that somehousehold run out charged electricity within a day while
the others does not, which resulted in difference of theirsatisfaction. Since if or not a user runs out charged
electricity depends on amount of discharged electricity
and capacity of ESS, the importance of life style andintroduction of ESS with an adequate capacity wasconfirmed. In addition, this survey implies that users withdeeper understanding of ESS and Home Energy
Management System (HEMS) are more satisfied withESS.

Companies•Sales promotion•Certain and fastmoney collection•Disposal of property
after termination57

1.2.3 Consumer preference for leasing scheme

Table 3 Variables for regression and clustering
eq.1

Some studies investigated consumer preference forleasing scheme of home appliances and cars. [11][12][13]They revealed that the users were satisfied with cheapnessof monthly payment and found that leasing scheme caused
concerns about damages to borrowed equipment andunsatisfaction due to the lack of ownership.

2. Objectives and Contribution

Since there are no surveys targeting actual ESS
leasing users, their situation and attitudes toward ESS and
its leasing are not revealed. For the further diffusion of
ESS, it is significant to know consumers’ actual situation,
especially their subjective satisfaction and economic
benefits, and to empirically propose further ESS diffusion
strategies. Therefore, the objectives of this study are toinvestigate:(1) the ESS users’ motivation and satisfaction with ESS(2) the relationship between the satisfaction level andeconomics benefits for the users under leasing scheme
By suggesting further ESS diffusion strategies basedon empirical results, companies can improve consumers’satisfaction with ESS. Higher satisfaction and solving
user’s complaints contribute to further ESS diffusion andits sustainable use.

3. Data collection and methodology
3.1 Data collection

With the cooperation of ONE Energy,
questionnaires were sent to 500 customers. They used ESS
for at least 1 year or longer and live in Japan, except inboth Hokkaido and Okinawa as there are no users in those
two regions. The collection period for the survey was
between 11th November 2016 and 16th December 2016.
For those who responded this survey, honorarium wasprovided, which was equivalent to JPY1,000. All private
information about the users were kept confidentially sothat the questionnaires were sent and collected by ONE
Energy while the design of the questionnaire was made by
the author and the author’s affiliated research group.
The questionnaire consisted of three sections:section A) Questions about your behavior and opinion toelectricity and its system, section B) Questions about yourbehavior and opinion to ESS and its leasing, and sectionC) Questions about your basic information. For example,
there were questions for electricity bill, electricity billsaving by ESS, introduction of PV system, and so on insection A, questions for satisfaction of ESS leasing,financial satisfaction of ESS leasing, a sense of security
from ESS, and so on in section B, and questions forgender, age, household income, and so on in section C.

𝑃 𝑌3 > 𝑗 𝑥
𝑃 𝑌3 ≤ 𝑗 𝑥

=

ln

eq.1

𝛽3.- 𝑥3.- − 𝜇2

eq.2

𝑌%

-

𝑉1

𝑥%.%

𝑌3

𝑉2

𝑥%.3

-

𝑉3

𝑥%.8

-

𝑉4

𝑥%.:

𝑥3.%

𝑉5

𝑥%.<

𝑥3.3

𝑉6

𝑥%.>
𝑥%.?
𝑥%.@
𝑥%.A
𝑥%.%.
-

𝑥3.8
𝑥3.:
𝑥3.<
𝑥3.>
𝑥3.?
𝑥3.@

-

-

𝑥3.A

-

Average monthly electricity
bill saving (JPY)

-

𝑥3.%.

-

Amountelectricity
(kWh)

-

𝑥3.%%

-

Monthly lease fee (JPY)

3.2.2 Cluster analysisofmonthly
consumption

Range
1~5
1~5
1~5
1~5
1~5

2~5
1~4
0~4
1~4
1~7
-2,000~13,500
5
~
1,592
1,000
~
4,900

To identify group of users and their common
characteristics, cluster analysis was conducted. Table 3
shows variables for this analysis. All of them are 5-ranked
categorical variables. With regarding them as quantitative
variable, they were not standardized. The detailed
calculation method is hierarchical cluster analysis withward’s linkage method by Euclidean distance.

%.

𝛽%.- 𝑥%.- − 𝜇2

Meaning (Unit)
Satisfaction of ESS leasing(C5)
Financial satisfaction (C5)
Sense of security from ESS(C5)Satisfaction with having atechnically new equipment(C5)
Demand for new plan which
is suitable with future pricemenu for ESS (C5)
how
much
your
consciousness
about
electricity bill and electricity
menu is changed after
installing ESS (C5)
Understanding for ESS (C4)
Gender (Dummy)Age (C5)
PV system (Dummy)
Contraction (C4)Household income (C7)Here, 𝛽 is a coefficient. 𝜇 is a threshold. Sinceboth 𝑌% and 𝑌3 are categorical variable with 5 grade
evaluation, 𝑗 = 1,2,3,4. 𝑌% is the overall satisfaction of
ESS leasing. 𝑥%.% ( 𝑌3 ), 𝑥%.3 , 𝑥%.8 , 𝑥%.: ( 𝑥3.% ), and𝑥%.< ( 𝑥3.3 ) are specific satisfactions and opinions.
𝑥%.> (𝑥3.8 ) is understanding which is how precisely you
know capacity of your ESS and how deeply you
understand function of ESS. 𝑥%.A (𝑥3.> ) is if or not you
own a PV system. 𝑥%.%. (𝑥3.? ) is how deeply you wereinvolved in your leasing contraction, for example, if or not
you are a contractor. 𝑥3.A is an estimation of monthly
electricity bill saving by ESS. In the questionnaire survey,
respondents were firstly asked which month’s electricity
bill was the highest/lowest in the latest one year after
installing ESS. Secondly, they were asked how much themonths’ electricity bill were changed. Hence, electricity
bill savings of “highest-electricity-bill month” and“lowest-electricity-bill month” were obtained, respectively.
𝑥3.A is an average of those two electricity bill savings.
𝑥3.%. is monthly electricity consumption in May 2016.
𝑥3.%% is monthly lease fee. This fee varies depending on
subsidies from the central government and local
governments, and also some users are compensated forlease fee by house manufactures.
Sample numbers are 223 for eq.1 and 104 for eq.2.
Considering multicollinearity, all VIFs were confirmed tobe less than 10.

In order to find the factors affecting satisfaction of
ESS leasing (eq.1) and financial satisfaction (eq.2),
analyses with ordered logistic regression were conducted.
The variables are shown in Table 3. The model equations
are:
=

CA

CA = Cluster analysis
C(n) = Categorical variable which is n grade evaluation

3.2 Methodology
3.2.1 Ordered logistic regression analysis

𝑃 𝑌% > 𝑗 𝑥
ln
𝑃 𝑌% ≤ 𝑗 𝑥

eq.2

-/%
%%

-/%

58

4. Result
4.1 Results of Questionnaire survey

Table 4 Result of ordered logistic regression
eq.1
Odds ratio
eq.2
Odds ratio𝑥%.%6.21 ***
𝑥3.%
1.0296
𝑥%.3
0.89
𝑥3.3
1.6450 *𝑥%.8
2.15 ***
𝑥3.8
1.2772
𝑥%.:
1.01
𝑥3.:
3.8831 *
𝑥%.<
0.83
𝑥3.<
1.1353𝑥%.>
2.08 ***
𝑥3.>
0.9125
𝑥%.?
1.47
𝑥3.?
0.4921
𝑥%.@
0.83
𝑥3.@
1.0924𝑥%.A
1.79 *
𝑥3.A
1.0004 ***
𝑥%.%.
0.66
𝑥3.%.
1.0004𝑥3.%%
0.9991 ***
AIC
371
AIC
250
Puseudo-R2
0.329
Puseudo-R2
0.174
SampleSample223
104
NumberNumber(Note) ***：p<0.01、**：p<0.05、*：p<0.1

As the result of the survey, 316 responses werereceived, and 315 of them were valid. Response rate was
63.0%. 149 of 315 respondents perfectly answered, andthe other answers included blanks or mistakes.
In section C, respondents were asked their basic
information. Majority of respondents are male (74.3%).
Their ages are relatively older such as 30s (22.5%), 40s(31.7%), 50s (25.7%), 60s (18.1%). Household income isalso high. Those whose income is more than 4 million JPY
accounts for 80.0%. The median of Japanese household’s
income is 4.27 million JPY. [14] In addition, they are
assumed to be households consuming large amount ofelectricity. 67.6% of them live in all-electric houses.
In section A, respondents were asked their behavior
and opinion to electricity and its system. Regarding PV
system, only 37.2% of them own the systems. The
respondents who do not have PV systems were asked how
much they would like to introduce PV systems. 23.2 % ofthem expressed positive response. For electricity bill,
respondents answered two electricity bill saving as alreadyexplained in 3.2.1. Figure 1 is a comparison of averagemonthly electricity bill saving and monthly lease fee. For
the horizontal axis, right-side households have higher
lease fee and higher average electricity bill saving.
Although this calculation is not mature, there are 25.8% of
the respondents who might not obtain financial benefit
from ESS leasing.
In section B, respondents were asked their behavior
and opinion to ESS and its leasing, for example,
satisfaction of ESS leasing, its reasons, financial
satisfaction, sense of secure from ESS, and understanding
for ESS. For satisfaction of ESS leasing, 69.5% (223people) of the respondents are satisfied, 21.3 % (67people) are “satisfied and unsatisfied”, and 6.6 % (21people) are unsatisfied. For the reason of “satisfied”,
73.5% of 223 people chose “saving electricity bill”. Only
17.0 % of them chose “sense of secure”. For the reason of“unsatisfied”, 38.1 % of 21 people chose “Not saving
electricity bill”. 19.0 % of them said “The capacity of ESS
is not enough”. Another 19.0 % of them chose “The meritof ESS is not clear”. Also, 152 people answered freecomments about their unsatisfaction and anxiety for ESS
leasing. 29 people of them complained or wondered about
financial benefit of ESS leasing. 25 people worried about
what would happen when ESS leasing is expired, forexample, if or not they can continue ESS leasing after it,
and some of them complained about additional costs whenremoving ESS. 23 people of them complained about thecapacity of ESS in this question as well.

4.2 Results of ordered logistic regression

Table 4 shows the results of ordered logistic
regression. Puseudo-R2 of eq.1 is 0.329, and that of eq.2 issatisfaction),
0.174.
In
eq.1,
𝑥%.% (Financial
𝑥%.8 (Satisfaction with having a technically new
equipment), and 𝑥%.> (Understanding for ESS) are
statistically significant at 1 % level. Those odds ratio is
more than 1, which means that those have positive effecton satisfaction of ESS leasing. On the other hand, in eq.2,
𝑥3.A (Average monthly electricity bill saving) and𝑥3.%% (Monthly lease fee) are statistically significant at 1 %level. Odds ratio of 𝑥3.A is more than 1 and has a positiveeffect on Financial satisfaction. That of 𝑥3.%% is less than
1 and has a negative effect.

4.3 Cluster analysis

Figure 2 shows dendrogram for cluster analysis.
Because of data availability for V1, V2, V3, V4, V5 andV6, sample number is 278. To understand detailed
characteristics of user groups, dividing respondents into 7
clusters are chosen here. Figure 3 shows average scores ofeach variable for each cluster. V1 and V2 have a strong
positive correlation. Relationships between V1 and theothers are conditional on each cluster. Reviewing it, eachcluster’s characteristics are described in Table 5.

5. Discussion

From questionnaire survey, it was firstly confirmedthat respondents were elder and rich. Even though theintroduction cost is mitigated due to leasing scheme, users,
who are early adopters, are such wealthy people. Also,
they seem to be households consuming a large amount of

Average electricity bill saving and Lease fee
Average electricity bill saving

Lease fee

20,000(JPY/month)

15,00010,0005,000
0-5,000Household

Figure 2 Dendrogram for cluster analysis

Figure 1 Average electricity bill saving and lease fee
59Average scores of each cluster
C7-1
C7-2
C7-3
C7-5
C7-6
C7-7

5

more significant in the future to keep or increase current
satisfaction level, and understanding of ESS will be also
important because it can be controlled by others whilesatisfaction with having a technically new equipment
cannot. On the other hand, it was not statisticallyconfirmed that sense of secure affected satisfaction of ESS
leasing, though it is one of the main merits of ESS.
From cluster analysis, respondents are divided into
7 groups, and each cluster has specific characteristics. It is
confirmed that a cluster (C7-7) had very low average of
V1, V2, and V5. This might mean that they do not havestrong motivation to improve current situation. If there is
no help for them, their motivation of continuous ESS use
might be lost.

C7-4

Average score

4.5
4
3.5
3
2.5
2
V1

V2

V3

V4

V5

V6

Variable

6. Conclusion

Figure 3 Average scores of each cluster

This study conducted questionnaire survey andanalyses with ordered logistic regression and cluster
Cluster
Expected characteristics
analysis, aiming for investigating (1) the ESS users’
C7-1
They are very satisfied with current situation andmotivation and satisfaction with ESS and (2) the(n=55) strongly desire optimized ESS usage plan.
relationship between the satisfaction level and economics
C7-2
They are very satisfied with current situation and do
benefits for the users under leasing scheme. In cooperation(n=33) not strongly desire optimized ESS usage plan.
with ONE Energy, questionnaires were distributed to 500
C7-3
They feel relatively little financial benefit andcustomers, and 315 responses were obtained. For (1), most(n=55) strongly desire to improve it.
C7-4
They are satisfied with current situation, but theirof ESS leasing users are satisfied with it, and it is mainly(n=46) attitude toward electricity bill was not so changed.
because of financial benefit. For (2), the satisfaction level
C7-5
They focus on financial benefit more than otheris increased by economics benefit. Additionally, it was(n=22) clusters.
confirmed that satisfaction with having a new equipment
C7-6
Their satisfaction is very low but strongly desire
and understanding for ESS also increased the satisfaction(n=43) optimized ESS usage plan.
C7-7
Their satisfaction is very low and do not strongly
level. However, it is assumed that future ESS users will
(n=24) desire optimized ESS usage plan.
have less satisfaction with a technically new equipment,
electricity. In fact, 215 of 315 respondents agreed that and financial benefit and understanding for ESS will beelectricity bill saving was one of the reasons to start ESS more significant. Thus, it is necessity to be considered thatleasing. Secondly, introduction of PV systems was the border line of loss and gain might be susceptible tochecked. Majority of respondents do not own PV systems changes of lease fee, and there is an unsatisfied groupand did not express high motivation to introduce PV which does not have a strong motivation for more
systems in the future. It may imply that a use of ESS itself financial benefit.
does not directly cause PV system introduction. Since a
Therefore, in order to achieve further ESS diffusion
tangible reason for this result was not revealed in this and its sustainable use, it is significant for users to know
study, future works are hoped to identify it. Third, it was an effective use of ESS, which leads users’ financial states
confirmed that many users were satisfied with ESS leasing, above the border line and which can provide opportunities
and it is mainly because of electricity bill saving. However, to deepen understanding for ESS. However, it should be
as shown in reasons of unsatisfaction and Figure 1, some considered that the effective use needs to be friendlyof users does not feel financial benefit or does not obtain enough for those who do not have a strong motivation for
financial benefit, and they are not satisfied. Financial issue more financial benefit.
is, of course, significant. In fact, because of termination of
governmental direct subsidy to ESS, lease fee is recently Reference
increasing. If all lease fees in Figure 1 were 4,900 [1] DBJ, (2013), 蓄電池産業の現状と発展に向けた考察
田頭直人, 池谷知彦, 土屋依子, 馬場健司, (2012), 電気自動車の蓄電機能
JPY/month, 68.8 % of the respondents would not obtain [2]と家庭用蓄電池に対する一般消費者の意向, 電力中央研究所報告, Y11021
profit. Although this is not accurate calculation, it is [3] 宮内義彦, (2008), リースの知識<第 9 版>, 日本経済新聞出版社[4] 加藤建治, (2015), 図解入門ビジネス 最新リース取引の基本と仕組みがよ〜
obvious that relationship between electricity bill saving くわかる本[第 6 版], 株式会社秀和システム
ONE エネルギー株式会社, ホームページ
and lease fee is very sensitive, considering current [5][6] 遠藤弘, 小川洋, (1991), 個人住宅用太陽光発電システムの経済性, 電気学会論
assumed electricity bill saving. Finally, it is also 文誌 D, 112(3), 235-243
山城迪, 小山貴之, 仲村宏一, 手塚博文, 柿添重光, (1995), 蓄電池機能付き住considered that lack of capacity can be a reason of [7]
宅用太陽光発電システムの運用と経済評価, 電 気学会論文誌 B, 115(10),unsatisfaction. In the questionnaire survey, this point was 1234-1241
大嶺英太郎, 小林広武, 浅利真宏, 上野剛, (2013), PV 設置需要家におけるnot directly asked, but some respondents complained [8]蓄電池の経済性評価 –蓄電池設置により経済メリットが生じる条件の検討-, 電力中央研究所報告, R12018
about it.
[9] Cucchiella, F., D’Adamo, I., Gastaldi, M., (2016), Photovoltaic energy systems with
From ordered logistic regression analysis, it was battery storage for residential areas an economic analysis, Journal of Cleaner Production,
confirmed that satisfaction of ESS is increased by 1-15
[10] 積水化学工業株式会社, (2013), 「家庭用蓄電池利用実態アンケート調査」
financial satisfaction, satisfaction with having a について
和田安彦, 中野加都子, (2006), サービサイジング普及に向けた家電リース・
technically new equipment, and understanding for ESS. [11]システムの消費者意識に関する研究 –液晶テレビ を事例として-, 土木学会論文
Since high satisfaction with having a technically new 集 G, 62(2), 211-219
今堀洋子, (2006), 家電のグリーン・サービサイジングに対する利用者の需要
equipment and deep understanding for ESS are considered [12]
性と事業化の方策, 環境科学会誌, 19(4), 347-353[13]石井康夫,
寺見佑弥, (2013), 個人向け自動車リースに関する生活意識の調査
to be unique characteristics of early adopters, satisfaction
分析, 国際研究論叢, 27(1), 37-58
of ESS leasing will be decreased in the future where users [14] 厚生労働省, (2015), 平成 27 年 国民生活基礎調査の概況
are more ordinal. Therefore, financial satisfaction will be
Table 5 Characteristics of clusters

60

Estimation of environmental and economic impacts induced by the promotion of accommodation
sharing services based on input-output table
Student Number: 15M18109

Name: Yosuke SOEJIMA

Supervisor: Naoya ABE

産業連関表に基づく民泊事業推進による環境影響と経済波及効果の推定
副島 洋介
本研究は 2020 年の東京都を事例に、民泊事業の推進を増大する宿泊需要に対する新規宿泊施設建設の代替案として捉え、その環境影響と経済効果を産業連関表に基づき定量化した。その結果、2020 年までに民泊の客室を現行の計画より 10%増やす毎に年間あたりの経済効果を 2.3%〜3.2%失うが、CO2 排出量を 1%〜2%削
減でき、これは東京都が 2020 年までに掲げる目標 CO2 削減量の 0.067%〜0.13%に相当することが示された。
operation emitted relatively huge amount of CO2.
Similarly, average CO2 emission by the operation of hotel
1.1 Accommodation capacity problem and rise of rooms was 125.2 kg-CO2/m2 while general household
accommodation sharing services
sector was 59.3 kg-CO2/m2 in 2012 [3], which is about
Today, the capacity of accommodation facilities in Japan half of the emission from the hotel operation.
has drawn serious attentions as the country will host the
There is a case that the increasing visitors’ demand
Olympic and Paralympic Games in Tokyo in 2020 and the might decrease after 2020. In that cases, hotels which willaccommodation capacity in that year may be insufficient be constructed in the future would be a problem in terms
for inbound foreign visitors for the significant event. of profitability and environmental burden. Once hotels are
Along with this concern, ICT based accommodation constructed, it should be used for decades so that decision
services are now growing rapidly. For instance, Airbnb [1], making whether constructing traditional hotels or
ICT based accommodation matching service, has 60 promoting accommodation sharing services should be
million users all around the world since its foundation in carefully concerned.
2008. The Japanese government is also testing the impactof accommodation sharing by deregulating the laws in Ota 1.3 Existing studies relating construction andward, Osaka and Kitakyushu city in 2016. These accommodation service industries
insufficient accommodation and accommodation sharing There are several studies, which estimate economic impact
could be inter-related with each other as the growth of of tourism activities. Musha [4] examined economic
latter may mitigate the concern of the shortage of the impact throughout several prefectures induced by aaccommodation in 2020 and beyond.
tourism event held in Nara. Sakurai et al. [5] evaluated theAn important property of the promotion of contribution of tourism industry in Hokkaido economy byaccommodation sharing is that it doesn’t require large setting some future scenarios.
scale construction so that there is little cost and time for
In addition, estimation of environmental impact induced
infrastructure development, which can consequently by construction and operation of buildings have been
mitigate environmental burden too. In contrast, traditional investigated for a long time. National institute forhotels need large scale construction which can induce environmental studies [6] developed database whichlarger economic impact as well as environmental impact reflect environmental impact per unit of production among
since construction involves various industries’ production 400 industrial sectors. Urushizaki et al. [7] analyzed time
activity such as materials and machineries. In short, there series CO2 emission generated by construction sector.
can be trade-off relationship between economic and Yoshioka et al. [8] estimated the change of CO2 emission
environmental effect of these two accommodation modes. including construction stage and operation stage after the
Therefore, it is important to quantify the effects of the two introduction of energy saving facilities in general houses.
aspects for the two different approaches to accommodate
Some studies pointed out characteristic of users of
the increasing foreign visitors to Tokyo.
accommodation sharing services and analyzed its effect toaccommodation service industry in economical point of
1.2 Energy consumption and GHG emission in view. Hamari et al. [9] and Tussyadiah et al. [10]accommodation sectors
investigated people’s motivation of using Airbnb. Zervas
The increase of accommodation facilities can be divided [11] analyzed correlation between traditional hotels andinto construction stage and operation stage with different the diffusion of Airbnb.
main operators: construction industry and accommodation
However, those existing research mainly focused onservice industry or hosts of accommodation sharing one aspect, either of economic or environmental aspect,respectively. AccordingtoTokyo
metropolitan and there is little study demonstrating the effect of certain
government [2], the amount of CO2 emission in Tokyo economic activity from multiple sides.
was 54M t-CO2 in 2010, and construction industry
While accommodation sharing services are expected toaccounted for 2.8% (1.5M t-CO2), and accommodation contribute to solve the concern, it is still unclear that what
service industry accounted for 1.8% (1.0M t-CO2) among kind of specific impacts will be generated by thethem. Accommodation sectors including construction and promotion of these services. In this study, Tokyo is chosen

1 Introduction61

as targeted areas where lack of accommodation facilities is
especially a significant problem.

is represented by 𝜟𝑭𝑑 . Moreover, for the environmental
impact analysis, modified Leontief inverse matrix)𝟏𝒅 𝑰− 𝑰−𝑴 𝑨
is used. Here 𝒅 is diagonal matrix

2 Objectives and contribution

whose components are 𝑑3 =The objectives of this study are (1) to demonstrate induced
effects by the promotion of accommodation sharing
services in terms of environmental and economic aspects
quantitatively, (2) to estimate exactly how much CO2emission will be reduced by the avoidance of new
construction and to prove its effectiveness, and (3) toprovide the range of environmental and economic impact
based on some scenarios in Tokyo in 2020.
Through this study, the trade-off relationship between
environmental and economic impacts triggered by thepromotion of accommodation sharing services and its
significance are clarified. The main contribution of thisresearch will be to help decision making of Japanese
government and municipalities regarding the promotion ofaccommodation sharing services which is currently underdiscussion.

Construction
Effect(Infrastructuredevelopment)

Operation
Effect(Operation of
the services)

In this analysis, input-output table which indicates how
much products and services are sold and bought in eachindustry in a given year in a single country or region isused. CO2 emission (t-CO2) and production amount (yen)induced by the change of final demand are estimated forenvironmental and economic impacts respectively.
The calculation flow and its boundary are shown in Fig.
1. More specifically, in the case of economic impact
analysis, at first, there is increased demand to various
industrial sectors (e.g., construction, retail &transportation), and then producers react to meet theseincreased demand (direct effect). This increased
production results in additional supply to the above
mentioned industrial sectors (indirect effect). These direct
and indirect effects raise the level of household income,which will be re-spent on final goods and services(induced effect). The sum of these three effects is
economic impact. Similarly, environmental impact is also
estimated by calculating how much CO2 emission will be
generated by the increased final demand.
Basic input-output analysis model is given as follows:
𝜟𝑿𝟐 = 𝑰 − 𝑰 − 𝑴 𝑨)𝟏(t-CO2/M yen), where 𝑑3

Indirect EffectHotels3.1 Input-output analysis)𝟏65

represents environmental impact per unit of production inindustry 𝑖, 𝐷3 is annual CO2 emission in industry 𝑖 and𝑋3 is annual production amount. Regarding construction
effect, annual impacts are calculated by dividing average
operation duration of hotels and general houses (35 years
for hotels and 30 years for general houses). Finally, annual
economic and environmental impacts are estimated forconstruction effect and operation effect respectively.

3 Data collection and methodology

𝜟𝑿𝟏 = 𝑰 − 𝑰 − 𝑴 𝑨

45

Induced EffectInducedproduction [¥]Householdexpenditure

Inducedproduction [¥]CO2 emission

Householdexpenditure

CO2 emission

Expenditure by
Hotels’ guests

Inducedproduction [¥]Householdexpenditure

Inducedproduction [¥]Expenditure by
Airbnb’s guests

CO2 emission

Householdexpenditure

CO2 emission

Constructing
buildings

AirbnbRenovatinghouses[t-CO2 ][t-CO2 ][t-CO2 ][t-CO2 ]Fig. 1 Analytical flow and its boundary

3.2 Dataset
Dataset indicating the situation of accommodation demandand supply in Tokyo in 2020 was collected from statics ofJapanese government and research institutes (Table 1).
Table 1 Estimation of accommodation demand and supply
situation in Tokyo in 2020 [12] [13] [14]Expected no. of increasing foreign visitors’ stay(comparison with 2014, million people’s stay)
Required no. of rooms(comparison with 2014, thousand rooms)

9.31
13.84

Ave. construction fee per hotel room (million yen)

9.91

Ave. renovation fee per accommodation sharing room
(million yen)

0.98

Ave. expenditure of foreign visitors (thousand yen)

100.48

3.3 Scenario setting

𝑰 − 𝑴 𝜟𝑭𝑑,

Environmental and economic impacts are estimated basedon scenarios, which represent accommodation demandand supply situation in Tokyo in 2020. Table 2 represents
scenarios depending on accommodation modes to coverinsufficient accommodations. Scenario (a) assumes that
hotels will be constructed as planed in 2020 and the rest ofrooms will be covered by accommodation sharing.
Scenario (b) and (c) suppose the situation that
accommodation sharing will be actively promoted andscenario (d) and (e) assume the situation that hotels will bestrongly promoted. In addition to these five scenarios,
other scenarios which assume expenditure of guests will
be decreased by -10% and -20% except foraccommodation fee are established (Table 3). Since guestsof accommodation sharing tend to have consciousness of
cost reduction, scenario (2) and (3) are considered as more
realistic scenarios. By combining scenarios in Table 2 and𝑰 − 𝑴 𝒄𝑘𝒘𝜟𝑿𝟏

where
𝜟𝑿𝟏 : Direct effect + Indirect effect
𝜟𝑿𝟐 : Induced effect
𝑰: Identity matrix
𝑰 − 𝑴: Self-sufficiency rate matrix
𝑨: Input coefficient matrix
𝑭𝑑 : Domestic demand vector
𝒄: Private final consumption expenditure rate vector
𝑘 : Propensity to consume (scalar)
𝒘: Employees’ compensation rate vector.
)𝟏

Here 𝑰 − 𝑰 − 𝑴 𝑨 is called Leontief inverse matrix,
which represents how output rises in each sector due to theunit increase in final demand. The change of final demand62

Table 3 as Scenario 1-a, 1-b, etc., environmental andeconomic impacts are estimated.

accommodation sharing services decreases economic
impact and increases environmental impact in Tokyo. This
result attributes to higher CO2 emission per unit ofproduction in operation effect of accommodation sharing
services, which was determined in the process of guests’
expenditure setting.
As seen in Table 4, if hotel rooms are replaced byaccommodation sharing rooms, environmental impact will
gradually decrease. In scenario 2, if accommodation
sharing is promoted by 10%, environmental impact will bedecreased by 0.94%, and there is 8 billion yen loss and2,795 t-CO2 reduction, and its elasticity is 0.41 (Table 5).
In scenario 3, if accommodation sharing is promoted by
10%, environmental impact will be decreased by 2%, andthere is 11 billion yen loss and 5,650 t-CO2 reduction, andits elasticity is 0.60 (Table 5). Among the three scenarios,
scenario 3 will lead the most effective outcome in terms of
CO2 reduction, which implies that reduction of final
demand involving operation effect has significant effectfor the CO2 reduction.

Table 2 Scenarios depending on accommodation modes(AS: accommodation sharing)
Scenariosa. BAUb. AS +10%
c. AS +20%
d. Hotels +10%
e. Hotels +20%

No. of hotel
rooms
9,690
8,306
6,922
11,074
12,459No. of AS
rooms
4,153
5,537
6,922
2,769
1,384

Table 3 Scenarios depending on guests’ expenditure (yen)Scenarios
1. Same as hotels’ guests
2. Expenditure of AS guests -10%
3. Expenditure of AS guests -20%

Hotel guests
100,476
100,476
100,476

AS guests
100,476
91,63382,790

4 Results
4.1 Economic impact and environmental impactof planned case in 2020

Table 4 Total economic and environmental impacts per
year depending on scenarios

The amount of economic and environmental impacts of
scenario 1 are considered as upper bound since scenario 2
and 3 are more feasible scenarios. In scenarios 1-a. BAU
case, economic impact induced by hotels is 259 billion
yen in Tokyo and 98 billion yen in the other area of Japan,
while environmental impact is 214K t-CO2 in Tokyo and
164K t-CO2 in the other area of Japan. Economic impact
by accommodation sharing services is 96 billion yen inTokyo and 4 billion yen in the other area of Japan, whileenvironmental impact is 92K t-CO2 in Tokyo and 69K
t-CO2 in the other area of Japan. 70% of economic impact
induced by both hotels and accommodation sharing
services are spread inside Tokyo, while over 40% of
environmental impact are induced outside of Tokyo. Thisis because industrial structure of Tokyo is service
intensive and most economic activity done by service
industries whose CO2 emission per unit of production isquite low stays in Tokyo while economic activity by raw
materials and infrastructure whose CO2 emission per unitof production is high flows out of Tokyo.
Moreover, operation effect accounted for over 90% ofeach impact. This is because in terms of annual impact,
construction effect was divided by operation duration andthus its annual impact was decreased. Consequently,
operation effect induced by the guests of hotels andaccommodation sharing services had significant impact.

Scenario a.
BAU

Scenario b.
AS+10%

Scenario c.
AS+20%

EC(bil. yen)

ENV
(K t-CO2)EC(bil. yen)

ENV
(K t-CO2)EC(bil. yen)

ENV
(K t-CO2)

Scenario 1

355

307

350
(-1.40)307(0.02)

345(-2.81)307(0.04)

Scenario 2

346

298

338
(-2.30)295(-0.94)330(-4.59)293(-1.88)Scenario 3

337

290

326
(-3.24)284(-1.95)315(-6.47)278(-3.90)

Table 5 Trade-off relationship between promotion ofaccommodation sharing and decrease of hotels
Economic impact(bil. yen)

Scenario 1
Scenario 2
Scenario 3

-5
-8
-11

Environmental
impact (t-CO2)
61-2,795-5,650

Elasticity(∆CO2%/∆yen%)-0.014
0.408
0.603

Fig. 2 shows the transition of CO2 emission per 100
rooms and year depending on the setting of operation
duration. As seen in this figure, the longer the operation
duration is, the lower the CO2 emission of hotels per year.
The main reason of this reduction is that construction
effect which is induced in the initial stage is divided byoperation duration so that the impact per year is gradually
decreased. On the other hand, there is few change for theCO2 emission of accommodation sharing services due to afew construction effect. As shown in the figure, even if
hotels are operated for 15 to 30 years, its annual CO2
emission will be still higher than upper bound CO2
emission of accommodation sharing services. If it will be
operated for over 30 years, its CO2 emission will be lower
than the upper bound CO2 emission of accommodation
sharing at last. In scenario 2 and 3, CO2 emission will
constantly be lower than CO2 emission of hotels regardlessof operation duration. This result implies that hotels haveto be operated in long duration when considering
environmental impact, however accommodation sharing
services have no such limitation.

4.2 Trade-off relationship between environmental
and economic impacts
Table 4 shows total economic and environmental impacts
including hotels and accommodation sharing services
depending on scenarios. The change rate from BAU caseis shown in bracket. In scenario 1, if accommodation
sharing is promoted by 10%, 37 billion yen economic
impact and 30.6K t-CO2 environmental impact of hotels
will be lost, instead 32 billion yen economic impact and
30.7K t-CO2 environmental impact of accommodation
sharing will be produced. In total, there is 5 billion yen
loss and 61 t-CO2 increase, and its elasticity is -0.014(Table 5). In this scenario, the promotion of63

CO2 emission (t-CO2 /year100 rooms)

5 Conclusion

2,300

The environmental and economic impacts induced by thepromotion of accommodation sharing services tocompensate insufficient accommodation in Tokyo in 2020
was demonstrated depending on several scenarios throughthis study. By regarding the impacts in scenario 1 as upperbound, the following findings were obtained:

2,200
2,100
2,000
1,900
1,800

• If 10% of planned hotel rooms are replaced byaccommodation sharing rooms, there will be 8 billion
yen loss and 2,795 t-CO2 reduction in scenario 2, and 11
billion yen loss and 5,650 t-CO2 reduction in scenario 3.
It indicates 2.3% to 3.2% economic impact loss, and 1%
to 2% of CO2 reduction comparing with planned case.

1,700
15

20

25
30
35
Operating duration (year)

40

50

CO2 emission of AS (Scenario 1)CO2 emission of hotels (Scenario 1)CO2 emission of AS (Scenario 2)

CO2 emission of AS (Scenario 3)

Fig. 2 Transition of CO2 emission per 100 rooms & year

• 70% of economic impact induced by both hotels andaccommodation sharing services are spread inside
Tokyo, while over 40% of environmental impact are
induced outside of Tokyo.

4.3 Evaluation of environmental impact ofpromotion of accommodation sharing services
Amount of reducible CO2 emission also need to beevaluated. As mentioned above, CO2 emission ofandconstructionindustrywas
1.5M
t-CO2,
accommodation service industry was 1.0M t-CO2 inTokyo in 2010. Table 6 shows reducible CO2 emission ineach scenario and its ratio in each industrial sector inTokyo. Ratio of CO2 emission in construction effectcorresponds to construction industry and operation effectcorresponds to accommodation service industry. In
scenario 2, 0.03% CO2 reduction in construction industry
and 0.24% CO2 reduction in accommodation service
industry will be possible with 10% increased
accommodation sharing rooms. In scenario 3, its
contribution will be 0.03% and 0.53% respectively.
Tokyo metropolitan government set a goal that aims to
reduce GHG emission by 25% by 2020 (comparison with
2000). In other words, CO2 emission need to be reduced to
44M t-CO2 per year, where required reduction amount is
4.2M t-CO2 (comparison with 2014). If scenario 2-b and2-c are achieved, totally 2,795 t-CO2 and 5,590 t-CO2
reduction will be possible, and these amount is equivalent
to 0.067% and 0.13% of required CO2 reduction,
respectively. If scenario 3-b and 3-c are achieved, totally
5,650 t-CO2 and 11,300 t-CO2 reduction will be possible,
and these amount is equivalent to 0.13% and 0.27% ofrequired CO2 reduction, respectively. As a result, if theaccommodation sharing is increased by 10%, it can lead to
0.067% to 0.13% of required CO2 reduction, and if theaccommodation sharing is increased by 20%, it cancontribute to 0.13% to 0.27% of required CO2 reduction.

• Hotels need to be operated for over 30 years to be lower
the upper bound CO2 emission by accommodation
sharing services.
• If 10% of planned hotel rooms are replaced byaccommodation sharing rooms, it will result in 0.067%
to 0.13 % of required CO2 reduction to achieve the goal
setting in Tokyo by 2020, and if it becomes 20%, it willbe 0.13% to 0.27% of required CO2 reduction.
The achievement of scenarios that the number ofaccommodation sharing rooms will increase by 10% to
20% could be feasible if there are more deregulation anddiffusion of ICT based services. Accordingly, theaccommodation sharing services are considered as
effective solution to cover insufficient accommodations interms of easy infrastructure development, mitigation ofenvironmental burden, and sustainable ways when
considering after 2020 as well.
There are several points to be improved regarding
methodology and scenario setting for more accurate
estimation. Furthermore, the estimation is required not
only in Tokyo but also in Osaka and Kyoto, where the lackof accommodation facilities will also be significant
problem because the impacts would vary depending on
industrial structure and energy supply configuration.

Reference[1] Airbnb, Inc. (2016). Airbnb. https://www.airbnb.jp/ (accessed 2016.12.12).
[2] 東京都環境局. (2013). 都における温室効果ガス排出量総合調査(2010 年度実績).
[3] 東京都環境局. (2016). 低炭素ベンチマーク.
[4] 武者加苗. (2010). 地域経済における観光事業の産業連関分析. 産研論集(関西学院大学), 37 号, pp.113-124.
[5] 櫻井賢一郎, 細江宣裕. (2005). 北海道観光振興計画はアド・バルーンか? 外国人観光客数予測と産業連関分析. 運輸政策研究, Vol 8, No.1.
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ク(3EID). 国立環境研究所.
[7] 漆崎昇串, 水野稔, 下田吉之, 酒井寛二. (2001). 産業連関表を利用した建築業の環境
負荷推定. 日本建築学会環境系論文集, Vol. 549, pp.75-82.
[8] 吉岡完治, 早見均, 池田明由, 菅幹雄. (1993). 環境分析用産業連関分析表の応用(3)：省エネ住宅のすすめ. イノベ－ション&I-O テクニ－ク, Vol.4, No.2, 環太平洋産業連関分
析学会.
[9] Hamari, J., Sjöklint, M., and Ukkonen, A. (2015). The sharing economy: Why people
participate in collaborative consumption. Journal of the Association for Information Science andTechnology, 67(9), pp.2047-2059.
[10] Tussyadiah, I. P., and Pesonen, J. (2015). Impacts of Peer-to-Peer Accommodation Use on
Travel Patterns. Journal of Travel Research, 55(8), pp.1022-1040.
[11] Zervas, G., Proserpio, D., and Byers, J. (2014). The Rise of the Sharing Economy:
Estimating the Impact of Airbnb on the Hotel Industry. Boston University School of Management
Research Paper No. 2013-16.
[12] 大和香織. (2015). インバウンド観光と宿泊施設不足. みずほ総合研究所.
http://www.mizuho-ri.co.jp/publication/research/ (accessed 2016.12.24).
[13] 国土交通省. (2016). 建築物リフォーム・リニューアル調査報告.
http://www.mlit.go.jp/toukeijouhou/chojou/rrs.htm (accessed 2016.12.24).
[14] 東京都産業労働局. (2016). 平成 27 年東京都観光客数等実態調査.
http://www.metro.tokyo.jp/INET/CHOUSA/2016/05/60q5q300.htm (accessed 2016.12.24).

Table 6 Reducible CO2 emission and its ratio in eachindustrial sector in Tokyo
Scenario b.
AS+10%

Scenario 1

Scenario 2

Scenario 3

Construction
effectOperationeffectConstruction
effectOperationeffectConstruction
effectOperationeffectScenario c.
AS+20%

Difference
from BAU(t-CO2)CO2 ratio(%)

Difference
from BAU(t-CO2)CO2 ratio
(%)

-425

0.03

-850

0.06

486

0.05

971

0.10

-425

0.03

-850

0.06

-2,3700.24-4,739

0.48

-425

0.03

-850

0.06

-5,225

0.53-10,450

1.06

64

Effects of ICT based urban mobility as a case of collaborative economy in Indonesia
Student Number: 15M18227

Name: Benny LEONO

Supervisor: Naoya ABE

ICT を活用した都市内モビリティーの効果
ーインドネシアにおける共有型経済の事例研究ー
Benny LEONO
本論文は、インドネシアにおける情報通信技術（ICT）を活用した都市内の二輪モビリティーサービ
スの基礎効果を検討し、同サービスが利用者と提供者（運転手）にもたらす影響という観点から、その実態把握を行ったものである。その結果、スマートフォンに代表される ICT の普及により、都市内モビ
リティーの利用及び提供方策が効率化され、特にサービス提供者の労働時間における柔軟性、満足度、所得水準が向上したことを明らかにした。同時に課題として、提供者の「専門性」の向上余地が限られているため，同サービスが将来，彼らの自立に対する影響がまだ不明瞭である。
people get around and interact with city. GoJek is atechnology based ride-hailing service, one of thephenomenon that connect Ojek drivers and ride hailers inreal time, by using app platform in smartphone. Gojek
enhances available advanced technology to enable people
fulfill mobility demands in a more efficient way, hints tothe term of collaborative economy. Over the last few years,
there has been much speculation as to whether theso-called collaborative economy will impact positively or
negatively (Santi et al., 2014; Carvero, R. G. 2007), butlittle hard evidence exists to support either position. In thispaper, we consider workers who choose to provide
motorcycle rides service using the Gojek app platform.
Drivers who partner with Gojek provide transportationand life-style services to users requesting rides via Gojek’s
app on smartphones devices.
Gojek realize how collaborative economy idea may
able to clarify the previously unregulated Ojek system toenable the circulation of activities and generate potential
benefits by fostering access over ownership of motorcycleand enhance the advance of Information andCommunication Technologies (ICT). It is still unclear
whether Gojek as a new disruptive innovation of urban
transport is truly could enhance people with productive
working environment and generate a more efficient option
for mobility. (A. Schlze, 2015) mentioned that the
turbulence from global demand towards advanced
innovation technology has always affect the market andimpact to people afterwards. Another study (Horpedahl,
2015) highlighted that innovation in technology nowadays
allow people to bypass traditional ways. Moreover, (B.
Cohen, 2014) also reminded that the developments of ICT
have started to challenge traditional thinking, andmentioned that collaborative economy in mobility provide
solutions which can be attributed to multiple stakeholders.

1. Introduction
1.1 Motorcycle rise and its use
In Indonesia, mass transport modes are still lacking,private vehicles are the primary modes of transport thatprovide everyday trips among the urban commuters.
Motorcycle is a common transportation mode which ispreferable than any other modes because it is cheaper thana car and more importantly because it can travel faster. In
2015, motorcycle ownership in Indonesia has reached thenumber of 92.9 million, while Indonesia population itself
is 257.5 million people (Badan Pusat Statistik, 2016). It
means 36% of the citizen commute with motorcycles andno wonder that the streets today are filled withmotorcycles. In the 1980s, realizing that there was anarbitrage, those who started to work, illegally, as a
motorcycle-taxi driver, which is known as Ojek,
responding promptly to provide mobility needs whenother public transport modes are not so available. The
perceived performance of public transportation system isconsidered as very low in Indonesia, therefore, the roleand contribution from ride-hailing service, Ojek to support
mobility needs becomes significant (S.B. Nugroho, 2010).
Local government has no choice but to allow Ojek service
going on as far as no alternative which may give a better
solution. Ojek at very beginning also supported the society
by providing job for those who possess low skill (A.K.M.
Tarigan, 2010). However, for most of its history, Ojek isknown as a low-income job and becomes an option whenpeople have no choice but to make use full of theirmotorcycles for earnings. As these issues remain aproblem in Indonesia, Sustainable Development Goal 8
encourage everyone especially the low-income groups towork efficiently to achieve productive employment, anddecent work in order to get an equal pay for work of equalvalue.

1.2 ICT based ride-hailing with collaborative 2. Objectives and Contribution
2.1 Objectives
economyThis study aims to shed some light to see what
collaborative economy is all about and what the impacts tothe society are by providing the first analysis of arepresentative, sample of Gojek driver-partners and Gojek
users. Gojek, which enhance the use of information
technologies to provide services can now benefits not only

Today people are living in digital and informatics era,
when everything is connected and information transfers
fast. Technology is transforming transportation, the ability
to work efficiently and conveniently request, track, andpay for trips via mobile devices is changing the way

65

Gojek users but also Gojek driver-partners. Regardless ofthe technological aspect, the underdeveloped performanceof public transportation system and working environment
may be related to Gojek driver-partners and Gojek user’sviews of this new ICT-based ride-hailing service.
The objective of this study is to examine the basic
effects of collaborative economy through a case of Gojek
under following two perspectives:To explore the benefit of ICT use in increasingsocial welfare for Gojek driver-partners.
To study how this new ICT based ride-hailingservice affect Gojek user preference for commuting
while capture the reduction of potential time saving.

4. Findings to Gojek driver-partners
4.1 Change of income level
Many of previously Ojek driver are attracted topartner with Gojek by the level of income Gojek offers at
the first time. While other respondents who worked atlabor market attracted by the flexibility, as data result
computed in Table 1. Combining the advanced technology,
Gojek attracted more users by using smartphone app,which results in increasing orders for Gojek
driver-partners to earn at any time.
Table 1. Cross tabulation of motivation to partner with
Gojek and driver-partners’ age range [unit: %]Motivation to join as
Gojek driver-partner
Increase in Income
Flexibility
Loan (smartphone etc.)
OthersTotal

2.2 Significance of the study
From the result of this study, the basic effects of ICT
based ride-hailing service Gojek has been identified.
Therefore, this research hopes will contribute to propose abetter transportation policy-making by ICT in the future,
to enhance solution for efficient working life-style, and tounderstand how transport mode combined with ICT cansave time.

Analysis Item
Advance Booking
Hiring Method
Payment

A total of 220 responses was gathered, including 68
and 152 responses from Gojek driver-partners and Gojek
user respondents respectively. Cross tabulation analysis
is employed to analyze the results of the survey data. The
data collected provides a bunch of information regarding
to relationship between variables. Then, visualization byusing chart bar, diagram, or graph is used to comprehend
the relationship.

47
52
1
0
100

Cash

Gojek (ICT-Based)NO
Smartphone App
Account Charge/Cash/Credit
cardTransparent
AvailableHigh
Flexible and Independent
17-55
Reward and Bonus
Available
Smartphone GPS /Self-skill
Organized and modernized
Structured and formalized

INCOME AFTER PARTNER WITH
GOJEKFrom the survey results, most of Gojek
driver-partners (61 respondents) experienced inincreased-income level after partnered with Gojek,
depicted by dots above 45 degrees’ line in Figure 1.

3.2.2 Clustering Analysis
To elicit different types of group among Gojek
driver-partners, clustering analysis is employed. This
study selects Agglomerative Hierarchical Clustering to
produce a hierarchical series of nested clusters, which
finally displayed as a tree diagram called a dendrogram.
Equation (1) shows the formula of the calculation ofdissimilarity measures by using Euclidean distance.
2

Ojek (Traditional)
YES
On the spot/Reservation
Call/Flag

Pricing Structure
Negotiation
Rating System
NA
Reliability
Low
Working Style
Unsecure
Age Limit
No limitation
Value Capture to company NA
ETA to pick up pointNA
Route to destination
Self-skill
SystemNA
Law and regulation
Grey Area

3.2 Methods of Analysis
3.2.1 Cross Tabulation(𝑋𝑘𝑖 − 𝑋𝑘𝑗 )Total

Table 2. Competitive analysis between Ojek and Gojek

This study attempts to examine people’s view orperspective toward Gojek. Some approaches likequestionnaire, face-to-face interview were employed.
Target respondents were people with Indonesiannationality. Should be noted, respondents in this study
were divided into 2 groups, Gojek partner-drivers andGojek users. Paper version of questionnaires was preparedand distributed from November to December 2016 in
Surabaya, Indonesia.

D(i, j) = √∑Range Age
31-40 41-50
19
11
26
15
0
0
0
0
46
26

Table 2 shows the competitive analysis between Ojek and
Gojek according to interview result which elaboratesignificant differences between the two modes.

3. Data collection and methods of analysis
3.1 Data collection

𝑁

21-30
17
11
1
0
28

*Value is in IDR Million

6
5
4
3
2
1
0

… (1)

0

𝑘=1

𝑖, 𝑗 ∶ 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑘 ∶ 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠
𝑁 ∶ 𝑇𝑜𝑡𝑎𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠

2
4
6
INCOME BEFORE PARTNER WITH GOJEK

8

Figure 1. Most of Gojek driver-partners experienced inincreased-income level and earned the minimum wage
compared to before they partnered with Gojek
66

The 45 degrees’ line indicates no change in income
level even after partnered with Gojek. Thus, from thefigure we also captured some driver-partners who
experienced in no change of income level (4 respondents)
and decreased-income level (3 respondents), whichindicated by dots running on the 45 degrees’ line and dots
below the 45 degrees’ line, respectively. In addition, green
vertical and horizontal line indicates the minimum wageof IDR 3.05 million. From Figure 1, we figured out thatmost driver-partners earn minimum wage of IDR 3.05
million after partnered with Gojek, indicated by dots
above green horizontal line. Previously, most ofdriver-partners did not earn as much as the minimum
wage before partnered with Gojek.

examining variable by variable to reveal which variable
that leads to the clusters. We assumed that Cluster 1
belongs to driver-partners focusing on both earnings andworking-flexibility. Cluster 2 is driver-partners who
prefers working-flexibility, while Cluster 3 isdriver-partners focuses more on earnings.
Cluster 1 has the highest value in increased-income
level after partnered with Gojek. This group of drivers put
earnings as important value to maintain a decent life. All
drivers in this group are married, we consider family to be
an influencing factor on how drivers work and earn for
their family. Moreover, drivers in Cluster 1 earn as muchas above the minimum wage of IDR 3.05 million in
average. The average of working hour per day in Cluster 1
shows 9.107, which is almost similar to Cluster 2 by 9.091.
But in fact, Cluster 1 could generate more earnings than
Cluster 2. This means Cluster 1 group knows how to work
more efficiently. Satisfaction level shows high scale for all
variables income, flexibility and overall working.
Result from Table 3 also shows that the level ofincome in average for Cluster 2 as 2.864, although theincome level indeed has increased, the value is less thanthe minimum wage. When we investigate further, theaverage result in other variables such as total working
hours, total trips, and total running distances, indicate thelowest value among other clusters. We assumed thatdriver-partners in Cluster 2 are more attracted to work due
to flexible working schedule Gojek offers. Satisfactionlevel towards flexibility shows highest value of 4.818
compared to other satisfaction variables and other clusters.
In addition, driver-partners who experienced in
Figure 2. Comparison of drivers’ income in monthly basis, decreased-income level, are belong to cluster 2. From
before and after partnered with Gojek
interview result, one driver stated that the earning from
Gojek still enough to maintain life for the whole family
Figure 2 shows a more detail illustration in change of even he experienced in decreased-income level. The driverincome level from 68 driver-partners before and after previously worked as a truck driver and often leave thepartnered with Gojek. The x-axis is in IDR million unit. hometown, which result in almost no time for family. Now
Only 7 drivers experienced in decreased- or no change in he can manage to increase more time for family as he canincome level after partnered with Gojek. While 61 drivers settle his schedule for work.
generated more income after joined as Gojek drivers.
Cluster 3 shows the highest in value of total working
hours, total trips, and total running distance variables
4.2 Cluster analysis of 68 driver-partners
among all clusters. From interview result, drivers in
In order to take a different perspective on the survey Cluster 3 tend to take any orders regardless of distance.
result data of Gojek driver-partners, we conducted Cluster We assumed that driver-partners from Cluster 3 are not
Analysis. The result of clustering 68 driver-partners picky in taking orders and work for earnings. They earned
simply aims to segment drivers’ survey data into as much as the minimum wage in average. All not-married
meaningful clusters, and to further investigate the drivers belong to Cluster 3. From the interview result,difference in motivation towards working as Gojek drivers. indeed the drivers mentioned that they have more time for
As shown in Table 3, we divide Gojek driver-partners into themselves without any responsibilities for the family yet,
3 clusters.
but planning to save up for one. The time results inwillingness to run further distance and to focus on
Table 3. Cluster centroid of 68 Gojek driver-partners
earnings. Satisfaction level towards income level shows
IncomeIncomeDaily
Workinghigh value of 4.036 compared to other satisfaction
Cluster
Age
BeforeAfter
IncomeHourvariables.
1

39.607

2.704

3.996

0.173

2

32.727

2.464

2.864

0.109

9.107
9.091

3

32.643

2.389

3.675

0.142

10.571Cluster

Total
Trips

1

13.179

2.714

4.679

4.714

2

9.364

1.909

3.455

4.818

3.909

3

14.286

3.286

4.036

3.643

3.714

5. Findings to Gojek users
Gojek users was divided into 2 groups, the students
from universities in Surabaya with ninety-seven (97)
respondents and the local workers with fifty-five (55)
respondents. Gojek users’ behavior towards Ojek seem tohave changed, which leads to preference of using Gojek.
As Gojek combined technology in delivering service,fewer people use Ojek recently. Some of Gojek users are

Running
IncomeFlexibility Working
Distance Satisfaction Satisfaction Satisfaction
4.821

From the cluster results, we made investigation by67

partly- and fully-shifting their transport mode from Ojek toGojek, as shown in Figure 3. From the survey result, wefound out that fifty-nine (59) over one hundred andfifty-two (152) user respondents are new-users of
ride-hailing service. These respondents never used Ojek
previously, yet they ever used the ICT based Gojek for thevery first time. Gojek had attracted many young people inIndonesia, who are more likely to be updated with ICT
innovation.

6. Conclusion

This paper has provided the first comprehensive
analysis of Gojek’s driver-partners and users, based on
survey data. Gojek permits flexible work and increases
income of level to some group of driver-partners, who
seemed satisfy to have partnered with Gojek. Moreover,
the availability of modern technology, like the Gojek app,provides advantages for Gojek users compared with thetraditional Ojek, and this has boosted new demand for ride
services, and proved to cut travel time of previously usedmode.
With more demands in ride services, Gojek increased
demand for driver-partner, who appears to be attracted inlarge part because of the flexibility Gojek offers, the levelof compensation, and the income. Many driver-partners
valued the flexibility to choose their hours of work. Gojek
also becomes an attractive option to those who want towork part-time, as other part-time jobs in the labor market
Figure 3. Two groups of respondents’ view towards Gojek typically disadvantages in terms of pay. With the help of
ICT development, Gojek serves as a bridge for many
service with comparison to the use of Ojek.
seeking employment opportunities. Most Gojek
We compared Gojek with other transportation modes driver-partners experienced in increased income and earnt
used previously by Gojek new-users, and found out that the minimum wage. Cluster analysis reveals 3 main types
Gojek contributed in terms of time-saving. From survey of drivers who prioritize: income, flexibility, both income
result as shown in Table 4, Gojek users can cut travel time and flexibility. Easy access by smartphone also has made aof other modes used previously by 57% of angkot and taxi, way to commute much more convenient because driver
can now come to pick-up location with estimated time
52% of car, bus, and Bemo, 43% of Becak.
arrival displayed on Gojek app. Gojek driver now able toTable 4. Comparison of travel time (TT) and time cut come to pick-up location which reduces Gojek users’
walking time compared to when they commuted by Ojek
between Gojek and previous mode trips
previously.
Mode Ave. TT by mode
Ave. Gojek TT
Time
Finally, increasing Internet use by citizens in(minutes)(minutes)
Cut
Indonesia have resulted in opportunities to support urban
Car
41.51
21.60
52%
mobility while public transportation system still not able
Taxi
41.52
23.79
57%
to compete with mobility demands. The high ownerships
Angkot
33.61
19.17
57%
of private motorcycle and smartphone users which
Bus
31.11
16.11
52%
linkages the advances in technology, can now make it
Bemo
31.43
16.43
52%
possible to respond to specific needs in increasing social
welfare to some group of people and providing option for
Becak
17.50
7.50
43%
Gojek seemed to contribute in convenience and mobility inside the city.
time-saving for the users. Survey result shows one
hundred and twenty-four (124) respondents among one Reference
hundred and fifty-two (152) respondents answered they  A. Schlze, J. M. (2015). Introduction: Knowledge Generation andInnovation Diffusion in the Global Automotive Industry. Change
experienced in time-saving after using Gojek.
and Stability during Turbulent, Industrial and Corporate Change,
Oxford University Press.
 A.K.M. Tarigan, Y. S. (2010). Negative experiences andwillingness to use paratransit in Bandung, Indonesia: An
exploration with ordered probit model. Proceeding of the 89th
Annual meeting of Transportation Research Board.
 B. Cohen, J. K. (2014). Ride on! Mobility business model for the
sharing economy. Organ. Environ. 27 (3).
 Badan Pusat Statistik Indonesia (2016).
Link source: http://www.bps.go.id/linkTabelStatis/view/id/1413
 Carvero, R. G. (2007). Informal transport: A global perspective.
Transport Policy 14, 445-457.
 Horpedahl, J. (2015). Ideology Uber alles? Economics bloggers
on Uber, Lyft, and other transportation network companies pp.
360-374. Econ. J. Watch 12 (3).
 S.B. Nugroho, T. L. (2010). Measuring Social Capacity for Urban
Air Quality Management in Megacities in South East Asia basedon Attitudinal Survey in Jakarta and Hanoi. Proceeding of the
16th Conference of Sustainable Development Research
Conference. The Kadoorie Institute, University of Hongkong.
 Santi, P. (2014). Quantifying the benefits of vehicle pooling withshare ability networks. Proc.Natl.Acad.Sci.111, 13290–13294.

Figure 4. Result of the two groups towards possible
activities engaged from the time saved after using Gojek.
Respondents mentioned activities they prefer to engage
from the time-saving after using Gojek, as depicted in
Figure 4. Workers group are more likely to work, socialize
and spend time for family. While students group are more
likely to rest, and spend time for self-entertainment suchas, reading books, café, or internet browsing, and eat.
68

Current status of subsidized taxi operations by NPOs in Tokyo for the elderly anddisabled mobility
Student Number: 15M18167

Name: Kazuhiko MATSUMURA

Supervisor: Naoya ABE

東京都における高齢者・障がい者モビリティのための福祉有償運送事業の現状
松村和彦
本論文は東京都内の 33 の福祉有償運送事業所のサービス充実度を分析し、移動困難者モビリティの地域差を明らかにした。
「補助金受給額」「重度移動困難者へのサービス充実度」、「サービス供給量」、「サービス確実性」の観点からまとめ、その結果から地理的特性の顕著な差は見られなかった。また、補助金受給額と他のサービス充実度との関連が見られないことから、補助金交付基準の見直しが必要である
ことが明らかになった。

1. Introduction
1.1 Current situation of
movement limited person

mobilityfor

1.2 FUKUSHI-YUSHOUNSO
Figure 1 shows that relations between total cars of
FUKUSHI-YUSHOUNSO and population density ofpeople certified for long term care in each area of Tokyo.
Twenty areas are selected that ten areas are the largest
number of total cars and ten areas are the smallest numberof it in Tokyo area. Ten areas in Tokyo don’t provide a
FUKUSHI-YUSHOUNSO service even though somemovement limited persons live in these areas. Thus, thesegaps might have an effect on their mobility depending on
where they live. Therefore, this study focuses on theactual operation of FUKUSHI-YUSHOUNSO.
Considering the actual operation, subsidy is an importantfactor because NPO has only limited budget for operation.
It means subsidy is directly related to service context.

Nowadays, Japan is facing a serious problem from aging
society. The ratio of population over 65 years people inJapan will continue to increase to over 30% by 2030 [1].
Since the number of people certified for long term care in
Japan has also increased to 6,077,435 in 2015 from5,250,404 in 2012 [2]. Almost all of them have difficulty
to go out due to their physical disability. Some of themcan go out by using a KAIGO taxi1 or transfer from theirfamily. However, others face financial limitation and nochance of access of transfer from their family or thepeople they can rely on. These people who have physicaland financial limitation of going out and no people to rely
on when they want to go out are defined as “movement
limited persons”. Movement limited persons are not only
the elderly people but there are three kinds of disabled
people who fulfilled the above conditions: physicaldisability, mental disability and intellectual disability
people.
Limitations in movement lead to be bedridden [3].
Besides, mobility of elderly and disabled people is defined
as likely to be human right in Western countries and it isensured by law [4]. Therefore, mobility of movement
limited person must be secured.
Since movement limited people are suffering from goingout, FUKUSHI-YUSHOUNSO2 is the vest way for them.
It is because that the service provided by
FUKUSHI-YUSHOUNSO has fulfilled the preferences of
movement limited person; it is not necessary for family
help, it has a welfare car that makes movement limited
person easier to get on and off the car and service fee is
about half of the price of normal taxi in operation area.
However, actual service operation might be different fromeach NPO.

1.3 Selection of targeted area
There are two reasons why this study has targeted Tokyo
area to investigate the actual operation of
FUKUSHI-YUSHOUNSO.
First, Tokyo has a characteristic of like that of small
Japan on the perspective of population density. It meansthat investigating Tokyo area is important to get the
picture of whole Japan.
Second, it will continue to increase the number ofmovement limited person in Tokyo. Tokyo is the largest
number of migrants from other prefectures that are over
65 years old.

1.4 Objectives
This research categorizes the NPOs which conduct a
FUKUSHI-YUSHOUNSO service based on theircharacteristics. There are two purposes of this research:1.

To clarify the relation between adequacy of
FUKUSHIYUSHOUNSO service and subsidy for
FUKUSHI-YUSHOUNSO.

2.

To clarify regional differences of mobility of
movement limited person.

1

KAIGO taxi is a taxi service for those people who havedifficulty of going out which is conducted by ordinalcompany [5].
2
FUKUSHI-YUSHOUNSO is a taxi service for thosepeople who have difficulty of going out which is
conducted by NPO [6].

69

3.2 Methodology
3.2.1 The flow of methodology
There are three steps to categorize the NPOs. First step isdata standardization to be able to compare with eachindicator. Second step is to extract synthetic variable fromthe data table 1 as show by principal component analysis(PCA). It is a method to shorten the multivariate data toprevent from data deviation. Third step is to categorize theNPOs by cluster analysis based on new indicators which
are extracted by PCA.

3.2.2 Principal component analysis
PCA extracts few numbers of independent data that can
simply indicate service adequacy. Equation (2) shows
principal component. Later P means the number ofvariables. In this study, P would be six. Later m means thenumber of principal component.
𝑃

Figure1
Relations
between
total
cars
of
FUKUSHI-YUSHOUNSO and population density ofpeople certified for long term care in each area of Tokyo

𝑍𝑚 ≡ ∑ 𝑎𝑝𝑚 𝑥𝑝
𝑍𝑚 ∶ 𝑝𝑟𝑖𝑛𝑐𝑖𝑝𝑎𝑙 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡
𝑎𝑝𝑚 ∶ 𝑐𝑜𝑢𝑝𝑙𝑖𝑛𝑔 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑥𝑝 ∶ 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒

2. Significance of the study
From previous researches, there are three issues about
FUKUSHI-YUSHOUNSO.
①
②
③

(2)

𝑝=1

But there is a condition, if m is 1.

The
recentactualoperationof
FUKUSHI-YUSHOUNSO is unclear.
The
informationofsubsidyfor
FUKUSHI-YUSHOUNSO is not organized.
The
relation
betweenadequacyof
FUKUSHI-YUSHOUNSO service and subsidy for
FUKUSHI-YUSHOUNSO is unclear.

𝑃∑ 𝑎𝑝𝑚 𝑥𝑝 = 1

(3)

𝑝=1

This study uses principal components as indicators byover around 80% of cumulative contribution ratio because
the number can present the original data.

3. Data collection and methodology
3.1 Data collection

3.2.3 Cluster analysis
Cluster analysis is a multivariate analysis method to makea group according to some analogy data. The cluster ismade of samples that are the highest similarity ofdissimilarity matrix in samples. There are some
calculation methods of dissimilarity matrix. This study
uses Mcquitty method because this method is suitable todeal with outlier minority sample when cluster number isover four [7]. Equation (4) shows how to calculate thedissimilarity matrix by Mcquitty method. The formula
simply means taking an average between two clusters.

This research used the data of NPOs which conduct a
FUKUSHI-YUSHOUNSO service in Tokyo. Table 1
shows the data of NPOs. These data which are subsidy,
trip, the number of registered user and the number ofdriver are collected by hearing survey since 2016 August
26 to November 24. These data of total car and wheelchair
car are collected from Director-General of Tokyo District
Transportation at 2016 July 8.
Table 1: The data of NPO (n=33)
Variable
Duration
Average
Subsidy [JPY/year]
In 20152,880,050
Trip [time/year]
In 2015
3,100
Total car [car]In 2016 at June
6.1
Wheelchair car [car]In 2016 at June
3.7
The number of
In 2015
118
registered user [people]
The number of driver
In 201512.9[people]D(𝐶𝑘𝑙 , 𝐶𝑖 ) =D(𝐶𝑘 , 𝐶𝑖 ) + D(𝐶𝑙 , 𝐶𝑖 )2

(4)

D(𝐶𝑘𝑙 , 𝐶𝑖 ) ∶ 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑐𝑙𝑢𝑠𝑡𝑒𝑟𝑠
𝐶 ∶ 𝑐𝑙𝑢𝑠𝑡𝑒𝑟

4. Hearing survey and results
4.1 Hearing survey
This study conducted hearing survey to collect the data ofNPOs and understand what factor is important to evaluate
actual operation for movement limited person. Five factors
are clarified.

70

Ⅰ.Whether the NPO conducts bed-to-bed service or not?
Ⅱ. How many wheelchair cars does the NPO have?
Ⅲ. What is the operation style of NPO?
Ⅳ. Who are the targeted people?
Ⅴ. Does the NPO motivate to help going out for many
people?
Recently, the demand of bed-to-bed service andwheelchair car is growing for severe movement limited
person according to aging society. and Ⅰ factors Ⅱ canhelp going out for them.
Additionally, operation style is strongly related tofinancial resources of NPO. Basically, NPO of single
operation style gains operation fee from municipality as a
subsidy. NPO of multi operation style is operated bymoney from the other businesses which are almost nursingcare.
On the other side, targeted people are different by NPO
based on the types of disability and physical condition.
Besides, some NPOs don’t motivate to help going out for
many people. The service is just part of the other careservice for them.

Figure 2 Result of cluster analysis
Six indicators which are about NPO data used by PCA
before, kinds of operation style and targeted people arecomprehensively
consideredtoevaluatea
FUKUSHI-YUSHOUNSO service adequacy by eachcluster.
The two research objectives are clarified as explained
below;In the first, the information about NPOs are organized into
four aspects; the amount of subsidy, service adequacy forsevere movement limited person, the amount of trips andservice certainty.
The service adequacy for severe movement limited
person is composed by the number of wheelchair car andprovision of bed-to-bed service. Service certainty iscomposed by the number of total cars, user and driver. It isbecause higher these numbers indicates lower rate ofrejection of a service provided from NPO due to their highcapacity.
Second step is to clarify the relationship between theamount of subsidy and service adequacy. As a result, theamount of subsidy is not related to service adequacy
perspective of three aspects; service adequacy for severe
movement limited person, the amount of trips and service
certainty.
Third step is to clarify the regional characteristics of
FUKUSHI-YUSHOUNSO service adequacy. Based on
above conditions except the amount of subsidy, the
FUKUSHI-YUSHOUNSO
serviceadequacyisconsidered.
First cluster is composed by only NPO2. Total car (8cars), user (270 people) and driver (19 people) are far
higher than the average number. It means first cluster ishigh service certainty. Additionally, wheelchair car (8cars) is higher than the average and bed to bed service isconducted. First cluster is high service adequacy forsevere movement limited person. But trip (886 times/year)
is much lower than average. Thus, first cluster is highservice certainty and service adequacy for severe
movement limited person. The other clusters by eighteenth
have been evaluated like the above steps.
Summarizing the result, seven clusters which arecomposed by ten NPOs are high service adequacy forsevere movement limited person. Targeted operation areas
are shibuya-ku, tama-shi, kita-ku, kiyose-shi, itabashi-ku,

4.2 Results
Six variables in table 1 are extracted by PCA. The resultof this analysis can express about 83% of cumulative
contribution ratio by third principal component (table 2).
These principal components are named based on thesenumbers on Table 2 as; first principal component iscomprehensive service adequacy axis. Second principal
component is independent large scale service engagement
axis. Third principal component is dependent onmunicipality service engagement for light movement
limited person axis.
First to third principal components are used by cluster
analysis as the indicators of NPOs. The result of cluster
analysis shows that thirty three NPOs are categorized into
eighteen clusters (Figure 2). The number of clusters is
minimum requirement that any cluster doesn’t have NPOs
which are not different motivation by hearing survey (Ⅴ).
Table 2 Result of PCA
First
Second
Items
PC
PC

Third
PC

Subsidy [JPY/year]

0.35

-0.58

0.40

Trip [time/year]

0.36

0.41

0.55

Total car [car]

0.35

0.64

-0.076

Wheelchair car [car]The number ofregistered user
[people]The number ofdriver [people]

0.41

-0.17

-0.65

0.46

-0.24

0.20

0.51

0.029

-0.27

Eigenvalue

2.87

1.17

0.85

Contribution ratio

0.48

0.20

0.14

Cumulative
contribution ratio

0.48

0.68

0.82

71

inagi-shi, shinagawa-ku, koganei-shi, hino-shi andtachikawa-shi.
Six clusters which are composed by eight NPOs are highamount of service provided. Targeted operation areas aremitaka-shi, inagi-shi, higashiyamato-shi, edogawa-ku,
koganei-shi, hino-shi, tachikawa-shi and setagaya-ku.
Six clusters which are composed by eight NPOs are highservice certainty. Targeted operation areas are shibuya-ku,
tama-shi, mitaka-shi, kokubunzi-shi, higashiyamato-shi,
edogawa-ku, koganei-shi and setagaya-ku.
Furthermore, some NPO have fulfilled multi conditions.
NPO2 and NPO28 are high service adequacy for severe
movement limited person and service certainty. Targeted
operation areas are shibuya-ku and tama-shi. NPO5 andNPO25 are of high amount of service provided and highservice adequacy for severe movement limited person.
Targeted operation areas are hino-shi and tachikawa-shi.
NPO35 and NPO7 have high amount of service provided
and high service certainty. Targeted operation areas aremitaka-shi and setagaya-ku. NPO30 and NPO17 aresatisfied three conditions. Targeted operation areas arekokubunzi-shi and koganei-shi.
On the other hand, four clusters which are composed by
fourteen NPOs have not fulfilled any conditions. Targeted
operation areas are fussa-shi, ota-ku, nishitokyo-shi,
mizuho-cho, kodaira-shi, adachi-ku, Machida-shi,
katushika-ku,
ome-shi,hamura-shi,komae-shi,akiruno-shi, fuchu-shi and nerima-ku. Figure 3 shows of
the result of regional characteristics of mobility for
movement limited person on map of Tokyo area.
Overall, some NPOs which are evaluated above three
conditions are mixed. Moreover, middle area of Tokyo hassome NPO which fulfilled with multi conditions of service
adequacy. But that area also has other NPOs which are notfulfilled with any conditions. Therefore, peculiar trend ofregional characteristic of FUKUSHI-YUSHOUNSO
service adequacy have not bene found.

5. Conclusion
According to research objectives, this research has
clarified two points:
1.

The amount of subsidy is not related to service
adequacy perspective of three aspects; service
adequacy for severe movement limited person, theamount of trips and service certainty.

2.

Peculiar trend of regional characteristic of
FUKUSHI-YUSHOUNSO service adequacy havenot been found.

It is necessary for the municipality to reconsiders thestandard of subsidy delivering. The municipality should
consider severe movement limited persons when theydecide to how much they deliver subsidy for NPO. It isbecause that the most of NPOs which conducts bed to bed
service don’t gain subsidy even though the service
demand is large and it is expensive to deliver bed to bed
service.
However, the subjects of this study are only 33 NPOs
out of 120 in Tokyo. It means that this research cannot
understand operation of whole FUKUSHI-YUSHOUNSO
NPOs in Tokyo. Additionally, the way of choosing NPOsin this study may have involved a selection bias. Moreover,service adequacy doesn’t include operation fee eventhough Operation fee is important to choose a
FUKUSHI-YUSHOUNSO service for movement limited
person. In the case of including operation fee in service
adequacy, it must consider use characteristics as well.

Reference

Figure 3 regional characteristics of mobility for
movement limited person in Tokyo72

1.

高齢社会白書(2015) 「第 1 章 高齢化の状況 第 1 節 高齢化の状況」
http://www8.cao.go.jp/kourei/whitepaper/w-2015/html/gaiyou/s1_1.html （アクセス日2016 年 1 月 19 日）

2.

介護保険事業報告(2015) 「介護保険事業状況報告（暫定）平成 27 年 4 月分」
http://www.mhlw.go.jp/topics/kaigo/osirase/jigyo/m15/1504.html （アクセス日 2016
年 1 月 19 日）

3.

藺牟田洋美・安村誠司・阿彦忠之（2004 年）「準寝たきり高齢者の自立度と心理的 QOL の向上を目指した Life Review による介入プログラムの思考とその効果」『日本公衛誌』第 51 巻第 7 号,471-482

4.

清水浩志郎（1995）「高齢者・障害者交通研究の意義と今後の展望」『土木学会論文集』第 518 巻第 28 号,17-29

5.

阿部名保子（2010）「福祉有償運送事業の運営実態から見た持続可能な移動サー
ビスの現状と今後のあり方」『運輸政策研究』第 13 巻第 3 号,24-34

6.

国土交通省(2008)「福祉有償運送ガイドブック」
http://www.mlit.go.jp/jidosha/sesaku/jigyo/jikayouyushoryokaku/GB-honbun.pdf （アクセス日 2016 年 1 月 19 日）

7.

金森弘昇・松田眞一（2010）「周期性のあるデータを中心とした独立成分分析の考察」http://www.seto.nanzan-u.ac.jp/st/nas/academia/vol_010pdf/10-011-025.pdf （アクセス日 2016 年 1 月 19 日）

8.

総務局（2015） 「東京都の人口（推計） 平成 27 年 4 月」
http://www.metro.tokyo.jp/PROFILE/map_to.htm （アクセス日 2016 年 1 月 19 日）

9.

都道府県別人口密度（2015）「都道府県，年齢（3 区分），男女別人口－総人口 平成 27 年 1 月」 http://www.stat.go.jp/data/jinsui/2014np/ （アクセス日 2016 年 1
月 19 日）10.

高齢社会白書(2015) 「第 1 章 高齢化の状況 第 2 節 高齢者の姿と取り巻く環境の現状と動向」
http://www8.cao.go.jp/kourei/whitepaper/w-2015/html/zenbun/s1_2_1.html （アクセス日 2016 年 1 月 19 日）

FACTORS AFFECTING MONGOLIAN PRIMARY SCHOOL LEADERS'
ATTITUDES TOWARD ICT IN EDUCATION
Student Number: 15M18055
Name: Yusuke Ohya
Supervisors: Shinobu Yamaguchi and Jun-Ichi Takada
During the multiple education reforms with policy development and project implementation since late 1990s in
Mongolia, responsibilities of school leaders have increased. One of the focuses of its policy is to integrate ICT in
education, and thus, school leaders’ positive perceptions on use of ICT have become essential. This study aims to
explore the factors that affect the attitudes of primary school leaders in Mongolia toward ICT use in educational
activities. The study of 222 school leaders found that two factors, namely, teacher cooperation and availability of ICT
resources are affecting school leaders’ attitudes toward ICT in education. These identified factors are expected to help
policy makers in Mongolia to develop effective education policies. Further, this study also revealed that other factors
such as school leaders’ age, school location, and availability of ICT specialist are not found to be significant factors.
This finding also contributes to the current literatures on attitudes of school leaders.

educational reform. A considerable amount ofevidence exists that school leaders are crucial for
determining the success of changes within theschools [3] [4].
As discussed earlier, Mongolia is
experiencing educational reform supported bypolicies and project implementation. Hence, it isimportant to look at the attitudes of school leaders
toward ICT use in educational activities, which isone of the significant factors in the success of theeducational change.

1 Introduction
After the collapse of the Union of Soviet
Socialist Republics (USSR), Mongolia faced
financial difficulties in every sector. Especially in
education sector, as education expenditure
decreased by 56% between 1990 and 1992, both anaccess to schooling and its quality were deteriorated[1]. In this context, education reform has been takenplace in Mongolia with policy development andproject implementation. In 2006, “Master Plan toDevelop Education of Mongolia in 2006-2015” wasestablished, and one of the focuses of its policy is to
explore the use of ICT to improve education,
contributing to increased access to education andteacher development. Mongolian governments havedeveloped plans to intensify their investments
regarding ICT integration in education .
Given new national policies, responsibilities ofschool leaders have increased to effectively utilize
ICT in school activities. As the role of schoolleaders is vital in making decisions on educational
activities, the positive perceptions of school leaderson the use of ICT at school have become essential.
Given this background, this study aims to
explore the factors affecting the attitudes ofMongolian primary school leaders toward ICT use
in education.

2.2 Attitude study
There are multiple definitions of people’s
attitudes. Kreijns, Acker, & Vermeulen defined
attitudes as person’s overall feeling of sympathy or
antipathy toward an object. Further, number ofstudies supported that attitudes are the mostdistinctive and indispensable concept determining
people’s behavior [5].
In developing scales to measure people’s
attitudes, Smith established the tripartite model. It
categorized the concept of attitudes into three
aspects: 1) affective domain; 2) behavioral domain;and 3) cognitive domain. Affective domain reflects
feelings toward an object/event. Behavioral domainreflects behavioral intentions and action with respect
to an object/event. Cognitive domain refers toperceptions and beliefs about an object/event [6].
This model has been adopted and utilized indifferent types of attitude studies in both developed
and developing countries [7] [8].

2 Literature Review
2.1 Educational change theory

2.3 Potential factors affecting schoolleaders’ attitudes toward ICT in
education

There are two important theories supporting
this study. One is the Fullan’s educational change
theory. The educational change is recognized as asynonym to education reform [2]. The theory statesthat transformation of beliefs of educators is thefundamental component for the sustainable
implementation of educational change. Schoolleaders, especially, are expected to lead changes as
facilitators among those who are involved in

An extensive literature review identified thefollowing eight potential factors which affect schoolleaders’ attitudes toward ICT in educational
activities: 1) teacher cooperation; 2) school ICT
strategy; 3) pedagogy using ICT; 4) school ICT
specialist; 5) ICT resources; 6) gender; 7) age; and8) location of school. Accordingly, eight hypotheses
73

from the baseline survey of “Quality Improvement ofPrimary Education Teachers Through Developmentof Training Materials Using ICT”, conducted in 2012.
A total of 222 responses were collected for data
analysis from school principals and training
managers from five regions, reflecting geographicaldifferences. (Fig 2). This sample represents 9.9% ofprimary school leaders in Mongolia. As for thequalitative analysis, face-to-face interviews andquestionnaire survey took place in Khentii, Khovd,
Bayankhongor provinces, and Ulaanbaatar city. A
total of 13 school leaders and local government
representatives participated in the survey.

were developed as follows (Fig 1):
Hypothesis 1: School culture that teachers
cooperate and share their experiences is positively
affecting school leaders’ attitudes toward ICT in
education
Hypothesis 2: Development of school strategies
for ICT is positively affecting school leaders’attitudes toward ICT in education
Hypothesis 3: School culture that teachers shareand discuss the pedagogy using ICT is positively
affecting school leaders’ attitudes toward ICT in
education
Hypothesis 4: Availability of school ICT specialist
is positively affecting school leaders’ attitudes
toward ICT in education
Hypothesis 5: Availability of ICT resources ispositively affecting school leaders’ attitudes toward
ICT in education
Hypothesis 6: Gender is affecting school leaders’attitudes toward ICT in education
Hypothesis 7: Age is negatively affecting schoolleaders’ attitudes towards ICT in education
Hypothesis 8: School location is affecting schoolleaders’ attitudes toward ICT in education

N=62
N=25

N=43

N=50

N=42

Fig 2. Data collection sites in Mongolia

3.3 Data analysis process

Fig 1. Hypotheses on potential factors affecting school leader’s attitudes

3 Methodology
3.1 Significance of focusing on cognitive
attitudes toward ICT in education
Applying the tripartite model to measureschool leaders’ attitudes, this study particularly
focuses on cognitive aspect for the following
reasons.
First, in discussing technology introduction
into school setting, the previous studies found theimportance of cognitive domain to analyze theattitudes of school leaders. For instance, Pelgrum
found that there is a clear association between
cognitive attitudes of school leaders and theemphasis on computer integrated learning at
schools [9]. Further, regarding the Mongolian
context, local experts emphasized the importanceof people’s positive cognitive attitudes to promote
ICT usage in school settings. The expert pointed
out that some Mongolian people are notenthusiastic to utilize ICT tools due to the lack ofbelief of positive impact of ICT [10].

The quantitative data analysis had four steps.
First, data cleaning including data re-coding andcreating dummy variables were performed. Second,question items were categorized into different
variables based on the literature review. Third,
exploratory factor analysis was conducted to avoid
redundancies in question items and examine thestructure or relationship between variables. Forth,multiple regression analysis was employed to
identify the significant factors affecting schoolleaders’ attitudes toward ICT use in education.
Descriptive analysis of the data sample
supplements the analysis to understand its
characteristic. A set of demographic information,
including position in school, gender, age, years ofservice at the position, and school location is
described.

4 Findings
4.1 Demographic information
The respondents are composed of school
principals (48.4%, N=104) and training managers(51.6%, N=111). Nearly three quarters are female(73.2%, N=161). As for its age distribution of the
respondents, 44.7% (N=93) are age of 40-49,
followed by 30-39 years olds (26.0%, N=54). In
terms of years of experience as school leaders,
45.7% (N=85) worked for 20-29 years and another
18.3% (N=34) worked for 30-39 years. As for thelocation of the schools, 74.4% (N=157) are locatedin villages while 13.7% (N=29) are situated inprovincial center, and 11.8% (N=25) are in
Ulaanbaatar.

3.2 Data source
For quantitative analysis, data were retrieved74significant variables affecting school leaders’attitudes toward ICT use in educational activities,
namely, teacher cooperation and ICT resources.
Regarding the model fits, this model explains 27.7%of the variance of Mongolian primary school leaders’attitudes toward ICT in educational activities.

4.2 Exploratory factor analysis
A set of 15 question items measuring schoolleaders’ attitudes and affecting factors were extracted
from the baseline survey. Factor analysis was
conducted on these items to avoid redundancies inquestion items. Kaiser-Mayer-Olkin (KMO) test andBartlett’s test were conducted to detect sampling
adequacy and correlation between variables. As aresult, the KMO measure is 0.843 which confirmed
the adequacy of data. Also, Bartlett’s test of
sphericity was found statistically significant, whichindicates that null hypothesis (there are nocorrelations between any of the variables) is rejected.
Hence, these result proved that the data are suitablefor factor analysis.
Subsequently, factor rotation was conducted
to recategorize the factor structure of question items
for adequate interpretations. It was found that 15question items were recategorized into four latentvariables with 13 question items. As for the
reliability of each category, Cronbach’s alpha (0.7) confirmed an internal consistency (Table 1).

Table 2. Regression and Multicollinearity Statistic
Variables
B
Std.
t
Sig.
Error
(Constant)
.937
.457
2.053 .041
Teacher cooperation
.384
.113
3.412 .001
Pedagogy using ICT
.127
.081
1.571 .119
School ICT strategies
.070
.089
.783 .435
ICT resources
.153
.045
3.406 .001
School ICT specialist
-.030
.121
-.251 .802
Gender-.020.099-.198 .844
Age
-.001
.006
-.125 .900
Location of school 1
-.021
.140
-.151 .880
Location of school 2
-.093
.177
-.525 .600

As for the Variance Inflation Factor (VIF), theresult revealed no infringement on multicollinearity
issue, suggesting that the independent variables do
not exhibit strong correlation to each other. On theother hand, other factors including school ICT
strategy, pedagogy using ICT, school ICT specialist,
gender, age, and location of school were not foundstatistically significant factors affecting schoolleaders’ attitudes. These results are discussed further
in the latter sections. The data analysis supports two
hypotheses introduced in this study.
Hypothesis 1: School culture that teachers cooperateand share their experiences is positively affecting
school leaders’ attitudes toward ICT in education
Hypothesis 5: Availability of ICT resources ispositively affecting school leaders’ attitudes toward
ICT in education

Table 1. Cronbach’s Alpha for latent variables
Latent variables
Number of
Cronbach’s
items
Alpha
Pedagogy using ICT
3.777
Teacher cooperation
4.833
Leaders’ attitudes in ICT
3.847
integrated education
School ICT strategies
3.804

In addition to four categories, variables
which were not tested in factor analysis suchas ICT resources, school ICT specialist, gender,
age, and school location are also utilized for
multiple regression analysis.

4.3

Multiple regression analysis

Multiple regression analysis was conducted to
identify the factors affecting school leaders’attitudes toward ICT use in education activities.
Nine independent variables, namely, teacher
cooperation (TC), school ICT strategy (SS),pedagogy using ICT (PI), school ICT specialist (IS),ICT resources (IR), gender (G), age (A), andlocation of school (LS1 and LS2). Dependent
variable in this model is school leaders’ attitudes
toward ICT use in educational activities (ATTD).
The regression model is developed as follows.
ATTD =

1

2

5 Discussion
This section discusses five major findings inrelation to current literature.
First, teacher cooperation was found significant
for school leaders’ attitudes toward ICT use ineducational activities. This finding is consistent
with previous studies [11] [12]. The qualitative
survey result also supports this finding. Schoolleaders recognized that teacher cooperation isessential for teachers to promote the use of ICT for
educational activities. Especially, rural schools arelocated in dispersed environment with limited
educational resources, and thus, sharing knowledge,
skills and new information were essential to copewith new ICT tools to be introduced into theireducational activities. Further, improvement of ICT
infrastructure also created the culture of sharing
among teachers.
Second, availability of ICT resources are also
found to be a significant factor affecting Mongolian

1

Table 2 summarizes the result of regression analysis.
Based on the analysis, model of Mongolian primaryschool leaders’ attitude was shown as the following.
ATTD = 0.937 + 0.384 (TC) + 0.153 (IR)2Equation (2) shows that there are two statistically

75

VIF
1.523
1.580
1.396
1.124
1.091
1.084
1.189
1.890
1.831
.277

school leaders’ attitudes toward ICT use ineducation. In Mongolian primary school context,
with national policies and strategies of expanding
ICT into educational settings, school principals
have become more responsible to establish school
ICT infrastructures. Therefore, the level of ICT
environment is a vital question for school leaders
to be dealt with on daily basis.
Third, the analysis found that an availabilityof school ICT specialist does not affect schoolleaders’ attitudes toward ICT in education. Thisfinding was contradictory with the current
literature indicating the importance of the ICT
specialist. Further, in the interviews and survey
conducted in urban and rural schools, the majorityof the respondents agreed with the current
literature emphasizing the role of ICT specialists
in schools. This contradiction cannot be explainedat this moment. A comment of a training manager,
however, indicates that hiring an ICT specialist
may prevent closer collaboration among teachers
as they could rely on the specialist. The view
point might imply less importance of a specialistin schools in Mongolian context.
Fourth, the analysis found that age is notimportant for the school leaders to promote ICT
integration in schools. As mentioned earlier,Mongolian government implements ICT policies
and strategies through multiple project activities
throughout Mongolia. School leaders are in theposition to put these strategies into action at
school level. Thus, they are obliged to train
themselves to upgrade their ICT skills andknowledge regardless of their age.
Fifth, location of school was not found to be afactor affecting school leaders’ attitudes toward
ICT use in educational activities. This finding was
not supported by the previous study in Mongolia[13]. This contradiction of finding can be
explained
by
rapid
ICT
infrastructure
improvement in rural area. Due to those efforts
nationwide, school location is not an important
issue to affect leaders’ attitudes toward ICT use ineducational activities.

infrastructure, it would affect positively to theattitudes of school leaders to integrate ICT inteaching and other school activities. It is hoped that
this finding can contribute to further policy
development in primary education.
This study also found the importance of national
policies and strategies. The national efforts
nationwide focusing on ICT use in educational
activities are so powerful that demographic
information such as gender, age, school location are
not as much as influential for the school leaders.
Further research is recommended to explore
other potential factors influencing school leaders’attitudes toward ICT in education. Additional
qualitative data help to understand the local context
in Mongolia. Further sampling is also recommended
to illustrate school leaders’ attitudes more precisely.

References[1] Wu, K. (1994). Mongolia: Financing Education during Economic
Transition. World Bank Discussion Papers 226. East Asia
&amp; Pacific Region Series.
[2] Education Resources Information Center. (1966). ERIC Thesaurus - Educational Change.
[3] Fullan, M. (2002). The change. Educational Leadership.
[4] Hallinger, P. (2003). Leading educational change: Reflections on
the practice of instructional and transformational leadership.
Cambridge Journal of Education.
[5] Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A
theoretical analysis and review of empirical research.
Psychological Bulletin, 84(5), 888–918.
[6] Ajzen, I. (1988). Attitudes, personality, and behavior.
[7] Albirini, A. (2006). Teachers’ attitudes toward information andcommunication technologies: the case of Syrian EFL teachers.
Computers & Education, 47(4), 373–398.
[8] Kay, R. (1994). An exploration of theoretical and practicalfoundations for assessing attitudes toward computers: The
computer attitude measure (CAM). Computers in Human
Behavior.
[9] Pelgrum, W. (1993). Attitudes of school principals and teachers
towards computers: does it matter what they think? Studies in
Educational Evaluation.
[10] USA International Business Publications. (2015). Mongolia
Investment and Business Guide: Strategic and Practical
Information.
[11] Peansupap, V., & Walker, D. (2005). Factors affecting ICT
diffusion: a case study of three large Australian construction
contractors. Engineering, Construction and Architectural
Management, 12(1), 21–37.
[12] LLouis, KS, HM Marks, and S Kruse. 1996. “Teachers’
Professional Community in Restructuring Schools.” American
Educational Research.
[13] Tsogtsaikhan, O. (2014). Factors Affecting Information
Communication Technology (ICT) Integration of Primary
School Teachers in Mongolia.

6 Conclusion
Since early 2000s, the Mongolian governmenthas been promoting the use of ICT into teaching
and learning through different policies and project
implementations. The responsibilities of schoolleaders have increased to effectively utilize ICT inschool activities, and thus, their positive perceptions
on the ICT integration at schools have become
indispensable. The study found that teacher
cooperation and ICT resources are the two
important factors affecting Mongolian primaryschool leaders’ attitudes toward ICT use ineducation. If schools can build an environment forteachercooperation
andimproved
ICT76

APPLICATION OF DRONE FOR RIVERBANK LANDSCAPE
MONITORING IN THE WORLD HERITAGE SITE OF LUANG PRABANG,LAO P.D.R.
Student Number: 15M18180
Name: Hironari Yu
Supervisors: Shinobu Yamaguchi and Jun-Ichi Takada
With the rapid increase in tourists in the world heritage town of Luang Prabang, there is an urgent need to manage
riverbank landscape of the main heritage zone using an appropriate ICT tool. In collaboration with the Department ofWorld Heritage, Luang Prabang (DPL), drone was selected as a tool to effectively visualize the current conditions along
Mekong river. Landscape analysis was conducted to identify the gaps between current conditions and the preservation
criteria. Analysis found the followings: 1) riverbank areas accessible from the street intersections are particularly notmeeting preservation criteria; 2) five areas with large scale temporary tents and storages are located near boat parkings
on Mekong river; and 3) restaurant owners are prioritizing tourist needs rather than preservation criteria. These findings
may imply that local residents have limited understanding about the preservation criteria, and therefore, raisingpreservation awareness targeting these populations is found to be essential. A set of video data of riverbank landscapeproduced in this study serve as baseline data for future preservation policy development.
identified to understand and analyze current riverbank
landscape to effectively implement Riverbank
Management Plan.

1. Introduction

1.1. Site background
Luang Prabang of Lao P.D.R was inscribed as a
World Heritage site of UNESCO in 1995 for its
outstanding values [1]. Since its inscription, the town
has faced drastic increase of tourists. Furthermore,
there is a concern that its landscape may change due tounplanned urban expansion [2]. UNESCO monitoring
report of 2008 noted the possibility for Luang Prabang to
be listed as World Heritage in Danger [3]. Thus, there isa keen need to keep a balance between development andpreservation in Luang Prabang. Tokyo Institute ofTechnology (Tokyo Tech) has been collaborating with
DPL aiming at the sustainable development managementof heritage site using Information and Communication
Technology (ICT) since 2004. In 2015, riverbank
landscape management is identified as one of the core
activities to keep the value of Luang Prabang as a World
Heritage site.

1.3. Research objectives
Four research objectives were formulated: 1) to
identify the appropriate tool for visualization and toanalyze its use; 2) to collect riverbank landscape visual
data; 3) to develop an evaluation tool to analyze
riverbank landscape; and 4) to evaluate and analyze thecurrent landscape in Mekong riverbank.

2. Literature Review

2.1. Importance of riverbank management
There are two importance points in riverbank
management in Luang Prabang. First, riverbank
landscape is recognized as a part of outstanding values
by UNESCO. Heritage Preservation and Development
Master Plan (PSMV) established in 1999 specifies theimportance of riverbank landscape, and thus, it is a
responsibility of DPL to preserve. Second, green
environment on riverbank has the important function forpreventing landslides by strengthening the root
networks of trees and plants. Therefore, the protectionof greenery is the key in maintaining the safe
environment along the riverbank. This research collects
detailed information about riverbank landscape andanalyzes the current landscape with DPL.

1.2. Problem statement
The Heritage Master Plan of Luang Prabangstates that Mekong riverbank and Namkhan riverbank
were originally covered by greenery such as vegetablegardens. However, after its inscription, decreasing
tendency of greenery was found in both riverbanks [3].
These potentially contribute to the degradation of
Luang Prabang as a World Heritage site. The
Riverbank Management Plan was developed in 2015
to preserve its landscape as riverbank landscape is oneof important heritage in Luang Prabang. However,
little information on current condition of riverbank
landscape is available, and thus, an urgent need was

2.2. Riverbank Management Plan
Riverbank Management Plan covers the protection
area of 4.5 km of both Mekong riverbank and Namkhan
riverbank. The plan shows the comparison between thecurrent landscape and the ideal landscape synthesized fromthe photos of current landscape on 11 important77

preservation points. This management plan contains
characteristics of the protected zone together withprotection criteria. The management plan only restricts
the landscape from the street side. However, landscapeconditions in the riverside are also important for future
management, and therefore this study focuses on thevisualization of the riverbank.

Second, landscape analysis was carried out on green
environment and built architectures in the pilot site. Third,
video shooting instead of photo taking was conducted torecord the continuous views of Mekong riverbank
landscape using Parrot Bebop drone with recording
documentation written in English. Forth, the video data
were improved by editing, stitching and tagging with a map
using GPS. These processes produced a video of green
environment (9.15 minutes) and a video of built
architectures (11.15 minutes) (Fig. 2).

2.3. Landscape monitoring tools
Five potential monitoring tools were identified to
examine appropriate tools in the field from existing or
ongoing projects around the world: 1) drone; 2) balloon;3) kite 4) crane; 5) manned helicopter [4][5][6][7]. These
tools are utilized to monitor coastal situation to prevent
natural disaster, to analyze damages caused by earthquake,as well as to monitor biodiversity of the world heritage site.
Each tool contains both advantages and shortcomings.
This study analyzes the use of these tools reflecting local
conditions of Luang Prabang.

3. Methodology
Fig. 2 A sample green environment video

Four major steps were involved prior to thelandscape analysis: 1) identification of monitoring
tools; 2) visualization of riverbank landscape; 3)
development of a landscape analysis method; and 4)
selection of landscape evaluators. As a first step, thecomparative analysis of the five possible monitoring
tools was conducted through field survey.
Considering seven selection criteria, i.e. recordingfrom riverside, recording from high view, distance tothe objects, cost, training needs for operation,difficulty in maintenance, and possible recordingtime, drone was selected as an appropriatemonitoring tool. Four aspects were found important:
1) capabilities of recording from riverside; 2)capabilities of recording from high view; 3)capabilities of close monitoring to the objects; and 4)
relatively low cost.
For a second step, riverbank landscapevisualization took place using the drone in 2016. This
step includes 1) selection of pilot site, 2) identificationof landscape monitoring indicators, 3) collection oflandscape visual data focusing on the indicators, and 4)
improvement of landscape data. First, 1.5 km ofprotected zone along Mekong riverbank was selected(Fig. 1).

Source: Created by the author, 2016

As for the third step, an evaluation tool wasdeveloped. In this process, important landscapepreservation criteria for greenery and built architectures
were clarified reflecting the Riverbank Management Plan
and discussion with DPL staff members. Four specificcriteria were identified: 1) no temporary tents; 2) no
temporary storages; 3) no other temporary objects; and 4)
no piling of fire woods, sands and gravels. These criteria
are important because such temporary objects on theriverbank degrade the landscape of Luang Prabang. On theother hand, the criteria of built architectures focus on thelocation and interior of the architectures. Three specificcriteria were: 1) (restaurants) not located in front ofintersections; 2) no roof (white umbrella can be used); and3) no wall. Riverbank landscape is considered importantfrom both riverside and street side, and thus, the view of thebuilt architectures needs to be evaluated accordingly.
Finally, these criteria are reflected to produce a set of
evaluation sheets for landscape analysis.
As a fourth step, landscape evaluators were selected
within DPL. Specifically, ten and nine evaluators wereselected for analysis of green environment and built
architectures respectively from different units in DPL.
Landscape analysis was performed using the landscapevideo data and evaluation sheets in October and November,
2016.

4. Results / Analysis

Fig. 1 Important intersections

4.1. Analysis results of green environment

Source: Riverbank Management Plan, 2015, p. 978

The greenery landscape analysis found the gaps
between current landscape and the preservation criteria.
Specifically, 14 areas were identified as not meeting thecriteria by evaluators. Among them, five areas wereidentified by all ten evaluators (Fig. 3).

criteria were situated in the areas between V4 and V5 andbetween V6 and V7. These areas have multiple tourist
attractions such as Luang Prabang Main Street, Mt. Phousi
and the National Museum. This result shows that
restaurant owners prioritize the tourist needs rather thanthe preservation criteria. At the local workshop in DPL,
difficulties of satisfying both tourist needs andpreservation criteria were discussed. Specific measuresto raise awareness of the residents to understand theimportance of preservation criteria are needed for the
future activities. Furthermore, 11 restaurants out of 22
were using white tents. White tents are not permitted forthe restaurants although white umbrella are allowed. It
was interpreted in the local workshop that such
regulation may not be understood precisely by therestaurant owners. Therefore, the importance ofeffective dissemination of regulation was recognized.

Fig. 3 Analysis results for greenery
Source: Created by the author utilizing Riverbank Management
Plan (2015, p.9), 2016

In these five areas, large temporary tents and storages
were found against the preservation criteria. This result
suggests that these five areas are subject to carefulmonitoring for landscape preservation. In the analysis,
seven areas out of 14 were situated in riverbank accessible
from the intersections. Interviews with local residents
clarified that tents or storages found in nine areas were
owned and utilized by the boat owners. Their preservation
awareness is vital for the sustainable riverbank
management.

4.3. Advantages
methodoflandscapeanalysis

The landscape analysis method makes the longterm monitoring of Mekong riverbank landscape. There
are two reasons. First, all evaluators from different unitsof DPL could identify areas and architectures notmeeting the preservation criteria. It suggests that thisanalysis method can be utilized by any staff members
regardless of their technical background. Second, it
enables to analyze the current landscape in detailthrough the comparison between landscape video dataand preservation criteria. This method can be applied tounderstand the changing situations of the landscapecontributing to the chronological landscape comparison
over time.

4.2. Analysis results of built architectures
The landscape analysis for built architectures also
found the gaps between current condition and the
management plan. Twenty-two restaurants out of 32 in
the pilot site were not meeting the preservation criteria.
For example, 20 restaurants had roofs that are notpermitted.

4.4. Issues of landscape analysis method to be
discussed
Three issues were identified to be considered for the
improvement. Two technical issues specific to the drone
used for video shooting were identified. First, thecurrent digital zoom function of the video processing
software of Parrot Bebop drone cannot capture detailed
information on the landscape. Therefore, it is suggested
to use the drone with optical zoom which can which canshoot specific objects with magnified images. Second,the video data taken by drone contained some blurred
images. Evaluators experienced difficulties to evaluate
the landscape due to the low resolution. To tackle thisproblem, it is necessary to develop recording manual
including important check points for clear images.
Third, the study found that some of the preservation
criteria of Riverbank Management Plan are not clear

Fig. 4 Analysis results for architectures
Source: Created by author utilizing Riverbank Management Plan
(2015, p.9), 2016

Further, figure 4 illustrates 14 restaurants against the79

6. Conclusion

enough for the evaluators to make decision in theanalysis. For the effective landscape analysis, it isnecessary to carefully define the preservation criteria.

Riverbank landscape management is an importantpart of heritage preservation in the world heritage townof Luang Prabang. The landscape analysis identified thegaps between current conditions and the Riverbank
Management Plan. To deal with the gaps, it is necessary
to raise preservation awareness understanding thecriteria among local residents. Although this study
covered 1.5 km of protected zone along Mekong
riverbank as a pilot site, it is suggested that future study
applies the monitoring method to the extended 3 km
along Namkhan riverbank to achieve complete datacollection.

5. Discussion
Through the landscape analysis using drone, twofindings were identified. First, the training of drone
operation on the site is vital for its effective use.
Because the operators should have the ability not only
to operate it smoothly but also to respond unexpected
incidents when capturing the images. This is consistent
with the previous study that the specific training on thesite can contribute to efficient workload and wide
range of applications [8]. Second, countermeasures forrisk of crash were identified. They are: 1) to keep ashort distance between operators and drone forprevention of unstable Wi-Fi connection during theflight; and 2) to refrain from operating drone near thetrees and other objects, 3) to avoid rainy and windy
weather conditions. These countermeasures werepartly confirmed by a previous study [4].
Further, based on the analysis results of both green
environment and built architectures of the pilot site of
Mekong riverbank area, three effective measures wereclarified for future landscape preservation. As forgreen environment, there are two specific activities tobe promoted from the landscape analysis. One is topromote boat owners’ preservation awareness. The
other is to cooperate with local residents in order tomonitor the riverbank areas accessible from theintersections more frequently. Large scale tents andstorages are owned by the boat owners, and awarenesscampaign targeting them would be a vital strategy for
DPL. For another measure, it is important to plan andimplement activities to motivate residents toparticipate in preservation of their village. One
suggestion is to introduce mobile application
developed by Tokyo Tech that include quizzes andimportant information on riverbank management [9].
Cooperative monitoring with local residents cancontribute to the preservation in these areas.
Regarding built architectures, discussion sessions
between local residents and DPL are highly
recommended. The landscape analysis clarified: 1)
difficulties to keep a balance between satisfying tourist
needs and following preservation criteria, and 2)
possibilities of misunderstanding about the criteria
among local residents. To cope with these issues,
opinion from the restaurant owners is considered vitalfor an effective preservation.

Reference[1] UNESCO. (1995): Town of Luang Prabang, UNESCO World
Heritage Center, [Online], Available:
http://whc.unesco.org/en/list/479/, (Accessed on January 9, 2017)[2] Leong, C, Yamaguchi, S, Takada, J, Sitthirath, R, Vannathy, P,
Phonesawat, X, Sengraly, P. (2010): Introducing GIS(Geographical Information System) to monitor development inworld heritage site of developing country: Case of Luang
Prabang, Lao P.D.R, 3rd joint international conference on
information & communication technology electronic engineering(JICTEE), pp. 503-507, Luang Prabang Lao P.D.R.
[3] ICOMOS. (2008): Reactive monitoring mission to the town ofLuang Prabang world heritage property Lao People’s Democratic
Republic, WHC-07/31.COM/7B , pp. 22-28 November 2007,[Online], Available: http://whc.unesco.org/document/10057(Accessed on January 12, 2017)[4] Mariella, D., Giuseppe, D, C., Massimo, R., & Antonio S.
(2012). Use of unmanned aerial vehicles (UAVs) for
photogrammetric surveys in rockfall instability studies, Società
Geologica Italiana, Roma 2012.
[5] Boike, J & Yoshikawa, K. (2003). Mapping of Periglacial
Geomorphology using Kite/Balloon Aerial Photography.
Permafrost Periglac. Process. 14: 81-85[6] Terunuma, H., Yokouchi, N., & Okada, T, (2007): 日本映画にみる海の風景の意味と演出方法に関する研究. 景観・デザ
イン 研究講演集. No.3
[7] Uchiyama, S. (2015). 有人ヘリコプターによる空撮画像を用いた平成 27 年 9 月豪雨における災害対応地図の作成.
Research Abstracts on Spatial Information Science: CSISDAYS 2015.
[8] Hing, J, T, Oh, P, Y. (2008): Development of an unmanned
aerial vehicle piloting system with integrated motion cueing fortraining and pilot evaluation, J Intell Robot Syst, 54:3–19.
[9] Poong, Y, S, Yamaguchi, S, Takada, J. (2016): Study ofperceptions on behavioral expectation to preserve world heritagesite through mobile learning application in Luang Prabang, Lao
P.D.R. doctoral thesis, International Development Engineering,Tokyo Institute of Technology, Japan80

Estimating Near-surface Wind Field by Aerial Thermal Image Velocimetry
Student Number: 15M18138

Name: Motoyuki HIJIKATASupervisor: Manabu KANDA

熱画像地表面風速場推定法の航空機観測への応用
土方 基由
本論文では，熱画像地表面風速場推定法（TIV）をヘリコプターからの観測に適用し，数百メートルス
ケールにおける移流の可視化及びベクトルの推定を行った．Scale Invariant Feature Transform(SIFT)を用いた座標系統一補正と時系列及び空間フィルタリングを施すことにより，本観測に対してTIV を適用することができ，その結果時間及び空間的に局所性を有する風速場の測定に成功した．さらに，追跡している乱流構造のスケールや地表面パラメータの空間分布の推定手法の提案，TIV 速度と地
表観測値とのバリデーションを行った．

1

100Hz were captured using an FLIR SC5000 Thermal
IR camera, which was mounted on the helicopter flying
900 m above ground level. The spatial field of view andgrid size are 624 m×764 m and 256×320 respectively.
The left side of figure. 1 shows the observation site.

Introduction

Turbulent structure inside an atmospheric boundary
layer is dependent on atmospheric conditions and thegeometric nature of the surface. However,measurement method for horizontal wind velocity
distribution is limited. Recently, a method to estimate
two-dimensional wind velocity called thermal image
velocimetry (TIV) was established. The concept behind
it lies in the relationship between the changes in surface
temperature measured by a thermographic camera andthe near-surface wind speeds. TIV has successfully
detected wind fields near building walls and vortices
similar to dust devils, which are tens of meters in scale[1]. To date, the application to larger spatial scales isstill limited [2]. In this study, I applied TIV to a larger
scale by conducting an aerial observation on-board ahelicopter. The outcome of this study suggests thepossibility of applying TIV to wider scales, hundredsof meters despite the thermographic camera being notstationary.

2

3.2. Tamagawa Riverbed
Another observation above the Tamagawa riverbed
was conducted from 14:35 to 14:50 at December 21,
2016 on a sunny condition. The installed camera andthe helicopter altitude were same with that of the Meiji
Shrine observation. However, frequency of filming was
set to 50Hz. The spatial field of view and grid size are
666 m×800 m and 256×308, respectively, as shownin the right side of figure. 1. In addition, fieldobservation of wind velocity using a supersonicanemometer (Model 81000 Ultrasonic Anemometer)
set-up on the ground was simultaneously conducted.
The location is indicated in figure. 1.

4

Principle of TIV

Consider a case where the surface is heated and thetemperature difference between air and surface
becomes extreme. Active turbulence will develop near
the surface. As air circulates horizontally aloft, heattransfer between surface and air occurs, resulting to a
non-uniform surface temperature distribution. Thisphenomenon is captured using a thermographic camera
and afterwards brightness will be extracted. Applying
an image correlation analysis to retrieve brightness
temporal variation similar to the particle image
velocimetry, horizontal wind distribution is derived.

3

Analysis Procedure

Figure. 2 shows the analysis procedure of aerial TIV(henceforth called ATIV). There are three steps in theprocess, namely: coordinate correction, filtering
analysis, and image correlation analysis.
In the first step, coordinate correction was conductedto adjust the images to have a common perspective, anecessary requirement for TIV. Differences in image
coverage and perspective was due to the helicopter’sObservation Location and Set-up

3.1. Meiji Shrine
The observation above the Meiji Shrine wasconducted from 11:45 to 11:50 at March 21, 2013 on aclear sunny condition. Surface temperature data at

Fig. 1: observation site and scale of Meiji Shrine (left),and Tamagawa riverbed (right)
81

Fig. 2: analysis procedure of ATIV

movement. In this study, SIFT (Scale-Invariant Feature
Transform), which can detect a feature point and matchthem between images was conducted [3]. Subsequently,
the Homography matrix based on the detected featurepoints were multiplied to original images, and thencorrected coordinates of each images were acquired.
This study achieved the first-ever application of SIFT
to TIV.
In the second step, filtering analysis, which iscomposed of the temporal and spatial direction, wasconducted. In the temporal direction, firstly, the pixel
value was normalized by the standard deviation valueof that pixel. Thereafter, low-pass and high-passfiltering were performed. This process can remove highfrequency and low frequency fluctuation, respectively.
This enables the detection of turbulence structure atcertain desired scaling for each observations. In thisstudy, low-pass×high-pass filter was set to 80(s)×40(s) for Meiji Shrine, and 30(s)×15(s) for Tamagawa
riverbed. In the spatial direction, spatial smoothing
using surrounding pixels was performed. Generally,
pixels that correspond to top of trees have larger
fluctuation of temperature than internal gaps of trees of
low turbulence intensities. In this study, the area
considered was the top of trees. In the smoothing
process, averaging was conducted in which each pixels
were weighted by the standard deviation. Images
obtained through this process are called brightness
temperature deviation.
In the final step, image correlation analysis, which isthe same methodology with PIV was applied. In TIV,
the pattern of brightness temperature deviation is used,while particles in images is used in PIV. In thisprocedure, two images of brightness temperaturedeviation that have a specified time difference were
initially acquired. Next, the inspection domain, which
is a square surrounding the focused pixels were set.
Thereafter, the area in the second image having themost similar pattern to the first image’s focus pixel was
detected. This was evaluated by correlation function.
Divided by the time difference, TIV vectors between
those time differences were estimated. Selected
inspection domain and time differencing were 31 grid(75m) and 1(s) for Meiji Shrine, and 31 grid (81m) and3(s) for Tamagawa riverbed respectively.
In this study, coordinate correction part andnormalization by standard deviation in the filtering
analysis for temporal direction were added to thestandard TIV methodology to make it applicable toaerial-acquired thermal images. The former effectively
considers the helicopter’s movement, and the latter isfor the spatial difference of heat properties in a widearea, of few hundreds meters in scale.

5

Result and Discussion

5.1. Meiji Shrine
After the filtering analysis, the movement of
turbulence fluctuation was visualized using the

Fig. 3: brightness temperature deviation at a partof Meiji Shrine observation
82

Fig. 4: spatial distribution of TIV vectors in Meiji Shrinebrightness temperature deviation (for example, see
Figure. 3). From 0(s) to 15(s), advection from north isvisible. In summary, apparent motion-like behavior inbrightness temperature deviation from north to south in
Meiji Shrine and from southeast to northwest in
Tamagawa riverbed were detected.
Figure. 4 shows TIV vectors in Meiji Shrine resulting
from image correlation analysis. Initially, the vectors
are from northwest direction in almost uniformly,
however after 42(s), divergence occurred at around(200, 200) and (400, 500) resulting in the upset of
uniformity. After 109(s), another divergence at (300,500) occurred and flow seems to be going around thebuilding. In figure. 4, spatial and temporal change ofTIV vectors were confirmed in Meiji Shrine andTamagawa riverbed throughout the whole area.
Figure. 5 shows the temporal averaging of spatial
distribution of TIV vectors in Meiji Shrine observation(left) and Tamagawa riverbed observation (right). In
Meiji Shrine, 83.2% was north-northwesterly wind,while in Tamagawa riverbed, 66.5% was south
southeasterly. At the same time, the observed winddirection was north-northwest and south-southeast
respectively at Otemachi weather station (AMeDAS),
which is 5 km east-northeast from Meiji Shrine and 14
km northeast from Tamagawa riverbed observation site.
However, there are slight differences between the TIV
result and AMeDAS data in both wind condition, good
accuracy between them were confirmed. The averagewind velocity was 2.44ms-1 and 1.42ms-1 in Meiji
Shrine and Tamagawa riverbed respectively.

5.2. Estimation of the height of turbulent
structure
As shown in Fig. 3, the brightness temperaturedeviation means a visualization of the footprint of
turbulent structure, and TIV velocity corresponds to thevelocity of the center of that structure. From thisassumption, the height of turbulent structure that TIV
has been focusing on can be estimated using the MoninObukhov similarity in unstable condition as shown inequation 1. From the result of Meiji Shrine observation,that height of turbulent structure was calculated.








 x 2  1 x0  12 
uadv 1 1 z coh   z  2 tan 1 x  tan 1 x0 dz
ln    ln  022 0u * k zcoh
 x  1 x  1   z0 

1/ 4





1/ 4

z 
z


x  1  16  , x0  1  16 0 
L
L



(1)

zcoh corresponds to the height of turbulent structure. L:
Obukhov length and z/L: atmospheric stability werereferenced to the observed data at the top of M05
building of Tokyo Tech. z0 was 1.09 derived by
z0/h=0.07 [4], and u*: friction velocity was assumed by
Rossby number similarity using MSM data provided by
JMA [5]. As a result, the height of turbulent structure
was evaluated as 122m from top of the forests. Fromthe low-pass for 40(s) and high-pass for 80(s)multiplied by 2.44 ms-1, horizontal scale of turbulent
structure could be assumed as 97.6–195.2m. We canconfirm that the estimated height of turbulence
structure is consistent with this result.

5.3. Estimation of spatial distribution offriction velocity
Friction velocity is the vital velocity scale to describe
turbulence and is used in many similarity theories inmeteorology field. Therefore, the attempt to obtain thespatial distribution of friction velocity was conductedusing TIV velocity and equation 1 in neutral condition
in Meiji Shrine. zcoh was assumed equal to the productof 2.44 ms-1 and 60(s), the average of low-pass andhigh-pass filtering. The result is shown in Figure. 6. In
accordance with the TIV velocity distribution, thefriction velocity has spatial locality too. Especially,

Fig. 5: temporal averaging of spatial distribution of TIV
vectors in Meiji Shrine observation (left) and Tamagawa
riverbed observation (right)
83

Fig. 7: validation of TIV vector direction and
Fig. 6: the spatial distribution of friction velocity

ground observation

windward side of the building and the area where wecan confirm strong divergence in the 42(s) of Figure. 4
have higher value than in other areas.

Proper filtering analysis and coordinate correction
using SIFT algorithm made it possible to realize ATIV
and rough agreement with AMeDAS data wasconfirmed.
For Meiji Shrine observation, using obtained TIV
velocity and Monin-Obukhov similarity, the height of
turbulent structure that TIV is focusing on was
estimated. Compared with the low-pass and high-passfiltering scale, the result was appropriate. In addition, amethodology to estimate the spatial distribution offriction velocity was also suggested.
For Tamagawa riverbed observation, validation ofTIV vector direction using ground observation was
conducted. Some errors in TIV vectors were found dueto the stratus clouds that might have passed below thehelicopter or enhanced noise due to the sudden
movement of the helicopter. Despite this, moderateagreement was confirmed (r = 0.44).

5.4. Validation with the ground observation
In Tamagawa riverbed observation, the validation ofTIV velocity with the ground observation using
supersonic anemometer was conducted. TIV velocity
has proportional relationship between surface windvelocity; however, the ratio between them isunpredictable [6]. Therefore, for validation, theobserved wind direction and TIV vector direction at theobservation point were compared. The result is shownin Figure. 7. The blue line shows the groundobservation and red line shows the TIV vectors. In thisfigure, 0°shows the southerly wind, and positive iswesterly, while negative is easterly. From thecomparison, ground data was about south-southeast
wind, which matches with the AMeDAS data shown in
Sect. 5.1. Changing the value of parameters: inspection
domain (dom) =10, 20, 30, 40 grid, time step in
calculating TIV vectors (step) =1, 3, 5 (s), the highest
correlation r = 0.44 were obtained in the case (dom) =30 grid, (step) = 3(s). However, there are some large
fluctuation in TIV vectors that were not observed by theground measurements at 14:43:20, 14:45:20 and
14:48:00. From the original thermal image, it was
found that during these times radial noise were foundthat led to significant errors arising from the sudden
movement of the helicopter. The noise is expected to be
removed by calibrating the thermal camera using a
black body furnace before observation. Anotherpossibility for the error is the presence of stratus clouds,
that might have passed below the helicopter slightly
affecting the measured ground temperature.

Reference[1] Inagaki,

[2]

[3]

[4]

[5]

6

Conclusion[6]

The visualization of turbulent structure and TIV
vectors in a few hundreds of meters in scale wereattempted by applying TIV to aerial observation.

84

Atsushi, et al.: ‘Thermal image
velocimetry.’ Boundary-layer meteorology 149.1,
1-18, 2013
Inagaki A.: ‘熱画像風速測定法(TIV)による風の空間分布計測’，水文・水資源学会誌，Vol.29，
No.3，pp.186-195，2016
David G Lowe.: ‘Object recognition from local
scale-invariant features’, International Conference
on Computer Vision, Corfu, Greece, pp.11501157, 1999.
Javis P G., James G B., Landsperg J J.:‘Coniferous forest in Vegetation and atmosphere’,Vol.2, Monteith J L. Ed, Academic, London,
pp171-240, 1976
Kondo J.: ‘水環境の気象学’，朝倉書店，2014
年第 16 刷Iwatsuka E.: ‘サーモカメラで観測した屋外地
表面温度の移流速度と近傍風速の関係’，土木学会論文集 B1（水工学），Vol.71，No.4，I_427I_432，2015

Projection of High Tide Inundation under Rapid Land Subsidence and Sea Level Rise
―Effectiveness and Limitation of Coastal Dykes in Jakarta
Student Number: 15M18144

Name: Daisuke Fujii

Supervisor: Hiroshi TAKAGI

ジャカルタ沿岸域における地盤沈下と海面上昇に伴う将来浸水域予測 ―海岸堤防の効果と限界
藤井 大祐

Abstract
ジャカルタは近年の急激な人口増加・経済発展に伴い，飲料用水・産業用水として地下水を大量に揚水しており，その結果著しい地盤沈下が起きている。低平な沿岸域ではすでに頻繁に洪水被害が発生しているが，最も沈下の激しい Pluit 地区で行った現地調査では海岸
堤防は高潮を防ぐには不十分な高さであり，貧弱な構造のため決壊の可能性があることがわかった。本研究では，地盤沈下と海面上昇を予測した複数のシナリオを作成し，2050 年までの浸水シミュレーションを行うことで海岸堤防整備の効果と限界を検証した。沈下が止まらない状況で異常潮位が発生すると，越流により 2050 年の浸水範囲は 40 km2 に広がりジャカルタ全域の問題に発展する。陸地が海面より低いため自然に水が引くことはなく，浸水は 2 週間程度続く可能性がある。堤防は洪水抑止の対策となり得るが，地盤沈下が止まらない場合その効果は一時的であり，逆に引き潮時に堰として働き，浸水を長期化させる要因となることが示された。

extreme high tides occur. It is especially considered thatan amplification of tide in November 2007 flooding is
caused by these abnormal high tides.

1 Introduction
Jakarta, the capital of Indonesia, is one of the largest
coastal megacities with a recent high population increaseand economic growth. A resident population exceeding
9.6 million in 2010 is estimated to reach 30 million by
2030. The population growth rate reached 1.39%/year
over the period of 2000-2010. Although Jakarta has also
been experiencing rapid economic growth over the last
decades, Jakarta has resorted to extract groundwater fordomestic and industrial use because of insufficient watersupply system. This led to drastic extraction ofgroundwater and consequently to sinking groundwaterlevel and severe damage to buildings and infrastructuresdue to land subsidence. Land subsidence rate in Jakarta
is the highest among Asian megacities at over 21.5
cm/year (Fig.1.1). This trend is showed notably incoastal areas. Rapid land subsidence causes
approximately half of land areas in Jakarta to be lower
than sea surface. This problem makes it difficult to drainwater to the sea and frequently causes an extended flood
due to high tide.

Fig. 1.2 Flood due to high tide in Pluit district, 2007

Jakarta’s government is planning to construct greaterdykes with a distance of 34 km along the coastline of
Jakarta, the outer sea wall over 50 km and 17 of theartificial islands. The total investment costs are between$8.6 and $20.1 billion (present value). However, thefeasibility of the project is not ascertained yet because ofmany uncertainties such as financial issue andenvironmental impact.
It is important to understand effectiveness and limitationof coastal dykes against future flood events under rapid
land subsidence in Jakarta in order to consider mitigation
strategies for those critical issues such as sea-level rise,abnormal high tide and land subsidence. Thus, the author
applied the model, which was adjusted to the abnormal
tide that happened in 2007, to project future coastal flood
under land subsidence and sea level rise.

Fig. 1.1 Land subsidence in Asian megacities

2 Methods

The range of tide level in Jakarta Bay is as small as 1 m.
However, there is a long-period tide constituent referred
to as the “18.6-year lunar nodal cycle” aside from thegeneral diurnal or the semi-diurnal tide constituents. Thistidal oscillation can be generated by the positional
relationship between the moon’s orbit and the earth's
equatorial surface and these inclinations have a period of
18.613 years. In this term, there are regional and extreme
sea levels added to the water levels for dozens ofcentimeters to typical tide in Jakarta. Pluit district, which
is sinking at over 10 cm/year in the northwest of Jakarta,
has suffered damage due to flooding as often as when

The present study is mainly consisted of two
components: (1) understanding the situation of Jakarta’s
coast by conducting field surveys and (2) performing
numerical simulations, all aiming at discussing future
flood and the effectiveness of dykes under land
subsidence.

2.1 Field Survey
The author conducted a series of field surveys in order to
identify coastal issues and measure ground elevations
and sea levels around the district of Pluit, where the most85

severe land subsidence in Jakarta is taking place. The
Pluit district also has the reservoir and the largest pumpstation (drainage capacity of 50m3/s) to drain water fromupstream to the sea.
Actual effect of sinking was observed visually for a few
days in September 2015, February 2016, and May 2016.
The distance between sea surface and ground elevation
was measured by a measuring staff and calibrated themean sea level at 0 m. The ground elevation in thevillage behind the dyke was examined using a laser
distance meter. The ground elevation was measured at alarge number of locations along small residential paths.
The sea levels in Jakarta Bay were observed by using awater pressure gauge at the interval of 10 minutes from
September 2015 to January 2016.

were considered between 0.5 m and 5 m above mean sea
level to see how the characteristics of coastal flood could
be influenced by initial heights of the dyke.

2.2 Numerical Simulation
2.2.1 Model outline
The hydrodynamic model Delft-3D was applied to thedetailed simulation of future flood in Jakarta. Calculation
settings are listed in Table2.1. Flood simulation was
performed by using the topography and tidal condition
showed Section 2.2.3.
Moreover, hypothetical dykes with different heights wereplaced all along the coastline in order to investigate
whether coastal floods can be effectively mitigated bysuch flood protections (Fig.2.1, blue).

Fig.2.2 Simulation scenarios

2.2.3 Input Data
The tidal condition in this study was calibrated by tidedata observed during the 2007 coastal flood event, which
demonstrated an abnormal high tide component up to
20-30 cm. Topography data between 2030 and 2050 werecreated by subtracting annual land subsidence
continuously observed at 10 points in Jakarta between
1997 and 2001 from the satellite data of SRTM Digital
Elevation as of 2000.

Fig.2.1 Calculation area
Table 2.1 Setting
Calculation area
Tide condition

38.8km × 33.1 km
High tide in 2007 flood event ※2.2.3
Mean sea level at Tanjung Priok(+0.0 m)

Grid size

Number of grid: 434×290
Minimum size: 150m × 20 m (neardyke)Δt = 0.5 min
2 weeks
SRTM satellite data in 2000 ※2.2.3

Time step
Calculation time
Topography
Bathymetry
Roughness(Manning)

Fig.2.3 Tide calibration and annual land subsidence

Flood area maps were made by masking the output offlood areas from calculation result with water areas atmean sea level in 2015. Future flood areas and flood
volume were analyzed from these results andsubsequently images were created. Additionally,
simulation outputs of the change of water level behindthe dyke (Fig.2.1, water level output point) weregenerated.

Water areas: 0.02, downtown: 0.04,
mangrove: 0.3 (s/m1/3)

2.2.2 Scenarios
Simulation scenarios were assumed between 2030 and
2050 which were split every 5 years with the year 2015
used as a reference. These scenarios were further
separated into two: 1) one scenario is that land
subsidence stops by 2030 and only sea level rise
continues afterwards and 2) the other assumed that bothland subsidence and sea level rise continue by 2050.
It is assumed that coastal dykes were built along thecoastline at 2030, and a total of 6 cases of dyke height

3 Results
3.1 Field Survey
The crest width of dyke in Pluit district was measured to
be 50 cm. Because of the thinness, the dyke could be
breached in the not too distant future. The height of thedyke was 70 cm above mean sea level, which was as low
as the high tide level. These results showed that the dyke86

In the case where land subsidence would continue, a 2 mdyke will undergo an extensive overflow from 2040, andeven if the 3 m dyke were built, it will be overflowed in
2050. Projected flood areas are similar with those in thecase of no dyke.
Fig.2.6 shows the behavior of water level behind thedyke under abnormal tide in 2050.
Compared to the case of without dyke, the case of with 1
and 2 m dyke both make the velocity slower when thesea water returns to the sea, causing stagnant water
behind the dykes. In the case of 3 m dyke, the seawaterthat once overflowed the dyke is trapped by the dyke andnot returned to the sea and consequently the water level
rises up to the high sea level.

height is not sufficient enough to prevent the sea waterfrom overflowing.

Fig. 2.4 The height of coastal dykes and community behind

The elevation of the coastal community behind a thincoastal dyke is lower than 1.5 m on average and 3 m at
maximum below sea level (Fig.2.4).
Although these dykes were raised by about 1 m after the2007 abnormal tidal flood event, the present height of thedyke appears to be not enough and the concrete has been
badly corroded with many cracks.
3.2 Numerical Simulation
Fig.2.5 shows the expansion of flood areas in Jakarta,
simulated for several scenarios (no dyke, 2 m and 3 m
dyke in 2030, 2040 and 2050).
Overflow starts from the northwest coast. Flood event iscurrently happening within the coastal area, but it isanticipated to expand into the entire Jakarta area by
2050.

Fig.2.6 Water level behind the dykes

Fig.2.7 Future flood areas and volumes

Fig.2.5 Projections of future flood areas87

50 m3 in the Pluit district would not be able to drain out
this amount of inundation in a short period of time. The
residential areas and main roads in Pluit would sufferfrom both economic loss and sanitation issues.

Fig.2.7 shows the future flood areas and volumes in the
different heights of dyke. In continuing land subsidence
scenarios, 40 km2 sea water will flow at high sea level in
2050, and it will be 20 km2 in the discontinued sinking
scenarios.
If dykes were built and land subsidence was terminated,flood areas would be less than 10% of those of thecontinuing land subsidence case. Furthermore, in thecontinuing land subsidence case, the extent of flooding
will expand severely even if the initial dyke elevation is
as high as 3 m. Lastly, compared to the no dyke case, thehigher the built dyke is, the smaller the difference in the
resulting flooded areas between high and low sea level.

5 Conclusions
The dyke construction has a great reduction effectagainst abnormal tide. However, if the land subsidence is
not mitigated appropriately, the effect of this coastal
dyke would only work for short term as it would allow
the sea water to overflow in the near future and further
cause longer periods of inundation. On the other hand, if
land subsidence is terminated by 2030, flooding could be
prevented by a short dyke with 1-m or slightly higher.
Even if the government of Jakarta can complete theconstruction of the extensive coastal dyke along theshoreline by 2030 as planned, the simulation results
reveal the occurrence of both the overflow of sea waterand the collapse of dyke unless the issues of land
subsidence is completely solved. The flood projections
by 2050 show that damaged flood areas could expand
over five times as large as the present flood area.
It is expected that it would take more than 10 years or a
few decades to be able to completely terminate land
subsidence. Given the magnitude of the catastrophe as
resulted from the simulation scenarios, therefore, coastal
flood mitigation plans should not only be focused onbuilding dykes and floodgates against abnormal tide but
more importantly, eliminating the ground subsidence
issue should be more prioritized.

4 Discussion
Field survey revealed that ground elevations in thecommunity of Pluit district are 1 m or even lower thansea level, and that the sea water that once flowed overthe dyke is considered to spread to the lowest center ofthe village rapidly. Seawater cannot return to the sea only
by gravity. Therefore, mechanical treatments such as
pumps are necessary to drain seawater out to thereservoir and ultimately to the sea.
Even though the local government raised the thin dykes
by about 1 meter after the flood event, the difference
between the top of the dyke and sea level is only 70 cm
in the present condition. This indicates that ground issinking together with the dyke. If land subsidence iscontinuing at its current rate, the dyke elevation will be
the same as the sea surface within the next 10 years. The
cracks and inclination on the dyke due to increasing sea
pressure can accelerate the risk and the expanded effectcan induce collapse by itself.
The current condition on the dyke renders long periodsof flooding induced by the sea water in the adjacentcoastal communities. Subsequently, human damages
would be more tragic mainly due to the boats and trees
being drifted towards the shore.
The simulation results such as the future flood maps, thewater levels behind coastal dyke and the future flood
volumes due to high tide all illustrate the difference of
future flood damage between the continuing anddiscontinued land subsidence cases.
The present (2015) no-dyke condition induces a
maximum of 7 km2 of flood area. In the year 2030, themaximum flood area would be expanded to 20km2,
which is twice as large as the minimum flood area
(10km2). The flooded area would then be drastically
expanded to 40km2 by 2050.
This indicates that the effect of land subsidence is higher
than that of the abnormal tide events such as that in 2007
and that the flood damage in the future high tide days ismuch worse due to the influence of land subsidence.
Although the dyke construction is effective as a flood
reduction strategy under the discontinued land
subsidence case, the 3-m high dyke will still allow
overflow by 2050 if the sinking continues. As water
overflows across the dyke, the dyke acts like a wall thatprevents the floodwater to return to the sea. The
overflowed water could remain inland for a long period.
These prolonged inundations may occur regardless of theheight of the dyke. Even if a 3-m dyke was constructed
in 2030, seawater would start to overflow after 2045 andcause extensive inundation up to 59.7 million m3 by 2050.
The pump station with a maximum drainage capacity ofReferences
1) Takagi H., Esteban M., Mikami T., Fujii D.
Projection of coastal floods in 2050 Jakarta, Urban
Climate, No.17 p.135-145,2016a.
2) Takagi H., Mikami T., Fujii D., Esteban M.
Mangrove Forest against Dyke-break induced
Tsunami in Rapidly Subsiding Coasts, National
Hazards
andEarth
SystemSciences,
doi:10.5194/nhess-2016-128, 2016b.
3) Djaja, R., Rais, J., Abidin, Z.H., Wedyanto, K. Landsubsidence of Jakarta Metropolitan Area. 3rd FIG
Regional Conference, Jakarta, Indonesia, October 3–
7, 2004 (14 pp.).
4) Farr, T.G., et al. The shuttle radar topography mission.
Rev. Geophys. 45, RG 2004.
http://dx.doi.org/10.1029/2005RG000183, 2007.
5) Hasanuddin Z. Abidin, H Andresa, Rochman Djaja,
Dudy Darmawan, M. Gamal. Land subsidence
characteristics of Jakarta between 1997 and 2005, as
estimated using GPS surveys, GPS Solut, 12:23-32
doi 10.1007/s10291-007-0061-0, 2008.
6) IPCC. Working group I contribution to the IPCC
Fifth Assessment Report Climate Change 2013, 2013.
7) JICA, YACHIYO Engineering Co., LTD.
8) The Institutional Revitalization Project for FloodManagement in JABODETABEK
http://open_jicareport.jica.go.jp/pdf/11995024_02.pdf.
p.2-56-p.2-62, 2010.
9) NCICD. NCICD Implementation Model 2025, 2013.

88

Photocatalytic Degradation of Methylene Blue Using TiO2 supported on Natural Zeolite and Activated Carbon
Student number: 14_18106 Name: Takahiro Takamatsu Supervisor: Hirofumi HINODE,
担持光触媒を用いたメチレンブルーの分解
高松 貴大
染料廃水は 1 ppm 以下のわずかな濃度でさえも染料は色を放ち、環境に悪影響を与える。新規の処理方法として酸化チタンによる光触媒分解があるが、処理後の分離が困難であるという問題を抱えている。
そこで光触媒をゼオライトと活性炭の表面に担持することで触媒の粒径を大きくし、反応系からの分離をより簡単にすることを目的として研究を行なった。本研究ではゼオライトと活性炭の表面にチタニアを担持し、粒径が大きく、かつ光触媒能力の高い触媒を合成しその担持条件等を検討した。

1. Introduction

its particle size and make the separation of theDye pollutants are one of the primary sources ofphotocatalyst from the wastewater stream after theenvironmental contamination[1][2].

treatment process easier. Different amounts, (by wt.

It has beenispercent) of commercial TiO2 (DeGussa P25), were

disposed in wastewater during the synthesis andsupported onto natural zeolite and activated carbon

processing [2,3].

(AC). These materials are known as adsorbent, andEven in small amounts (< 1 ppm), dyes are clearlyusing them as support of TiO2

visible and can affect the water environment [3]. So it

beneficial.

is necessary to treat water contaminated with dyes.

supported TiO2

The usual methods that are employed to remove dyedegradation of methylene blue.

estimated that 15% of produced dyes worldwide

may also be

The photocatalytic activity of thephotocatalysts were tested forfrom wastewater streams are activated sludge method
and coagulation method. However, these methods

2. Experimental

have some concerns. Activated sludge method

2.1 Synthesis of TiO2 / zeolite (TZ) or
TiO2 / AC(TC)[8]produce excess sludge and is difficult to control[4].

2.1.1 Pretreatment of adsorbent supports (Zeolite

Coagulation method produces waste aggregates[5].

and AC)

Treatment and disposal of the excess sludge and waste
aggregates entails costs and landfill space. While in

Zeolite used in this study was natural zeolite taken

the landfill, these wastes can also leach in thefrom Sukabumi, Indonesia. Zeolite or AC powder was

groundwater[6].

placed in a beaker and then 0.1 mM HCl was added.

One of the alternative methods to remove dyes in the

The mixture was placed in a magnetic stirrer for 6h,

water is by photocatalytic degradation using TiO2.

and then rinsed with deionized water afterwards. The

Photocatalytic degradation of dyes using TiO2 have apowder was filtered and dried at 80℃.

distinctive advantage. During photocatalysis, TiO2
completely mineralizes the dye pollutant and does not

2.1.2 Supporting TiO2 onto adsorbent

produce excess sludge, so there is no need forDeionized water, the adsorbent, and commercial TiO2

secondary treatment. However, separation of TiO2

(Degussa P25) were mixed in the beaker andfrom the wastewater stream after treatment is anotherultrasonicated for 1 h. TiO2 loading rate was 25, 40,

problem, because TiO2 particle size is very small[7].

60, and 90 wt%. Afterwards, the mixture was washed

To address this setback, this study aims to support

thoroughly by deionized water and dried at 80℃. The

TiO2 photocatalyst on different materials to increasezeolite samples were named, TZ25, TZ40, TZ60,
89

TZ90 and activated carbon samples were named TC25,

2.5 Measurement of TiO2 loading amount in TZ

TC40, TC60, TC90, accordingly.

and TZa
After TZ or TZa was dissolved by hydrofluoric acid

2.2 Catalyst characterization(HF), Ti cation concentration was measured by

The synthesized catalysts were subjected to X-ray

ICP-AES and loading rate of TiO2 of TZ and TZa wasdiffraction to identify any crystal structures present incalculated.

TZ and TC samples. Their morphologies were

HF(27M, 20 ml) and TZ or TZa (50 mg) was put into

observed using SEM and their particle sizes were

tefron beaker and the mixture was stirred for 24 h. The

determined using laser diffraction particle sizing

concentration of Ti cation was adjusted by deionizedtechnique.

water. Afterwards, Ti cation concentration wasmeasured by ICP-AES and loading rate of TiO2 of TZ
and TZa was calculated.

2.3 Photocatalytic activity test
Methylene blue (MB) was used as a representative
cationic dye to test the photocatalytic activity of the

2.6 Measurement of loading rate of TiO2 in TC

synthesized catalysts. The initial concentration of MB

and TCawas adjusted from 40-80 ppm to become around 35

AC can not be dissolved by HF, so TiO2 loading rate

ppm after dark adsorption. Catalyst (0.01g) was addedof TC and TCa was calculated from weight reduction

into the beaker and MB was allowed to adsorb on therate by heating. TiO2 weight reduction rate by heating

surface of the catalyst in the dark for 30 min. Afteris negligible, thus all weight loss after heating can be

dark adsorption, UV lamp was switched on, and theattributable to the weight loss of AC, as shown in thesolution was sampled every 15 or 30 min. The MB

following formula:concentration in each sample was measured by UV-vis

Loading rate of TiO2（％） ＝ 100 – Weight reduction ofspectrophotometer.

TC（％）÷ Weight reduction of AC（％）・・・Eq (1)In this study, TC or TCa is heated at 800℃ andweight reduction was measured by TG.

2.4 Test of anchoring strength of TiO2 onto
adsorbents
To test how stable the TiO2 was supported on the

3.Results and discussion
3.1 XRD analysis

absorbents, test of anchoring strength was conducted,

The anatase, rutile, clinoptilolite-Ca and mordenite

In this test, the amount of TiO2 detached frompeaks can be observed in TZ’s XRD pattern.

adsorbent during prolonged time of stirring was

Clinoptilolite-Ca and mordenite are kinds of zeolite

measured.

structure. As TiO2 loading rate became higher, TiO2

TZ or TC samples (0.5 g) were put into beaker withpeak became higher and zeolites peak became lower.

300 ml deionized water and stirred for 1 h and settled

The anatase and rutile peaks could also be observed infor 2 h. Afterwards, The cloudy liquid part was

TC’s XRD pattern. TiO2 loading rate of TC did notremoved and the TiO2 samples were washed witheffect these peak intensity.

deionized water. This procedure was repeated twice,
and then the catalysts were dried at 80℃. The washedsamples were named TZa and TCa.

90

(b)
Fig.2．SEM image of (a) TZ25 and (b) TC25

(a)

3.3 Loading rate of TiO2
Table1. Result of loading rate of TiO2(b)
C: Clinoptilolite-Ca(KNa2Ca2(Si29Al7)O72 ・ 24H2O)
M: Mordenite ((Ca, Na2, K2)Al2Si10O24·7H2O)
Q: Quartz (SiO2)
A: Anatase (TiO2)
R: Rutile (TiO2)

Fig.1. XRD patterns of TZ(a) and TC(b) at different
TiO2 loading rate.
(a)
3.2 Morphology
The morphology of the synthesized samples wasstudied via SEM, and the SEM images of TiO2 coated zeolite and AC are shown in Fig.2. Small whiteparticles observed on surface of samples are TiO2
particles.
These SEM images show support of TiO2 on zeolite or(b)
Fig.3.Particle sizes distribution of (a) TZ and (b) TC
by laser diffraction particle size analyzer

AC was successful.

Result of loading rate of TiO2 is shown in table 1.
The result indicates that TiO2 was strongly anchoredon TZ and TC. Analyzed loading rate of TiO2 isalmost same theoretical.
3.4 Particle size distribution(a)91

Table 2 shows the mean volume diameter (MV) of TZ

loading rate of TiO2 caused decrease of active site,

and TC was about 2~15 times bigger than that of P25.

which lead to the decrease in degradation removal rate.

The particle size of TC is bigger than TZ because of

Adsorption amount of TC is higher than TZ because

the bigger particle size of AC. TC60 have the biggestof high adsorption amount of AC.

MV (39.2 μｍ). TZ90 and TC90 have high amount ofTable 3．Adsorption amount and degradation removal
rate

small particle (< 1.0 μｍ). This is attributable to thepresence of small TiO2 particles. From this result, it
can be concluded that some of TiO2 was notsupported on the adsorbent.
Table 2. Mean Volume Diameter（MV）

4. Conclusion
Support of TiO2 was successfully support a zeolite and

3.5 Degradation removal rate

AC. TC25 had the highest degradation removal rate of
78.7 %. TC25 have higher degradation removal rateand MV (78.7 %, 25.0 μm) than P25. Excess loading
rate of TiO2 resulted in the decrease of adsorption
amount and caused the presence of TiO2 particles thatcan not be supported on the adsorbents. Too little
loading rate of TiO2 caused decrease of active site,which lead to the decrease in degradation removal
(a)

rate.
Test of anchor strength showed TiO2 was strongly
anchored on TZ and TC.
Particle sizes of TZ and TC are bigger than that of
P25.
Reference[1] K. Nakata, A. Fujishima, TiO2 photocatalysis: Design andapplications, J.
Photochem. Photobiol. C Photochem. Rev. 13 (2012) 169–189.
[2] A. Fujishima, T.N. Rao, D.A. Tryk, Titanium dioxide
photocatalysis, J.
Photochem. Photobiol. C Photochem. Rev. 1 (2000) 1–21.
[3] A. Fujishima, X. Zhang, D. Tryk, TiO2 photocatalysis and related
surfacephenomena, Surf. Sci. Rep. 63 (2008) 515–582.
[4] 羽賀清典、農業技術研究機構畜産草地研究所
http://lin.alic.go.jp/alic/month/dome/2003/oct/CHOUSA-2.HTM[5] 前馬恵美子、富山大学、中和凝集沈殿法とは？
http://www3.u-toyama.ac.jp/environ/shige/lecture/2004/b04_02_2.htm[6] 三菱総合研究所、平成 22 年度地球温暖化問題等対策調査事業
http://www.meti.go.jp/meti_lib/report/2011fy/E001477.pdf[7] R.L Pozzo et al,Catalysis Today 39 (1997) ,219-231[8] 水中に分散した TiO2 光触媒による水浄化 – Panasonichttps://www.panasonic.com/jp/corporate/technology-design/ptj/pdf/.../p0105.pdf(b)Fig.4. Degradation removal rate of MB by (a) TZ
and (b) TC
Fig.4 shows the removal rates of TZ and TC.
Degradation removal rate were compared withadsorption amount in table 3.
TC25 had the highest degradation removal rate of
78.7 %. TC25 have higher degradation removal rateand MV (78.7 %, 25.0 μm, respectively) than P25(73.3 %, 2.5 μm, respectively). Excess loading rate ofTiO2 caused decrease of adsorption amount. Too little

92

Estimation of CO2 Emission from Chinese Manufacturing
Industry using Hybrid Method
Student Number: 15M18061

Name: GUAN Jianxin

Supervisor: Shinya HANAOKA

ハイブリッド手法を用いた中国製造業 CO2 排出量の推計
関建新

本研究ではプロセス分析と産業連関分析を組み合わせたハイブリッド手法を用いて中国の乗用車一台当たりの素材，生産，輸送，使用段階の CO2 排出量を推計する．また，シナリオ分析のため産業構造の変化，技術進歩，工場移転及び需要の予測も行う．推定結果から，使用段階の排出量が最も多く，今まで算定されていない素材段階の排出量も無視できないほど多いことが分かった．シナリオ分析により，技術進歩が排出量削減に最も効果的である一方，需要増加による全体の排出量は現状の 2.6 倍となることを示した．
studies propose a hybrid method in other country, but not
appropriate to Chinese automobile industry.
Because of the high demand of Chinese automobile market,
CO2 emission of automobile industry is a big problem as well.
And it can be estimated increase rapidly in the future. Therefore,a high accuracy estimation method to quantify CO2 emission ofChinese automobile industry is necessary.
However, the existing method to estimate automobile
industry`s CO2 emission in China, process based analysis and IO table analysis, have their own limitation, it is difficult toestimate CO2 emission accurately.
The objective of this study is to estimate CO2 emissions by ahybrid methodology of I-O table analysis and process based
analysis of automobile industry in China. Furthermore, becauseof the structure of Chinese industry changes, technology
improvement, and factory shift in the future, this study makeseveral scenarios to quantify the effect of these changes, and toclarify which is the policy contribute to CO2 emission reductionin the future.

1. Introduction
The IPCC concluded that the rapid rise of GHG emissions
concentration is the result of human activities in its 5th
assessment report (AR5)[1]. Carbon dioxide (CO2) accounts for
65% of GHG emissions in 2014, CO2 emission reduction is avery important issue all over the world.
China started the reform and opening-up policy since 1978. In
these 30 years, Chinese government changed its planned
economy to market economy, which resulted in rapid increase of
economy. On the other hand, with the economy increasing, CO2
emissions of China increased as well. On the basis of Emissions
Database for Global Atmospheric (EDGAR) [2], Chinese CO2
emissions surpassed U.S., became the world`s largest CO2
emission country in 2005. Moreover, in 2013 the CO2 emissionof China is 10.64 Gt-CO2, which accounted for 29% of global
emission. One of the reason is that depending on fossil energy.
Amongst CO2 emission in China, manufacturing industry is thebiggest emission sector, the direct emission of the manufacturingsector is 6.6 Gt-CO2. Manufacturing of automobile and othertransportation equipment is the 6th among contained emission of
manufacturing sector[3]. Moreover, manufacturing is the sector
which is most sensitive to economy increasing in China. On theother hand, not only automobile manufacturing industry has avery high CO2 emission, the sector of transportation emitted
0.95Gton CO2 as well, which is 5th of all sectors. Therefore,
increasing CO2 emissions, especially of automobile industry is aserious problem in China.
Looking at automobile industry in China, total sales of vehicle
already became world largest in 2009, and exceed 25 million in
2015. However, car ownership per 1000 people, is only 71.42 in
China, only a half of world average (140), and much less thandeveloped countries (727). Because of increase of economy,
population and purchasing power, it can be assumed that demandof automobile in China will rapidly increase in the future.
In U.S.-China Joint Announcement on Climate Change inNovember 2014, the Chinese government announced that China
would strive to bring its spiraling CO2 emissions to a peak by2030. In the 21st annual Conference of Parties (COP21) [4] of UN
Framework on Climate Change (UNFCCC) in Paris, Chinese
government submitted the Intended Nationally Determined
Contributions (INDCs): to lower carbon dioxide emissions perunit of GDP by 60% to 65% from the 2005 level by 2030.
Considering the automobile ownership in the future, the sectorof transportation and automobile equipment`s emission will
increase as well, which will become a big influence factor of
INDC in COP21.
CO2 emission of automobile method still remains startup step.
There are only some studies using process based analysis or I-O
table analysis. However, estimate CO2 emission only by one ofthem is difficult to get a high accuracy result. There are some

2. Methodology
This study using life cycle viewpoint, estimate one
automobile`s CO2 emission from material phase, production
phase, transportation phase and using phase (Table 1). And
estimate whole automobile industry`s emission by using sales
data of different body type and car level.
Table 1 Emission phase of car life cycle
Phase
Emission sector
Material Phase
Manufacturing
Production
Parts Production
Manufacturing
Phase
Assembly
Manufacturing
Transportation Parts Transportation Transportation
Phase
Assembled car
Transportation
Transportation
Using Phase
Transportation

2.1 Data
To estimate CO2 emission by a hybrid method, the data ofprocess based analysis and I-O table analysis is necessary as
follows,
I-O table: the I-O table used in this study is from national
bureau of statistics of the People`s Republic of China. The yearof 2002, 122 sectors, 2007, 135 sectors and 2012, 139 sectors is
be used.
China Energy Statistical Yearbook: to calculate basic unit of
direct and indirect emission, the energy using of every sector is
obtained. In this study, 2008 China Energy Statistical Yearbook
is used.

93

Price of material: there is no official statistic data of price ofmaterial in China, therefore this study collect material`s pricefrom the biggest E-commercial website, Alibaba
Data about parts production and assembly: because these are no
this kind of database in China. The energy using of production
and assembly, type and weight of parts are obtained from aJapanese database, JLCA database.
Data about transportation: this study uses number and locationof supplier and dealer to estimate transportation phase`s CO2
emission.
Using data: this study use`s Shanghai Volkswagen`s car Lavida
as a case study to calculate CO2 emission of using phase. Henceusing phase data obtain from Shanghai Volkswagen`s official
website.

ER 

Assembled car transportation from port of assembly factory toport of dealer can use Eq. (6)EM 

From Eq. (1) to Eq. (7) CO2 emission from transportation phaseis:ET   Ei   Ec  EDic..............................................................(8)ET: CO2 emission of transportation phase per car (t-CO2);
Ei: CO2 emission of parts transportation per car (t-CO2);
Ec: CO2 emission of assembled car transportation by road percar (t-CO2);
ER: CO2 emission of assembled car transportation by rail per car(t-CO2);
EM: CO2 emission of assembled car transportation by maritime
per car (t-CO2);
ED: CO2 emission of assembled car transportation per car (tCO2);
ns: Dealer number in province s;
Wi : Weight of parts (t);dij: Transportation distance (km);c: Basic unit of improved tonnage method (l/tkm);
H: Heat generation per unit (GJ/kl);
F: emission factor (t-C/GJ);i, j: number of supplier i=1~97;
u: number of unit;
q: number of assembled car;
W: weight of assembled car (t);dkl drn dlm: Distance from assembly factory to dealer (km);(iv) UsingTo estimate CO2 emission of using phase, it is necessary toconsider how consumer use car. Which means to setup car using
pattern. Based on Chinese car using habit, 3 different car using
patterns are set in this study.
Heavy user: commuting every day by car and use car at
weekend for leisure as well. This kind of patter, annual travel
distance is set as 20,000KM.
Normal user: sometimes commuting by car on weekdays, anduse car at weekend for leisure. Annual travel distance is set as
10,000KM.
Light user: only user car on weekend for leisure. This kind ofpatter, annual travel distance is set as 3,000KM. CO2 emissionof using phase is calculated in Eq. (9).
dyEU  den  fe....................................................................(9)EU: CO2 emission of using phase (t-CO2);
d: annual travel distance (km);y: service life of car (set as 10 years);
e: fuel consumption (km/L);
f : emission factor of gasoline (kg- CO2/kg);
den: density of gasoline (kg/L);
I-O Table Analysis
To apply I-O table analysis, firstly to calculate emission basic
unit by using I-O table. After calculating basic unit, it is able toestimate CO2 emission of different material by using Eq. (10).
E  e  Price ...................................................................(10)In this study, firstly to apply process based analysis to estimate
production phase, transportation, and using `s CO2 emission. IO table analysis is used for material phase. To combine this two
analysis`s boundary (Figure. 1) is the estimation methodology ofhybrid method.

Figure 1 Boundary of 2 analysis
Process Based Analysis(i) Parts production
In this study, CO2 emission of parts production is estimated as
Eq. (1) use JLCA database.
...............................................................(1)E p   g n, j  EMFjn

Ep: CO2 emission of parts production per car (t-CO2);
gn,i: electricity use of producing parts n in region j (mWh);
EMFj: emission factor of region j (t-CO2/mWh);
n：number of parts, n=1~31;(ii) Assembly
Similar as parts production, CO2 emission of assembly also
estimated by using JLCA database. Electricity of assembly isused in Eq. (2).
Ea  g  EMF...........................................................................(2)g: electricity use of assembly phase (mWh)
EMF: emission factor (t-CO2/mWh)；(iii) Transportation
Calculation of transportation phase is divided into partstransportation and assembled car transportation. And also
divided into road, maritime and rail transportation. Road
transportation is calculated by improved tonnage method while
railway and maritime transportation are calculated by tonnage
method. These calculations are made by estimation tool of the
Policy Research Institute for Land, Infrastructure, Transport andTourism (PRILIT). This tool was established under Ministry ofLand, Infrastructure, Transport and Tourism (MLIT) of Japan [5].
According to improved tonnage method, CO2 emission of partstransportation is calculated by Eq. (3).
Wi  dij  C  H  F  44
12
Ei 
u
.......................................................(3)Assembled car transportation from assembly factory to dealer
can use improved tonnage method (road, Eq. (4)) or tonnage
method (rail or maritime, Eq. (5)).
Ec 

Wc  dij  C  H  F  44uW  dlm  FMq........................................................................(6)Since there are all 876 dealers considered in this research,emissions of assembled vehicles transportation phase fromassembly plant to dealers can be calculated as follows:n  E  EC n  E  EC nE
ED   s M
or  s r
or  s C
876
876
s
s
s 876 …………(7)2.2 Hybrid MethodjWc  d rn  FR
q........................................................................(5)

m



k ,i

i

i

Em: CO2 emission of using phase;
ek,i: emission basic unit of sector i;
Pricei: material price of material i;12........................................................(4)94Hybrid method
The flow of a hybrid method shows as Figure 2. Firstly, use
process based analysis to estimate using, transportation andproduction phase`s CO2 emission, then the phase of material
which can`t estimate by process based analysis, I-O table
analysis is applied. Finally, add the result of process based
analysis and I-O table analysis is the result of hybrid method.

in 2013. Secondly, in order to keep performance at lower
displacement, turbo technology is widely utilized. Similarly,
same car of golf, in 1992 all of them are naturally aspirated
engine, but in 2013 all changed to turbo engine. Finally, fordecrease fuel consumption at same speed, transmission
technology also improvement form MT to 4AT, 6AT, and DSG
in these years.
It can be assumed technology improvement will also continue
in the future to decrease fuel consumption, therefore in this study,
fuel consumption set as 2.79 L/100KM in 2050.
(iv) Scenario 3: factory shift
Because of increasing of Chinese income, the labor cost ofautomobile company increased these years, and will increase in
the future as well. Therefore automobile company will shift their
manufacturing factory to somewhere with lower labor cost.
Scenario 3.1: domestic factory shift: the tariff to importassembled car is 25% in China. If automobile company shift
their assembly factor aboard, although labor cost decrease, tariff
will be the new cost, and it will reflect in the price of car.
Therefore in this study, automobile company will shift theirassembly factory to another different province with a lower laborcost. The shift target set as Gansu province, which is the lowest
income in 2050[3]. Parts will transport from parts factory aspresent to Gansu, assembled car transport from Gansu to dealers.
Scenario 3.2: aboard factory shift: in this scenario, automobile
company will shift their parts factory to the country of lower
labor cost. Because of the import policy of China, the main partsof car (engine, frame, transmission parts) have a 25% tariff, so
only unimportant parts factory will shift to abroad in the future.
The target will set as Myanmar which is near to China and havelower labor cost among ASEAN countries. Prats will transport
from Myanmar to assembly factory in China.

Figure 2 Hybrid method

2.3 Scenario Building(i) Scenario 0: BAU
It assumed that there is no change of Chinese industry structure,develop as the same pattern at present. Therefore, it is necessary
to forecast I-O table in the future.
To forecast I-O table in the future, RAS method is applied inthis study. RAS is a method to forecast the input coefficientmatrix of target year. To forecast the input coefficient matrix,
total output, intermediate demand, value added and intermediate
input have to be forecasted beforehand.
(ii) Scenario 1: 3rd industry development
China is the biggest manufacturing country in the world, whichcall “the world's factory”. However, consider the future
condition of China, to develop an environment-friendly andsustainable society, it can be assumed that the 3rd industry will
increase. Refer to the developed countries such as U.S. and Japan, 3. Result
it can also assume that the main industry will shift from 2nd 3.1 Present Emission Estimationindustry to 3rd industry.
(i) Result of one car
However, China has its own industry structure, because of
The emission estimation result of one car by hybrid method
protection of domestic industry, even in the future, 1 st and 2ndshows in Table 3.
structure will not decrease as low as U.S. or Japan.
Table 3 Emission result of one car (t-CO2)
Therefore in this study, the percentage of Chinese industry
Material Phase
0.429
st
nd
rd
structure in 2050 set as 1 industry – 1%, 2 industry – 35%, 3
Production
0.777 Parts Production
0.271
industry – 64%.
Phase
Assembly
0.506
Table 2 Industry share setting
Transportation
0.218 Parts Transportation
0.008
1st Industry
2nd Industry
3rd Industry
Phase
Assembled car
0.210
BAU
2.4%
66.5%
31.1%
Transportation
2030
5.6%
46.4%
47.9%
Using Phase
6.6~44.2
2050
1%
35%
64%
Estimation result shows the largest emission comes from using(iii) Scenario 2: technology improvement
phase. However, the result of using phase is very sensitive to
Scenario 2.1: electricity generation improvement
service life and annual travel distance (car using pattern). The
Firstly, according to Nakamichi et al3): the efficient of coalsecond is production phase, in this study, part production andpower generation increase. The emission factor of coal was
assembly factory are located in China mainland, which has adecreasing from 2004 (1.112 kg-CO2/kWh), became 0.934 kgvery high emission factor. The third is material phase. In otherCO2/kWh in 2009. This study assumes that it will decrease tostudies this phase always be omitted, but according to result in
0.57 kg-CO2/kWh in 2050.
this study, emission of this phase is also very high and should be
About the share of power generation method, Chinese
noticed. Finally, transportation phase`s emission is the lowest.
government does a lot of work on nuclear power, wind power
The reason is that both supplier and dealer of Shanghai
and solar power, try to make the share of non-fossil fuel up to
Volkswagen are located in south-east coastal areas in China,
30%.In this study, the share of nuclear set as 10% in 2050. Abouttransportation distance is not that long.
wind power, assume that government will stop subsidy from(ii) Result of automobile industry
2020, the share of wind power will set as 6% in 2050. About
The CO2 emission result of Chinese automobile industry
solar power, because of subsidy, the share will be 2% in 2050.
different by body type and level shows in Figure 3. The total
Finally, water power is 17% at present, and it will keep thisemission in 2015 is 36.10 Mt-CO2. Sedan is the highest emission
percentage in the future.
body type. Among the level, A level is the highest emission level
Scenario 2.2: fuel consumption improvement
because the demand is highest. Non sedan is the second,
There are several technology improvements these years ofalthough the demand of non sedan is not that high, but because
Volkswagen. Firstly, a decrease of displacement, for example,
non sedan always heavier than the same level sedan, emission of
the displacement of car golf is almost 2.0, but it decreases to 1.4
non sedan also very high. Crossover is a niche market, so the95emission only very less.

Scenario1x2x3.1(Rail)

60
50
40
Mt-CO2

Sedan Current
Non Sedan0
Cross Over

A00
A0(Small SUV)
A(Compact SUV)
B(Middle size SUV)C(Middle large SUV)
D(Full size SUV)

30
20
10

Non Sedan Cross Over

Figure 3 Emission result of automobile industry in 2015

1.5
Mt-CO2

Mt-CO2
100

The estimation result of one car shows, the highest emission
phase is using phases. On the other hand, CO2 emission ofmaterial phase shows a very high result as well, which cannot beignored. According to scenario analysis, technology
improvement is the most effective way to reduce CO2 emissionof one car. The scenario of manufacturing factory changes can
reduce CO2 emission of one car, however, transportation phase`s
CO2 emission will increase dramatically. Moreover, although
CO2 emission of one car can be decreased to 0.841 t-CO2 in the
future, because of demand of car increase, total emission of
automobile industry will increase finally.
To achieve INDC of COP21, improvement of power generation
is a fundamental solution. Moreover, to reduce CO2 emission of
total automobile industry, a solution of one car`s emission is notenough. Some policy to reduce automobile demand such as
improve public transportation, automobile tax, limitation of
parking also plays a very important role.
In this study, data of parts obtain from a Japanese database –JCLA database. These data are collected in Japan, may havesome difference from China. In the future, if parts data can becollected in China domestic, uncertainty of result can decrease
still more.
I-O table used in this study is only 139 sectors, much less thanJapan and U.S., it also makes some uncertainty because ofaggregation of I-O table. In addition, the price of I-O table is an
assumed average price, not the real price of products, it also
makes some uncertainty to result.
To calculate CO2 emission of material phase, a price of material
is necessary, these data are collected from Chinese Ecommercial site, so the price changes in the future also affectCO2 emissions of this study.
Finally, this study only considers about GV, however in the
future EV, HEV or other new energy car also become popular in
the market. Therefore in the future effect of new energy car
should be included in this study.

1
0.5
-50%

Assembled Car Transportation
Assembly
Material80

4. Conclusion

0%

0

60

Finally, compare CO2 emission per GDP of 2015, 2030 and
2050, the GPD data of HSBC is used. The result shows in Table
4, CO2 emission per GDP is 5.24 in 2015, 6.59 in 2030 and 25.89
in 2050. Consider about automobile industry, the ability toachieve INDC of COP21 difficult, but because of GDP
increasing and population reduction, it substantially decrease in
2050.

3.2 Future Forecast
As the result shows in Figure 4, different scenarios havedifferent emission reduction result in the future. Firstly, the mosteffective way to reduce CO2 emission is technology
improvement (scenario 2). It can contribute 34% of CO2
reduction. And the industry structure changes (scenario 1) can
reduce 21% of CO2 emission. Secondly, manufacturing factory
shift scenario (scenario 3.1, 3.2) also contribute to CO2 emission
reduction, because the emission factor is much lower. However,
because of the location change of manufacturing factory, CO2
emission of transportation increased dramatically. Especially, if
transportation keeps using truck or trailer, it will even higher
than current situation.

40

Figure 6 Forecast result by body type in 2050
Table 4 Emission per GDP
2015
2030
2050
Emission/GDP(t-CO2/GRMB)
5.24
6.59
2.59
Percentage
100%
126%
49%

0

Sedan

20

Parts Transportation
Parts Production
Changes Rate

Figure 4 Forecast result of scenarios in 2050
Figure 5 shows automobile industry`s CO2 emission of eachprovince in China. CO2 emission of all province increased in
2050. The reason is that although emission of on car decrease to
0.841 t-CO2 in 2050, demand is increased of car leads to CO2emission increase of automobile industry from 36.10 Mt-CO2 to117.48 Mt-CO2 in 2050. U.S. Energy Information
Administration (EIA)[6]`s data shows that in 2015 the emissionof transportation equipment is 28.57 Mt-CO2 in U.S. Compare
to U.S., CO2 emission of automobile industry is a very big
problem.
Therefore, to reduce CO2 emission of one car`s lifecycle isimportant. On the other side, to reduce CO2 emission of all
automobile industry, some policy to reduce automobile demandsuch as improve public transportation, automobile tax, limitationof parking also plays a very important role.
2015

References[1] Intergovernmental Panel on Climate Change: Fifth Assessment Report(AR5), 2014, http://www.ipcc.ch/.
[2] EDGAR: CO2 time series 1990-2013 per region/country.
http://edgar.jrc.ec.europa.eu/overview.php?v=CO2ts1990-2013.
[3] 中道久美子, 花岡伸也, 関建新: 中国における家計消費に基づく
省市区別 CO2 排出量の推計と削減策の検討, 土木学会論文集
G( 環 境 ), Vol.72, No.6 ( 環 境 シ ス テ ム 研 究 論 文 集 , 第 44巻),pp.II_95-II_106,2016.10[4] Conference of the Parties. http://www.cop21paris.org/.
[5] 経済産業省 国土交通省：ロジスティクス分野における CO2 排出量算定共同ガイドライン Ver.3.0，2007．
[6] EIA: Fuel Consumption,
2010,https://www.eia.gov/consumption/manufacturing/data/2010/pdf/Ta
ble3_1.pdf

2050
0
Anhui
Guangxi
Heilongjiang
Jiangxi
Shandong
Tianjin
Chongqing20
Beijing
Guizhou
Hubei
Liaoning
ShanxiXizang40
60
80
FujianGansu
Hainan
Hebei
Hunan
Jilin
Neimenggu
Ningxia
Shaanxi
Shanghai
Xinjiang
YunnanMt-CO2
100
Guangdong
Henan
Jiangsu
Qinghai
SichuanZhejiang

Figure 5 forecast result by province96Production of high concentration bioethanol from cassava stem
Student Number: 15M18115
Supervisor: Ryuichi EGASHIRA

Name: Kazumasa TANAKA
Cosupervisor: Kiyohiko NAKASAKI

キャッサバの茎を原料とした高濃度バイオエタノール発酵
田中一正
本研究はキャッサバの茎からの高濃度エタノール発酵を目的としてキャッサバの茎の酸加水分解法を検討した。キャッサバの茎の酸加水分解液を再利用することで液中のグルコース濃度を従来の約 2 倍に濃縮することに成功した。濃縮
した酸加水分解液に Saccharomyces cerevisiae IAM4178 を接種してエタノール発酵することで最大 40 g/L 程度のエタ
ノールを得ることができ、ほとんどが有効利用されていないキャッサバの茎から高濃度のエタノールを生産する方法を示した。

ethanol productivity. So, it is required to remove these
inhibitors by detoxification.
The research purpose is to produce around 40 g/L ofbioethanol from cassava stem. In this study, cassava
stem was used as bioethanol substrate. The amount of
cassava stem produced in a year is 35 million tons, but
about 80% of cassava stem is abandoned or burned in
the wild [2]. For the purpose, fermentation of synthetic
broth, fermentation of cassava stem broth andfermentation of condensed cassava stem broth wereconducted.

1 Introduction
Recently, there is a growing concern about the
depletion of petroleum based-fuels reserves. Carbon
dioxide which is produced in combustion of petroleum
based-fuels tends to increase the temperature of theplanet. It leads to many negative effects to environment.
Furthermore, the intensive demand of petroleum basedfuels increases the crude oil price. These factors haveheightened the need to search for alternative energy
resources.
Bioethanol is an attractive solution to address above
problems as it is a carbon neutral energy source.
Bioethanol is produced by the fermentation of sugar by
microorganism. Today, almost all bioethanol is
produced from corn and sugarcane. However, theproduction of bioethanol from food crops causesinterference between food demands.
Agricultural residues are collecting attentions asalternative substrate of bioethanol fermentation, owing
to its low cost and abundant amount in the world. It isestimated that 491 billion liters of bioethanol can beproduced from sum of agricultural residues and foodwastes in the world which is 16 times higher than thecurrent total production. During bioethanol
fermentation, the ethanol concentration should reach atleast approximately 40-50 g/L, because several studies
have concluded that this target concentration can makethe process economically feasible, especially by savingenergy for the distillation [1]. So, it is important toproduce high concentration ethanol in fermentation ofagricultural residue.
Agricultural residue is composed of lignocellulose.
Starch in the crops can be fermented directly, but
lignocellulose has to be pretreated before fermentation.
The acid hydrolysis by hydrochloric acid or sulfuric
acid is the most common method to break thelignocellulosic structure. However, acid hydrolysis
pretreatment produces fermentation inhibitors such as
furfural and phenolic compounds which can lead low

2 Materials and methods
2-1 Fermentation of synthetic broth
The fermentation ability of IAM4178 on glucose andxylose was examined using synthetic broth. IAM4178
was precultivated in 50 mL of YM broth at 30ºC for 2days. Then, 50 mL of the preculture was centrifuged at
6,000 rpm for 5 min. The supernatant was discarded
and the wash solution which was composed of 5 g/L of
peptone and 3 g/L of yeast extract was added to washthe pellet. The mixture of wash solution and the pellet
was centrifuged again in the same condition. Aftercentrifuging, the supernatant was discarded and thepellet was inoculated to 50 mL of synthetic broth which
was composed of 40 g/L of glucose, 20 g/L of xylose,
5 g/L of peptone and 3 g/L of yeast extract.
Fermentation was conducted at 30ºC, 120 rpm.
The chemical formulas of ethanol production fromglucose is shown below.
C6H12O6 → 2 C2H5OH + 2 CO2According to the formula, 0.51 g of ethanol is produced
from 1 g of glucose. The ethanol fermentation
efficiency was calculated for all experiments based on
an assuumption that the ethanol concentration
calculated from above formula is 100%.

97

mixture. The volume of the added hydrolysate wassame with the volume of distilled water which wasadded before the first heating. Acid concentrarion after
adding the hydrolysate became 28%. The mixture of
CS and hydrolysate was heated in autoclave at 111ºC
for 1 hour. The hydrolysate was kept stand at room
temperature for 1 hour, and calcium carbonate wad
added to adjust pH around 5.5. The hydrolysate after
neutralization was centrifuged at 6000 rpm for 5 min
and supernatant was filtered with a paper which haspore size of 0.45 µm. Then, peptone and yeast extract,
5 g/L and 3 g/L each were added to the filteredhydrolysate. The hydrolysate after these procedures
was used as CS 400. The theoretical sugarconcentration is shown in Table 1. The value wascalculated using CS composition measured by NREL
method. IAM4178 was inoculated to CS 400 in thesame manner with chapter 2-1. After the fermentation
started, IAM4178 was inoculated again in 6d as the celldensity decreased.

2-2 Fermentation of cassava stem broth
2-2-1 Fermentation of CS 200
Cassava stem hydrolysate was fermented by
IAM4178 to investigate the fermentation ability of
IAM4178 in the cassava stem hydrolysate and compare
the fermentation characteristic of the CS broth andsynthetic broth. Cassava stem (CS) was obtained fromTrang Bom region, Dong Nai prefecture, Vietnam.
Firstly, CS was ground to make the particle size smaller
than 0.5 mm. The ground CS was stored in thedesiccator. Ground CS and 72wt% sulfuric acid weremixed at the weight ratio 1:1.25. The mixture was putin the incubator at 30ºC for 1 hour. During incubation,
the inside content was stirred evey 15 min to make it
uniformity. After 1 hour, distilled water was added todecrease the acid concentrarion to 16.4% and the
mixture of CS and acid was heated in autoclave at
111ºC for 1 hour. The hydrolysate was kept stand atroom temperature for 1 hour, and calcium carbonate
wad added to adjust pH around 5.5. The hydrolysate
after neutralization was centrifuged at 6000 rpm for 5
min and supernatant was filtered with a paper whichhas pore size of 0.45 µm. Then, 5 g/L of peptone and 3
g/L of yeast extract were added to the filteredhydrolysate. The hydrolysate after these procedures
was used as CS 200. The theoretical sugarconcentration is shown in Table 1. The value wascalculated using CS composition measured by NREL
method [3]. IAM4178 was inoculated to CS 200 in thesame manner with chapter 2-1.

2-3 Analytical methods
Sugar, ethanol, and furfural were measured with a
HPLC system with a Shodex Sugar SH1011 column
and a refractive index detector. Mobile phase was 5 mM
H2SO4, flow rate was 0.6 mL/min and the temperaturein the column oven was 60°C. Cell density wasmeasured by dilution plating method with YM agar
plate. Incubation of the plates was conducted at 30°C,
for 2 days. Phenolic compound was measured by FolinCiocalteu method referred from [4].

2-2-2 Fermentation of CS 400

3 Results and discussion

In order to produce high concentration of ethanol,
condendsation of sugar in CS broth by two times
hydrolysis and fermentation characteristic of thecndensed CS broth were investigated. Ground CS and
72wt% sulfuric acid were mixed at the weight ratio
1:1.25. The mixture was put in the incubator at 30ºC for
1 hour. During incubation, the inside content wasstirred evey 15 min to make it uniformity. After 1 hour,
distilled water was added to decrease the acidconcentrarion to 16.4% and the mixture of CS and acidwas heated in autoclave at 111ºC for 1 hour. The
hydrolysate was kept stand at room temperature for 1
hour, and filtered with a paper which has pore size of
0.45 µm.
Ground CS and 72wt% sulfuric acid were mixed andincubated for 1 hour in the same manner, and thefiltered hydrolysate mentioned above was added to the

3-1 Fermentation of synthetic broth
The courses of sugars, ethanol and cell density are
shown in Fig. 1 and 2. IAM4178 consumed 40 g/L of
glucose within 1 day and 20 g/L of xylose wasdecreased to 10 g/L in 96h. As a result, ethanol andxylitol were produced. The ethanol fermentation
efficiency was 84.4%. The cell density was stablearound 108 CFU/mL. These results indicated that
IAM4178 can produce ethanol from glucose at highefficiency and can produce xylitol from xylose as a
byproduct. The present study also demonstrated that thexylitol production from 5 g/L of xylose by IAM4178 in
the exisitence of 10 g/L of glucose was higher than 5
g/L of xylose alone (data not shown). So, the xylitol
production was enhanced possibly by the existence of
glucose.
98

Table 1 The theoretical sugar concentration50

CS 200CS 40030

Xylose
(g/L)
31.08
57.50

20
10
0

0

24

48
Time (h)7296Fig. 1 The courses of sugars and ethanol

9

GlucoseXyloseEthanol
Xylitol50

0.8Furfural

40

0.6

30

0.4

20
0.2

10
0

0

6

12

18
24
Time (h)30036Fig. 3 The courses of sugars, ethanol, and furfural

8

10
7

0

24

48
Time (h)72

96Log. cell density (CFU/mL)

Log. cell density (CFU/mL)

10

60

Furfural concentration (g/L)

170

Glucose, xylose, xylitol andethanol concentration (g/L)

Concentration (g/L)

Glucose(g/L)
73.94
136.79Run

GlucoseXyloseEthanol
Xylitol

40

Fig. 2 The course of cell density

3-2 Fermentation of CS 200
The courses of sugars, ethnaol, furfural and celldensity are shown in Fig. 3 and 4. The initial sugarconcentration of CS200 was 63 g/L, which was about
90% of the theoretical value (see Table 1). All glucose
was consumed within 12h while about a half (i.e. 10g/L) of xylose was consumed in 36h. Ethanol was
produced with the decrease of glucose but xylitol was
not produced altough xylose decreased. SchirmerMichel, Ângela Cristina, et al. [5] reported that theCandida guilliermondii produces xylitol from xylose in
the synthetic broth, but does not produce xylitol in therice hull hydrolysate. The author speculated theexistence of inhibitor of xylose reductase activity in therice hull hydrolysate. In this study, CS 200 might also
contained xylose reductase inhibitor. The ethanol
fermentation efficiency from CS200 was 73.4%, andthe cell density was almost stable at 108 CFU/mL (seeFig. 4). These results indicate that IAM4178 canproduce ethanol in the CS hydrolysate without any
detoxification. However, the ethanol fermentation
efficiency was 10% lower than that of the synthetic
broth. It is possibly because of the delay in glucose
consumption by furfural [6], and/or the consumption of
ethanol by the yeasts.

9

8

7

0

6

12

18
24
Time (h)Fig. 4 The course of cell density

30

36

3-3 Fermentation of CS 400
The courses of sugars, ethanol, furfural, cell density,and phenolic compound are shown in Fig. 5, 6 and 7.
The sugar concentration in CS 400 was about 70% of
theoretical value in Table 1. This is possibly becausesome sugar was degraded excessively by two times
hydrolysis. From 0d to 2d, about 40 g/L of glucose wasconsumed. However, the glucose consumption wasceased from 2d to 6d. The glucose consumption begun
after IAM4178 was inoculated in 6d, and all of theremaining glucose was consumed in 9d. The glucose
consumption was slower than that of CS200, whichshowed the degradation of 60 g/L glucose within 12h.
It suggests that the inhibitor concentration increased bycondensation, and caused the delay in glucose
consumption.

99

3Concentration (g/L)

100

Concentration (g/L)

The final ethanol concentration and ethanol
fermentation efficiency of CS 400 were 40 g/L and
73.3%, respectively. It showed that 40 g/L of ethanol
concentration is achieved by condensation in the newhydrolysis method. The ethanol fermentation efficiency
was similar value with CS 200. These results indicatedthat the inhibition delayed the sugar consumption but
did not affect the fermentation efficiency.
The cell density decreased from 108 CFU/mL in 0d to
zero in 4d. Then, the cell density recovered to 108
CFU/mL because of second inoculation in 6d, but it
decreased again to zero in 9d. The total phenol wasaround 2.5 g/L and it was almost stable. In the previous
research, 2 g/L of phenoic compound could inhibit theethanol fermentation [7]. Thus it can be said that 2.5
g/L of phenolic compound in the CS 400 was enoughhigh to affect the cell increase of IAM4178. Highconcentration of ethanol could be produced from CS
400, but IAM4178 decreased during the fermentation.
In summary, the present study indicated that thecondensation of CS broth increases both fermentablesugars and inhibitors, but high concentration ethanol is
produced by the addition of yeast during thefermentation. The removal of the inhibitory substances
will be the next challenge.

1

4
6
8
10
Time (d)Fig. 5 The courses of sugars and ethanol

Log. cell density (CFU/mL)

9
8
7
6

4

0

～
～
2

4
6
8
Time (d)Fig. 6 The course of cell density

10[1] Mitsunori Yanagisawa, Kanami Nakamura, Osamu
Ariga, Kiyohiko Nakasaki, Production of highconcentrations of bioethanol from seaweeds that
contain easily hydrolyzable polysaccharides Process
Biochemistry 46 (2011) 2111–2116.
[2] Minhee Han, Yule Kim, Youngran Kim, Bongwoo
Chung, and Gi-Wook Choi, Bioethanol production
from optimized pretreatment of cassava stem,
Korean J. Chem. Eng. 28(1) (2011) 119-125.
[3] A. Sluiter, B. Hames, R. Ruiz, C. Scarlata, J. Sluiter,
D. Templeton, and D. Crocker, Determination of
Structural Carbohydrates and Lignin in Biomass,
Technical Report NREL/TP-510-42618 Revised
July (2011).
[4] Matteo Bonoli, Vito Verardo, Emanuele Marconi,and Maria Fiorenza Caboni, Antioxidant Phenols in
Barley (Hordeum vulgare L.) Flour: Comparative
Spectrophotometric Study among Extraction
Methods of Free and Bound Phenolic Compounds, J.
Agric. Food Chem. 52 (2004) 5195−5200.
[5] Angela
Cristina
Schirmer-Michel,
SimoneHickmann Flores, Plinho Francisco Hertz, Gilvane
Souza Matos, Marco Antonio Zachia Ayub,
Production of ethanol from soybean hull hydrolysate
by osmotolerant Candida guilliermondii NRRL Y2075, Bioresource Technology 99 (2008) 2898–2904.
[6] L. J. Boyer, J. L. Vega, K. T. Klasson, E. C. Clausen
and J. L. Gaddy, The Effects of Furfural on Ethanol
Production by Saccharomyces Cerevisiae in Batch
Culture, Biomass and Bioenergy Vol. 3 No. I (1992)
41-48.
[7] J. P. Delgenes, R. Moletta, and J. M. Navarro, Effects
of lignocellulose degradation products on ethanol
fermentations of glucose and xylose by
Saccharomyces cerevisiae, Zymomonas mobilis,
Pichia stipitis, and Candida shehatae, Enzyme andMicrobial Technology 19 (1996) 220-225.

2

～
～

8

References

20

5

4
6
Time (d)The cassava stem broth (CS 400) was successfully
condensed by two times hydrolysis. Fermentation of
the condensed broth with IAM4178 achieved ethanol
concentration of 40 g/L, which is the target
concentration of bioethanol from agricultural residue.

40

0

2

4 Conclusions

60

0

0

Fig. 7 The course of phenolic compound

GlucoseXyloseEthanol

80

2

10

100

Liquid-liquid equilibrium in concentration and purification of bioethanol
by solvent extraction
Student Number: 15M18150

Name: Tomonori MASUDA

Supervisor: Ryuichi EGASHIRA

溶媒抽出法によるバイオエタノールの濃縮および精製における液液平衡
増田 知徳
エタノール水溶液を原料，直鎖アルカン又は軽質軽油を溶媒として液液平衡を実測及び活量係数モデル
により推算した。エタノールは水よりも選択的に溶媒相中に移動した。分子中の炭素原子数(炭素数)の大
きい溶媒及び平衡時の温度の低い範囲の条件においてはエタノール-水系の共沸混合物よりもエタノール
濃度の高い範囲への濃縮が可能であった。平衡時の温度の上昇又は溶媒の炭素数の減少と共に，エタノー
ルの分配係数は増加し水に対する分離の選択性は低下した。

were selected as solvent. The Physical properties ofmaterials are shown in Table 1. The selected solvents are
expected to separate from ethanol easily by distillation,
because each boiling point of solvents is much higher
than that of ethanol.
The experimental conditions of the mutual solubility
and LLE measurement are summarized in Tables 2 and 3.
The specified amounts of feed and solvent were put inconical flask and shaken in isothermal bath. After the
specified time, raffinate and extract phases wereseparated into each other by separating funnel. The
obtained solutions were analyzed by gas chromatograph(GC) with flame ionization detector, and Karl Fischer
titration (KFT) to determine their compositions.

1. Introduction
Bioethanol is well known for the substitute of motor
gasoline and considered to the promising fuel since it isobtained from renewable source. Although it is necessary
to concentrate ethanol more than 99.5vol% to blend withmotor gasoline [1], it is impossible to attain the highconcentration by the conventional distillation, because ofazeotrope mixture at the ethanol mass fraction of 0.96.
The techniques to concentrate ethanol other thandistillation have been studied for a long time. One of thetypical methods of ethanol concentration is azeotropic
distillation, whereas this method should require muchenergy. Other concentration methods for less energy
requirement have been proposed to replace the methodwith high energy requirement, such as adsorption andmembrane separation and so on. The solvent extraction is
also one of them. For most cases, the ethanol extraction
was studied with the solvents giving relatively highethanol solubility. Generally these kinds of solvents
showed lower separation selectivity of ethanol relative towater, and it was difficult to concentrate ethanol up to
the specified concentration. Therefore it is necessary to
study the application of the extraction with the solventsof high separation selectivity of ethanol for thebioethanol production.
This study aims the application of solvent extraction toconcentration and purification of ethanol for thebioethanol production. The extraction solvents whichhave potential to concentrate ethanol more thanazeotrope composition were selected and the liquidliquid equilibrium (LLE) of the ethanol-water-solvent
was measured under various conditions. The measured
LLE was compared with the estimation results by theactivity coefficient model. Then the development ofbioethanol concentration process with the employedsolvent was discussed.

Table 1 Physical properties of materials
Component
Ethanol
Tetradecane
TridecaneLight gas oil

Boiling point[K]
351.5
526.6
507.1
443~643

Density[kg/L]
0.789
0.764
0.756
0.840

Table 2 Conditions of experiment of mutual solubilityof solvent-ethanol and solvent-water
Feed, F

Ethanol(e), Water(w)Solvent, S

Tridecane (C13),
Tetradecane(C14),Light gas oill(LGO)
10~30
1~1.5
289~308
48

Mass of feed
Mass ratio of S to F
Temperature, T
Time

[g]
[-]
[K]
[h]

Table 3 Conditions of experiment of liquid-liquid
equilibrium in ethanol-water-C14 systemFeed, F
Solvent, S
Mass of feed
Mass fraction of
ethanol in feed
Mass ratio of S to F
Temperature, T
Time

2. Liquid-liquid equilibrium measurement
2.1. Experimental
According to previous studies, alkane compounds ofhigher carbon numbers in molecule were expected toshow higher separation selectivity of ethanol relative towater [2]. Tridecane (C13), tetradecane (C14), and lightgas oil (LGO), consisting mainly of alkane compounds,
101

[g]

Ethanol aqueous solution
Tetradecane(C14)
10~30

[-]

0.20~1.00

[-]
[K]
[h]

1
298~33348

2.2 Results and discussion
Figure 1 shows the mutual solubilities between ethanoland solvent under several temperatures. Although thesolvent used formed a heterogeneous phase with ethanol,
the systems formed a homogeneous phase at higher
temperature. The temperature required to form ahomogeneous phase rose in order of C13 (298K), LGO(303K), and C14 (313K), and this might follow the orderof increasing number of carbon atom in molecules (CN).
The mutual solubility of ethanol with solvent increased,
as temperature was elevated and CN decreased.
The solvent compound in the raffinate phase and waterin the extract phase were not detected by GC or KFT
analysis, in other words, the mutual solubility between
water and C14 or LGO was negligibly small. Then it
could be expected that this kind of solvent should givehigh separation selectivity of ethanol relative to water.
Moreover it must be unnecessary to consider the
separation between solvent and water.
Figures 2 and 3 show the phase diagrams of ethanol,
water, and C14 systems. The solubility of ethanol andwater in the extract phase and that of C14 in the raffinate
phase increased with temperature, that is to say, twophase region became smaller as the temperature rose. In
this measuring conditions, C14 formed homogeneous
phase with some kinds of the feed solutions of higher
ethanol concentrations, and the temperature and feed
compositions are shown in Table 4. Then, by adjusting
temperatures and other conditions appropriately, it ispossible to concentrate ethanol up to higher than thatspecified for the bioethanol production by extraction.

Fig.2 Phase diagram of C14-water-ethanol(experimental results show by points, estimatedresults show by curves)

Fig.3 Enlarged view of extract phase on Fig.2(experimental results show by points, estimatedresults show by curves)
The LLE of the systems measured in this study were
estimated by the UNIFAC method, one of thethermodynamic techniques to estimate activity
coefficients with interaction parameters between
functional groups composing molecular of component
and so on[3]. The interaction parameters were
determined by fitting the experimental results at therespective temperatures. The estimation results in thecase of ethanol-water-C14 system are also shown in
Figures 2 and 3 by the curves. The UNIFAC method
could favorably estimate the LLE of C14-water-ethanol,
especially the compositions of the extract phases.

Fig.1 Mutual solubility of ethanol and solvent
Table 4 Conditions which C14 and feed became
homogeneous phase
T[K]
313
323
333

Mass fraction of ethanol in feed
1.00
0.99, 1.00
0.98, 0.99, 1.00

102

The distribution ratio of component i, mi, the separation
selectivity of component i against component j, βi,j,solvent-free mass fraction of ethanol in extract, ZE, andsolvent-free mass fraction of ethanol in raffinate, ZR were
defined as follows:mi  y i / xi
 i , j  mi / m j
Z E  ye /( y e  y w )Z R  x e /( x e  x w )

(1)
(2)
(3)
(4)

Fig.4 Relation of solvent-free ethanol concentrationin each phase
The LLE of alkane-water-ethanol system was estimatedby the UNIFAC method under various temperature.
Figure 4 shows the relation between ZE and ZR. The ZE
was always larger than ZR. In other words, ethanol could
be concentrated in the extract relative to water. The ZE
increased as the temperature increased, and that waslarger than that with CN. Figure 5 shows the relation
between me and ZR. The me increased with ZR,
temperature, and decreasing CN. Figure 6 shows therelation between βe,w and ZR. The βe,w decreased withincrease of ZR, elevation of temperature and decrease of
CN. The actual feed solution from the fermentation step
generally contained ethanol of 0.1 by mass fraction. In
this concentration range, me. was so small whereas βe,w
was large. Then the recovery of ethanol from the feedsolution must be difficult even though me could be
improved by temperature elevation.

Fig.5 Relation between distribution ratio of ethanoland solvent-free ethanol concentration in theraffinate phase

3. Bioethanol concentration process by solvent
extraction
Figure 7 shows the outline of extraction process forbioethanol concentration and purification. The solvent
should form heterogeneous two liquid phases with highly
pure ethanol in the enriching section between the feed
entry point and the top of the extractor, where the ethanol
is enriched up to nearly pure with concentration higher
than in azeotrope by reflux operation.(shows on Figure8) On the other hand, high solubility of ethanol into thesolvent is required in the stripping section lower than thefeed entry to reduce ethanol flowing out as a raffinate
product. Although these two criteria of the solvent
conflict with each other as results of the equilibrium
shown in Figures 5 and 6, they can be satisfied at thesame time by selecting different operating temperatures
for the enriching and stripping sections, for example.
Other method for reduction of ethanol flowing out as araffinate product is use of distillation before extraction.

Fig.6 Relation between separation selectivity of
ethanol relative to water and solvent-free ethanol
concentration in the raffinate phase

103

Mass fraction of ethanol in vapor phase and that ofethanol in liquid phase were defined as ZV and ZL. Figure
8 shows ZE and ZR on LLE of C14-water-ethanol system
and ZV and ZL on vapor-liquid equilibrium(VLE) of
ethanol-water system. The VLE data was quoted fromthe literature.[4] Within the range of high concentrationof ethanol(around 96wt%), the difference between ZV
and ZL is much small, so distillation is difficult. On theother hand, in the range of relatively low concentrationof ethanol (60-80wt%), the difference between ZV and ZL
is large enough to distill raw bioethanol feed into
60-80wt% ethanol easily. Figure 9 shows outline ofextraction and distillation. The raffinate containing
ethanol is introduced into distillation column, then theethanol in raffinate is distilled into a distillate product.
Accordingly it is possible to recover ethanol fromraffinate product by using distillation.
The technique which needs lower energy is required ofbioethanol purification process. When operating
conditions of extraction process are optimized for
reduction of energy consumption, these plans such as
above are important.

Fig.7 Outline of extraction with product reflux

4. Conclusions
The mutual solubility of alkane compounds and ethanol,or alkane compounds and water was measured, andalkane compounds were employed as the solvent for thebioethanol production. Liquid-liquid equilibriums of
ethanol-water-solvent were clarified, and it was possible
to concentrate ethanol up to more than ethanol azeotrope
composition in the range of lower temperature and larger
number of carbon in molecules. The distribution ratio of
ethanol increased, with elevate of temperature anddecrease of number of carbon in molecules, and theseparation selectivity of ethanol relative to water
decreased with increase of distribution ratio of ethanol.
Based on LLE of experimental results and estimation
results by activity coefficient model, bioethanol
production process employing extraction using productreflux, and distillation was discussed.

Fig.8 LLE of C14-water- ethanol system andvapor-liquid equilibrium of ethanol-water system

Nomenclaturex = mass fraction of component in raffinate phase, y = mass fraction ofcomponent in extract phase, Z = mass fraction of ethanol without
solvent, CN = number of carbon in molecule, T = temperature, m =distribution ratio, β = separation selectivity, γ = activity coefficient, a =interaction parameter, C10 = decane, C12 = dodecane, C13 = tridecane,
C14 = tetradecane, C16 = hexadecane, LGO = light gas oil, CN =number of carbon atom in molecule, LLE = liquid-liquid equilibrium,
VLE = vapor-liquid equilibrium

Subscript
E = extract phase, R = raffinate phase, V = vapor phase, L = liquid
phase, w = water, e = ethanol, s = solvent, i = component i, j =component j, p = phase

References[1] JIS K 2190:2011 燃料用エタノール[2] Roddy.J.W Ind. Eng. Chem. Proc. Des. Dev. p104 (1981)[3] Fredenslund A AIChE Journal vol.21 p1086-1099(1975)[4] 社団法人日本化学工学会 “化学工学基礎編改訂第 5 版”(2004)

Fig.9 Outline of extraction and distillation

104Forecasting extreme storm surges in Manila Bayan adverse combination of unusual tropical cyclone tracks and southwest monsoon
Student Number: 15M18279

Name: Jerome SILLA

Supervisor: Hiroshi Takagi

During the last 56 years, 4% or 66 cases of the total tropical cyclones (TC) that matured
over the Northwestern Pacific Basin developed originally in the South China Sea (SCS) andtraversed northeast towards the northern Philippines including Metro Manila. Despite the rarity ofthese tropical systems, their wind speeds may be exacerbated by an interaction with southwest
monsoon (SWM) which could further induce disastrous storm surges. Analysis of the NCEP FNL
wind data revealed an average wind speed of 5.4 m/s and a maximum wind speed of 14.1 m/s
southwest monsoon in Manila Bay. Two east-tracking hypothetical tropical cyclones, TD W28(1993) and TY Halong (2008), were utilized in the simulation based on 2013 Typhoon Haiyan’s
characteristics. The largest surge induced by TD W28 was simulated at Navotas station with a peakwater level of 5.5m. The largest surge induced by TY Halong was also observed at Navotas station
with a maximum surge of 6.6m. The results demonstrated that the additional 5.4 m/s averagemonsoon produced up to 40cm increased surge (7% relative change) and the additional 14.1 m/s
maximum monsoon increased by 70cm in water level (13% relative change).
landfall point of most TCs from the Pacific
Ocean, the bay is particularly vulnerable tocoastal disaster due to the presence of extremely
densely-populated communities over a lowlying coast. In September 2011, despite being
200 kilometers north of Manila Bay and only
having 80 knots maximum sustained windspeed, Typhoon Nesat (TY Nesat) generated
one of the most destructive storm surges to ever
hit the bay. The maximum storm surge
coincided with the high tide of the tidal cycle
and tropical cyclone winds interacted withsouthwest monsoon (SWM) that contributed toan augmented wind field (Morin, 2016),
battering the low-lying informal housing
scattered around the Manila Bay shoreline.
The objective of this research is toplace an increasing emphasis on thevulnerability of Manila Bay to, not just storm
surge per se, but also to the disasters brought
about by the peculiar characteristics of tropical
cyclones affecting the Philippines. This researchassumes a worst-possible scenario in Manila
Bay by incorporating a straightforward
approach of integrating tropical cyclone,southwest monsoon and high tide components.

1 Introduction
Tropical cyclones (TC) that enter thePhilippine Area of Responsibility typicallycome from the east in the Pacific Ocean. In
addition to this predominant source of TC, lowpressure systems that eventually develop into
tropical cyclones in the South China Sea (SCS)(hereby referred to as SCS TC) also endanger
the western side of the Philippines (Figure 1).
Although these SCS TCs have relatively weakerwind speed and are less frequent, this paper
stresses that the existence of such is clearly
indisputable and potentially disastrous.

Figure 1. Tropical systems that enter thePhilippines develop from either the west
(South China Sea) or the east (Pacific Ocean)
Although Manila Bay at first glance
may seem to be sitting safely on the westernside of the Philippines which faces the opposite

105

to roughly estimate the southwest monsoontropical cyclone synthesis. To perform this, theNational Center for Environmental Prediction
Final Operational Model Global TroposphericAnalyses (NCEP FNL, 2016) data for u- and vcomponents of wind height above sea surface
from 2001-2014 in the southwest monsoon
months (June to September) were extracted(Figure 2.2). The reliability of NCEP FNL data
as input wind to simulate the storm surge in
Manila Bay was evaluated using Delft3D Flowand verified accordingly.
The 6-hourly wind data information ondays without any occurrence of typhoon wereobtained (hereby referred to as no-TC days). It
was found that the velocities were fluctuating ineach month of the 14-year data and exhibited nosignificant trend. Results also showed that the
four-month average wind speed on no-TC days
was 5.4 m/s (9.8 knots) with a u-component of
3.7 m/s (7.2 knots) and a v-component of 3.4
m/s (6.6 knots). Additionally, it was discoveredthat the daily maximum monsoon wind strength
based on the NCEP-FNL data could rise as highas 14.1 m/s (27.4 knots).

2 Data Analysis
2.1 SCS TC Component
Tropical cyclones that originated in theSouth China Sea may track either eastward
towards the northern Philippines and Taiwan or
westward away from the Philippines andtowards Vietnam and southern China. Using thetyphoon track data from the Joint Typhoon
Warning Center (JTWC) from 1959-2014 (56years), it was found that among the total
tropical systems (1477 cases) that matured in
the entire Northwestern Pacific Basin, 153 casesor 10% originated from the South China Sea
and tracked either eastward (4%) or westward(6%). It should be stressed out that the scope oftropical cyclones in this study focused only on
those that initially tracked eastwardly in which
66 cases were observed. Any indication offluctuation in the number of occurrence of these
SCS TCs was investigated in an annual basis
although no pattern was observed. Further,
these tropical systems were classified into two— 1) cyclogenesis above 14°N and 2)
cyclogenesis below 14°N — on the basis thatthe southernmost latitude of Manila bay is
14°16 ́N (Figure 2.1).

Figure 2.2. Off-Manila Bay wind rose
diagram from 2001-2014 shows theprevailing wind coming from the southwest

2.3 Tide Component
The tide analysis was achieved using
predicted tide data produced from WXTide32 at
two tidal stations. Hourly tide values from2001-2015 (15 years) were gathered during theSWM months (June to September). Days thatexhibited the highest tide level for each station
were selected for each month for each year andwere plotted. Since the tide peak time for themonths of June, July and September all aligned
within a 2-hour range, it was deemed reasonable
to compute for the average high water level ofthese months to obtain the representative
maximum tide value (Figure 2.3). The mean

Figure 2.1 East-tracking SCS TCs
generated above 14°N (left) and below
14°N (right)

2.2 Southwest Monsoon Component
A straightforward calculation by adding
the zonal (u) and meridional (v) windcomponents of SWM winds in Manila Bay withthose of a tropical cyclone used as total wind
input into a storm surge model was considered106high water values in these months ranged from
1.43-1.46m with a peak time generally between
10:00-11:00h.

typhoon model to roughly estimate the total
wind surge attribute. The non-wind surge
attribute from the tidal component was inputtedin time series form at the open boundary of thecomputational domain. The simulation was run
using Delft3D-Flow which is based on nonlinear shallow water equations.

4 Simulation Results
Four locations were under observation
throughout the simulation to represent four
highly-populated communities along thecoastline of Manila Bay: Navotas, Roxas
Boulevard, Baseco and Cavite City. . It shouldbe emphasized that the typhoon model used togenerate the preliminary wind speed did notconsider the change of topography.

Figure 2.3. (Left) Manila Port andCorregidor tidal stations and (Right) theirderived hypothetical maximum tide timeseries (unit: meter)

3 Storm Surge Simulation
Two SCS TCs, TD W28 (1993) and TY
Halong (2008), which exhibited an east-tracking
characteristic from the South China Sea wereselected in the analysis (Figure 3).

4.1 TD W28 with SWM
For the case of TD W28, Navotas Sta.
exhibited the largest water level with peaksurges of 5.5m (With-Tide Case) and 2.9m
(Without-Tide Case) as compared to the otherthree stations (Figure 4.1).

Fig 3. Paths of TD W28 and TC Halong
A model that generates an improved
estimation of wind and pressure distribution
particularly for typhoons that exhibit a centralpressure of 935 hPa or lower was utilized(Takagi and Wu, 2016). The characteristics of
TD W28 and TY Halong were adopted fromthose of Typhoon Haiyan (with a minimum sea
level pressure of 895 hPa) to forecast extreme
events in Manila Bay. The maximum sustained
wind, translation speed, central pressure andradius of 50-knot wind of the two tropical
cyclones were patterned from that of Typhoon
Haiyan and thereby produced hypotheticallyextreme versions of TD W28 and TY Halong.
The determined values of SWM wind based on
NCEP data analysis were directly added to theu- and v- wind components produced by the

Figure 4.1 Time series of the water level (inmeters) at each station for TD W28
The value of 2.9-m peak surge for the
Without-Tide Case was not expected mainly
due to the fact that Haiyan’s exceptional
characteristics were included in the reproduced
tropical cyclone. This water level was found to
be rather low given the extremely high windspeed and extremely low pressure of Haiyan. Itshould be clarified that the input wind for the
Without-Tide Case was exactly the same as thatof the With-Tide Case, and therefore, the only
difference between the two was the tide input atthe boundary condition.
107

for low-lying regions around the coastline of
Manila Bay.

4.2 TY Halong with SWM
The results of the numerical simulationof TY Halong were generally similar to thetrend of the results of that of TD W28.
Although both cases for TD W28 and TY
Halong employed a 30-hour simulation run, thebiggest difference between the two was theangle of approach of the wind and the timeinterval before landfall in which the input wind
and pressure time series of TY Halong was 6hours longer than those of TD W28. This result
ascertained the sensitivity of the hydrodynamic
model to both the input wind and pressure.
Another main difference in the analysis
was that the latitude coordinates of TY
Halong’s original track were adjusted (lowered)
to render the storm eye closer to Manila Bay.
Several trial simulations were performed todetermine by how much the latitude should be
adjusted such that the largest storm surge would
be generated. It was later discovered that a
reduction factor range of 2.1-2.2° onto thelatitude coordinates would induce the highest
water level at the four observation points(Figure 4.2).

5 Conclusion
Despite the rarity of SCS-generated
TCs, a TC-SWM interaction is potentiallycatastrophic particularly for Manila Bay which
is surrounded by a densely-populated
metropolitan area. This may be further
exacerbated by the lack of awareness of thepeople regarding the existence of such unusual
SCS TCs.
Irrespective of Typhoon Haiyan’s
extreme characteristics that ultimately claimed
lives of 6,300 people in 2013, one of thecontributing reasons for such a great number ofcasualties was the lack of understanding of theconcept of storm surge in the Philippines. In thesame logic as what had transpired in the case of
Haiyan, thorough understanding of themechanism of tropical cyclone-southwest
monsoon interaction ushers a more pertinent
disaster mitigation scheme. Before successful
strategies could be formulated and policies be
operationalized towards an effective integrated
coastal resource management, awareness is thefirst measure that must be undertaken in orderto prevent yet another Haiyan tragedy.

References
Morin, V. M., Warnitchai, P., & Weesakul, S.
(2016). Storm surge hazard in Manila Bay:Typhoon Nesat (Pedring) and the SW monsoon.
Natural Hazards, 81(3), 1569-1588.
Figure 4.2. TY Halong track adjustment
Takagi, H., & Wu, W. (2016). Maximum windradius estimated by the 50 kt radius:
improvement of storm surge forecasting over
the western North Pacific. Natural Hazards andEarth System Sciences, 16(3), 705-717.

4.3 TY Halong without SWM
The additional 5.4 m/s average monsoon
increased the surge height by ~40cm (7%relative change) whereas the additional 14.1 m/s
maximum monsoon prompted a ~70cm-rise inwater level (13% relative change). It should be
noted that only extreme tropical cyclone
scenarios were utilized in this study. The
increase of surge from SWM which reached up
to 13% may seem insubstantial to surges
brought about by extreme tropical cyclones but
this may be significant enough in the case oflower category tropical systems, most especially

National
CentersforEnvironmental
Prediction/National Weather Service / NOAA /U.S. Dept. of Commerce. 2000, updated daily.
NCEP FNL Operational Model Global
Tropospheric Analyses, continuing from July
1999.https://doi.org/10.5065/D6M043C6.

108

Utilization of Indonesia Natural Bentonite for Dye Removal
インドネシア産天然ベントナイトの染料除去への有効利用
15M18285

Harish Reza Septiano Warsono

Supervisor: Hirofumi HINODE

本研究ではインドネシア産ベントナイトの有効利用を目的とし、キャラクテリゼーション、ピラリ
ング処理、及び染料除去能の評価を行った。インドネシア産ベントナイトはスメクタイト族の結晶相を含むことが確認され、陽イオン交換容量及び比表面積を測定した。ピラリング処理により、ベントナイ
トの特性を改善することに成功した。また、インドネシア産ベントナイトの未処理及び、処理後の染料除去能を検証した。

1.

bentonite; to conduct pillaring treatments as efforts
to improve the properties of bentonite as well as toelucidate the effects of the pillaring treatment itself;
to evaluate the capability of bentonite and its modified
form in removing dye pollutants as an alternative
application of Indonesian natural bentonite.

INTRODUCTION

Bentonite is clay mineral with many applications
such as adsorbent, ion exchanger or catalyst support.
It exists naturally in area within the volcanic region,and is very abundant in Indonesia. However, its
utilization in Indonesia is still not yet fully maximized.
Indonesia still imports 20% of its bentonite
requirements [1].
This condition occurred mainly due to two
reasons: firstly from the demand side, consumers
avoid the use of local bentonite due to its unknown
characteristics which often lead to substandard
performance. Secondly, from the supply side, there
are only limited ways of use known for local bentonite,because almost all bentonite consumption in Indonesia
is for palm oil purification process. Efforts to overcome
these challenges in order to improve local bentonite
use, such as characterization and the proposal ofother usage of local bentonite, have to be conducted.
Additionally, as a natural clay, bentonite still has
a potential to be processed and improved. Many
researches have been conducted to improve bentonite
utility such as acid treatment, thermal treatment,
organobentonite and pillaring. The first threemodification treatments already have credibility as
robust treatments due to their clear mechanisms. As
for the pillaring treatment, despite many studies having
reported its ability to improve many of bentonite’scapability, it still contains a black box regarding its
mechanism and other treatment conditions which arevery challenging to be identified.
Therefore, addressing those problems andpotentials, the aims of this study are as follow: tocharacterize the properties of Indonesian natural

2.

MATERIALS AND METHODS

2.1. Material
The material used as the main target sample isnatural bentonite (NB) taken from Bogor Indonesia.
The clay was grinded and mixed in one batch in
the industrial grinder prior the usage in the laboratory.
No additional treatment conducted before experiment.

2.2. Characterization
Characterization of NB was conducted using thefollowing methods: x-ray diffraction analysis (XRD)
to analyse the crystalline structure of NB;
thermogravimetry and differential thermal analysis(TG-DTA) to investigate the thermal stability of NB;
Fourier transform infrared spectroscopy (FTIR) to
investigate the functional groups in NB; nitrogen
adsorption desorption analysis to measure the specificsurface area (SSA) and pore size; cation exchange
capacity (CEC) analysis using Chapman method[2]; and chemical composition analysis measured byinductively coupled plasma atomic emission
spectroscopy (ICP-AES).

2.3. Pillaring Treatment
Pillaring treatments were conducted using Al, Cu,
Mn and Ti metal precursors. The technique used in109the Mn and Cu pillaring experiment refers to themethod used in Drljalca et al. [3] which is also known
as the Base-Acid method. Al pillaring method usedin this research is the widely used method for Al
referring to the experiment conducted by Bertella etal. [4]. A simple method for intercalating and pillaring
bentonite with Ti by directly mixing bentonite with
Ti precursor inside ethanol was proposed. For
comparison, thermal treatment was also conducted.
All the details of the procedure used in this research
are shown in Figure 1. The bentonite treated using
Mn, Cu, Ti, Al and thermal treatment henceforth arecalled HMnB, HCuB, HTiB, HAlB and HB,
respectively.
All the modified bentonites were also characterizedusing the same analyses and methods used in the
NB characterization experiments. In addition,
experiment to determine the exchangeable cation
amount of each sample was conducted. 0.1 g ofsample was mixed to 12 mL sodium acetate 1.0 M
for 5 minutes, and was separated using centrifugation,
followed by collection of the supernatant. This
procedure was repeated for three times, and thecollected liquids were measured using ICP-AES for
cation other than Na.

The effect of time was investigated by mixing
0.05 g of bentonite to 50 mL of dye solution withknown concentration (Table 1). Samples were taken
at regular intervals and analysed using ultraviolet–
visible spectrometer (UV-VIS).
Table 1 Experiment Condition
Dye

Concentration

BentoniteMB

300 mg/L

NB

MO

200 mg/L

NB

NG

200 mg/L

HMnB

The study of the effect of initial dye concentration
to the dye removal performance was conducted by
the mixing constant amount of bentonite (0.05 g)
to 50 mL solution of dyes with varying concentrations.
The concentration range for MB, MO and NG were
50-500 mg/L, 25-300 mg/L and 25-300 mg/L,
respectively. The mixing was conducted in room
temperature and in natural pH of each dye.
The effect of pH was studied by mixing 0.05
g of bentonite to 50 mL of dye solutions with known
concentration, whose pH already adjusted to 3,5,7,9
or 11. The pH of the solutions were adjusted using
0.1M HNO3 and 0.1M NaOH. The dye concentration
and bentonite used in this experiment were the samewith the effect of time investigation experiment asshown in Table 1.
The wavelength used in the UV-Vis measurementof MB, MO and NG concentration in each experimentwere 663 nm, 464 nm and 714 nm, respectively.

2.4. Dye Removal Evaluation
In this study, the effect of time, the effect of
initial dye concentration and the effect of pH to thedye removal capability of bentonite and its treatedform were investigated. Methylene blue (MB),methyl orange (MO) and naphthol green b (NG)were used as the target dyes.

Figure 1 Details of Procedures
110Table 2 Basal Spacing and Intensity

3. RESULTS AND DISCUSSION

Before Calcination

3.1. Characterization
XRD pattern of NB shows that it contains smectite
mineral structure, which may be a mixture ofmontmorillonite and nontronite with the interlayer
spacing around 1.5 nm. This result was in good
agreement with the data from chemical composition
analysis that shows there are significant percentageof Mg and Fe exist as the structural cation in theclay. The existence of tridymite impurity was also
detected through the XRD and FTIR analysis.

After Calcination*d001(nm)I (%)

d001(nm)I* (%)

NB

1.57

102

1.00

41

AlB

1.81

100

1.60

67

MnB

1.57

110

0.98

49

CuB

1.48

78

0.99

60

TiB

1.51

112

1.01

48

I* = Intensity of d001/ intensity of d100
All the conducted pillaring treatment also
successfully increased the CEC and SSA of NB.
However, each pillaring agent caused different resultsin terms of average pore size of the bentonite bulk.
The result of CEC, SSA values and average poresize values of the samples are shown in Table 3.
Table 3 CEC, SSA and Average Pore Size
CEC

SSA

AVG.

(meq/100g)(m2/g)

Pore Size (nm)

NB

98

70.7

8.25

Figure 2 XRD pattern of NB

HB

83.2

69.8

8.32

NB was also found to have a relatively high cationexchange capacity (CEC) and specific surface area
(SSA) which were determined to be 98 meq/100
g and 70.8 m2/g, respectively.

HAlB

107.5

151.3

5.18

HMnB

107.4

92.4

7.08

HCuB

91.2

82.8

6.35

HTiB

125.3

113.9

4.96

3.2. Pillaring Modification

3.3. Dye Removal Evaluation

The intercalation of pillaring agent that is shownby the increase of basal spacing was observed insome of the pillaring treatment (Al and Mn). However,
Only AlB could maintain the basal spacing due tothe newly made structure bonds after heating. The
decrease in basal spacing in other pillaring treatments
after calcination was caused by the loss of coordinatedwater inside the basal spacing. Details of the basal
spacing data and peak intensities are shown in Table
2.
Despite the inability to maintain the basal spacing,the intercalation of metal was confirmed in thechemical composition analysis. Moreover, the additionof 5.61 wt% of Al, 1.57 wt% of Mn, 2.01 wt% for
Cu and 13.33 wt% of Ti were found to have remained
in bentonite bulk, not as exchangeable cation.

3.3.1. Equilibrium Time StudyIn the equilibrium time study, almost 48% of thetotal amount removal was achieved for all 3 target
dyes in the first 5 minutes. Equilibrium was alsoachieved in around 2 hours after mixing. Thisequilibrium time was used to determine the period
for the study of initial dye concentration effect.

3.3.2. Effect of Concentration
All of NB and its modified forms showed capabilityof MB and MO removal. However, in NG removal
test, only HMnB showed the removal capability. The
removal increased as the initial dye concentration
increased until the adsorbent maximum capacity wasreached. This means that the removal occurred dueto the existence of limited removal sites in the
bentonite.
111

In MB removal test, HMnB showed the highest
removal capability, followed by NB, HB, HCuB, HAlB
and HTiB, respectively. The removal process issuggested to be highly influenced by the synergistic
effect of the overall negative charge of bentonite
structure shown by CEC values and the average poresize which determines the likelihood of MB to enter
the bentonite bulk. HMnB has one of the highest
CEC values and a sufficiently large average pore size
which enable a high amount of MB removal.
In MO removal test, NB achieved the highest
removal amount, continued by HB, HTiB, HCuB, HAlB
and HMnB, respectively. The removal of MO isbelieved to be affected by the number of locally
positive-charged edge of bentonite platelet. The
thermal and pillaring treatment conducted in this study
bonded the edges with each other and decreased
the positive charge of the bentonite, causing a dropin the MO removal capability in the treated bentonite.
Only HMnB showed capability in removing NG
from the solution due to the size of NG which is
too big to enter the bentonite bulk. However, only
Mn had the capability to attack Fe in the NG structureand degrade it to smaller compounds which areremovable.

(A)

(B)

3.3.3. Effect of pH
From the study of pH, it is observed that theprocess of dye removal by bentonite was significantly
affected by the pH condition of the solution and thedifferent characteristics of each dye pollutant.
For cationic dye such as MB, the removal processcompetes with positive charge from proton so theamount of removal is reduced in lower pH andincrease in higher pH. Reversely, for anionic MO
and NG dyes, in the removal shows higher removal
in lower pH than in the higher pH due to thecompetition with hydroxide ion in the solution.

(C)

Figure 3 MB Removal Test (A), MO Removal
Test (B), NG Removal Test (C)5. REFERENCES
1. Panjaitan, Rumintang Ruslinda. The Usage ofBentonite in Industry. Surabaya : Surabaya Research and

4. CONCLUSIONSStandardization Center, 2010.
2. Cation-Exchange Capacity. H.D.CHAPMAN. s.l. :Agonomy Mnograph, Methods of Soil Analysis, 1965.
3. A new method for generating chromium(III)intercalated clays. A. Drljaca, J.R. Anderson, L. Spiccia,
T.W. Turney. s.l. : Inorganica Chimica Acta, 1997, Vol.
256.
4. Pillaring of bentonite clay with Al and Co. Francine
Bertella, Sibele B.C. Pergher. s.l. : Microporous andMesoporous Materials, 2015, Vol. 201.

Characterization of Indonesian natural bentonite
was successfully conducted and the result shows it
has adequate properties to be utilized. The conductedpillaring treatment also managed to intercalate themetal pillaring agent to the bentonite bulk. NB andits modified form were able to remove some dyepollutants from aqueous water system.

112

SYNTHESIS OF N-DOPED MESOPOROUS TiO2 BY FACILE ONE-STEP
SOLVOTHERMAL PROCESS FOR VISIBLE LIGHT PHOTOCATALYSIS
Student Number: 15M51800

Name: Erlandy Dwinanto TOESupervisor: Hirofumi HINODE

Abstract
In this study synthesis of modified TiO2 by non-metal N-dopant was done with one-step solvothermal
method. Under this process, both N-dopant and CTAB could be introduced thus creating N-doped
mesoporous TiO2 after template removal using calcination. Several characterizations were done inorder to determine the properties of the as-synthesized photocatalyst and also explaining the possible
band-gap reduction mechanism. Settling test was done to give qualitative description in the easinessof separation process. Performance of the as-synthesized photocatalysts were tested under visible
light (λ>400 nm) and was compared to other commercially available TiO2 photocatalyst.

1. Introduction
Water is one of the essential needs to support
human life, and as the population is projected to keepon increasing, not to mention the decreasing effective
amount of clean water due to contaminant and pollution
from industrialization, the needs for effective andefficient waste water treatment is imminent. Since mostpollutant is in the form of organic type, advanced
oxidation process is deemed good solution with the helpof photocatalyst. TiO2 is one of the well-known material
for photocatalyst because of good thermal andchemical stability and the fact that it is relatively
inexpensive. However, its band-gap of around 3.0 – 3.2
eV means TiO2 only active in the UV region wavelength.
Whereas, most of the solar radiation consist of visible
light with up to more than 40% and UV light is consistedof only 3 – 5 % from total radiation spectrum.
As a result, in this study band-gap modification of
TiO2 was done using nitrogen as non-metal doping toextend the workability range. The material was
synthesized under one-step solvothermal method inorder to get mesoporous structure. Porous structure isintended to have larger specific surface area without theneed for small particle size, thus help easing thephotocatalyst separation and recovery process. In
addition, characterization was done to give explanation
regarding the synthesis condition and mechanism of thevisible light photocatalysis.

2. Experimental Method
2.1. N-doped porous TiO2
In a typical synthesis, 1ml of TTIP (Ti{OCH(CH3)2}4,
Kanto Chemical co., inc.) is inserted into 10 ml ofethanol (99.5% Wako pure chemical industries ltd.) andmixed together with 0.185 g CTAB ((C16H33)N(CH3)3Br,
Kanto Chemical co., inc.) and 1 ml of 10x diluted HCl(35-37% Wako pure chemical industries ltd.). CTAB isused here as the template to create porous TiO2. The
mixture is stirred in magnetic stirrer for 30 minutes. After
that, certain amount of ethylenediamine (C2H4(NH2)2,
Wako pure chemical industries ltd.) are introduced as
nitrogen source (Table 1), and the mixture further stirredfor 60 min.
After that, the solution is transferred into teflon-lined
stainless-steel autoclave for solvothermal process at
200 ˚C for 16 h. The obtained precipitate was thenfiltered and washed after that dried overnight at 80 ˚C.
Afterwards, the powder form sample was calcined intube furnace under air flow with flowrate of 350 ml/h at
400˚C for 3 h. The ramp rate of the calcination is being
controlled at a low rate of 1 ˚C/min.

2.2. Settling Test
Settling test was done so as to determine the rate of
sedimentation of each used photocatalyst (assynthesized NTi1, Degussa P25, Wako anatase)113

Table 1. Variation of starting N:Ti molar ratio.
Titanium
Isopropoxide(TTIP)

1

EthanolicHClsolution

Cetyltrimethylam
monium bromide(CTAB)

Molar ratio: 50: 0.15Mixing for 30
minEthylenediamine
Added
C2H4(NH2)2
dropwiseMixing for 1 h
Heating at 200 ˚Cfor 16 h
Filtration andwashingDrying at 80 ˚CCalcination at
400 ˚C for 3 hFigure 1. Flowchart of synthesis process.

therefore easiness of separation and retrieval process
for each catalyst can be relatively compared with eachother. In this test, condition similar to the actual
photocatalytic test was used, where 100 ml of deionizedwater with 0.5 g/l catalyst concentration were stirred for
3 h after which each were placed in graduated cylinderand left observed for 1 h, 2 h, 4 h and lastly 24 h.

2.3. Photocatalytic Activity Test
Activity of the as-synthesized photocatalyst was tested
using Heraeus UV immersion lamp TQ 150, with the
distance between light source and solution being kept
at 10 cm. During the experiment cooling jacket wasused to maintain the temperature constant and film filter
was used so as to cut out the UV part (λ < 400 nm) of
the spectrum. Methylene blue (MB) (Kanto ChemicalCo., Inc) and methyl orange (MO) (Wako pure chemicalindustries ltd.) were used as model pollutant with initialconcentration of 10 ppm and catalyst amount of 0.5 g/l.
Prior to each activity test, dark adsorption was done bystirring the solution with catalyst for 60 min without any
light source. Next, photocatalytic activity was sampledperiodically. Afterwards the concentration was checkedby measuring the absorption using Shimadzu UV-1800
UV-vis spectroscopy.

SampleN : Ti molar ratio

Undoped

-NTi0.5

1:2

NTi1

1:1

NTi1.5

3:2

NTi2

2:1

3. Results and Discussion
First, by comparing the XRD pattern of the samples
to a standard reference, all of the as-synthesized
photocatalysts has been synthesized into thephotocatalytically active form of anatase phase TiO2
with peak at 2θ angle of 25.4°, 37.8°, 48.3° allconforming to the standard JCPDS card #84-1286 foranatase TiO2.
Moreover, the effect of solvothermal temperature
was also observed by varying the oven temperaturefrom 150 °C, 175 °C and 200 °C. It can be seen that at
solvothermal temperature of 150 °C, Ostawld-ripening
process was just started and that crystallization of TiO2
has not been achieved. In the solvothermal process
there will be breaking of metal alkoxide bond in titanium
tetraisopropoxide (TTIP) reactant. After that somenucleation will take place, and further excess heat will
be used for transformation from the amorphous phase
into crystallization and crystal growth. At the same time,
solvothermal was also intended so as to make themicelles of CTAB, which act as the pore forming
template, to undergo self-assembled and attached on
the TiO2 surface before later removed by thermal
treatment during calcination.

Figure 2. SEM images at x4000 magnification.

Based on SEM images, the as-synthesized powder
form TiO2 had a spherical morphology with relatively

114

narrow particle size distribution as shown in Fig.2 andaverage particle size of around 3 μm. FTIR spectrum for
all the synthesized samples were shown. Broad peak at
3400 cm-1 indicated the surface hydroxyl and absorbed
water. In addition, peak present at 1630 cm-1 for all
samples indicated the vibrations of O-H bonds, while
the deep peak observed at 700 cm-1 stands for Ti-O
stretching. The difference between undoped and allother N-doped TiO2 samples can be well observed
between the region of 1000 – 1550 cm-1 which is shownin more detail on Fig.3. The presence of peaks showedsome indication that N-doping had been successfully
inserted into TiO2, although further verification isneeded and will be done by XPS spectroscopy.

NTi2
NTi1.5NTi1
NTi0.5
Undoped1080 cm
1250 cm
1474 cm~0.1 eV band-gap reduction compared to the undoped
TiO2.
Average pore size and specific surface area were
calculated using BET method. Both the N2
physisorption characterization results of undoped andNTi1 showed relatively large surface area, that is of
87.82 m2/g and 69.18 m2/g, respectively. The porous
size of the as-synthesized catalyst is around 15 nm
which suggest that it is in the mesoporous region.

-1

-1

-1

Ti-N bondvibration

Figure 4. XPS survey scan of NTi1.

Figure 3. FTIR spectrum

Referring to the XPS survey result in Fig. 4, using
pass energy of 187.85 eV major peaks such as that of
O 1s at 532 eV, Ti 2p1/2 and Ti 2p3/2 chemical state at
465 eV and 461 eV, respectively can be observed.
Moreover, peak of C 1s which act as the reference forcalibration position is also seen at around 285 eV andN 1s is seen for energy level of 400 eV. For purpose ofdetailed analysis, narrow scans of each observed major
peaks were given under the smallest pass energy of
23.5 eV. In this respect, for the case of N 1s chemical
state, it was seen that only a single peak at around 400
eV is present. According to reference on several
possible N 1s spectrum, N 1s peak in TiO2 at 400 eV isindicating a Ti-O-N bonds, whereas peak at lower
energy of 397 eV indicated Ti-N bonds. This means thatit can be said that N-dopants has been successfully
inserted through the solvothermal synthesis route, andthat in this case the nitrogen atom would only act ininterstitial doping type in the form of Ti-O-N. Next, in
DRS characterization it was shown that NTi1 sample
slightly red-shifted to longer wavelength resulting in

Figure 5. Tauc plot comparison of all samples.

NTi1 catalyst have faster settling time, compared tothe other two photocatalyst of Degussa P25 (middle)
and Wako anatase (right), which means easierseparation and recovery process with properties
summarized in Table 2.
The photocatalytic activity of methylene blue
degradation went according to the following order: NTi1
> Undoped > Degussa P25 > Wako anatase > blank.
The photocatalytic activity of NTi1 under the visible lightirradiation (λ>400 nm) showing the highest MB
degradation rate with the methylene blue fully
decolorized under the NTi1 after 5 h of light irradiation,
whereas Degussa P25, Wako anatase or undoped TiO2

115

photocatalyst only show slight degradation, with MB
concentration still more than 50% even after 5 h ofirradiation while blank test condition showed very small
MB degradation. The same can be noticed for methyl
orange degradation, however with faster degradation
rate. After 2 h of visible light irradiation the 10 ppm MO
was showing complete photocatalytic degradation.
Based on both photocatalytic performance of NTi1, it iscan be said that the presence of N-doping in the form ofinterstitial doping of Ti-O-N, is able to extend theworkability range of TiO2 and induce the visible light
responsive of TiO2. This idea is supported by Nakamura,et al [1] which also found that vis light activity not caused
by Ti-N bond which come from the notion ofsubstitutional doping or oxygen vacancies previously
stated by many studies [2-3].
Table 2. Properties of NTi1 compared with otherphotocatalyst.
NTi1

Degussa, P25

Wako,

N-dopedTiO2; Mixed

TiO2
TiO2;

TiO2; Anatase

Anatase andAnatase~ 3 μmRutile~ 25 nm~ 125 nm

~ 70-80 m2/g

~ 50 m2/g

Chemical;
Form
Avg.
Particle Size
Specificn/aSurface Area

Photocatalytic Activity in MB
1

C/C0

0.8
0.6
0.4
0.2
0
0

50

100

150

200

250

300

Time (min)Blank

Undoped

NTi1

P25

Wako

Figure 6. MB degradation in vis light photocatalysis.

The mesoporous pore channel in as-synthesized Ndoped mesoporous TiO2 might not only enhance theseparation of photogenerated electron-hole pairs anddecrease their recombination rate, but also in this caseincrease the effectiveness of interfacial electrontransfer. The notion of increased electron transfer inporous structure, especially in the mesoporous channel,
is actually given before in multiwalled carbon nanotubes[4] as well as Co3O4-CeO2/C as highly active
electrocatalyst in oxygen reduction reaction [5]. This
would mean that e- photoreduction reaction especiallyof multi-electron reduction (+0.68 V) which will result in
H2O2 is the dominant degradation path-way:
O2 + 2 H+ + 2 𝑒 −  H2O2

(1)This H2O2 is a strong oxidizing agent which could
degrade MO more easily due to the cleavage of azo
bond. This was proven in further experimental test using
only H2O2 as oxidizing agent, with MO degradation rate
is found to be 36.6% compared to MB of 22.7%. In
addition, measurement of MB and MO solutions pH,
both are in the range of 6.0. Considering the point ofzero charge of TiO2 is at 6.80, there might be somepositive surface charge build up on the surface of TiO2
in the solution which will favour the adsorption of anionicdye of MO, whereas MB as cationic dye will have lower
adsorption due to charge repulsion.

4. Conclusion
The synthesis process of N-doped mesoporous TiO2
has been successfully done by using facile one-stepsolvothermal method. This synthesis method enabled
successful introduction of N-doping into the crystal
lattice of TiO2 as well to use CTAB as porous template.
The as-synthesized N-doped TiO2 managed relatively
large surface area of more than 70 m2/g, although theaverage particle size is around 3 μm. This is due to thepresence of mesoporous structure with average poresize of ~15 nm. NTi1 also had faster settling
performance as well as higher photocatalytic
performance under visible light source as comparedwith Degussa P25 and Wako anatase. Based on thecharacterization this photosensitization towards longer
wavelength was caused by the Ti-O-N interstitial dopingthat had the effect on extending the workability range
by providing band-gap reduction by introduction of N2p
energy band level just above the O2p valence band level.

References[1] Nakamura, R., et al. (2004). The Journal of Physical
Chemistry B, 108(30), 10617-10620.
[2] Asahi, Ryoji, et al. science 293.5528 (2001): 269-271.
[3] Ghicov, Andrei, et al. Nano Letters 6.5 (2006): 1080-1082.
[4] Nugent, J. M., et al. Nano letters 1.2 (2001): 87-91.
[5] Liu, Kun, et al. ACS applied materials & interfaces 8.50
(2016): 34422-34430.

116

DEVELOPMENT OF GPSDO-SYNCHRONIZED CHANNEL SOUNDER USING SOFTWARE
DEFINED RADIO
Student Number: 15M51740

Name: DEEPAK Gautam

Supervisor: TAKADA Jun-ichi

Abstract
In this thesis, time and phase synchronization using GPS Disciplined Oscillator(GPSDO) in low cost Software DefinedRadio (SDR) based channel sounder is presented as an alternative to existing synchronization techniques. Phase stabilityof carrier signals generated in Transmitter (Tx) and Receiver(Rx) using reference signals of GPSDO output in indoor
environment is studied thoroughly. Finally, two path channel is measured with developed GPSDO-synchronized channelsounder and result is evaluated by comparing with the result of reference sounder.

1

Introduction

tual procedure to obtain channel impulse response, computation is done in frequency domain. If Y (f ) is thefourier tansform of y(t) and X(f ) is fourier transform ofx(t), then channel transfer function H(f ) is obtained as

Performance of wireless communication system is heavily dependent on the environment between transmitter andreceiver known as propagation channel. The directionaland delay characteristics of the propagation channel areof great importance for designing such systems. Channel properties need to be measured in order to characterize
wireless propagation channel. Measurement system used
for channel measurement is known as channel sounder andprocess of measuring channel response is called channelsounding.
Channel sounder detects the electromagnetic waves
transmitted via particular communication channel todetermine the statistics of either channels time varianceimpulse response or time variant frequency response [1].From early literature to till date, channel sounders are
implemented either using off-the-self devices like Vector
Network Analyser (VNA) [2], Spectrum analyser or they
are designed using dedicated hardware components [3, 4].
Designing channel sounder using dedicated hardware
is being popular as off-the-self devices does not offer
flexibility to customize for measurement in more complex
environment. For example, VNA where transmitter andreceiver are in the same device is not suitable for large
distance channel measurement due to significant loss incoaxial cable extended from transmit and receive port
of VNA. Thus, in this study, we use SDR platform with
GPSDO to increase measurement range, reduce cost andfor convenience to carry due to its compact size.
1.1

H(f ) = Y (f )/X(f )Inverse Fourier Transform(ifft) of the channel transformfunction gives the channel impulse response h(t)H(f ) −→ h(t)ifft

Figure 1: Principle of channel sounding

1.2

Methods of Synchronization in channel sounding

Accuracy of the estimated parameters from the measured
data in delay and angular domain depends on the precisionof the time, frequency and phase synchronization between
Tx and Rx. In channel sounding system, phase synchronization depends on the performance of the local oscillator
and Phase Locked Loop (PLL) system used to synthesize
the carrier frequency.Phase noise affected channel transfer
function can be represented as:

Channel sounding methodology

In channel sounding as shown in Fig.1, known sounding
signal x(t) is sent from the transmitter at the desired
RF frequency. After being propagated through channel,
signal is received as y(t) in the receiver and is stored inmemory. From the information of the received signal,
channel impulse response h(t) of the propagation channel
is obtained

Hpn (f ) = H(f )exp(j(φ1 (t) − φ2 (t)))(1)

Here, φ1 (t) and φ2 (t) are phase noise introduced by local oscillator at Tx and Rx respectively.Phase noise is oneof the major source of directional characteristic estimation
error [5]. exp(j(φ1 (t) − φ2 (t))) in this work is represented
as the phase difference between carrier signals generated
in Tx and Rx. Tx and Rx are said to be phase synchronized when phase difference approaches to 0. Phase synchronization is necessary for accurate measurement of directional characteristics.

y(t) = x(t) ∗ h(t) + n(t)where, n(t) is the noise introuduced by the channel. In ac117

Highly accurate phase synchronization is achieved in
VNA based channel sounding where carrier signal fromsame local oscilaltor is shared in both Tx and Rx. For
frequency and phase synchronization in conventional
channel sounder, highly stable 10 MHz reference signal from Rubidium and cesium oscillators are used tosynthesize local signal. Either 10MHz signal from one
frequency reference source is shared between Tx and Rx
or separate signals from two or more than two frequency
reference sources are used separately. Later case which
is used in distributed channel measurement, frequency
offset between Tx and Rx exists due to free running
mode.Rubidium and cesium oscillators cost very highand extra synchronization affort is required in distributed
channel measurement. So, we are interested to investigate
the use of GPSDO as synchronization unit with Software
Defined Radio (SDR) based channel sounder. Motivationof selecting GPSDO is due to its low cost and also better
phase synchronization between spatially separated Tx and
Rx is expected as GPSDOs are phase locked with highly
accurate 1 PPS GPS signal.

2

signal generated by reference clock is utilized to trigger
the start of transmitting and receiving. For phase synchronization, highly stable 10 MHz signal generated bycommercial oscillator is utilized in Tx and Rx chain to
generate carrier signal. Three modes of synchronizations
are supported in USRP. In default mode, Temperature
Compensated Crystal Oscillator(TCXO) generates masterclock and reference to carrier signals. Secondly,in externalreference mode, USRP accepts 1PPS and 10 MHz signals
from any external oscillator. Finally in GPSDO mode,
1PPS and 10 MHz signal of GPSDO is utilized in which
GPS information can be tracked and GPS time can beused for time synchronization.

3

GPS Disciplined Oscillator (GPSDO) utilize GPS signal
received from satellites to generate stable frequency of 10
MHz.GPSDO connected to GPS antenna has to be lockedwith satellites in clear line of sight condition for at least 48
hours to perform at its best [6]. In order to use GPSDO under required condition, GPS antenna is installed in rooftop
of 4 stories building at south 6, Ookayama campus, Tokyo
Institute of Technology and the signal is carried to groundfloor with low loss cable,3D-FB as shown in Fig.3

SDR based sounder structure

Universal Software Radio Peripheral(USRP), low cost andflexible hardware peripheral hosted in the general purpose computer is used with free and open source software,
GNU Radio to make software radio. Structure of USRP
and GNU Radio platform is shown in Fig.2. In transmit
chain, complex signals generated from eq.(2) are sent fromhost PC to USRP through gigabit ethernet cable at complex sampling rate of 25MS/s. After digital processing inmotherboard, signals are upconverted to desired RF signal in analog quadrature modulator of daughterboard. Finally, RF signal is sent through antenna. In receiver chain,
RF signal received is downconverted in RF front end into
baseband signal. Then that signal is digitized by ADC andis sent to field-programmable gate array(FPGA). Finallythe data is sent to host PC through gigabit ethernet cable.
x(t) =

1
N

Description of GPSDO experiment setup

N/2−1

X

exp(j2kπ4F t + jφk )(2)

k=−N/22Here, φk = π kN , Newman phase is added to reduce crest
factor of multitone signal, 4F is frequency spacing between tones and N is total number of tones.

Figure 3: GPS antenna installed in rooftop

4

Experiment: Phase synchronization

Before utilizing output of GPSDOs directly to synthesize
carrier signals. Phase synchronization between 10 MHz
signals of two GPSDOs is presented in Section 4.1 bothin free running mode and GPS locked mode. After the information of phase synchronization of reference signals,
phase synchronization of 4.85 GHz carrier signals is presented in Section 4.2.
4.1
Figure 2: Structure of GPSDO-synchronized USRP and GNU
Radio based channel sounder

Between 10 MHz reference signals of GPSDOs

Performance of phase synchronization between output of
two GPSDOs in free running mode and GPS locked modeis examined from the experiment setup as shown in Fig. 4
and 5 respectively.

For time synchronization between Tx and Rx, 1PPS
118

Here, a and b are amplitude of baseband signal at transmitter and receiver respectively.

Figure 4: Phase synchronization measurement setup for freerunning GPSDOs

Figure 7: Back to back connection between Tx and Rx

Phase difference from measured data is plotted over timein Fig.8. From the result , it is found that phase is rotatedby 478o within 8 seconds and phase difference between Tx
and Rx signals seems to be increased linearly over time.
From this graph, it can be said that there is possibility ofprediction of phase rotation between Tx and Rx while using two GPSDOs.

Figure 5: Phase synchronization measurement setup for GPS
locked GPSDOs

Phase coherency between two GPSDO reference signals
is much improved when they are locked with GPS signal.
Fig.6 shows that the phase difference between two 10 MHz
reference signals measured over 72 seconds has standard
deviation of phase difference of 101.1706o when they are
free running. After being locked with GPS antenna, phasedifference deviation reduces to 2.5354o . From this result,it can be said that oscillators can be phase synchronizedwith GPS signal in indoor environment.

400

Phase Difference(Degree)

300

200
Free running
Locked to GPS signal

Phase difference(degree)

150

Measurement data
Linear fit

350

250
200
150
100
50
0

100
-50
50

-100
0

1

2

3

4

5

6

7

8

0

Time (sec)-50

Figure 8: Phase synchronization between 4.85 GHz signals
synthesized from separate GPSDOs

-100
-150

4.3

-200
10

20

30

40

50

60

70

Time (second)

Discussion on Coherency between 4.85 GHz carrier signals

To examine the phase difference between Tx and Rx at
4.85 GHz synthesized from 10 MHz frequency referencesof separate GPSDOs, back to back connection between Tx
and Rx is made. In this connection, Tx and Rx are directly
connected with 1m cable and 30dB attenuator as shown in
Fig.7. Complex sinusoid signal,x[n] of 10 Hz is sent fromTx with complex sampling rate of 12.5MSps at 4.85GHz
and received as y[n] at Rx.

Temporal correlation of phase is used as a mean to examine the phase coherency between transmitted and received
signal in this work. Higher the value of correlation between samples in time domain, higher the degree of coherency between them. Data received from back to backconnection, y[n] is used for this purpose. Autocorrelation is calculated from received data with the resolutionof 0.8 ms over the period of 4 second using eq. (5). Result presented in Fig.9 shows that phase of received signal
has high correlation over the period of 4 second.From thisresult, it can be said that two GPSDOs configuration canbe used for the directional measurement with duration of
PN/2
4 second∗
i=1,j=τ y(i).y (i + j)
(5)
R(τ ) =
PN/2∗
i=1 y(i).y (i)x[n] = a · exp(2πfb n)

Where, N is total number of received samples andτ ∈ [0 N/2]

Figure 6: Phase synchronization between 10 MHz signals of
two GPSDOs

4.2

Between 4.85 GHz signals of Tx and Rx synthesized from separate GPSDO(3)

If δφ is phase difference introduced due to Tx and Rx
chain, receives signal is represented asy[n] = b · exp(2πfb n + δφ)

5Evaluation of channel sounderTo validate the operation of the developed channelsounder, two path channel is measured using this channel

(4)
119

VNA
USRP

-50

0.98

-60
0.96

Gain(dB)correlation coefficientPower Delay Profile of Two Path Channel

-40

1

0.94

-70
-80

0.92

-90

0.9

-100
-110

0.88
0

Time lag(Sec)2
Delay(seconds)

Figure 9: Autocorrelation of received signals

Figure 11: Power Delay Profile of two path channel

0.5

1

1.5

2

2.5

3

3.5

0

4

sounder and also using Vector Network Analyzer as a reference for comparison. T-junction channel as in Fig.10 isused as two path channel . Multitone signal is sent from Tx
and received at Rx .Signal propagating through T junction
has two paths. One is direct path from terminal A to terminal B and another path is from C.This channel is measured
using developed sounder and VNA in similar setup whose
measurement parameters are tabulated in table 1. Power
delay profile of this channel measured using both GPSDO
synchronized channel sounder and VNA is compared.

aggreement.

Figure 10: Two path simulator setup

References

6

sounding signal
Transmit power
Frequency reference

3

4
×10-6

Conclusions and Future Research

In this work, GPSDO-synchronized channel sounder using
commercially available USRP and free and open source
tool, GNU Radio is presented for low cost channel sounding. Operation of developed channel sounder is validatedby comparing with VNA in two path channel at 4.85 GHz.
Coherency of carrier signals at 4.85 GHz synthesized fromseparate GPSDO in Tx and Rx is confirmed for 4 second
duration.
In future, phase coherency of carrier signals using separate GPSDOs for longer duration will be examined. This
GPSDO-synchronized channel sounder is expected to beextended in directional channel measurement at 4.85 GHz[1] S. Salous, Radio propagation measurement and channel modelling. John Wiley & Sons, 2013.

Table 1: Measurement parameters for two path channel

Center frequency
Bandwidth
Sampling frequency
Number of tones

1[2] S. S. Ghassemzadeh, R. Jana, C. W. Rice, W. Turin,
and V. Tarokh, “Measurement and modeling of an
ultra-wide bandwidth indoor channel,” IEEE Transactions on Communications, vol. 52, no. 10, pp. 1786–
1796, 2004.

4.85 GHz
12.5 MHz
25 MSps
128
Multitone signal with
Newman phase
0 dBm
GPSDO (Separate)[3] Y. Konishi, Y. Chang, M. Kim, and J.-i. Takada, “Versatile radio channel sounder for double directional andmulti-link mimo channel measurements at 11 ghz,”
IEICE Transactions on Electronics, vol. 97, no. 10,
pp. 994–1004, 2014.

Cable used in this setup is HUBER+SHUNER SYUCOFLEX100. Propagation speed in cable is 77% of speedof light. Length of cable connected at terminal C is 23 m.
Distance of second path is 46 m
46dsecondpath == 206 ns.
2.9 · 108 · 0.77
Where, dsecondpath is time taken to propagate throughsecond path. From our calculation, delay of second path isfound to be 240 ns. This is almost equal to the calculated
value of delay time. This result validates the operation ofchannel sounder to be correct. Also power delay profile
of two path measured with GPSDO-synchronized channelsounder and VNA as presented in Fig.11 shows good[4] M. Kim, K. Wangchuk, S. Sasaki, K. Fukawa, and J.i. Takada, “Development of low cost mm-wave radio
channel sounder and phase noise calibration scheme,”
COST Action IC1004 (EU), TD (15), vol. 12036,
2015.
[5] M. Landmann, Limitations of Experimental Channel
Characterisation. PhD thesis, 2007.
[6] “Manual FireFly-1A.” Available: http://www.jacksonlabs.com/assets/uploads/main/firefly 1A manual.pdf.

120

Estimation of National Logistics Cost Using Macro Economic Data
Student Number: 15M51733 Name: Shidong FENG Supervisor: Shinya HANAOKA
The measurement and comparison of national logistics cost are the focus of this study. By three criteria, Transportation,
Inventory and Administration Cost are found to be most appropriate components for national logistics cost measurement
and comparison. Cost elements of components are found similar across countries but differences still exist, according to
the literature review. Linear model and linear-log models are built for Transportation and Inventory Cost using macro
economic data. With concern of multicollinearity, all feasible models are kept. All independent variables have expected
signs and are consistent across models. Transportation and Inventory Cost have a trade-off with urban population and
GDP per capita, are significantly affected by industry share as % to GDP and urban population respectively, and are
negatively related to fuel price.

2 Logistics component & cost elements
2.1 Logistics components for national logistics
cost measurement & comparison

1 Introduction
Logistics cost can take up a high percentage in national
economy. Reduction of national logistics cost can benefit
national economy. Cost information is needed for the cost
reduction and can be obtained by national logistics costmeasurement and comparison. Because of the trade-off
between many components in logistics, not only should
the measurement and comparison should be conducted forthe whole national logistics cost, but also for the costs ofindividual logistics components.
According to Bowersox et al. (2005), the national
logistics cost measurement is hard to carry for mostdeveloping countries, as the rollup summation method
which is proposed by Heskett, Glaskowsky and Nicholas
in 1973 asks for too much data. Though Bowersox et al.
(2005) managed to solve this by using widely available
macro economic data like GDP, Population, Territory
Area, etc. and cost data from Heskett Method, they only
estimated national logistics cost as a whole without costestimates of individual logistics components and theArtificial Neural Network model they built is a black box
for interpretation. Despite the above, the use of macro
economic data for cost estimation of individual logistics
component on national level looks feasible.
For the countries that can measure national logistics
costs using Heskett Method, there are various logistics
components they use to report national logistics cost,which reduces comparability of their reports, a set ofcomponents for national logistics cost measurement andcomparison is needed for unification of components. As
cost for each component is a rollup summation ofcorresponding cost elements, cost elements also affectcomparability of components. A review of cost elements
in countries is needed.
Rantasila (2013) tried to unify components by
categorizing them into 4 categories. However, after
application, some categories indicate no expense for somecountries which is logically not likely. Also, as thecategories have never been used in any of the studies tothe best of my knowledge, the results it generates by thecategories may not be so referential and suggestive.
Other studies on logistics component and cost element
seldom solve problems for cross country comparison.
In the light of the foregoing discussion, there is thus aneed to (1) Identify the logistics components for national
logistics cost measurement and comparison and review
the cost elements in components and (2) Estimate the costfor logistics component on national level using macro
economic data.

A set of components for national logistics costmeasurement and comparison is desired for components
unification. To identify the components, a few criteria are
identified.
For the measurement, logistics components should
cover entire logistics-related costs. As there are trade-offs
between components like a lower inventory may be at thecost of more expensive transportation, the measurementof national logistics should cover entire logistics-related
costs to achieve global optimization of national logistics
cost.
For comparison, first, the most used components should
be used. Naturally, these components have highest
comparability. Second, there should be a balance between
number of components and comparability. Because themore components to keep, the less possible a comparisonof national logistics cost is. If the more disaggregatedcomponents are used for comparison, one should expect it
is more likely some components used in one country
should be broken down to be in line with the moredisaggregated component. However, in many cases there
seem to be no publication of detailed national logistics
cost estimation method or proper data sources to make thebreakdown possible. In contrast, if more aggregatedcomponents are used for comparison, it is easier forcountries to add up their disaggregated components to be
the aggregated components. But this aggregation makes
the comparison less informative about logistics activities,
so a level of disaggregation that provides possibly moredetail with a reasonable comparability is desirable.
Components that meet the above three criteria are
identified as components for national logistics costmeasurement and comparison.
Rantasila (2013) identified the times each component isused in national logistics cost studies by literature review.
According to the 23 studies about macro logistics costwith well-established model, components are listed frommost used to least used.
The total cost concept of logistics developed byLambert et al. (2006) identified 5 necessary logistics costgroups and basically they all can be included in the top 4
components in Table 1.
The Warehousing and Inventory Carrying Costs arereferred to as storage cost and inventory related cost
respectively like in Brazil, Finland, while Japan, USA
only use Warehousing for both, which makes it hard to121

converted to % to GDP by dividing the country’s
corresponding year’s GDP. National logistics costs as %to turnover cannot be converted, because turnoverincludes intermediate inputs, but GDP only refers to added
value, and there is no data on intermediate inputs onnational level.
For some countries where there are available cost data
for multiple years, 3 years that are not next to each otherare picked out to alleviate the bias of using continuous
years which may lead to a higher-than-real R2 andsignificance and also to provide enough degree of freedom
for independent variables. Given some independent
variables have available data only after 2005, the 3 years
are after 2005. Now, 14 Countries with 29 years are
considered as suitable data sources for model building.

compare. Also as the storage cost and inventory related
cost are closely positively related. A combination of
Warehousing and Inventory Carrying Costs to beInventory Cost promotes comparability without reducing
the information out of comparison too much.
According to above, components for national logistics
cost comparison and measurement are identified to beTransportation, Inventory and Administration. Various
logistics components will be adjusted to fit into these threecomponents according to their characteristics.
Table 1 Most often used components
in macro logistics cost studies

Table 2 National logistics cost measured as % to GDP
Country
Indonesia

ThailandChina

2.2 Cost elements in components

Japan

For the cost reports found, Japan, USA, Korea andSouth Africa give most details about how the national
logistics cost is obtained, and what the cost elements are,so the reports of these countries are reviewed.
Findings are (1) cost coverage of cost elements issimilar across countries and (2) differences in costcoverage do exist. (3) It is hard to make sure how different
one component is between two countries when how
disaggregated the cost elements are is different in the twocountries and (4) it is also hard to make sure whether twoelements refer to different coverage of costs when they are
not disaggregated in the same way.
Discussions are (1) the differences in disaggregation
may be a result of data source, so data availability appears
to be a decisive factor for the unification of cost elements.
(2) Basic comparability of logistics cost exists between
the four countries.

USA

South Africa
Korea
SwedenIndia
Netherland
Finland
Brazil
NorwayGermany

3 National logistics cost estimation
3.1 Cost estimation method
Among the cost estimation techniques in the review ofby Niazi, et al. (2006), the Regression Analysis Models
are preferred for this study, as it is applicable and allows
interpretation of results, which makes possible the use ofprevious knowledge to examine the results.

3.2 Dependent variables and data collection
Previously
identified
logistics
components
Transportation, Inventory and Administration for national
logistics cost measurement and comparison are intended
to be the dependent variables. The data sources areprevious studies on national logistics cost.
National logistics costs are measured as absolute value, %to GDP or % to turn over. This study chooses the metrics
of logistics cost measured as percentage to GDP, because
this term is widely used and comparable between
countries. National logistics costs as absolute value are

122

Year

Transportation Costs

Inventory Costs

Administration Cos

2005
2008
2011
2005
2007
2010
2005
2008
2010
2005
2007
2009
2005
2007
2009
2005
2008
2011
2005
2008
2005
2005
2008
2011
2007
2013
2008
2007
2014

12.81
11.04
11.63
8.50
8.90
8.70
10.20
10.00
9.60
5.30
5.50
5.20
5.90
6.20
5.00
7.69
7.55
7.69
8.90
8.80
2.94
6.70
6.60
6.60
3.34
3.74
6.90
6.69
3.83

9.92
9.64
8.73
8.10
8.20
7.60
5.80
5.90
6.00
2.70
3.00
3.00
3.20
3.50
2.60
4.01
4.89
3.41
2.00
2.80
3.68
0.10
0.10
0.10
3.46
6.05
4.20
5.66
4.16

4.77
4.34
4.28
1.70
1.70
1.60
2.50
2.10
2.20
0.40
0.40
0.30
0.40
0.40
0.30
1.20
1.46
1.51
0.80
0.90
1.40
1.90
2.80
4.00
0.82
0.98
0.40
1.16
0.58

All 14 countries included Transportation Cost,
Inventory Cost or/and Warehousing Cost andAdministration Cost in their national logistics cost studies.
It is just that some countries included more components
like Order Processing, Insurance, etc.
These extra components are usually part ofTransportation Cost, Inventory Cost or Administration
Cost according to both practices in other countries and thetotal cost concept of logistics developed by Lambert et al.
(2006). With easy summation, these extra components canbe aggregated with the Transportation Cost, Inventory
Cost and Administration Cost making a unified set ofcomponents. An example is Norway uses components ofTransportation, Warehousing, Cost of capital,
Obsoleteness and wastage, Insurance, Packaging, andAdministration. For unification, Transportation andPackaging are added up to be Transportation Cost, theWarehousing, Cost of capital, Obsoleteness and wastage,
Insurance are added up to be Inventory Cost andAdministration remains unchanged.
It is found there is no well-established method formeasuring Administration Cost in previous studies. Not
like Transportation Cost and Inventory Cost for which thecost elements are added up to measure the cost, theAdministration Cost is measured often as a ratio toTransportation Cost and Inventory Cost, and the decisionof the ratio though fully decides the Administration Costis usually not explained. So the regression model will only
be built for Transportation, Inventory.
Inventory Cost data of India is considered as outliers,
and taken out. It is too low compared to all the othercountries, even when developing countries often have ahigher Inventory Cost, and adding Inventory Cost data of
India into the model results in a worse R2 and significance.
It seems India fails to consider some necessary costs in
Inventory.

interpreted.
It should be noted that though Urban Population (% ofTotal) shows a positive sign in Transportation Cost, which
may be a result of more transportation activities in cities,
it has a negative sign in Inventory Cost, which may be
because of more aggregated demands that are cheaper for
inventory to server in cities. The above indicates a tradeoff between Transportation Cost and Inventory Cost with
Urban Population (% of Total). The sum of Urban
Population’s coefficients in Transportation and Inventory
Cost is negative, which means higher urbanization lowers
national logistics cost. This is in line with previous studies,though they only discuss the total effect without knowing
the individual effect on Inventory and Transportation Cost.

3.3 Independent variables and data collection
Many studies discuss factors about logistics cost on the
micro level. The data drivers can hardly find available
data for countries. So the studies about macro logistics
cost are referred to in the search of possible cost driverson the national level.
According to Rantasila (2013), all the factors inlogistics cost are affective directly or indirectly to all
logistics components. So all the possible cost drivers
found in literatures will be used for building
Transportation Cost and Inventory Cost model. In total 14
independent variables for each model.
DataBank of World Bank is chosen as the data sourceof independent variables for its wide coverage of items,
years and countries as well as the consistence, reliability
and comparability of World Bank data. Independent
variables use data that are corresponding to the year of
dependent variables’ data.

Table 4 Transportation cost linear models

Table 3 Independent variables and data source

Table 5 Inventory cost linear models

GDP per capita also has the similar case as Urban
Population. It has negative sign on Transportation Cost,
which seems to owe to more efficient transportation
realized by better infrastructure and facility when GDP
per capita is high. GDP per capita has positive sign on
Inventory Cost, which can be because of high percentageof labor cost in inventory cost when GDP per capita ishigh. The above indicates a trade-off between
Transportation Cost and Inventory Cost with GDP per
capita. The sum of the coefficients shows a higher GDP
per capita reduces national logistics cost, which agrees
with previous study, though they did not go into this detail.
Of course, full view of the effects on logistics cost shouldcome after inclusion of Administration Cost, but till now,
it is a good sign.
Fuel price is found negatively correlated toTransportation Cost. This can be explained, as it was
found with the increase in fuel price, there can be more

3.4 Multicollinearity
Multicollinearity is certainly an issue in regression
model building. There are several ways to solve it asreviewed by Dormann et al. (2013). To take out themulticollinear variable is one of the methods and is chosen
to be used in this study for its easiness of interpretation of
the results.
To use this method, it is necessary to find the cost
drivers, so that the multicollinear variables can be takenout. However, as there is no firm ground on what the cost
drivers are on national level, all feasible models are to be
discussed as a footstone for future studies.

3.5 Transportation/Inventory cost linear modelAll feasible models are with high R2 and adjusted R2
and all significant independent variables at 0.01 level.
Coefficients in models are consistent and can be properly123decrease in fuel consumption of transportation thantransportation activities, which means a more efficienttransportation that generates GDP with less cost.

4 Conclusion
A unified set of logistics components is good forcomparison across countries. With three criteria,
Transportation, Inventory and Administration are selected
to be the components for national logistics costcomparison and measurement.
This guarantees as many countries to do costcomparison as possible which gives countries morereference to evaluate their logistics performance moreaccurately.
As the costs for components are measured by summing
up cost elements, cost elements also affect comparability.
A review of cost elements in 4 countries is done. It isfound (1) cost coverage of cost elements is similar acrosscountries and (2) differences in cost coverage do exist. (3)It is hard to make sure how different one component is
between two countries when how disaggregated the costelements are is different in the two countries and (4) it isalso hard to make sure whether two elements refer todifferent coverage of costs when they are notdisaggregated in the same way.
The lack of data keeps countries from knowing andreducing their national logistics cost. An effort to estimate
national logistics cost by components is necessary. Data
from previous studies about national logistics cost andmacro economic data from World Bank are used to build
linear and linear-log models for Transportation Cost andInventory Cost. With concern of multicollinearity,
multiple feasible models are found for discussion. All
models can be properly interpreted and are consistent.
Industry share in economy is found to have a trade-off
between Transportation Cost and Inventory Cost, as it ispositively related to Transportation Cost while negatively
to Inventory Cost.
Also a trade-off between Transportation and Inventory
Cost is found on urbanization. Urban population as % tototal has negative relation with Inventory Cost andpositive relation with Transportation.
Fuel price is found to be negatively correlated withTransportation and Inventory Cost.
Industry share as % to GDP is found to have strong
positive correlation with Transportation Cost while urban
population as % to total has strong negative correlation
with Inventory Cost.
Limitation for this study is national logistics cost data
are not enough for the models to extrapolate to countries
that cannot measure their logistics costs.

3.6 Transportation/Inventory cost linear-log
model
The linear models measure whether one factor explains
the dependent variable, but it does not measure how strong
the explanatory power is, because all the factors arehaving different units. Linear-log model and log-log
model can measure how strong the power is because theymeasure the effect of 1% change in independent variables.
This study uses linear-log model to measure theexplanatory power as the log-log model is found to havetoo different independent variables from the linear models.
In this study, linear-log models can tell us how strong
explanatory power the variables have, and linear models
add confidence to the results when the significance andsigns of variables are in line with linear-log models, which
can also be referred to as being consistent.
Table 6 Transportation cost linear-log models

Table 7 Inventory cost linear-log models

Reference

All feasible models are with high R2 and adjusted R2
and all significant independent variables at 0.05 level.
Most independent variables are the same as those in thelinear models. All same variables are consistent in allmodes of both kinds and can be properly interpreted.
Transportation Cost is found to have a strong positive
correlation on the value added share of industry sector,
which can result from more demand of transportation inindustry sector. Inventory Cost was found strongly
negatively correlated to urban population. Fuel priceshows negative relation with Inventory Cost, which may
also be because of the more efficient use of fuel like theinterpretation in Transportation cost linear model.

1 Rantasila (2013). Measuring logistics costs-designing a generic model for assessing
macro logistics costs in a global context with empirical evidence from the
manufacturing and trading industries.
2

Bowersox, D. – Rodrigues, A. – Calantone, R. (2005) Estimation of Global andNational Logistics Expenditures: 2002 Data Update. Journal of Business Logistics, Vol.
26 (2), 1–16.
3

Lambert, D. – Grant, D. – Stock, J. – Ellram, L. (2006) Fundamentals of Logistics

Management. European edition. McGraw-Hill, Maidenhead, Berkshire, UK.
4

Niazi, A., Dai, J. S., Balabani, S., & Seneviratne, L. (2006). Product costestimation: Technique classification and methodology review. Journal of manufacturing
science and engineering, 128(2), 563-575.
5

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G. &Münkemüller, T. (2013). Collinearity: a review of methods to deal with it and asimulation study evaluating their performance. Ecography, 36(1), 27-46.

124

PASSENGER’S AIRPORT AND AIRLINE CHOICE IN A MULTIPLE AIRPORT REGION:
THE CASE OF GREATER JAKARTA
Student Number: 15M51785

Name: Fajar Fauzie RAKHMAN

Supervisor: Shinya HANAOKA

Presently, passengers in Greater Jakarta are having varied flight options due to two airports, Soekarno-Hatta andHalim Perdanakusuma, operated in a multiple airport region together with service that offered by two major airlines,
Garuda and Lion group, performed in the both airports. This study aims to estimate choice of passenger in the same region
by investigating significant factors of airport and airline choice, including the case when passengers are undertaking
departure and arrival flight using multinomial logit model. The investigation, furthermore, resulted that airfare, frequency,
access time, and airport queue were identified as significant factors. From segmentation estimation of departure andarrival, it resulted that the differences in access cost, airport queue and number of frequency become major differences.

significant factors in an opposing scenario between
departure and arrival activity as part of the choice model.
The prospective result is expected to give Indonesia’s airtransportation stakeholder insight about airport and airline
choice model, for instance for the government whether
Halim airport is still need to be opened as contingency
action after congestion in Soekarno-Hatta..
Competition between Soekarno-Hatta and Halim
airport and also the airline that operated in both airports
occurred quite intense. For instance, Surabaya and Solo as
one of the competitive route due to high frequency andcompetitive capacity production in both airports. From thegraphic below we could see that from full-service airlines
to low-cost carrier operated with average variation fare thatcan cover all segmentation of passenger.

1. INTRODUCTION
Greater Jakarta becomes region with the mostnumerous air traveler passenger in Indonesia. In 2009, thenumber of passengers that flew in Jakarta airport reached
37 million and it grown significantly to 56 million
passengers five years later by 51%. Initially, Greater
Jakarta operated solely one main airport, called SoekarnoHatta International Airport as main hub of all airlines inIndonesia. With the high number of passengers using
Soekarno-Hatta airport, however, the condition exceeded
more than double from the airport proper capacity and leadto congestion problem. To confront the situation, anotherexisting airport which located in city center of Jakarta,
Halim Perdanakusuma airport, a smaller airport locatednear city center which previously used for military airport.
Along with that, major airline groups in Indonesia, Garuda
and Lion group operated in both airports and served several
dense routes for the passenger through their full-service
and low-cost carrier, Garuda Indonesia and Citilink for
Garuda group; Batik Air and Lion Air for Lion group. The
situation, therefore, leads passenger to choose airport andairline which give them better utility as their flight pointcompared to the other alternative provided.

Figure 1 Location of airport in the Greater Jakarta

Since passengers in Greater Jakarta are provided with
two airports and several airlines which operated in both
airports, this study aims to estimate choice of passenger’s
in the same region by investigating significant factors of
airport and airline choice in the case of departure andarrival when undertaking a flight. The inventive
investigation is being examined in this study by comparing

Figure 2 Flight schedule between Jakarta and other cities

125

and daily frequency which can be seen the detail in thetable below as follow:

The discrete choice model is a group of proceduresthat explains decision maker, alternatives, attributes, anddecision rule. In general, consumer decision will be derived
by choosing an option which has greater utility function
compared to the other alternative. In this research, we
analyzed about which airport and airline that will be chosen
by passenger in Greater Jakarta.
Loo (2008) investigated passenger choice in themulti-airport region in Hong-Kong with stated preference
method and analyzed by multinomial logit model. The
result showed that airfare, access time, flight frequency,
and the number of airlines operated as significant factors
as the most important level-of-service attributes. Jiang-tao(2008) used multinomial logit model as a method to focus
on the prediction of airport choice in a multiple-airport
region and estimation using passenger data in Chinese
metropolitan area. For the airline choice, Hess and Polak(2005) conducted airport, airline, and access mode choicein the San Francisco Bay area with the main discussion ofpassenger choice was simultaneously determined in thatarea with airfare and aircraft size become attractivenessattributes as significant factors. Hopefully, this researchalso can add another insight towards airport airline choicemodel that has been conducted previously.

Table 1 Alternative and attribute setting for model estimation
Attribute

Description

Airline fare

Ticket price

Delay
probability
Airlineservice(dummy)Access time
Access cost
Airportqueue

Parameter
Indonesia
Rupiah (IDR)

Data Source
Respondent
data

Airline on time
performance
(OTP)

%

Airline OTP
reportAirline type ofservice

Full service (1), LCC (0) –dummy

Travel time to
airportTravel cost to
airport
Queuing time
(check-in orbaggagedelivery)

Hour
Indonesia
Rupiah (IDR)
Minutes

Respondent
data
Respondent
data
Respondent
data

According to economic theory, consumer orpassenger will choose alternative that has greater utility
function; where 𝑈 is utility function; 𝑖 is alternative; 𝛽
is parameter to be measure; 𝑋 is attribute explained; 𝜀 isthe disturbance term.
𝑈𝑖 = 𝛽0 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + ⋯ + 𝛽𝑛 𝑋𝑛 + 𝜀(1)=

𝛽𝑛 𝑋𝑛 + 𝜀𝑛Logistic regression or logit model is one of the mostpopular practical methods to analyze passenger choice byassuming the choice of probabilities among alternative
provided; where 𝑃 is probability of passenger choice; 𝑖is alternative provided; 𝑈𝑖 is utility function; 𝑘 is choicealternative.
exp(𝑈𝑖 )𝑃𝑖 =(2)∑𝑘 exp(𝑈𝑖 )2. METHODOLOGY
For this research, the survey to airport passengers was
conducted during 8th to 17th March 2017 in Jakarta’s
airports, Soekarno-Hatta and Halim Perdanakusuma,
which can covers all segmentation of passengers inIndonesia domestic market. It obtained total 671 answers
from respondents of both airports, with specification 317
respondents from Soekarno-Hatta airport and 354 answers
from Halim airport. There are two major purposes of thepassenger from survey obtained, with 56% traveled forbusiness purpose, and 44% went for non-business purposessuch as holiday or leisure, visit relatives or friends, andgoing back to their hometown. Since the survey used thestratified method, the survey divided by respondents fromseveral major airlines which dominate in Indonesia
aviation and belong to the major competitive group,
Garuda Indonesia and Citilink from Garuda Group, andLion Air and Batik Air from Lion Group.
Variables that generated during the survey is mostly
represented important and significant factors that affectpassenger decision in choosing the flight. Since thisresearch used four alternatives of choice for passenger
which combination of airport and airline choice called;
Soekarno-Hatta – Garuda Group, Soekarno-Hatta – LionGroup, Halim – Garuda Group, and Halim – Lion Group,
therefore, the model estimation that built in this research
was established by using multinomial logit model. Airport
attributes consist of airport access time, airport access cost,and airport queue. On the contrary, airline attributes consist
with airfare, delay probability, airline service (dummy),

This study exercised logistic regression to conductedsignificant factors that determined passenger preference infor airport and airline choice. To conduct whether
passenger decision is determined by airport or airline
attribute, the level nested model conducted to solve theproblem.
3. RESULTS
3.1. Survey Results
Passenger background was examined to know
characteristic of passenger in both airports. The majority of
respondent dominated by an active production age between
26 to 55 years old. From the survey, Most of them are
employee from public and private institution in Indonesia,
where the higher income (>20 million IDR) tend to choose
Halim rather than Soekarno-Hatta airport. For Garuda
passenger, it distributed in low-income (1-3.9 million IDR)
and high income (10-19.9 million IDR), while Lionpassenger spread in medium income (3-9.9 million IDR).
It revealed that passengers in Halim airport are morefamiliar to fly from both airports, while passengers inSoekarno-Hatta airport was not familiar fly as passenger in
Halim airport. It shows that only 39% of passenger of126

Hatta choose their flight because of airline factor andHalim passenger chooses their flight because of airport
factor. It revealed that greater schedule that leads airline tooffer more frequency of flight create Soekarno-Hatta
Airport advantage point to attract the passengers. Moreover,for passengers who are sensitive to level of service and not
too considered ticket price, such as Garuda Indonesia
passengers, choose the airport Soekarno-Hatta Airport
since the airline only operates at this particular airport. On
the contrary, Halim airport is very reliable with its location
as the main attraction of passenger to choose the airport to
reduce access time and access cost to the airport.

Soekarno-Hatta had ever flew from Halim airports.
However, almost all respondents from Soekarno-Hatta
airport already know if Halim airport now operated again
in Jakarta, as 90% of respondent knowing that currently
there are two airports carried on in Jakarta.

Figure 3 Reason to choose airport

For respondents who chose Soekarno-Hatta airport,
flight frequency and airport location become importantreasons why they choose the airport. It is known that since
Soekarno-Hatta capacity is larger compared to Halim, theairport offers more schedule frequency rather than Halim.
Therefore, passengers who are sensitive to schedule time,
especially the frequency of flights tends to choose
Soekarno-Hatta rather than Halim airport. Halim
passengers revealed that airport location, access congestion,
and access cost to the airport became important reasons forthe passenger in Halim to choose the airport. Because of
Soekarno-Hatta airport is 27 km away from business
district center and located in west part of Jakarta, while
Halim airport located 12 km in the east, the location of the
airport can be one of advantage to be chosen by respondent.
We analyzed segmentation between departure andarrival passenger and asked them reasons to choose airport
and airline in Greater Jakarta. It revealed that, there are no
significant different factors between departure and arrival
passengers when choose the airport. For the airline choice,departure and arrival passengers valued airfare ticket, fitin schedule as significant factors, and good service andgood safety become the difference factors between both ofthem.
All in all, from preference survey that had been
conducted in both airports, the passenger in Soekarno127

Figure 4 Reason to choose flight

3.2. Model Estimation
Endogenous data sampling method used in thisresearch to follow actual condition of Indonesia’s airtransportation and minimize bias of the result. SoekarnoHatta passenger picked more than Halim passenger tofollow actual condition based on current capacity
production in each airport. Number of Lion grouppassengers are greater than Garuda group’s passenger
according to current market share of seats in Indonesia’s
domestic market. Furthermore, respondents who don’tknow their airfare and two airports operated in Jakarta are
excluded in the model estimation. Several attributes
introduced to be estimated in the model including bothalternative factors namely; airline service (ALSVC),
airport cost (APCOST), airport queue (APQUEUE),
airport access time (APTIME), airline fare (FARE), andairline number of frequency (FREQ). Alternative specificconstant (ASC) was observed and estimated in the model
to represent the utility of respondent that is not captured byintroduced attributes but well known as respondents
consideration. Total number of observations is calculated
based on market share seats capacity of airline in Indonesia

domestic market.
In the full model estimation, airport queue (value = 10.3) and airport access time (value = -0.982) havenegative utility value which explains that respondents inGreater Jakarta are happier with shorter time to access the
airport, quicker check-in queuing time before boarding to
the aircraft, and faster baggage delivery after they landed
in destination airport. Airline fare (value = -3.40) andairport access cost (value = -2.85) also show negative
estimation, indicate that passenger valued lower ticket
price and total trip cost to or from the airport. On thecontrary, airline service (value = 0.890) and the number of
frequency (value = 1.44) have positive utility value whichmeans that passenger in Greater Jakarta is the more valued
full-service carrier in choosing an airline and greaternumber of frequency to be picked as their flight choice. In
this model estimation, airport queue (p-value = 0.00),
airline fare (p-value = 0.00), and number of frequency (pvalue = 0.00), and airport access time (p-value = 0.01)
were identified as significant factors at level of 5%.

Table 3 Multinomial logit (departure and arrival)
Name

Unit

Value

Std err

ASC_CGKGA

0.00

Fixed

ASC_CGKLION

0.0605

0.546

0.11

ASC_HLPGA

-1.61

0.672

-2.39**-0.565

0.546

-1.03

0.890

0.647

1.38

ASC_HLPLION

b_APCOST

-2.85

5.09

-0.56

b_APQUEUE

Minutes

-10.3

1.10

-9.34**

b_APTIME

Hours-0.9820.390-2.52**

b_FARE

IDR

-3.40

0.772

-4.40**

b_FREQ

Number

1.44

0.490

2.94**b_ALSVC

Number of observations

218

Adjusted rho square

0.593

t-value

ArrivalValue

t-value

0.00

0.00

ASC_CGKLION-0.267-0.42-0.593-1.22

ASC_HLPGA

-1.21

-1.33

-3.83

-3.74**-1.71-2.21**-5.29-5.03**b_ALSVCFSNC (1)/
LCC (0)1.37

2.21**

2.49

3.06**

b_APCOST

IDR

-22.5

-2.74**

-9.84

-0.99

b_APQUEUE

Minutes-2.61-2.29**-0.896-0.69

b_APTIME

Hours

-1.08

-2.11**-3.15-4.52**

b_FARE

IDR-3.81-5.47**-5.27-5.33**

b_FREQ

Number

1.48

2.62**-0.580-1.14

Number of observations

115

103

Adjusted rho square

0.431

0.439

** Significance at level 5%, * Significance at level 10%

Moreover, a nested logit model, was estimated toobserve which factor that majorly considered by thepassenger when undertaken a flight. Airline service, airline
fare, and the number of frequencies belong to airline nest,
and the other; airport cost, airport queue, and airport accesstime are included in airport nest. After the simulation had
been run, however, the program diagnosed that model
estimation does not fit because diagnostic of the model
resulted that maximum number of iterations reached,which fails to maintain random maximized utility. We
assumed that the error caused by the number of samples
that observed in the model was not enough to be estimated,thus larger samples required in the future.

t-value

FSNC (1)/
LCC (0)IDR

Departure
Value

ASC_CGKGA

ASC_HLPLION

Table 2 Multinomial logit (full model estimation)
Name

Unit

4. CONCLUSION
From the study, it can be concluded that airport choice
in Greater Jakarta is determined by airport location andaccess congestion for Halim respondent, and for SoekarnoHatta’s passengers, number of frequency and airport
location become the main reason in choosing the airport.
For the airline choice, good service and good safety
become the difference factors between both of them
departure and arrival passengers. The choice of Halim
airport is based on airport factor and Soekarno-Hatta isbased on airline factor. In multinomial logit, full model
estimation resulted that airfare, frequency, access time, andairport queue as significant factors and segmentation
estimation resulted the differences in access cost, airport
queue and number of frequency for passenger when
undertaking departure and arrival flight. From nested logit
experiment, the result showed that structure doesn’t fit thisresult because radius of the trust region is too small.

** Significance at level 5%, * Significance at level 10%

To conduct deeper investigation, segmentation
analysis for departure and arrival passengers was observed.
From the result, there are different motives of choice
between departure and arrival passenger. Access cost,
airport queue, and the number of airline frequency become
differentiator factors between both of segmentation.
Number of frequency (p-value = 0.01) become significant
factor at 5% for departure passenger, but it did not reflectin arrival passenger result (p-value = 0.25). We assume thatdeparture passenger is more valued number of frequency
so that they tend to find an alternative which offers themmore flight frequencies. Furthermore, access cost become
significant factor for departure passenger (p-value = 0.01),
while the result did not led to departure passenger (p-value= 0.32), and airport queue is significant for departure
passenger with (p-value = 0.02), while for arrival passenger
is tend not to be significance with (p-value = 0.49).

REFERENCES[1] Hess, S and Polak, J. W. (2005). Mixed logit modelling of airport choice in multi-airport
regions. Journal of Air Transport Management. 11, p59-68.

[2] Jiang-tao, L. (2008). Airport Choice in Multi-airport Regions: An Empirical Study for
[3]

128

Chinese Metropolitan Area. International Conference on Intelligent Computation
Technology and Automation. p329-332.
Loo, B.P.Y.. (2008). Passengers’ airport choice within multi-airport regions (MARs): someinsights from a stated preference survey at Hong Kong International Airport. Journal ofTransport Geography. 16 (1), p117-125.

Operational sustainability of rural electrification program through an effective fee collection
strategy: Case study of the Philippines
Student Number: 15M51779

Name: Keshav Raj POKHREL

Supervisor: Naoya ABE

Rural electrification in remote areas of developing countries with renewable energy sources has been a major phenomenon
for the past few decades. Operational sustainability is a fundamental concern amid the rapid diffusion of renewable energy
technologies in the developing countries. This research assessed the present status and problems being faced by the Solar
Home System (SHS) users in a rural and remote island of the Philippines. It has been found that more than 90% of theusers have already stopped paying their monthly tariff despite the ability and willingness of payment. Observation of theincome and expenditure pattern of those users revealed a major strategical difference between the payment for SHS andthe payment for other daily basic utilities. This study suggests a fee collection system for electricity on a daily basis rather
than on a monthly basis to ensure the operational sustainability of SHS projects.
Keywords: Rural electrification program, Operational sustainability, Fee collection strategy

1. Introduction
Rural electrification is a process of electrifying rural andremote households. Renewable energy sources have beenplaying a vital role to address the energy scarcity problemof the developing countries. Over the past five years, themarket for basic solar powered lights and small homesystems with multiple lights, and basic appliances havegrown rapidly in developing countries, with more than 24
million units sold [1]. Approximately 1.2 billion people arestill without access to clean energy in 2016 [2]. Rural
electrification plays a significant role in poverty reductionand economic improvement of these people. However, theimplemented projects in rural areas are facing financial andtechnical challenges, which complicates the long-term
operation of these systems. The story does not end withinstallation of new technologies, and thus operationalsustainability has been a major concern.
The major problems faced in the operation of rural
electrification programs in developing countries are related
to financial ones, such as: (a) users’ inability to pay theusage fee because of unaffordability, (b) users’
unwillingness to pay the usage fee which might result fromlow satisfaction with the electricity provision or unfeasible
payment strategy, and (c) budget deficiency for
maintenance of the project due to stoppage of subsidy fromnational and international entities and lack of collected
tariff [3,4]. Williams et al. and Hong and Abe also
highlighted the negative impacts of poor payment morality
and budget deficiency, which challenges the sustainabilityof rural electrification projects [5,6].
This research is a field survey based study. First,
literature review was conducted to identify the prospects ofoff-grid electrification in developing countries and to
explore the existing challenges. A field visit was made to
the study area to find out the obstacles faced by the program
through interviews and questionnaire surveys. Data
analysis was performed with the collected primary andsecondary data. At last, an effective solution is proposedthat would help to ensure the operational sustainability ofrural electrification program in the study area.

2. Objectives and Significance
2.1 Research Question
Why do rural electrification programs face budget
deficiency?

2.2 Objectives
1. To investigate the present status and the problems in acase of rural electrification program
2. To analyze the income pattern and the expenditurepattern of users for basic daily utilities
3. To propose an effective fee collection strategy that
ensures the operational sustainability
2.3 SignificanceBy analyzing a real case scenario, this study proposes aneffective strategy to address the underlying problems forsustainable operation. It aims to contribute to the national
and international bodies to make rural electrification
program more robust and sustainable. Moreover, this study
will support the researchers and academicians working onsustainability of renewable energy projects by shedding alight on the practical aspects of sustainable development.

3. Study Area
The study area is “Alumar Island”, which is a small ruralisland located at 3-hrs boat ride from Cebu Island,
Philippines. It has a population of about 1160 (228
households) with fishing, seaweed and stores as major
occupations. In 2008, the Philippine Department of Energy(DOE) in partnership with Japan International Cooperation
Agency (JICA) provided solar home systems (SHS) to 50
households of this island [7]. This project was under thePhilippine government’s program titled as “Sustainability
Improvement of Renewable Energy Development in
Village Electrification”. Each of the 50 households were
provided with a complete SHS that is composed of solar
panel (50-75 Wp), a charge controller, a lead-acid battery,
and a few lightning bulbs. Users paid 1,500 pesos as an
initial fee to receive the system. Barangay Alumar Power
Association (BAPA) was established as an electriccooperative responsible for monitoring and maintenance ofthe solar units. Each household is required to pay 200 pesos
as a monthly tariff, which is used as savings for the
replacement of damaged battery, panel or controller and anhonorarium for fee collector and technicians.(1$= 50 pesos)

4. Methodology
4.1 Case Investigation
Interviews were conducted with the electric cooperative
BAPA to understand the overall scenario of the project andto find out the problems that challenges the sustainabilityof the program. The interview revealed the existence of129

poor payment morality among the SHS users. Because ofthis, BAPA was undergoing a serious financial crisis andcould not respond to users’ request for the replacement of
the batteries and other support.
4.2 Data Collection
Face-to-face interviews were made with 48 users of SHS of
Alumar island using questionnaire survey to obtain the
primary data of the categories as mentioned in Table 1.
Table 1: Variables of data collection.
Category
Present Status of
SHS
Training
and
Maintenance
Additional
Sources of Energy
Satisfaction levels
with SHS
Personal
Information
Incomeandexpenditurepattern
Priorities Ranking
Miscellaneous

Details
Components of SHS, Appliances being
operated, Record of repair or replacement
Things learnt during training program, Typesof maintenance services, Satisfaction levels
Types of additional sources of energy being
used, Expenses for them
Capacity of SHS, Availability of electricity,
Monthly tariff
Gender, Age, Occupation, Education, Income,Number of family members
Sources of income, Methods of earnings,
Expenses for other basic utilities, Methods ofexpenditure
Rank the priorities among the basic needs
Renewable Energy Knowledge, Willingness topay (WTP)

4.3 Data Analysis
4.3.1 Descriptive Analysis
The descriptive analysis was performed on the
demographic data of the users like gender, education, age,income level and satisfaction ratings of the users tounderstand the proportions of users in different categories
and to understand the percentage of users who weresatisfied or not satisfied with the current SHS.
4.3.2 Chi-square Test of Independence
It is used to determine a significant association between thetwo categorical variables. In this study, the pricesatisfaction data is used as Variable A with two categories(Satisfied and Not Satisfied) and 13 other categoricalvariables as Variable Bm (m = 1,2,……,13). This test is
used to identify the association of price satisfaction withother users’ responses and attributes.
At first, a contingency table is created using Variable A
and Variable B1. The expected value for each cell of thecontingency table is calculated using eq. 1. Using eq. 2 andeq. 3, the Chi-square value (χ2) for the table is calculated.
Using this χ2 value and degrees of freedom (df) (eq. 4), pvalue is obtained from Chi-square table of significances.
𝐸𝐴𝐵1 =𝑀𝑅 ∗𝑀𝐶𝑁(eq. 1)

Where:
EAB1 = Expected value of that particular cell
MR = Sum of row values for that cell
MC = Sum of column values for that cell
N = Total sample size
2
𝜒𝐴𝐵1
=

(𝑂𝐴𝐵1 −𝐸𝐴𝐵1 )2𝐸𝐴𝐵12
𝜒 2 = ∑𝐶𝐵1=1 ∑𝑅𝐴=1 𝜒𝐴𝐵1

(eq. 2)(eq. 3)df = (Number of rows - 1) * (Number of columns - 1) (eq. 4)

Where:
OAB1 = Observed value (actual value)2
𝜒𝐴𝐵1= Chi-square value of each cell2
χ = Sum of Chi-square values of all cells
If the p-value of the table is less than 0.05 (significancelevel), it indicates that there is a significant association
between the two categorical variables.
4.3.3 Cluster Analysis
This study uses cluster analysis, an exploratory dataanalysis technique, to classify the SHS users’ data into
various clusters in order to obtain the similarities anddissimilarities of responses among the users. This study
uses hierarchical agglomerative method in which thealgorithm joins together similar observations one level at atime until all observations are categorized as one group.
Similarity and dissimilarity is a term to depict the
distance measure among groups whose criterion is termed
as linkage method. There are three major linkage methods
to join the observations: single-linkage (closest pairbetween two clusters), complete-linkage (farthest pair), andaverage-linkage (average distance between all data points).
This study adopted complete linkage method in which the
distance between the farthest pair of observations in two
clusters is measured and the two clusters that have thesmallest complete linkage distance are combined.
The output of the analysis gives a cluster dendrogram
which shows the tree of clusters formed. In this study, 4different clusters were considered and peculiar
characteristics of the users in each cluster were analyzed.
4.3.4 Income and Expenditure Distribution
Since the major occupations of people in Alumar island
were fishing, collecting shells, owning a store, the income
depended on the factors such as number of working days,
amount of catch of fish, types of fish, market price of the
day etc. Thus, the residents responded their monthly
income in intervals like less than 3k pesos, 3k-6k pesos, 6k9k pesos and so on. Using this data, the mean and standard
deviation of the income were calculated. Lognormal
distribution was used to make a good approximation ofincome distribution for daily and monthly pattern taking
reference from [8]. For the expenditure pattern, 2 major
studies were taken as references. Secondary data were
mostly used for this analysis, household final consumption
expenditure data from Philippines Statistics Authority andexpenditure pattern for poor households in Metro Manila ofPhilippines from [9]. Adjustments were made according to
the scenario directly observed in Alumar island and an
expenditure distribution was created.
4.4 Solution Proposal Criteria
An effective solution to the existing problem of nonpayment has been proposed. Here “effective” is defined interms of 3 criteria:a) Affordable: The amount of tariff must be less thanusers’ WTP for electricity which in turn should be arealistic response in comparison with their income
amount.
b) Feasible: The payment method for electricity should
be compatible with users’ expenditure strategy forother basic utilities

130

c)

Table 2: p-values for association with Price Satisfaction.

Sustainable: The collected tariff should provide
enough revenue for BAPA to replace the damaged
batteries, panels or controllers and provide anhonorarium to fee collectors and technicians.

Variables (Bm)
Availability
Capacity
MaintenanceTraining
GenderAge
Education

5. Analysis and Discussion
5.1 Descriptive Analysis
Gender1

23

13

FemaleAge Group (yrs)
22
15

11

Number of users

34

Variables (Bm)p-value
Family Size
0.26
Keep Solar
0.11
Knowledge
0.04*
Available Hours
0.07
WTP
0.01*
Income0.17* means significant at 0.05 level

5.3 Cluster Analysis
Four clusters were selected from 25 users for theobservation. Table 3 summarizes the major properties ofeach cluster. Users were making significant payment foralternative fuels and their WTP for electricity wassignificantly high in each group despite having low pricesatisfaction. The average WTP was 334 pesos while only
10 users responded less than 200 pesos. Thus, amount oftariff being expensive was not a problem. However, users
were not making payment for SHS. To explore the reasons
behind this, income and expenditure pattern were analyzed.

PrimaryMale

25

Number of users

Education Levelp-value
0.2
0.2
0.94
0.43
0.08
0.17
0.34

High
SchoolUniversity

Monthly Income(pesos)
25
11
7
2 3

Table 3: Average data of 4 clusters.

<40 40-60 >60

FactorFigure 1: Demographic Characteristics of SHS users.

Figure 1 reflects the basic demographic characteristics of
SHS users in Alumar island. Alumar, being a rural andremote island, most of the users’ education level was
primary and monthly income was quite lower (mostly in the
3k-9k range) than national average (22k pesos/household).
Approximately 90% of the users have already stopped
their monthly payment of 200 pesos for more than a year.
The general assumption is that users were either dissatisfied
with the electricity provision or price or services from
BAPA. The satisfaction rating from Figure 2 shows thatusers were quite satisfied with all the services they werereceiving from BAPA (maintenance and trainings) and withthe solar home system itself (capacity and availability ofelectricity). However, half of the users were not satisfied
with the price. To understand the reasons behind this pricesatisfaction/dissatisfaction, chi-square test was performed.
Satisfaction Levels
MaintenanceTraining
Price
Capacity
Availability

NumberAge (yrs)
Price Satisfaction
Alternative fuels
payment(pesos)
WTP (pesos)
Available hours

Group
1
9
53
3

Group
2
9
39
3.7

Group
3
1
35
2

Group
4
6
47
1.8

138

590

200

132

192
4

578
4

1200
3

258
1

5.4 Income and Expenditure Distribution
The earnings from fishing, seashell collections and stores is
on a daily basis and is uncertain and vulnerable to manyfactors. The average number of working days for afisherman varied from 18 to 22 days with daily income
ranging from 200 to 400 pesos (Figure 4). With thecalculated mean and the standard deviation of the monthly
income as 5,600 pesos and 3,000 pesos respectively, alognormal distribution of monthly income has been
generated using exponential of normal distribution functionin EXCEL software and is shown in Figure 3. The income
varies each day and each month for all households.

0%
20%
40%
Satisfied
Neutral60%
80%
Unsatisfied

Income (pesos)

8000

100%

6000
4000
2000

Figure 2: Satisfaction Levels of SHS users.

5.2 Chi-square Test of IndependenceChi-square test was performed using price satisfaction as
variable A and all other 13 variables mentioned in Table 2
below as variable Bm (m ranges from 1,2,…., 13). It reveals
that price satisfaction has no dependency on monthly
income, demographics, electricity provision and services
from BAPA. Users having renewable knowledge andhigher WTP were found to be satisfied with the price. To
understand the spread of WTP data among the users(measured by stated preference open ended WTP method)and find the similarities and dissimilarities by classifying
them as groups, cluster analysis was performed next.

Monthly Income
Average
Figure 3: Monthly income distribution pattern

The expenses for the daily basic utilities close to 200
pesos are summarized in Table 4. With the data available
for clean water and fuels expenses as shown in Table 5,
expenditure pattern for these utilities were analyzed. For
clean water, an average household consumed 6-7 containers
per month. However, they bought only 1-2 containers at
once. Water stands were visible where people could buy
300 ml of drinking water with 1 peso.
For

131

collectors. However, centralized deposits such ascollection in teashops and school can be introduced tomake the collection cost effective and to sustain theactivities of BAPA.
Amount of Money(pesos)

telecommunications, people were using re-loader from 5
pesos despite having recharge cards of 100/200/500 pesos.
For alternative fuels like diesel, kerosene, they were buying
1-2 liters at once even though they consumed in higher
quantities when accumulated together for a month (Table5). Thus, people were habitual in making payments on aunit-by-unit pattern.
Table 4: Expenditure amount (pesos)
Item
Expenses
Electricity
200
Clean Water
240
Other Fuel Sources
210
Health151
Education
221
Housing194
Clothing and Personal Care
237
Recreation and Culture151
Source: [9], Philippines Statistics Authority and modified by author for the
scenario of Alumar.

300
200
pesos

200
100

10 pesos0

1 2 3 4 5 6 7 8 9 1011121314151617181920

Daily Income

Income Days
Daily

Monthly

Figure 4: Monthly and daily payment strategies inproportion to daily income.

6. Conclusions

Table 5: Expenses pattern for water and fuels
Type
Quantity
Clean water
6 containers
Water stand
300 mlDiesel4-5 liters
Flashlights
3-4 batteries
Butane3-4 containers
Source: Interviews and household surveys

400

Price /unit
40 pesos
1 peso30 pesos
10 pesos
14 pesos

5.5 Proposal of Solution
A fee collection strategy of 10 pesos on a daily basis rather
than 200 pesos on a monthly basis is proposed as anaffordable, feasible and sustainable solution to payment
morality problem in the scenario of Alumar island as
explained below:a) Affordable: The average WTP was 334 pesos (>200
pesos) and SHS users’ income amount also represents thevalidity of these responses. Since the earnings are on adaily basis (Figure 4) and low income households lacksaving habits which is barely 1% of their income [10],200 pesos at once was not affordable even though 200
pesos per month was affordable. Thus, 10 pesos per day
is an affordable option.
b) Feasible: Despite having higher WTP, the reasons
behind the poor payment morality was the unfeasibilityof monthly based payment procedure. As seen from
Figure 4, 200 pesos occupies a major portion of their
daily income and there are days when the earnings areeven lesser than 200 pesos. The expenditure pattern also
shows that the residents of Alumar island were making
payments in smaller units for their basic necessities likeclean water, fuels, recharge cards for cellphones etc.
despite the monthly consumption being high. Thus,
cooperating with similar strategy, 10 pesos/day isconsidered a feasible strategy.
c) Sustainable: A successful collection of 10 pesos per day
will give BAPA 200 pesos a month. From 200 pesos,
BAPA is depositing 86 pesos for the replacement ofdefective batteries, 87 pesos for the replacement of either
panel or controller and the remaining as payment for fee
collector and technicians. Daily basis collection will
increase the operation cost because of addition of fee

1. Poor payment morality was found among SHS users in
Alumar island, Philippines
 Price satisfaction had no significant association withmonthly income and electricity provision.
 Contrary to common hypothesis, amount of tariff
was not a problem because users’ average WTP was
significantly higher than payment for SHS.
2. Earnings were found to be made on a daily basis andexpenses for basic utilities were preferable in smaller
units rather than purchasing for a whole month at once.
3. A fee collection on a daily basis has been proposed as aneffective strategy to ensure the operational sustainabilityof rural electrification program in Alumar island.
For further studies, it is recommended to propose thisstrategy to the electric cooperative BAPA and observe thechange in users’ payment behavior after the application ofthis strategy in Alumar island and explore similar scenarios
in other developing countries as well.

References[1] BNEF (2017), Global Trends in Renewable Energy Investment 2017by UNEP and Bloomberg
New Energy Finance[2] WEO (2016), World Energy Outlook 2016 by International Energy
Agency[3] Nieuwenhout, F. D. J., Van Dijk, A., Lasschuit, P. E., Van Roekel, G.,
Van Dijk, V. A. P., Hirsch, D., … Wade, H. (2001). Experience with solar
home systems in developing countries: A review. Progress in
Photovoltaics: Research and Applications, 9(6), 455–474.
[4] Tamir, K., Urmee, T., & Pryor, T. (2015). Issues of small scale
renewable energy systems installed in rural Soum centres in Mongolia.
Energy for Sustainable Development, 27, 1–9.
[5] Williams, N. J., Jaramillo, P., Taneja, J., & Ustun, T. S. (2015).
Enabling private sector investment in microgrid-based rural electrificationin developing countries: A review. Renewable and Sustainable Energy
Reviews, 52, 1268–1281.
[6] Hong, G. W., & Abe, N. (2012). Sustainability assessment of
renewable energy projects for off-grid rural electrification: The Panganan Island case in the Philippines. Renewable and Sustainable Energy
Reviews, 16(1), 54–64.
[7] Hong, G. W., Abe, N., Baclay, M., & Arciaga, L. (2015). Assessing
users’ performance to sustain off-grid renewable energy systems: The
capacity and willingness approach. Energy for Sustainable Development,
28, 102–114.
[8] Bartošová, J. (2007). Probability models of income distribution, 2–6.
[9] Rufino, C. C. (2013). Consumption Pattern of Poor Households inMetro Manila – A Microeconometric Evaluation, 1, 10–24.
[10] Joseph, A. B. J. (2015). The Quality of Life of Fishermen Community
A micro level study Final Report of the Minor Research Project.

132

Adsorption Equilibrium of Metal Cations of Acid Mine Drainage Treatmentusing Indonesian Natural Zeolite
Student Number: 14M51641

Name: Hironobu HASEGAWA

Supervisor: Ryuichi EGASHIRA

インドネシア産天然ゼオライトによる酸性炭鉱廃水の処理における金属陽イオンの吸着平衡
長谷川 博信インドネシア・ボゴール産の天然ゼオライトを用いて，モデル酸性廃水中の重金属イオン(Mn, Zn, Cu, Fe)の吸着平衡を実測した。ゼオライトおよびモデル酸性廃水を接触させることによりNa，Mg，K，Caイオンが溶出した。初期pH(2～5)は，平衡時において7～8に上昇した。pHの上昇により金属水酸化物が析出したと考えられ，Zn，Cu，Feの水溶液中濃度は大きく減少した。Mnは主としてゼオライトによる吸着により濃度が減少した。拡張Langmuir式は実験結果を良好に推算することができた。

1. Introduction
Indonesia is a country that has large quantity ofmineral resources, especially huge reserves of coal, andthis country has been a great exporter of coal. Thus
mining industry in Indonesia is expected to further
develop in the future[1]. On the other hand, Indonesia
has been suffered from acid mine drainage (AMD)caused by mining activities. The AMD is acidic
wastewater containing heavy metals sulfate, generated
through the oxidization of metal sulfide at the surface ofmineral deposit with water or air, and polluting theecological systems[2]. The AMD is mostly drained fromabandoned mining sites where no one has duty to treat
AMD, and therefore economical and simple treatment
to mitigate the high acidity and remove the heavy metal
ions is required[2].
For treatment of AMD, neutralization method ismost commonly used. Although the method is effective
to remove the heavy metal ions in the AMD, large
amount of sledges should be generated and the treatmentof the sledges also is necessary. On the other hand, thedeposits of Indonesian natural zeolite were foundabundantly in West Java Province and the utilization of
the natural zeolite for the treatment of AMD has beenstudied. The base components of the zeolites are mainly
clinoptilolite and mordenite. Although the previous
studies of our research group reported that the naturalzeolite from Bogor deposit was effective for removal ofheavy metals from real AMD of coal mine andmitigation of acidity in the solution[3], the effects ofprecipitation of heavy metals and other cations in thesolution on the mechanism of heavy metal removal werestill necessary to study.
In this dissertation, the characterization of the zeolite
samples and batch treatment of the model AMD
solutions containing single and binary heavy metalusing Indonesian natural zeolite were carried out tostudy the effects of pH and concentration of metal
cations on the adsorption equilibrium in order to clarify
the mechanisms of the AMD treatment.

2. Experimental
2.1 Materials
Natural zeolite sample was obtained from Bogor
deposit, Indonesia. According to XRD result of previous
study[3], the major components of the zeolite wereclinoptilolite-Ca and mordenite. In addition, the sample
contained albite, cristobalite, and calcite as non-zeolitic
material. Before the experiment, the zeolite sample wascrushed (Wonder Blender, WB-1, Osaka Chemical Co.,
Ltd.), and screened (Testing Sieve, aperture 150x10-6 m,
wire diameter 100x10-6 m, Tokyo Screen Co., Ltd.)Then obtained powder was washed with deionized waterand dried to remove water.
In the cases of adsorption equilibrium
measurements, four kinds of heavy metal ions, such as
Mn2+, Zn2+, Cu2+ and Fe3+, were selected as the model
heavy metal ions contained in AMD, and MnSO4∙5H2O,
ZnSO4 ∙ 7H2O, CuSO4 ∙ 5H2O, and Fe2(SO4)3 ∙ nH2O
were used as source of heavy metal ions to prepare themodel AMD. The pH of the feed solution was adjusted
using H2SO4. All reagents used here were in analyticalgrade and purchased from Wako Pure Chemical Ind., Ltd.
2.2 Characterization of the zeolite sample
The cation exchange capacity (CEC) of the naturalzeolite sample also was measured with Method-9081 by
the US Environmental Protection Agency. In order toquantify exchangeable cations in the zeolite, leaching of
the zeolite using ammonium acetate (NH4OAc) solution
were also carried out. The 4g of natural zeolite sample
was contacted with 30mL of 1 mol/L aqueous NH4OAc
solution and shaken for 5 minutes. After phaseseparation using centrifuge, fresh 30mL NH4OAc
solution was added to the solid and this operation was
repeated 10 times. The liquid phase obtained from each
operation was analysed to determine metal
concentrations.
2.3 Batch AMD treatment
The experimental conditions of batch adsorption aresummarized in Table1. Single metal solutions and 3
types of binary metal solutions (Mn-Zn, Zn-Fe, and MnFe) were used as feed solutions. The initial metal133

concentrations were varied from 1.0x10-3 to 1.9x10-2
mol/L. In the cases of binary metal solutions, summationof initial metal concentrations of both metal ions werevaried from 1.0x10-3 to 1.9x10-2 mol/L and massconcentration ratio of both metals was set as unity. All
experiments were carried out as follows. The specified
volume of feed solution V0, and amount of naturalzeolite sample S0 were put in a 100mL volume plastic
bottles with screw cap and shaken at 300K for 240 hours
to be equilibrated. After reaching equilibrium the
mixture was filtrated with paper filter. The feed solution
and the obtained liquid phases were analysed todetermine metal concentrations and pH values.
2.4 Analysis
The liquid phases were analysed to determine metal
concentrations in the solutions by ICP-AES (SPS7800,
SII Nano Technology). The pH values of the solutions
were measured by digital pH meter (F-51, HORIBA).

CaCO3 leaching were larger, the effects of the leachingon the CEC measurement should be negligible. The
concentration of Ca ion at each shaking steps were much
lower than the concentration of Na in the sodium acetate
solutions which were used for the CEC measurement.
Then, the obtained CEC was considered to bereasonable.

Ei [keq kg-3]

0.002
Key
○
□
△
◇

0.001

x10-3

0
0

0.1

0.2

0.3

Total volume of NH4OAc solution
used [m3]Fig.1 Accumulated equivalent of leached cations in the
NH4OAc solution(equivalent based)
Key
○
□
△
●
■
▲

9
8
pH

Table1 Experimental conditions of batch adsorption
SoluteMnSO4 5H2O,
ZnSO4 7H2O,
CuSO4 5H2O,
Fe2(SO4)3 nH2O
Adsorbent
Natural zeolite sample from
Bogor deposit
Volume of solution,[m3]
50 × 10−6
V0
Initial metal[kmol m-3]
1 × 10−3 -1.9 × 10−2
concentration, Ci,0Initial pH of solution,
2, 3, 5(Mn, Zn, Cu)[-]
pH0
2 (Fe, binary metals)
Mass of zeolite, S0[kg]
2.5 × 10−3

7
6
5

pH0
2
3
5
2
3
5
2
3
5
2
2

4
1

3 Results and discussion
3.1 Characterization of the zeolite sample
Based on the method-9081, the CEC of the zeolite
sample was determined as 8.42 x 10-4 eq/g. This value
was lower than the theoretical CECs of pure
Clinoptilolite-Ca (2.44 x 10-3 eq/g) and Mordenite (2.24
x 10-3 eq/g)[4]. Total equivalent of metal ion i leachedfrom unit mass of the zeolite sample until the k time
contacts, Ei,k, was defined as follows,
𝐸𝑖,𝑘 = ∑𝑘𝑗 𝑚𝑖 𝐶𝑖,𝑗 𝑉𝑗 ⁄𝑆0(1)
where Ci,k and Vk (10 ≧ k ≧ 1) represent theconcentration of metal ion i at the k-th contact and added
volume of solution at the k-th contact, respectively.
Figure 1 shows the plots of Ei,k against the total
volume of NH4OAc contacted with the zeolite samples,
and the CEC of the zeolite sample measured above wasshown as the dashed line. The metal ions of Ca, Na, Mg,
and K dissolved into the NH4OAc solution, and Ei,k of
Ca was the largest, followed by those of Na, K, and Mg.
The Ei,k of Na, Mg, and K increased until k=2 and kept
almost constant in the range of k>2. On the other hand,
Ei,k of Ca kept increasing until 10 time contacts and Ei,kof Ca at k=10 was almost twice as much as the CEC.
According to the previous study, the zeolite sample
contained CaCO3 [3] and most of Ca ions in the solution
should be derived not from the ion exchange sites of thezeolite, but from CaCO3. Although the amount of

i
Ca
Na
Mg
K

2

3

4

5

6

2

pH0

2

Sys.
Mn

Zn

Cu
Fe
MnZn
ZnFe
MnFe

Fig.2 pH changes in the equilibrium solution
10-1

Key
○
□
△
●
■
▲

Ci [kmol m-3]

10-2
10-3
10-4
10-5
10-6
10-7
10-4

10-3

10-2

pH0
2
3
5
2
3
5
2
3
5
2

Sys.
Mn

Zn

Cu
Fe

10-1

Ci,0 [kmol m-3]

Fig.3 Concentration changes of heavy metals in
equilibrium solution

3.2 Batch AMD treatment
Figure 2 shows pH changes of the single and binary
heavy metal solution between initial and equilibrium.
The pHs in all model AMD solutions at equilibriumincreased from the initial ones and settled from 7 to 8,
in spite that their initial pHs were varied from 2 to 5. The
pH increased as the initial concentration of the metal ion
decreased. Although this increment of pH might becaused by adsorption of hydrogen by the zeolite,

134

dissolution of CaCO3, from the zeolite, and so on, thesubstantial contributions of these reasons to the pH
increment were unclear.
Figure 3 shows the comparison of initial andequilibrium concentrations of heavy metals in the model
AMD with the single heavy metal systems. All metal
concentrations in the solution were lowered bycontacting with the natural zeolite. Especially theequilibrium concentrations of Zn(pH0=3, 5), Cu, and Fe
in the single heavy metal systems. In the cases of binary
metal systems, the concentrations of all metals also
decreased and that of were almost zero.
Figure 4 shows the relationship of the metal cation
concentrations and pH in the equilibrium solution in thecases of the single heavy metal systems. The
concentrations of leached cations and heavy metal ionsincreased as pH decreased. The cation most leachedfrom the zeolite sample was Ca and its concentration atequilibrium increased as the initial heavy metal
concentrations increased in all cases. In the binary heavy
metal systems, Ca was most leached and its
concentrations increased with the increment of initialheavy metal concentrations, as same as in the single
heavy metal systems.
Figure 5 shows the relationship between theequilibrium pH and concentrations of the metal ions atequilibrium, in which the solubility curves of respective
metal hydroxides estimated from their solubility
products also were indicated as solid lines. The curves
represent solubilities of their metal ions atcorresponding pH, and the solubility products werecited from the previous studies [3] and used as thereference solubilities. In all conditions, theconcentrations of Mn2+ at equilibrium were smaller than
the solubility, and the concentrations of the other metal
ions were close to or larger than the estimated solubility
curves. Since concentration of Mn2+ was larger thanestimated solubility curve, Mn2+ was removed byadsorption onto zeolite. The concentrations of Zn2+ wereclose to the estimated solubility of Zn(OH)2 and somepart of Zn2+ might be precipitated to be removed fromthe solution. The concentrations of Cu2+ and Fe3+ atequilibrium were larger than solubility of Cu(OH)2 orFe(OH)3 estimated at each equilibrium pH. The
concentrations were low enough to be negligible
10-1

Fe

Cu

Zn

compared to other heavy metal. Thus most of Cu and Fe
might be removed by precipitation of hydroxide form inall conditions. The concentration of Ca was the largest
among metal ions dissolved from the zeolite sample andthe concentration at equilibrium increased as initialheavy metals concentrations increased.

Ci [kmol m-3]

0.02

0.01

6

Key
○
□
△
●
■
▲

Mn

Ci [kmol m-3]

10-5
10-6
10-7
5

6

7

8

9

7

8

pH

9

Na
Mg
K

Sys.
Mn

Zn

Cu

Fe

Finally, we tried to predict adsorption isotherm ofheavy metals by Langmuir model. The adsorbed amountof heavy metal cation i was calculated from materialbalance equation and was expressed as,
𝑞𝑖 = (𝐶𝑖 − 𝐶𝑖,0 )𝑉0⁄𝑆0(2)
In this experiment we assumed that initial heavy metal
concentrations in solid phase were zero (qi,0=0) andvolume of the solution and mass of the zeolite weresame before and after equilibrium (V0=V and S0=S). The
extended Langmuir equation was expressed as,
𝑞𝑖 = 𝑞∗ 𝐾𝑖 𝐶𝑖 ⁄(1 + 𝐾Ca 𝐶Ca + 𝐾Mg 𝐶Mg + 𝐾𝑖 𝐶𝑖 ) (3)where qi* and Ki were the saturated amount of metal ion
i on the zeolite and the Langmuir parameter of metal ion
i, respectively. Then it was assumed that only divalentcations were adsorbed; adsorption of Ca2+, Mg2+ andMn2+ or Zn2+ were considered, and the saturated amountof adsorption, qi*, was decided based on the CEC
measured here as 4.21x10-4 mol/g, and Ks of Ca and Mg
were same as that of Mn2+which had no precipitation ofhydroxides.

10-4

4

Ca
Na
Mg
K
Ca
Na
Mg
K
Ca
Na
Mg
K
Ca

Fig.4 Equilibrium concentrations of eluted cations form thezeolite into the solution at pH0=2

10-3

3

i

〇
□
△
◇
●
■
▲
◆

0

10-2

2

Key

10

11

pH0
2
3
5
2
3
5
2
3
5
2

i

Sys.

Mn

Mn

Key

pH0
2
2

Zn

Zn

Cu

Cu

Fe

Fe

2

i
Mn
Zn
Zn
Fe
Mn
Fe

Sys.
MnZn
ZnFe
MnFe

pH
Fig.5 Estimated solubility curves of the metal hydroxides and the relations between the metal concentrations and pH
values at equilibrium

135

Table2 Determined Langmuir parameters of Mn and Zn in
adsorption experiment

Figure 6 shows the adsorption isotherms of various
kinds of metal ions in the single heavy metal solutions.
The natural zeolite could remove most of Zn at pH0=3
or 5, Cu and Fe, and their concentrations in the solutions
were almost zero. These ions should be removed as theforms of their hydroxides by elevation of pH. The
obtained parameters were shown in Table 2 and theestimation results by Eq. (3) were shown in Fig.6 aslines. The K of Mn under the condition of pH0=2 waslower than those of pH0=3 or 5. The K of Zn was muchlarger than those of Mn adsorption because qi included
the effects of precipitation.
The adsorption isotherms of heavy metal ions in thebinary heavy metal solutions are shown in Figure 7. The
concentrations of Fe were almost zero and the qZns inbinary metal systems were lower than those in single Zn
system at same equilibrium concentrations of Zn. Since
the divalent cations were major among all cations in thesolutions at equilibrium, the effects of divalent cations
on the removal of heavy metals were considered. Then,the extended Langmuir model was used to predict theadsorption isotherm of the metal ions in the binary
system as same as the single system, and prediction
results are shown in Fig. 7 by lines. The Ks determined
here are listed in Table 3, where Ks of divalent cations,
Ca and Mg, were set as same as that of Mn. The Ks of
Mn were the same as those measured in single Mn
system. Whereas K of Zn was higher than that of Mn inthe binary metal ion system, it was much smaller than K
obtained in the single metal ion system. The amount ofprecipitated Zn(OH)2 in the binary system might besmaller than those in the single system because theconcentration of Zn2+ in the binary system lied in wider
range than that in the single system.

qi [kmol kg-1]

4x10-4

Key
○
□
△
●
■
▲

K=900
2x10-4
K=30
K=20

0
0

0.01

0.02

pH0
2
3
5
2
3
5
2
3
5
2

qi [kmol kg-1]

K=60
K=20

i
Mn

0.005
Ci [kmol m-3]

Zn

Zn

2
3
5
2

q*
[kmol kg-1]

20
30
30
900

4.21x10-4Kiq*[m3 kmol-1][kmol kg-1]

System

Metal

pH0

Mn
-Zn
Zn
-Fe
Mn
-Fe

Mn
Zn

2
2

20
20

Zn

2

60

Mn

2

20

Nomenclature
C -concentration in liquid phaseq-concentration in solid phaseq* -saturated amount of adsorption
V-volume of liquid phaseS-mass of zeoliteE-total equivalent of metal leached
K -Langmuir constantm -valency of ion

Zn

Cu
Fe

4.21x10-4[kmol m-3]
[kmol kg-1][kmol kg-1]
[m3]
[kg]
[keq kg-1][m3 kmol-1][-]

Subscript0-at initial statei-component ik-at k-th contact

Sys.
Mn
-Zn
Zn
-Fe
Mn
-Fe

Reference[1]一般財団法人石油エネルギーセンター, コールデータバンク[2]JOGMEC 酸 性 炭 鉱 廃 水 問 題 の 対 処 動 向 に つ い て
http://mric.jogmec.go.jp/public/current/03_03.html[3]Fernando, 東京工業大学大学院 2015年修士論文

0
0

Mn

Ki
[m3 kmol-1]

4. Conclusions
The natural zeolite sample from Bogor deposit wasused as an adsorbent to study the treatment of the modelAMD. CEC of the zeolite sample was determined to
8.42x10-4 mol/g by US-EPA method 9081. Part of eluted
Ca ions were not derived from exchangeable sites.
The zeolite sample could increase pH of the model
AMD solutions to appropriate level (pH=7-8). In
addition, the natural zeolite could remove heavy metal
Mn2+, Zn2+, Cu2+, and Fe2+ in model AMD. Zn2+, Cu2+,
and Fe2+ were removed not only by the adsorption withthe zeolite, but also by precipitation to form metalhydroxides. The Mn2+ was mainly removed byadsorption onto the zeolite samples. The extended
Langmuir model was used to organize the adsorption of
Mn and Zn considering the effects of other metal ionsdissolved from the zeolite sample, and favorably
predicted the adsorption behavior.

2x10-4

1x10-4Mn

pH0

adsorption experimentCi [kmol m-3]

i
Mn
Zn
Zn
Fe
Mn
Fe

Metal

Table3 Determined Langmuir parameters of Mn and Zn in

Fig.6 Adsorption isotherm of heavy metals onto zeolite insingle heavy metal solutions

Key

System0.01[4]https://www.mineralienatlas.de/lexikon/index.php/Min
eralData

Fig.7 Adsorption isotherm of heavy metals onto zeolite inbinary heavy metal solutions
136

A MARS ROLLING-ROVER INTEGRATED WITHENTRY, DESCENT AND LANDING SYSTEM
Student Number: 15M51704 Name: Masaaki ICHIMURA Supervisor: Daisuke AKITA

大気突入機をかねた転がり型火星探査機の可能性
市村 優明
本論文では極めて軽量かつ簡易な構造を持つ, 単独で火星大気突入から着陸, 表面探査までを行うような転がり移動型の火星表面探査機を提案する. 探査ミッションの各フェーズにおいて、満たすべき条件について考察し、実現可能な機体構成を見積もる. 探査機に対する全ての要求について考察した後、機体構成とミッションの一例を示すことで探査機の実現可能性について論じる

market currently. Furthermore, this probe does notneed additional landing systems such as parachutesor airbags. The probe proposed in this study is an allin –one system which contains the functions ofdecending, landing and roving. This study
investigates the possibility of this all-in-one system.
The mission scenario of the probe is shown in Figure2.

1. Introduction
Although a number of Mars exploration had been
conducted, in-situ explorations are very effective inunderstanding Mars’ environment. However, Mars
surface exploration mission is difficult because Mars’
atmosphere is very thin despite heavy entry
probes.[1] So atmospheric braking tends toinsufficient, and then probes collide with Mars’surface at very high speed. It results in 2/3 failure ofMars surface exploration mission. This study
proposes extremely lightweight probe as shown in
Figure 1.

Fig 2. Mission scenario

At first, the probe is deployed from mother spacecraft,
and then enters into Mars atmosphere. Duringcontained in the spacecraft and atmospheric entry, theshaft of the probe is shortened. Before touchdown toMars surface, the probe descends and decelerates byusing atmospheric braking. After sufficiently
decelerated, the probe lands on Mars surface. By
using impact of touchdown, the shaft can be extended
by spring just like a knock-type pen. Finally, theprobe plays a role as rover, rolling down the slope on
Mars surface. During surface exploration, this probe
is intended to take some pictures of Mars surface. The
data of the pictures will be sent to mother spacecraftby communication device.
To achieve Mars surface exploration mission, four
requirements below are need to be satisfied.
(1) To endure aerodynamic heatingFig 1. The concept of the rover

This probe takes two types of configurations;aeroshell and rover. The aeroshell, which works foratmospheric braking and heat shielding, has a shape
like a cone. The rover is a mobile probe exploring theplanets or asteroids surface. This probe uses framesof the aeroshell as wheels. The two aeroshells areconnected with a shaft, which can be extended andshortened like a telescope. This system enables theprobe to roll down the slope on Mars. Wheels are
made of super elastic alloy, to avoid plastic
deformation caused by collision with rocks. The
aeroshells are made of Zylon® or alumina fiber cloth
membrane. All materials of the probe is available on137

(2) To avoid twist buckling of wheels caused byaerodynamic force(3) To endure impact of landing on Mars surface(4) To roll down a gentle slope and run overobstacles
This study investigates the possibility of the probe byconsidering the configurations of the probe to satisfy
the four requirements.

dashed line represents the max temperature on themembrane in the case of alumina fiber cloth. The
alumina fiber have a characteristic that emissivity
depends on its temperature. In some cases, thesurface temperature of the probe at Condition A
exceeds its heatproof temperature. On the other hand,
it does not exceed the heatproof temperature at
Condition B. Thus, in order to endure aerodynamic
heating, Condition B seems to be more desirable.

2. Mars Atmospheric Entry

2.2. Aerodynamic Force

To calculate the trajectory from the orbit of themother spacecraft to Mars’ surface, this study
considers two cases of initial condition. The first one
is a direct entry at initial height of 125[km] and initial
velocity of 5.6[km/s] (Condition A). The other one isinitial Mars orbit 27,000[km] × 3,700[km](Condition B). The orbit of the probe is determinedby a ballistic coefficient, expressed by the equation
below.
𝑚
(1)
β=
𝐶𝐷 𝑆

The aerodynamic force yields a tensile force on themembrane of the aeroshell. This tensile force
possibly causes out-of-plane buckling of the frames.
Out-of-plane buckling load of a ring is expressed byan equation below.[2]
9𝛼 𝐸𝐼
(2)
𝑝𝑐𝑟 =
1 + 4𝛼 𝑅 3(𝑝𝑐𝑟 : buckling load[N/m], 𝐸 : modulus of elasticity[Pa], I:
moment of inertia of area[m4], R: ring radius[m])(𝑚: mass[kg], 𝐶𝐷 : Drag coefficient, S: Projected area[m2])

From this equation, a required thickness of theframe can be calculated. To examine the validity ofthis equation, a hypersonic wind tunnel test had been
conducted. The test was conducted at HypersonicWind Tunnel at University of Tokyo Kashiwa
Campus.[3] Mach number of the wind tunnel is M=7.
At this Mach number, the dominating parameter ofthe flow is Mach number. Thus, the wind tunnel testis goodly reproduced despite Reynolds number of thewind tunnel is different from actual case. The probe
model used at the test is shown in Figure 4.

2.1. Aerodynamic Heating
The aerodynamic heating had been estimated by the
trajectory calculation. Figure 3. shows the estimatedresult of the max temperature of the probe surface
varying with the ballistic coefficient β.

Fig 4. Probe Model

The model is made of Zylon® membrane and NiTi shape memory alloy. The diameter of the model
projected area is 30[mm]. To examine the validity ofeq. (2), the effect of the ring thickness wasinvestigated. From eq. (2) estimates, the ring
thickness of d=0.454[mm] where the out of plane
buckling occurs. Then, preparing the ring of 0.35, 0.4,
0.5[mm] thickness, the deformation of the ring isobserved. Figure 5 shows the appearance of the windtunnel test.

Fig 3. Ballistic Coefficient vs. Max Temp

The local curvature of the probe is set to be 1.0 (m)and emissivity is set to be 0.8. The max temperature
on Zylon® membrane surface is set to be the same asthat at the stagnation point. The max temperature
increases as β increases. Lower β is derived by larger
area of membrane, thus larger wheel radius. The

138

This structure is lightweight and have significant
shock absorption performance. The energy
absorption is estimated by equation as follows.
𝐸𝐶 = 𝜎𝑐𝑟 ∙ 𝑇𝐶 ∙ 𝐴(3)(𝐸𝐶 : absorbed energy [J], 𝜎𝑐𝑟 : crush strength[Pa], 𝑇𝐶 :crush stroke[m], 𝐴: cross section area[m2])For example, if we consider a case at 𝛽 = 5[kg/m2]
and m=5[kg], the touchdown speed would be
55[km/s]. The honeycomb’s crush strength and,density is assumed to be 8[MPa] and 110[kg/m3],
respectively. The cross section has a circular shape of
0.1[m] diameter. If the speed of the probe is 6[m/s]after the shock absorption, the crush stroke of
0.12[m] is needed. Since the crush stroke is 3/4 of thehoneycomb full stroke, honeycomb weight would be
138[g]. Figure 8 depicts an example of the shock
absorbing system.

Fig 5. Wind Tunnel Test
The out of plane buckling occurs at ring thick of 0.35,
0.4[mm]. At the ring thick of 0.5[mm], deformationof the frame was not observed. Thus, the validity ofeq. (2) had been proven.
Fig 6. shows the requirement for ring thickness toendure the aerodynamic force by using eq. (2). For 1
to 5 [m] ring diameter, the ballistic coefficient varies
2.2 to 5 [kg/m2]. Furthermore, Condition B requires
smaller ballistic coefficient. Thus, Condition B is also
desirable in terms of to endure the aerodynamic force.
So the following discussion is focused on Condition
B.

Fig 8. An Example of Shock Absorbing System

4. Mars Surface Exploration
The probe moves on Mars surface by rolling down
the slope. The wheels of the probe are affected byrolling resistance effect, where prevents the rolling
motion of wheels. The effect is expressed by therolling resistance coefficient, 𝐶𝑟𝑟 . The slope which
the probe can start roll down is determined as follows.
Fig 6. Ring Diameter vs. β and Ring Thickness

tan−1 (𝐶𝑟𝑟 ) > θ(4)(𝐶𝑟𝑟 : rolling resistance coefficient, θ: slope [deg.])

3. Landing
When the probe reaches in the vicinity of Mars
surface, the probe generally have a velocity of 30 to55 [m/s]. Without any shock absorber, the probe mustbe broken. A shock absorber using aluminumhoneycomb is considered. Aluminum honeycomb ismade of laminar sheets of aluminum as shown in
Figure 7.

To start rolling down at 10[deg.] slope, 𝐶𝑟𝑟 < 0.17
is needed to be satisfied.
The relationships between the wheel distortion
caused by self-weight and the rolling resistancecoefficient is modeled by NASA.[5] The assumption
is following
𝐶𝑟𝑟 = [𝑝𝛿 (1 − 𝑝𝛿 )]1⁄2(5)(𝑝𝛿 : wheel deformation [m])From this equation, requirement for wheel thickness
can be estimated.
Consider to explore 10 [deg.] slope, thus therequirement is 𝐶𝑟𝑟 < 0.17. And assume the ballisticcoefficient should be less than 5[kg/m2]. If β < 5

Fig 7. Aluminum honeycomb

139[kg/m2], the probe can endure the aerodynamic
heating and aerodynamic force. Note that the shapeof cross section of the wheels are set to rectangular.
Figure 9. shows the result of estimation ofrequirements. For the wheels diameter of 1 to 5 [m],any size can be selected. The thinner wheels would
be favorable because the mass of the probe needs tobe small for the sake of reduction in the cost of
mission and the landing impact.

Figure 11 shows the result of estimation ofrequirements for rover’s size and initial rolling speed.
In the case of the initial speed of 3[m/s], about 90%
of Martian rocks can be rolled over.

5. Rover and Mission Example
An example of probe configuration is shown in Fig
11. This rover can endure the aerodynamic heating
and aerodynamic force, land on Mars surface
successfully, and start rolling down at 10[deg.] slope.
If the initial rolling speed exceed 3[m/s], 90% of
Martian rocks can be rolled over. Valles Marineris,
Martian extremely huge canyon that has 4,000 [km]length, 200 [km] width and 7 [km] depth could be oneof the most hopeful exploration sites.

Fig 9. Requirements for Wheel’s Size and Thickness

To roll over the rock is important matter because
there are many rocks. To roll over rocks on Mars
surface. It is modeled like in Figure 10.
Fig 11. An Example of probe

6. Conclusion
By examining the requirements to achieve themission, a feasible configuration of the proposed
probe is illustrated. The all-in-one probe must be very
cost effective and robust system for Mars surface
exploration.

Fig 10. Model of the Rolling Over the Rocks

This model considers rover’s collision with a rock.
After the collision with the rock, rover have avelocity vector normal to Mars surface. Then, if the
kinetic energy of the normal to surface component
exceeds the potential energy at the rock height, rover
can roll over the rock.

References
[1]

[2]

[3]
[4]

[5]

Fig 11. Requirement for rolling speed

140

R. D. Braun and R. M. Manning, “Mars Exploration
Entry, Descent, and Landing Challenges”, Journal ofSpacecraft and Rockets, vol. 44, no. 2, 2007.
Thein Wah, “Buckling of Thin Circular Rings under
Uniform Pressure”, Int. J. of Solids Structure, vol.3,
pp. 967-974, 1967.
http://daedalus.k.u-tokyo.ac.jp/wt/, accessed Aug. 25,
2017
小田 周平・野口 元,"ハニカムの衝撃エネルギ
ー吸収性について", 圧力技術, Vol.15 No.1 p4149, 1977
J. Antol, et al, “Low Cost Mars Surface Exploration:
The Mars Tumbleweed”, NASA TM-2003-212411,
2003.

DEVELOPMENT OF A MULTI-BEAM STRUCTURE BY STACKING COMB-SHAPEDRUBBERS
Student Number:15M51710 Name: Mikhail SALYUKOV Supervisor: Kunio TAKAHASHI

くし状構造の積層による側面接触型弾性梁集合体の試作
サリュコフ ミハイル
本研究では、マニピュレーション用デバイスとしての利用可能性が示唆される斜め梁構造の梁集合梁の試作と把持脱離の容易性の評価を行うため、梁の取り付け角が任意に調整可能な梁集合体の手法を提案して梁集合体の試作を行い、その凝着力の水平力依存性を実験的に測定した。その結果、提案した手法で試作可能な取り付け角が小さい梁集合体は水平力による把持脱離操作が容易に可能であり、マニピュレーション用デバイ
スとしての利用可能性が高いことが示された。

1

2Introduction

In recent years, automation and speed enhancing of
the manipulation process has been carried out on various production sites. Although, conventional devices
for manipulation have difficulties operating the objects
that have surface with unevenness and roughness,
which hinders the progress of automation and speed enhancing. In order to solve this problem, a concept of a
gecko-inspired manipulation device (GIMD) has been
introduced. Gecko has a remarkable ability to grip on
uneven and rough surfaces, which is thought to haveapplication possibility to achieve the manipulation of
objects that conventional devices have difficulty operating.
It is known that gecko’s ability is made possible by
the microscopic oblique hairs existing on their toes[1].
Analytical and experimental results suggest that theoblique structure of the hairs contributes to the increase
and tangential force dependency of the adhesion force,
which are both important properties for easy grip andrelease of the surface[2,3]. Thus, it is suggested that
GIMD could be realized by mimicking gecko’s fibrillar
structure in a form of multi-beam structure with beams
mounted obliquely. On the contrary, conventional
methods commonly used to develop the multi-beam
structures have difficulties in achieving an obliquemounting angle of beams due to the technical constraints, thus not suitable for developing the
GIMD[5,6,7,8].
In this paper, an improved method of developing themulti-beam structure with oblique mounting angles ofbeams is proposed to achieve the GIMD with improved
sufficiency. The development of multi-beam structure
was done for evaluation of the proposed method. Developed multi-beam structure was tested for tangential
force dependency of the adhesion force, to consider theability of easy grip and release operations which is ahighly important aspect in manipulation.

Method of developing the multi-beam
structure

The molding process of a multi-beam structure isshown in Figure 1. In the beginning of the process,blocks with beams at the bottom arranged in a row (i.e.
comb-shaped rubbers) are molded. Two kinds of flatmolds are used in the process. One has single rectangular hole in the middle and an opening on the top of it.
The other mold has additional multiple rectangular slits
for the formation of beams. Two molds are mutually
fixed between two plates to form a disassemblable
mold and filled with curable liquid material. After curing, mold is disassembled and the comb-shaped rubber
is removed. Molded comb shaped rubbers are thenstacked vertically with fixed intervals in a horizontal
direction. Finally, stacked comb-shaped rubbers are put
into a container filled with curable liquid material for a
fixation. Using the method described above, it is possible to develop a multi-beam structure with obliquemounting angles of beams. By changing the length of
fixed intervals in a horizontal direction, mounting angleof beams 𝜃 can be arbitrarily set within an adjustment
range of 0 < 𝜃 ≤ 90°.
In this study, flat molds were made from sheets of
SUS304 by metal etching. Height, width and length of
the slits for the formation of beams were each set to
𝐻 = 100, 𝑊 = 300, 𝐿 = 1000μm . For formation of thedisassemblable mold, mold with the multiple slits wasplaced over the glass plate, in order to transfer thesmooth glass surface to the contact face of the beams.
Mold without the slits was placed over the thin metal
sheet coated with mold release agent (DAIKIN,DAIFREE GW-250), for easy disassembling of themold after material curing. PDMS (sylgard®184 SILICONE ELASTOMER KIT, Dow Corning) that has
high mold releasability and transferability, was used asa curable liquid material for molding the comb-shaped
rubbers. PDMS was filled into disassemblable mold in
low vacuum atmosphere and cured at a temperature of141

65℃ on a hotplate. Stacking of the comb-shaped rubbers was done using stepwise jig, designed to achieve
the mounting angle of 𝜃 = 20° which has not yet beenachieved by conventional methods.

3

Measurement method of tangential force
dependency of adhesion force of the developed multi-beam structure

To evaluate the easiness of grip and release operationsof the GIMD that uses multi-beam structure developedby the method proposed in this study, the tangential
force dependency of the adhesion force of the developed multi-beam structure was measured experimentally using the set-up shown in Figure 2. A 5mm-thinflat glass plate (Matsunami Glass, Blue-board glass
plate) was mounted on x-axis motorized linear stage(Suruga Seiki, K101-20MS-M) and z-axis motorized
linear stage (Suruga Seiki, KS322-8N) along with angle regulation stages. The test sample of developed
multi-beam structure was mounted on a 3-axis force
sensor. After the horizontal adjusting, the test sample
was first pressed vertically against the glass plate(i.e.
loading process), then pulled off at a fixed angle of 𝜑until the beams of the sample had completely detached(i.e. unloading process). The amount of displacement of the beam from the moment of the contact withthe plate until the end of the loading process was set to327μm to avoid the contact between beams. In orderto change the direction and magnitude of the tangential
force during the unloading process, experiments wereconducted for 11 different values of pull off angle 𝜑.
Both normal and tangential forces working on the test
sample were simultaneously measured during the loading-unloading process using the 3-axis force sensor(KYOWA, LSM-B-10N). The deformation state of thebeams during the experiment were observed using theimages captured by the digital microscope (Saitoh Kougaku, SKM-3000B-PC).

4

Figure 1. Schematic illustration of developing process of amulti beam structure by stacking comb-shaped
rubbers

Results and discussions

Observation of the developed multi-beam
structure
Developed multi-beam structure shown in Figure 4 wasobserved with digital microscope (RH2000, HiROX).
Photo images of the contact side of the beams suggested that shape of the mold was successfully transferred to the beams. Additionally, the surface of thebeams on the contact side transmitting light without
scattering suggested low roughness on the contact sideof the beams. Mounting angle of beams 𝜃 was confirmed to be approximately the same to the target valueof 20° by visual observation. Deducing from these results, the method proposed in this study is suitable fordeveloping a multi-beam structure with beams in submillimeter scale, with smooth contact surfaces and adjustable mounting angles.
4.1

Figure 2. Schematic illustration of experimental set-up for
loading-unloading experiment

Figure 3. Photo images of developed multi-beam structure

142

4.2

Experimental results of the measurementof tangential force dependency of adhesion
force of the developed multi-beam structure

The results of the experiments measuring the tangential force dependency of adhesion force of the developed multi-beam structure at pull of angle of 𝜑 = 45°
and φ = 135° are shown in Figure 5 (a) and Figure 5(b), respectively.
When pull of angle was 𝜑 = 45° , negative normal
force 𝐹n (i.e. adhesion force) working on the multibeam structure was observed during the unloading process. Tangential force 𝐹t took a negative value during
the loading but increased during the unloading process
and stayed positive while adhesion force was observed.
The beams of the developed multi-beam structure were
deformed by bending as they formed a side contact withthe plate during the loading process. The magnitude of
deformation of the beams by bending kept decreasing
during the unloading process while the magnitude of
deformation by elongation in the axial direction increased. Immediately after adhesion force became
maximum, “Stick-slip” phenomenon was observed
around the contact area of the beams causing the valueof the tangential force 𝐹t to decrease gradually, followed by the detachment of the beams.
In contrast to the results described above, when thepull off angle was set to 𝜑 = 135° , tangential force 𝐹ttook a negative value during both loading and unloading processes and adhesion force was not observed.
The magnitude of deformation of the beams by bending kept increasing during the unloading process, causing a large deformation. During the increase of bending
deformation, a shift of a contact state from side contact
to tip contact and to line contact was observed.
The experimental results of forces working on themulti-beam structure during loading-unloading processes for all 11 different values of pull off angle 𝜑 areshown in Figure 6. For 18° ≤ 𝜑 ≤ 90°, the changes in
the forces working on the multi-beam the contact weresimilar to those of 𝜑 = 45°, as long as the changes incontact state of the beams. Larger maximum adhesion
force at a larger 𝐹t was observed at smaller values of𝜑. For 108° ≤ 𝜑 ≤ 162°, he changes in the forces working on the multi-beam the contact were similar to thoseof 𝜑 = 135°, as long as the changes in contact state of
the beams. Larger maximum repulsive force at asmaller 𝐹t was observed at larger values of 𝜑.
As is evident from the results described above, multibeam structure with oblique beams has a tangential
force dependency of adhesion force that allows the easy
grip and release operations by changing the direction oftangential force working on it. Results suggest that thistangential force dependency occurs due to the difference in deformation state of beams owing to a difference in direction of applied tangential force, which affects the energy state of the beams. The total energy ofa beam 𝑈total that adheres with rigid surface could be
described as

Figure 5. Experimental results during the unloading
process at the unloading angle of (a) 45° and (b) 135°143

multi-beam structure. From the experimental results ofloading-unloading test with multiple pull-off angles,
we suggested that the multi-beam structure with small
mounting angles shows tangential force dependency of
the adhesion force, thus is applicable to develop the device for manipulation with easy grip and release operation ability.
Methods for designing a beam with the geometry anddetermining an optimal pull off angle that maximize
tangential force dependency of the adhesion force need
to be confirmed in further studies for achieving an optimal manipulation process, along with testing the ability of a multi-beam structure with inclined beams tomanipulate objects with uneven surfaces.

𝑈total = 𝑈elastic + 𝑈surface, where 𝑈elastic is the elastic energy attributable to thedeformation of the beam and 𝑈surface is the energy attributable to the work of adhesion. When tangential
force was applied to the positive direction, a decreaseof 𝑈elastic is estimated to cause the shift of energy minimum of 𝑈total causing the decrease of 𝑈surface . This
resulted in beams adhering to the surface at a stable energy state with low 𝑈total , generating the adhesion
force. On the contrary, when tangential force was applied to the negative direction, an increase of 𝑈elastic is
assumed to cause an unstable energy state with high𝑈total , generating the repulsive force against the contact
surface.
The obvious difference in beam deformation during
the unloading process that is suggested to cause the tangential force dependency of the adhesion force isachieved by the highly directional geometry of thebeams that have a mounting angle of 𝜃 = 20°. Thus, it
could be suggested that multi-beam structure for easy
grip and release operations should have the oblique
beams with small mounting angles.

6[1] Autumn, K., et al., “Frictional adhesion: a new angle on gecko attachment”, The Journal of Experimental Biology, Vol. 209, pp. 3569–3579, 2006
[2] Sekiguchi, Y., et al., “Adhesion between side surface of an elastic beam and flat surface of a rigidbody” Journal of Adhesion Science and Technology,
Vol. 26, No. 23, pp. 2615–2626, 2012[3] Shimahara, M., “Effect of tangential force on adhesion between an elastic beam and a rigid body”,
Master thesis of Tokyo Institute of Technology, 2016[4] Parness, A., et al.,“A microfabricated wedgeshaped adhesive array displaying gecko-like dynamic
adhesion, directionality and long lifetime”, Journal of
The Royal Society Interface, vol.6, pp.1223–1231,
2009[5] Day, P., et al., “Microwedge machining
for the manufacture of directional dry adhesives”,
Journal of Micro Nano-Manufacturing, 1(1), pp.
011001–011001-10, 2013[6] Murphy, M., et al., “Gecko-inspired directionaland controllable adhesion” Small 5, pp.170–175. 2008
[7] Aksak, B., et al., “Adhesion of Biologically Inspired Vertical and Angled Polymer Microfiber Arrays”, Langmuir, Vol.23, pp.3322–3332, 2007
[8] Qu, L., et al.,“Carbon Nanotube Arrays with
Strong Shear Binding-On and Easy Normal LiftingOff”, Science, Vol. 322, Issue5899, pp.238–242, 2008

Figure 6. Experimental results of measured normal andtangential forces for different pull-off angles

5

References

Conclusions

In this paper, we proposed a new method of developing the multi-beam structure that allows to adjust
mounting angle of the beams to an arbitrary value. The
validity of the method was confirmed by analyzing theexperimental results of observation of the developed144

A Huge and High Resolution Large Eddy Simulation Coupling with Energy Balance Model in
Urban Area
Student Number: 15M18090

Name: Takafumi SUEISHISupervisor: Manabu KANDA

街区のエネルギー収支を考慮した都市の大規模高解像度 LES 数値解析
居石貴史
本研究では、LES (Large Eddy Simulation) Model と Energy Balance Model のオフラインカップリングを行い、2m の高解像度で、都市街区の 3 次元的な空間構造を考慮した気象場の解析を実施した。RCP (Representative
Concentration Pathways)シナリオと SSP (Shared Socio-economic Pathways)シナリオを用いて、インドネシアのジ
ャカルタにおいて、現在と将来の気象状況を再現・予測し、都市街区内の温熱環境を評価した。

1. Introduction
Global temperature is increasing at a rate of 0.64℃/100
years [1]. In urban areas, this condition is exacerbated by
the urban heat island (UHI) effect, phenomenon whereinurban areas have higher temperatures than urban areas.
UHI is also detected to affect temperature trends in cities(e.g. annual average temperature in Tokyo increased about
3.2℃/100 years, a rate higher than the global temperaturerise [2].) On the other hand, Asian megacities areprojected to expand rapidly resulting to serious in the
future atmospheric environment. Thus, detailed analysis
of urban meteorology for Asian megacities is needed toprescribe optimum countermeasure for these issues.
Future mesoscale can be downscaled using PGW(Pseudo Global Warming) Method developed by Kimura
et al (2007) [3]. Through PGW, Varquez et al (2017)
succeeded to simulate future mesoscale weather condition
including global warming effects and UHI effects by using
RCP (Representative Concentration Pathways) and SSP(Shared Socio-economic Pathways) scenarios [4].
Mesoscale models cannot consider very fineresolution which includes detailed, realistic roughness
effects such as actual buildings and vegetation. Recently,
LES (Large Eddy Simulation) has been developed to
calculate weather condition inside urban area with a few
meter resolution. For example, Keck et al (2014)
conducted LES simulation for real city with 2m resolution,and investigated characteristics of urban flow [5]. At thesurface of LES, thermal comforts at the pedestrian levelcan be estimated.
Thermal comfort at pedestrian level is largely
dependent on heat balance within the city. An energybalance model developed by Ashie et al (2011)
successfully calculates energy balance of sun radiationand evapotranspiration from vegetation [6]. Bakkali et al(2015) simulated weather
condition inside real city byusing coupling of LES andenergy balance model [7].
In this study, a buildingresolving
large
eddy
simulation coupled with anenergy balance model isconducted for a highly
urbanized district in central
Jakarta (Figure 2) on typical

daytime hours. Six cases were considered; two cases
which utilizes present urban scenario and four cases
represent different urban configurations in 2050s.
Meteorological inputs were acquired from mesoscale
simulation by Varquez et al (2017).

2. Simulation Outline

2.1. Simulation Methods

Figure 1 shows the simulation flow of this study. The
results from GCM (General Circulation Model) were
downscaled using WRF (Weather Research andForecasting) Model through PGW. The simulation results
were then further downscaled using an energy balance
model and LES.
Future scenarios are based on RCP (future climate)and SSP (urbanization) scenarios. RCP2.6 and SSP1
represents best scenario for 2050s and RCP8.5 and SSP3
represents worst scenario for 2050s. Based on thesescenarios, PGW Method was applied to WRF Model, andfuture urbanization and AHE (Anthropogenic Heat
Emission) were determined.
LOCALS-UCSS developed by Ashie et al (2011) was
the energy balance model used. It calculates energybalance of sun radiation and evapotranspiration fromvegetation, and outputted sensible and latent heat flux
from ground surfaces for thermal boundary condition of
LES. AHE from buildings and roads were added tosensible heat flux. AHE was acquired from a global
database by Dong et al (2016) [8].
PALM (PArallelized LES Model) developed by
Raasch et al (2001) was the LES used. It simulates
weather condition inside a city at 2m resolution.
Simulated parameters were then used to evaluate thermal
comfort at pedestrian level through SET* (Standard new
Effective Temperature) [9]. SET* can evaluate thermal

Figure 1. Simulation Flow145

comfort based on the effects from temperature, humidity,
wind speed, radiation, clothing and metabolism.

Table 1. Simulation Cases

2.2. Simulation Settings and Objectives
Table 1 shows 6 cases of this study. Figure 2 shows
simulation domain (inside red rectangle) and observationroute (orange line). Table 2 shows initial conditions usedin PALM taken from Varquez et al (2017). Table 3 shows
its simulation settings. Figure 3 shows the building height
map and land use map for 6 cases.
Moving observation was conducted around 14:00 on
August 13, 2016. Case Observation reproduced thisobservation condition for validation of simulation results.
Initial condition of Case Current was based on August
2006~2015 results of the WRF, which utilized present
climate condition. Building height map and land use map
in Case Observation and Case Current were created by
combining the data acquired by Emporis and satellite
imageries.
Initial condition of Case Best Scenario, Case Worst
Scenario, Case Complex City and Case Green City werebased on August 2046~2055 results of WRF Model by
Figure 2. Simulation Domainusing PGW Method. RCP2.6 and SSP1 scenarios wereused for Case Best Scenario, Table 2. Initial Condition of LES from WRF Model
which represents best scenario for2050s. On the other hand, RCP8.5
and SSP3 scenarios were used forCase Worst Scenario, which
represents worst scenario for2050s. Homogeneous building
array was applied to the central
area of Case Best Scenario andCase Worst Scenario to increase
Table 3. Simulation Settings for LES
total building volume because of increasing population
inside city in the future.
Objective of Case Complex City and Case Green City
was to investigate how urbanization change weather
condition inside a city. More complex urbanization wasapplied to Case Complex City and added more green area
in Case Green City with keeping total building volume.
RCP8.5 and SSP3 scenarios were used for Case Complex
City and Case Green City to compare the results from
Case Worst Scenario.

3. Simulation Results
3.1. Validation of Simulation Results
To validate simulation results, moving observation along
an orange line in Figure 2 was conductedaround 14:00 on August 13, 2016. From thisobservation, WBGT (Wet Bulb Globe
Temperature) was calculated to evaluate
thermal comfort.
Figure 4 compares simulation results ofCase Observation (left) and moving
observation results (right). Both area A and C
in simulation results and those in observation
show relatively lower WBGT, because
vegetation interrupted sun radiation. On theother hand, both area B and D in simulation
results and those in observation show
Figure 3. Building height map and land use map

146relatively higher WBGT, because there was no obstacle tointerrupts sun radiation. The distribution of WBGT in
simulation results and observation results corresponded
well with each other such as in the circled regions. Someareas don’t match because of vegetation area mismatchand the sun radiation change during observation. We
concluded that our model performs relatively well toestimate actual thermal comfort at pedestrian level.

3.2. Thermal Comfort at Pedestrian Level
Figure 5 shows sensible heat flux three dimensionallyfrom south-east direction by Energy Balance Model.
North and west surface and roof had higher sensible heat
flux because sun radiation came directly. But south andeast surface and shade of buildings and vegetation had
lower sensible heat flux because sun radiation wasinterrupted. Road had also high sensible heat flux becauseof AHE from road.
Figure 6 shows SET* contours at 1m height by LES
Model. Radiation affected SET* results significantly,
SET* at shadowed areas by buildings and vegetation
was lower and SET* at area with direct sun radiation
was higher. So, central area of Case Best Scenario,
Case Worst Scenario, Case Complex City and Case
Green City (black rectangle in Figure 6) had lower
SET* because added high-rise buildings interrupted
sun radiation.
Table 4 shows averaged potential temperature,humidity, wind speed, MRT (Mean RadiantTemperature), SET* at 1m height inside central area
(black rectangle in Figure 3). Potential temperature
in Case Current was higher than Case Best Scenario,
but lower than other cases. However, Case Current
had the highest SET* of 5 cases because there was nohigh-rise buildings in central area of Case Current and it
resulted to increase MRT and SET*. Case Best Scenario
had lower potential temperature than Case Current thoughinitial potential temperature was higher in Case Best
Scenario. This is because there were higher sensible heat
flux near the surface and air near the surface was heatedin Case Current.

Figure 4. WBGT distribution comparison forsimulation and observation

Figure 5. Sensible heat flux from surfaces

3.3. Urbanization Effects
Figure 7 shows profiles of wind speed, potential
temperature and heat flux. In Case Best Scenario and Case
Worst Scenario, the profiles changed significantly at theshear level of homogeneous buildings (80m). Within the
canopies, wind speed was weak and potential temperature
was high in these cases. On the other hand, above buildingheight, wind speed increased rapidly and temperature
decreased rapidly. Heat flux also decreased rapidly near
Figure 6. SET* contours at 1m heightbuilding height. It means thathomogeneous buildings separated Table 4. Averaged parameters inside central area
the air below building height andabove building height, and heat
flux from surface was kept below
building height. As a result,potential
temperature
below
building height in Case Worst was
higher than those of Case Complex

147

Figure 7. Profile data inside central area
City and Case Green City.
Figure 8 shows potential temperature contours at
y=1000m (black line in Figure 3). The vectors depict windspeed velocity for Case Worst Scenario, Case Complex
City and Case Green City. In Case Worst Scenario, wind
above the building height was almost horizontal and clear
potential temperature difference below building height
and above building height can be seen. On the other hand,
extremely high rise building (300m) generates updrafts inCase Complex City and it generates downdrafts in Case
Green City. These high rise buildings promote mixing of
air between high altitudes and near-ground level.

4. Conclusion
In the future, average temperature in Jakarta will increaseby 0.4 ~ 1.2 ℃ because of global warming (frommesoscale model). But there will also have high risebuildings inside a city with an increase of population
density. These high rise buildings will interrupt sun
radiation. As a result, future SET* averaged over central
area was 1~2℃ lower than current SET* at pedestrianlevel. Vegetation also interrupts sun radiation and makes
cooler environment at pedestrian level. Homogeneous
buildings kept heat flux within the canopies and it makes
higher temperature inside canopies. There are little mixingof air between high altitudes and near-ground level. It may
prevent to reduce temperature if cooler air comes to thisarea. On the other hand, height distribution of buildings
inside city generates updrafts and downdrafts and it
promotes mixing of air between high altitudes and nearground level. Therefore, optimum urbanization is high risebuildings with height distribution and adding vegetation
reduces SET* by interrupting sun radiation.

Figure 8. Potential temperature contour at
y=1000m with speed vectors
atmospheric boundary layer”, Journal of Heat Island
International, Vol 9-2, 39-43, 2014
[6] Ashie Y. and Kono T., “Urban scale CFD analysis insupport of a climate-sensitive design for the Tokyo Bay
area”, International Journal of Climatology, Int. J.
Climatol, Vol 31, Issue 2, 174-188, 2011[7] Bakkali M., Inagaki A., Ashie Y., Yoshida Y., Kanda
K. and Raasch S., “Thermal large eddy simulation withsensible heat flux distribution from 3D building
geometries”, Journal of Hydraulic Engineering, Vol 71,
I_433-I_438, 2015[8] Dong Y., Varquez A.C.G. and Kanda M., “Global
anthropogenic heat flux database with high spatial
resolution”, Atmospheric Environment, Vol 150, 276-294,
2017[9] Raasch S. and Schroeter M., “PALM-A large-eddy
simulation model performing on massively parallel
computers”, Meteorol. Z., Vol 10, No 5, 363-372, 2001

References[1] IPCC (Intergovernmental Panel on Climate Change),
AR5 (Assessment Report 5), WG I (Working Group 1),
SPM (Summary for Policymakers), 2013
[2] Web site of Japan Meteorological Agency[3] Kimura F. and Kitoh A., “Downscaling by pseudo
global warming method”, In The Final Report of ICCAP,
Research Institute for Humanity and Nature (RIHN), 2007
[4] Varquez A.C.G., Darmanto N., Kawano N., Takakuwa
S., Kanda M. and Xin Z., “Representative urban growing
scenarios for future climate models”, Journal of HydraulicEngineering, Vol 73. I_103-I_108, 2017[5] Keck M., Raasch S., Letzel M. O. and Ng E., “First
results of high resolution large-eddy simulation of the148Studies on Human Motion Recognition through Wireless Sensing with
Communication Devices
Student Number: 15M51762 Student Name: Haitao PANG Supervisor:Jun-ichi TAKADA

Abstract
This research studies using electromagnetic (EM) signals from wireless communication systems for human motion
recognition (HMR) via radio frequency (RF) sensing. Modern high data-rate RF communication systems have inherent
sensing capabilities for moving objects. Contact-less, device-less, camera-less HMR by ordinary radio signals is very
advantageous compared with traditional method by computer vision or wearables. However, the physical phenomenaof RF scattering from biological bodies are complicated and dependent on many factors such as shape, material
properties, polarization, geometry, etc. To demonstrate this, the author focus on the characteristics of time variant
scattering from a human body performing various kinds of motions, in a data-centric manner. Using inputs of measured
motion data, generative human models in various postures, and a numerical EM simulator based on a high frequency
asymptotic method of physical optics (PO), large datasets can be generated. This can be used for better and morecomprehensive understanding of the RF interaction with human body, and for development and comparison of futurerobust recognition RF algorithms and joint communication and radar (RAdio Detection And Ranging) devices.

1

Introduction-Radar and wireless communication use the same medium
of EM waves as information carrier. Potentially the twoclasses of technologies can converge, so that the same
RF devices could allow double use. Recent advancements in miniaturized, high frequency, high performanceûdevices, advanced communication technologies and computing power have made RF sensing possible with ordinary communication devices. To list a few examples,
mobile networks could be used for vehicular radar, andIEEE 802.11 series Wireless LAN signals used for human behaviour recognition [1]. Human motion recognition has many important applications such as human computer interface, smart home, etc. In 5G era, an exploding
number of RF devices will operate at higher frequencies
and wider bandwidth, equipped with directional massive
Figure 1: Ubiquitous RF sensing of human body
MIMO (Multiple Input Multiple Output) which can exploit spatial diversity, and other advanced technologies.
These advancements will bring about higher resolution
and better sensing performance. This 5G perspective may
provide a ubiquitous and more powerful distributed radar
sensing service for wireless recognition of human activities. can be generated. This is crucial for development of dataHowever, most of the past researches are built on empiri- driven methods/algorithms, whereas it is diﬃcult, timecal data from site-speciﬁc, target-speciﬁc, scenario-speciﬁc consuming and costly to acquire by actual measurements.
measurement campaigns, sometimes conducted with speciﬁc unmodiﬁable commercial-grade devices with speciﬁcation partially unknown. Consequently, these results are
sometimes not fully reproducible, with limited generality,
considering the complicated diﬀerent propagation channel and other important factors. Also these results areusually not openly, easily available for other researchers’
usage or comparison. Therefore, the author turns to fundamental theories and physically sound EM simulations.
This will allow us to better understand the potentials
and fundamental limitations, and to study the characteristics of the complicated time variant scattering problem
with modiﬁable parameters. These factors include various
motions, human body shape, EM properties of clothing,carrier frequencies, transmitter-target-receiver geometry,channel characteristics, etc. Also, a large amount of data

2

Radar principles and the recognition problem

Relevant radar principles is shortly introduced in this section and detailed explanation can be found in [2]. Fundamentals of RF sensing is illustrated as in Figure 1. RFsignal is emitted by the transmitter and scattered by target of interest, and then the scattered signal reaches thereceiver antenna. A moving target performing certain motion, which models a part of human body in motion in ourcase, will modulate the signals in terms of three parameters:

149• Time delayτh = 2Rt,h + Rr,h
,
c0

(1)

where c0 is the speed of EM wave in air, Rt,h and Rr,hare the distances from target to the transmitter andto the receiver, respectively.
• Doppler frequency isvrel,hfD,h = 2
fc ,
c0

(2)

where vrel,h is the relative radial velocity of the target
and fc the carrier frequency.

where B is the bandwidth. For example in 802.11acwith maximum bandwidth of 160MHz this is just 0.94m.
• Velocity resolution
△v =

c0
,
2fc M TOFDM(5)

where TOFDM is the time duration of one OFDM symbol and M is the number of symbols used in coherent
signal processing for extraction of Doppler frequency.
For 802.11ac WLAN, integration of 60 ms of OFDM
symbols means a velocity resolution of 0.5 m/s

Low resolution means only coarse estimation of wholebody posture in one single snapshot, which may not beenough for imaging or localization of body parts. However,combining data from more snapshots where estimated parameters are diﬀerent, recognition of motion could be possible. Human body parts are inherently constraint, so thatexhibit diﬀerent but interrelated velocity proﬁle while perEquation 3 assumes a point target model in free space forming certain kinds of motions. Scattering from eachusing radar cross section (RCS) σh . h is the index type of motion can be represented by a set of these parameters with innate eigen characteristics. In later sections,
number of the target.
the author generates receive signals to show this.
These parameters are true values related to the targets and describe the state of the targets. Hence they
are the source of information in radar systems. RF sens- 3
Methodology
ing hardware processes the modulated receive signals to
extract∑these parameters. Finally, these estimated param- Figure 2 shows the overall methodology in this research.
eters (τ̂h , fˆD,h , α̂h ) in snapshots collected from a period• Step 1 Human motion data is recorded using moof observation are used to recognize the state of the target
tion capture technology (MOCAP). Here data fromof human body motion. Mathematically, the recognition
an open adataset [4] is used, in which key points ofprocess is a mapping from a set of the estimated paramehuman body are captured at 120 Hz.
ters to a set of labels.
Estimation performance of these parameters are funda• Step 2 A skeleton form constructed by 17 points ismentally limited by speciﬁcations of the whole RF system,
extracted from motion data in each snapshot.
such as the waveform in use and hardware. Targets are
considered distinguishable by a radar system, only if the• Step 3 Transform skeleton form data in each snapdiﬀerences of their parameters are larger than the system
shot to a proper 3D human model suitable for EM
resolution. In most modern high data-rate communicasimulation.
tion systems, Orthogonal Frequency Division Multiplexing(OFDM) is used and its resolution is given by [3] as:• Step 4 Execute EM simulation to model received signal using 3D models in each snapshot.
• Range resolution, or the smallest resolvable unit indistance measurement, is• Step 5 Apply time-frequency analysis to the received
signals.
c0
△R =
,
(4)
2B
• Complex amplitude αh , which is related to theshape and EM properties of the target and can bemodelled as√σh c2oαh =(3)
2 R2 f 2
3(4π) Rt,hr,h c

1. Recordmo!on using
MOCAP

2.Input ofskeleton data

3.Model humanin 3D

4.Generate
Radar data

Figure 2: Overview of research methodology

150

5.Dataanalysis

of Doppler frequencies in the receive signals due to themodulations by target’s complex movements.
Spectrogram(t, ω) = |STFT(t, ω)|2

STFTx,w [n, ω] =∞∑x[n + m]w[m]e−jωm

(8)

(9)

m=−∞

Gaussian windows is used as window function w[m] in
STFT. For the radar signal sampled at 1200 snapshots per
second, FFT size and window size of w[m] is set to 512.
And overlap is used with a hopsize of 1/8 of the window
size.

Figure 3: Spectrogram patterns from a walking human4

EM simulation with ellipsoidal
4.3 Results for Doppler signaturemodel

For fast calculation and without loss of generality, in thissection human body is modelled as a combination of 19
ellipsoids similar to [5]. The modelled human body is as
illustrated in step 3 in Figure 2. With this model, patternsof micro Doppler due to diﬀerent velocity of body parts
can be identiﬁed.

4.1

Signal model

Consider single carrier RF signals in frequency fc , in every
snapshot the receive signal is modulated by each moving
ellipsoid h, which can be parametrized as (τh , αh ). Receive
signal at the antenna consists of reﬂected signals from all
19 ellipsoids, superimposed during one snapshot. The energy at the receiver Erx then has a relationship to thetransmitted signal in each snapshot as

Erx = Etx

18∑

αh ej2πfc (t−τh )(6)Figure 4: Spectrogram patterns from some sports activities

h=0√
Without loss of generality, σh is used as αh for later
simulation, omitting propagation attenuation. And an approximate bistatic RCS solutions for perfect electric conductor (PEC) ellipsoid from [6] is used for calculation ofσh , which is represented as Equation 7.
where ϕi , θi are the incidence angle and azimuth angle
of incident wave to the ellipsoid, and ϕ, θ are observation
angles, respectively.
Once the data in each snapshot is calculated, time sequence received data is available for joint time-Doppler
analysis.

Simulation is conducted at frequency of 5 GHz, withtransmitter and receiver located in the same position(monostatic) at diﬀerent locations. Figure 3 shows theresults of monostatic backscattering from a walking human at speed of 1 m/s towards the +x axis direction. An
enlarged version is in Figure 6. And Figure 4 shows the
spectrogram from several sports activities.
First, the patterns of spectrogram are clearly and visually recognizable and classiﬁable. Some activities do havean innate Doppler signature, which can be used in classiﬁcation algorithm. Second, sensors at some locations(e.g. at (2m, 5m, 2m)) cannot generate recognizable pattern. This is because radial velocity is angle dependent
4.2 Time frequency analysis
and given the antenna location, it is not detectable. Third,
Spectrogram represented by short time Fourier transform sensors at several locations show diﬀerent but similar pat(STFT) is used for the analysis of the time variant change terns, due to the fact that the radial velocity is diﬀerent

σh = [

4πa2 b2 c2

[(
)
(
)
]2
1 + cos ϑi cos ϑ cos ϕ − ϕi + sin ϑi sin ϑ22a2 (sin ϑ cos ϕ + sin ϑi cos ϕi ) + b2 (sin ϑ sin ϕ + sin ϑi sin ϕi ) + c2 (cos ϑ + cos ϑi )
151

2

]2

(7)

of zero from top. Result shows that the co-polarizationand cross-polarization scattering from the complex-shaped
human body is diﬀerent and this suggests the use of apair of orthogonally polarized antenna could help improve
the sensing performance by exploiting this polarimetricdiversity.

6

Conclusion and future work

This research proposes the general idea of ubiquitous RF
sensing for human motion recognition, and simulation approaches are taken to demonstrate useful characteristics.
Figure 5: Example of polarimetric RCS from a PEC human Using these approaches, large datasets can be generated
body in Figure 1 with vertically polarized incident wave from for development of actual hardware system and recognitop
tion algorithms, also for their veriﬁcation and comparison. Still, better modelling techniques that could modelthe diﬀerent dialectic clothing components with diﬀerent
EM properties, and better simulation techniques such as
from diﬀerent observers. This also suggests that a multiiterative PO could help improve accuracy.
static conﬁguration with multiple measurement link could
The relevant algorithms used in this research would bepossible provide extra spatial diversity to increase sensing
put on GitHub to encourage open research. As a prelimiperformance.
nary trial classiﬁcation, a mini VGG Convolutional Neural
Network was used with inputs of noisy spectrogram ﬁgures
5 Polarimetric signature by PO as in Figure 4 to classify sports activity. And the result
accuracy is between 65% ~80%.

simulation

In section 4, electromagnetic polarization was ignored. To
solve the polarimetric EM scattering problems, a high frequency asymptotic method of Physical optics (PO)[7] isapplied to meshed human models.

5.1

References[1] S. Youseﬁ, et al. "A Survey on Behavior Recognition Using WiFi Channel State Information," in IEEE
Communications Magazine, vol. 55, no. 10, pp. 98104, Oct. 2017.

Physical optics approximation[2] M. I. Skolnik, Introduction to Radar Systems, 3rd
Edition, McGraw-Hill, New York, 2001.

PO is based on the ﬁeld equivalence principle so that only
surface equivalent currents on the surface illuminated by
the incident wave, are considered in the calculation of
scattered ﬁeld at the observation point. It is a common
method for calculation of RCS of complex shaped objects,
known for its good balance between fast calculation, lesscomputation resources and good accuracy for backscattering region with relatively small bistatic angle. In this simulation, only ﬁrst order PO currents are taken into accountand Ludwig’s method [8] [9] is used for fast calculation of
the PO integrals for triangular meshes.

5.2[3] K. M. Braun. "OFDM Radar Algorithms in Mobile
Communication Networks." PhD thesis, Karlsruher
Institute of Technology, 2014.
[4] http://mocap.cs.cmu.edu/[5] V. C. Chen, "Doppler signatures of radar backscattering from objects with micro-motions," in IET Signal Processing, vol. 2, no. 3, pp. 291-300, September
2008.
[6] K. D. Trott, "Stationary Phase Derivation for RCS of
an Ellipsoid," in IEEE Antennas and Wireless Propagation Letters, vol. 6, pp. 240-243, 2007.

Human mesh model

To create realistic, highly accurate, conﬁgurable 3D
meshed human models, a generative algorithm of Skinned
Multi-Person Linear Model (SMPL) [10] is used. SMPL
can represent a wide variety of body shapes in natural human poses. One example of a generated meshed modelis showed in Figure 1. To fulﬁl the small size mesh requirements for highly accurate PO simulation, surfaces ofgenerated models are subdivided until the length of all triangular meshes are smaller than 1/10 of the wavelength.

5.3[7] C.A. Balanis. Advanced Engineering Electromagnetics, 2nd Edition. Wiley, 2012.
[8] A. Ludwig. Computation of radiation patterns involving numerical double integration. IEEE Transactions
on Antennas and Propagation, 16(6):767-769, 1968.
[9] Tokio Miyake. Numerical simulation of scatteringfrom random rough surface by using physical optics.
2018.

Result for polarimetric signature

Figure 5 shows a result for polarimetric RCS from a PEC
human body in posture as illustrated in Figure 1. The
incident wave is vertically polarized, with incidence angle[10] M. Loper, et al. SMPL : A Skinned Multi-Person Linear Model. ACM Trans. Graphics (Proc. SIGGRAPH
Asia), 34(6):248:1|-248:16, 2015.

152