Doctoral Dissertation
The Multidimensional Impacts of Multi-service Transport Platform(MSTP) on Activity-travel Behavior and Urban Form:
A Case of Jakarta, Indonesia

MAYA SAFIRA

Graduate School for International Development and Cooperation
Hiroshima University

September 2021

The Multidimensional Impacts of Multi-service Transport Platform(MSTP) on Activity-travel Behavior and Urban Form:
A Case of Jakarta, Indonesia

D185311
MAYA SAFIRA

A Dissertation Submitted to
the Graduate School for International Development and Cooperationof Hiroshima University in Partial Fulfillmentof the Requirements for the Degree ofDoctor of Engineering
September 2021

Abstract

ABSTRACT
Background and motivations
Information and communication technology (ICT) is the critical enabler of innovation.
Recently, there has been a rapid escalation in the number of Internet users worldwide. In
2020, there were 4.54 billion Internet users, the equivalent of 59% of the world’s population.
This number increased by 7% in January 2019 (Global Digital Reports, 2020). Some benefits
may be obtained from ICT, such as better information and access to goods and amenities,
increased connection anywhere and anytime, and the convenience of the sharing economy
and delivery services. On the other hand, the evolution of ICT has consequences that cannot
be ignored, such as the possibility of job losses and bankruptcy in the economic sector, lackof data privacy, reduced personal interactions, and reduced physical activity.
The interaction between information and communication technologies (ICT) andhuman activity-travel behavior has become an important theme in transportation research inrecent years. Researchers have recognized that an increase in the use of ICT may lead tochanges in the location, timing, and duration of people’s activities, and the widespread useof ICT will likely be associated with new patterns of activity and travel in space-time (Kwan,
2020; Dijst, 2004). Analyses of these patterns could provide part of the empirical basis and/or
behavioral foundation for enhancing our understanding of the interaction between ICT,
activity-travel behavior and urban form.
Recently, several issues concerning the interaction between transportation and urban
form in the era of ICT have arisen. Changes in the concentration of urban activities,
transportation systems, and individual mobility are expected. ICT may improve people’s
access to goods, services, and even jobs through virtual connectivity, which can allow
activities to take place almost anytime and anywhere. Several studies have investigated how
urban space and ICT covary. Nevertheless, it is possible that ICT only affects activity spaces
and schedules (and not the converse). There is growing interest in questions related to thebidirectional relationships between urban form and ICT.
One significant ICT innovation is multiservice transport platforms (MSTP). MSTP
are innovative platforms that integrate transportation systems and daily service provision. In
Indonesia, some MSTP companies (e.g., GoFood by Gojek, Grab-Food, and Uber Eats) have
expanded their services, including their online food delivery (FD) services. They utilize fleet
drivers to provide transportation and offer other daily services such as delivering food frommerchant partners to the consumer. However, to maintain the quality of the food, they set
maximum service areas. The service area determines the number of food merchants thatusers can access from their location. Maximum distances were set to maintain the quality
and freshness of food delivered to the consumer. Thus, the accessibility of local foodmerchants and the extent of delivery service areas are critical factors in the delivery systemand consumer choice.
As one ICT innovation, MSTP also change people’s daily activity-travel behavior in
the short run and possibly city development in the long run. Because users do not need toallow time for travel when they use online food delivery services, they may order foods fromfar afield. These changes could increase the spatial mismatch between the customer location(i.e., home or office) and that of restaurants/food merchants, resulting in urban sprawl andloss of vitality in cities. Thus, if people can access the same quality of goods and services
without commuting, they may be willing to live in far suburbs where more affordable land
can provide good living conditions. For this reason, the presence of MSTP may causeiAbstractchanges that cannot be ignored in travel behavior, activity patterns, and commercial location
decisions. Further analysis of the impacts of MSTP is needed to provide a comprehensive
understanding of the potential benefits or risks to be considered by policymakers andresearchers.
Research questions and aims
This thesis aims to provide a comprehensive understanding of the impacts of MSTP andexamine them empirically based on a case study in Jakarta, Indonesia. The specific objective
is to assess the impact of MSTP in Indonesia on urban form and activity-travel behavior. In
this study, three research questions regarding the impact of MSTPs are included:(1.1) How to quantify the concentration of facility distribution?
(1.2) How is the association between facility distribution and city-level characteristics?
(2.1) What distribution changes do MSTP bring about on the facility distribution?
(2.2) How do these induced changes in urban form?
(3.1) How to capture the virtual activities on daily activity-travel behavior?
(3.2) How MSTP change the distribution of activities?
(3.3) What factors that influence people to choose online activities?
(4.1) How the presence of MSTP’s online food delivery service will affect people’s
eating behavior?
(4.2) What factors that affects MSTP’s service level?
(5.1) How to extend the current dynamic discrete choice model for activity-travel
analysis to incorporate the impact of MSTP use on activity-travel pattern?
Methodology
In this study, to analyze the impact of MSTPs on the urban form, we analyze facilities' spatial
distribution through agglomeration index analysis and its correlation with the city-level
characteristics. We employ the agglomeration analysis across the different scales of the areaand different types of facilities. Regarding analyzing the impact of MSTP on activity-travel
behavior, both revealed preference (RP) and the probe-person (PP) data are collected tocapture the current activity-travel behaviors and preference of using MSTPs. A travelactivity diary survey was conducted in Jakarta within 14 days (from January 28th to February
10th, 2020), along with two additional questionnaires about the individual characteristics
and their preference of MSTPs usage, mainly, the online-based food delivery service. To
achieve this research aim, we extend the dynamic discrete choice model by adding thecomponent of MSTPs to assess the impact of MSTPs on activity-travel behavior.
Main Findings
In recent years, the presence of MSTP as one of the innovations of ICT in transportation anddaily service provision has rapidly expanded and had a significant impact on our daily
activity. Daily activities and travel are inseparable since travel results from an individual’s
desire or need to engage in an activity. While the location to perform activities is spatiallydistributed over a wide range of areas. Hence, these activities cannot be carried out at thesame location. Then, the result is the desire to conduct some trip or travel to another location.
With this realization in mind, this thesis had presented the result of an analysis that examined
the multi-dimensional impact of MSTP on urban form and activity-travel behavior.
This research aimed to examine the impact of Multi-Service Transport Platformii

Abstract(MSTP) on the urban form and activity-travel behavior. Based on the analysis of the impactof MSTP on the urban form and activity-travel behavior, it can be concluded that MSTP
changes the behavior of individuals toward online activities and change the decision locationof food merchants. The results indicate that MSTP brings the new agglomeration forces thatinduce the new distribution pattern of facilities (i.e., food merchants), where the distributionof facilities itself will affect the MSTP’s services level and affect the individual’s preferences
toward online activities and services as well as their activity-travel behavior (i.e., eating
behavior).
In this study, to analyze the impact of MSTP on the urban form, this study develops
a new approach in quantifying the distribution of facilities through the agglomeration index
analysis. The presence of MSTP brings a new agglomeration force for a certain type of
facility (i.e., online food merchants) through the flexibility in the location decision. Since
the online food merchants can provide the food through online delivery services and thecentral area is not a prime location for the merchants since the MSTP services available. In
our analysis, we also found that the agglomeration index is highly correlated with the citytransportation characteristics, such as the share of public and private transport mode, the area
size of the city, population density, and the average travel time.
To analyze the impact of the MSTP on the activity-travel behavior, several approaches havebeen utilized, including the improvement of activity-travel diary survey that can incorporate
with the MSTP and online activities component, analyzing the changes in activity and travelcomposition, and we also propose the new framework in activity-travel behavior analysis byimproving the current dynamic discrete choice model with the MSTP components.
Adding the online activities and MSTP component on the Future Mobility Sensing(FMS) activity-travel diary survey application can enrich the activity-travel diary survey.
We observed that the presence of MSTP influences the decision of people for conduct
activity and choosing mode. Under the influence of the MSTP services, for eating andshopping, more than 50% of activity was conducted online. People are preferred to use
MSTP’s transport service (i.e., online ojek and ride-hailing) rather than public transit for the
daily transport mode.
The presence of MSTP also affects the individual’s eating behavior through online
food delivery services. This research clearly illustrates that the improvement of MSTP’s
online food delivery service can increase the changes in an individual’s eating behavior,
which the improvement of the service level is associated with the distribution of online foodmerchants itself. People will gain more benefits from MSTP in the area with a greaternumber of online food merchants.
This study also simulates the impact of MSTP on activity-travel behavior by adding
the MSTP component in the dynamic discrete choice model for activity-travel behavior
analysis. Through the simulation, we found that MSTP changes the activity scheduling andthe choice of conducting activities. However, the further application of this framework withthe actual data is needed to see how the models are interacting.
While the existing studies do not consider the presence of MSTP, this study attempt
to provide a comprehensive understanding of the impact of MSTP on urban form andactivity-travel behavior through the agglomeration analysis of facilities and the eating
behavior analysis. The results are well explained the impact of MSTP and answering all of
the research questions. However, there are some insights that arose in the process that will
be addressed as the recommendation for future study.

iii

Abstract

Significant Contributions
Academic Contributions
The concept of Multi-service Transport Platforms (MSTP), a part of ICT innovation, is
expanded in the field of activity-travel behavior research. In particular, this study could
successfully:1) It is the first study in the literature to explore the impact of MSTP on urban form andtravel behavior in Indonesia comprehensively.
2) Establish the comprehensive survey framework to capture the individuals’ activitytravel behavior simultaneously with the usage of online activities and theirpreferences towards the online services (MSTP).
3) Propose the new approach in quantifying the distribution of facilities through theagglomeration index analysis and shows the importance of the selection ofcounterfactuals and distance metrics.
4) Propose the new framework of activity-travel behavior modeling by expanding the
dynamic discrete choice model component with the addition of the MSTP
component.
Practical Contributions
1) This study brings new insight for the online food merchant’s location decision, where
the fringe area can be an alternative location as long as they keep agglomerated withother online food merchants to keep a certain number of drivers around their area.
Since the number of drivers is associated with the additional waiting time that
affecting MSTP’s service level.
2) MSTP could be an alternative in the unusual condition like nowadays COVID-19
pandemic, where people can access to many services while staying at the same
location (e.g., home). The practitioner may consider improving the quality of
MSTP’s services, in terms of delivery time and delivery cost may increase thepotential demand of the services.
Contributions to Policy Implications
Given the strong interrelationship between urban form and transport, along with the presenceof MSTP as the ICT-based innovation in transport, the integration of land-use andtransportation planning represents a unique policy opportunity. When MSTP brings the new
agglomeration forces for online food merchants by giving some flexibility to decide theirlocation while still keep a certain number of consumers through online food delivery
services, the new neighborhood-scale center may develop. The type of service that MSTP
provides is the service that relies on the spatial interaction among food merchants as the
supply side, among the users as the demand side, and the spatial interaction among the user
and the food merchants. By having the flexibility to be located, the presence of MSTP has
the potential to change the structure of cities. In the long run, this kind of service may leadto a less structured city—the further regulation and policy regarding the location of an
online-based food merchant. When the online food merchants are scattered agglomerated
across the city, the practitioner should consider putting some regulation in the location of
online food merchants. To avoid the new trip attraction increased in the neighborhood-scale,the practitioner could allocate the new center at the bigger scale (e.g., district-scale), assumes
that a bigger area scale can accommodate a bigger activity scale and bigger traffic volume.
Since the provision of strategic infrastructure is one of the most critical public policy
instruments informing the long-term shape and characters of a city at any stages iniv

Abstractdevelopments, MSTP as a part of transport infrastructure and services play a key role indetermining urban mobility patterns within urban planning, including modal choice anddelivery services. Regulatory policy instruments also play a key role in shaping urban
transport performance, including the limitation of MSTP’s fleet in one area to avoid
overcrowding traffic.
Content of Chapters
The dissertation consists of nine chapters. The first chapter provides an introduction,
including the research background, research objective and questions, research framework,and significance and contribution of this study. The remainder of the dissertation isorganized as follows.
Chapter Two broadly reviews the literature regarding MSTP, which has seldom been
examined in existing research. The general explanation of the interaction between activity,
urban form, and transportation will be explored at the beginning of this chapter, following
by the exploration of the impact of ICT on activity, urban form, and transportation system.
This chapter will also explain the general definition and working system of the Multi-Service
Transport Platform (MSTP).
Chapter Three descript the research design and methodology of this study. Including
the theoretical and conceptual framework, research aims and objectives, general design ofthis study, survey design, and the context-awareness of stated preference survey and thebroad analysis methodology that will be used. The characteristics of two different locationsof the case study (i.e., Jakarta, Indonesia, and Japan) will also be explained in this chapter.
Chapter Four analyzes the association between the agglomeration index and the citylevel characteristics in 69 cities in Japan. By utilizing the agglomeration index approach, we
analyze the concentration of facilities in the city and its association with the city-level
characteristics, including the transportation and socio-geographic characteristics.
Chapter Five empirically assesses the impact of MSTP on the urban form in Jakarta
through the analysis of the agglomeration index of facilities. Adopting the agglomerationindex calculation approach, we explored the spatial distribution of food merchants under the
MSTP’s influences.
Chapter Six broadly explore the changes in the activity and travel distribution under
the presence of MSTP. This chapter explores the data from the improved activity-travel diarysurvey that includes online activities such as online food delivery, online shopping, online
working, online study, online social activity, and online leisure/entertainment, with the casestudy in Jakarta, Indonesia.
Chapter Seven examines the impact of online-based food delivery services on
individual meal choice behavior, i.e., eating out or using a delivery service. This chapter
attempts to identify empirical factors affecting people’s preferences for online-based fooddelivery services using adaptive stated preference (SP) survey data.
Chapter Eight aims to extend the existing dynamic discrete choice activity-travel
model to incorporate ICT use into activity-travel patterns. We argue that the presence ofMSTP increases the utility of activity links by allowing people to access services virtually
in local areas without travel, i.e., MSTP drivers bring services to the clients’ homes.
Chapter Nine summarizes the findings and draws conclusions concerning themultidimensional impacts of MSTP on urban form and activity-travel behavior from a casestudy of Jakarta, Indonesia.

v

Acknowledgements

ACKNOWLEDGEMENTS
Alhamdulillah. Thanks to the almighty Allah SWT for giving me the strength and ability tounderstand, learn, and complete this study.
I would like to give my special thanks to the JICA Innovative Asia Program,financially support me in doing and finish my doctoral degree at Hiroshima University. Also,
the Ministry of Land, Infrastructure, Transport, and Tourism of Japan that financially support
my surveys. I would like to thank the following people, without whom I would not havebeen able to complete this study.
I would like to address my deep sense of thanks and gratitude to my supervisor (thesuper-best-Sensei-ever), Dr. Eng. Makoto Chikaraishi, for his generous help since I camehere. He was the best supervisor and teacher that I ever met. Almost the past four years, I’ve
learned so much from you. But the most important thing I’ve appreciated is your belief inme. Because you set such high expectations for me, I tried to raise my academic level tomeet them. I’m a better researcher and writer because of you. Still, I have so many things tolearn in the future. Again, thank you for seeing something in me and encouraging me to bebetter. Thanks for believing in me enough to finish my study. Your support means more tome than I can express.
I would like to address my gratitude to my sub-supervisors, Prof. Akimasa Fujiwara
and Prof. Junyi Zhang, who helped me a lot in improving my research by providing
encouragement and suggestions all the time. I also want to thank Dr. Eng. Makoto Tsukai
and Ibnu Syabri, Ph.D. to take the time to be my reviewer and committee members. Your
insightful suggestions, comments, and encouragement help me to broaden my research.
Thank you to my lovely family, my mom, my little sister, my late father, and all myfamily members. Thank you for your support, prays, and everything. They are everything tome, my biggest motivation and reason to survive in doctoral study, in Japan. It has been very
tough, especially in my last year of doctoral, but they are always by my side and make mestrong again. Even though they cannot come to my graduation, next time, I would like totake my mom and my little sister to go to Saijo and having my favorite okonomiyaki.
Thank you for all HiIRM, HiTEL, and TSG Lab Members, especially for Dr. Varun
Varghese, Dr. Hong Nguyen, Nur Diana Safitri, Setyo Nugroho, and Hyewon Namgung. I
feel really grateful to meet all the teachers and lab mates. Thank you for all your suggestions
on my study, it really helped me to expand my perspective of research. I also want to thankall Indonesian Student Association (PPI) Hiroshima members that make me less lonely inJapan. Thank you very much for helping me, for becoming my friend in Saijo.
I would like to address my gratitude for all my professor in Bandung Institute ofTechnology that always support me from the beginning till I accomplished my study.
Especially to Wilmar Salim, Ph.D. that give me a chance to join the JICA Innovative Asia
Program, Ridwan Sutriadi, Ph.D. and Dr. Denny Zulkaidi, who give a recommendation forme and still support me today. Also, I would like to thank Dr. Niken Prilandita and Dr. I
Gusti Ayu Andani, for all your support and help.
It is difficult to say goodbye to all people that I met in Saijo and every memory wemade. There is always a first time for everything, first snow in winter, first Sakura in spring,first Momiji in autumn, first wonderful fireworks in summer, and all those ‘first-moments’that I get in Saijo, Higashi Hiroshima was incredible and unforgettable. I always believe thateverything has happened for a reason and the way we cross a path is the best scenario of lifethat made for me, for us. I enjoyed and will never forget my life in Saijo. I am really thankfulfor everyone’s help and I am ready for the new chapter in my life. See you again!
vi

Contents

CONTENTSABSTRACT .......................................................................................................................... i
ACKNOWLEDGEMENT ................................................................................................. vi
CONTENTS ....................................................................................................................... vii
LIST OF TABLES ............................................................................................................... x
LIST OF FIGURE .............................................................................................................. xi
Chapter 1: INTRODUCTION .......................................................................................... 1
1.1. RESEARCH BACKGROUND .............................................................................. 1
1.2. RESEARCH OBJECTIVE AND QUESTIONS .................................................... 2
1.3. RESEARCH FRAMEWORK................................................................................. 2
1.4. OVERVIEW OF FINDINGS ................................................................................. 3
1.5. RESEARCH SIGNIFICANCE AND CONTRIBUTION ...................................... 5
1.6. OUTLINE OF THE DISSERTATION................................................................... 6
Chapter 2: LITERATURE REVIEW .............................................................................. 92.1. ACTIVITY, URBAN FORM, AND TRANSPORTATION .................................. 92.2. ICT ON ACTIVITY, URBAN FORM, AND TRANSPORTATION .................. 122.2.1. ICT on Activity-Travel Behavior .............................................................. 13
2.2.2. ICT on Urban Form ................................................................................... 16
2.2.3. ICT on Transportation ............................................................................... 17
2.3. THE MULTI-SERVICE TRANSPORT PLATFORM (MSTP) .......................... 18
Chapter 3: RESEARCH DESIGN ................................................................................. 22
3.1. RESEARCH FRAMEWORK............................................................................... 22
3.1.1. Conceptual Framework for Analyzing the Impact of MSTP on The Urban
Form .......................................................................................................... 23
3.1.2. Conceptual Framework for Analyzing the Impact of MSTP on the Changes
in Activity-Travel Behavior....................................................................... 24
3.1.3. Conceptual Framework for Analyzing the Impact of MSTP on The ActivityTravel Behavior ......................................................................................... 25
3.2. RESEARCH AIMS AND OBJECTIVES ............................................................ 26
3.3. SURVEY DESIGN ............................................................................................... 27
3.3.1. Activity-Travel Diary Survey .................................................................... 27
3.3.2. Online Activities Diary Survey ................................................................. 33
3.3.3. Socio-Demographic Questionnaire............................................................ 34
3.3.4. Context-aware Stated Preference Surveys ................................................. 35
3.4. STUDY LOCATIONS ......................................................................................... 39
3.4.1. Study in Jakarta, Indonesia ........................................................................ 39
3.4.2. Study in 69 Cities of Japan ........................................................................ 41
3.5. TARGET RESPONDENTS ................................................................................. 42
3.6. ANALYSIS METHODOLOGY........................................................................... 43
3.6.1. Agglomeration Index ................................................................................. 43vii

Contents

3.6.2.
3.6.3.
3.6.4.
3.6.5.

Correlation Analysis .................................................................................. 44
Decision Tree Analysis .............................................................................. 44
Propensity Score ........................................................................................ 46
Dynamic Discrete Choice Model .............................................................. 46

Chapter 4: THE ASSOCIATION BETWEEN AGGLOMERATION INDEX AND
CITY-LEVEL CHARACTERISTICS ....................................................... 49
4.1. INTRODUCTION ................................................................................................ 49
4.2. METHODOLOGY ............................................................................................... 51
4.3. STUDY AREA ..................................................................................................... 52
4.4. DATA ................................................................................................................... 53
4.5. RESULT AND DISCUSSION ............................................................................. 55
4.5.1. Selection of Counterfactuals and Distance Metrics ................................... 55
4.5.2. Association with City-Level Characteristics: Correlation Analysis .......... 57
4.5.3. Association with City-Level Characteristics: Decision Tree Analysis ..... 59
4.6. CONCLUSION ..................................................................................................... 62
Chapter 5: THE IMPACT OF MSTP ON THE URBAN FORM: A CASE OF
JAKARTA, INDONESIA ............................................................................ 63
5.1. INTRODUCTION ................................................................................................ 63
5.2. METHODOLOGY ............................................................................................... 64
5.3. STUDY AREA ..................................................................................................... 65
5.4. DATA ................................................................................................................... 66
5.5. RESULT AND DISCUSSION ............................................................................. 68
5.5.1. Identified Agglomeration Indices .............................................................. 68
5.5.2. Association of Agglomeration Index and City Characteristics ................. 70
5.6. CONCLUSION ..................................................................................................... 75
Chapter 6: CHANGES IN ACTIVITY-TRAVEL DECISION UNDER THE
INFLUENCE OF MSTP .............................................................................. 77
6.1. INTRODUCTION ................................................................................................ 77
6.2. METHODOLOGY ............................................................................................... 78
6.3. DATA AND STUDY AREA ............................................................................... 79
6.4. RESULT AND DISCUSSION ............................................................................. 80
6.4.1. Socio-Demographic Characteristics .......................................................... 80
6.4.2. Activity and Travel Behavior Characteristics ........................................... 81
6.4.3. Online Virtual Activities ........................................................................... 82
6.4.4. Physical vs. Virtual Activities: Influence of Variables ............................. 82
6.5. CONCLUSION ..................................................................................................... 86
Chapter 7: THE IMPACT OF ONLINE FOOD DELIVERY SERVICE ON
EATING-OUT BEHAVIOR ....................................................................... 88
7.1. INTRODUCTION ................................................................................................ 88
7.2. METHODOLOGY ............................................................................................... 89
7.2.1. Survey Methodology and Data Preparation .............................................. 90
7.2.2. Modeling Framework ................................................................................ 93
7.3. DATA ................................................................................................................... 95
7.4. RESULT AND DISCUSSION ............................................................................. 96
7.5. CONCLUSIONS ................................................................................................ 102

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Contents

Chapter 8: THE IMPACT OF MSTP ON ACTIVITY-TRAVEL BEHAVIOR (THEIMPROVEMENT OF DYNAMIC DISCRETE CHOICE MODEL FOR
ACTIVITY-TRAVEL BEHAVIOR ANALYSIS UNDER THE
PRESENCE OF MSTP)............................................................................. 104
8.1. INTRODUCTION .............................................................................................. 104
8.2. METHODOLOGY ............................................................................................. 105
8.2.1. Modeling Approach ................................................................................. 105
8.2.2. Dynamic Discrete Choice Model (DDCM) ............................................. 107
8.3. PROPOSED MODEL ......................................................................................... 109
8.4. CONCLUSION AND WAY FORWARD ......................................................... 115
8.5. SIMULATION RESULTS ................................................................................. 117
Chapter 9: CONCLUSIONS AND RECOMMENDATION ....................................... 119
9.1. CONCLUSION ................................................................................................... 119
9.2. IMPLICATION FOR POLICY AND PLANNING ........................................... 122
9.3. LIMITATION OF THE STUDY AND RECOMMENDATION ....................... 123
REFERENCES ................................................................................................................. xiii
LIST OF PUBLICATIONS ........................................................................................... xxiii
CURRICULUM VITAE ................................................................................................. xxv
LIST OF APPENDICES ................................................................................................ xxix

ix

List of Tables

LIST OF TABLES
Table 1.1. Research objectives, research topics, and research questions ............................ 2
Table 3.1. Research objectives, research topics, and research questions .......................... 26
Table 3.2. Smartphone-based travel activity surveys: A review ....................................... 28
Table 3.3. Variable of activity-travel diary........................................................................ 29
Table 3.4. Attributes and levels for context-aware SP survey for eating out activities ..... 36
Table 3.5. The characteristics of the study area ................................................................. 40
Table 4.1. The 69 cities in Japan ....................................................................................... 53
Table 4.2. General transportation (modal share) characteristics of the case study ............ 54
Table 4.3. Average pairwise distance and agglomeration index ....................................... 55
Table 4.4. Average pairwise distance of facilities ............................................................. 56
Table 4.5. Summary of agglomeration index of 69 Japanese cities .................................. 56
Table 4.6. List of correlation and decision tree analysis variables .................................... 58
Table 4.7. Correlation coefficients .................................................................................... 61
Table 4.8. Share of commercial facilities by the type of agglomerated areas ................... 63
Table 4.9. City-transport characteristics ............................................................................ 63
Table 5.1. The characteristics of the study area ................................................................ 66
Table 5.2. Facility type ...................................................................................................... 67
Table 5.3. The service of food merchants .......................................................................... 67
Table 5.4. Average agglomeration indices with different spatial scales ........................... 68
Table 6.1. Data description of socio-demographic variables............................................. 80
Table 6.2. Distribution of activity participation and time allocation in primary activities 81
Table 6.3. Distribution of mode choice for travel activities .............................................. 82
Table 6.4. Distribution of online activities ........................................................................ 82
Table 6.5. Effect of variables on the choice of physical vs. virtual activities ................... 85
Table 7.1. Attributes and levels for context-aware SP survey for eating out activities ..... 92
Table 7.2. Data Description of Variables .......................................................................... 95
Table 7.3. Context-aware SP survey variable .................................................................... 96
Table 7.4. The estimation results of the propensity score model ...................................... 97
Table 7.5. The model estimation results of eating choice behavior .................................. 99xList of Figures

LIST OF FIGURES
Figure 1.1.
Figure 1.2.
Figure 2.1.
Figure 2.2.
Figure 2.3.
Figure 2.4.
Figure 2.5.
Figure 2.6.
Figure 3.1.
Figure.3.2.
Figure.3.3.
Figure.3.4.

Conceptual framework ..................................................................................... 3
Structure of the dissertation.............................................................................. 8
The land use formation of urban landscapes and transportation .................... 10
The daily travel-activity behavior embedded with ICT usage ....................... 14
Association of ICT activities and other activities .......................................... 15
MSTP’s components and application interface .............................................. 19
Components of MSTP .................................................................................... 19
The function of online food delivery provided by MSTP .............................. 20
The general conceptual framework ................................................................ 23
Workflow for analyzing the impact of MSTP on the urban form .................. 24
Workflow for analyzing the impact of MSTP on the urban form .................. 25
Workflow for analyzing the impact of MSTP on the activity-travel behavior........................................................................................................................ 26
Figure 3.5. The interface of X-ING application ............................................................... 30
Figure 3.6. The smartphone-based activity-travel survey framework .............................. 31
Figure 3.7. A typical activity and travel log in X-ING application .................................. 32
Figure 3.8. Follow-up questions (prompt pop-up on the application) for eating out
activities ......................................................................................................... 33
Figure 3.9. Follow up question for online activities ......................................................... 34
Figure 3.10. Context-aware SP methodology ..................................................................... 35
Figure 3.11. Example of questionnaire ............................................................................... 37
Figure 3.12. RP and SP surveys for online activities ......................................................... 37
Figure 3.13. Map of study area in Jakarta, Indonesia ......................................................... 39
Figure 3.14. Land use map of Jakarta 2009 ........................................................................ 40
Figure 3.15. Map of Jakarta city’s structure ....................................................................... 41
Figure 3.16. Map of study area in Japan ............................................................................. 42
Figure 3.17. Location of sampling selection ...................................................................... 43
Figure 3.18. Example of decision tree analysis .................................................................. 45
Figure 4.1. Map of 69 Japanese cities as the study area ................................................... 53
Figure 4.2. Facility distributions of Nara, the most agglomerated city ............................ 57
Figure 4.3. Facility distributions of Imabari, the least agglomerated city ........................ 57
Figure 4.4. Correlation matrix of variables ...................................................................... 59
Figure 4.5. The results of the decision tree analysis ......................................................... 60
Figure 5.1. Map of study area in Jakarta, Indonesia ......................................................... 66
Figure 5.2. Distribution of facilities ................................................................................. 67
Figure 5.3. Agglomeration index of food merchants (combination) ................................ 69
Figure 5.4. Agglomeration index of online food merchants ............................................. 69
Figure 5.5. Agglomeration index of dine-in food merchants ........................................... 70
Figure 5.6. Facility densities at the neighborhood level ................................................... 71
Figure 5.7. Comparison of agglomeration indices at the neighborhood level .................. 72
Figure 5.8. Distance from central area.............................................................................. 72
Figure 5.9. Average densities with respect to distance to the central area ....................... 72
Figure 5.10. Average agglomeration indices with respect to distance to the central area .. 73
Figure 5.11. The percentage of commercial land use in Jakarta ........................................ 74
Figure 5.12. Average facilities densities and the share of commercial land use ................ 74
Figure 5.13. Average agglomeration indices and the share of commercial land use ......... 75xi

List of Figures

Figure 6.1.
Figure 6.2.
Figure 6.3.
Figure 7.1.
Figure 7.2.
Figure 7.3.
Figure 7.4.
Figure 7.5.
Figure 8.1.
Figure 8.2.
Figure 8.3.
Figure 8.4.
Figure 8.5.

Spatial distribution of home locations of the survey participants .................. 79
Physical vs. virtual activities: The share of activity participation .................. 83
Variations in activity participation based on time of day ............................... 84
Map of study area in Jakarta, Indonesia ......................................................... 91
Context-aware SP questionnaire for eating out behavior ............................... 93
Maps of 262 zones and the road networks of Jakarta .................................. 100
Distribution of food merchants .................................................................... 101
Correlation between service efficiency index and the density of combinationand online food merchants ........................................................................... 102
History of the development of the activity-based model ............................. 106
Markov Decision Process (MDP) ................................................................ 107
The daily travel-activity behavior embedded with ICT usage ..................... 110
Tensor for action-state pair .......................................................................... 114
Tensor for action-state pair including time dimension................................. 116

xii

Chapter I – Introduction

Chapter 1: INTRODUCTION
This chapter provides an introduction, including the research background, research objective
and questions, research framework, and significance and contribution of this study.
1.1.

RESEARCH BACKGROUND

Information and communication technology (ICT) is the critical enabler of innovation.
Recently, there has been a rapid escalation in the number of Internet users worldwide. In
2020, there were 4.54 billion Internet users, the equivalent of 59% of the world’s population.
This number increased by 7% in January 2019 (Global Digital Reports, 2020). Some benefits
may be obtained from ICT, such as better information and access to goods and amenities,
increased connection anywhere and anytime, and the convenience of the sharing economy
and delivery services. On the other hand, the evolution of ICT has consequences that cannot
be ignored, such as the possibility of job losses and bankruptcy in the economic sector, lackof data privacy, reduced personal interactions, and reduced physical activity.
The interaction between information and communication technologies (ICT) andhuman activity-travel behavior has become an important theme in transportation research inrecent years. Researchers have recognized that an increase in the use of ICT may lead tochanges in the location, timing, and duration of people’s activities, and the widespread useof ICT will likely be associated with new patterns of activity and travel in space-time (Kwan,
2020; Dijst, 2004). Analyses of these patterns could provide part of the empirical basis and/or
behavioral foundation for enhancing our understanding of the interaction between ICT,
activity-travel behavior, and urban form.
Recently, several issues concerning the interaction between transportation and urban
form in the era of ICT have arisen. Changes in the concentration of urban activities,
transportation systems, and individual mobility are expected. ICT may improve people’s
access to goods, services, and even jobs through virtual connectivity, allowing activities totake place almost anytime and anywhere. Several studies have investigated how urban spaceand ICT covary. Nevertheless, it is possible that ICT only affects activity spaces andschedules (and not the converse). There is growing interest in questions related to thebidirectional relationships between urban form and ICT.
One significant ICT innovation is multiservice transport platforms (MSTP). MSTP
is an innovative platform that integrates transportation systems and daily service provision.
In Indonesia, some MSTP companies (e.g., GoFood by Gojek, Grab-Food, and Uber Eats)
have expanded their services, including their online food delivery (FD) services. They utilize
fleet drivers to provide transportation and offer other daily services such as delivering foodfrom merchant partners to the consumer. However, to maintain the quality of the food, they
set maximum service areas. The service area determines the number of food merchants thatusers can access from their location. Maximum distances were set to maintain the quality
and freshness of food delivered to the consumer. Thus, the accessibility of local foodmerchants and the extent of delivery service areas are critical factors in the delivery systemand consumer choice.
As one ICT innovation, MSTP also changes people’s daily activity-travel behavior
in the short run and possibly city development in the long run. Because users do not need toallow time for travel when they use online food delivery services, they may order foods fromfar afield. These changes could increase the spatial mismatch between the customer location
1

Chapter I – Introduction(i.e., home or office) and restaurants/food merchants, resulting in urban sprawl and loss ofvitality in cities. Thus, if people can access the same quality of goods and services without
commuting, they may be willing to live in far suburbs where more affordable land canprovide good living conditions. For this reason, the presence of MSTP may cause changes
that cannot be ignored in travel behavior, activity patterns, and commercial location
decisions. Further analysis of the impacts of MSTP is needed to provide a comprehensive
understanding of the potential benefits or risks to be considered by policymakers andresearchers.
1.2.

RESEARCH OBJECTIVE AND QUESTIONSThis research aims to provide a comprehensive understanding of the impacts of MSTP andexamine them empirically based on a case study in Jakarta, Indonesia. The specific objective
is to assess the impact of MSTP in Indonesia on urban form and activity-travel behavior.
Follows are the detail of research objectives, research topics, and questions for this study.
Table 1.1. Research objectives, research topics, and research questions
Research Objective
To analyze theimpact of MSTP on
the urban form

To analyze theimpact of MSTP on
the activity-travel
behavior

Research Topic(1) The impact ofMSTP on facility
distribution andcity characteristics(2) The impact ofMSTP on thefacility distribution(3) Changes in
Activity-Travel
Distribution underMSTP(4) Impact of MSTP
on the Individual’s
Eating Behavior(5) Impact of MSTP
on the ActivityTravel Behavior

1.3.

Research Question(1.1) How to quantify the concentration of facility
distribution?
(1.2) How is the association between facility
distribution and city-level characteristics?
(2.1) What distribution changes do MSTP bring about
on the facility distribution?
(2.2) How do these induced changes in urban form?
(3.1) How to capture the virtual activities on daily
activity-travel behavior?
(3.2) How does MSTP change the distribution ofactivities?
(3.3) What factors influence people to choose online
activities?
(4.1) How the presence of MSTP’s online fooddelivery service will affect people’s eating
behavior?
(4.2) What factors affect MSTP’s service level?
(5.1) How to extend the current dynamic discrete
choice model for activity-travel analysis toincorporate the impact of MSTP use on theactivity-travel patterns?

RESEARCH FRAMEWORK

Recently, MSTP has become one of the important aspects of an individual’s daily activity.
On a daily basis, to fulfill their needs and desire, an individual may need to conduct some
activities (e.g., shopping, eating, working, and so on). However, as we know, some activities
may need to be conducted at a certain place scattered across space (e.g., shopping at themarket, working at the office, study at school, and so on), and the facility distribution reflects
it. In the activity-travel behavior study, we know that travel is a “derived demand” fromindividuals to fulfill their needs at a certain location. To access the location of a certain
facility, an individual will be traveling or conduct a trip from their original location to their
desire destination. Therefore, the association between activity, facility distribution, and2

Chapter I – Introduction

transportation system need to be comprehensively understood to develop a better public
policy and management.
Recently, there are many innovations invented in the field of transportation to maketravel faster, cheaper, and more convenient. The integration of the transportation field andthe use of information and communication technologies (ICTs) has become an importantresearch topic in transportation. As one of the ICT-based innovations in transportation,
MSTP provides many kinds of online and transportation services to connect the service
suppliers and the consumers. It has also become one of the most used transportation modesin the city. The presence of MSTP may significantly impact an individual’s behavior and the
distribution of facilities.
In this study, we aim to provide a comprehensive study exploring the impact ofMSTP on both activity-travel behavior and the urban form. There are some hypotheses that
we want to test in this study. Those hypotheses including:(1) MSTP will change the facility location distribution through the provision of
online food delivery services. The food merchants may gain some incentives tobe located away from the central area. At the same time, they still maintain thesame number of customers as demand through MSTP’s online food delivery
services.
(2) MSTP will change an individual’s activity and travel decisions through MSTP’s
online delivery services and MSTP’s online transportation services.
(3) MSTP will change an individual’s eating behavior by providing a better online
food delivery service.
(4) MSTP will change an individual’s daily activity-travel behavior.

Figure 1.1. Conceptual framework
1.4.

OVERVIEW OF FINDINGSThe innovation of information and communication technology has influenced many aspectsof our daily lives, such as economic activities, study, medical, and others. In Indonesia, oneof the innovations of ICT that very popular and widely used by many people since 2010 is3

Chapter I – Introduction

the Multi-Services Transport Platform (MSTP). There are more than 21.7 million users of
MSTP in Indonesia, where most of them are concentrated in the metropolitan and big cities
such as Jakarta, Bandung, and Surabaya. With a high demand for the MSTP, it indicates that
MSTP becomes an essential aspect in people's daily life, and it may affect their activity andtravel behavior and change how the cities work.
This study included four parts of analysis: (1) the analysis of facilities distribution
through agglomeration index analysis, (2) the descriptive analysis of the online and physicalactivity and transportation behavior, (3) the analysis of the impact of MSTP on the individual
eating behavior, and we also attempt to develop the (4) dynamic discrete choice model for
activity-travel behavior that includes the component of MSTP to see whether the MSTP
components will affect the decision of our daily activity and travel. Some findings anddiscussion of the studies are showed to empirically explain the impact of MSTP on the urban
form and activity-travel behavior.
First, MSTP changes the distribution of online food merchants by introducing the
new agglomeration forces or the mechanism to be agglomerated from attracting more MSTP
drivers into their area. Using the agglomeration index approach, we analyze the distribution
and concentration of facilities in Jakarta, Indonesia. Due to the presence of MSTP, we foundthat a high agglomeration of online food merchants happened around the central area and12-14 km away from the central area (e.g., fringe area) of Jakarta city. This may happen due
to the mechanism of MSTP that relies on the fast service and high accessibility level to serve
the consumers, so they set a particular distance as their maximum area coverage to keep theirfood quality for the consumers. By considering the cities' geographical and trafficcharacteristics, MSTP set their maximum coverage area within 6 kilometers away from theconsumer's location. In other words, the online food merchants tend to be agglomerated
within the MSTP's area coverage (i.e., 6 km) in order to attract more drivers into their area.
This has contributed to exploring the behavior of the facilities under the influence of MSTP.
Second, the descriptive analysis of activity-travel behavior showed that the MSTP
services have been more favorable toward eating and shopping activities. This has
contributed to the reduction of the physical trip of the users. Based on a two-week activitytravel diary survey, we found that more than 55% of eating and shopping activities havebeen preferable to conduct by using MSTP. Those percentage indicates a high demand for
MSTP services. If our samples represented the actual characteristics of all Jakarta's
population, then this might be some significant changes in the daily behavior. The shift inpeople's behavior of conducting physical trips to online activity can have significant
implications for transportation planning and urban development. If many people were
shifting to conduct online activities and less conduct the physical trips, fewer people will use
the road, and less congestion may happen. Less congestion may lead to some sustainable
development of the cities.
Third, based on the activity-travel diary survey, we found that using MSTP's
transport services is more favorable than public transport in Jakarta. Despite their role as an
online delivery service, there are a significant number of MSTP transport service usage inJakarta. Having a high number of demands does not make MSTP can replace the usage of a
private vehicle and reduce traffic congestion. The basic idea of MTSP to optimize the fleetsby allowing them to provide many kinds of services was a good initiative. However, as we
mentioned above, it is uncertain that MSTP can reduce traffic congestion due to the unknown
driver's behavior.
Forth, the delivery time and delivery cost that MSTP proposed is the significant
variable that affects people using online food delivery services rather than conducting aphysical trip. The findings suggested that utilitarian orientation is an important determinant
4

Chapter I – Introductionof users' behavior toward MSTP online food delivery services. People find that using online
food delivery services is relatively cheaper in the term of value of time than conducting around trip of the trip for eating purposes. The improvement of delivery time and delivery
cost may increase people's tendency to use MSTP online food delivery services. In thedelivery time of MSTP online food delivery services, there are three main components: thetime for food preparation by the merchants, the travel time by online ojek, and the additionaltime to find nearby drivers to take the orders. Where the time for food preparation is givenby the condition of the cross-side network effect of demand and supply, the additional timeis influenced by the same-side network effect. The number of drivers in the nearby area willaffect the additional time. The additional time will decrease in the area with the increase in
the density of online food merchants. In general, drivers would standby around the area withhigher demand. Having more drivers in the area may reduce the additional time for searchingfor the driver and reduce the delivery time.
Fifth, the exploration of MSTP impact on individual activity-travel behavior throughthe simulation on the dynamic discrete choice model showed that the MSTP components(i.e., online transportation services, the availability of online food delivery services, and thenumber of food merchants as the attraction of each area) show that MSTP may change thetiming decision, destination choice, and the activity purpose of individuals in their daily
activity-travel pattern. As mentioned above, some studies can address some limitations byusing the improved DDCM framework.
1.5.

RESEARCH SIGNIFICANCE AND CONTRIBUTION

Theoretical contribution:
This study constructs a research framework for MSTP of both supply and demand, based on
the relationship of the travel and spatial context of urban form with activity-travel behavior.
In particular, it supplements previous studies of MSTP, which is an ICT innovation that
affects the urban form and activity-travel behavior. To date, to the author’s knowledge, thisstudy will be the first comprehensive analysis of the impact of MSTP on activity-travel
behavior and urban form, particularly in a developing country. There is no publishedacademic research on this topic, and a decade after the rise of MSTP in Indonesia, theirimpact on activity-travel behavior and urban form remains unexamined. Chapter Two
explores the concept, systems, and mechanisms of MSTP to understand how they work andwhat components are vital.
Methodological contribution:
This study will explore a new method of using a smartphone-based diary app to capture
travel activity and consider context-dependent factors affecting ICT use and activity-travel
behavior. By this method, users can voluntarily report their activities in real-time. Several
new methods and approaches are explored in this study, including the new approach tocapturing online activity data by modifying the activity-travel diary survey explained in
Chapter Three. Chapter Four proposes a new approach to calculating the agglomerationindex by developing a new counterfactual for agglomeration analysis. Chapter seven
expands the current dynamic discrete choice model by adding online activity (i.e., MSTP)
components to the models and assessing their impact on activity-travel behavior.

5Chapter I – Introduction

Practical contribution:
This research is an empirical assessment of the impact of MSTP on urban form and travel
behavior, particularly in Indonesia. The findings may indicate ways for policy to maximize
the presence of MSTP while maintaining a balance of urban economy activities and city
structure. In terms of policy relevance, the results of this study will provide a framework to
improve the provision of MSTP that incorporates activity-travel behavior and distribution ofcommercial facilities. In Chapters Four and Five, we empirically applied the agglomeration
analysis to examine the distribution of online food merchants across the area and its impact
on the urban form. Then, in Chapter Seven, we empirically analyze the impact of MSTP on
individuals’ eating behavior through their preferences for using MSTP.
1.6.

OUTLINE OF THE DISSERTATION

The dissertation consists of nine chapters. This chapter provides an introduction, including
the research background, research objective and questions, research framework, andsignificance and contribution of this study. The remainder of the dissertation is organized asfollows.
Chapter Two broadly reviews the literature regarding MSTP, which has seldom been
examined in existing research. The general explanation of the interaction between activity,
urban form, and transportation will be explored at the beginning of this chapter, following
by the exploration of the impact of ICT on activity, urban form, and transportation system.
This chapter will also explain the general definition and working system of the Multi-Service
Transport Platform (MSTP). Some current trends and analytical challenges are also
explained in this chapter.
Chapter Three descript the research design and methodology of this study. Including
the theoretical and conceptual framework, research aims and objectives, general design ofthis study, survey design for activity-travel diary survey, and the context-awareness of statedpreference survey and the broad analysis methodology that will be used. The characteristics
of two different locations of the case study (i.e., Jakarta, Indonesia, and Japan) will also be
explained in this chapter.
Chapter Four analyzes the association between the agglomeration index and the citylevel characteristics in 69 cities in Japan. By utilizing the agglomeration index approach, we
analyze the concentration of facilities in the city and its association with the city-level
characteristics, including the transportation and socio-geographic characteristics. We
develop the agglomeration index by considering three types of distance metrics, and we arecarefully select the counterfactual facilities in the analysis.
Chapter Five empirically assesses the impact of MSTP on the urban form in Jakarta
through the analysis of the agglomeration index of facilities. Adopting the agglomerationindex calculation approach, we explored the spatial distribution of food merchants under the
MSTP’s influences. This chapter explains how the distribution of online food merchants isdifferent from other facilities, and we also showed the importance of spatial boundary setting
in agglomeration analysis.
Chapter Six broadly explore the changes in the activity and travel distribution under
the presence of MSTP. This chapter explores the data from the improved activity-travel diarysurvey that includes online activities such as online food delivery, online shopping, online
working, online study, online social activity, and online leisure/entertainment, with the casestudy in Jakarta, Indonesia.
6Chapter I – Introduction

Chapter Seven examines the impact of online-based food delivery services on
individual meal choice behavior, i.e., eating out or using a delivery service. This chapter
attempts to identify empirical factors affecting people’s preferences for online-based fooddelivery services using adaptive stated preference (SP) survey data.
Chapter Eight aims to extend the existing dynamic discrete choice activity-travel
model to incorporate ICT use into activity-travel patterns. We argue that the presence ofMSTP increases the utility of activity links by allowing people to access services virtually
in local areas without travel, i.e., MSTP drivers bring services to the clients’ homes.
Chapter Nine summarizes the findings and draws conclusions concerning themultidimensional impacts of MSTP on urban form and activity-travel behavior from a casestudy of Jakarta, Indonesia.

7Chapter II – Literature Review

Figure 1.2. Structure of the dissertation
8

Chapter II – Literature Review

Chapter 2: LITERATURE REVIEWThis chapter broadly reviews the literature regarding MSTP, which has seldom been
examined in existing research. The general explanation of the interaction between activity,
urban form, and transportation will be explored at the beginning of this chapter, following
by the exploration of the impact of ICT on activity, urban form, and transportation system.
This chapter will also explain the general definition and working system of the Multi-Service
Transport Platform (MSTP). Some current trends and analytical challenges are also
explained in this chapter.
2.1.

ACTIVITY, URBAN FORM, AND TRANSPORTATION

It is widely known that the relationship between urban form and transport systems iscomplicated, given that land use and transportation are part of a retroactive feedback system,with one influencing the other (Giuliano, 2004). Transportation has a strong influence on theurban form through its spatial structure at the local, regional and global levels. A historicalperspective on the evolution of transport systems underlines the impacts of technologicalinnovations and how transportation improvements were interdependent with economic,
social, and spatial changes. Thus, the current transport systems are the outcome of a longevolution marked by periods of rapid changes where new transport technologies wereadopted. Transportation systems are composed of a complex set of relationships between the
demand, the locations they service, and the networks that support movements. Such
conditions are closely related to the development of transportation networks, both in capacity
and in spatial extent. The same forces will likely shape future transportation systems as in
the past, but it remains to be seen which technologies will prevail and their impacts on the
structure. The unique purpose of transportation is to overcome space, which is shaped byvarious human and physical constraints such as distance, time, administrative divisions, andtopography. The specific purpose of transportation is to fulfill a demand for mobility sincetransportation can only exist if it moves people, freight, and information around. Otherwise,
it has no purpose. This is because transportation is dominantly the outcome of derived
demand.
Transport represents one of the most important human activities worldwide. It is anindispensable component of the economy and plays a major role in spatial relations between
locations. Transport creates valuable links between regions and economic activities, between
people and the rest of the world. The development of activities reflects the cumulative
relationships between transport infrastructure, economic activities, and the builtenvironment. The important factors that shaped the spatial structure are included:• Costs. The spatial distribution of activities is related to distance factors, and thelocational decisions are taken to minimize costs of transportation and production.
• Accessibility. All locations have a level of accessibility, but some are more accessible
than others because of transportation. Some locations are perceived as more valuable
than others.
• Agglomeration. There is a tendency for activities to agglomerate to take advantageof the value of specific locations. The more valuable a location, the more likely
agglomeration will take place. The organization of activities is essentiallyhierarchical, resulting from the relationships between agglomeration andaccessibility at the local, regional, and global levels.
9

Chapter II – Literature Review

One of the most basic transportation relationships involves how much space can beovercome within a given amount of time. The faster the mode, the larger the distance can beovercome within the same amount of time. Transportation, notable improvements intransport systems, changes the relationship between time and space. When this relationshipinvolves easier, faster, and cheaper access between places, this result is defined as space/time convergence because the amount of space that can be overcome for a similar amountof time increases significantly. It is, however, a spatially and socially uneven process since
it will impact the accessibility of locations differently. The outcome has been significant
differences in space/time relationships, mainly between developed and developing countries,
reflecting differences in the efficiency of transport systems. Five major factors are related to
the relationship between transportation and space, including speed, economic scale,
expansion of transport infrastructures, and the evolution of information technologies.

Figure 2.1. The land use formation of urban landscapes and transportation
Source: Adapted from Taaffe E. J. et al. (1996)

In the past decades, the innovation of the transportation system has been impacting
the urban form, as we know that the urban form is an outcome of spatial differentiation andspatial interaction. The spatial differentiation itself is the outcome of a cumulative process
as several elements of the spatial structure, such as urban areas, are the outcome of a longaccumulation process, which tends to change slowly. The spatial interactions where
attributes such as origins, destinations, and flows are also illustrative of inequalities.
Transportation not only favors economic development but also has an impact on spatialorganization.
On the other side, the urban form is one of the more essential outputs of those location
decisions and transportation developments. In a free-market economy, the urban form is theoutcome of location choices made by thousands of households, private firms, and publicagencies. An urban agglomeration is one of the important outputs of those location decisions,
shaping urban form together with transportation developments. The literature identifies threesources of agglomeration of commercial facilities: matching, sharing, and trip chaining

10Chapter II – Literature Review(Takahashi, 2013; Koster et al., 2019). Takahashi (2013) considers that consumers seek abetter matching between their preferred varieties and the varieties sold in each commercial
area and confirms that agglomeration would occur when consumers exhibit taste
heterogeneity while the available information is imperfect. Alternatively, agglomeration
may also occur when there are positive externalities through sharing. Shopping malls are
typical, i.e., shops in a mall share fixed costs such as facility maintenance and customer
acquisition costs (Pashigian and Gould, 1998).
In the past decades, many researchers in urban and regional science have tried toshow that the agglomeration economies may be one source of the uneven distribution of
economic activities and economic growth across cities and regions (see Strange, 2004). A
number of theoretical studies indicate that transportation cost plays a significant role indetermining agglomeration and dispersion forces (Krugman, 1991). For example, Tabuchi(1998) shows that agglomeration would reduce transport costs, and then re-dispersion would
come in when transportation costs monotonically decrease. The effect of agglomeration
economies on localized firms' behavior can be expected to differ, however, across sectors,
space, and time (McCann and Folta, 2008; (Groot et al., 2009). At the same time, little is
known about the importance of agglomeration economies for individual firms' location
decisions (Acs and Armington 2004; Martin et al. 2008).
Although a number of empirical studies have been conducted (see Combes andGobillon, 2015, for example), providing empirical evidence on these theoretical works is
generally difficult, essentially because of the difficulties in establishing appropriate
counterfactuals. Another major problem is that, since theoretical models simplify the realworld, it is often difficult to establish an appropriate mixture of deductive and inductive
techniques to derive empirical evidence. For example, most theoretical models do not
explicitly deal with multiple transport modes, making it challenging to execute theappropriate empirical analysis.
Given that, scholars have argued that the death of distance through digital evolution
is still premature. However, many innovations in the field of ICT have significantly impacted
our daily lives. As one ICT innovation, MSTP also brings changes to the transportation
system. How urban space and ICT co-vary remains as elusive as ever. While it is still
possible that ICT only affects activity spaces and schedules (and not the converse), there is
growing interest in questions related to the bi-directional relationships between urban form
and ICT. Having virtual connectivity can allow activities to take place almost anytime andanywhere. Thus, if people are allowed to telecommute, they might be willing to live far out
in the suburbs, where larger land plots at affordable prices can provide high-quality living
conditions. Even if travel to the workplace needs to be done, ICTs become more and more
immersed in various transportation modes (especially for public transport and ridesharing
services today and presumptively for autonomous vehicles in the future). When ICT (MSTP)
presence leads to unignorable changes in people's travel behavior and activity patterns, it
may also affect the facilities' location decisions. It is possible that, in this case, ICT would
contribute to the concentration of urban space. Since the users do not need to allocate time
for travel when they use online-based food delivery services, there is a possibility that people
tend to order foods provided in a distant place. These changes could increase the spatial
mismatch between the location people stay (i.e., home and office) and restaurants/foodmerchants' locations, resulting in urban sprawl and the city's loss of vitality.
In this study, an activity-travel pattern is defined as a sequence of performed
activities and trips that start and end at home located in a whole day. Each day’s uniquenessof individuals’ activity-travel pattern is not only based on a single dimension, but also theinteraction between multiple dimensions, such as the combination of travel mode, travel
11

Chapter II – Literature Review

purpose, activity location, and arrival time (Koppelman and Pas, 1985; Schlich andAxhausen, 2003; Susilo and Axhausen, 2014). Individuals with different daily constraints ineconomic, social-cultural, temporal, and geographical perspectives would have different
day-to-day activities configuration and scheduling. They had different time-spaceconstraints that can also associate with how an individual has more varied/more predictable
activity-travel behaviors on a different day.
Theories on the relationship between urban form and travel patterns are mainly basedon the concept that travel results from an individual's desire or need to engage in an activity.
The location to perform activities is spatially distributed over a wide range of areas. Hence,these activities cannot be carried out at the same location. Then, the result is the desire to
conduct some trip or travel. Theoretical reflections on the potential effects of urban form
typically concern the spatial distribution of essential activity locations such as residences,jobs, and shops. Shortening distance between these types of locations is often represented asa means to decrease mobility growth. The nearest facilities and service from the home may
be the best alternative as destinations for activity participation for some individuals.
2.2.

ICT ON ACTIVITY, URBAN FORM, AND TRANSPORTATION

Back in the past, the ability of an individual to access people, goods, and information are
restricted by several constraints such as capability constraints, coupling constraints, andauthority constraints. The capability constraints are related to the limited ability to perform
certain tasks within a given transportation technology and the fact that we can be in only one
place at a time. Coupling constraints that related to the need to undertake certain activities
at certain places with other people. Then, the authority constraints related to the social,political, and legal restrictions on access. Other constraints that widely discussed in the late1960s and 1970s is the time-space constraints.
The time-space constraint concept that experts in human geography have discussed
since. It emphasizes that time and space delimit an individual's opportunities to participate
in activities and travel, imposing restrictions on people's access and mobility (Hägerstrand,
1970; Burns, 1979; Schwanen, 2008). A number of activity-based models (e.g., Kitamura
and Fujii, 1998; Pendyala et al., 2002; Liao et al., 2013) have been developed based on thistime-space concept, together with the assumption that travel is a derived demand of activity
engagement at destination (i.e., people are traveling for extrinsic motivations rather thanintrinsic motivations).
With the presence of ICT and its innovation in our daily life, many aspects have been
influenced by it can reduce the constraints that we faced. Some advantages anddisadvantages of using ICT in our daily life have been discussed by researchers in many
research filed, including the transportation and urban planning field. One of the advantagesof ICT in the transportation and urban planning field is that ICT can improve people’s
accessibility to their needs and preferences through virtual spaces. People can gain more
information about their needs and even get their needs without traveling by using online
shopping or online delivery services. On the other hand, in the current situation, when thesource of information is still limited, not all real-world conditions can be represented in thevirtual space. For example, for those who did not have access to the internet (e.g.,conventional shops at the traditional market), their shops' information will not be accessedby other people unless they or other people share their shop’s information internet or people
make a physical trip to the market and found the shops. In this sub-chapter, we explore thediscussion related to the presence of ICT in many aspects of our daily life, including ICT on
12

Chapter II – Literature Review

activity-travel behavior, ICT on Urban Form, and ICT on transportation.
The impacts of information and communication technologies (ICTs) infuse thesociety, spanning from the reorganization of cities and large businesses to modifications in
an individual’s daily activities. ICTs contribute to reshaping the way we work, live, andinteract with each other, the way we participate in social and leisure activities, and the way
we travel. Definitively identifying the impacts of ICT in each of these areas may be
impossible because of the many overlapping relationships between (1) the development andadoption of ICT and (2) the headlong rate of development of modern technologies, which
continuously modifies the available applications and services.
2.2.1. ICT on Activity-Travel Behavior
In this 21st century, several issues about the interaction between transportation and urban
form in the era of information technologies have been brought forward. In the era of
information and communication technology (ICT), changes in urban activities concentration,
the transportation system, and individual mobility are expected. For example, based on
Kakujo et al. (2019), the introduction of fully autonomous vehicles (AVs) may allow users
to do a multitasking behavior while traveling, and it is generating a positive utility. The AVs
may potentially lead to more extended travel. In the long run, AVs may also induce
residential relocation since the users get more benefits from traveling, along with conductingsome multitasking may reduce the value of time. Shaw and Yu (2009) also claimed that
virtual activity engagement via ICT cannot be represented and explained by the classicaltime-space constraint framework. Also, such virtual activity engagement would reduce
travel if travel were a purely derived demand of activity engagement, but this may not be
entirely true since people would travel not only for extrinsic but also intrinsic motivations(Mokhtarian et al., 2015). These arguments call for further investigations on observing andmodeling interdependencies between ICT use and travel.
The interaction between ICT use and human activity-travel behavior is highly
complex. It cannot be simply described in terms of substitution and/or generation. As
Mokhtarian (1990, p. 240) suggested, the most important impact of ICT use is that “it permits
much more flexibility in whether, when, where, and how to travel, and thus loosening theconstraint of having to be at a certain place at a certain time.” She made the important pointthat including considerations of the decision, location, timing, and duration of activities andtravel in the analysis will likely be more fruitful than focusing only on one particular aspectof such interaction. We hope the articles in this special issue will help stimulate further
research on the complex interaction between ICT and individual behavior, especially thefragmentations and regrouping of daily activities and trips.
Based on the time-space constraint concept, several activity-based models have beendeveloped together to assume that travel is a derived demand of activity engagement at thedestination. On the other hand, several researchers have discussed how ICT usage affects
travel behavior from various angles, such as how ICT affects the mode choice, route choice,and scheduling (e.g., Lenz, B. & Novis, C., 2007, Aguiléra, A. et al., 2012, Fiore, F. D., etal., 2014, Ben-Elia, E., 2018). However, most studies focus on the overall impact of ICT on
activity-travel behavior. At the same time, they do not take into account context-dependent
factors affecting ICT use and activity-travel behavior. For example, the use of an online fooddelivery service for lunch may depend on the time pressure the person is under at that time.
Dynamic discrete choice models have received widespread acceptance in transport
research and are used in travel demand modeling and behavioral analysis. In the context oftravel behavior analysis, dynamic discrete choice models have been used for modeling route
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Chapter II – Literature Review

choice behavior (Fosgerau et al., 2013; Oyama and Hato, 2019) and recently used formodeling whole activity-travel patterns in a given period of time (Västberg, O. B., Karlström,A., Jonsson, D., & Sundberg, M., 2019), i.e., a sequence of activity-travel decisions is
considered as a path choice in a time-space prism (Chikaraishi et al., 2018). While the model
strictly reflects time-space constraints, the current version does not represent virtual activity
engagement through MSTP that would virtually nullify time-space constraints. This study
attempts to fill in this gap.
Figure 2.2. shows how a person's activity-travel behavior, the utility that they getfrom the "real" experience, may be different from the utility they get from the "virtual"
access; there may be some reduction in the amount of utility. However, their benefits arestill higher than the cost they are made for the trips/movements. From the user's perspective,
they have more alternatives or options to access the service or the other location's needs byhaving virtual access. This relates to how virtual access utility maximization can be achievedby staying at one location. This is similar to the concept of route choice in choosing the bestroute, which is considered to maximize its utility. The utility function used is a utility
obtained through the "real" access and a utility obtained through "virtual" access.

Figure 2.2. The daily travel-activity behavior embedded with ICT usage
By looking at the ICT usage of people within a particular time slot, we can know thatsomeone can improve the utility by having virtual access to their needs through the Multiservice Transport Platform (MSTP). By having that virtual access would relax users' timeand space constraints to reach the services or their needs. In this case, ICT will increase thecumulative utility obtained by someone. ICTs as a trip replacement strategy have been seen
as a solution to many societal problems, including urban congestion, dependence on nonrenewable energy sources, air pollution, and greenhouse gas emission, as well as rural
underdevelopment, reduced economic opportunity for the mobility-limited, and the struggle
to balance job and family responsibilities (Mokhtarian, Salomon, & Choo, 2005; Salomon,
1998). ICTs certainly do replace “a lot” of travel, but at the same time, they can generate
additional travel as well. ICTs can influence an individual’s space-time constraints and theresulting activity participation and travel behavior in many ways, including imposing new
constraints and relaxing some old ones. As with e-shopping, the interaction between ICT
and travel behavior can include several possibilities, including ICTs can have no relevant
effect on travel (neutrality), generate new travel (complementarity, or stimulation), alter
travel that would have occurred anyway (modification), or reduce travel (substitution)(Salomon, 1986; Salomon & Mokhtarian, 2008).
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Chapter II – Literature Review

Figure 2.3. Association of ICT activities and other activities
Source: Adaptation from Rodrigue, J. P., Comtois, C., & Slack, B.2016.

Mokhtarian (2009a) discusses a number of reasons that favor complementarity as thedominant impact. She first notes that not all ICT based activities reduce travel because:(1) Not all activities have an ICT counterpart.
(2) Even when an ICT alternative exists in theory, it may not be practically feasible.
(3) Even when feasible, ICT is not always a desirable substitute.
(4) In particular, travel carries a positive utility in its own right, not just as a meansof accessing specific locations.
(5) Not all uses of ICT constitute a replacement for travel. She then presents several
reasons why ICT actively increases travel.
In the short run, ICT may save time and/or money for other activities, some of which
may involve travel. Specifically, it permits travel to be sold more cheaply (e.g., through lastminute airline deals, or airfare and hotel bundles) through Intelligent Transportation Systems(ITS) and other technologies it increases the efficiency (and thus the effective capacity) of
the transportation system, making travel less costly and therefore more attractive andpersonal ICT use can increase the productivity and/or enjoyment of travel time, thereby also
increasing the attractiveness and/or decreasing the disutility of travel. Importantly, ICT
directly stimulates additional travel through its ability to inspire and facilitate transactions(among other ways). In the long run, ICT is an engine driving the increasing globalizationof commerce, facilitating shifts to more decentralized and lower-density land patterns.
On the other hand, ICT may also reduce travel in the following ways:(1) It may directly substitute for making a trip.
(2) It consumes time (and/or money) that might otherwise be spent traveling.
(3) When travel becomes more costly, difficult, or dangerous, ICT substitution increases.
(4) It can be deployed to make shared means of transportation more attractive (reducing
drive-alone trips).

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Chapter II – Literature Review(5) It can reduce unnecessary travel (such as when “letting your fingers do the walking”
prevents driving to several stores to look for an item) (see Mokhtarian, 2009;
Mokhtarian & Tal, 2013).
2.2.2. ICT on Urban Form
The current form of urban areas results from complex interactions among the numerous
needs that influence the location of economic activities and residences. Living in cities
allows individuals to save on transportation costs and provides diversity in offering goods
and services (e.g., access to theaters, sports centers, restaurants, libraries, stores), facilitating
communication and social relations. Similarly, it allows the firm to benefit fromagglomeration economies, minimize the cost of transporting people and goods, and ensure
proper access to information and connection with commercial partners, consumers, andsuppliers (O’ Sullivan, 2011). The organization of cities is not always efficient in all
dimensions (e.g., as attested by the congestion level in many urban areas). However,nonetheless, cities continue to maintain a strong attractive power because of the benefits they
provide.
Technology has gradually modified the distance constraints that limit the mobility ofgoods and people in two ways. First, it has allowed for an increase in travel speed bydeveloping new modes of transportation and/or the improvement of road and rail
infrastructure. This has led to a contraction of the average travel time needed to reach thespecific destination (a process known as time-space convergence) (Janelle & Gillespie,2004) or alternatively to an expansion of the set of destinations that can be reached in a
specific unit of time (relaxing time-space constraints). Second, new technologies havehelped make travel cheaper, reducing the cost of moving goods and people over long
distances and making it practical to shop goods farther (or to travel longer distances, forpassenger trips) than in the past. Over the years, these factors have allowed cities to expand
beyond previous limits. The relationships between technological development and urban
form and urban form have been studied in terms of technology-land substitution (Kim, Claus,
Rank, & Xiao, 2009). Technological innovations can facilitate greater efficiency in the useof land, as happened in the process of urbanization in the first half of the 20th century, or
they can improve accessibility, reducing the friction of distance and favoring low-density
urban expansion, as happened in the second half of the last century.
Despite a consensus that ICTs can further lift space-time constraints, disagreements
exist about the extent of that relaxation (Janelle, 2012; Nijkamp & Salomon, 1989). The
most extreme position holds that ICTs portend the “end of geography” (O’Brien, 1992) andthe “death of distance” (Cairncross, 2001). Physical proximity is a less binding constraint in
the decision of where to locate economic activities and/or residence at a time “whendominant forces such as globalization and telecommunication seem to signal that place andthe details of the local no longer matter” (Sassen, 2000, p. 144).
Modern ICTs are changing the urban geography of commercial and retail activity.
The internet has given birth to new ways of buying and selling (including consumer-toconsumer, business-to-consumer, and business-to-business) that were simply not possible
before. Furthermore, technologies can support the recent trend, at least among somesegments of society, toward purchasing used items rather than new, toward bartering rather
than paying money, and in general toward the reduced level of materialism (a phenomenon
referred to as the “sharing economy” or “collaborative consumption”; Belk, 2014)
On the other side, ICT helps relax time and space constraints, which affects theorganization of individuals’ schedules and consequently the spatial distribution of economic

16Chapter II – Literature Review

activities and residences. But does it also help overcome physical marginality and reduce
accessibility? Traditionally, cities have facilitated agglomeration of activities that help
overcome time constraints by minimizing distance constraints: the large concentration ofactivities in the most central areas increases their cumulative attraction. It allows retailers toincrease their sales volumes, and it allows consumers to satisfy more needs while minimizing
travel distances.
2.2.3. ICT on Transportation
ICT innovations have profoundly modified individuals’ personal decisions and travel
behavior. For example, ICTs allow teleworkers to accept jobs far from home without
residential relocation or have more freedom to choose a residential location by reducing
constraints on commuting trips. Modern ICTs are also modifying users’ relationships withthe adoption and use of private vehicles. ICT-enabled carsharing, on-demand ride services,and bike-sharing services are a familiar presence in many urban areas: they substitute for theownership and use of a private vehicle, allow users to enjoy automobility when needed while
avoiding the fixed costs of owning a vehicle, and extend the area of coverage of public
transportation.
A clear dominant effect of ICT on either reducing or stimulating travel cannot be
confirmed in many cases. The specific impacts vary depending on local contexts and otherconcurrent causes. ICTs have contributed to reshaping work organization through increased
opportunities for distributed teamwork and the devolution of important functions to remotelocations. They have contributed to reshaping the demand for space in urban areas. While
many residents are moving back into livelier and more central parts of American cities, firms
require reduced space for front offices in these areas. They are reorganizing their production
activities closer to important transportation hubs, in particular airports and freeway corridors.
Similar changes affect the organization of retail stores, which are increasingly integrated into
mixed forms with their virtual counterparts (“bricks and clicks”). In contrast, the physicalorganization of traditional stores has often evolved toward the model of entertainment
centers, which are less subject to competition with e-shopping.
For a long time, policymakers have hoped ICT would be a valid substitute forphysical trips as a way to reduce traffic congestion and increase transportation sustainability
in urban areas. We have shown, however, that ICT can increase travel demand through bothits direct and indirect effects on individuals’ behaviors. For instance, ICT can (1) generate
additional business travel via the increased number of ICT-based businesses; (2) reducetransportation costs and allow reinvesting some of the money and time savings in additional
travel; and (3), in the longer term, promote economic growth and stimulate new activities
that generate additional travel. ICT also provides alternatives to travel, and it increases
people’s freedom to eventually choose not to travel. It increases the efficiency oftransportation so that more travel can be accommodated within the existing infrastructure.
ICT has a central role in all these strategies: The way public policies will be implemented infuture years will greatly affect its capability to reduce environmental externalities oftransportation and ensure safe and reliable transportation options that satisfy travelers’mobility needs. To date, only limited aspects of the relationships between ICT andtransportation have been uncovered: research has mainly focused on specific impacts of ICT
on urban form and the organization of some activities, and on specific components ofindividuals’ decisions and travel behavior—primarily commuting trips and to a lesser extent
business, leisure, and shopping trips.
Many other impacts of ICT remain to be explored, including effects on congestion,
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Chapter II – Literature Review

fuel consumption, and greenhouse gas emissions. Future technologies have the potential tofurther induce dramatic changes in lifestyles and travel behavior, for example, throughtechnologies for driverless vehicles that will automate the use of roads or technologies thatwill make point-to-point air travel feasible and affordable to a mass market. Important shifts
are also associated with the type of technologies that will be used, with a larger prevalenceof two-way communication solutions: not only will ICT provide services to users (e.g.,
information about travel time), but it will also be used increasingly for interactions between
users and providers of services, and among users, for example, through an increased
automatic collection of personal data and the introduction of additional location-aware
services.
However, potential threats associated with the adoption of ICT relate to privacy
issues, as in the case of the automatic collection of personal data through mobile
communication devices, and equity, due to the increased technological gap suffered by nonICT users. The latter topic, in particular, deserves renewed attention: new technologies arecontributing to reshaping the world in unprecedented ways, but this has come at the expenseof those individuals who, owing to economic conditions, physical disabilities, or otherreasons, do not have access (permanently or even temporarily) to many of thesetechnological solutions. In an increasingly globalized and connected world, policymakers
should pay increased attention to how technological innovations affect these populations.
Technological development will continue to evolve and will further reshape transportationin the 21st century. In this continuous transition, it will be the responsibility of future
generations of transportation planners and policymakers to ensure that nobody is left behindin the modern digital society.
2.3.

THE MULTI-SERVICE TRANSPORT PLATFORM (MSTP)

In recent years, one of the significant innovations of ICT is multi-service transport platforms(MSTP). By definition, MSTP is an online-based multi-service platform that relies on drivers
providing access to a wide range of services, including ride-hailing transport, food delivery
service, courier service, and daily need services (e.g., cleaning service, massage, hair salon,
and other services). MSTP is an innovative platform that integrates transportation systems
and daily service provision by managing the demand from consumers and the available fleet
as the supply provides many services to the consumer. MSTP integrates several
methodologies, including assortment optimization, vehicle routing, scheduling, and pricing,
to design travel options and daily service provision in real-time. MSTP's system is included
many components, including the consumer, the availability of the vehicle, the number andvariance of merchants that can provide many services and goods, and the traffic condition.
Figure 2.5. shows the overall system of MSTP and how the interaction among
components has happened. First, a consumer requests a ride or chooses the service/goods
using a smartphone. MSTP handles the demand and supply by optimizing the source or fleet
they have to accommodate consumer requests. They allow consumers to request the service
and choose the best options by offering several service options. After the reservation is
confirmed, one vehicle in the fleet will be notified of the schedule through their smartphone
application. Since each driver is also attached to the GPS through their smartphone, they willcontinuously upload GPS data that the platform and the consumer can track. It can give theconsumer information to estimate their driver's location and when they will arrive. Then,after using the service or getting the goods from the driver, the consumer can give real-time
feedback to the driver and the platform regarding their experience directly on their apps. In
18Chapter II – Literature Review

this sense, the consumer, MSTP, and the driver’s fleet are in a triangular relationshipinterconnected and provided mutual feedback.

Figure 2.4. MSTP’s components and application interface
One area of MSTP that is rapidly expanding is their online food delivery (online fooddelivery) service. Online food delivery refers to the systematic process whereby the foodordered through an online application or website is prepared and delivered to the consumer.
In Indonesia, some of the MSTP company (e.g., GO-FOOD by GOJEK, Grab-Food, andUber eats) has been expanding their services, including the online food delivery services.
They utilize their fleet drivers to provide the transportation service and provide daily service,
such as delivering the food from partnered food merchants to the consumer. By utilizing theexistence MSTP system, the online food delivery service from MSTP serves a variety offunctions, including providing the consumer with a wide variety of food choices, themonitoring of payment, the provision of tracking facilities, real-time feedback to theapplication platform, sometimes the MSTP also provide some monetary incentives toincrease the consumers' tendency to order more food (Figure 2.6).
The platform (MSTP) has a different business model in online-based food service
provision compared to other services. For the online-based food delivery service (online fooddelivery), the MSTP has set some maximum service range within 6 km from the destination
location. The maximum boundary size for the services was placed to keep the quality of thefood. By setting the maximum range, they can keep the maximum delivery time to keep their
quality. In this sense, the accessibility and the range of service areas are the critical factorsof online food delivery services. The user's locations are the crucial factors determining thewide range of food that user can get nearby their area. The users may gain more choices ifthey are located in an area with many food merchants available, such as the central area. On
the other side, to provide some online food delivery service in certain areas, the platforms
need some sufficient demand (users) and supply (food merchants partner) within the area,
which implies that within a specific location, there is a minimum number of merchants thatjoin the platform system that needed to attract the driver to come and get around those areas
to provide some delivery.

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Chapter II – Literature Review

Figure 2.5. Components of MSTP

Figure 2.6. The function of online food delivery provided by MSTP
In Indonesia, 58% of MSTP users have used food delivery daily (Global Digital
Reports, 2020). Based on Azzuhri, A. A. et al. (2018), some of the reasons why online fooddelivery is very popular are (1) save time/effort to queue and wait, (2) save time/energy intraveling to buy food, (3) there are many promotions / attractive offers, (4) there are practicalpayment options, and (5) attractive cash discounts, many choices (food outlets) to choose
from, practical to order food whenever they want, make it easier to determine the type offood to be ordered through a favorite or bestseller list, and also it saves money from travel
20Chapter II – Literature Review

costs to buy food from food merchant.
Due to their popularity, the rise of MSTP has given some benefits to society, such asprovided job opportunities for many people across various employment types, including
delivery people (drivers), programmers behind the apps/online platforms, or chefs andadministrative staff in restaurants. For example, GOJEK in 2018, GOJEK, employs around2 million people to work as a driver, 600 thousand as food partners, and more than 5,000
people as office workers. Likewise, Grab in Indonesia has more than 2.5 million people
works as a driver and 400 thousand food partners, and 6,000 office employees. Meantime,
there is no doubt that the MSTP has provided many jobs, especially in the transportation anddelivery sector.
MSTP also directly impacts traditional restaurant or food merchants. Recently, many
food merchants have had to change the way they operate to stay in business. The
conventional food merchants with a physical storefront noticed the decreased in-store dining
and the increased food delivery. The decreasing number of people who did in-store dining
may have happened because more of their consumers began ordering online food and eating
it away from the restaurant, more likely at home or in the workplace. As online food delivery
started to gain a foothold in the market, many conventional food merchants need to react
quickly to consumer demand change by embracing online food delivery services to sufferedfrom a declined profit.
With MSTP services, many food businesses have realized that they can reduce theirdining area by utilizing the online food delivery service, thus saving some costs associatedwith space provision for in-dining locations. Regarding the study of Li, C., Mirosa, M., &Bremer, P. (2020), many cities in the UK, US, and India has been started to develop sometrend called ghost kitchen (also known as cloud kitchens or dark kitchens) due to theincreasing online food delivery service. These food merchants are shifting from owning thephysical store to the food delivery businesses where they do not have any physical store for
their daily operations. It may give some advantages for those food merchants in reducing theoperational cost by eliminating the store rent, reducing the number of staff, and virtually andlimitlessly increasing the diversity of their menu, concepts, or even their brands only one
kitchen. In the context of daily activity, MSTP has changed the interaction between
consumers and their food. One example is by modifying the way foods are obtained,
processed, and consumed by consumers. Online food delivery services can save timeotherwise spent on grocery shopping, cooking, or cleaning up afterward. According to Liu,
C. and Chen, J. (2019), by shifting to use online food delivery, at least two hours a day could
be "saved," and these customers liked to order online after their commute so that they could
rest and enjoy the food when they arrive home.
These days, especially during the 2020 COVID-19 pandemic for the many people
quarantined at home, another influence from the online food delivery and other MSTP
application distribution and delivery individuals are given a vital lifeline. Online fooddelivery provided meals and employment for the people who prepared or delivered the food.
Most online food delivery platforms adapted their food distribution software during this era,so delivery individuals and customers did not have to come into face-to-face contact. In
comparison, many people's meal preferences changed from dining out or venturing out tobuying groceries and preparing at home to online ordering frozen food under the lockdown
conditions imposed in certain countries due to the pandemic. For instance, to devote moretime to their jobs, many employees began or increased their online food delivery use.
Globally, the rise of online food delivery services has changed how consumers and foodmerchants/suppliers interact.

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Chapter III – Research Design

Chapter 3: RESEARCH DESIGNThis chapter describes the research design, including the research framework, research aims
and objectives, survey design, study location, target respondents, and a brief introduction to
the analysis methods used in this study. In this chapter, we attempt to answers one of ourresearch questions, “How to capture the virtual activities on daily activity-travel behavior?”(RQ 3.1.). The research question will be answered through the survey design that will be
explained in this chapter.
3.1.

RESEARCH FRAMEWORKTo the best of our knowledge, existing studies into the relationship between urban form andmobility have typically looked at specific trip purposes instead of a comprehensive activitytravel pattern. In the activity-based models, we assume that new (and existing) urban areas
constitute an environment for individuals and households to live in. The urban environment
forms a stage for people to act their lives. In doing so, individuals and households try to meettheir basic needs and personal preferences, while the environment they live in offers them
opportunities and constraints to doing so. Since activities cannot all be conducted at the same
location, individuals have to travel between activity locations. This means that activity
participation leads to activity-travel patterns, which show what kind of activities are
executed and where at what times, and which transport modes are used.
The Figure 3.1. shows the conceptual framework underlying this study. It is
important to know that individuals and households are assumed to organize their daily
activity-travel patterns. Personal and household characteristics primarily influence such
patterns. For example, the presence of children in a household will induce particular
activities and hence travel. Children need to go to school, maybe be involved in sports,
etcetera. Likewise, a double-earner household’s activity-travel pattern is likely very different
from an unemployed individual’s activity-travel pattern. Personal and household
characteristics lead to particular needs and preferences, hence inducing particular activities
that need to be conducted at particular locations.
While the urban environment and transportation system offers opportunities to
execute the activities, different transport modes’ availabilities give options people canchoose from; likewise, the spatial distribution of workplaces, shops, and other facilities
directly determines how far people minimally have to travel to conduct their activities.
Similarly, these facilities’ attractiveness will influence whether they choose the nearest
location or whether they will trade-off distance (or travel time) and attractiveness. The urban
environment and transportation system, however, do not only provide opportunities but also
constrain behavior. The role of urban form in this complex conceptualization is thatparticular forms, characterized in terms of variables such as urban shape, density, land use
configuration, and network types. The way urban or city is formed may influence the spatial
distribution of residences, jobs and facilities, and the relative accessibility of activity
locations. As such, it potentially influences the opportunities and constraints offered by theurban environment.

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Chapter III – Research Design

Figure 3.1. The general conceptual framework
In this research, we tried to provide a comprehensive understanding regarding theimpact of MSTP through two components, including (1) the impact of MSTP on the facility
distribution and (2) the impact of MSTP on activity-travel behavior. There are somehypotheses that we want to test in this study. Those hypotheses including:(1) MSTP will change the facility location distribution through the provision of
online food delivery services. The food merchants may gain some incentives tobe located away from the central area. At the same time, they still maintain thesame number of customers as demand through MSTP’s online food delivery
services.
(2) MSTP will change an individual’s activity and travel decisions through MSTP’s
online delivery services and MSTP’s online transportation services.
(3) MSTP will change an individual’s eating behavior by providing a better online
food delivery service.
(4) MSTP will change an individual’s daily activity-travel behavior.
3.1.1. Conceptual Framework for Analyzing the Impact of MSTP on The Urban Form
To analyze the impact of MSTP on the urban form, the exploration on facility distribution
analysis was done. By developing an agglomeration index, this study tries to quantify the
distribution of the facility, and it is changed due to the presence of MSTP. The agglomerationindex was developed by the ratio of the average pairwise distance of the targeted facility andcounterfactual facility. The specific type of facilities that we want to analyze theirdistribution is denoted by the term “targeted facility” and we also use the term“counterfactual facility” to represent the type of facilities that we assume will be equallydistributed across the area or there will be no agglomeration forces happened in distribution
the counterfactual facilities. In this study, there are several types of targeted facilities that
we will be analyzed, including the commercial facilities, combination food merchants,
online food merchants, and dine-in food merchants, where the detailed explanation of eachfacility's characteristics later will be explained in chapter 4 and chapter 5.
In the development of the agglomeration index, three major components will becarefully selected. Those components including (1) distance metrics, (2) counterfactual
facilities type, and (3) spatial boundary. To select distance metrics, we calculate the average

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Chapter III – Research Design

pairwise distance of each facility by using three types of distance metrics, including the
Euclidean distance, the network distance, and travel time using a car as the transport mode.
In Chapter 4, we develop the agglomeration index that will be used in Chapter 5. Bydeveloping an agglomeration index, this study tries to quantify the facility's distribution, and
it is changed due to the presence of MSTP. Using the agglomeration index, we can quantify
the distribution of facilities, analyzing the distribution pattern, and comparing with otherlocations or other facility types. Some considerations in developing agglomeration are also
discussed in Chapter 4, including selecting counterfactual facilities type and selecting
distance metrics. Later in Chapter 5, we show that the selection of spatial boundary as thescale of analysis will significantly impact the agglomeration index result. The spatial
boundary included in this study includes the metropolitan scale, the city scale, the district
scale, and the neighborhood scale as the smallest area. By developing the agglomerationindex for the targeted facilities, we explore the result in each scale through the agglomeration
pattern analysis. As the next steps, several statistical analyses will be used to explore theassociation of the agglomeration index and the city’s characteristics, including correlation
analysis and decision tree analysis.

Figure.3.2. Workflow for analyzing the impact of MSTP on the urban form
3.1.2. Conceptual Framework for Analyzing the Impact of MSTP on the Changes in
Activity-Travel Behavior
The presence of MSTP as one of the ICT-based innovations in transportation has become
one of the important researches focuses that needs to be explored in the future. In this study,
we attempt to explore the new survey method to capture the daily online activities together
with the activity-travel diary and the context-awareness stated preference survey. Using thefuture mobility survey (FMS) application called X-ING, we improve the current activitytravel diary survey by adding the ICT (i.e., MSTP) components. On the other side, by
utilizing the actual data or revealed preference data that we obtained from the activity-travel
diary survey, we construct a context-awareness stated preference survey towards online
activities. This study analyzes the distribution of physical and online activity-travel patterns
24Chapter III – Research Design

and the individual’s eating behavior by using the panel binary mixed logit model. Later in
Chapter 6, we will explain the changes in activity-travel distribution under MSTP influences.
In Chapter 7, we will explain our findings regarding the changes in individuals’ activity andeating behavior by exploring the factors that influence people to conduct an online activity,
the value of using online services, and the factors that affect MSTP’s service level.

Figure.3.3. Workflow for analyzing the impact of MSTP on the urban form
3.1.3. Conceptual Framework for Analyzing the Impact of MSTP on The ActivityTravel Behavior
This study aims to extend an existing dynamic discrete choice activity-travel model toincorporate the impacts of ICT use on activity-travel patterns. This study focuses on asituation in Indonesia where multi-service transport platforms (MSTP), particularly GOJEK
and Grab, have been widely used and have now become a vital part of people’s daily lives,
leading to unignorable changes in people’s travel behavior and activity patterns. With thepresence of the MSTP, the way of interacting between the supply-side (merchants) anddemand-side (users) might change, essentially because the platform would relax users’ timeand space constraints to reach the services. We have attempted to develop a survey andmodeling framework to comprehensively understand the impacts of MSTP on the urban
form and activity-travel behavior. We believe that it is worth sharing the proposed
framework together with empirical findings with other travel behavior scholars since the
MSTP has been showing both unignorably positive and negative impacts on the society inIndonesia and putting the MSTP in a proper position of entire transport systems is one of thesignificant challenges we face.

25

Chapter III – Research Design

Figure.3.4. Workflow for analyzing the impact of MSTP on the activity-travel behavior
3.2.

RESEARCH AIMS AND OBJECTIVESThis thesis aims to provide a comprehensive understanding of the impacts of MSTP andexamine them empirically based on a case study in Jakarta, Indonesia. The specific objective
is to assess the impact of MSTP in Indonesia on urban form and activity-travel behavior.
Follows are the detail of research objectives, research topics, and questions for this study.
Table 3.1. Research objectives, research topics, and research questions
Research Objective
To analyze theimpact of MSTP on
the urban form

To analyze theimpact of MSTP on
the activity-travel
behavior

Research Topic(1) The impact ofMSTP on facility
distribution andcity characteristics(2) The impact ofMSTP on thefacility distribution(3) Changes in
Activity-Travel
Distribution underMSTP(4) Impact of MSTP
on the Individual’s
Eating Behavior(5) Impact of MSTP
on the ActivityTravel Behavior

Research Question(1.1) How to quantify the concentration of facility
distribution?
(1.2) How is the association between facility
distribution and city-level characteristics?
(2.1) What distribution changes do MSTP bring about
on the facility distribution?
(2.2) How do these induced changes in urban form?
(3.1) How to capture the virtual activities on daily
activity-travel behavior?
(3.2) How does MSTP change the distribution ofactivities?
(3.3) What factors influence people to choose online
activities?
(4.1) How the presence of MSTP’s online fooddelivery service will affect people’s eating
behavior?
(4.2) What factors affect MSTP’s service level?
(5.1) How to extend the current dynamic discrete
choice model for activity-travel analysis toincorporate the impact of MSTP use on theactivity-travel patterns?

26Chapter III – Research Design

3.3.

SURVEY DESIGN

To empirically examine the impact of MSTP comprehensively, the exploration of data andmethodology is necessary. In this study, we attempt to collect the data regarding the impactof MSTP on the activity-travel behavior and facility distribution by conducting an activitytravel diary survey, online activity diary survey, socio-demographic survey, and contextawareness stated preference survey.
3.3.1. Activity-Travel Diary Survey
Recently, innovation in information and communication technologies (ICTs) have rapidlychanged the daily activities, work organization, and social habits of everyone across theworld. Many studies have been done to identifying the interaction between ICT and travelin many different fields. Some studies (Mokhtarian, 2013; Helling and Mokhtarian, 2001;
Salomon and Mokhtarian, 2007) have attempted to identify the substitution andcomplementary effect of ICT on travel and found that ICT has a potential to substitute travel.
Based on Hanson, S. & Giuliano, G. (2004), the effect of ICT on transportation can bedivided into three categories, i.e., long-term, medium-term, and short-term. In the long-term
impact, we discuss how ICT may affect the location, e.g., where to live, in the medium-term
decision, e.g., buying a car. ICT revolutionizes many aspects of travel behavior in the shortterm impact, such as activity participation and other trip-making aspects. ICTs have foreverchanged our society in ways that were barely imaginable only a few years ago. However,quantifying the impacts of these changes on urban transportation and activity-travel behavior
is not easy. The challenges of identifying the impact of ICT rely on data availability.
Recently, the smartphone-based travel activity survey has become one of the solutions tothis challenge.
Smartphone-based travel activity surveys are gaining popularity as they provide
solutions to the various limitations of traditional face-to-face and/or paper-based surveys.
Unlike traditional paper-based questionnaires, smartphone-based surveys can easily
integrate visual effects and provide more accurate descriptions of attributes for the
respondents. In addition, most smartphones nowadays are equipped with multiple sensors,
including GPS, Wi-Fi, GSM, and accelerometer, to provide a better data resolution andadditional information on the user’s activity and travel behavior. Also, the smartphone has
become a vital component of a user’s daily life which almost always will be carried andcharged by the user. Moreover, using smartphone-based survey tools may reduce the cost of
conducting the survey because the survey’s device itself belongs to the users, making
smartphones ideal “life-loggers” (Zhao et al., 2015).
One of the survey platforms used in existing studies is called Future Mobility Sensing(FMS), developed by MIT and commercialized by Mobile Market Monitor (MMM) and was
first applied in Singapore, where both revealed preferences of travel and activity patterns ofindividuals were observed. The same application has been modified and used in different
countries. Nahmias-Biran et al. (2018) used FMS in Israel and collected information overtwo days. Meanwhile, Zegras et al. (2018) used the same application to collect data from
Tanzania and presented the challenges of collecting data from a developing country as mostapplication applications had been in the developed world. Similarly, Qudratullah andMaruyama (2019) presented the challenges to conducting a smartphone-based travel survey
in the cities of Afghanistan. However, they did not utilize the FMS application. Other
applications were also developed to collect either travel or travel and activity participation
information with pilot studies from different countries, mainly from the global north. Table
27

Chapter III – Research Design

3.2. presents a summary of the different studies which have used smartphone-based
applications for surveys.
The table lists out the primary focus of these surveys, the method, the country of
application, the number of days participants were surveyed, and whether information on
online virtual activities or MSTP was captured in those surveys or not. Most studies wereobserved to capture revealed preference (RP) data over multiple days (see Table 1). RP data
are generated by a choice process in the real world (Ben-Akiva et al., 1994), and it is also
known to have a high reliability and face validity. However, RP data are relatively inflexible
and often inappropriate if we wish to forecast a real-world situation in the future. Statedpreference (SP) choice data can be used to model the hypothetical world, which can be theinput of the practitioner to make some policy for the future. The SP survey’s main limitationis that they only record choices made in hypothetical scenarios (Fifer et al., 2014), resulting
in different biases such as inattentiveness, attribute non-attendance, and inconsistency withactual behavior (revealed preferences). For example, Murphy et al. (2005) show that
respondents’ willingness to pay tends to be higher in SP surveys than in RP.
To address these biases, some researchers suggest a “pivoting” method, where thelevels of attributes in the SP survey could be created based on the chosen RP alternatives,and an “SP-off-RP” method, where the choice experiment is conducted simply by
hypothetically changing RP attributes while maintaining the RP context (Train and Wilson,2008). Smartphone-based travel activity surveys have developed ‘context-aware’ SP surveys,
which suggest attribute levels based on the RP information observed from the survey
application. Cox (2015) and Danaf et al. (2019) used FMS to develop SP attributes and mode
choice levels in Singapore and Boston, respectively. In the process, they also collected
attribute-level information on certain MSTP, such as ride-sharing service applications.
The literature review highlights specific pertinent gaps in the literature that the survey
methodology developed in this study will address. First, only a few studies have tried todevelop context-aware smartphone-based SP surveys. Studies that have done that too havemainly focused on traditional mode choice. Many other aspects of activity participation, suchas the decision to “eat out” or “order online,” have not received much attention. Second,
these smartphone-based survey applications rarely focus on capturing information on online
activities performed during travel or during other activities. Capturing this information isimportant in order to fully understand the impacts of MSTP applications such as Uber and
Go-Jek. Finally, only a few studies have implemented the use of smartphone-based
applications in the countries of the global South. The developing economies pose uniquechallenges with respect to the acceptance of such methods and comprehensively understandthe impact of MSTP on travel and activity participation behavior. There have been instancesof extensive long-term activity-travel data collection in developing economies (such as byDharmowijoyo et al., 2015) in Indonesia). However, the focus on identifying the impacts of
ICT and the use of smartphone-based survey frameworks has been rare. As a part of thisstudy, MMM’s FMS was modified to incorporate features that addressed the literature gaps
and were implemented in Jakarta, Indonesia. The survey methodology and framework are
explained in the next section.
Table 3.2. Smartphone-based travel activity surveys: A review
Sl
no
1.
2.
3.
4.

Study
Cottrill et al. (2013)
Safi et al. (2015)
Cox (2015)+
Berger and Platzer (2015)

Focus

Method

A; T
T
A; T
T

RP
RP
RP-SP
RP

OnlineActivities
No
No
No
No

MSTP

Days

Country

No
No
Yes
No

14
3
Multiple
3

Singapore
New ZealandSingapore
Austria28

Chapter III – Research Design

Sl
no
5.
6.
7.
8.
9.
10.
11.
12.
##

Study

Focus

MethodMaruyama et al., (2015)
Xiao et al. (2016)
Allström et al. (2017)
Zegras et al. (2018)+
Nahmias-Biran et al. (2018) +Thomas et al. (2019)
Danaf et al. (2019) +Qudratullah and Maruyama(2019)
Our survey (this study)

T
T
T
A; T
A; T
T
A; T

RP
RP
RP
RP
RP
RP
RP-SP

OnlineActivities
No
No
No
No
No
No
No

T

RP

A-T

RP-SP

MSTP

Days

Country

No
No
No
No
No
No
Yes

1
7-12
7
24
2
28
Multiple

Japan
China
SwedenTanzania
IsraelNetherlands
USA

No

No

14

Afghanistan

Yes

Yes

14

Indonesia

Notes: Primary focus: on Activity (A) or Travel (T) or both A; T; Survey method: Revealed preference (RP) or pivoted
RP- stated preference (SP) (RP-SP); Online activities refer to capturing information on online or virtual activities conductedduring the trip or primary activity; MSTP refers to capturing information on multi-service transport platforms such as Uber,etc. + refers that these surveys used the basic Future Mobility Survey (FMS) architecture developed by MMM, same as
Cottrill et al. (2013).

A number of researchers have discussed how the usage of ICT affects travel behavior
from various angles, such as how ICT affects the mode choice, route choice, and scheduling(e.g., Lenz, B. & Novis, C., 2007; Aguiléra, A. et al., 2012, Fiore, F. D., et al., 2014, BenElia, E., 2018). However, most studies focus on the overall impact of ICT on activity-travel
behavior. At the same time, they do not take into account context-dependent factors affecting
ICT use and activity-travel behavior. For example, an online food delivery service for lunch
may depend on the individuals’ time and space constraints. This calls for an improved travel
survey scheme, while there has been relatively little attempt to explore new data collection
schemes unique to the problem. This activity-travel diary survey is a survey of the MSTP
user’s activity-travel pattern, including the destination choice, mode choice, activity choice,and virtual activities.
Travel diaries are widely accepted as one of the proxies for getting insights into
individuals’ and groups’ travel behavior. On the other side, the amount of useful information
extracted from travel diaries is matched by the difficulty of obtaining travel diaries. One of
the main challenges in collecting travel diaries in this modern area is where the response rate
to traditional travel diary collection methods has decreased in most countries (Prelipceana
A.C. et al., 2018).
The survey’s core part is an app called X-ING (by Mobile Market Monitor (MMM)).
The X-ING app is an application that is installed on the smartphone that required an internet
and GPS connection. This application records and infers a wide range of travel attributes,
including location (origin and destination), travel time, travel purpose (activity), route choice(by GPS tracking), and mode choice, and provides a user-friendly interface for users to verify
the auto-generated timeline and answer additional questions. Using this application, we are
not only handling the obstacles of the previous travel diary methods, but we also collect
much higher resolution data in real-time. We use the combination of revealed preference of
trips and the uses of GPS mobile phones or other mobile communication tools and web diaryon the internet to record trip information of person and vehicle. The variables to be recorded
include (For the detailed explanation of variables, the choices, and some other notes for thefilling, see appendices 3):
Table 3.3. Variable of activity-travel diary
No
1
2
3

Definition
Household ID
Household person ID
Participant referral code

No
27
28
29

Definition
Type of parking area
Money spends on parking fee
Bus type

29

Chapter III – Research Design

No
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

Definition
Household member name
Unique ID for each segment
Indicates if segment is stop or trip
Start time of the segment
End time of the segment
Final trip mode
Source of interval
Source of stop
Source of mode information
Original algorithm for mode of the trip
Original algorithm for latitude of the stop
Original algorithm for longitude of the stop
Unique ID for each user
Professional driver ability
Original algorithm for start time
Original algorithm for end time
Latitude of the stop
Longitude of the stop
Text value of mode_id
Specification of other modes
Number of other people in the traveling party
Relationship with the accompanied
Identification of driving activities

No
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52

Definition
Car type
Identification of other types of car ride
the fare of car ride service
identification of driving the motorbikeMotorbike ride hailing service
Other motorbike ride hailing service
Motorbike ride hailing service fare
All types activities in the segment
Other types of activities
Main activities
Type of escort
Passenger activities
Type of shopping
Type of groceries
Other type of groceries
Type of non-groceries
Other type of non-groceries
Shopping expenses
Food Type
Other type of food
Food expenses
Online activity
Other type of online activities

Figure 3.5. The interface of X-ING application
Source: Adaptation from X-ING, 2020

The smartphone-based survey framework utilizes a modified version of MMM’s FMS using
the X-ING application to capture participants’ travel-activity patterns. The FMS platform
consists of four interconnected technology components:(1) a mobile app that unobtrusively collects raw sensor data, such as GPS, GSM,
Wi-Fi, accelerometer, from iOS and Android smartphones.

30

Chapter III – Research Design(2) A machine-learning back-end that houses intelligent algorithms to detect stops
and infer modes and activities using a) mobile sensor data, b) local contextual
data such as transit network files, and c) user data, including household andpersonal characteristics collected in a recruiting survey and user-verified travel
and activity history.
(3) An interface accessible via the smartphone apps through which users view andverify a daily travel and activity timeline and provide supplementary trip details.
(4) A data management system for monitoring and management of data collection
activity.
The back-end inference algorithms, as well as the user-friendly interfaces, help
reducing user burden when multiple days of travel data are collected. This platform wasselected for our study as it had been applied in large-scale travel surveys in several cities andproven to collect high-quality data. In addition, the system can be customized to incorporate
study-specific questions, which is required in our study. More details on the FMS platform
can be found in Zhao et al. (2015). The data-collection framework for this study can bebroadly divided into four parts (see Figure 1), described in detail in this section. The four
different parts of the survey were the following:
1. Socio-demographic questionnaire.
2. An activity-travel survey using X-ING application: (2a) Travel diary and (2b)Information of activities.
3. Online activities survey using X-ING application.
4. Context-aware stated preference surveys:
a. SP for eating out activities.
b. SP for online activities.

Figure 3.6. The smartphone-based activity-travel survey framework
The application records and infers a wide range of travel attributes, including location(origin and destination), travel time, travel purpose (activity), route choice (by GPS tracking),
and mode choice, and provides a user-friendly interface for users to verify the auto-generated
timeline and answer additional questions. Using this application, we are not only overcomingsome of the obstacles of the traditional travel diary methods, but we also collect much higher
31

Chapter III – Research Design

resolution data in real-time. The application uses a combination of revealed preference ofusers, GPS tracking, machine learning back-end for activity diary inference, and user
verifications in order to record the complete trip information.
The use of the application makes this survey one of the first smartphone-based
activity-travel diary surveys that have been conducted in Indonesia. Participants were thenrequested to install the application, log their activity, and travel for 14 days (from January
28th to February 10th, 2020). The participants were awarded a 300,000 IDR (21 USD) cash
prize for finishing the two weeks’ survey.
For every trip made, the travel diary will record:
1. The location of origin and destination of the travel.
2. The duration of trips.
3. The route taken in the trips.
4. After the respondents made their trips, there will be a follow-up or confirmationquestion that asked some details about their trips, including (i) Mode of transportthat they used; (ii) Travel cost; (iii) The number of people in the vehicle (ifapplicable); and (iv) The parking fee (if applicable).
5. Modification for MSTP:
This part of the survey tried to capture the use of MSTP in their travel by adding
additional alternatives in the mode choice for on-demand mobility services. Suchan exclusive provision has been rarely tested in previous smartphone-based
surveys. In addition, other transportation mode options specific to the study area,
Jakarta, which includes the MSTP-based two-wheeler taxi (e.g., Online Ojek)
and ride-hailing services, were also included in the list of alternatives.
The application (X-ING) can automatically detect if the users are moving, i.e.,making a trip or ‘staying,’ i.e., participating in an activity. It uses a similar approach to thetravel diary to capture a participant’s activity behavior. Figure 3.7. illustrates a typical log
entry for the travel and activities on the application.

Figure 3.7. A typical activity and travel log in X-ING application
For every stay made, the application will record:
1. The location they stayed at.
2. The duration (starting and ending time) when they stayed at a place.

32

Chapter III – Research Design

3. Activities performed at the location.
4. Modification for certain activity types.
We modified the questionnaire to capture additional information for specific activity
purposes; if the participant performed one of the following activities, eat-out, grocery
shopping, or non-grocery shopping, the respondents were asked additional details of theiractivity. These questions include what items the user purchased and how much money theyspent on shopping or eating. Figure 3.8. shows the screenshots for the follow-up questions
for eating out activities. The responses from these questions were later utilized to develop
the context-aware SP questionnaire.

(1) Type of Food

(2) Other Food Type(3) Food Price(4) Verification

Figure 3.8. Follow-up questions (prompt pop-up on the application) for eating out
activities
3.3.2. Online Activities Diary Survey
The third major component of the survey framework involved collecting information on
additional online or virtual activities performed during the primary activity (both during thestay and while moving, i.e., during traveling). This aspect of capturing secondary online
activities using a smartphone-based survey has not received much attention in the literatureand is a necessary component in understanding the impact of ICT on activity-travel behavior
comprehensively. This survey was conducted for each activity or trip with a minimum
duration of 20 minutes. Twenty minutes is chosen as the cut-off because many studies
recently have observed that people frequently check their mobile phones (Ofcom, 2018), andthe division between physical and virtual activities is blurring (Deloitte, 2018). In addition,
20 minutes provides a sizeable time limit to perform additional online activities along withthe primary activity. For every such activity (i.e., duration greater than or equal to 20
minutes), an additional prompt question was presented to users, asking them about the typesof additional online activities they performed during the time duration of the primary activity.
The list of online activity categories are included (1) online food delivery (FD), (2) online
grocery shopping, (3) online non-grocery shopping, (4) online banking or payment, (5)online meeting, (6) online class, (7) online entertainment (e.g., playing games, watchingfilms, etc.), (8) other online activities, and (9) no online activity.
33

Chapter III – Research Design

Figure 3.9. Follow up question for online activities
3.3.3. Socio-Demographic Questionnaire
The first part of the activity-travel diary survey captured personal socioeconomic anddemographic characteristics from users. It included questions on gender, age, income, family
size, vehicle ownership, education level, and occupation type, among other variables. Each
person who participated in the survey responded to these questions before filling out travel
diaries, constituting the second part of the survey. The component of the questionnaire is asfollows (See the appendix 4 for the detail):
1. Gender.
2. Marital status.
3. Phone number.
4. Email address.
5. Home address.
6. Office address.
7. Education level.
8. Job type.
9. Driving license ownership.
10. Household vehicle(s) ownership.
11. Household member data.
12. Average household monthly income (including all of household member’s income).
13. Average household monthly expenses.
14. Average respondent’s monthly travel expenses.
15. Average respondent’s monthly meal expenses.
16. Average respondent’s monthly grocery expenses.
17. Average respondent’s monthly non-grocery expenses.
18. Credit card ownership.
19. E-money ownership.
20. Smartphone(s) ownership.
34

Chapter III – Research Design

21. Wi-Fi ownership.
22. Average time spent in an activity.
23. Working schedule.
24. Lunch behavior.
25. Provision of lunch from the office.
26. The daily schedule of working and arriving at home.
3.3.4. Context-aware Stated Preference Surveys
Stated preferences surveys are most commonly used to provide behavioral insights on
hypothetical travel scenarios such as new transportation services or attribute ranges beyond
those observed in existing conditions. When designing SP surveys, considerable care isneeded to balance the statistical objectives with the realism of the experiment (Ben-Akiva etal., 2019). The realism of experiments involves accounting for the market, personal, or
contextual constraints and presenting alternatives in the same way as their market framing.
These objectives can be met by designing context-aware SP surveys, which pertain to a
specific context already faced by the respondent. For example, a transportation mode SP
survey would refer to a trip performed by the respondent but present different alternatives
and attributes from those originally experienced by this respondent. In this study, we conduct
two context-awareness stated preference surveys, including the context-aware statedpreference survey for eating behavior and the context-aware stated preference survey for
online activities.

Figure 3.10. Context-aware SP methodology
Source: Adaptation from Atasoy B. et al., 2018

3.3.4.1. Context-aware stated preference survey for eating behavior
This survey was designed particularly to capture changes in eating out behavior.
Hypothetical scenarios denoting online food delivery options were provided to users, and35

Chapter III – Research Design

their choice of whether they will shift to online food delivery or continue to conduct theeating-out activity was observed. This is an important aspect with respect to understanding
the effects of ICT on travel behavior. It will aid in analyzing if ICT will substitute physicaltravel in case of eating out trips. Four different stated preference (SP) attributes were selected
viz. a) delivery time for online food delivery, b) delivery cost for online food delivery, c)
combination of ordered food types, and d) food cost for online food delivery.
Each attribute’s levels were decided based on revealed preference informationcollected from other parts of the survey. For each user, one of their eating out activities was
selected at random from the first week of their travel, and then based on their revealed
preference (RP), attribute levels were decided. Each user was then provided with five choice
scenarios to choose between the online food delivery option and their present eating-out trip.
This context-aware stated preference survey is deemed better than when all choice contexts
in an SP survey are purely hypothetical. It is because such a design accounts for contextdependent factors such as motivation and constraints they had at that time. In addition, thiskind of context-aware survey design could capture the complex interdependencies between
ICT use and travel since their travel decisions may come from extrinsic motivations (e.g.,
getting a lunch meal) and intrinsic motivations (e.g., interacting with friends traveling, andhaving lunch).
The attribute levels for the context-aware stated preference survey were generatedusing the following RP information. For a), i.e., delivery time for online food delivery, thetravel time information captured from travel before eating out the activity (for one randomly
selected context) was utilized to create five different levels (see Table 2). Meanwhile, for b),i.e., delivery cost, travel distance information for the previous trip before eating out activity
captured automatically through GPS sensors was utilized and multiplied with an assumed
per km cost for delivery of 6,000 IDR (0.43 USD) across five different levels. For c), i.e., a
combination of ordered food types, the same categories offered to users for their eat-out trips
were utilized to create four different levels, denoting the combination and the number offood items ordered. Finally, for d), i.e., food cost for online food delivery and informationfrom RP on the user’s actual expenditure on the eating out activity was utilized to create fivedifferent levels. The variations in the food cost are an important factor as often it is seen that
MSTP collaborate with food merchants to provide services at discounted rates. The attributes
and their corresponding levels are shown in Table 3.4.
Table 3.4. Attributes and levels for context-aware SP survey for eating out activities
Attributesa) Delivery time for online food delivery.
(Based on the actual travel time from thetravel diary data; revealed-preferencebased question)b) Delivery cost for online food delivery.
(Based on the actual travel distance fromthe travel diary data; revealed-preferencebased question)c) Combinations of Online food delivery's
Food Types(1. Beverages, 2. Snacks/Sweets, 3. Fast
food, 4. Indonesian food, 5. Western food,
6. Eastern food, 7. Bakso/Noodles)d) Food cost for online food delivery.
(Based on the actual food cost from the

1.
2.
3.
4.
5.
1.
2.
3.
4.
5.
1.
2.
3.
4.

Level
0.4* actual travel time
0.7*actual travel time
1.0*actual travel time
1.3*actual travel time
1.6*actual travel time
0.4*6,000 IDR*actual travel distance
0.7*6,000 IDR*actual travel distance
1.0*6,000 IDR*actual travel distance
1.3*6,000 IDR*actual travel distance
1.6*6,000 IDR*actual travel distanceOne food typeThree food types
Five food types
Seven food types

1.
2.

0.8*actual food cost
0.9*actual food cost36

Chapter III – Research Design

Attributes
activity information data; revealedpreference-based question)

3.
4.
5.

Level
1.0*actual food cost
1.1*actual food cost
1.2*actual food cost

The five scenarios were presented to participants with two choices in each, with theactual eating-out trip information on the left side and the attribute levels for the online fooddelivery option on the right (see Figure 4). The questions show context-aware SP scenarios
for an online food delivery service introduced in Jakarta. As the context-aware SP survey
was conducted at a later time, we strived to make respondents remember the actual
conditions they felt at the time of participating in that activity. By showing the date when
they took the eating-out trip, it is hoped that the respondents will be able to remember theconditions and constraints they had at that time. The question was then posed to the
respondents as part of the SP survey, “by considering all the activities and constraints you
have at that time if the following online food delivery service is available, will you be shiftingfrom eating out to ordering an online food delivery service?”

Figure 3.11. Example of questionnaire
We asked users if they would replace the eating-out with an online-based fooddelivery service if the service is available with specified price and service quality parameters
by pivoting the revealed preferences (e.g., Hensher and Greene, 2003). In those fivequestions, the respondent will be provided with their actual eat-out data, shown by the pink
37

Chapter III – Research Designtable on the left side and the purposed online-based food delivery service showed by the blue
table on the right side. In each questionnaire, the respondents are required to choose thealternatives. This context-aware SP survey will be using the actual eat-out activity from thefirst-week data of the activity-travel diary survey respondents’ data. This allows us toconfirm whether ICT use substituted a trip or not. Importantly, this pivoted stated preference
design allows respondents to represent their preferences given all context-dependent factors
such as motivation and constraints they had at that time. This feature is of particularimportance to capture the complex interdependencies between ICT use and travel since theirtravel decisions may come from extrinsic motivations (e.g., getting a lunch meal) andintrinsic motivations (e.g., interacting with friends while traveling and having lunch). The
respondent would not choose an online-based food delivery service, even when the service
level is very high if they made a trip for intrinsic motivations.
3.3.4.2. Context-aware stated preference survey for online activities
Additional context-aware SP surveys were conducted to understand ICT’s impact on
activity-travel behavior better. Users’ RP information from the first week of the survey was
obtained from part 3 of the survey framework, i.e., the survey for online activities, whereusers were prompted to record the online activities that they performed along with a primaryactivity (when its duration was more than 20 minutes) was utilized for this SP survey.
Whenever the users reported having conducted online food delivery orders and online
shopping (both grocery and non-grocery), an additional SP survey was conducted with ahypothetical scenario where that particular online activity which they performed ceased toexist.

Figure 3.12. RP and SP surveys for online activities
In the case of online food delivery order, the users were provided with six alternatives
to choose from, including 1) give up having a meal, 2) make a trip to the nearby restaurant,3) cook by themselves, 4) use traditional food delivery services (such as calling therestaurants), 5) ask other people to bring them food, and 6) others. Meanwhile, in the case38

Chapter III – Research Designof online shopping (both grocery and non-grocery), the users were provided with four
alternatives, including 1) give up the online shopping activity, 2) make a trip to the nearby
shop or market, 3) ask other people to bring them or buy them the goods, and 4) others. The
following question was then posed to the respondents as part of these SP surveys “if there
are no MSTP services available at that time, which of the following alternatives will you
choose?”. All of X-ING feature was shown in English, while the SP questionnaire wasshown in Bahasa Indonesia.
3.4.

STUDY LOCATIONSIn this study, we employ research in Jakarta, Indonesia, and 69 cities in Japan. In analyzing
MSTP’s impact on the urban form, we employ an agglomeration analysis in Japan andJakarta. While analyzing MSTP’s impact on the activity-travel behavior, we altogether
employ the data that we collected in Jakarta, Indonesia, where the MSTP is available andpopularly used.
3.4.1. Study in Jakarta, Indonesia
Jakarta (the Special Capital Region of Jakarta or Daerah Khusus Ibukota (DKI) Jakarta) isthe capital and the largest city of Indonesia. With an area size of 664.13 Km2 and 10,770,487
population, enact DKI Jakarta is the world’s second-most populous urban area after Tokyo,
with a population density of 21,974 people/Km2. Based on UN indicators (UNDP, 2020),
Jakarta is the largest metropolitan area in Southeast Asia, acknowledged for a tremendous
rate of population growth.

Figure 3.13. Map of study area in Jakarta, Indonesia
In this study, we analyze at the aggregate level of the metropolitan area of Jakarta
and analyze within a disaggregated level, including five administrate cities, 42 districts, and262 neighborhoods. Table 3.5. shows the general characteristics of the case studies following
39

Chapter III – Research Design

with Figure 3.14 and Figure 3.15, which will show the land use and city’s structure of Jakarta.
Table 3.5. The characteristics of the study area
City NameCentral Jakarta
South Jakarta
West Jakarta
East Jakarta
North Jakarta

Area Size
(Km2)
52.38
154.32124.44
182.70139.99Population(People)1,138,346
2,188,457
2,324,121
2,944,493
1,711,386

Population Density(people/Km2)
21,732
14,181
18,676
16,116
12,225

Number ofDistricts
8
10
8
10
6

Number of
Neighborhood
44
65
56
66
31

Source: Badan Pusat Statistik Provisi DKI Jakarta, 2021

Based on PTSP Jakarta (2019), the allocation of land for housing occupies the largest
proportion, namely 48.41% of the mainland area of DKI Jakarta. Meanwhile, the area forindustrial, office, and commercial buildings only reached 15.68%. As part of their spatial
structure, Jakarta has two activity center systems, including the primary activity center andthe secondary activity center shown in Figure 3.15. The location of Jakarta's main activity
will be determined by the central area or central business district (CBD) area that will be
used in the following analysis (see Chapter Five). We will only use the primary activity
center location for the analysis, including an 11-point location throughout Jakarta.

Figure 3.14. Land use map of Jakarta 2009
Source: Province Government of DKI Jakarta

40Chapter III – Research Design

Figure 3.15. Map of Jakarta city’s structure
Source: Province Government of DKI Jakarta

3.4.2. Study in 69 Cities of Japan
Japan is an island country located west of the Pacific Ocean in the Northern Hemisphere.
Japan comprises 6,852 islands covering 377,974.17 square kilometers (145,936.64 square
miles) (Geospatial Information Authority of Japan, 2019). Japan is divided into 47
administrative prefectures and eight traditional regions, with Tokyo as its capital city. Witha total number of populations around 125,929,817 (Statistics Bureau, Ministry of Internal
Affairs and Communications, 2020), Japan is the most densely populated country globallyand the eleventh most populous country in the world. We select the 69 Japanese cities
according to the availability of other city-level information, particularly from the nationwide
person trip survey conducted by the Ministry of Land, Infrastructure, Transport, and Tourism(2008). Therefore, we select 69 cities in Japan as our case study with regards to theavailability data.
Concerning the availability of MSTP, there is no MSTP kind of services available
yet. In several big cities such as Tokyo, Osaka, and Kyoto, uber eats food delivery services,
but they are not integrated with the transportation system as befits in Indonesia. Regulatory
issues related to transportation modes related to safety, legality, and market competition withexisting transport fleets make it difficult for ride-hailing services to be established in Japan.
Until now, based on the knowledge of the author, there is no MSTP service in Japan.

41

Chapter III – Research Design

Figure 3.16. Map of study area in Japan
3.5.

TARGET RESPONDENTS

This study will be collecting data from 300 individuals who will complete the 14 days (2weeks) application-based travel-activity diary survey from Jan January 28th to February 10th,
2020. The respondent is a user of an online-based multi-service platform who lives andworks (home-work place-based sampling) in South Jakarta City, Indonesia. Respondents
were randomly chosen based on their home and workplace location. Respondents will be
asked to complete three types of surveys, including the travel-activity diary and ICT usage,
online-based questionnaire, and stated preference. All surveys will be done within the travelactivity dairy survey period. The criteria of target respondents of this study are:(1) People with age 18 years old and above.
(2) Having a smartphone that can support relatively accurate GPS detection.
(3) An active user of MSTP’s online services (i.e., online food delivery service).
(4) Lives or works (home-work place-based sampling) nearby South Jakarta City.

42

Chapter III – Research Design

Figure 3.17. Location of sampling selection
3.6.

ANALYSIS METHODOLOGY

In this section, we briefly explain the analysis methodology that will be used in this study,
including (1) agglomeration index, (2) correlation analysis, (3) decision tree analysis, (4)propensity score analysis, and (6) the dynamic discrete choice model for activity-travel
analysis. The detail of each methodology will be explained in Chapters 4 to Chapter 8.
3.6.1. Agglomeration Index
Agglomeration index is an index that is used to represent the distribution of facilities acrossspace. To calculate the agglomeration index, the analytical procedure will be divided into
four steps analysis: (1) calculating the average pairwise distance of each targeted facility(i.e., combination food merchants, online food merchants, and dine-in food merchants); (2)constructing the counterfactuals, i.e., calculating the average pairwise distance of public
facilities; (3) developing the agglomeration index; and (4) comparing the agglomerationindex result across different spatial boundaries and different facility types.
1) Calculating the average pairwise network distance of targeted facilities
Following Safira and Chikaraishi (2019), to calculate the average pairwise distanceamong facilities, we adopt the Euclidean-based distance, the network distance, andthe travel time to reflect the actual distance between facilities The pairwise network
distance for targeted facilities 𝑑̅ 𝑇𝐹 is define as:
∑ ∑
𝑖

𝑗(≠𝑖)

𝑇𝐹
𝑑𝑖𝑗
/(𝑛𝑇𝐹 (𝑛𝑇𝐹 − 1))

(3.1)

𝑇𝐹
where 𝑛𝑇𝐹 is the total number of targeted facilities, and 𝑑𝑖𝑗
is the pairwise distance43

Chapter III – Research Design

between facility 𝑖 and 𝑗.
2)

Constructing the counterfactuals
Counterfactuals need to control for the overall tendency of the facility to agglomerate.
The locations of public facilities serve as counterfactuals. The selection of the public
facilities as counterfactuals assumes that the provision of public facilities in urban
areas is based on the principle of equitable distribution or need-based on the locationof the community residence. It would be reasonable to assume that the spatial
distribution of these facilities is free from agglomeration forces. Similar tocommercial facilities, the pairwise network distance of public facilities 𝑑̅ 𝐶𝐹 is
defined as:
∑ ∑
𝑖

𝑗(≠𝑖)

𝐶𝐹
𝑑𝑖𝑗
/(𝑛𝐶𝐹 (𝑛𝐶𝐹 − 1))

(3.2)

𝐶𝐹
where 𝑛𝐶𝐹 is the total number of public facilities, and 𝑑𝑖𝑗
is the pairwise distance
between facility 𝑖 and facility 𝑗.

3)

Developing the agglomeration index
We define the agglomeration index as the ratio of the average pairwise network
distance of commercial facilities divided by the average pairwise network distance
between public facilities as:
𝑑̅ 𝐶𝐹
(3.3)
𝐴𝐼 = 𝑇𝐹𝑑̅
where AI is the agglomeration index of a city, with a higher value indicating the moresignificant agglomeration of commercial facilities.

3.6.2. Correlation Analysis
Correlation analysis is used to test the relationship between each variable to measure how
things are related. Correlations are useful because if you can find out what relationshipvariables have, you can make predictions about future behavior. Knowing what the futureholds is very important in the social sciences like government and healthcare. Businesses
also use these statistics for budgets and business plans. In this study, we explore thecorrelation among variables using the correlogram matrix. A correlogram is a graph of thecorrelation matrix. Useful to highlight the most correlated variables in a data table. In thisplot, correlation coefficients are colored according to the value. The correlation matrix canalso be reordered according to the degree of association between variables.
3.6.3. Decision Tree Analysis
The decision tree is a supervised learning model used for both classification and regression.
Answer sequential questions that send us down a certain route of the tree the model behaves
with if this than those conditions leading us to a final prediction. A Decision Tree Analysis
is a graphic representation of various alternative solutions available to solve a problem. The
manner of illustrating often proves to be decisive when making a choice. A Decision Tree
Analysis is created by answering several questions that are continued after each affirmative
or negative answer until a final choice can be made. A Decision Tree Analysis is a scientificmodel and is often used in the decision-making process of organizations. When making adecision, the management already envisages alternative ideas and solutions. By using adecision tree, the alternative solutions and possible choices are illustrated graphically, as a
44

Chapter III – Research Design

result of which it becomes easier to make a well-informed choice. This graphic
representation is characterized by a tree-like structure in which the problems in decisionmaking can be seen in the form of a flowchart, each with branches for alternative choices.
The Decision Tree Analysis makes good use of the ‘what if’ thought. Several
alternatives consider both the possible risks and benefits that are brought about by certain
choices. The possible alternatives are also made clearly visible, and therefore the decision
tree provides clarity with respect to the consequences of any decisions that will be made.
There are several ways in which a decision tree can be represented. Lines, squares, andcircles commonly represent this analysis. The squares represent decisions, the lines represent
consequences, and the circles represent uncertain outcomes. By keeping the lines as far apartas possible, there will be plenty of space to add new considerations and ideas. The
representation of the decision tree can be created in four steps:(1) Describe the decision that needs to be made in the square.
(2) Draw various lines from the square and write possible solutions on each of the lines.
(3) Put the outcome of the solution at the end of the line. Uncertain or unclear decisions
are put in a circle. When a solution leads to a new decision, the latter can be put in a
new square.
(4) Each of the squares and circles is reviewed critically so that a final choice can be
made.

Figure 3.18. Example of decision tree analysis
The advantages to using decision trees:
1. Easy to interpret and make for straightforward visualizations.
2. The internal workings are capable of being observed and thus make it possible
to reproduce work.
3. Can handle both numerical and categorical data.
4. Perform well on large datasets.
5. Are extremely fast.
The disadvantages of decision trees:
1. Building decision trees require algorithms capable of determining an optimal
choice at each node. However, choosing the best result at a given step does not
45

Chapter III – Research Design

ensure you will be headed down the route that will lead to the optimal decision
when you make it to the final node of the tree, called the leaf node.
2. Decision trees are prone to overfitting, especially when a tree is particularly deep.
This is due to the amount of specificity we look at, leading to a smaller sample
of events that meet the previous assumptions. This small sample could lead tounsound conclusions.
Ideally, we would like to minimize both errors due to bias and errors due to variance.
Enter random forests. Random forests mitigate this problem well. A random forest is simply
a collection of decision trees whose results are aggregated into one final result. Their ability
to limit overfitting without substantially increasing error due to bias is why they are powerfulmodels.
3.6.4. Propensity Score
The propensity score is the probability of treatment assignment conditional on observedbaseline characteristics. The propensity score allows one to design and analyze an
observational (nonrandomized) study to mimic some of the particular characteristics of arandomized controlled trial. In particular, the propensity score is a balancing score:
conditional on the propensity score, the distribution of observed baseline covariates will besimilar between treated and untreated subjects. Propensity score analysis (PSA) arose as away to achieve exchangeability between exposed and unexposed groups in observational
studies without relying on traditional model building. Exchangeability is critical to ourcausal inference. In experimental studies (e.g., randomized control trials), the probability of
being exposed is 0.5. Thus, the probability of being unexposed is also 0.5. The probabilityof being exposed or unexposed is the same. Therefore, a subject’s actual exposure status is
random. This equal probability of exposure makes us feel more comfortable asserting thatthe exposed and unexposed groups are alike on all factors except their exposure. Therefore,
we say that we have exchangeability between groups.
3.6.5. Dynamic Discrete Choice ModelIn this study, the fundamental modeling approach is a dynamic discrete choice model basedon random utility-maximizing principles. Following Västberg, O. B. et al. (2020), we briefly
introduce the Dynamic Discrete Choice Model (DDCM) and its use for estimations.
Dynamic discrete choice models have received widespread acceptance in transport research
and are used in travel demand modeling and behavioral analysis. Following the random
utility maximization model, specifically the standard nested logit model, in DDCM, the pathchoice problem is formulated as a link choice sequence. At each state, the decision-maker or
agent chooses the utility-maximizing outgoing link with link utilities given by the
instantaneous cost, the expected maximum utility to the destination (value function), andi.i.d. extreme value (with zero means) (M Fosgerau, 2013).
The DDCM was constructed based on the Markov Decision Process (MDP) to model
the choice of daily activity-travel pattern. The activity-travel pattern can be defined by thesequences of states 𝐬 and actions 𝐚 transverse during a day. Giving that the individuals'
preference for taking a decision 𝑎𝑘 in a specific state 𝑠𝑘 and reaching the state 𝑠𝑘+1 isrepresented by a one-stage utility function. It is assumed that an agent makes an action thatcan maximize his or her total utility in a given period of time. Observed an individual who
has made sequences of actions 𝐚𝑛 = {𝑎0,𝑛 , 𝑎1,𝑛 , … , 𝑎𝐾𝑛,𝑛 } and reached states 𝐱 𝑛 ={𝑥0,𝑛 , 𝑥1,𝑛 , … , 𝑥𝐾𝑛 +1,𝑛 }, The total utility 𝑈(𝐬, 𝐚) is defined as the sum of utilities obtained46

Chapter III – Research Designfrom reaching a specific state and from conducting some actions as follows:𝐾

𝑈(𝐬, 𝐚) = ∑ 𝑢 (𝑠𝑘 , 𝑎𝑘 , 𝑠𝑘+1 )

(3.4)

𝑘=0

Where the 𝑘 is an index to denote the order of the state 𝑠𝑘 in the sequence of the statethat is transverse during the day. By assuming that individuals behaved as if they choose theutility-maximizing travel pattern, a rational agent that starts in a state 𝑠 would behave
according to policy 𝜋, determining the action 𝑎𝑘 = 𝜋(𝑠𝑘 ) that maximize the expected future
utility of a day. Then, the one-stage utility 𝑢(𝑎𝑘 , 𝑥𝑘 ) of taking an action 𝑎𝑘 in the state 𝑠𝑘 is:𝑢(𝑠𝑘 , 𝑎𝑘 ) = 𝑢(𝑎𝑘 , 𝑥𝑘 ) + 𝜖𝑘 (𝑎𝑘 )(3.5)

Where 𝑢(𝑎𝑘 , 𝑥𝑘 ) is the utility function of the observed variable at state 𝑠𝑘 and𝜖𝑘 (𝑎𝑘 ) is the random terms that will be assumed as i.i.d Gumbel distribute. Finding the
optimal policy and then calculating the choice probabilities requires computing the expectedvalue function in each state 𝑥𝑘 . The expected future utility conditional on a state is the valuefunction in the state:𝐾

𝑉(𝑠) = max 𝐸𝑠 {∑ 𝑢 (𝑠𝑘 , 𝑎𝑘 , 𝑠𝑘+1 )|𝑠𝑘 = 𝑠, 𝑎𝑘 = 𝜋(𝑠𝑘 )}
𝜋

(3.6)

𝑘=0

Where 𝐸𝑠 is respected to the stochasticity of 𝑠𝑘 given the decision rule 𝑎𝑘 = 𝜋(𝑠𝑘 ).
With the assumption that 𝑞 (transition probability), 𝑢 (one-stage utility function), and 𝐶(𝑠𝑘 )(choice set) is under Markovian condition, so they are independent of the history. The
Markovian assumption is not a problem in theory because it could include all previous
history in a finite horizon model. Following Rust (1987), we have assumed that the random
state variable 𝜖𝑘 is conditionally independent of the previous state and action and enters theone-stage utility additively. Observe that value function 𝑉(𝑠) can be defined recursively
through Bellman's equation as (Bellman 1957, Rust 1987).
𝑉(𝑥𝑘 , 𝜖𝑘 ) = max{𝑢(𝑥𝑘 , 𝑎𝑘 ) + 𝜖𝑘 (𝑎𝑘 ) + 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 )}
𝑎𝑘

(3.7)

Where the 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) is the expected value of the value function of the state reached when
taking action 𝑎𝑘 in state (𝑥𝑘 , 𝜖𝑘 ). If 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) is known for each state-action pair, theprinciple of optimality states that the optimal policy π is obtained by, conditionally on a state
𝑠𝑘 , choosing the action 𝑎𝑘 that maximizes the utility function, then 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) is given by:𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) = 𝐸𝑡 (𝑘+1),ℎ(𝑘+1),𝜖𝑘+1 [𝑉(𝑥𝑘+1 , 𝜖𝑘+1 )|𝑥𝑘 , 𝑎𝑘 ]𝑁ℎ= ∫ (∑ 𝑞ℎ (ℎ𝑗 |𝑡 ′ , 𝑡, ℎ, 𝑝̃) ∙ 𝑉̅ (𝑥𝑘+1 )) 𝑑𝑞𝑡 (𝑡 ′ |𝑡, 𝑙, 𝑚
̃ , 𝑑̃ )
𝑡′

(3.8)

𝑗=1

Where in turn 𝑉̅ (𝑥𝑘 ) = 𝐸𝜖𝑘 [𝑉(𝑥𝑘 , 𝜖𝑘 )]. When 𝜖𝑘 (𝑎𝑘 ) is independent and identically
distributed (i.i.d.) with Gumbel distributed (with zero means), 𝑉̅ is given by the following
log-sum:𝑉̅ (𝑥𝑘 ) = log ( ∑𝑒 𝑢(𝑥𝑘,𝑎𝑘)+𝐸𝑉(𝑥𝑘,𝑎𝑘) )(3.9)

𝑎𝑘 ∈𝐶(𝑥𝑘 )47

Chapter III – Research Design

With i.i.d. Gumbel distributed 𝑘, the probability that an action 𝑎𝑘 will be the utilitymaximizing alternative in a state 𝑥𝑘 when 𝑘 is unobserved is simply given by the MNLmodel:𝑒 𝑢(𝑥𝑘,𝑎𝑘)+𝐸𝑉(𝑥𝑘,𝑎𝑘)(3.10)𝑃(𝑎𝑘 |𝑥𝑘 ) =∑𝑎𝑘 ∈𝐶(𝑥𝑘 ) 𝑒 𝑢(𝑥𝑘,𝑎̃𝑘)+𝐸𝑉(𝑥𝑘,𝑎̃𝑘)After we suppressed the parameters θ from the utility functions' specification and theindividual's dependence in that the model describes, the likelihood for the observation of anindividual is then given by:𝐾𝑛

𝐿𝑛 (𝐚𝑛 , 𝐱 𝑛 |𝑥0 , 𝜃) = ∏ 𝑃𝑛 (𝑎𝑘,𝑛 |𝑥𝑘,𝑛 , 𝜃𝑢 ) ∙ 𝑞(𝑥𝑘+1|𝑎𝑘,𝑛 , 𝑥𝑘,𝑛 , 𝜃𝑞 )(3.11)

𝑘=0

Let N observations construct the set of observations 𝜕𝑁 ON. The log-likelihood
function for 𝜕𝑁 based on the conditional likelihoods becomes:
𝑁

𝐿̅𝐿(𝜕𝑁 ; 𝜃) = ∑ log(𝐿𝑛 (𝐚𝑛 , 𝐱 𝑛 |𝑥0 , 𝜃))(3.12)

𝑛=1

48

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

Chapter 4: THE ASSOCIATION BETWEEN AGGLOMERATIONINDEX AND CITY-LEVEL CHARACTERISTICS
This chapter explores the association between agglomeration index and city-level
characteristics with a case study in 69 cities of Japan. This study attempt to answer the
research questions regarding “How to quantify the concentration of facility distribution?”(RQ 1.1) and “How is the association between facility distribution and city-level
characteristics?” (RQ 1.2) through the development of the agglomeration index and theassociation analysis. This chapter also contains the introduction of the study, methodology
approach, study area, data used, result and discussion, and conclusion.
4.1.

INTRODUCTION

It is widely known that the relationship between urban form and transport systems iscomplex, given land use and transportation are part of a retroactive feedback system, with
one influencing the other (Giuliano, 2004). This suggests a lack of independence in theevolution of transportation and urban form, i.e., changes in transportation systems influence
location decisions and vice versa (Anderson et al., 1997). In a free-market economy, theurban form is the outcome of location choices made by thousands of households, private
firms, and public agencies. An urban agglomeration is one of the important outputs of thoselocation decisions, shaping urban form together with transportation developments. While
there is no unique definition of urban agglomeration (Fang and Yu, 2017), we use the term
urban agglomeration to simply indicate the concentration of socio-economic and humandevelopment, following Combes and Gobillon (2015), Fang and Yu (2017), and Uchida and
Nelson (2009).
In the past few decades, the urban agglomeration has been the subject of intensive
analysis in the field of urban economics and geography studies (e.g., Anas, et al., 1998;
Combes et al., 2008), particularly with the emergence of the new economic geography(Krugman, 1991; Fujita et al., 1999). Problematically, as observed, an urban agglomerationis the outcome of various agglomeration, and dispersion forces operating at various scales,
establishing a solid link between empirical work and theoretical models remains achallenging task (Combes and Gobillon, 2015; Akamatsu et al., 2017). In this study, we
focus on the agglomeration of commercial facilities within a city with special attention to
the role of transport systems. Although a number of empirical studies have been conducted
on interregional agglomeration (e.g., Malmberg et al., 2000; Malmberg and Maskell, 2002),
few have addressed agglomeration within a city (Koster et al., 2019).
The literature identifies three sources of agglomeration of commercial facilities:
matching, sharing, and trip chaining (Takahashi, 2013; Koster et al., 2019). Takahashi(2013) considers that consumers seek a better matching between their preferred varieties andthe varieties sold in each commercial area and confirms that agglomeration would occur
when consumers exhibit taste heterogeneity while the available information is imperfect.
Alternatively, agglomeration may also occur when there are positive externalities throughsharing, shopping malls being a typical example, i.e., shops in a mall share fixed costs suchas facility maintenance and customer acquisition costs (Pashigian and Gould, 1998). Lastly,
Koster et al. (2019) explain the agglomeration of commercial facilities in shopping streetsfrom the viewpoint of consumer trip chaining behavior, whereby consumers can reducetransport and search costs owing to the spatial proximity of shops, while shops receive more49

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

customers due to the increase in the number of pedestrians passing by.
In this study, we conduct an exploratory analysis to argue that transportation systems
moderate these agglomeration forces. Although a number of studies have repeatedly shownthat transportation systems account for both agglomeration and dispersion forces such ascongestion (e.g., Tabuchi, 1998; Glaeser and Kahn, 2004), a few concerns with howtransportation systems moderate agglomeration forces. Through an empirical analysis, we
argue that it may be better to pose the question “In what conditions would agglomeration
force play a more significant role?” instead of repeatedly asking, “Which agglomeration
force is the most relevant determinant?” While recent analyses explore the presence ofcomplex agglomeration forces in interregional industry agglomeration (Faggio et al., 2017),
there is no existing investigation on the agglomeration of commercial facilities within a city.
Intuitively, the moderation effects of transportation systems are as follows. As
pointed out by Koster et al. (2019), European cities maintain active shopping streets better
than cities in other developed countries mainly because consumer trip-chaining behavior
contributes to the reduction of transport and search costs and thereby increases the numberof customers in shopping streets. However, this is only valid when cities rely on public transit
or non-motorized transport. When cities become car-dependent (as in the US), the costs ofaccessing shopping streets become prohibitively expensive owing to high parking costs andtraffic congestion, thereby reducing the effectiveness of the agglomeration force derived
from trip chaining. For these car-dependent cities, rather than maintaining shopping streets,
agglomeration through sharing may be more feasible, for example, by establishing a largescale shopping mall in a suburban area where congestion and parking costs are sufficiently
low. These extreme cases of European and US cities imply that transportation systems
moderate the effectiveness of each agglomeration force.
Although not directly mentioning transportation systems as a moderator, narratives
similar to the aforementioned are available in the literature (e.g., Glaeser and Kahn, 2004).
However, to the authors’ knowledge, there is little empirical confirmation of the moderation
effects of transportation systems on agglomeration forces. As appropriate urban andtransport policies depend on the type of the dominant agglomeration/dispersion forces, it isessential for policymakers to have a proper understanding of the role of transport systems inshaping them. Particularly in Japan, there are various types of cities, ranging from the very
car-dependent to the very public transport dependent, and thus the suitable urban andtransport policy decisions vary by city.
Given these considerations, this study empirically examines the association between
the agglomeration of commercial facilities and city-level characteristics (includingtransportation, socio-economic and geographical characteristics) in 69 Japanese cities. We
particularly discuss how transportation systems moderate agglomeration forces influencing
the spatial distribution of commercial facilities. In the empirical analysis, we employ asimple agglomeration index. We first calculate the pairwise network distance between
commercial facilities and among public facilities in a city, where the pairwise distanceamong public facilities serves as a counterfactual to calculate the agglomeration index, given
the assumption that agglomeration phenomena do not exist for public facilities. We thenidentify the association between the agglomeration indexes, including city-level
characteristics, using a decision tree analysis to discuss the moderation effects of transportsystems in shaping agglomeration forces. Note that the present work is an exploratory
analysis with a particular focus on the association between an agglomeration index andtransport systems, not one directly identifying actual agglomeration forces in Japanese cities.
Despite this, we believe that our study makes a significant contribution to the existing
literature: one of the most critical arguments derived from our empirical analysis is that50

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

agglomeration mechanisms seem to be far more complicated than most theoretical models
on agglomeration forces suggest. Of course, theoretical models need not necessarily berealistic. Nonetheless, it does matter when we derive policy implications from the theoreticalmodels: if what the theoretical models indicate is quite different from our empiricalobservations, policymakers should carefully consider the implications arising from these
theoretical models.
4.2.

METHODOLOGY

There are two important ideas in our empirical analysis. First, to explore the association
between the agglomeration of commercial facilities and transportation systems, we conducta nationwide comparison analysis (of 69 Japanese cities), including cities varying from cardependent to public transport dependent. Given other possible confounding factors such as
population and area size could affect this association, we conduct exploratory analysis using
a decision tree model including a number of other city-level characteristics. Second, inspiredby the work of Duranton and Overman (2005), we use a travel time-based or distance-based
agglomeration index where space is treated as continuous instead of other popular indicessuch as the Isard Herfindahl and Theil indices, which require arbitrary geographical units.
Specifically, we define the agglomeration index based on the difference between (1) the
actual spatial distribution of commercial facilities where various agglomeration forces would
work and (2) the counterfactual spatial distribution where facilities are randomly distributed,i.e., none of the agglomeration forces are effective.
Unlike other indices involving aggregation at a specific spatial level, the continuous
index possesses the following two properties: (1) the values are comparable across spatial
scales, and (2) the values are unbiased with respect to arbitrary changes to spatialclassification (Combes et al., 2008). These properties are particularly important for thecurrent study where we compare the index values across cities: other zone-based
agglomeration indices would invoke bias as the spatial scales of administrative boundaries
differ by city (for example, zone size tends to be typically larger in less populated areas).
Notably, we compare the pairwise distance result from three different distance metrics,
including Euclidean distance, network distance, and travel time (by car). By comparing threedifferent distance metrics, we attempt to explore the importance of using travel time andnetwork-based distance measures that can reflect the actual distance between facilities
accurately considering the geographic conditions restricting the developable area. In theremainder of this section, we first introduce the data used, followed by details of theanalytical procedure.
The analytical procedure applied in this study can be divided into four steps: 1)
calculating the pairwise network distance of commercial facilities, 2) constructing thecounterfactuals, 3) establishing the agglomeration index, and 4) analyzing the association
between the agglomeration index and city-level variables through correlation and decision
tree analyses.
1) Calculating the pairwise network distance of commercial facilities
We first calculate the pairwise distance of commercial facilities. While most previous
studies (e.g., Duranton and Overman, 2005) employ Euclidean distance, we adopt
the network distance and travel time to reflect the actual distance between facilities
accurately considering the geographic conditions restricting the developable area.
More specifically, the pairwise network distance 𝑑̅ 𝑐 is defined as:

51

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

∑ ∑
𝑖

𝑗(≠𝑖)

𝑐
𝑑𝑖𝑗
/𝑛𝑐 (𝑛𝑐 − 1)

(4.1)

𝑐
where 𝑛c is the total number of commercial facilities, and 𝑑𝑖𝑗
is the pairwise distance
between facility 𝑖 and facility 𝑗.

2) Constructing counterfactuals
The locations of public facilities (i.e., school, medical, community center, park, andthe combination among them) serve as counterfactuals. It would be reasonable toassume that these facilities' spatial distribution is free from agglomeration forces andthat the pairwise network distance between schools and medical facilities within acity can then serve as a counterfactual. Similar to commercial facilities, the pairwise
network distance of public facilities 𝑑̅ 𝑝 is defined as:
∑ ∑
𝑖

𝑗(≠𝑖)

𝑝
𝑑𝑖𝑗
/𝑛𝑝 (𝑛𝑝 − 1)

(4.2)

𝑝
where 𝑛𝑝 is the total number of public facilities, and 𝑑𝑖𝑗
is the pairwise distance
between facility 𝑖 and facility 𝑗.

3) Calculating the agglomeration index
We define the agglomeration index as the ratio of the average pairwise network
distance of commercial facilities divided by the average pairwise network distance
between public facilities as:
𝑑̅ 𝑝
(4.3)
𝐴𝐼 = 𝑐 ,𝑑̅where AI is the agglomeration index of a city, with a higher value indicating thegreater agglomeration of commercial facilities.
4) Analysis of association with city-level variables
To explore the association between the agglomeration index and city-level
characteristics, we perform two analyses. First, we calculate the Pearson correlation
coefficients between the agglomeration index and city-level characteristics. Second,
we develop a decision tree model using the significant variables identified from thecorrelation analysis. There are two reasons to use decision tree analysis. First, manycity-level characteristic variables are highly correlated, as shown in the manuscript,
and thus using standard multivariate statistics would be problematic. Second, we canclassify cities into several groups based on their characteristics. This allows us to takethe further analysis of the association between agglomeration index and its
transportation characteristic and its share of commercial facilities in agglomerated
areas. Note that Safira and Chikaraishi (2019) made the first attempt to obtain an
agglomeration index using Euclidian distance and revealed no significant association
with car dependence. The second group comprises city-level characteristics,
including the city population, the city's total area size in a square kilometer, the city's
population density, the number of commercial facilities, and the number of public
facilities.
4.3.

STUDY AREA

We select the 69 Japanese cities according to the availability of other city-level information,

52

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

particularly from the nationwide person trip survey conducted by the Ministry of Land,
Infrastructure, Transport, and Tourism (2008). Therefore, we select 69 cities in Japan as ourcase study with regards to the availability data. The cities are included:
Table 4.1. The 69 cities in Japan
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

City Name
Nara
Otake
Fukuoka
Yokohama
Kawasaki
Kitakyushu
Matsuyama
Saitama
Kobe
Sendai
NagoyaSapporoHiroshima
Koriyama
Kumamoto
Utsunomiya
Chiba
Osaka

No
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

City Name
Tokorozawa
Yamanashi
Oyabe
Shizuoka
Yokkaichi
Kyoto
MatsudoAkashi
Kochi
Takasaki
Ome
JoetsuKomatsu
Matsue
Gifu
Tsushima
Sakai
Odawara

No
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54

City Name
Tokushima
Kanazawa
YasugiMoriokaOmihachiman
Uji
Usuki
Iwata
Shiogama
Inagi
TorideKasugai
DazaifuIsahayaKagoshima
HirosakiIzumisanoNankoku

No
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

City Name
Tokai
Soja
Hitoyoshi
Toyohashi
Toyonaka
YuzawaImabari
Ina
Otaru
Kameyama
Urasoe
Kure
NagatoChitoseKainanFigure 4.1. Map of 69 Japanese cities as the study area
4.4.

DATA

To conduct the analysis, we used commercial facility location data from the Census ofCommerce conducted by the Ministry of Economy, Trade, and Industry in Japan in 2014.
This data includes the street address of the commercial facility and the name and type of53

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

facility. The commercial facilities in this study comprise department stores, textile, clothing,fashion, personal items, food and beverage, building materials, mineral/metal materials,
machine tools (i.e., automobile, bicycle, equipment, etc.), general supermarkets and others(e.g., furniture, fuel, book stationery, sporting goods, office for mail order/online shop,
vending machines, etc.). Although making a distinction in service type is an importantresearch topic, particularly when identifying co-agglomeration phenomena (e.g., Kolko,
2007), it lies outside the scope of the present analysis.
One unique characteristic of the Census of Commerce data is that it contains
information on whether commercial facilities are located in agglomerated areas with thefollowing five categories: (1) station, (2) central business district (CBD), (3) residence, (4)roadside and (5) others (i.e., tourism site, religious site, etc.). We can expect that higher
transit dependency would increase the number of commercial facilities falling into thecategory of “station” while shops in the shopping streets of European cities would typicallybe included in “CBD”. Conversely, we may observe that additional commercial facilities fall
into the category of “roadside” when cities are more car-dependent.
In the empirical analysis, in addition to the geographical coordinates of commercial
facilities, we use the public facility location data to obtain the counterfactual distribution.
The candidate public facilities include schools (i.e., elementary school, junior high school,senior high school), medical facilities (i.e., hospital and clinic), community centers, andparks in 2015. The public facilities data was obtained from the website of the Ministry ofLand, Infrastructure, and Transport (available from http://nlftp.mlit.go.jp/ksj/).
After obtaining the agglomeration index, we analyze the association between the
agglomeration index and the city-level characteristics, including transportation, socioeconomic and geographical characteristics. For transportation characteristics, we used thedata from the nationwide person trip survey conducted by the Ministry of Land,
Infrastructure, Transport, and Tourism in 2008 that including the average of trip distance, anaverage of travel time, and the share of transport mode usage (i.e., car, motorbike, walk,train, bike, and bicycle). For socio-economic and geographical characteristics, population
size, population density, and area size data that obtained from the population census datafrom the Ministry of Internal affairs and Communication in 2015. Using data from theperson-trip survey enables us to analyze the association between the agglomeration index
and city-level travel-related variables. Note that transport infrastructure and consequent
travel behavior do not have a one-to-one correspondence. In this study, we use variables on
the revealed travel behavior as proxy indices of the service level of transportation systems,
which are the outcomes of the combination of transport infrastructure and service operations.
The selection of 69 Japanese cities is corresponding to the availability of other city-level
data, particularly the nationwide person trip survey.
Table 4.2. General transportation (modal share) characteristics of the case study
Components
Transportation Variable% of Walk Share% of Bicycle Share% of Motorbike Share% of Car Share% of Bus Share% of Train ShareArea Size
Number of Population

Average Value
15.39
9.54
2.00
61.72
1.83
9.50
342.11
503,527.28

Source: MILT, 2020

54

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

4.5.

RESULT AND DISCUSSION

In this section, we first introduce the selection results of public facility types forcounterfactuals and distance metrics. We then explore the association between the
agglomeration level and city-level characteristics through correlation analysis and decision
tree analysis.
4.5.1. Selection of Counterfactuals and Distance Metrics
Ideally, the use of residential distribution at the disaggregate level would be the best to obtaincounterfactuals. However, we could not obtain the data for this analysis, and thus we decided
to use the distribution of public facilities to obtain the counterfactuals. The choice of public
facility type needs to be carefully done since the agglomeration would occur for some typeof public facility. To empirically identify a suitable counterfactual, we empirically compare
the average pairwise distances for different public facilities (including schools, medicals,
community centers, parks, the combination of schools and community centers, andcombination of schools, community centers, and parks) and select the facility type that gives
the largest average pairwise distance, assuming that the longer pairwise distance implies thatless agglomeration occurs. Table 4.3. shows the results. We confirm that the combination ofschool and community center (SC) has the largest average pairwise distance and thus use it
to obtain counterfactuals with the assumption that there is no agglomeration for the school
and community centers.
For distance metrics, we compare three different distance metrics, including the
Euclidean-based distance, the network-based distance, and car travel time in the calculationof average pairwise distance and its agglomeration index. In this study, car travel time waschosen for the subsequent analysis since both shopping destination choice (by the consumer)
and commercial facility location choice (by supplier) would be made based on travel timerather than distance. However, for some cities or countries, the availability of travel distancedata was difficult to obtain. Our results shown in Table 4.3 confirm that the results with
network distance would be quite similar to those with car travel time, while the results with
Euclidean distance are considerably different. The dataset of all cities’ average pairwise
distance and the agglomeration index by public facility type and distance metric is provided
in Appendices.
Table 4.3. Average pairwise distance and agglomeration index
Facility Type
Commercial
School (S)Medical (M)Community center (C)Park (P)
School & medical (SM)
School & community
center (SC)
School, community center,and park (SCP)

Average pairwise distance
Euclidean Network
Traveldistance
distance
time
5.07
6.09
14.42
7.42
8.07
18.16
5.37
5.90
14.27
7.69
8.37
18.05
6.04
6.67
16.06
6.45
6.42
15.21

Agglomeration index
Euclidean Network
Traveldistance
distance
time
1.49
1.35
1.29
1.04
0.96
0.97
1.51
1.36
1.26
1.16
1.08
1.08
1.29
1.05
1.05

7.75

8.49

20.06

1.59

1.41

1.36

7.56

7.78

17.88

1.51

1.28

1.25

Using the travel time-based distance metric with school and community centers ascounterfactuals, we calculate the average pairwise distance and agglomeration index for all
55

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

69 Japanese cities. Table 4.4. summarizes cities with the longest and shortest average
pairwise distance for both public facilities and commercial facilities, and Table 4.5.
summarizes the most and the least agglomerated cities. We found that Yokohama has thelongest average pairwise distance for commercial and public facilities, while Shiogama has
the shortest average pairwise for public facilities, and Hitoyoshi has the shortest average
pairwise for commercial facilities. We also found that there are significant differences
between the average pairwise distance of commercial facilities and that of public facilities(Welch's t-test result: 4.09, statistically significant at the one percent significance level).
Table 4.4. Average pairwise distance of facilities(Distance metric: car travel time; public facility type: school & community center)
Travel time (min)Longest (City)
Shortest (City)
Mean
Std. dev.
Observations

Commercial facility
32.80 (Yokohama)
4.97 (Hitoyoshi)
14.42
5.90

Public facility
49.79 (Yokohama)
4.94 (Shiogama)
20.06
9.79
69

As mentioned above, we took the ratio between the average pairwise distance of public
facilities and commercial facilities to obtain the agglomeration index. As shown in Table 4,
Nara City is the most agglomerated city with an agglomeration index of 1.82, and ImabariCity is the least agglomerated city among 69 Japanese cities with an agglomeration index is
0.77.
Table 4.5. Summary of agglomeration index of 69 Japanese cities
Variable
Highest Agglomeration Index (City)
Lowest Agglomeration Index (City)Mean
Std Dev
Observations

Agglomeration Index
1.82 (Nara)
0.77 (Imabari)
1.36
0.21
69

Nara, the most agglomerated city, is a famous historical tourist destination in Japan.
From Figure 4.2., we can confirm that commercial facilities agglomerate in an areaconnecting Nara Station and the Todaiji Temple. In contrast, there appears to be lessagglomeration in the public facilities, largely because the distribution of public facilities
follows the distribution of the residents. The least agglomerated city is Imabari, a typical
local city in Japan, where population decline has accelerated (down about 20% from its peak).
We also found that, out of 11 cities with one million or more inhabitants, the agglomerationindex exceeds 1.5 for 9 cities, implying that the larger cities tend to be agglomerated.
In contrast, the five least-agglomerated cities (Imabari, Shiogama, Urasoe, Tokai, andDazaifu) are less populated (populations of these five cities are from 56,256 to 167,872).
This indicates that the city scale could be one of the important factors affecting theagglomeration level. Given that population typically influences the development of transportsystems as well, we should carefully discuss the impacts of transport systems on the
agglomeration level. As revealing the actual causal structure may not be possible with crosssectional data, in the following sections, we conduct an exploratory analysis (correlation anddecision tree analyses) to discuss the possible associations between the agglomeration index
and city-level characteristics, including scale-related variables such as population and area
size, and transportation variables such as modal share.

56

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics(a) Commercial facility distribution(b) Public facility distribution

Figure 4.2. Facility distributions of Nara, the most agglomerated city(a) Commercial facility distribution(b) Public facility distribution

Figure 4.3. Facility distributions of Imabari, the least agglomerated city
4.5.2. Association with City-Level Characteristics: Correlation Analysis
To identify city-level characteristics that are highly correlated with the agglomeration index,
the Pearson correlation is first confirmed. Table 4.7. and Figure 4.4. show the results. We
found that the area size, population density, average travel time, the share of car usage, andthe share of train usage are significantly correlated with the agglomeration index. The major
findings from the identified correlation coefficients are as follows.
1. Population density and area size have positive associations with the agglomerationindex.
2. The average travel time (trip duration) has a negative association with theagglomeration level, implying that the greater agglomeration would emerge together
with the shorter travel time.
3. Regarding the association between the agglomeration level and modal share, more
car-dependent and transit-dependent cities are less agglomerated. In contrast, agreater share of walk and bicycle increases the agglomeration level, though these are
not statistically significant. Note that the impacts of train share are non-linear, andthus we could not simply conclude that transit-dependent cities are always lessagglomerated.
Overall, the correlation results indicate that there is a significant association between
transport systems and the agglomeration level. Simultaneously, there are high correlations
among the city-level characteristics, implying that it may be difficult to establish a one-toone relationship between the agglomeration level and the state of the transportation system.
57

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

Table 4.6. List of correlation and decision tree analysis variables
Variable
Description
Agglomeration index
AI_TT_SC
Agglomeration index of city based on travel distance calculation with school
and community center as the counterfactualCity-level attributes
Population
The logarithm of the total city population
Area
The total area of the city in km2
PopDensity
The city population density
N_Com
The total number of commercial facilities in the city
N_SCH
The total number of schools in the city
N_CC
The total number of community centers in the city
Dist_Com
The average pairwise network distance of commercial facilities
Dist_SCThe average pairwise network distance of public facilities (schools andcommunity centers)
Transportation variables
Travel Time The average travel time per trip within the city
Distance
The average distance per trip within the city
Car
Percentage of modal share, car
Motorbike
Percentage of modal share, motorcycle
BicyclePercentage of modal share, bicycle
Walk
Percentage of modal share, walking
Train
Percentage of modal share, train
Bus
Percentage of modal share, bus

Figure 4.4. Correlation matrix of variables

58

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

Table 4.7. Correlation coefficients
Variables
Population
Area Size
PopDensity
N_Com
N_SCH
N_CC
Dist_Com
Dist_SC
Travel TimeDistance
Car
Motorbike
Bicycle
Walk
Train
Bus

Estimates Value
4.35E-07
4.82E-03
2.61E-02-8.36E-07-2.16E-05
2.27E-04-5.46E-04
4.64E-04-3.22E-02
4.74E-01-5.37E-02
2.65E-01
2.27E-01
3.47E-01-3.71E-02
3.19E-01

t-Value
1.58
3.08
3.21
-0.08
-1.28
0.22
-1.42
1.38
-3.11
0.35
3.49
0.03
0.34
0.31
3.02
0.03

Sig. Sign
**
**

**
**

**

** Significant at 0.01 level.

4.5.3. Association with City-Level Characteristics: Decision Tree Analysis
Based on the correlation analysis in the previous section, we include five significant
variables in the decision tree analysis, including area size, population density, average travel
time, car share, and train share. We apply a Classification and Regression Trees (CART)
algorithm (Breiman et al., 1984), which is one of the most widely used algorithms for
decision tree analysis, in order to classify cities into several groups based on theircharacteristics. For the analysis, we used the R package “rpart” (Therneau et al., 2019),
where the “ANOVA” method was chosen to produce a group of cities based on the degreeof similarity in their characteristics. We set the minimum split and minimum bucket as 10,
ensuring that final nodes (called groups in this study) contain at least ten cities.
Figure 4.5. illustrates the results of the decision tree analysis. The analysis divides
the 69 cities into six groups. The first split is done based on area size (whether it is smaller
than 199 km2 or not), confirming that cities with larger area sizes tend to be agglomerated.
One of the possible reasons is the cities with larger area sizes would obtain larger benefits
from agglomeration: benefits from matching and trip chaining would be getting larger with
the increase in area size.
In the second split, cities with smaller area sizes are further divided into two groups
by area size (whether it is smaller than 64 km2 or not), confirming that smaller cities tend to
be less agglomerated (group 1). For cities with larger area sizes they are further divided into
two groups based on car share (whether it is larger than 56% or not): we confirm that cities
with larger area sizes and lower car share tend to be agglomerated (group 6). Actually, mostof the cities in group 6 are metropolitan cities, such as Kyoto, Osaka, Sapporo, Fukuoka, andHiroshima. For cities with larger area sizes and higher car share are further divided into twogroups by train share (whether it is smaller than 1.8% or not). It is confirmed that cities witha lower train share tend to be less agglomerated (group 3) compared to cities with a higher
train share. Most of the cities in group 3 are local cities, such as Hirosaki, Hitoyoshi, Imabari,etc. Cities with higher train share are further divided into two groups by travel time (whether
it is smaller than 26 minutes or not). It is found that cities with longer travel times tend to beless agglomerated (group 4), and cities with shorter travel times tend to be more
agglomerated (group 5). In order to further understand the characteristics of each group, in
the remaining part of this section, we conduct an additional aggregation analysis.
59

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

Group 1
Group 2
Group 3
Group 4
Group 5
Group 6

Akashi, Dazaifu, Inagi, Izumisano, Matsudo, Shiogama, Tokai, Toyonaka, Tsushima, Urasoe
Iwata, Kainan, Kameyama, Kasugai, Kawasaki, Nankoku, Odawara, Ome, Omihachiman, Otake, Oyabe,
Sakai, Tokorozawa, Tokushima, Toride, Uji
Hirosaki, Hitoyoshi, Imabari, Ina, Kochi, Komatsu, Matsue, Nagato, Yasugi, Yuzawa
Kagoshima, Kitakyushu, Koriyama, Kumamoto, Kure, Morioka, Sendai, Soja, Takasaki, Toyohashi,
Utsunomiya, Yokkaichi
Chitose, Gifu, Isahaya, Joetsu, Kanazawa, Matsuyama, Otaru, Shizuoka, Usuki, Yamanashi
Chiba, Fukuoka, Hiroshima, Kobe, Kyoto, Nagoya, Nara, Osaka, Saitama, Sapporo, Yokohama

Figure 4.5. The results of decision tree analysis
Table 4.8. presents the share of commercial facilities by the type of agglomerated
areas for each group, and Table 4.9. presents the average city-level characteristics for eachgroup. From Table 4.8., we confirm that cities in group 1 and group 2 have more commercial
facilities around stations. It is common for the smaller cities in Japan to have morecommercial facilities near stations since the station is often designed or dedicated as a central
area of the city. This finding is also supported by the share of the train in group 1 and group
2 that is 15.53% and 11.59%, respectively. Although their train shares are relatively highcompared to other groups, their agglomeration levels are the lowest across groups. This
would be because their area sizes are small: as discussed above, sharing and trip chaining
would be less beneficial for a city with a smaller area size.
In contrast, cities in group 3 have larger area sizes, resulting in more agglomerationcompared to cities in groups 1 and 2. On the other hand, the agglomeration index of group 3
is lower compared to groups 4, 5, and 6. This would be because of the characteristics of thetransportation systems in group 3: Group 3 has the highest car share (77.27%), leading to
the highest agglomeration level on roadside areas (19.16%). This indicates that
agglomeration forces that occurred in group 3 are different from those in groups 1 and 2:
Residents in cities belonging to group 3 may access commercial facilities in CBD androadside areas by car.
Groups 4 and 5 have similar characteristics in terms of the type of agglomeration
areas and modal share. They have a higher car share, while agglomeration occurs inresidential areas. These imply that these cities may have a polycentric urban form, but further
analysis is needed to make a general conclusion.
60

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

In cities belonging to group 6, commercial facilities are agglomerated in station and
CBD areas, while they have the highest train share among the six groups. Their average
travel distance and travel time are the largest, indicating that agglomeration benefits are
obtained with the higher travel cost in these cities.
Table 4.8. Share of commercial facilities by the type of agglomerated areas
Group
Group 1
Group 2
Group 3
Group 4
Group 5
Group 6All Group(1) Station
56.74%
56.87%
12.08%
22.81%
24.01%
41.83%
35.72%(2) CBD
9.69%
11.30%
35.96%
31.78%
28.37%
35.10%
24.54%

Type of agglomerated areas(3) Residence(4) Roadside
19.29%
11.25%
26.04%
5.00%
28.19%
19.16%
34.02%
11.23%
33.08%
9.68%
15.23%
6.32%
26.81%
10.44%(5) Others
3.02%
0.79%
4.61%
0.16%
4.87%
1.52%
2.49%

Note: Shaded cells indicate the highest share in each group.

Table 4.9. City-transport characteristics
Average AverageGroup Travel
TravelDistance
Time
1
2
3
4
5
6
Average

11.89
12.09
12.17
11.76
11.77
12.80
12.08

29.26
27.77
22.78
26.91
24.57
30.59
27.10

Average Percentage of
Average
Average
AverageTransport Mode Usage (%)
Population
Density
Area Size
Size(population
Motor
(Km2)
Train
Bus CarBicycle Walk
(people)/km2)
bike
15.53 1.18 54.43 2.28
9.20
17.39
179,01035.92
4,806
11.59 0.87 61.10 2.24 10.13
14.07
268,511123.872,241
1.27 0.84 77.27 1.68
8.48
10.46
123,615467.56300
4.56 2.61 67.79 1.73
8.60
14.72
490,789483.601,083
3.50 2.16 69.35 2.07
8.38
14.54
274,971524.87628
19.31 3.57 41.61 1.94 12.07
21.51 1,707,435
503.364,502
9.50 1.83 61.73 2.00
9.55
15.39
503,527
342.11
2,257

Taking the types of agglomeration forces into account, we found that (1) cities withlarger area sizes and higher train shares enjoy agglomeration presumably through matching
and/or trip chaining, while cities with smaller area sizes less enjoy agglomeration even theirtrain share is high, (2) car-dependent cities enjoy agglomerations presumably throughsharing, particularly by agglomerating in their residential and roadside areas. These results
indicate that transportation systems may moderate agglomeration forces, i.e., the dominant
agglomeration forces vary across cities depending on the transportation systems. These also
imply the importance of handling how transportation systems moderate the agglomeration
economy in the development of theoretical and empirical models, rather than simply
identifying to what degree the level of accessibility or density (which can be seen as
simplified transportation system performance measures) lead to the agglomeration economy.
The above-mentioned findings are crucial in shaping relevant policies, particularly
in transport investment appraisals. Recently, evaluating the broader economic impact oftransport infrastructure investment in an agglomeration economy has gained popularity(Graham, 2007; Chatman and Noland 2011, 2014; Kidokoro, 2015; Graham and Gibbons,2019). Graham (2007) estimates the additional benefits from the agglomeration would bearound 25%, while Horcher et al. (2020) show that agglomeration benefits strongly affectoptimal public transport policies, indicating that optimizing transportation services without
considering agglomeration effects would not be optimal in the long run.
Thus, existing studies clearly indicate the importance of considering agglomeration
effects in decision-making on transport investment and management strategies. However,there are a number of important limitations in the existing literature. Chatman and Noland(2011) state that “… the challenges are numerous in conducting research to determine
61

Chapter IV – The Association Between Agglomeration Index and City-Level Characteristics

whether and when public transport improvements increase agglomeration economies. The
possible agglomeration mechanisms at work imply a dizzying array of possible measuresand methods. Tracing the links between public transport and agglomeration is an important
step that has not been explored yet”. Our study contributed to addressing this research gap
as we empirically show that the significant association between agglomeration and citycharacteristics, including transport characteristics.
4.6.

CONCLUSION

In this study, we conducted an exploratory analysis on the association between the urban
agglomeration of commercial facilities and the city-level characteristics of 69 Japanese cities
with a particular focus on transportation systems. We develop a simple agglomeration index
inspired by Duranton and Overman (2005) that we can compare across cities. Major findings
from our empirical analysis are (1) cities with larger area sizes, and higher train shares enjoy
agglomeration presumably through matching and/or trip chaining (Takahashi, 2013; Koster
et al., 2019), while cities with smaller area sizes less enjoy agglomeration even their train
share is high, (2) car-dependent cities enjoy agglomerations presumably through sharing(Pashigian and Gould, 1998), particularly by agglomerating in their residential and roadsideareas. These findings also lead to two academic implications. First, our results highlight theimportance of handling how transportation systems moderate the agglomeration economy in
the development of theoretical and empirical models, rather than simply identifying to what
degree the level of accessibility or density leads to an agglomeration economy. Second, in
empirical analysis, it is crucial to use travel time or network distance rather than Euclidian
distance, as the results could differ substantially between them.
We should also note that this study involves a number of limitations. First, while we
simply use a city’s administrative boundary to define the unit of analysis, this could beinappropriate. One simple solution would be to deal with the whole of Japan as a unit andcompute the pairwise distance between all facilities. Unfortunately, this would becomputationally expensive. For example, there are 52,394 commercial facilities in Osaka,and thus around 2.7 billion pairs (52,394  52,393) exist in just one city. As the number ofpairs exponentially increases, there is a need for an alternative way to solve this issue.
Second, the approach we took in this paper was more statistical than economic, and thus it
is not possible to connect our analysis directly with the discussion on transport investment
appraisal. To do this would require a solid microeconomic foundation that could differ
depending on the agglomeration forces. Third, in the analysis, we did not distinguishagglomeration in a building (like a shopping mall) from agglomeration on the street, but thedifference between these two would be important for urban planners. Related to this, thedesign of streets, including size, speed, and width, and zoning constraints, would also affectthe emergence of agglomeration. Extending the method that can consider these aspects is an
important remaining challenge. Lastly, Safira and Chikaraishi (2021) argue that on-demandtransport services change the type of the dominant agglomeration forces. More empiricalstudies on how these emerging mobilities change agglomeration forces would also be needed
to discuss their wider impacts.

62

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

Chapter 5: THE IMPACT OF MSTP ON THE URBAN FORM: A
CASE OF JAKARTA, INDONESIA
This chapter explores the impact of MSTP on facility distribution (i.e., combination foodmerchant, online food merchant, and dine-in food merchant) through the agglomerationindex analysis in Jakarta, Indonesia. This study attempt to answer two research questions
regarding the impact of MSTP on the urban form, including “What distribution changes do
MSTP bring about on the facility distribution?” (RQ 2.1) and “How these induced changes
in urban form?” (RQ 2.2). This chapter also contains the introduction, methodology, study
area, data used, result and discussion, and conclusion of the study.
5.1.

INTRODUCTION

Recently, exploring the role of information and communication technologies (ICT)in human activity-travel decisions has become a significant theme in transportation researchdue to the rapid development of ICT tools. Researchers have recognized that an increase in
the use of ICT may lead to changes in the location, timing, and duration of people’s activities,
and the widespread use of ICT will induce new patterns of activity and travel in space andtime (Kwan, 2020; Dijst, 2004).
Multi-service transport platforms (MSTPs) are one of the ICT innovations in the fieldof transportation, which has been used by many people recently. In Indonesia, there are two
major MSTPs companies called GOJEK and GRAB. The core of their operating system isthe presence of motorbike drivers. MSTPs companies utilize motorbike drivers as their fleetsto provide not only transportation services called ojek online (motorbike ride-hailing), butalso other life services, including online food delivery (FD) service, grocery, and nongrocery shopping agency service, cleaning service, massage services, and so forth. Amongothers, the popularity of online FD services is growing rapidly. GoFood by GOJEK and Grab
Food by GRAB, started in 2015, are the most popular online FD services in Indonesia.
Increases in the number of online food merchants and hence food options and provision ofmonetary incentives (e.g., discount and voucher) have further led to the increasing numberof service users. The presence of online FD services from MSTPs allows people to accessmany food merchants nearby without traveling.
Replacement of physical access with virtual access through MSTPs would lead tochanges in facility distribution in a city. Numerous literatures has emerged in the field ofregional science and urban economies that examines the questions of whether spatial
circumstances give rise to agglomeration economies where firms can benefit through colocation (Glaeser et al. 1992; Rosenthal and Strange, 2003). However, the benefits from theagglomeration could become lower under the presence of MSTPs due to the reduced needfor physical proximity. For example, Rodrigue (2020) pointed out that ICT innovation in thetransportation field has broadly led to changes in urban form. The more extreme the changes
in transportation technology have been, the more changes in the urban form could happen.
Some researchers predict the dissolution of the city due to the increasing ease oftransportation and communication (e.g., Webber, 1968; Fathy, 1991), where the ease oftransportation and communication may be able to break the spatial barriers resulting in
spatial dispersion in community. A similar phenomenon with the increasing ease oftransportation and communication might happen due to the presence of MSTPs, increasing
the spatial mismatch between the place people stay (i.e., home and office) and food merchant63

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

locations, potentially resulting in urban sprawl and the loss of vitality of the cities in the longterm.
Given the above background, this study aims to empirically explore the impact ofMSTPs on the distribution of food merchants by exploring differences in the density andagglomeration of the facilities between dine-in and online food merchants. Our main
hypotheses to be tested are:(1) dine-in food merchants may tend to be agglomerated in the center of the metropolitan(agglomeration at the metropolitan level) to increase their area’s attraction for the
consumer to come, and(2) online food merchants may tend to be agglomerated at the neighborhood level toattract a certain amount of motorbike drivers to keep the delivery service level.
Confirming these hypotheses is crucial since, if the above hypotheses are true, thediffusion of online food merchants could lead to the degradation of the city’s vitality, and in
the long run, it may also change the structure and form of the city.
In order to test the above hypotheses, we utilize the method to calculate theagglomeration index of facilities used in Safira and Chikaraishi (2019), where the index isdefined as the ratio of the average pairwise distance of target facilities and that of public
facilities. In addition, we also pay attention to the areal unit or boundary of the analysis. The
selection of the areal boundary has long been discussed in spatial and geographical analysis,
which is well known as the Modifiable Areal Unit Problem (MAUP). Based on Arbia andPetrarca (2011), the MAUP refers to the representation of data whose value is affected, often
signed by the spatial unit employed. More specifically, to explore the agglomeration at thecity level, we may have to employ the larger spatial boundary. In comparison, the smaller
spatial boundary would be more appropriate to explore the agglomeration at theneighborhood level. To handle this, we apply the method to different spatial units (i.e., cities,
districts, and neighborhoods).
5.2.

METHODOLOGYThis study explores the density and agglomeration index of facilities to explore the impactof MSTP on the distribution of facilities. Following Safira and Chikaraishi (2019), we
calculate the agglomeration index of each type of facility by taking the average pairwise
distance of each food facility and divided by the average pairwise distance of public facilities.
Unlike other indices involving aggregation at a specific spatial level, the continuous index
used has the following two advantages: (1) the values are comparable across spatial scales,and (2) the values are unbiased with respect to arbitrary changes to spatial classification.
Although these properties partially avoid the MAUP mentioned in the introduction yet, theindex values would still depend on the spatial boundary employed. Thus we calculate theagglomeration indices with four different spatial boundaries, including metropolitan, city,
district, and neighborhood.
The analytical procedure will be divided into four steps analysis: (1) calculating the
average pairwise distance of each targeted facility (i.e., combination food merchants, online
food merchants, and dine-in food merchants); (2) constructing the counterfactuals, i.e.,
calculating the average pairwise distance of public facilities; (3) developing theagglomeration index; and (4) comparing the agglomeration index result across different
spatial boundaries and different facility types.
1) Calculating the average pairwise network distance of targeted facilities
In this study, there will be three types of target facilities that will be used, including
64Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia(i) the combination of food merchants, (ii) online food merchants, and (iii) dine-infood merchants. Following Safira and Chikaraishi (2019), to calculate the average
pairwise distance among facilities, we adopt the network-based distance and travel
time (by car) to reflect the actual distance between facilities, considering thegeographical condition that restricts the developable area. The pairwise network
distance for targeted facilities 𝑑̅ 𝑇𝐹 is define as:
∑ ∑
𝑖

𝑗(≠𝑖)

𝑇𝐹
𝑑𝑖𝑗
/(𝑛𝑇𝐹 (𝑛𝑇𝐹 − 1))

(5.1)

𝑇𝐹
where 𝑛𝑇𝐹 is the total number of targeted facilities, and 𝑑𝑖𝑗
is the pairwise distance
between facility 𝑖 and 𝑗.

2)

Constructing the counterfactuals
The locations of public facilities (i.e., park, school, mosque, church, fire station,library, and post office) serve as counterfactuals. The selection of the public facilities
as counterfactuals assumes that the provision of public facilities in urban areas isbased on the principle of equitable distribution or need-based on the location of thecommunity residence. It would be reasonable to assume that the spatial distributionof these facilities is free from agglomeration forces. Similar to commercial facilities,
the pairwise network distance of public facilities 𝑑̅ 𝐶𝐹 is defined as:
∑ ∑
𝑖

𝑗(≠𝑖)

𝐶𝐹
𝑑𝑖𝑗
/(𝑛𝐶𝐹 (𝑛𝐶𝐹 − 1))

(5.2)

𝐶𝐹
where 𝑛𝐶𝐹 is the total number of public facilities, and 𝑑𝑖𝑗
is the pairwise distance
between facility 𝑖 and facility 𝑗.

3)

Developing the agglomeration index
We define the agglomeration index as the ratio of the average pairwise network
distance of commercial facilities divided by the average pairwise network distance
between public facilities as:
𝑑̅ 𝐶𝐹
(5.3)
𝐴𝐼 = 𝑇𝐹𝑑̅
where AI is the agglomeration index of a city, with a higher value indicating the moresignificant agglomeration of commercial facilities.

4)

Association of Agglomeration Index and City Characteristics
The agglomeration indices are obtained for each facility type with different spatial
units. The facility type includes commercial facilities, food merchants (combination),
online food merchants, and dine-in food merchants. The spatial unit employed in thisstudy includes metropolitan area, city-scale, district-scale, and neighborhood-scale.

5.3.

STUDY AREA

As mentioned in the previous section, we analyze the agglomeration index within a different
scale in this study. Our study's area is the metropolitan DKI Jakarta. Its sub-metropolitan
area included five administrative cities (i.e., Central Jakarta, South Jakarta, West Jakarta,
East Jakarta, and North Jakarta), 42 districts under those five administrative cities, and 262
neighborhoods under those 42 districts. Figure 5.1. shows the map of our case study andTable 5.1. shows the general characteristics of the area.

65Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

Table 5.1. The characteristics of the study area
City NameCentral Jakarta
South Jakarta
West Jakarta
East Jakarta
North Jakarta

Area Size
(Km2)
52.38
154.32124.44
182.70139.99Population(People)1,138,346
2,188,457
2,324,121
2,944,493
1,711,386

Population Density(people/Km2)
21,732
14,181
18,676
16,116
12,225

Number ofDistricts
8
10
8
10
6

Number of
Neighborhood
44
65
56
66
31

Source: BPS, 2020

Figure 5.1. Map of study area in Jakarta, Indonesia
5.4.

DATA

This study obtains the commercial and public facilities data from Google maps by using web
crawler methods. Mapping data is becoming increasingly relevant in the Internet age,creating market value and supporting decision-making. Such data are commonly used in
sectors; for example, catering firms may determine where to open a new restaurant by
evaluating map data and competitors in the vicinity. Nowadays, web scraping and web
crawling are the most used methods to extract data from websites, including the data frominteractive maps (e.g., Google Maps). Web crawlers are computer programs that
automatically collected several web documents from one or more web pages. A web crawler
processes the obtained data and prepares the data to be eventually analyzed by other systems.
The data includes the name of the facilities, type of facilities, street address,
coordinate location, opening hours, and other variables. Table 5.2. shows all categories anddetail of the facilities that we use in this analysis. Although making a distinction in service
type is an important research topic, particularly when identifying co-agglomeration
phenomena (e.g., Kolko, 2007), it lies outside the present analysis scope.

66

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

Table 5.2. Facility type
Category
Food Facilities
Commercial
Facilities
Public Facilities

Detail(1) Restaurant; (2) Café; (3) Coffee shop; (4) Bakery; and (5) Street food vendor(1) Automobile shop; (2) Groceries shop; (3) Market; (4) Bank; (5) Pharmacy; (6) Hairand beauty salon; (7) Appearance store; (8) Home appliance store; (9) Electronic store;and (10) Movie theater(1) Park; (2) School; (3) Mosque; (4) Church; (5) Fire station; (6) Library; and (7) Post
officeThis study classifies food merchants into three groups, as shown in Table 5.3. The
first type is called “combination” food merchants, which provide both dine-in and online
food delivery services. The second type is online food merchants, which only provide online
delivery services without any dine-in facility. It is also called ghost kitchens in cities in the
UK, US, and India. These food merchants are shifting from owning the physical store to thebusinesses where they do not have any physical store. It may give some advantages for thosefood merchants in reducing the operational cost by eliminating the store rent and reducing
the number of staff. The last type is dine-in food merchants, which is the food merchants
only providing dine-in services without any delivery services. Using the web-crawler
methods, we successfully obtained 7,408 public facilities' locations; 6,810 commercial
facilities' locations; 4,458 combination food merchants' locations; 3,718 online foodmerchants' locations; and 586 dine-in food merchants' locations.
Table 5.3. The service of food merchants
Type of Food Merchant
Food Merchant (Combination)
Online Food MerchantDine-in Food MerchantDine-in Facility
Available
Not Available
Available

Online Delivery Facility
Available
Available
Not Available

Figure 5.2. Distribution of facilities
67

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

5.5.

RESULT AND DISCUSSION

5.5.1. Identified Agglomeration Indices
Table 5.4 summarizes the calculated agglomeration indices (AI) for different facility types
with different spatial boundaries. Also, Figures 5.3., 5.4., 5.5. show the spatial distributionsof agglomeration indices by merchant type and by the spatial scale. The details of calculated
agglomeration indices are shown in appendix. The main findings can be summarized asfollows:
1. Although AI values are similar across merchant types at the metropolitan scale,
online food merchants (AI: 0.99) are less agglomerated than the combination anddine-in food merchants (1.06 and 1.09, respectively). This indicates that, while
dine-in food merchants tend to be agglomerated, for example, in CBD (Central
Business District) area at the metropolitan scale, online food merchants do not,implying that little agglomeration forces exist for online food merchants.
2. The AI values tend to be more significant when we employ a finer spatial scale.
In particular, the results show that, while the agglomeration level of dine-in foodmerchants is not so high at the neighborhood level (1.40), that of online foodmerchants reaches 2.21. The higher agglomeration level was observed for online
food merchants presumably because of the necessity for attracting motorbike
drivers under MSTPs to keep the service level of delivery.
3. The higher agglomeration level of online food merchants at the neighborhood
level was observed in the urban core and urban fringe (Figure 5.4).
The above first and second findings support the first and second hypotheses mentioned
in the introduction of chapter 5, indicating that the spatial distribution of food merchants
would be affected by MSTPs. The 3rd point is not expected, but it is an interesting finding to
be further explored. Since higher agglomeration levels in the urban core were observed for
combination and dine-in food merchants as well, the unique characteristic of online foodmerchants would be in the agglomeration of facilities within a neighborhood in the urban
fringe. The following subsection is dedicated further to explore this unique nature of online
food merchants.
Table 5.4. Average agglomeration indices with different spatial scales
Scale
Metropolitan
City
District
Neighborhood

Combination Food Merchants
Mean
Min
Max
1.06
1.08
0.94
1.25
1.09
0.70
1.53
1.84
0.24
6.24

Agglomeration Index
Online Food Merchants
Mean
Min
Max
0.99
1.01
0.94
1.06
1.13
0.84
2.45
2.21
0.31
7.12

Dine-in Food Merchants
Mean
Min
Max
1.09
1.12
0.90
1.47
1.06
0.30
2.51
1.40
0.37
4.41

Based on Figure 5.3, we found that South Jakarta has a higher agglomeration index
for combination food merchants than other areas. However, on the district scale, the areawith a higher agglomeration index was located in South Jakarta but scattered across districts.
On the other side, the agglomeration index in the neighborhood scale shows some spatial
patterns. The area with a higher agglomeration index for combination food merchants tends
to be located in the central area or near the central area and some in the fringe area.

68Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

Figure 5.3. Agglomeration index of food merchants (combination)
On the other side, Figure 5.4. shows the spatial distribution of the agglomerationindex for online food merchants. On the city scale, we can identify that West Jakarta has thehighest agglomeration index for online-food merchants (with 2.11) than the other area,
followed by Central Jakarta and South Jakarta with 2.05 and 2.04, respectively. Similar to
the agglomeration index for combination food merchants, the spatial distribution of theagglomeration index for online food merchants also changed in the district scale. The spatial
distribution was changed, but the agglomeration index's range also changed on the district
scale. However, the result of the agglomeration index in neighborhood-scale shows sometendency in spatial distribution. The area which has a higher agglomeration index was
located in the central area and the fringe area. In this sense, online food delivery may lead todeveloping the new service core in the central and fringe areas, which will be discussed in
the next sub-section.

Figure 5.4. Agglomeration index of online food merchants
Figure 5.5. also shows the changes in the area that has a higher agglomeration index
across space. On the city scale, most dine-in food merchants are located in Central Jakarta.
This led to Central Jakarta has a higher agglomeration index of dine-in food merchants
compare to other cities. However, on the district scale, many districts located in Central
Jakarta are in the middle to low range of agglomeration index value. The district with thehigher agglomeration index is located in the West and South Jakarta. On the other side, thedine-in food merchant's spatial tendency is to be located, as shown in the neighborhood scale
agglomeration index. We can see that the dine-in food merchant tends to be agglomerated
nearby the central area and less agglomerated in the fringe area.

69Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

Figure 5.5. Agglomeration index of dine-in food merchants
5.5.2. Association of Agglomeration Index and City Characteristics
To further understand the characteristics of the distribution of online food merchants, wefirst explore the densities of combination, dine-in, and online food merchants. Then, we also
do the same for commercial facilities for comparison purposes. We also compare how
densities and agglomerations are different depending on the distance from the central areaof Jakarta for each facility type. Finally, we examine how densities and agglomerations areassociated with the share of commercial land use.
Figures 5.6. and 5.7. show the density and agglomeration indices for each facility
type at the neighborhood level. We also aggregate densities and agglomerations by the
distance from the central area of Jakarta. Figure 5.8. shows the distance information1, andFigures 5.9. and 5.10. show the average density and agglomeration indices with respect todistance from the central area. Thus, we can confirm that dine-in food merchants are moreconcentrated in the urban core than calculated facility densities. In contrast, another facility
type is more dispersed over the area, including commercial facilities. This indicates that
dine-in food merchants are more agglomerated at the metropolitan scale, while others arenot. On the other hand, if we look at agglomeration indices at the neighborhood level (Figure5.7.), dine-in food merchants and commercial facilities are agglomerated only in the central
area of the cities. In contrast, combination and food merchants are agglomerated in the urban
fringe as well. Figures 5.9. and 5.10. also support the above findings, i.e., more online foodmerchants exist in the urban core and urban fringe, and they tend to be agglomerated in theurban core and urban fringe.

1

We use 7 locations of central primer as our central area of the Central Business District (CBD) area. Fourlocation of central primer was omitted due to the under-planning status of these areas.

70Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia(6a) Combination food merchant(6c) Dine-in food merchant(6b) Online food merchant(6d) Commercial facility

Figure 5.6. Facility densities at the neighborhood level(7a) Combination food(7b) Online food71

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia(7c) Dine-in food(7d) Commercial

Figure 5.7. Comparison of agglomeration indices at the neighborhood level

Figure 5.8. Distance from central area

Density of Facilities (units/Km2)

Combination Food Merchants

Online Food Merchants

Dine-In Food MerchantsCommercial Facilities

0.30
0.25
0.20
0.15
0.10
0.05
0.00
<0.5

0.5-3.5

3.5-6.5

6.5-9.5

9.5-12.5

12.515.5

15.518.5

Combination Food Merchants

0.23

0.17

0.15

0.10

0.08

0.09

0.15

Online Food Merchants

0.24

0.21

0.15

0.08

0.07

0.08

0.14

Dine-In Food Merchants

0.22

0.16

0.14

0.14

0.13

0.11

0.11

Commercial Facilities

0.28

0.17

0.15

0.11

0.11

0.10

0.09

Distance to The Central Area (Km)

Figure 5.9. Average densities with respect to distance to the central area72

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

Combination Food Merchants

Online Food Merchants

Dine-in Food MerchantsCommercial Facitilies

Agglomeration Index

0.25
0.20
0.15
0.10
0.05
0.00
<0.5

0.5-3.5

3.5-6.5

6.5-9.59.512.5

12.515.5

15.518.5

Combination Food Merchants

0.20

0.14

0.14

0.11

0.13

0.14

0.17

Online Food Merchants

0.22

0.14

0.12

0.10

0.10

0.13

0.19

Dine-in Food Merchants

0.19

0.16

0.15

0.13

0.13

0.14

0.11

Commercial Facitilies

0.21

0.18

0.14

0.12

0.11

0.11

0.14

Distance to The Central Area (Km)

Figure 5.10. Average agglomeration indices with respect to distance to the central area
A remarkable difference between combination and online food merchants from dinein food merchants and commercial facilities is that they do not require users to travel. In theliterature, the benefits of agglomeration are considered to be rooted in (1) matching: the costof walking around to find preferable shops and restaurants would be lower in an
agglomerated state (Takahashi, 2013), and (2) trip chaining: consumers can reduce transportand search costs owing to the spatial proximity of shops and restaurants (Koster et al., 2019).
Thus, one logical explanation we could make on the above observation is that the distributionof online food merchants would not be affected by these agglomeration forces, resulting in
the different spatial distribution from dine-in food merchants. Also, it is important to mention
that a new agglomeration force would exist for online food merchants as mentioned above:
online food merchants need a certain amount of MSTP drivers to keep their service level,
and thus a certain level of agglomeration would be crucial. This could be a reason why higher
agglomeration in the urban fringe was observed for online food merchants. Although thisstudy could not further investigate the mechanism of the above-mentioned agglomeration
forces, it would be worth further investigating changes in agglomeration forces due to thepresence of MSTPs from both theoretical and empirical perspectives.
In the daily operations, the MSTPs’ driver can travel and standby everywhere within
the city without any distance restriction. However, when it comes to the online food delivery
services, the driver who got an order to deliver the food will have some distance restriction
or maximum area coverage that the drivers can cover. The online food delivery service
provided by MSTPs, and other food delivery services have a similar rule in considering the
distance between food merchants and the customer’s location. Specifically, the distance
between the origin (i.e., the location of food merchants) and destination (i.e., the customer’s
location) will undoubtedly have an impact on the quality of services. In Indonesia, the online
food delivery service provided by MSTP has an average maximum coverage area or
maximum delivery distance up to 6 km from the location of food merchants to the locationof their customers in principle. Therefore, the distribution of food merchants will
significantly impact the online delivery service’s quality.
Another phenomenon that we should discuss is that the reason why neighborhoods
between urban core and fringe areas tend to have lower densities and agglomeration indicesfor online food merchants. One of the possible reasons is that the middle area gets access toboth central and fringe areas, and thus facilities may not need to be located in thoseneighborhoods. However, it requires further analysis to identify whether this notation is73

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

likely to be true or not.
Finally, we explore how densities and agglomerations are associated with the share ofcommercial land use. According to the Jakarta provincial government’s city planningdocument, 15.68% of the total land is allocated for commercial use. We calculate thepercentage of commercial land use in each neighborhood (Figure 5.11.) and then calculate
the average densities and agglomeration indices by the share of commercial land use shownin Figures 5.12. and 5.13. The main finding is that, while densities and agglomeration indicesof combination and dine-in food merchants and commercial facilities are getting larger with
the increase in the share of commercial land use, online food merchants are less affected by
the share. This implies that the distribution of online food merchants is less consistent with
the intended land use that the government specifies compared to other merchant types. This
may require city planners to pay special attention to the spatial distribution of online foodmerchants and further discuss how properly embed them into their city planning.

Figure 5.11. The percentage of commercial land use in Jakarta

Density of Facilities (units/Km2)

Combination Food Merchants

Online Food Merchants

Dine-In Food MerchantsCommercial Facilities

0.30
0.25

0.20
0.15
0.10
0.05
0.00

0.020.12

0.120.22

0.220.32

0.320.42

0.420.52

0.520.62

0.620.72

Combination Food Merchants

0.08

0.07

0.12

0.12

0.19

0.19

0.25

Online Food Merchants

0.11

0.12

0.13

0.13

0.14

0.16

0.19

Dine-In Food Merchants

0.11

0.11

0.14

0.13

0.14

0.18

0.19

Commercial Facilities

0.11

0.09

0.12

0.11

0.14

0.16

0.22

Percentage of Commercial Land Use (%)

Figure 5.12. Average facilities densities and the share of commercial land use
74

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

Average of Agglomeration Index

Combination Food Merchants

Online Food Merchants

Dine-in Food MerchantsCommercial Facitilies

0.25
0.20

0.15
0.10
0.05
0.00

0.020.12

0.120.22

0.220.32

0.320.42

0.420.52

0.520.62

0.620.72

Combination Food Merchants

0.12

0.11

0.13

0.15

0.15

0.16

0.20

Online Food Merchants

0.15

0.14

0.14

0.12

0.14

0.14

0.15

Dine-in Food Merchants

0.12

0.11

0.14

0.14

0.15

0.17

0.19

Commercial Facitilies

0.11

0.11

0.14

0.15

0.13

0.17

0.20

Percentage of Commercial Land Use (%)

Figure 5.13. Average agglomeration indices and the share of commercial land use
5.6.

CONCLUSION

Multi-service transport platforms (MSTPs) such as GOJEK and GRAB may lead to changes
in activity and travel decisions, in turn leading to changes in the distribution of facilities,
evidenced by a rapid increase in the number of online food merchants that only provide
online food delivery services. This study has investigated to empirically confirm whether or
not these rapidly emerging online food merchants have different spatial patterns comparedto the conventional dine-in food merchants with a particular focus on the agglomeration anddensity of the facilities. Our empirical results showed that, while dine-in food merchants are
more agglomerated at the metropolitan scale, online food merchants are more agglomerated
at the neighborhood scale, presumably due to the necessity to attract MSTP drivers to keep
the delivery service level. We also found that online food merchants tend to be agglomerated
in the center and fringe area of the city. We discuss possible mechanisms on it, particularly
from the perspective of changes in agglomeration forces by the MSTPs.
There are a number of limitations of this study. First, although this study took an
empirical approach, theoretical investigations would also be needed to understand theimpacts of MSTPs on urban form fully. The possible changes mentioned in this paper, i.e.,
weakening existing agglomeration forces (i.e., matching and trip-chaining) and emerging
new agglomeration forces (i.e., attracting MSTP drivers to keep the delivery service level),would provide a valuable tip to conduct theoretical works. Second, it is clearly better to take
into account the demand side, for example, by using daytime and nighttime population
information, but this study could not do it due to the lack of access to the data. This would
be one of the major future tasks. Third, the impact of the current pandemic situation in the
distribution of the potential demand needs to be considered. As we know, in a pandemic
situation like nowadays where many people tend to conduct work-from-home activities maychange the distribution of potential demand (i.e., customer) of online food delivery services,
especially in the daytime. In this situation, the number of online food delivery service orders
from home-based locations may increase compared to the “normal” condition where theincreased work-from-home activities may change the distribution of potential demand (i.e.,
customer) of online food delivery services, especially in the daytime. The number of online
food delivery service orders from home-based locations may increase compared to office75

Chapter V – The Impact of MSTP on The Urban Form: A Case of Jakarta, Indonesia

based locations. However, the further analysis of the changes in the demand distribution due
to the pandemic situation will be considered as our future task. Fourth, street food vendors
play an important role in the food industry in Indonesia, but we could not really take theminto account due to the lack of data. Maybe a field observation would be needed to properlyreflect it in the future. Fifth, the results would also be different depending on the choice ofdistance metrics and facility type for constructing counterfactuals. In the future, we should
test the robustness of the results by changing the metrics. Sixth, we only explore thedifferences between dine-in and online food merchants in Jakarta. However, the impacts
would be pretty different depending on city size, development stage, and so forth. Further
empirical analysis should undoubtedly be needed to understand the impacts of MSTPs on
urban form comprehensively.

76

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

Chapter 6: CHANGES IN ACTIVITY-TRAVELDECISION UNDER THE INFLUENCE OF MSTP
This chapter describes the changes in activity-travel decision and distribution under MSTP
influence. In this chapter, we attempt to answer two research questions, including “HowMSTP changes the distribution of activities?” (RQ 3.2) and “What factors influence people
to choose online activities?” (RQ 3.3). This chapter also contains the introduction of thestudy, methodology approach, study area, data used, result and discussion, and conclusionof the study.
6.1.

INTRODUCTION

Information and communication technologies (ICT) may improve people’s access to goods,
services, and even jobs through virtual connectivity, allowing them to participate in activities
across space and time. The ICT systems are evolving fast and have the potential to causechanges in people’s activity and travel patterns. In the short term, ICT has been observed to
substitute, complement, and modify physical travel (Andreev et al., 2010) while improving
access to activity opportunities (Novo-Corti et al., 2014). Meanwhile, in the long-term, it has
the potential to affect congestion, emission levels, and urban form. With such important
policy-level impacts, it is evident that analyzing the complex interrelationships between ICT
and activity-travel behavior has become an essential theme in transportation research inrecent years.
ICT systems have seeped into the lives of people and are continually evolving. Oneof the latest ICT innovations is the multi-service transport platform (MSTP). MSTP can be
defined as an online-based platform that provides access to a wide range of services,
including ride-hailing transportation, food delivery service, courier service, and daily need
services (such as cleaning service, massage, hair salon, etc.). Besides playing the role of amediator between the demand from the consumer’s side and the supply from the provider’sside, MSTP also gets involved in the direct distribution of goods and services by relying on
their drivers and fleets. MSTP allows people to virtually access the services, relaxing theirtime and space constraints. Therefore, they have the potential to severely impact the activity
and travel behavior patterns of people.
The speed of the evolution of analytical and survey methods capturing theinterrelationships between ICT and activity-travel behavior has not kept up with theevolution of ICT systems. To comprehensively understand the full extent of these
relationships, good quality data is required, and the recent advancements in smartphonebased surveys provide the scope to capture that adequately. Studies recently have highlighted
the advantages of smartphone-based activity and travel surveys (Cottrill et al., 2013; Zhao
et al., 2015), which collect the activity and travel diaries in an inexpensive and non-intrusive
manner (Prelipcean et al., 2015). However, there are many opportunities to improve such
survey frameworks to improve the data quality and, subsequently, the analysis. This study is
an attempt towards that. Most of the current smartphone-based surveys still focus oncollecting conventional travel and activity diary survey data.
Further expansion of this method is needed to allow researchers and practitioners to
observe virtual or online activities and analyze their impacts on activity-travel behavior.
Hence, this calls for improving the survey scheme, while there has been relatively little
attempt to explore new data collection schemes concerning online or virtual activities. In
77

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

addition, most of these surveys have been conducted in the developed world, while very little
is known about the effect of ICTs on activity-travel behavior in the fast-changing cities ofthe developing world (Lila and Anjaneyulu, 2017; Varghese and Jana, 2019).
This study presents the framework for a smartphone-based activity-travel survey
conducted in Jakarta, Indonesia. The paper explains and discusses the approaches undertaken
to modify and improve existing smartphone-based activity-travel behavior surveys. It
describes the data collection effort that can be used to comprehensively understand theinterrelationships between ICT use and activity-travel behavior with a special focus on
capturing the impact of newly popularized MSTP. Also, it presents the preliminary findings
from the survey with a particular focus on the choice between physical and virtual activity
engagements.
6.2.

METHODOLOGY

In this study, we explore the activity-travel diary data that we obtain from our travel-diarysurvey and describe changes that happened in the individual’s activity and travel decision
under the influence of MSTP. This study also tests the effect of the variables mentioned
above on the binary choice between virtual and physical activities using a mixed logit modelwith random error components accounting for inter-individual heterogeneity of unobserved
variables. In addition, we calculate the influence of these variables and compare them with
the influence of unobserved variables. This was done by decomposing the total variance ofutility differences as a sum of variations in observed and unobserved variables (Chikaraishi
et al., 2011a, 2011b). The total variance of utility difference between virtual and physicalactivities could be denoted by:
The total variance of utility differences between virtual and physical activities
2(6.1)
𝑉𝑎𝑟(𝑈2𝑖𝑡 − 𝑈1𝑖𝑡 ) = 𝑉𝑎𝑟(𝛽̂2 𝑋2𝑖𝑡 − 𝛽̂1 𝑋1𝑖𝑡 ) + 𝜎𝑖2 + 𝜋 ⁄3
Where,
𝑈1𝑖𝑡 = Utility for physical activity engagement for individual 𝑖 for 𝑡-th activity
𝑈2𝑖𝑡 = Utility for virtual activity engagement for individual 𝑖 for 𝑡-th activity
𝛽̂1, = A vector of estimated coefficients for physical activity engagement𝛽̂2, = A vector of estimated coefficients for virtual activity engagement
𝑋1𝑖𝑡 = A vector of observed variables for physical activity engagement for individual
𝑖’s 𝑡-th activity
𝑋2𝑖𝑡 = A vector of observed variables for virtual activity engagement for individual
𝑖’s 𝑡-th activity
𝜎𝑖2 = Variance of a random term representing unobserved inter-individual variations
𝜋 2⁄ = White noise, i.e., variance due to other unobserved variables
3
Using the above formulation, the contribution of observed and unobserved variables
towards the total variance of utility differences could be calculated by using the following
formulations:Contribution of observed variables (%)= (𝑉𝑎𝑟(𝛽̂2 𝑋2𝑖𝑡 − 𝛽̂1 𝑋1𝑖𝑡 )/ 𝑉𝑎𝑟(𝑈2𝑖𝑡 − 𝑈1𝑖𝑡 )) × 100
Contribution of unobserved inter-individual variables (%)= (𝜎𝑖2 /𝑉𝑎𝑟(𝑈2𝑖𝑡 − 𝑈1𝑖𝑡 )) × 100

(6.2)
(6.3)
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Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

Contribution of other unobserved variables including intra-individual variations (%)(6.4)= (3−1 𝜋 2 /𝑉𝑎𝑟(𝑈2𝑖𝑡 − 𝑈1𝑖𝑡 )) × 100
Four separate binary choice models were developed modeling the effect of variableson the choices of four activity types 1) online food delivery (base alternative: eating out), 2)
online shopping (base alternative: physical shopping), 3) online leisure (base alternative:
physical leisure), and 4) online social (base alternative: physical social). Work and education
activities were not modeled as the choice of virtual activities for these activity types could
be constrained based on the workplace requirements and the school. However, the users were
free to choose between the physical and the virtual alternatives for the other four activity
types.
6.3.

DATA AND STUDY AREA

A total of 225 users participated in the survey conducted in and around Jakarta, Indonesia,
for 14 days between January 28th to February 10th, 2020. The study location was in Jakarta,
Indonesia. Jakarta (the Special Capital Region of Jakarta or Daerah Khusus Ibukota (DKI)
Jakarta) is Indonesia’s capital and the largest city. The city has an area size of 664.13 sq. km
and a population of 10.77 million, with a population density of 21,974 people per sq. km(Central Bureau of Statistics, 2020). Figure 6.1 shows the distribution of the home locationsof these participants. The spatial distribution shows a relatively higher concentration ofparticipants from the South Jakarta region, where Jakarta’s central business district is located.
The participants were recruited based on a workplace-based sampling done in the South
Jakarta region of the city, which meant that all the users who participated worked in theSouth Jakarta region. An additional recruitment questionnaire explaining the research
project’s scope and objectives was developed and shared on social media websites. Initially,
312 participants were shortlisted to participate in the survey based on the online recruitment
questionnaire’s responses. However, only 225 participants managed to complete the entire
14 days of the survey.

Figure 6.1. Spatial distribution of home locations of the survey participants
79

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

6.4.

RESULT AND DISCUSSION

6.4.1. Socio-Demographic Characteristics
The socio-demographic information of participants shows that 55.56% of participants werefemale. Meanwhile, the highest percentage of respondents belonged to the age group 23-27
years (56.89%), indicating that the survey sample distribution was slightly biased towards a
younger population. A possible reason behind this could be that as the survey only recruited
people with a smartphone and an active internet connection, this might have resulted in thebiased recruitment of young people who have a relatively higher propensity to use ICT-based
services. Expectedly, 67.56% of the respondents were single. Meanwhile, 59.55% of the
respondents had at least one college degree. In addition, it was observed that people whohave a fixed work schedule and need to go to the office, i.e., the office workers constituted
91.11% of the total respondents.
Table 6.1. Data description of socio-demographic variables
Variable
Total number of respondents
GenderAge

Marital
Education

Job
Provision Lunch by Workplace
Car Ownership
Motorbike Ownership
Family Size

Average Income (in Mil. IDR
per month)

Category
Male
Female
18-22
23-27
28-32
33-37
38-42
SingleMarriedJunior High School
Senior High School
DiplomaBachelor
Master and Doctor
Office Worker
Non-office Worker
Provided
Not Provided
Yes
No
Yes
No
1
2
3
4
>5
Less than 1
1-3.994-7.998-9.99More than 10

Total
225
100
125
21
128
43
29
4
152
73
1
90
14
110
10
205
20
42
183
158
67
71
154
105
29
58
33
10
8
58
118
31
9

Percentage
100.00%44.44%
55.56%
9.33%
56.89%
19.11%
12.89%
1.78%
67.56%
32.44%
0.44%
40.00%
6.22%
48.89%
4.44%
91.11%
8.89%
18.67%
81.33%
70.22%
29.78%
31.56%
68.44%
46.67%
12.89%
25.78%
14.67%
4.44%
3.56%
25.78%
52.44%
13.78%
4.00%

In contrast, the group of people who do not have fixed work schedules or do not needto go to the office, i.e., the non-office workers, constitutes the remaining 8.89%. There are
42 respondents that have their lunch provided by their workplace. 70.22% of respondents
80Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

owned a car for vehicle ownership, whereas 31.56% of them owned a two-wheeled motor
vehicle. A high percentage of 46.67% of the respondents lived alone with a family size equal
to 1, whereas only 14.67% of the respondents had a family size of 4 or more people (seeTable 3). Finally, the distribution of income showed that the majority of respondents
belonged to the mid-income ranges of 1 to 3.99 million IDR (25.78%), 4 to 7.99 million IDR(52.44%), 8 to 9.99 million IDR (13.78%), with 4.00% of the respondents with an incomeof 10 million or more.
6.4.2. Activity and Travel Behavior Characteristics
In the empirical analysis, we collected data from 272 individuals who complete 14 days (2weeks) smartphone app-based travel-activity diary survey, from January 28th to February
10th, 2020, in Jakarta, Indonesia. We use office-based sampling. The respondents are usersof the online-based food delivery service provided by MSTP (e.g., Go-Food and GrabFood)
who work in South Jakarta City, Indonesia. The data include individual activity and travel
behavior on a given 14 days survey period and their online activity behavior. Before
addressing the analyses of the travel performance indicator derived from the activity-diaries,
this section discusses the activity-travel pattern in these diaries in a more general way.
We used the term “travel activities” is referring to the number of observed linked
trips and the “stay activities” as the number of observed stops with a purpose. Within the 14
days of observation, 225 respondents total 7,112 travel activities (trips) and 7,154 stay
activities. It was observed that of all the stay activities, 38.51% were stay-at-home activities,
whereas 32.07% were work activities. Other activity types such as eating out, shopping,education, leisure, social, and other activities witnessed a share of .56%, 5.10%, 1.10%,
1.80%, 8.05%, and 5.80% of all activities, respectively (see Table 4). It should be noted thatthese stay activities denote the primary physical activities in which the users participated.
The duration of these activities also varied, e.g., most activities were either done fora duration between 0-60 minutes (26.98%) or greater than 300 minutes (50.87%) (see Table
4 for the complete distribution). In addition, the distribution across activity types also variedwith the time duration. Activities such as eating out, social activities, and shopping activities
were mainly done for a shorter time period between 0-60 minutes. Meanwhile, work andstay-at-home activities were highly conducted for short and long durations (0 to 60 minutes
and greater than 300 minutes, respectively).
Table 6.2. Distribution of activity participation and time allocation in primary activities
Activity Types
Eating out
Shopping
Working
Education
LeisureSocialHome
OthersTotal (2)
% (2)

0-60
324
236
366
35
38
288
376
267
1930
26.98%

61-120
96
51
154
5
24
91
140
51
612
8.55%

Duration (in minutes)
121-180 181-240 241-300
40
24
16
29
10
9
135
119
122
8
6
6
33
13
2
50
37
20
101
85
65
25
10
8
421
304
248
5.88%
4.25%
3.47%

>300
41
30
1398
19
19
90
1988
54
3639
50.87%

Total (1)
541
365
2294
79
129
576
2755
415
7154
100.00%

% (1)
7.56%
5.10%
32.07%
1.10%
1.80%
8.05%
38.51%
5.80%
100.00%On the other side, for travel activities (trips), every movement that the users madewas identified and associated with a travel mode. It was observed that 17.83% of the travel
activities were conducted on foot. Meanwhile, 29.96% and 19.76% of all the travel activities
81

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

were conducted using private cars and motorbikes, respectively. Public transport modes,
including angkutan kota (angkot)/minibus, bus, and trains, were used for 10.46% of the trips,
whereas Ojek online, an MSTP-based taxi motorbike, was used in 16.65% of the total travel
activities. Four-wheeler ride-hailing and taxi services were used in 2.35% of the trips,
whereas other modes (such as bicycles, airplanes) were used in 2.99% of the trips.
Table 6.3. Distribution of mode choice for travel activities
Travel mode
Walk
Car
Motorbike
Public Transit
Ojek Online
Ride-Hailing/Taxi
OthersTotal (7112 trips)

Average Travel Time (min)
95.82
47.18
70.92
77.59
51.40
36.83
230.31
319.59Average Distance (km)
2.65
19.24
12.42
15.16
5.31
15.34
25.39
19.18

Percentage of trips (%)
17.83%
29.96%
19.76%
10.46%
16.65%
2.35%
2.99%
100.00%6.4.3. Online Virtual Activities
The third part of the survey captured information on the online activities performed byrespondents during primary activities, which had a duration of greater than or equal to 20
minutes. A basic descriptive analysis of the data showed that users participated the most in
online leisure activities (34.69%), followed by online social (25.56%), online food delivery
services (17.32%), shopping (8.31%), work (9.21%), and education activities (4.91%). The
survey also captured the primary activities during which these online activities were
performed, and it was observed that the most significant number of online activities were
performed during stay at home (39.36%) and travel activities (26.18%), followed by work
activities, which accounted for 21.46% of the total online activities. This trend is reasonably
expected as these activity types were also observed to be the most performed “stay” activities
in the activity and travel diaries.
Table 6.4. Distribution of online activities
Physical Activities
Eating out
Shopping
Working
Education
LeisureSocialHome
OthersTravelTotal (2)% of total (2)Virtual/Online Activities
Eat

Shopping

Working

Education

LeisureSocial

35
14
230
5
2
26
371
12
251
946
17.32

9
18
128
2
3
2
152
7
133
454
8.31

2
3
180
1
0
8
202
3
104
503
9.21

0
2
57
16
1
12
109
4
67
268
4.91

72
23
318
23
26
57
868
62
446
1895
34.69

30
10
259
5
4
73
448
138
429
1396
25.56

Total (1)% of total (1)

148
70
1172
52
36
178
2150
226
1430
5462

2.71
1.28
21.46
0.95
0.66
3.26
39.36
4.14
26.18

6.4.4. Physical vs. Virtual Activities: Influence of Variables
Finally, an analysis of the effect and influence of variables on the choice of virtual online
activities was performed. A direct comparison was made between the primary physicalactivity types and their virtual online counterparts. Figure 6.2. shows this comparison
between the shares of physical and virtual activities across six different activity types. It

82

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

could be seen that social, educational, leisure, shopping, and eating activities, the share ofvirtual activities is higher than physical activities. Only in the case of work activities was it
observed that people participated in many physical activities (more than 90% of the total
work activities).

1192

Social576
1657

Leisure129

221

Education

340

Work

79
2294

454

Shop

20%

Physical

365

946

Eat
0%

Online

541
40%

60%

80%

100%

Figure 6.2. Physical vs. virtual activities: The share of activity participation
It is expected that these shares, i.e., the choice of whether to participate in an activity
type, would depend on several variables. Figure 8 shows the variations in activity
participation across the six activity types based on the time of day. Certain activities such as
work are constrained in time and space, with most people having non-flexible choice options(start time is mostly during morning hours). Meanwhile, other activity types provide a higher
level of flexibility for users to choose from. In addition, virtual activities may relax thesestrict space and time constraints. In almost all activity types, the influence of time of the day
is evident, with varying peaks throughout the day. Apart from the time of the day, othersocio-demographic variables such as gender, age, marital status, education level, job type,car ownership, motorbike ownership, family size, and average monthly income could
influence the choice between virtual and physical activities.
The binary logit models’ results show that most socio-demographic observed
variables included in the model did not have a statistically significant relationship with the
choice of virtual activities relative to their respective physical activities. For an online fooddelivery (eating behavior), we found that the average monthly income and family sizes havea significant negative value. The result may indicate people with a lower income, and asmaller number of family member tend to have a meal by him/herself through online fooddelivery services. People with a higher income and bigger family size tend to eat out, which
may indicate some tendency to have some social interaction through eating together. For
online shopping activities, people with higher education tend to conduct online shopping.
The result may indicate that with a higher education level, the ability to access theinformation of the goods online and operate the application led them to conduct some online
shopping where they found it more convenient than conducting shopping physically. For
social activities, age and marital status are the significant variables affecting people’s
decision to conduct online social activities. Younger people tend to conduct social activities
online rather than physically, but married people tend to conduct social activities. This may
be the initial indicator of the social interaction changes of the younger people in urban areasthat tend to communicate and interact online socially. It is noted that the result is produced83

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

with the majority of the respondents in the age range 23-27 years old, which may describe
some similarities in the daily behavior or lifestyle, especially their eating behavior. Basedon Demografi L. (2017), 77% of the MSTP users are in the range age of 20-39 years old,with 70.4% of them are using online food delivery services for their daily consumption.
People find it is more convenient and cheaper to order food by using MSTP rather thancooking by themselves.

Figure 6.3. Variations in activity participation based on time of day
On the other hand, the time of the day seemed to have a stronger relationship withthe activity choices. Twenty-four hours were divided into six time zones, and a dummy
variable for each was tested in the model (with the time between 00:00:00 to 03:59:59 as thereference category, time 1). It was observed that for eating activities, users were less likely
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Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

to order food online (relative to going out for eating) from 12 PM to 12 AM as compared toafter 12 AM till 4 AM in the morning, as three dummy variables for time zones 4, 5, and 6
showed significant negative relationships (see Table 6.5). Similarly, for shopping activities,
it was observed that people were more likely to shop online between time one, i.e., between
00:00:00 and 03:59:59 as compared to between 08:00:00 and 23:59:59. Meanwhile, forleisure, time 2 (04:00:00-07:59:59), time 4 (12:00:00-15:59:59), time 5 (16:00:00-19:59:59)
showed a negative relationship. Indicating that people are more likely to perform online
leisure between 12 AM and 4 AM. For online social activities, similar negative relationships
were observed for time 5 (16:00:00-19:59:59) and time 6 (20:00:00-23:59:59). It is interesting
to note that the time period after midnight and before the early morning was the mostconducive time to participate in online virtual activities across different activity types.
Table 6.5. Effect of variables on the choice of physical vs. virtual activities
Explanatory Variables
Constant
Gender (0=Male; 1=Female)
Age
Marital (0=Single; 1=Married)
Education (0= Not graduate; 1=
Bachelor’s degree or above)
Job (0=Non-office worker;
1=Office worker)
Car ownership (0= No; 1=Yes)
Motorbike ownership (0= No;
1=Yes)
Family size
Avg. income (in mil. IDR)
Time 2 (04:00:00-07:59:59)
Time 3 (08:00:00-11:59:59)
Time 4 (12:00:00-15:59:59)
Time 5 (16:00:00-19:59:59)
Time 6 (20:00:00-23:59:59)
Sigma (random parameter)
Number of individuals
Sample size
LL (0)LL (Final)
AIC
Variance Explained by (%)
Observed Variables
Unobserved inter-individual
variables
Other unobserved variables
including intra-individual
variations.

Eating (Online
food delivery)
Est.
t-stat
-1.90
-0.99
0.54
1.16
0.12
1.53
0.25
0.35

Shopping(online)Est.
t-stat
-2.26
-0.81
0.4
0.55
0.11
0.83
-0.6
-0.55

Leisure(online)Est.
t-stat8.22
1.98 b
0.91
0.65
-0.03
-0.17
0.51
0.35

Est.
6.58
-0.57
-0.25
1.5

t-stat2.80 a-0.97-2.33 b1.66 c

0.2

0.37

-1.31

-1.77 c

-1.5

-1.17

-0.62

-1.01

0.75

0.81

0.76

0.63

2.17

0.8

-0.71

-0.66

-0.22

-0.45

0.82

1.19

-0.39

-0.38

0.02

0.03

0.65

1.18

-0.16

-0.25

-1.86

-1.33

-0.76

-0.95

-0.11
-0.19
-2.05
-1.09
-1.6
-2.11
-0.29
-3.95

-0.33
-1.16
-2.51 b-1.32
-2.27 b-3.42 a-0.37
-5.56 a121
1864
-1292.03-358.92
749.83

0.12
-0.07
-0.53
0.05
-0.31
-0.72
-0.73
3.55

177
1592
-1103.49-714.881461.75

-0.78
0.88
-0.13
-2.10 b-1.74 c-2.52 b-2.48 b7.80 a137
840-582.24-338.28708.57

24.10

7.85

24.18

22.68

46.91

66.87

62.59

61.30

28.99

25.28

13.23

16.02

-0.28
-0.13
0.07
-0.002
-1.32
-1.15
-1.22
-2.31

c

-1.73
-1.66 c0.18
-0.004-3.93 a-3.68 a-3.69 a-9.00 a

-0.18
0.1
-0.08
-1.14
-0.98
-1.33
-1.32
2.95

Social (online)

0.79
-0.77
-1.46
0.14
-1.00
-2.15 b-2.11 b9.04 a163
2030-1407.09-738.021508.04

Notes: Est. Represents parameter estimates; variables Age, Family size, and Avg. income was introduced in the model as
continuous variables. (a) significant at the 1% level, (b) significant at the 5% level, (c) significant at the 10% level

The analysis of the contribution of variables towards the total variance of utility
differences shows that for all activity types, the contribution of observed variables is far lessthan that of unobserved variables. For eating activities, observed variables only explain
24.10% of the variance in the total utility difference. Meanwhile, unobserved interindividual variables explain 46.91%, and other unobserved variables, including intraindividual variations, explain 28.99% of the total variance. A similar trend was observed for85

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

other activity types as well, although the unobserved inter-individual variables explain arelatively higher 66.87%, 62.59%, and 61.30% of the total utility differences in shopping,leisure, and social activities, respectively (differences calculated relative to their respective
physical activities) . The unobserved variables include all individual-specific unobserved
variables, including the attitudinal and personality-based variables, making specificindividuals more prone to perform virtual activities than others. Although a detailed,comprehensive analysis on those variables is needed in the future to fully understand theeffect of MSTP on travel and activity behavior, it is worth emphasizing that, while travel
decisions rely more on contextual factors (Chikaraishi et al., 2010, 2009), decisions on
virtual activity engagement seem to be dependent more on individual specific attributes.
Collecting more attitudinal variables could be one way to explore further the factors affecting
virtual activity engagement.
6.5.

CONCLUSION

This study presented the framework for and preliminary findings from a smartphone-based
activity-travel survey conducted in Jakarta, Indonesia. In the first part, we showed theframework of the activity-travel survey using FMS. Then we showed the preliminary result
from our data analysis regarding the impact of ICT (MSTP) on activity and travel behavior
on the individual scale. The framework of the survey consisted of four distinct but
interconnected parts, which collected information regarding the effect of MSTP on activitytravel behavior. The four parts of the survey included, 1) socio-demographic questionnaire,2) activity-travel survey using X-ING mobile phone application (with separate diaries for
capturing information on travel and activities), 3) online activities survey using X-ING
application, and 4) context-aware SP surveys (with separate surveys for eating out behavior
and online activities). This study addressed certain pertinent literature gaps using andapplying the survey method in a developing country scenario. First, by developing contextaware SP surveys for “eating out” or “ordering online” activities based on the RP informationfrom users, the study collected important and relevant information to directly analyze theeffect and importance of MSTP. Second, it captured rich information regarding additional
online or virtual activities conducted by users along with their primary activities using asmartphone-based survey. This provided the scope to directly compare the two activity
alternatives, i.e., physical vs. virtual. Finally, the application of the method to Indonesia
helped accumulate new information about the changing trends in the use of MSTP and
activity-travel behavior in developing countries.
This study also describes the information collected from the survey and performs
basic analysis on the factors that influence activity participation behavior. The key findings
could be summarized as follows: 1) out of all the stay activities, a high proportion of themwas either stay-at-home activity (38.51%) and work (32.07%) activities. 2) The use ofprivate cars (29.96%) and motorbike (19.76%) were the most popular modes of transport,
representing how the respondents are more relying on a private vehicle. 3) Among the online
activities, leisure (36.96%), social (25.56%), and ordering food online (17.31%) were themost commonly conducted virtual activities. 4) For the SP surveys, it was observed that in
53% of the scenarios, users chose to shift to order food online than making a trip to eat out.
Meanwhile, in the case when MSTP ceased to exist, i.e., a case with no online alternatives,in 58% and 74% of the scenarios, the users chose to make a physical trip to the nearest
restaurant and shop to participate in eating and shopping activities, respectively. These
clearly reflect the importance of MSTP in the current context and how they could play an
86

Chapter VI – Changes in Activity-Travel Decision Under the Influence Of MSTP

important role in modifying activity-travel patterns. Finally, 5) an analysis of the effect ofvariables on the choice of performing virtual activities relative to physical activities showedthat unobserved variables, especially inter-individual unobserved variables, explained the
majority of the variations in the difference of utilities between virtual and physical activities.
This indicates the importance of individual unobserved variables such as attitudes andpersonalities of individuals towards ICT use. They might be pivotal in understanding the full
extent of interrelationships between ICT and activity-travel behavior.
The study's findings clearly highlight the importance of MSTP and how seamlessly
it has integrated with our lives. These could have major implications on transportation andland-use policies. MSTP could reduce user trips in the short run but could add additional
trips from the supplier’s end. Meanwhile, in the long run, these services could change theland use structure of the cities. In the past, especially in Indonesia, the function ofsmartphones has been limited to that of a daily communication tool (e.g., sending themessage, chatting, and video calls) (See Gifary, S., 2015). With the presence of MSTP, ourempirical results show that people are now significantly utilizing their smartphones not only
for communication purposes but also for using online services. On the other side, theavailability and use of a flexible smartphone-based survey tool like FMS have become oneof the solutions in fulfilling the challenges of collecting the activity-travel behavior data.
The flexibility of such tools provides the opportunity to keep up with the rapidly evolving
MSTP systems. In our case, we modified the tool to capture virtual activities.
The smartphone-based survey conducted in Jakarta, Indonesia, addressed importantresearch gaps and provided relevant information to comprehensively analyze the impact ofMSTP on activity-travel behavior. However, the study has several limitations that should be
addressed in the future. First, while capturing the online activities (i.e., 3rd part of the survey),
the actual duration of online activities was not captured since it is attached with the physicalactivities and trips’ duration. The actual duration is an important factor that helps incalculating the time allocation to activities. Studies in the future can ensure developing
applications which capture that without increasing the burden on respondents. Second, thesmartphone data might have some measurement error related to the sensors such as GPS(e.g., travel times and route trajectories). Third, as the survey was limited to people who
owned a smartphone and had internet access, the recruitment process might have led tobiases in sampling people who have a higher propensity to use ICT services, such as younger
people. Future studies must put considerable attention in ensuring the representativeness ofthe study sample; one way to do this is to provide smartphones on loan during the survey
period. Another important future research agenda is the utilization of such unique data. This
study only showed preliminary results and did not conduct a deeper analysis of therelationship between virtual and physical activities. We are now developing an activitybased model that can simultaneously handle both virtual and physical activities to capture
the impacts of MSTP on activity-travel behavior comprehensively. We believe that suchresearch has great importance in handling the impacts of MSTP on society properly.

87Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

Chapter 7: THE IMPACT OF ONLINE FOOD DELIVERY SERVICEON EATING-OUT BEHAVIORThis chapter explores the impact of MSTP’s online food delivery service on eating out
behavior through the analysis of context-aware stated preference data. In this study, we
attempt to answer two research questions regarding the impact of MSTP on the individual’s
eating behavior, including “How the presence of MSTP’s online food delivery service willaffect people’s eating behavior?” (RQ 4.1) and “What factors that affects MSTP’s service
level?” (RQ 4.2). This chapter also contains the introduction of the study, methodology
approach, study area, data used, result and discussion, and conclusion of the study.
7.1.

INTRODUCTION

Information and communication technologies (ICTs) are the critical enablers of innovationin transport systems and daily lives. ICTs improve people’s access to goods, services, andeven jobs through virtual connectivity, allowing them to participate in activities across space
and time. The ICT systems are evolving quickly and have the potential to influence people’s
activity and travel patterns. In the short term, ICTs have been observed to substitute,complement, and modify physical travel (Andreev et al., 2010) while improving access toactivity opportunities (Novo-Corti et al., 2014). Meanwhile, in the long term, they have thepotential to affect congestion, emission levels, and urban form. With such important policylevel impacts, analyzing the complex interrelationships between ICT and activity-travel
behavior has become an essential theme in transportation research in recent years.
One of the latest ICT innovations is multi-service transport platforms (MSTPs). By
utilizing the innovation of technology to improve people’s daily lives, MSTPs can be defined
as an online-based platform that provides access to a wide range of services, including ridehailing transportation, food delivery service, courier service, and daily need services (suchas cleaning services, massages, and hair salons). The main components of the MSTPs are(1) the efficient provision of transport services through real-time data processing and (2) the
integration of transportation services and other daily life support services. MSTPs play therole of a mediator between the demand from the consumer’s side and the supply from theprovider’s side and get involved in the direct distribution of goods and services by relying
on their drivers and fleets. In daily life, the presence of MSTPs can change how people
virtually access the services to fulfill their daily needs. MSTP allows people to fulfill theirneeds, including goods (e.g., meals and groceries) and services (e.g., massage service andcar repair) without traveling.
In Indonesia, MSTPs (e.g., Gojek and Grab) have become an important part ofpeople’s daily life. One of the most used MSTP services is the online food delivery service,where people can order foods from food merchant partners across Indonesia. MSTPs allow
people to virtually access the services, relaxing their time and space constraints. Because
they do not need to allocate time to travel to get their meal, they can use the time to perform
other activities. Pigatto et al. (2017) found that online food delivery services allow
consumers to have a more comprehensive range of options to optimize their time usage,
resulting in the rapid growth of online delivery services. From the behavior study perspective,
several studies found a positive relationship between attitude toward technology adoption
and behavioral intention (Ingham et al., 2015; Chang et al., 2012; Wagner et al., 2016). The
rise of such online food delivery services may change eating-out behavior (demand side) and88

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

merchant behavior (supply side), as well as the interactions between consumers and foodmerchants (Atasoy et al., 2019). While the impacts of online food delivery services havebeen explored, as mentioned, more empirical works are certainly needed particularly to
improve our understanding of the indirect impacts of food delivery services.
Given the presented background, this study empirically identifies the impacts of thecontextual factors (i.e., time-space constraints and having a meal with friends/colleagues) as
well as the service level factors (including delivery cost, delivery time, food cost, andavailable food types) on the use of online food delivery services using a stated preference(SP) survey data collected together with a multi-day smartphone-based travel diary survey
in Jakarta, Indonesia. We believe that controlling contextual factors is key to not mislead theimpacts, which have not been well addressed in existing studies. More specifically, a longer
waiting time for food would prevent people from using online food delivery services,
partially because of time-space constraints they have. For example, in the case of lunchtime,
people must go back to their office after getting lunch; thus, people may not be able to use
the food delivery service if the waiting time is too long. In other words, ordering foods froma distant place would be possible only when time-space constraints are satisfied, but this
aspect has not really been explored in the literature. Another critical point that needs to beconsidered is that people often eat out to interact with friends and colleagues. With theconsideration of this social interaction function of the meal, it seems evident that not all
eating-out trips would be replaced with an online food delivery service. If the online fooddelivery service reduced the number of merchants in the central area of the city, as discussed
previously, the social interaction function that merchants and transport systems have jointly
provided (Urry, 2007) would be decreased. Understanding such social impacts of online fooddelivery services is crucial in forming a better public policy, yet the relevant works are still
very limited.
Two efforts have been made in the empirical analysis to avoid potential biases in theestimated impacts. First, contextual factors vary across trips; thus, it is not easy to set thecontext in the standard SP technique where all information is hypothetical (Hensher D.A.,
Reyes A.J., 2000). To give a realistic context, we use a context-aware SP survey scheme:
people first join an app-based activity-travel diary survey, and they are asked about thepossibility of shifting to the use of online food delivery service for a particular eating-out
trip. This allows for reflecting on the actual context the person had. However, it inevitably
leads to another challenge, i.e., a self-selection issue: the population in the data set becomes
not all individuals in the society, but all individuals who made eating-out trips, potentiallyleading to bias in the model estimation results. To control this potential bias, we employ oneof the propensity score methods, the inverse probability weighting (IPW) method: we firstestimate the propensity of eating out then use it to generate the weight used in the final model
estimation to identify the preferences on online food delivery service use. Another important
point to note is that although the presented survey method would let people consider
contextual factors they had, the analyst typically cannot observe all contextual factors. We
employ the mixed logit model to control these unobserved contextual factors where therandom term varies across eating-out trips.
7.2.

METHODOLOGY

Given the above background, this study attempts to empirically identify the impacts of thecontextual factors (i.e., time-space constraints and having a meal with friends/colleagues) as
well as the service level factors on the use of online food delivery service, including delivery

89

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

cost, delivery time, food cost, and available food types. In the empirical analysis, we use astated preference (SP) survey data collected together with a one-week smartphone-based
travel diary survey in Jakarta, Indonesia. In the empirical analysis, there are a couple ofmethodological challenges. First, contextual factors vary across trips, and thus it is not easy
to set the context in the standard stated preference technique, where all information ishypothetical (Hensher D.A., Reyes A.J., 2000).
To give a realistic context, we use an adaptive SP survey scheme: people first join an
app-based activity-travel diary survey. They are asked about the possibility of shifting to theuse of an online food delivery service for a particular eating-out trip. This allows forreflecting on the real context the person had. However, it inevitably leads to anotherchallenge: the population in the data set becomes not all individuals in the society, but allindividuals who made eating-out trips, potentially leading to bias in the model estimation
results. To control this potential bias, we employ the inverse probability weighting (IPW)
method, which is one of the propensity score methods: we first estimate the propensity ofeating-out then use it to generate the weight used in the final model estimation to identify
the preferences on online food delivery service use. In this study, we found that the use of
IPW is critical to remove the bias.
Another important thing is that, although the above survey method would let people
consider contextual factors they had, the analyst may not fully observe all contextual factors.
We employ the mixed logit model to control these unobserved contextual factors where therandom term varies across eating-out trips. Our empirical results also show that introducing
the random term at the trip level is critical to control biases from omitted variables.
7.2.1. Survey Methodology and Data Preparation
This study focuses on Indonesia's, particularly in Jakarta’s situation where the multi-service
transport platforms (MSTP), particularly GOJEK and Grab, have been operated since 2010.
These platforms provide ride-hailing services and various services supporting their daily-life
activities, including food delivery service, medicine delivery service, grocery shopping
service, daily need services such as house cleaning service, online payment, and many more.
They allow people to access various services without travel at a relatively affordable costand relatively real-time service. They now have become one of the best alternatives thatmany people choose. With the presence of the MSTP, the way of interacting between the
supply-side (merchants) and demand-side (users) might change, mostly because the platform
would relax users' time and space constraints to reach the services.
In Indonesia, there were more than 500,000 food merchants in 2020 that partnered
up with MSTPs for food distribution and more than 22 million active users every week.
MSTPs’ online food delivery service has been dominating 70–75% of Indonesia’s online
food delivery order market (Gojek News, 2019). Our study area is Jakarta, the capital city ofIndonesia (Figure 1). Jakarta has a very high population density of 14,464 people per square
kilometer (37,460/sq mi), while the metro area has a density of 4,383 people per square
kilometer (11,353/sq mi). Jakarta also has the highest number of MSTP users among other
cities in Indonesia, where around 8.8 million people (30–40% of the population) are active
MSTP users. With regards to the online food delivery service activities, as of the year 2018,
the Central Bureau of Statistics Indonesia confirms that 8.59% of food and beverages wereordered using online services.

90

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

Figure 7.1. Map of study area in Jakarta, Indonesia
To analyze the impact of MTSPs on an individual’s eating behavior, we conducted acontext-aware SP survey together with the multi-day smartphone app-based travel diary(revealed preference (RP)) survey from January 28th to February 3rd, 2020, in Jakarta,
Indonesia (see Safira et al. (2021) for details). We used office-based sampling, and thus all
respondents were workers in South Jakarta, Indonesia, who have used online food delivery
services (i.e., Go-Food by Gojek and GrabFood by Grab). In the survey, we used asmartphone-based app called X-ING (by Mobile Market Monitor (MMM),
www.mobilemarketmonitor.com). The application provided a wide range of travel attributes,
including location (origin and destination), travel time, travel purpose (activity), route choice(by GPS tracking), and mode choice.
For respondents who made eating-out trips, we further asked them to answer SPquestions. The SP survey was designed and implemented to observe preferences on the
respondents’ eating behavior when the online food delivery service was improved. This is
one type of context-aware SP question (Danaf et al., 2019), which combines a pivoting
technique (Hess and Rose, 2009) and an SP-off-RP approach (Train and Wilson, 2008). By
doing this, the real RP context, including the time-space constraints the respondents had,
was reflected when answering the question. Note that there is also another similar SP survey
design called an adaptive SP survey design (Fowkes and Shinghal, 2002) in which theattribute levels are dynamically modified depending on the previous choice results, but we
did not employ this scheme.
This survey was designed particularly to capture changes in eating out behavior.
Hypothetical scenarios with regard to online food delivery options were provided to users,
and their choice of whether they will shift to online food delivery or continue to conduct theeating-out activity was observed. This is an important aspect with respect to understanding
the effects of ICT on travel behavior as it will aid in analyzing if ICT will substitute physicaltravel in the case of eating-out trips. For each user, one of their eating-out activities was91Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

selected at random from the first week of their travel, and then based on their RP, attributelevels in SP were decided. Each user was then provided with five choice scenarios to choose
between the online food delivery option and their present eating-out trip. This context-aware
SP survey is deemed better than when all choice contexts in an SP survey are purely
hypothetical. It is because such a design accounts for context-dependent factors such as
motivation and constraints they had at that time. In addition, this kind of context-aware
survey design could capture the complex interdependencies between ICT use and travel
because their travel decisions may come from extrinsic motivations (e.g., getting a lunchmeal) or intrinsic motivations (e.g., interacting with friends, traveling, and having lunch).
The attribute levels for the SP survey were generated using the RP information, as
shown in Table 7.1. For a) delivery time for online food delivery, the travel time information
captured from travel before the eating-out activity (for one randomly selected context) was
utilized to create five different levels. Meanwhile, for b) delivery cost, travel distanceinformation for the previous trip before the eating-out activity captured automaticallythrough GPS sensors was utilized and multiplied with an assumed per km cost for deliveryof 6,000 IDR (0.43 USD) across five different levels. For c) a combination of ordered foodtypes, the same categories offered to users for their eating-out trips were utilized to create
four different levels, denoting the combination and the number of food items ordered. Finally,d) food cost for online food delivery and information from RP on the user’s actual
expenditure on the eating-out activity was utilized to create five different levels. The
variations in the food cost are an important factor, as often it is seen that MSTPs collaborate
with food merchants to provide services at discounted rates.
Table 7.1. Attributes and levels for context-aware SP survey for eating out activities
Attributesa) Delivery time for online food delivery.
(based on the actual travel time from the travel diary
data; revealed-preference-based question)b) Delivery cost for online food delivery.
(based on the actual travel distance from the travel
diary data; revealed-preference-based question)c) Combinations of Online food delivery's Food Types(1. Beverages, 2. Snacks/Sweets, 3. Fast food, 4.
Indonesian food, 5. Western food, 6. Eastern food, 7.
Bakso/Noodles)d) Food cost for online food delivery.
(based on the actual food cost from the activity
information data; revealed-preference-based question)

Level
6. 0.4* actual travel time
7. 0.7*actual travel time
8. 1.0*actual travel time
9. 1.3*actual travel time
10. 1.6*actual travel time
6. 0.4*6,000 IDR*actual travel distance
7. 0.7*6,000 IDR*actual travel distance
8. 1.0*6,000 IDR*actual travel distance9. 1.3*6,000 IDR*actual travel distance
10. 1.6*6,000 IDR*actual travel distance
5. One food type
6. Three food types
7. Five food types
8. Seven food types
6. 0.8*actual food cost
7. 0.9*actual food cost
8. 1.0*actual food cost
9. 1.1*actual food cost
10. 1.2*actual food cost

The five scenarios were presented to participants with two choices in each, with theactual eating-out trip information on the left side and the attribute levels for the online fooddelivery option on the right (Figure 7.2.), where alternatives are (1) continuing to conduct
the eating-out trip, and (2) shifting to the online food delivery services. As the SP survey
was conducted at a later time, we strived to make respondents remember the actual
conditions they felt at the time of participating in that activity. By showing the date when
they took the eating-out trip, it is hoped that the respondents will be able to remember the92

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

conditions and constraints they had at that time. The question was then posed to the
respondents as part of the SP survey, "by considering all the activities and constraints you
have at that time if the following online food delivery service is available, will you be shiftingfrom eating out to ordering an online food delivery service?"

Figure 7.2. Context-aware SP questionnaire for eating out behavior
7.2.2. Modeling Framework
This section introduces a modeling framework to empirically identify the impacts of thelevel of service factors and contextual factors on the use of online food delivery services. As
we briefly discussed in Section 1, it is not easy to introduce all contextual factors in astandard SP survey where all choice contexts are purely hypothetical. A feasible way tointroduce realistic contexts is to ask respondents to answer SP questions in a real RP context(Huynh et al., 2017). We employed this context-aware SP survey approach. Specifically, we
randomly picked up observed eating-out trips and asked respondents to answer whether theywould like to shift to online food delivery services given the RP context. However, thisprocess would lead to a self-selection issue by excluding respondents who did not haveeating-out trips. To alleviate this self-selection issue, we used an inverse PWT (IPWT)
method. In the method, we first estimated the propensity of having an eating-out trip for eacheating behavior where the alternatives of eating behavior include eating-out and online fooddelivery service. Using the weights constructed from the estimated propensities, wedeveloped an SP model on the use of online food delivery services. This process could
remove the biases caused by the fact that the sample (i.e., people who made eating-out trips)
used for the model estimation is systematically different from the population (i.e., people
who made eating-out trips and who had a meal using an online food delivery service).
Note that because online food delivery services are already available in the market,
it apparently seems that the RP data are good enough to explore preferences on the use of
online food delivery services but taking the proposed SP approach is crucial to properlyreflect contextual factors. It is well known that contextual factors are dominant in decision
making; however, many of them are typically unobserved (Chikaraishi et al., 2009 & 2011).
To control these unobserved contextual factors, it would be straightforward to show different
online food delivery services to respondents repeatedly under the same RP context andobserve how respondents change their decisions. Such repeated observations allow for
introducing additional random terms representing unobserved trip-specific contextual
factors analogous to random effects in panel data analysis. In the empirical analysis of this93

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

study, we employed a panel mixed logit model to control such unobserved trip-specificcontextual factors. It should also be noted that a popular SP–RP combined model (BenAkiva and Morikawa, 1990) typically allows us to obtain statistically accurate estimation
based on actual and hypothetical behavior (Sanko, 2001), but a straightforward applicationof this approach is not appropriate for our case study because, different from Ben-Akiva andMorikawa (1990), the SP data were not obtained from the population.
7.2.2.1. Estimation of the propensity score
We assume that whenever people want to use food services, they have two options: going torestaurants (i.e., making eating-out trips) and using online food delivery services. Given thisassumption, we obtain the propensity of having an eat-out trip by estimating the following
logit model:
exp(𝑣𝑖𝑡 )
(7.1)
𝑝𝑖𝑡 =
exp(𝑣𝑖𝑡 ) + 1
where, 𝑝𝑖𝑡 is the probability of choosing eating-out in the 𝑡-th eating behavior ofindividual 𝑖 (called propensity score), 𝑡 ∈ 𝑇 = {𝑇𝑒 , 𝑇𝑓𝑑 }, where 𝑇 is a set of all observed
eating behavior, 𝑇𝑒 is a set of observed eating-out trips, and 𝑇𝑓𝑑 is a set of observed online
food delivery service uses, 𝑣𝑖𝑡 is the systematic utility for making an eating-out trip, and𝑣𝑖𝑡 = βxit where xit is a vector of explanatory variables, and β is a vector of parameters tobe estimated. After obtaining the estimated propensity score 𝑝̂ 𝑖𝑡 , we take the inverse ofpropensity score as a weight, i.e.,𝑤̂ 𝑖𝑡 = 1/𝑝̂ 𝑖𝑡 .

(7.2)

7.2.2.2. Model specification for eating choice behavior
We then develop a panel binary mixed logit on the use of online food delivery service using
SP data, where the utility is defined as follows:
𝑢𝑠𝑖𝑡 = αzsit + 𝜂𝑖𝑡 + 𝜀𝑠𝑖𝑡

(7.3)

where α is a vector of parameters to be estimated, zsit is a vector of explanatory
variables for the s-th SP question for individual i’s t-th trip (𝑡 ∈ 𝑇𝑒 ), and 𝜀𝑠𝑖𝑡 is the error term
following a standard Gumbel distribution. 𝜂𝑖𝑡 is another random term following a normal
distribution. This would capture the impacts of unobserved trip-specific attributes on the
choice. The probability of choosing an online food delivery is defined as follows:exp (αz+𝜂 )𝑖𝑡𝑝𝑠𝑖𝑡 = exp(αz sit+𝜂 )+1
sit

𝑖𝑡

(7.4)

The following weighted likelihood function 𝐿𝐿 is used in the model estimation tocontrol possible biases caused by the self-selection issue mentioned above.
𝐿𝐿 = ∫ ∑ ∑
𝑖

𝑡∈𝑡𝑒

𝑤
̂ 𝑖𝑡 ln(𝑝𝑠𝑖𝑡 )𝜑(𝜂𝑖𝑡 )𝑑𝜂𝑖𝑡(7.5)

For the model estimation, we use the glmer function of R-package lme4 (Bates et al.,2012).

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Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

7.3.

DATA

In this study, we collected data from the respondents who completed the multi-day activitytravel diary survey. We found that 114 respondents were conducting eating activities that
included both eating-out trips and ordering foods through online delivery services. Out of
557 eating activities, 272 were eating-out trips, and the remaining 285 were the use of online
food delivery services. We also captured personal socioeconomic and demographic
characteristics from users. It included questions on gender, age, income, family size, vehicle
ownership, education level, and occupation type. Table 7.2. summarizes the number ofindividuals, samples of SP questionnaire, number of eating activities, and the explanatory
variables used for the model estimation and their basic statistics.
Table 7.2. Data Description of Variables
Variable
Category
Eating behavior (RP)
Eating-out activities
Online food delivery services
Eating behavior (SP)Keep making an eat-out trip
Shifting to order online food delivery service
Explanatory Variables
GenderMale
Female
Age
18-22
23-27
28-32
33-37
38-42
Marital Status
SingleMarriedJob
Office Worker
Non-office Worker
Average Income
Less than 1
per Month (in1-1.99Mil. IDR)
2-3.994-5.996-7.998-9.99More than 10
Average Income
Less than 1
per Month (in1-1.99Mil. IDR)
2-3.994-5.996-7.998-9.99More than 10
Location Attributes of Activities
Home Before
Location of Previous Activity is Home
OtherwiseHome After
Location of Next Activity is Home
OtherwiseWork Before
Location of Previous Activity is Workplace
OtherwiseWork After
Location of Next Activity is Workplace
Otherwise

Total
557
272
285
570
271
299

Percentage
100.00%48.83%
51.17%
100.00%47.54%
52.46%

49
65
12
44
28
18
12
72
42
107
7
3
7
21
42
15
10
16
4
7
24
42
13
8
16

42.98%
57.02%
10.53%
38.60%
24.56%
15.79%
10.53%
63.16%
36.84%
93.86%
6.14%
2.63%
6.14%
18.42%
36.84%
13.16%
8.77%
14.04%
3.51%
6.14%
21.05%
36.84%
11.40%
7.02%
14.04%

55
515
65
505
150
420
110
460

9.65%
90.35%
11.40%
88.60%
26.32%
73.68%
19.30%
80.70%

95

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior
Variable
Social Interaction

Category
Having a Desire to Interact with Others
Otherwise

Total
390
180

Percentage
68.42%
31.58%For the individual and household attributes, we used gender, age, marital status,
respondents’ occupation type, average monthly income of respondents, average monthly
household expenses, dummy variables indicating the location before and after eating
behavior, and the desire of having social interaction. The last attitudinal variable on social
interaction was constructed from a 1–6 Likert scale attitudinal question, that is, “If I havesomeone to eat out with, I prefer to eat in a real restaurant rather than using online-based
food delivery services,” where negative answers (strongly disagree, disagree, and slightly
disagree) are set as zero, while positive answers (slightly agree, agree, and strongly agree)
are set as one.
For the eating choice behavior model estimation, we also use the variable from the SPquestionnaire as our explanatory variables. Table 7.3 describes the variables from the SPquestionnaire that represented the online food delivery services, including the variety of food,delivery time (in minutes), delivery cost (in IDR), and food cost (in IDR).
Table 7.3. Context-aware SP survey variable
Variable
Sample Size
Variety of Foods

Delivery Time (in Minutes)

Delivery Cost (in IDR;
10,000 IDR = 0.69 USD)

Food Cost (in IDR; 25,000
IDR = 1.74 USD)

7.4.

Category
1
3
5
7
< 10
10-19
20-29
30-39
40-49
50-59
> 60
< 10,000
10,000-29,999
30,000-49,999
50,000-69,999
70,000-89,999> 90,000< 25,000
25,000-74,999
75,000-124,999
125,000-174,99> 175,000

Total
570
145
133
142
150
248
165
57
31
17
20
32
242
158
68
40
14
48
148
240
75
28
79

Percentage
100.00%25.44%
23.33%
24.91%
26.32%
43.51%
28.95%
10.00%
5.44%
2.98%
3.51%
5.61%
42.46%
27.72%
11.93%
7.02%
2.46%
8.42%
25.96%
42.11%
13.16%
4.91%
13.86%RESULT AND DISCUSSION

To handle the self-selection issue, we first estimate the propensity score model. Table 7.4.
shows the estimation results; it is confirmed that people who are young, male, or married orhave high-income tend to choose eating out rather than online food delivery services.
Regarding the scheduling-related factors, it is confirmed that those who have a job with afixed schedule tend to choose eating out, and people who were staying at the workplace tend
to choose eating out as well. We also found that those who have a desire to interact with96

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

others while eating tend to choose eating out rather than online food delivery services.
Table 7.4. The estimation results of the propensity score model
Explanatory Variables
Estimate
t-Values
Constant
-0.71
-1.08
Age
-0.07
-5.14
Gender (0: Male; 1: Female)
-0.21
-2.21
Marital Status (0: Single; 1: Married)
0.54
3.03
Average Individual Monthly Income (mil IDR)
0.05
2.25
Average Household Monthly Expenses (mil. IDR)
0.03
2.11
Dummy for Occupation (0: occupation without a fixed
2.04
5.03
schedule; 1: occupation with a fixed schedule)
Dummy for Home Before (1: location of the previous activity is
0.21
1.06
home; 0: otherwise)
Dummy for Home After (1: location of next activity is home; 0:
0.04
0.21
otherwise)
Dummy for Workplace Before (1: location of the previous
0.61
3.08
activity is workplace; 0: otherwise)
Dummy for Workplace After (1: location of next activity is a
-0.01
-0.06
workplace; 0: otherwise)
Social interaction (1: having a desire to interact with others; 0:
0.32
3.87
otherwise)
AIC
284.10Initial log-likelihood-386.08
Final log-likelihood-294.95
Sample size
557(***) significant at 0.1% level; (**) significant at 1% level; (*) significant at 5% level

Sig. Sign
***
*
***
**
*
***

**

***

Based on the estimation results shown in Table 7.4, we calculate the weights used in
the following model estimations. Table 7.5 shows the estimation results of the models foreating choice behavior. We estimated how the presence of MSTP’s online food delivery
service would affect people’s eating behavior by including the variables such as the social
interaction, delivery time (in an hour), delivery cost (in 100,000 IDR), variety of food, foodcost (in 100,000 IDR), the actual travel time for an eating-out trip, and the actual travel costfor the eating-out trip (in 100,000 IDR). In total, we estimated four models to identify the
impacts of adding a random term representing unobserved trip-specific contextual factors
and the impacts of the weights introduced. The results confirm the significant impacts ofboth the random term and weights on the estimated parameters. More specifically, theintroduction of the random term changes the sign of parameter on delivery time, while taking
weights into account changes the statistical significance of delivery time. The latter indicatesthat the population would be more sensitive to delivery time than the sample (i.e., eating-out
trips). This can be logically understood because making eating-out trips implies that people
have fewer time constraints compared with those who used online food delivery services.
The estimated parameter values of the model with weights and random effects indicate thatthe delivery time, delivery cost, the actual travel time, and the actual travel cost are
significant, while the variety of foods, food cost, and attitudes toward social interaction are
not significant.
Based on our findings, we calculated the value of travel time for an eating-out trip
and the value of waiting time for an online food delivery service. Note that the absolutevalues of parameters for SP variables are higher than those for RP variables as indicated by
Ben-Akiva and Morikawa (1990), and thus the absolute values between RP and SP should
not be directly compared. To make the comparison possible, we calculated the value of travel
time for an eating-out trip and the value of waiting time for online food delivery services,

97

Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

where the former is 54,837 IDR/hour while the latter is 62,148 IDR/hour. This is apparently
counterintuitive because people can conduct other activities while waiting for online fooddelivery services, and thus the value of waiting time should be lower than the value of travel
time. However, several potential reasons make the value of waiting time greater than thevalue of travel time. First, using an online food delivery service is less flexible in terms ofschedule modifications. For example, when travel time/food delivery time is increasing
because of traffic congestions against their expectations, those who choose to eat out canchange their destination to obtain their meal within the time constraint, but those who choose
food delivery service may not be able to do that. Another possible reason is that a longer
delivery time implies that people may not be able to have a fresh-cooked dish. Although weshould further confirm whether these reasons are true in the future, our empirical results
indicate that the impacts of online food delivery services on the spatial distribution of online
food merchant’s world could be modest because most people may use online food delivery
services nearby, and thus merchants moving out to suburbs may have fewer online fooddelivery services’ customers. However, it should be noted that in eating-out trips, they must
make an additional trip to return to the original location, and thus the benefits of using online
food delivery service would still be high even when the value of waiting time is higher thanthe value of travel time.
As confirmed, delivery time is one of the main factors affecting the use of online
food delivery services. It may be natural to consider that delivery time will be decreased with
the increased number of merchants nearby since it makes it easier to find MSTP drivers. To
confirm this hypothesis empirically, we further explored the association between delivery
time and the density of online food merchants. We first prepared travel time and delivery
time data of online motorbike ride-hailing service (online ojek) using Google MAPs route
search service for OD pairs of 262 zones in Jakarta (Figure 7.3). More specifically, we
collected travel time and travel cost data of online ojek on Monday, November 2nd, 2020,
at 10.00 a.m. In Indonesia’s Google MAPs route search service, there is information on the
online ride-hailing service’s approximation cost for motorbike and car ride-hailing services.
We took the average cost of the services as the travel cost of online motorbike ride-hailing(online ojek). It is noted that to order the services, people cannot use the Google MAPs
directly; they must use the MSTP’s application to order the services, and thus actual cost
could be different from the one shown in Google MAPs. We also obtained information ondelivery time and delivery cost of MSTP’s online food delivery services. We used the Gojek
application to obtain the information of GoFood (online food delivery services) delivery timeand delivery cost from one zone to the other zones. It is noted that if we order the food using
MSTP’s online food delivery services, they might have some monetary incentives (e.g.,
coupons, price discounts, and other promotions) that may result in a different price at thetime of ordering the food. However, we only included the regular average price of MSTP’s
online food delivery cost.

98Chapter VII – The Impact of Online Food Delivery Service on Eating-Out Behavior

Table 7.5. The model estimation results of eating choice behavior
Without 𝜼𝒊𝒕

Constant
Social interaction
Delivery time (hour)
Delivery cost (100,000 IDR)
Variety of foods
Food cost (100,000 IDR)
Travel time (hour)
Travel cost (100,000 IDR)
Random effect: 𝜎𝜂2
AICInitial log-likelihood
Final log-likelihood
Sample size

Without Weight
Estimate
t-values-2.34-13.12
-0.21
-1.35
-0.33
-2.45
-0.03
-3.51
0.41
12.63
0.52
4.76
-0.14
-2.29
-0.07
-1.35
422.54-395.09-252.98

**
*
**
**
**
*

With 𝜼𝒊𝒕

With Weight
Estimate
t-values-2.37-14.00
-0.28
-1.40
0.76
1.68
-1.81
-2.92
0.46
11.40
0.49
3.25
-0.84
-1.0438
-0.43
-0.82

**
.
**
**
**

437.88-395.09-240.48

Without Weight
Estimate
t-values
-0.53
-0.41
-0.14
-0.69
-1.14
-2.13
-0.81
-1.36
-0.003
-0.08
-0.12
-0.74
-0.146-1.93-0.062-0.07
9.737
361.43-395.09-221.40*.

With Weight
Estimate
t-values
-0.21
-0.15
-0.37
-1.51
-1.15
-2.14
-0.63
-3.97
-0.01
0.13
-0.15
-1.00
-0.25
-1.74
-0.15
-1.88
9.915
246.74-395.09-215.54

*
**

.
.

570

(1) ** significant at 1% level; * significant at 5% level; (.) significant at 10% level(2) At the time of the survey was conducted 1 USD = 14,602.75 IDR

99

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior(a) Map of 262 zones(b) Map of Jakarta’s Road Networks

Figure 7.3. Maps of 262 zones and the road networks of Jakarta
We collect the data of several transportation modes on Monday, November 2nd, 2020,
at 10.00 a.m. The data that we collected includes:
1. Travel time and travel cost of a private vehicle.
As we know that in Google MAPs route search services, there is no information of
approximation cost for private vehicle usage available on the website. Therefore, to obtainthe travel cost of private vehicle usage, we use travel distance information to calculate theaverage fuel cost of the distance. In this study, we simplified the formula of the consumption
rate of fuel by Ipatov (1982) by ignoring all of the model coefficients and the vehicle capacityand ended up with:
𝑐
(7.6)
𝑡𝑐𝑖𝑗
= (𝑡𝑑𝑖𝑗 ∗ 𝑓𝑐𝑐 )/1000
Where,
𝑐𝑡𝑐𝑖𝑗= travel cost of the car from zone i to zone j
𝑡𝑑𝑖𝑗 = travel distance of from zone i to zone j
𝑓𝑐𝑐 = the average price fuel consumption of 1 liter for the car (543.24 IDR)
2. Travel time and travel cost of private motorbike.
Similarly, with the travel cost of a private vehicle, there is no information of theapproximation cost for private motorbike usage available on the website. Therefore, toobtain the travel cost of the private motorbike usage, we use travel distance information to
calculate the average fuel cost for the motorbike for the certain travel distance among thezones, with the assumption that the average travel price of fuel consumption of 1 liter for a
motorbike is cheaper than the car.
𝑚
(7.7)
𝑡𝑐𝑖𝑗
= (𝑡𝑑𝑖𝑗 ∗ 𝑓𝑐𝑚 )/1000
Where,
𝑚𝑡𝑐𝑖𝑗= travel cost of a motorbike from zone i to zone j
𝑡𝑑𝑖𝑗 = travel distance of from zone i to zone j
𝑓𝑐𝑚 = the average price fuel consumption of 1 liter for a motorbike (443.24 IDR)
3. Travel time and travel cost of public transit.
To obtain the travel time and travel cost of public transit, we calculate the average
travel time and travel cost from all public transit modes that individuals may use from one
zone to the other by neglecting the types of mode, transit time, and walking time.
100

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

4. Travel time and travel distance of walking.
Since we cannot quantify the cost of walking in the transportation research yet, weonly obtain the travel time and travel distance of walking for our transport service dataset.
5. Travel time and travel cost of online motorbike ride-hailing service (online ojek).
In Indonesia’s Google MAPs route service, there is information on the online ridehailing service’s approximation cost for motorbike and car ride-hailing services. Then we
took the average cost of the services as the travel cost of online motorbike ride-hailing(online ojek). It is noted that to order the services, people cannot use the Google MAPs
directly; they must use the MSTP’s application to order the services.
6. Travel time and travel cost of online car ride-hailing service.
In line with the data gathering for online ojek, we use the average cost of the online
car ride-hailing services as the travel cost.
7. Delivery time and delivery cost of MSTP’s online food delivery services.
To obtain the data of delivery time and delivery cost of MSTP’s online food delivery,
we only can access the information through their platform’s application. Therefore, in thisstudy, we use the GOJEK application to get the information of their GOFOOD (online fooddelivery services) delivery time and delivery cost from one zone to the others zone. It isnoted that if we order the food by using MSTP’s online food delivery services, they might
have some monetary incentives (e.g., coupons, price discounts, and other promotions) that
may be resulting in a different price at the time you order the food. However, we only include
the regular average price of MSTP’s online food delivery cost.
We also obtained the location information of online food merchants from Google
MAPs using web crawler tools. We extracted the location information on December 3rd,
2020. The extracted data include the name of facilities/stores, type of facilities, street address,
coordinate location, opening hours, and other variables. In this study, we categorized thefood merchants into two groups: (1) the “combination” food merchants who provide bothdine-in and online food delivery services and (2) the online food merchants who only provide
an online food delivery service (no dine-in service available at their store). Regardless of thetype of food merchants, we only considered the food merchants with the fixed location of
stores. The mobile food merchants such as mobile street food vendors were omitted. Using
the web crawler data collection method, we successfully obtained 4,458 combination foodmerchants and 3,718 online food merchants across the area in Jakarta.

Figure 7.4. Distribution of food merchants
101Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

The delivery time includes both travel time by online ojek and the additional timesuch as time for searching a driver. Because this additional time makes online food delivery
service less efficient, we employed the ratio of delivery time and travel time by online ojek
as a service efficiency index for online food delivery service. Figure 7.5. shows the indicatorvalues by the density of food merchants. The figure indicates that the additional timedecreases with an increased density of online food merchants. In other words, people havemore time-saving benefits if they order foods from an area that has a high density of online
food merchants.
In general, drivers standby around areas that have higher demand. If the drivers
standby in areas that have more online food merchants, the chance for them to obtain someorders is higher than in areas with a smaller number of online food merchants. Having more
drivers in the area may reduce the additional time for searching for a driver and hence reducethe delivery time. This new kind of agglomeration must be considered when we explore thelong-term impacts of online food delivery services on the distribution of food merchants.
From the modeling perspective, in the future, delivery time should be dealt with as an
endogenous variable because delivery time depends on the demand in the area.

Figure 7.5. Correlation between service efficiency index andthe density of combination and online food merchants
7.5.

CONCLUSIONSThis study examined the impact of online food delivery services on individuals’ eating
activity behavior. The empirical analysis was conducted using context-aware SP survey datacollected in Jakarta, Indonesia, together with multi-day smartphone-based travel diarysurvey data. Although this context-aware SP survey leads to a self-selection issue in thesense that all the respondents were persons who made eating-out trips, it allows us to elicit
respondents’ preference on the use of online food delivery services under the real-time–102

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

space constraints they had. In our empirical model estimation, we used the IPWT method tocontrol biases caused by the self-selection and introduced a random term to control
unobserved trip-specific contextual factors. The empirical results indicate the importance ofusing both IPWT and random-effect models to alleviate the biases in the estimates.
Empirical results showed that delivery time and delivery cost, along with the otherunobserved random variables, are key factors affecting people’s preferences on the use of
MSTPs’ online food delivery services. Our empirical results also confirmed that the valueof waiting time for online food delivery services (62,148 IDR/hour) is larger than the valueof travel time for an eating-out trip (54,837 IDR/hour), potentially because (1) using online
food delivery service is less flexible in terms of schedule modifications, and/or (2) longer
delivery time implies that people may not be able to have a fresh-cooked dish, though further
analysis is needed to reach a general conclusion.
The delivery time of MSTPs’ online food delivery service includes both travel timeby the online ojek and the additional time of searching for a driver who can pick up the order.
We empirically confirmed that the additional time could be substantially shorter with theincrease in the number of online food merchants nearby because drivers would standbyaround the area that has higher demand. This may become a new agglomeration force foronline food merchants that may need to be considered when evaluating the long-term
impacts of online food delivery services on urban form.
There are several major remaining tasks. First, we need to evaluate how merchants(as the supply side of MSTPs) react to the changes in users’ behavior. Second, the use of
online food delivery services should properly be embedded into an activity-based model.
Although the impacts on land use may be marginal, as indicated by our empirical results,
travel patterns are affected by the shift from eating out to the use of online food delivery
services. Another major challenge is the comprehensive evaluation of ICT tools on activitytravel behavior. Now ICT tools have tremendous impacts on our activity-travel behavior in
multiple ways, including online shopping and teleworking, and having a better
understanding of the negative/positive and direct/indirect impacts of shifting to these virtual
activities need to be further explored for better policy decisions.

103

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

Chapter 8: THE IMPACT OF MSTP ON ACTIVITY-TRAVELBEHAVIOR (THE IMPROVEMENT OF DYNAMIC DISCRETE
CHOICE MODEL FOR ACTIVITY-TRAVEL BEHAVIOR ANALYSISUNDER THE PRESENCE OF MSTP)
In this chapter, we proposed the improvement of the current dynamic discrete choice model
for activity-based analysis that incorporates the presence of MSTP. In this study, we attempt
to answer one of our research questions that “How to extend the current dynamic discrete
choice model for activity-travel analysis to incorporate the impact of MSTP use on theactivity-travel pattern?” (RQ 5.1). This chapter is containing the introduction of the study,
methodology, proposed model, simulation results, conclusion, and the way forward of the
proposed methodology.
8.1.

INTRODUCTION

Theories on the relationship between urban form and travel patterns are mainly based on theconcept that travel results from an individual's desire or need to engage in an activity. The
location to perform activities is spatially distributed over a wide range of areas. Hence, these
activities cannot be carried out at the same location. Then, the result is the desire to conduct
some trip or travel. Theoretical reflections on the potential effects of urban form typicallyconcern the spatial distribution of essential activity locations such as residences, jobs, andshops. Shortening distance between these types of locations is often represented as a means
to decrease mobility growth. The nearest facilities and service from the home may be thebest alternative as destinations for activity participation for some individuals.
According to the activity-based approach, it is assumed that individuals andhouseholds will try to meet their basic needs and personal preferences by participating in
activities. At the same time, the environment they live in offers them opportunities andconstraints to doing so. Then, travel is the result of the locations for conducting activities
where it is also spatially distributed. Activity-based is a comprehensive approach, including
all travel motives that use a disaggregate scale by focusing on individuals and households.
An activity-based approach's advantage is that it recognizes that travel patterns are theoutcome of a highly complex interplay between personal and household characteristics,
features of the urban environment, the transportation system, and the institutional context.
The activity-based model will be appropriate for evaluating how people will behave ororganize their activities in time and space.
In the activity-based approach, several activity-based models have been developed
together to assume that travel is a derived demand of activity engagement at the destination
based on the time-space constraint concept. This concept emphasizes that time and spacedelimit an individual's opportunities to participate in activities and travel, imposing
restrictions on people's access and mobility (Hägerstrand, 1970; Burns, 1979; Schwanen,2008). For example, Kitamura and Fujii (1998), Pendyala et al. (2002), and Liao et al. (2013)
have been developed based on this time-space concept, together with the assumption thattravel is a derived demand of activity engagement at destination (i.e., people are travelingfor extrinsic motivations rather than intrinsic motivations). Based on the time-spaceconstraint concept, the critical question here is how individuals (households) organize their
daily activity-travel pattern within the opportunities and constraints set by their immediateand larger urban environment. Moreover, ICTs, particularly MSTP, may also give some104

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

dynamic changes in each individual and household opportunities and constraints to conduct
some activities.
The recent studies, together with the rapid progress of ICT, have questioned these
two foundations of the model. Shaw and Yu (2009) claim that virtual activity engagement
via ICT cannot be represented and explained by the classical time-space constraint
framework. Also, such virtual activity engagement would reduce travel if travel is a purely
derived demand of activity engagement, but this may not be entirely true since people would
travel not only for extrinsic but also for intrinsic motivations (Mokhtarian et al., 2015). Thesearguments call for further investigations on observing and modeling interdependencies
between ICT use and travel.
On the other hand, several researchers have discussed how ICT usage affects travel
behavior from various angles, such as how ICT affects mode choice, route choice, andscheduling. However, most studies focus on the overall impact of ICT on activity-travel
behavior, while they do not consider the context-dependent factors affecting ICT use andactivity-travel behavior. A number of researchers have discussed how the usage of ICT
affects travel behavior from various angles, such as how ICT affects the mode choice, route
choice, and scheduling (e.g., Lenz, B. & Novis, C., 2007, Aguiléra, A. et al., 2012, Fiore, F.
D., et al., 2014, Ben-Elia, E., 2018). However, most studies focus on the overall impact of
ICT on activity-travel behavior. At the same time, they do not take into account contextdependent factors affecting ICT use and activity-travel behavior. For example, the use of an
online food delivery service for lunch may depend on the time pressure the person is underat that time.
In this study, we aim to extend an existing dynamic discrete choice activity-travel
model to incorporate the impacts of ICT use on activity-travel patterns. This study focuses
on a situation in Indonesia where multi-service transport platforms (MSTP), particularly
GOJEK and Grab, have been widely used and have now become a vital part of people’s daily
lives, leading to unignorable changes in people’s travel behavior and activity patterns. With
the presence of the MSTP, the way of interacting between the supply-side (merchants) anddemand-side (users) might change, essentially because the platform would relax users’ timeand space constraints to reach the services. We have attempted to develop a survey andmodeling framework to comprehensively understand the impacts of MSTP on the urban
form and activity-travel behavior. We believe that it is worth sharing the proposed
framework together with empirical findings with other travel behavior scholars since the
MSTP has been showing both unignorably positive and negative impacts on the society inIndonesia and putting the MSTP in a proper position of entire transport systems is one of thesignificant challenges we face.
8.2.

METHODOLOGY

8.2.1. Modeling Approach
Based on Rasouli, S., & Timmermans, H. (2014), three different modeling approaches canbe distinguished in developing activity-based models of travel demand shown in Figure 8.1.
includes:105

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

Figure 8.1. History of the development of the activity-based model(a) Constraints-Based Models
This model attempts to check whether any given activity agenda is feasible to be
performed in a specific space-time context instead of predicting individual or
household activity-travel patterns. The inputs of this model are activity programs that
describe a set of activities with a specific duration that can be performed at aparticular time. In this model, the space context is defined as the location where theactivities can be performed, including the available transportation mode andapproximation of travel time between locations by different transportation modes.
One of the exciting attributes included in this model is the opening and closing hoursof the facilities at the locations. Thus, these constraints-based models' strength is theirability to identify infeasible activity schedules in a changing time-space context.
However, there are several limitations to this model. First, most models consider
individual accessibility, not household accessibility. Second, most models andapplications have been based on deterministic representations of the urban and travel
environment: travel times are fixed, opening hours are fixed, space-time prisms tend
to be derived from the maximum speed, etc. Finally, individual and household choice
behavior's conceptualization does not include any mechanisms related to choosing
behavior under uncertainty.
(b) Utility-Maximizing Models
In the activity-based model, there is an assumption that individuals will maximize
their utility throughout a day by choosing between activity-travel pattern alternatives
or individuals a utility maximizer of their daily activity and travel. According to theutility maximization assumption, the activity-based model was extended thecomplexity of discrete choice models (in particular, the nested logit model)
developed earlier to model, for example, destination–transport mode decisions toinclude more nests and choice options. Early applications were straightforward
extensions in the sense that logit models were used to predict the probability of anindividual choosing a multifaceted activity-travel profile (e.g., Adler & Ben-Akiva,
1979; Recker, McNally, & Root, 1986a, 1986b). This nested logit model is consistingof five nests: (i) activity pattern, representing a choice of a pattern with and one
without travel, (ii) primary tour time of day, (iii) primary destination and mode, (iv)secondary tour time of day and (v) secondary tour destination and mode. A nested
logit model is used to predict activity-type choice. The upper nest represents the
choice of an in-home activity, activity at or near the fixed activity location, andgeneral out-of-home activity.

106Chapter VIII – The Impact of MSTP on Activity-Travel Behavior(c) Computational Process Models
Some researchers proposed the formulation of rule-based models to explain decision
heuristics in order to relax the rigid and behaviorally irrational assumption of utilitymaximizing behavior. These models were often formulated in the context ofcomputational process models, which mimic the underlying decision-making process.
In line with earlier theoretical notions of time geography, individuals and households
are assumed to conduct activities to attain specific goals. These activities need to bescheduled interactively with other individuals to decide who will participate in theactivities, when, where, how long, and how to travel between locations where theactivities can be performed. The model system that bears some resemblance withcomputational process modeling is AMOS, a dynamic micro-simulator of household
activities and travel over time and space (Pendyala, Kitamura, Chen, & Pas, 1997;
Pendyala, Kitamura, & Reddy, 1998). Choice sets delineated based on space-timeprisms, and other constraints are updated dynamically during the scheduling process.
8.2.2. Dynamic Discrete Choice Model (DDCM)
In this study, the fundamental modeling approach is a dynamic discrete choice model basedon random utility-maximizing principles. Following Västberg, O. B. et al. (2020), we briefly
introduce the Dynamic Discrete Choice Model (DDCM) and its use for estimations.
Dynamic discrete choice models have received widespread acceptance in transport research
and are used in travel demand modeling and behavioral analysis. Following the random
utility maximization model, specifically the standard nested logit model, in DDCM, the pathchoice problem is formulated as a link choice sequence. At each state, the decision-maker or
agent chooses the utility-maximizing outgoing link with link utilities given by the
instantaneous cost, the expected maximum utility to the destination (value function), andi.i.d. extreme value (with zero means) (M Fosgerau, 2013).
The DDCM was constructed based on the Markov Decision Process (MDP) to model
the choice of daily activity-travel pattern. Markov decision-making can be formallyrepresented with four components (see Figure 7.1), including (1) a set of states (S), which is
the outcome of the decision or actions that the decision-maker took, (2) a set of actions (A),(3) the transition probabilities (q) that can describe the dynamic of the environment, and (4)there is a real-valued reward or utility function (u) on states which is an instantaneous utility
function as a reward from the action that agent took.

Figure 8.2. Markov Decision Process (MDP)
Source: Adaptation from https://towardsdatascience.com, 2020

A sequence of actions forming a path between states where a state 𝑠𝑘 may define the location𝑙 and time of day t, among other things represents the daily activity-travel pattern. While S
107

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

denote the set of all states and the set of states available at a specific point in time t by 𝑠𝑡 . In
this model, the time t is a continuous state variable, but we assume that decisions are taken
at discrete points in time. The index 𝑘 is used to denote the order of the state 𝑠𝑘 in thesequence of states that are traversed during the day.
In each state, an individual can choose an action 𝑎𝑘 ∈ 𝐶(𝑠𝑘 ), where 𝐶(𝑠𝑘 ) ⊂ C defines
the subset of discrete actions that are feasible in the specific state 𝑠𝑘 . An action may define,e.g., the type and duration of an activity or destination and mode of transport of a trip. Whenthe environment is stochastic, the state 𝑠𝑘+1 reached when choosing 𝑎𝑘 in state 𝑠𝑘 may beuncertain and given by some probability density function 𝑞(𝑠𝑘+1 |𝑎𝑘 , 𝑠𝑘 ). Daily variations intravel times may cause such uncertainty. One could have a state that represents the need toperform an activity on a specific day and allow the stochastic process 𝑞 to model how thisneed evolves over a day, resembling how need-based models (e.g., Arentze, Ettema, andTimmermans, 2011) to capture how the need to perform activities evolves between days overa week. We assume that individuals are aware of the stochasticity introduced by 𝑞 and takeit into account when making decisions.
The activity-travel pattern can be defined by the sequences of states 𝐬 and actions 𝐚transverse during a day. Giving that the individuals' preference for taking a decision 𝑎𝑘 in a
specific state 𝑠𝑘 and reaching the state 𝑠𝑘+1 is represented by a one-stage utility function. It
is assumed that an agent makes an action that can maximize his or her total utility in a given
period of time. Observed an individual who has made sequences of actions 𝐚𝑛 ={𝑎0,𝑛 , 𝑎1,𝑛 , … , 𝑎𝐾𝑛,𝑛 } and reached states 𝐱 𝑛 = {𝑥0,𝑛 , 𝑥1,𝑛 , … , 𝑥𝐾𝑛+1,𝑛 } , The total utility𝑈(𝐬, 𝐚) is defined as the sum of utilities obtained from reaching a specific state and fromconducting some actions as follows:𝐾

𝑈(𝐬, 𝐚) = ∑ 𝑢 (𝑠𝑘 , 𝑎𝑘 , 𝑠𝑘+1 )

(8.1)

𝑘=0

Where the 𝑘 is an index to denote the order of the state 𝑠𝑘 in the sequence of the statethat is transverse during the day. By assuming that individuals behaved as if they choose theutility-maximizing travel pattern, a rational agent that starts in a state 𝑠 would behave
according to policy 𝜋, determining the action 𝑎𝑘 = 𝜋(𝑠𝑘 ) that maximize the expected future
utility of a day. Then, the one-stage utility 𝑢(𝑎𝑘 , 𝑥𝑘 ) of taking an action 𝑎𝑘 in the state 𝑠𝑘 is:𝑢(𝑠𝑘 , 𝑎𝑘 ) = 𝑢(𝑎𝑘 , 𝑥𝑘 ) + 𝜖𝑘 (𝑎𝑘 )(8.2)

Where 𝑢(𝑎𝑘 , 𝑥𝑘 ) is the utility function of the observed variable at state 𝑠𝑘 and𝜖𝑘 (𝑎𝑘 ) is the random terms that will be assumed as i.i.d Gumbel distribute. Finding the
optimal policy and then calculating the choice probabilities requires computing the expectedvalue function in each state 𝑥𝑘 . The expected future utility conditional on a state is the valuefunction in the state:𝐾

𝑉(𝑠) = max 𝐸𝑠 {∑ 𝑢 (𝑠𝑘 , 𝑎𝑘 , 𝑠𝑘+1 )|𝑠𝑘 = 𝑠, 𝑎𝑘 = 𝜋(𝑠𝑘 )}
𝜋

(8.3)

𝑘=0

Where 𝐸𝑠 is respected to the stochasticity of 𝑠𝑘 given the decision rule 𝑎𝑘 = 𝜋(𝑠𝑘 ).
With the assumption that 𝑞 (transition probability), 𝑢 (one-stage utility function), and 𝐶(𝑠𝑘 )(choice set) is under Markovian condition, so they are independent of the history. The
Markovian assumption is not a problem in theory because it could include all previous
history in a finite horizon model. Following Rust (1987), we have assumed that the random108

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

state variable 𝜖𝑘 is conditionally independent of the previous state and action and enters theone-stage utility additively. Observe that value function 𝑉(𝑠) can be defined recursively
through Bellman's equation as (Bellman 1957, Rust 1987).
𝑉(𝑥𝑘 , 𝜖𝑘 ) = max{𝑢(𝑥𝑘 , 𝑎𝑘 ) + 𝜖𝑘 (𝑎𝑘 ) + 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 )}
𝑎𝑘

(8.4)

Where the 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) is the expected value of the value function of the state reached when
taking action 𝑎𝑘 in state (𝑥𝑘 , 𝜖𝑘 ). If 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) is known for each state-action pair, theprinciple of optimality states that the optimal policy π is obtained by, conditionally on a state
𝑠𝑘 , choosing the action 𝑎𝑘 that maximizes the utility function, then 𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) is given by:𝐸𝑉(𝑥𝑘 , 𝑎𝑘 ) = 𝐸𝑡 (𝑘+1),ℎ(𝑘+1),𝜖𝑘+1 [𝑉(𝑥𝑘+1 , 𝜖𝑘+1 )|𝑥𝑘 , 𝑎𝑘 ]𝑁ℎ= ∫ (∑ 𝑞ℎ (ℎ𝑗 |𝑡 ′ , 𝑡, ℎ, 𝑝̃) ∙ 𝑉̅ (𝑥𝑘+1 )) 𝑑𝑞𝑡 (𝑡 ′ |𝑡, 𝑙, 𝑚
̃ , 𝑑̃ )
𝑡′

(8.5)

𝑗=1

Where in turn 𝑉̅ (𝑥𝑘 ) = 𝐸𝜖𝑘 [𝑉(𝑥𝑘 , 𝜖𝑘 )]. When 𝜖𝑘 (𝑎𝑘 ) is independent and identically
distributed (i.i.d.) with Gumbel distributed (with zero means), 𝑉̅ is given by the following
log-sum:𝑉̅ (𝑥𝑘 ) = log ( ∑𝑒 𝑢(𝑥𝑘,𝑎𝑘)+𝐸𝑉(𝑥𝑘,𝑎𝑘) )(8.6)

𝑎𝑘 ∈𝐶(𝑥𝑘 )With i.i.d. Gumbel distributed 𝑘, the probability that an action 𝑎𝑘 will be the utilitymaximizing alternative in a state 𝑥𝑘 when 𝑘 is unobserved is simply given by the MNLmodel:𝑒 𝑢(𝑥𝑘,𝑎𝑘)+𝐸𝑉(𝑥𝑘,𝑎𝑘)(8.7)
𝑃(𝑎𝑘 |𝑥𝑘 ) =∑𝑎𝑘 ∈𝐶(𝑥𝑘 ) 𝑒 𝑢(𝑥𝑘,𝑎̃𝑘)+𝐸𝑉(𝑥𝑘,𝑎̃𝑘)After we suppressed the parameters θ from the utility functions' specification and theindividual's dependence in that the model describes, the likelihood for the observation of anindividual is then given by:𝐾𝑛

𝐿𝑛 (𝐚𝑛 , 𝐱 𝑛 |𝑥0 , 𝜃) = ∏ 𝑃𝑛 (𝑎𝑘,𝑛 |𝑥𝑘,𝑛 , 𝜃𝑢 ) ∙ 𝑞(𝑥𝑘+1|𝑎𝑘,𝑛 , 𝑥𝑘,𝑛 , 𝜃𝑞 )

(8.8)

𝑘=0

Let N observations construct the set of observations 𝜕𝑁 ON. The log-likelihood
function for 𝜕𝑁 based on the conditional likelihoods becomes:
𝑁

𝐿̅𝐿(𝜕𝑁 ; 𝜃) = ∑ log(𝐿𝑛 (𝐚𝑛 , 𝐱 𝑛 |𝑥0 , 𝜃))

(8.9)

𝑛=1

8.3.

PROPOSED MODEL

In the context of travel behavior analysis, dynamic discrete choice models have been used
for modeling route choice behavior (Fosgerau et al., 2013; Oyama and Hato, 2019) and109

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

recently used for modeling whole activity-travel patterns in a given period of time (Västberg,
O. B., Karlström, A., Jonsson, D., & Sundberg, M., 2019), i.e., a sequence of activity-travel
decisions is considered as a path choice in a time-space prism (Chikaraishi et al., 2018).
While the model strictly reflects time-space constraints, the current version does not
represent virtual activity engagement through MSTP that would virtually nullify time-spaceconstraints.
Figure 8.3. shows how a person's activity-travel behavior, the utility that they getfrom the "real" experience, may be different from the utility they get from the "virtual"
access; there may be some reduction in the amount of utility. However, the benefits they get
are still higher than the cost they are made for the trips/movements. From the user's
perspective, they have more alternatives or options to access the service or the otherlocation's needs by having virtual access. This relates to how virtual access utility
maximization can be achieved by staying at one location. This is similar to the concept ofroute choice in choosing the best route, which is considered to maximize its utility. The
utility function used is a utility obtained through the "real" access and a utility obtained
through "virtual" access.

Figure 8.3. The daily travel-activity behavior embedded with ICT usage
By looking at the ICT usage of people within a particular time slot, we can know thatsomeone can improve the utility by having virtual access to their needs through the Multiservice Transport Platform (MSTP). By having that virtual access, it would relax users' timeand space constraints to reach the services or their needs. In this case, ICT will increase thecumulative utility obtained by someone. To handle the disadvantage, we are adding a log
sum term representing the expected maximum utility obtained from ICT use into theinstantaneous utility of the recursive logit model would allow us to represent the impacts of
ICT use on travel behavior.
In this study, we attempted to analyze the impact of MSTP on the whole daily
activity-travel behavior. However, due to the limitation in time and data preparation, we willconduct some simulations by using small-scale data instead of using a whole daily activitytravel behavior data. The challenge that we faced is the size of the data or model’s problem.
The actual problem size that we need to handle if we want to analyze the whole daily activitytravel behavior is.
• Time period: 05:00 a.m. to 11:00 p.m. (18 hours)• Number of zones: 262 zones• Number of respondents : 272 persons• Purposes: 6 purposes (working, eating out, shopping, recreation,

110

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

home, and others): 8 modes (stay, walk, car, motorbike, public transport,ojek online, ride-hailing, and others)
Later, we will show why this size of whole daily activity-travel behavior ischallenging to handle in the activity-behavior analysis. This study aims to extend the existing
dynamic discrete choice model for activity-travel behavior analysis by taking into accountthe presence of MSTP. To test the behavior of the proposed model, we used the small-scaledata for the simulation of lunch behavior with the specification as follows:• Time period: 11:00 a.m. to 14:00 p.m. (3 hours)• Number of zones: 4 zones• Purposes: 4 purposes (work, eating out, MSTP, others)• Transportation mode : 4 modes (stay, walk, car, public transport)• Transportation mode

To be able to perform the dynamic discrete choice model, several things need to be
specified in the network data, including:
1. Specification of actions (𝒂𝒌 )In the dynamic discrete choice model, we assumed that an individual will makes anaction that can maximize his or her total utility in a given period of time. The decisionvariable that defines actions 𝑎𝑘 are destination 𝑑̃ ∈ 𝐿; mode of transport 𝑚̃ ∈ 𝑀; andpurpose 𝑝̃ ∈ 𝑃. Here 𝐿, 𝑀, and 𝑃 define the set of locations, modes, and purposes,
respectively.
1,2, … , 𝑁𝐿
𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛
𝐿
𝑑̃
𝑚
𝑚𝑜𝑑𝑒
−
𝑜𝑓
−
𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡
𝑠𝑡𝑎𝑦 , 𝑚1 , … , 𝑚𝑁𝑀𝑀𝑚̃
𝑎𝑘 = ( ) = () ∈ { 𝑝 𝑝 ,…,𝑝}={ }𝑝𝑢𝑟𝑝𝑜𝑠𝑒
𝑃
1, 2
𝑁𝑃
𝑝
̃
̃
𝜏
𝑚𝑖𝑛.
𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛
𝑓𝑜𝑟
{𝑑
,
𝑚
̃,
𝑝
̃}
[0,
𝜏
𝑇]

(8.10)In this simulation, we define the action 𝑎𝑘 as follows.
1,2,3,4
𝑚𝑠𝑡𝑎𝑦 , 𝑚𝑤𝑎𝑙𝑘 , 𝑚𝑐𝑎𝑟 , 𝑚𝑝𝑡
𝑎𝑘 = {𝑝}𝑤𝑜𝑟𝑘 , 𝑝𝑒𝑎𝑡 , 𝑝𝑚𝑠𝑡𝑝 , 𝑝𝑜𝑡ℎ𝑒𝑟𝑠[0, 𝑇](8.11)

2. Specification of state (𝒔𝒌 )In the DDCM, the state is the position of the agents in a specific environment whereit should include all information necessary to formulate the choice set and one-stage
utility function. As state 𝑠𝑘 consist of (1) the time-of-day 𝑡, which are modeled as acontinuous variable between 0 and 𝑇 (𝑡 ∈ [0, 𝑇]); (2) the current location 𝑙 ∈ 𝐿; (3)the purpose of a previous action 𝑝 ∈ 𝑃; (4) the previous mode of transport 𝑚 ∈ 𝑀
was used to allow for interdependence among the mode choice between subsequent
trips; (5) activity history ℎ that stores the relevant history related to previously
performed activities in the form of an index; it stores the number of times eachactivity has been performed during the day; and (6) the non-modeled random
attributes of the available actions 𝜖𝑘 ∈ ℝ𝑁𝐶 . While 𝑥𝑘 = (𝑡, 𝑙, 𝑝, 𝑚, ℎ) is denote theobservable part of the state space, so that 𝑠𝑘 = (𝑥𝑘 , 𝜖𝑘 ).

111

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior[0, 𝑇]
𝑡𝑖𝑚𝑒 − 𝑜𝑓 − 𝑑𝑎𝑦𝑡
1,2,… , 𝑁𝐿
𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝑙𝑝1, 𝑝2 , … , 𝑝𝑁𝑃
𝑝𝑝𝑢𝑟𝑝𝑜𝑠𝑒 𝑜𝑓 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑎𝑐𝑡
𝑠𝑘 = 𝑚 =∈ 𝑚𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑚𝑜𝑑𝑒 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡
𝑠𝑡𝑎𝑦 , 𝑚1 , … , 𝑚𝑁𝑀ℎ
1,2, … , 𝑁ℎ𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 ℎ𝑖𝑠𝑡𝑜𝑟𝑦
(𝜖) (
) {
}
𝑟𝑎𝑛𝑑𝑜𝑚 𝑠𝑡𝑎𝑡𝑒 𝑣𝑒𝑐𝑡𝑜𝑟ℝ𝑁𝐶(8.12)In this simulation, we define the state 𝑠𝑘 as follows.
0,1,2, … ,18𝑡
1,2,3,4𝑙𝑝𝑤𝑜𝑟𝑘 , 𝑝𝑒𝑎𝑡𝑖𝑛𝑔−𝑜𝑢𝑡 , 𝑝𝑚𝑠𝑡𝑝 , 𝑝𝑜𝑡ℎ𝑒𝑟𝑠𝑝𝑠𝑘 = 𝑚 =𝑚𝑠𝑡𝑎𝑦 , 𝑚𝑤𝑎𝑙𝑘 , 𝑚𝑐𝑎𝑟 , 𝑚𝑝𝑡
ℎ
1,2
(𝜖) {
}
ℝ𝑁𝐶

(8.13)

With the assumption that every decision is made every 10-minute interval (∆𝑡).
3. Specification of the conditional choice set (𝑪(𝒙𝒌 ))Defining the conditional choice set on the state 𝑠𝑘 involves defining the set of
purposes, destinations, and modes that are available in a specific state 𝑠𝑘 . We assume
that the conditional choice set is independent of 𝜖 . With a few limitations, anindividual can in each time step decide to either stay (𝑚 = 𝑚𝑠𝑡𝑎𝑦 ) at the same
location 𝑙 and perform the previous activity 𝑝 for a while longer; or travel with amode 𝑚̃ ∈ 𝑀(𝑚) to a new destination 𝑑̃ ∈ 𝐿 and start a new activity 𝑝̃ ∈ 𝑃. In thissimulation, the choice set is restricted in the following ways:𝑛𝑚𝑤𝑎𝑙𝑘 , 𝑚𝑝𝑡 , 𝑚𝑜𝑗 , 𝑚𝑟ℎ𝑖𝑓 𝛿𝑐𝑎𝑟=0
𝑀𝑛 (𝑚) = {𝑛𝑚𝑐𝑎𝑟 , 𝑚𝑤𝑎𝑙𝑘 , 𝑚𝑝𝑡 , 𝑚𝑜𝑗 , 𝑚𝑟ℎ 𝑖𝑓 𝛿𝑐𝑎𝑟 = 1, 𝑚 = 𝑚𝑠𝑡𝑎𝑦𝐿
𝑖𝑓 𝑝̃ = 𝑝𝑒𝑎𝑡 , 𝑝𝑜𝑡ℎ𝑒𝑟
𝐿𝑛𝑎𝑐𝑡. (𝑝̃) = { 𝑛𝑙𝑤𝑜𝑟𝑘 𝑖𝑓 𝑝̃ = 𝑝𝑤𝑜𝑟𝑘 , 𝑝𝑚𝑠𝑡𝑝(8.14)

4. State Transition (q)The unspecified state transition is 𝑞𝑡 and 𝑞ℎ . In this simulation, we define theminimum time t that individuals need to take some actions; it is defined as:1
3
𝑚𝑖𝑛(𝑡 (𝑘) ) = 2
1{0

𝑖𝑓 𝑝 = 𝑤𝑜𝑟𝑘
𝑖𝑓 𝑝 = 𝑒𝑎𝑡𝑖𝑛𝑔 − 𝑜𝑢𝑡
𝑖𝑓 𝑝 = 𝑀𝑆𝑇𝑃
𝑖𝑓 𝑝 = 𝑜𝑡ℎ𝑒𝑟
𝑖𝑓 𝑝 = 𝑡𝑟𝑎𝑣𝑒𝑙(8.15)

h denotes the number of finished eating our MSTP activities; it is increased by one
whenever the activities are performed. It is defining as:ℎ(𝑘+1) = {ℎ(𝑘) + 1 𝑖𝑓 𝑝̃ = 𝑝𝑒𝑎𝑡 , 𝑝𝑚𝑠𝑡𝑝
ℎ(𝑘)

𝑒𝑙𝑠𝑒

(8.16)

5. One-stage Utility Function (u)We divide the one-stage into the utility of traveling 𝑢𝑡𝑟𝑎𝑣𝑒𝑙 (t, 𝑙, 𝑑̃ , 𝑚̃ ) and the utilityof staying 𝑢𝑠𝑡𝑎𝑦 (𝑡, 𝑙, 𝑝, 𝑝̃). Where the utility of staying is consisting of the utility ofeating out 𝑢𝑒𝑎𝑡,𝑠𝑖𝑧𝑒 (𝑙) , the utility of using an online food delivery service
112Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

𝑢𝑚𝑠𝑡𝑝,𝑠𝑖𝑧𝑒 (𝑙), utility to perform other activities 𝑢𝑜𝑡ℎ𝑒𝑟,𝑠𝑖𝑧𝑒 (𝑙), the utility obtained
from working 𝑢𝑤𝑜𝑟𝑘,𝑠𝑡𝑎𝑦 (𝑡)And the utility of staying at home 𝑢ℎ𝑜𝑚𝑒,𝑠𝑡𝑎𝑦 (𝑡). Belowis the specification of the utility functions used in this study.
1. The utility of Travelling (𝒖𝒕𝒓𝒂𝒗𝒆𝒍)𝑢𝑡𝑟𝑎𝑣𝑒𝑙 (t, 𝑙, 𝑑̃ , 𝑚̃ ) = 𝜃𝑚̃ + 𝜃𝑡𝑡 ∙ 𝑇𝑇𝑚̃ (𝑡, 𝑙, 𝑑̃ ) + 𝜃𝑐𝑜𝑠𝑡 ∙ 𝐶𝑚̃ (𝑡, 𝑙, 𝑑̃ )(8.17)The utility of traveling depends on the time of day when deciding to travel, thecurrent location, the destination, and the chosen mode of transportation. The utility
function of travel is constructed by the parameter of each mode 𝜃𝑚̃ , the travel timeof the chosen mode of transportation 𝑇𝑇𝑚̃ from the current location to the destination,and the travel cost of the chosen mode of transportation 𝐶𝑚̃ from the current location
to the destination.
2. The utility of Stay (𝒖𝒔𝒕𝒂𝒚)The utility of stay was constructed with the utility of eating out, the utility of ordering
online food delivery service (MSTP), and the utility of working.
a) Eating out
𝑢𝑒𝑎𝑡 (𝑙) = 𝜃𝐶,𝑒𝑎𝑡 + 𝜃𝑓𝑜𝑜𝑑 ∙ 𝑥𝑓𝑜𝑜𝑑 (𝑙) + 𝜃𝐼,𝑓𝑜𝑜𝑑 ∙ 𝐼𝑓𝑜𝑜𝑑 (𝑙)(8.18)The utility function of eating will depend on the current location where theindividual 𝑛 is located. Where the variable of eating-out utility including thenumber of food merchants at the location (𝑥𝑓𝑜𝑜𝑑 ) and agglomeration index offood merchants at the location (𝐼𝑓𝑜𝑜𝑑 ).
b) MSTP (Online food delivery service)𝑢𝑚𝑠𝑡𝑝 (𝑙) = 𝜃𝐶,𝑚𝑠𝑡𝑝 + 𝜃𝐶,𝑙𝑜𝑔𝑠𝑢𝑚𝑚𝑠𝑡𝑝 ∙ 𝑢𝑙𝑜𝑔𝑠𝑢𝑚𝑚𝑠𝑡𝑝 (𝑙)The log-sum variable of MSTP defines as follows.
𝑢𝑙𝑜𝑔𝑠𝑢𝑚𝑚𝑠𝑡𝑝 (𝑙)= ln [∑ exp (𝜃𝑚𝑠𝑡𝑝 ∙ 𝑥𝑚𝑠𝑡𝑝 (𝑙) + 𝜃𝐼,𝑚𝑠𝑡𝑝 ∙ 𝐼𝑚𝑠𝑡𝑝 (𝑙)(8.19)− 𝜃𝑑𝑒𝑙,𝑐 ∙ 𝐶𝑑𝑒𝑙 (𝑙) − 𝜃𝑑𝑒𝑙,𝑡 ∙ 𝐷𝑇𝑑𝑒𝑙 (𝑙))]The utility of using an online-based food delivery service (MSTP) will bereflected by the log-sum function and the size variable of MTSP, including:1) Number of online food merchants at the location (𝑥𝑚𝑠𝑡𝑝 )2) Agglomeration index of online food merchants at the location (𝐼𝑚𝑠𝑡𝑝 )3) Delivery cost to the current location (𝐶𝑑𝑒𝑙 (𝑙))4) Delivery time to the current location (𝐷𝑇𝑑𝑒𝑙 (𝑙))c) Utility from working
𝑢𝑤𝑜𝑟𝑘 (𝑡) = 𝜃𝑤𝑜𝑟𝑘

(8.20)

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Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

We conduct the simulation of our model by using a small amount of data to see how
the model will behave. The observation of the simulation’s result cannot be used to examine
how the MSTP affects the activity-travel behavior directly. However, in the activity-travel
behavior analysis, we need to handle 9 or more dimensions in the model, including thedimension for actions (𝑎𝑘 ) that consist of 4 dimensions (destination (𝑑̃ ), mode (𝑚̃ ), purpose(𝑝̃), and duration (𝜏)) and the dimension for states (𝑠𝑘 ) that consist of at least 5 dimensions(time-of-day (𝑡), previous location (𝑙), previous purpose (p), previous mode (m), history ofactivities (h)). With this number of dimensions, the conventional sparse matrix that has beenused in the recursive logit model for value function calculation will not be able to handle itdue to the huge amount of combination and high computational burden.
In this simulation, we introduce the new approach to calculate the value function byusing a tensor matrix. Conceptually, the tensor is similar to the sparse matrix, but it canhandle a multidimensional space. By using the tensor, we can handle the multidimensional
space of variables from the combination of action and state that will be used in the calculationof the value function. Following the framework of the recursive logit model, in this dynamicdiscrete choice model, the value function is also defined recursively through Bellman’sequation.
In the analysis, we want to handle the tensor problem as a matrix form to be able todo the calculation, make a product, and sum up different tensors. Then, to do that, we change
the tensor into matrix form (see Figure 7.6.). The combination of location, mode, andpurpose both in state and action can produce several combinations of action-state pair,including (1) change activity purpose, (2) activity soon after travel, (3) continue the same
purpose, and (4) traveling.

Figure 8.4. Tensor for action-state pairIn this study, we attempt to introduce the time (t) dimension into the activity-travel
behavior model. By adding the time component, it is means adding an additional dimension
into our matrix (see Figure 8.5). The diagonal block is the central part of the model where
the action-state is changing over time. Even though most of the values will be empty or zero114

Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

due to the limitation of the choice set, but not all of them will be zero because somecombination of action can be taken during the time period.
The expected value function is approximated in a number of discrete time steps. In
this simulation, we define the time steps ∆𝑡 is in every 10 minutes interval, then the
difference between T-1 and T is 10 minutes. When the value is needed between these time
steps, some approximating function is used. When travel time is 16 min and the time step(T) = 10 min, the agent is assumed to move to t+1 with 40% probability and to t+2 with 60%
probability. The same proximation also will be used as the weight in the calculation of travel
time. By using these weights, we can simulate such a condition where the travel time isplaced between the time steps. For example, if the travel time is 47 minutes, then the utility
value will be placed in the time steps 4.7. Then the time step before travel was taken is 4 andafter travel was taken is 5. By adding the weight into the utility calculation, we found that a
30% probability of the utility will be at T+4, and a 70% probability of the utility will be at
T+5. If we allocate 10 persons in that state, then by following the calculation, we found 3
persons will stay at the same state where 7 persons will go to T+5. However, they will go
through the same procedure in the next state and their decision may be changed over time.
In the same manner, we can set up a minimum time duration for activity. For example,
the previous one is travel, and you travel to the new places and start the new activity and atthat point, we can add the minimum time required. We forced them to spend time conducting
an activity, but it may not reflect the reality because some people may skip their activities at
the first destination due to several reasons, such as queuing, closed facilities, and otherreasons. However, this minimum duration for taking some activities is the part of actions or
decision variables that need to be considered from the decision maker’s point of view.
8.4.

CONCLUSION AND WAY FORWARD

Based on our simulation, we found that our proposed model is well-behaving as expected.
This proposed model is one of the major contributions of this study to construct themethodological framework for activity behavior analysis based on the dynamic discrete
choice model that taking account the presence of MSTP. In this simulation, all the parameteris given, while in the actual calculation, we will estimate all parameters for each action andstates. In this study, we also found that the use of tensor can handle the multidimensional
component of activity-travel behavior. We are also introducing the way to taking accountthe time (t) component into our model. Also, we introduce how we handle the travel time
uncertainty by using the weight in the calculation of the expected utility proportion that can
reduce the computational burden. Another dimension that we will add to our model is thehistory component of activities which is a part of states, and the individual characteristics
that can restrict the decision makers’ choice set.
Although this is still ongoing research, we have attempted to develop a modelingframework to comprehensively understand the impacts of multi-service transport platform(MSTP) on travel behavior with a focus on Indonesia, where MSTP services have been
widely used and have now become a vital part of people’s daily lives. We believe that it isworth sharing the proposed framework together with empirical findings with other travel
behavior scholars since the MSTP has been showing both unignorable positive and negative
impacts on the society in Indonesia, and putting the MSTP in a proper position of entire
transport systems is one of the significant challenges we faced.

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Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

Figure 8.5. Tensor for action-state pair including time dimension

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Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

8.5.

SIMULATION RESULTS

Log sum parameter = 0.1

Log sum parameter = 0.5

Log sum parameter = 0.9

Figure 8.6. Simulation results 1 (the impact of MSTP service level)

Car const = 0; walk const = 0

Car const = 5; walk const = 5.

Car const = 5; walk const = 10.

Figure 8.7. Simulation results 2 (impacts of travel impedance)

117Chapter VIII – The Impact of MSTP on Activity-Travel Behavior

Eat = 10, MSTP = 10

Eat = 20, MSTP = 20

Eat = 30, MSTP = 10

Figure 8.8. Simulation results 3 (the impacts of minimum duration of activities)

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Chapter IX – Conclusion and Future Works

Chapter 9: CONCLUSIONS AND RECOMMENDATIONIn recent years, the presence of MSTP as one of the innovations of ICT in transportation anddaily service provision has rapidly expanded and had a significant impact on our daily
activity. Daily activities and travel are inseparable since travel results from an individual’s
desire or need to engage in an activity. While the location to perform activities is spatiallydistributed over a wide range of areas. Hence, these activities cannot be carried out at thesame location. Then, the result is the desire to conduct some trip or travel to another location.
With this realization in mind, this thesis had presented the result of an analysis that examined
the multi-dimensional impact of MSTP on urban form and activity-travel behavior.
This research aimed to examine the impact of Multi-Service Transport Platform(MSTP) on the urban form and activity-travel behavior. Based on the analysis of the impactof MSTP on the urban form and activity-travel behavior, it can be concluded that MSTP
changes the behavior of individuals toward online activities and change the decision locationof food merchants. The results indicate that MSTP induces the new distribution of facilities(i.e., food merchants, where the distribution of facilities itself will affect the MSTP’s services
level and affect the individual’s behavior (i.e., eating behavior).
While the existing studies do not consider the presence of MSTP, this study attempts
to provide a comprehensive understanding of the impact of MSTP on urban form andactivity-travel behavior through the agglomeration analysis of facilities and the eating
behavior analysis. The results are well explained the impact of MSTP and answering all of
the research questions. However, there are some insights that arose in the process that will
be addressed as the recommendation for future study.
The contribution of this study to international literature include:1) examining the impact of MSTP on facility distribution through the agglomerationindex analysis.
2) capturing the online activities and MSTP services together with the improvement on
the activity-travel diary survey.
3) proposing the theoretical framework for activity-travel behavior analysis thatconsidering the presence of MSTP.
9.1.

CONCLUSION

The innovation of information and communication technology has influenced many aspectsof our daily lives, such as economic activities, study, medical, and others. In Indonesia, oneof the innovations of ICT that very popular and widely used by many people since 2010 is
the Multi-Services Transport Platform (MSTP). MSTP is an online-based multi-service
platform that relies on drivers/fleets providing access to a wide range of services, including
daily-needs services (e.g., online food delivery services, common delivery, grocery delivery,
car wash, and others) and transportation services (e.g., online car ride-hailing and online
motorbike ride-hailing/ojek online). MSTP is allowing people to virtually access services inother areas nearby without travel. In other words, MSTP may improve the accessibility andminimizing the cost (i.e., travel cost, travel time, energy). There are more than 21.7 million
users of MSTP in Indonesia, where most of them are concentrated in the metropolitan andbig cities such as Jakarta, Bandung, and Surabaya. With a high demand for the MSTP, it
indicates that MSTP becomes an essential aspect in people's daily life, and it may affect theiractivity and travel behavior and change how the cities work.
119Chapter IX – Conclusion and Future Works

This dissertation explores the impact of the MSTP on the activity-travel behavior andurban form in Jakarta, Indonesia. The purposes were to understand better how MSTP affects
the urban form and individual's activity-travel behavior. A dynamic change of daily activitytravel behavior and urban form through MSTP can induce more sustainable cities andconvenient lives. Those can only be achieved if the urban and transportation policies, spatial
development patterns, individual decisions, private sector decisions, and interdependencies
are understood and properly implemented. Therefore, this study included four parts ofanalysis: (1) the analysis of facilities distribution through agglomeration index analysis, (2)the descriptive analysis of the online and physical activity and transportation behavior, (3)the analysis of the impact of MSTP on the individual eating behavior, and we also attempt
to develop the (4) dynamic discrete choice model for activity-travel behavior that includes
the component of MSTP to see whether the MSTP components will affect the decision of
our daily activity and travel.
Some findings and discussion of the studies are showed to empirically explain theimpact of MSTP on the urban form and activity-travel behavior. First, MSTP changes the
distribution of online food merchants by introducing the new agglomeration forces or themechanism to be agglomerated from attracting more MSTP drivers into their area. Using theagglomeration index approach, we analyze the distribution and concentration of facilities in
Jakarta, Indonesia. Due to the presence of MSTP, we found that a high agglomeration of
online food merchants happened around the central area and 12-14 km away from the central
area (e.g., fringe area) of Jakarta city. This may happen due to the mechanism of MSTP that
relies on the fast service and high accessibility level to serve the consumers, so they set a
particular distance as their maximum area coverage to keep their food quality for the
consumers. By considering the cities' geographical and traffic characteristics, MSTP set theirmaximum coverage area within 6 kilometers away from the consumer's location. In otherwords, the online food merchants tend to be agglomerated within the MSTP's area coverage(i.e., 6 km) in order to attract more drivers into their area. This has contributed to exploring
the behavior of the facilities under the influence of MSTP.
Some studies (e.g., Akamatsu et al., 2017), Combes, P. P., & Gobillon, L. (2015),
and Fang, C., & Yu, D. (2017)) mentioned that the agglomeration might increase theproductivity of firms and the economic development. It also happened to the online foodmerchants, where they tend to be agglomerated on a neighborhood scale. Suppose all of theurban facilities have behaved like the online food merchants that rely on online delivery
services, where the physical movement of the customers is less required to access theservices. In that case, there might be some flexibility in the location of facilities that may
change the spatial equilibrium of the cities. However, an extensive exploration of the
behavior of the facilities and their interdependencies is needed to see how the MSTP can
affect facilities behavior, moreover whether MSTP can increase the productivity of the
facilities and city economic development. We need to explore how online-based facilities
interact with other types of facilities. Whether they will compete, support, or eliminate eachother's will be our remaining tasks.
Second, the descriptive analysis of activity-travel behavior showed that the MSTP
services have been more favorable toward eating and shopping activities. This has
contributed to the reduction of the physical trip of the users. Based on a two-week activitytravel diary survey, we found that more than 55% of eating and shopping activities havebeen preferable to conduct by using MSTP. Those percentage indicates a high demand for
MSTP services. If our samples represented the actual characteristics of all Jakarta's
population, then this might be some significant changes in the daily behavior. The shift inpeople's behavior of conducting physical trips to online activity can have significant
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Chapter IX – Conclusion and Future Works

implications for transportation planning and urban development. If many people were
shifting to conduct online activities and less conduct the physical trips, fewer people will use
the road, and less congestion may happen. Less congestion may lead to some sustainable
development of the cities.
It is uncertain that the high demand for MSTP may reduce traffic congestion. Eventhe consumers conduct fewer physical trips due to relying on MSTP online delivery services.
However, a certain number of MSTP drivers (fleets) are still conducting some physical trips
with their vehicles. Existing studies (see Suhartanto, D. et al. (2018)) show that MSTP
drivers induce some traffic at a specific time nearby the center of activities, such as university,
office, and shopping mall. To avoid some side-effects from an oversupply of drivers, we
may need to explore how the MSTP's drivers behave. Some drivers may only serve in one
area, while others serve in many areas. Some concentration of drivers may also relate to theagglomerated facilities that bring some potential demand or order for the drivers.
Another critical issue that arises is a way to decide the sufficient number of drivers
to serve an area without having an under or oversupply of drivers. To the best of the author's
knowledge, the number of drivers is decided by the natural market mechanism. We foundthat the market mechanism of MSTP is a complicated multi-sided market with a network
effect where the number of users, merchants, and drivers is affected will affect each otherdecisions. Without any regulation that can control and estimate how many drivers thatneeded to serve an area, an insufficient number of drivers may happen. If the number ofdrivers exceeds the number of orders, many drivers are standby on the road without anyorders, which may lead to traffic problems such as congestion and illegal parking. In thissense, the government must take action to regulate the optimal number of drivers to avoid
some negative impacts on the transportation system and urban development.
Third, based on the activity-travel diary survey, we found that using MSTP's
transport services is more favorable than public transport in Jakarta. Despite their role as an
online delivery service, there are a significant number of MSTP transport service usage inJakarta. Having a high number of demands does not make MSTP can replace the usage of a
private vehicle and reduce traffic congestion. The basic idea of MTSP to optimize the fleetsby allowing them to provide many kinds of services was a good initiative. However, as we
mentioned above, it is uncertain that MSTP can reduce traffic congestion due to the unknown
driver's behavior.
Based on the greater Jakarta commuter data in 2019, the usage of MSTP transport
service (i.e., online motorbike ride-hailing (online ojek) and online car ride-hailing) cannot
be used as the primary transportation mode for the whole trip due to their expensive fees
compare to the public transportation such as private car, private motorbike, MRT, and bus.
In Jakarta, people prefer to use their private motorbike for the first-mile trip, then sometransport hub (e.g., MRT, KRL, Bus), and using some online-based transportation for thelast mile. However, there is a similar percentage of MSTP transport service and private cars
for the first mile and last mile. This indicates that people feel MSTP transport service isrelatively cheap to be used as the first and last mile of transport mode, and it can provide
high flexibility compared to the other modes. It is implicated that if the MSTP transport
service quality is improved, the use of the car and other private vehicles for the first and last
mile of the trip may be reduced. It also may reduce the traffic congestion caused by private
vehicle usage. However, it may happen if there is a good integration between MSTP andpublic transport hub to reduce the use of private vehicles.
Forth, the delivery time and delivery cost that MSTP proposed is the significant
variable that affects people using online food delivery services rather than conducting aphysical trip. The findings suggested that utilitarian orientation is an important determinant
121

Chapter IX – Conclusion and Future Worksof users' behavior toward MSTP online food delivery services. People find that using online
food delivery services is relatively cheaper in the term of value of time than conducting around trip of the trip for eating purposes. The improvement of delivery time and delivery
cost may increase people's tendency to use MSTP online food delivery services. In thedelivery time of MSTP online food delivery services, there are three main components: thetime for food preparation by the merchants, the travel time by online ojek, and the additionaltime to find nearby drivers to take the orders. Where the time for food preparation is givenby the condition of the cross-side network effect of demand and supply, the additional timeis influenced by the same-side network effect. The number of drivers in the nearby area willaffect the additional time. The additional time will decrease in the area with the increase in
the density of online food merchants. In general, drivers would standby around the area withhigher demand. Having more drivers in the area may reduce the additional time for searchingfor the driver and reduce the delivery time. However, it is unsure that having MSTP can
improve the productivity of each individual in their daily activities by substitute all theessential tips into online delivery services. Future research by academics should focus on theimpact of MSTP on individual activity-travel behavior, especially on the timing decision to
see whether MSTP people can allocate more time for productive activities (e.g., working andstudying).
Fifth, the exploration of MSTP impact on individual activity-travel behavior throughthe simulation on the dynamic discrete choice model showed that the MSTP components(i.e., online transportation services, the availability of online food delivery services, and thenumber of food merchants as the attraction of each area) show that MSTP may change thetiming decision, destination choice, and the activity purpose of individuals in their daily
activity-travel pattern. As mentioned above, some studies can address some limitations byusing the improved DDCM framework. To decide whether MSTP can support thedevelopment of a sustainable city, another exploration needs to be done, such as analyzing
individual activity-travel behavior and their preference towards MSTP and other online
activities. By analyzing an individual's activity-travel behavior through some activity-based
analysis (i.e., dynamic discrete choice model (DDCM) for activity-based analysis), we can
explore the impact of MSTP on their activity decision (e.g., timing decision, mode decision
choice, destination choice, activity choice, and so on). The future improvement of DDCM isneeded to taking account the new component, including MSTP's transportation service,
MSTP's online food delivery service, the new attraction of the zones, and congestion. This
contributes to a comprehensive understanding of MSTP's impact on activity-travel behavior
on an individual scale and its implication to urban development.
9.2.

IMPLICATION FOR POLICY AND PLANNING

Given the strong interrelationship between urban form and transport, along with the presenceof MSTP as the ICT-based innovation in transport, the integration of land-use andtransportation planning represents a unique policy opportunity. When MSTP brings the new
agglomeration forces for online food merchants by giving some flexibility to decide theirlocation while still keep a certain number of consumers through online food delivery services,
the new neighborhood-scale center may develop. By having the flexibility to be located, thepresence of MSTP has the potential to change the structure of cities. In the long run, thiskind of service may lead to a less structured city—the further regulation and policy regarding
the location of an online-based food merchant. When the online food merchants areagglomerated across the city, the practitioner should consider putting some regulations in
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Chapter IX – Conclusion and Future Worksthe location of online food merchants. In some cases, when that agglomerated online foodmerchants will induce some new trip attraction, at least for the online drivers that need topick up the consumer’s orders. In some cities in Indonesia, where the food merchants haveto gain more popularity or maybe have some promotional programs, the number of orders
will be increased and the number of drivers that were going to the merchant’s location also
increased. If many online food merchants in the agglomerated area are gain more orders, thesignificant number of trips to the agglomerated area will happen and it may cause somechanges in the city’s traffic and mobility flows where the particular area has more trip
assignments compare to how they allocation.
To avoid the new trip attraction increased in the neighborhood-scale, the practitioner
could allocate the new center at the bigger scale (e.g., district-scale), assumes that a bigger
area scale can accommodate a bigger activity scale. Since the provision of strategic
infrastructure is one of the most critical public policy instruments informing the long-term
shape and characters of a city at any stages in development, MSTP as a part of transport
infrastructure and services play a crucial role in determining urban mobility patterns within
urban planning, including modal choice and delivery services. Regulatory policy instruments
also play a crucial role in shaping urban transport performance, including the limitation of
MSTP's fleet in one area to avoid overcrowding traffic.
Based on the analysis that we have done, to achieve a more sustainable urban
development by optimizing the potential that MSTP has, this study recommends a higher
facility density and higher agglomeration of economic activities in multiple locations,
including the policies that are promoting more mixed-use and denser development aroundresidential areas encourage changes in activity behavior, such as switching from making aneating behavior to using MSTP online food delivery service. Some researchers (see Ewing
R. et al., 2018) already mentioned that compact development that is diverse, dense, welldesigned, etc., produces fewer vehicle miles traveled than sprawling development. In thiscase, MSTP induces the concentration of activities based on their origin and destination thatgain a benefit from a compact design city. On the other side, the distribution of facilities has
become an attraction for people to choose MSTP online food delivery services. Closer
distance and more variance of online food merchants may interact more users of MSTP
online food delivery service. It can be applied for others MSTP delivery services that relyon accessibilities and delivery time. This study suggests that, in Jakarta, policies adapted
toward increasing facility density and the mix of land use to maximize the potential of MSTP,
and it is related to the multi-core urban development.
9.3.

LIMITATION OF THE STUDY AND RECOMMENDATIONIn this study, there are some limitations that can be encountered in the future to enrich thestudy. This section provides some direction for further research based on the limitations andfindings reported in this study. First, this study research clearly illustrates the impact ofMSTP on the urban form through the changes in the facility distribution. However, asystematic review of the literature was not possible given the limited academic work
available. As this is a burgeoning academic field, we encourage future researchers to adopt
a systemic approach to understand the sustainability impacts of the MSTP. Second, as we
mentioned above, the exploration of the behavior of the facilities and its interdependencies
is needed to see how the MSTP can affect facilities behavior, moreover whether MSTP canincrease the productivity of the facilities and city economic development. An extensive
exploration of the behavior of the facilities and their interdependencies is needed to see how123Chapter IX – Conclusion and Future Worksthe MSTP can affect facilities behavior, moreover whether MSTP can increase theproductivity of the facilities and city economic development. We need to explore how
online-based facilities interact with other facilities; whether they will compete, support, or
eliminate each other's will be our remaining tasks. Third, to avoid some side-effects froman oversupply of drivers, we may need to explore how the MSTP's drivers behave. Somedrivers may only serve in one area, while others serve in many areas. Some concentration ofdrivers may also relate to the agglomerated facilities that bring some potential demand ororder for the drivers. Forth, the development of the dynamic discrete choice model toincorporate the presence of MSTP is needed to provide a more comprehensive analysis.

124

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xxii

List of Publicattions

LIST OF PUBLICATIONSJournal Papers and International Proceeding(1) Chapter 4
a. Title: Association between Facility Location Agglomeration and Car
Dependency: A Case of Japanese Cities.
b. Status: Published.
c. Journal name: Journal of the Eastern Asia Society for Transportation Studies
d. Vol, Pages: 13, 1087-1098
e. Publication date: 2019/12/31(2) Chapter 5
a. Title: Exploring the Impacts of Online Food Delivery Service on Facility
Distribution: A Case of Jakarta, Indonesia.
b. Status: Accepted.
c. Journal name: Conference Proceeding of the 14th International Conferenceof Eastern Asia Society for Transportations Studies (EASTS) and nominatedfor Journal of Asian Transport Studies (ATS).
d. Publication date: 2021.
(3) Chapter 6
a. Title: Toward a Comprehensive Understanding of ICT Impacts on ActivityTravel Behavior: Preliminary Results from a Two-week Smartphone-based
Survey in Jakarta, Indonesia.
b. Status: Acceptedc. Journal name: Conference Proceeding of the 14th International Conferenceof Eastern Asia Society for Transportations Studies (EASTS) and nominatedfor Journal of Eastern Asia Society for Transportations Studies (EASTS).
d. Publication date: 2021.
(4) Chapter 5
a. Title: On the empirical association between spatial agglomeration ofcommercial facilities and transportation systems: A nationwide analysis ofJapan.
b. Status: 2nd round peer-review
c. Journal name: Journal of Transport and Land Use.
(5) Chapter 7
a. Title: Exploring the Impacts of Online Food Delivery Service on Facility
Distribution: A Case of Jakarta, Indonesia.
b. Status: Submitted.
c. Journal name: Journal of Transportation.
d. Date of submission: 2021/07/06.
International Conference•

Safira Maya, Chikaraishi Makoto. (2021). Toward a Comprehensive Understandingof ICT Impacts on Activity-Travel Behavior: Preliminary Results from a Two-week
Smartphone-based Survey in Jakarta, Indonesia. The 14th International Conferenceof Eastern Asia Society for Transportations Studies (EASTS). Hiroshima, Japan.
September 12-15, 2021.
xxiii

List of Publicattions

•

•

•

•

•

•

•

Safira Maya, Chikaraishi Makoto. (2021). Exploring the Impacts of Online Food
Delivery Service on Facility Distribution: A Case of Jakarta, Indonesia. The 14th
International Conference of Eastern Asia Society for Transportations Studies(EASTS). Hiroshima, Japan. September 12-15, 2021.
Safira Maya, Chikaraishi Makoto. (2020). The Impact of Online-based Food
Delivery Service on Individuals’ Eating Behavior: A Case Study on the Multi-service
Transport Platforms (MSTPs) in Indonesia. The 5th PlanoCosmo International
Conference, Institut Teknologi Bandung, Indonesia. October 20-21, 2020.
Safira Maya, Chikaraishi Makoto (2020). Observing and modelingInterdependencies between ICT use and travel in the presence of multi-service
transport platform: A case from Jakarta, Indonesia. hEART 2020: 9th Symposium of
the European Association for Research in Transportation, ENTEPE, Lyon, France.
April 7-9, 2021.
Safira Maya, Chikaraishi Makoto (2020). The Impact of Online-based Food Delivery
Service on Individuals’ Eating Behavior: A Case Study on the Multi-service
Transport Platforms (MSTPs) in Indonesia. The 5th PlanoCosmo International
Conference, Institut Teknologi Bandung, Indonesia. October 20-21, 2020.
Safira Maya, Chikaraishi Makoto (2019). Association Between Facility Location
Agglomeration and Car Dependency: A Case of Japanese Cities. The 13th
International Conference of Eastern Asia Society for Transportations Studies(EASTS), Sri Lanka. September 9-11, 2019
Safira Maya, Chikaraishi Makoto (2019). Association Between Facility Location
Agglomeration and Car Dependency: A Case of Japanese Cities. 2019 International
Conference on Climate Change, Disaster Management, and Environmental
Sustainability, Kumamoto University, Japan. September 19-21, 2019.
Safira Maya, Chikaraishi Makoto (2018). Agglomeration of Commercial Facilities
and Modal Share Case Study: 16 Cities in Japan. 58th ERSA Congress, Cork, Ireland.
August 28- 31, 2018.

xxiv

Curriculum Vitae

CURRICULUM VITAE
I.

Personal Information

Name
Place, Birth Date
E-mail Address
Phone NumberHome Address
Scopus ID
ORCID iD

II.

Maya Safira, ST., MT., Dr. Eng.
Bandung, 14 December 1993
safira.msa@gmail.com(+62) 817-7062-0626
Jalan Sam Ratulangi H136, Kel. Baros, Kec. Cimahi
Tengah, Kota Cimahi, Jawa Barat Kode Pos 40521
Safira (Last Name); Maya (First Name); Hiroshima
University
0000-0002-5568-6690

Education Background

Program
University

Bachelor (S-1)
Institut Teknologi Bandung

Major

Urban and Regional
Planning
2011
2015
3.24 / 4.00 (Very Satisfied)Study of Land Price
Changes in Public
Apartment in Cimahi,Indonesia

Entrance Year
Graduate Year
GPA
Thesis Title

Supervisor(s)Sugiyantoro, Ir., MIP

Master (S-2)
Institut Teknologi
Bandung
Urban and Regional
Planning
2015
2016
3.86 / 4.00 (Cum Laude)
System Development ofMunicipality AssetManagement. Case
Study: Kecamatan
Coblong, Kota Bandung
Dr. Ir.Denny Zulkaidi,
MUP

Doctor (S-3)
Hiroshima University
Urban and Transportation
Engineering
2018
2021
4.00 / 4.00 (Cum Laude)
The Multidimensional
Impact of Multi-Service
Transport Platform (MSTP)
on Activity-travel Behavior
and Urban Form: A Case of
Jakarta, Indonesia
1. Makoto Chikaraishi, Dr.
Eng.
2. Prof. Akimasa Fujiwara
3. Prof. Junyi Zhang

III. Publication and Research Participation
3.1. International Journal
No
Title
Role
1 On the empirical association between spatial agglomeration Firstof commercial facilities and transportation systems: A Author
nationwide analysis of Japan.
2 Exploring the Impacts of Online Food Delivery Service on First
Facility Distribution: A Case of Jakarta, Indonesia.
Author

Journal Details
Submitted to Journal ofTransport and Land Use.
(2021)
Submitted Journal of AsianTransport Studies (ATS).
(2021)
3 Toward a Comprehensive Understanding of ICT Impacts on First
Submitted Journal of the
Activity-Travel Behavior: Preliminary Results from a Two- Author Eastern Asia Society forweek Smartphone-based Survey in Jakarta, Indonesia.
Transportation Studies.

xxv

Curriculum Vitae

No

Title

Role

Journal Details(2021)
4 Road Network Vulnerability and City-level Characteristics: CoEnvironment and Planning
A Nationwide Comparative Analysis of Japanese Cities
Author B: Urban Analytics andCity Science. (2020)
5 Association between Facility Location Agglomeration and First
Journal of the Eastern Asia
Car Dependency: A Case of Japanese Cities.
Author Society for Transportation
Studies. (2019)

3.2. International Conference Proceeding
No
1

Title
Toward a Comprehensive Understanding of
ICT Impacts on Activity-Travel Behavior:
Preliminary Results from a Two-week
Smartphone-based Survey in Jakarta,
Indonesia.
Exploring the Impacts of Online Food
Delivery Service on Facility Distribution: A
Case of Jakarta, Indonesia.

Role
First
Author

Proceeding Detail
Proceeding of the 14th International
Conference of Eastern Asia Society forTransportations Studies (EASTS).
2021.

First
AuthorObserving and modeling Interdependencies
between ICT use and travel in the presenceof a multi-service transport platform: A casefrom Jakarta, Indonesia.
The Impact of Online-based Food Delivery
Service on Individuals’ Eating Behavior: A
Case Study on the Multi-service Transport
Platforms (MSTPs) in Indonesia.
Association Between Facility Location
Agglomeration and Car Dependency: A
Case of 69 Japanese Cities.

First
Author

Proceeding of the 14th International
Conference of Eastern Asia Society forTransportations Studies (EASTS).
2021.
Proceeding of hEART 2020: 9th
SymposiumoftheEuropean
AssociationforResearch
inTransportation. 2020.
Proceeding of the 5th PlanoCosmo
International Conference, Institut
Teknologi Bandung, Indonesia. 2020.

6Road Network Vulnerability: A Nationwide
Analysis of 69 Cities from Japan

CoAuthor

7

Association Between Facility Location
Agglomeration and Car Dependency: A
Case of Japanese Cities.

First
Author8

Agglomeration of Commercial Facilities
and Modal Share Case Study: 16 Cities in
Japan

First
Author

2

3

4

5

First
AuthorFirst
Author

Proceeding of 2019 International
Conference on Climate Change,
DisasterManagement,
andEnvironmental
Sustainability,
Kumamoto University, Japan. 2019
Proceeding of 2019 International
Conference on Climate Change,
DisasterManagement,
andEnvironmental Sustainability. 2019.
Proceeding of the 13th International
Conference of Eastern Asia Society forTransportations Studies (EASTS).
2019
Proceeding of the 58th ERSA Congress.
2018.

3.3. Participation in International Conferences
No
1
2
3

Conference
The 14th International Conference of Eastern Asia
Society for Transportations Studies (EASTS)
hEART 2020: 9th Symposium of the European
Association for Research in Transportation
The 5th PlanoCosmo International Conference

Date
12-15
Sept 2021
7-9 April
2021
20-21 Oct

Role asPresenter Participant
√
√
√

xxvi

Curriculum Vitae

No
4
5
6
7
8

Conference
2019 Transportation Research Board (TRB) Annual
Meeting2019 International Conference on Climate Change,
Disaster Management, and Environmental
Sustainability
The 13th International Conference of Eastern Asia
Society for Transportations Studies (EASTS)
2018 Transportation Research Board (TRB) Annual
Meeting
The 58th ERSA Congress

Role asPresenter Participant

Date
2020
13-17 Jan
2019
19-21
Sept 20199-12 Sept2019
7-11 Jan2018
28-31
Aug 2018

√
√
√
√
√

IV. WORK EXPERIENCE
No
1

2

Institution
Graduate School forInternational Development
and Cooperation, Hiroshima
University, Japan
Urban and Regional
Planning Departement;
School of Architecture,
Planning, and Policy
Development; Institut
Teknologi Bandung,Indonesia

Position
1. Teaching Assistant for Development
Technology Department
2. Research Assistant for Risk andInfrastructure Laboratory
1. Academic Assistant for the School ofArchitecture, Planning, and Policy
Development
2. Teaching Assistant for Urban andRegional Planning Department
3. Research Assistant for City Planningand Design Research Group
3. Facilitator of Functional Training for
National Government Officer (Diklat
JFP Pertama and Muda) Collaborationof BAPPENAS and ITB
Urban analysist

3Center of Urban Design
Study, Bandung, Indonesia

V.

TEACHING EXPERIENCE

No
1

Institution
Hiroshima
University

2

Hiroshima
University

3

Bandung
Institute ofTechnology
Bandung
Institute ofTechnology
Bandung

4
5

Subject
Seminar on risk management technology
for graduate program of development
technology department
Special seminar for linkage program II for
the graduate program of development
technology department
Urban planning studio for urban planning
concentration of the graduate program ofurban and regional planning department
Land management for the graduate
program of urban and regional planningdepartment
Urban facility planning for the graduate

Years
March 2018 –March 2021
August 2015 –September 2017

August 2014 –December 2014Year
20182021

Position
Teaching Assistant2020

Teaching Assistant

2017
2017

Teaching Assistantand Studio
Assistant
Teaching Assistant2017

Teaching Assistantxxvii

Curriculum Vitae

No
6
7
8
9

Institution
Institute ofTechnology
Bandung
Institute ofTechnology
Bandung
Institute ofTechnology
Bandung
Institute ofTechnology
Bandung
Institute ofTechnology

Subject
program of urban and regional planningdepartment
Site planning studio for the undergraduate
program of urban and regional planningdepartment
Urban planning studioYear

Position

2016

Teaching Assistant

2016

Planning process studio

2016

Introduction of spatial data (Geographic
Information System)

2015

Teaching Assistantand Studio
Assistant
Teaching Assistantand Studio
Assistant
Teaching Assistantand Lab Session
Instructor

VI. RESEARCH EXPERIENCE
No
1

Research Title
Evaluation of sediment-related disasterevacuation facility plan due to heavy rain under
COVID-19 situation (COVID-19 状況下での豪雨による土砂災害避難施設計画の評価)
Study on Interdependencies Between ICT and
Travel Behavior

Year
2020

Position
Research
AssistantInstitution
Hiroshima University
and Ministry ofTransportation of Japan

2020

Research
Assistant

3

Short-term Prediction for Travel Demand
Management in Disaster Situation

2019

Research
Assistant

4

Interaction Between Urban Form and Travel
Behavior in a Smart City Environment

2018

Research
Assistant5

Study of The Development of Communicative
City Towards Smart Cities

2017

Research
Assistant

6

Urban-Village Asset Management

2017

Research
Assistant

7

Formulating A Smart Region Concept in The
Context of Tropical Islands Cities System

2017

Research
Assistant

Hiroshima University
and Ministry ofTransportation of Japan
Hiroshima University
and Ministry ofTransportation of Japan
Hiroshima University
and Ministry ofTransportation of Japan
Lembaga Penelitian dan
Pengabdian kepada
Masyarakat (LPPM)
ITB
Lembaga Penelitian dan
Pengabdian kepada
Masyarakat (LPPM)
ITB
Lembaga Penelitian dan
Pengabdian kepada
Masyarakat (LPPM)
ITB

2xxviiiAppendices

APPENDICES LISTAPPENDIX 1.
APPENDIX 2.
APPENDIX 3.
APPENDIX 4.
APPENDIX 5.
APPENDIX 6.
APPENDIX 7.

AGGLOMERATION INDEX FOR 69 CITIESAGGLOMERATION INDEX OF JAKARTA (MSTPs)
ATTRIBUTE OF ACTIVITY-TRAVEL DIARY SURVEYATTRACTION EACH ZONE IN JAKARTAQUESTIONNAIRE
LIST OF VARIABLESPRESENTATION HANDOUT

xxix

Appendix 1 - Agglomeration Index of 69 Cities in Japan

APPENDIX 1. AGGLOMERATION INDEX FOR 69 CITIES
Average Pairwise Distance
No

City

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

Nara
Otake
Fukuoka
Yokohama
Kawasaki
Kitakyushu
Matsuyama
Saitama
Kobe
Sendai
NagoyaSapporoHiroshima
Koriyama
Kumamoto
Utsunomiya
Chiba
Osaka
Tokorozawa
Yamanashi
Oyabe
Shizuoka
Yokkaichi
Kyoto
MatsudoAkashi
Kochi
Takasaki
Ome
JoetsuKomatsu
Matsue
Gifu
Tsushima
Sakai
OdawaraTokushima
Kanazawa
YasugiMoriokaOmihachiman
Uji
Usuki
Iwata
Shiogama

Com
5.8
1.78
5.67
9.34
9.68
9.93
5.26
6.12
10.09
6.46
6.94
7.79
6.81
5.26
5.63
5.44
7.2
4.93
4.16
3.29
3
7.4
4.86
5.17
3.95
5.32
4.25
6.08
3.82
9.04
3.7
6.16
4.58
2.89
4.92
3.34
4.12
4.14
6.59
4.52
3.3
2.49
5.14
4.58
2.03

S
9.19
4.8
9.73
11.68
10.64
11.75
8.07
4.77
12.8
9.89
9.71
12.96
11.86
10.68
8.32
8.14
9.54
8.35
4.62
6.51
3.75
12.12
6.62
9.46
4.66
5.93
6.15
9.97
3.75
14.3
6.58
9.93
7.48
3.24
8.2
4.68
6.17
7.06
8.7
9.65
4.58
3.81
8.4
6.65
1.58

Average Pairwise Euclidean Distance
M
C
P
SM
5.33
10.89
5.07
9.07
1.76
5.15
2.92
3.38
7.26
9.3
10.47
8.46
12.28
10.78
12.45
12.76
9.78
10.52
10.84
10.55
10.96
10.96
12.11
11.78
5.83
13.58
8.65
6.97
6.64
6.97
7.44
7.57
11.96
11.49
14.46
12.74
7.36
10.85
10.57
8.14
8.41
8.81
10.89
9.38
8
12.06
14.18
10.9
8.24
12.97
10.47
9.52
5.22
13.67
5.84
7.36
7.13
8.29
8.13
7.51
6.61
11.35
8.13
7.58
8.47
9.18
8.91
5.37
7.78
2.08
8.4
7.96
3.37
4.47
4.26
4.04
3.85
5.99
1.84
5.28
2.87
5.29
0.51
4.23
7.88
15.81
9.77
9.26
5.85
8.13
6.99
6.73
6.99
9.75
9.34
8.18
0.5
4.36
4.42
4.85
6.28
6.06
5.48
6.35
4.02
9.46
6.45
5.11
6.9
8.19
6.86
8.24
3.94
5.07
3.11
4.05
10.48
17.87
5.97
12.44
4.09
5.88
6.26
5.47
6.45
11.04
8.51
7.99
5.9
7.3
7.37
6.68
2.18
2.79
1.22
3.03
5.52
4.9
5.94
6.5
3.42
2.66
5.29
4.25
3.79
6.74
6.25
4.99
5.5
7.05
6.88
6.21
5.97
11.89
10.32
8.25
5.72
14.52
7.1
6.7
3.35
5.19
1.86
3.84
3.2
2.96
2.88
3.86
5.06
9.7
0.54
6.57
5.31
6.84
3.9
6.45
0.87
0.46
2.42
1.74

SC
9.38
5.28
9.1
12.62
10.23
11.13
9.66
8.52
12.48
9.57
9.28
12.45
11.65
10.56
8.9
8.96
8.98
8.85
5.05
6.78
5.61
12.37
8.08
9.06
9.82
6.49
8.01
9.04
4.36
16.06
6.88
5.09
7.97
3.49
6.9
5.06
6.86
7.65
10.48
9.7
5.2
4.06
9.31
7.25
1.61

SCP
7.64
3.79
10.84
13.58
11.15
12.85
9.46
8.44
14.62
10.8
11.14
14.2
11.75
10.34
7.47
7.99
7.89
7.97
4.82
6.3
5.42
11.46
7.71
9.98
4.99
6.5
6.88
8.63
3.86
12.86
6.88
9
7.99
3.27
6.71
5.39
6.94
7.52
11.49
8.72
4.42
3.66
9.03
6.04
2.36

Com
6.83
2.9
6.65
12.11
10.07
10.93
6.35
7.48
11.26
7.4
8.23
8.2
8.43
6.28
7.04
6.71
8.14
6.71
4.44
4.41
3.64
8.5
5.96
6.95
4.79
5.57
5.22
7.05
4.12
10.67
4.7
7.42
5.63
3.14
6.42
4.17
5.23
5
8.31
5.89
3.91
3.52
6.46
5.98
2.35

S
10.54
7.15
10.41
13.71
10.56
12.47
8.72
4.97
13.96
10.41
9.83
11.63
13.35
10.72
9.12
9.17
9.81
8.78
5.16
7.44
4.43
13.85
7.82
10.34
5.18
6.27
7.22
10.1
4.43
14.38
6.85
10.98
7.97
3.44
8.57
5.39
7.01
7.47
10.1
9.74
4.84
4.52
9.31
6.89
2.18

Average Pairwise Network Distance
M
C
P
SM
5.71
12.67
6.28
9.83
2.75
6.78
3.87
3.98
7.95
10.12
11.32
8.27
12.84
14.24
13.56
12.73
9.9
10.54
10.58
9.88
11.66
11.41
13.32
11.7
6.56
13.54
9.69
6.86
6.97
7.25
8.12
7.26
12.44
11.53
15.59
12.69
7.8
11.13
10.63
7.92
8.83
10.36
10.85
8.95
9.22
12.16
12.52
9.21
9.12
14.31
12.22
9.56
5.88
13.58
5.82
6.96
7.22
8.73
8.79
7.47
6.72
11.04
8.29
7.11
8.8
9.35
9.45
9
7.77
2.36
9.05
7.87
3.82
4.45
4.72
4.01
4.11
6.64
2.13
5.25
3.43
5.46
0.55
4
8.35
20.4
10.23
9.26
6.27
8.5
7.64
6.57
7.8
9.69
9.65
8.23
4.71
4.23
4.93
4.77
5.96
6.51
6.14
6.01
4.91
10.93
7.08
5.29
7.28
8.2
7.16
7.75
4.26
5.29
3.38
4.3
10.65
18.54
5.82
11.84
4.54
6.05
6.59
5.24
7.43
12.01
9.14
8.17
6.28
7.76
8.03
6.52
2.92
3.08
1.54
3.01
6.5
5.38
6.7
6.51
4.11
2.99
5.75
4.31
4.89
7.83
7.57
5.21
5.58
7.33
7.07
5.92
6.49
13.17
11.97
8.17
5.46
14.98
7.68
6.24
3.45
5.58
2.37
3.79
3.95
3.21
3.8
4.04
5.55
10.47
0.64
6.9
6
7.01
4.66
6.18
1.79
0.77
2.68
1.87

SC
11.38
7.05
10.32
13.79
10.7
12.3
11.49
8.43
14.09
10.55
9.88
11.62
13.6
12.04
9.17
9.45
9.73
8.78
5.17
7.1
5.29
15.03
8.06
10.42
11.36
6.36
8.74
9.64
4.71
16.93
6.66
5.22
7.95
3.48
7.05
5.56
7.34
7.51
11.97
10.27
5.11
4.45
9.68
6.98
1.88

SCP
8.22
5.1
10.84
13.74
10.66
13.27
9.61
8.22
15.3
10.76
10.65
12.22
12.57
9.88
8.88
8.55
9.59
8.94
4.88
6.59
5.09
12.12
7.77
10.04
5.03
6.28
7.45
8.37
3.98
14.54
6.75
10.71
8.01
3.25
6.76
5.77
7.5
7.27
11.98
8.34
4.34
3.99
9.36
5.88
2.58

Com
18.86
6.92
19.67
32.8
29.96
27.69
15.76
25.9
23.6
18.11
22.88
20.08
19.39
13.81
18.01
15
22.77
21.34
13.02
9.82
6.93
22.23
16.06
21.7
14.73
14.38
12.29
16.58
11.06
18.84
8.67
15.17
15.23
7.29
18.09
11.47
12.26
11.66
13.89
13.26
8.56
9.97
11.08
12.79
6.08

S
27.52
13.48
29.41
37.99
31.51
30.77
20.04
9.19
28.77
24.34
27.28
27.42
29.66
21.14
22.41
19.35
26.37
28.06
14.69
15.4
11.73
31.74
19.2
30.57
15.74
16.07
16.35
21.89
11.73
24.87
12.02
21.34
20.42
8.07
29.55
14.18
15.98
16.47
16.52
19.35
10.16
11.81
15.73
14.42
5.84

Average Pairwise Travel Distance
M
C
P
SM
16.82
31.67
18.7
26.01
6.42
12.81
8.9
8.51
23.25
28.58
32.18
24.17
35.29
38.49
37.47
34.96
29.48
31.41
31.3
29.43
29.1
28.04
32.01
29.29
16.24
25.11
22.66
16.81
24.3
25.25
28.19
25.28
26.43
23.13
30.84
26.9
19.06
25.16
25.57
19.19
24.58
27.78
29.89
24.95
22.56
27.92
29.63
22.44
21.34
31.14
28.23
22.17
13.18
25.28
13.52
14.99
18.47
21.08
22.18
19
15.21
21.96
18.69
15.9
24.33
24.92
25.46
24.82
24.81
7.97
28.64
25.15
11.76
12.79
14.33
12.19
9.63
13.04
5.82
11.64
6.38
9.94
1.6
7.4
22.04
43.6
26.04
23.76
16.54
20.29
19.36
17.09
24.52
26.77
29.91
25.52
14.52
12.64
15.14
14.67
15.28
16.87
15.76
15.41
11.66
22.73
16.46
12.45
16.92
18.56
17.17
17.79
11.23
13.2
9.85
11.33
18.97
31.28
10.89
20.91
8.68
11.21
10.91
9.74
15.49
22.95
17.67
16.72
16.68
19.64
20.1
17.23
6.89
7.1
3.96
7.09
18.4
15.16
18.86
18.41
11.46
7.6
14.28
11.9
11.53
17.63
17.09
12.22
13.05
16.07
16.18
13.68
11.59
21.77
20.57
14
12.56
21.26
17.58
13.85
7.77
11.37
5.65
8.42
10.71
9.04
11.31
10.94
9.51
17.59
1.68
11.77
12.88
14.54
10.36
13.19
4.78
2.01
7
4.97

SC
29.26
13.42
29.19
38.08
31.88
30.4
24.69
29.07
28.94
24.52
27.42
27.39
30.08
23.09
22.48
19.77
26.13
28.07
14.66
14.43
9.65
33.99
19.66
30.7
21.94
16.36
19.15
21.11
12.24
28.92
11.88
15.84
20.26
8.12
19.85
14.28
16.6
16.55
19.64
20.13
10.58
11.76
16.29
14.57
4.94

xxx

SCP
22.92
10.66
30.91
37.88
31.58
31.95
22.24
28.51
30.66
25.52
29.33
28.93
28.57
19.85
22.33
19
25.81
28.42
14.51
13.7
9.34
29.24
19.47
30.5
15.4
16.15
17.06
19.13
10.99
25.12
11.6
20.68
20.2
7.68
19.04
14.47
16.95
16.42
19.63
18.28
9.38
11.48
15.81
12.61
6.76

Appendix 1 - Agglomeration Index of 69 Cities in Japan

No

City

46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

Inagi
TorideKasugai
DazaifuIsahayaKagoshima
HirosakiIzumisanoNankokuTokai
Soja
Hitoyoshi
Toyohashi
Toyonaka
YuzawaImabari
Ina
Otaru
Kameyama
Urasoe
Kure
NagatoChitoseKainan

Com
2.26
4.69
4.96
2.44
6.23
4.98
3.94
2.44
3.19
2.83
3.79
2.03
4.47
3.74
6.81
6.89
4.98
4.86
4.5
2.12
8.1
7.81
4.05
4.9

S
2.36
4.88
6.14
2.42
9.63
10.27
9.38
3.35
4.74
3.49
6.53
6.37
7.03
3.87
10.55
6.56
8.25
8.27
5.78
1.83
7.92
9.89
8.63
6.73

Average Pairwise Euclidean Distance
M
C
P
SM
SC
2.67
2.29
1.96
2.77
2.7
4.4
4.91
3.42
4.91
5.44
4.88
5.15
5.88
6.03
6.57
2.15
2.37
2.18
5.72
2.58
5.6
10.49
4.01
7.33
9.45
6.96
13.11
7.99
7.91
10.06
3.41
9.09
3.26
5.72
9.15
2.87
1.84
3.46
3.08
3.47
3.36
4.97
1.64
4.2
5.31
3.84
2.6
3.18
3.87
3.62
3.33
5.82
2.62
4.7
6.89
1.69
5.08
1.33
2.98
6.73
5.17
7.44
6.01
5.84
7.71
3.61
4.86
3.9
3.96
4.32
7.88
9.53
4.19
9.57
9.51
4.59
7.75
4.23
5.49
11.7
4.32
7.46
5.67
5.48
8.59
4.43
7.78
8.07
6.26
7.03
3.09
5.43
3.66
4.35
5.95
1.51
1.25
2.33
2.16
2.01
6.53
10.13
8.75
7.12
9.34
7.84
8.99
8.15
9.04
11.2
4.01
7.73
4
2.56
8.94
4.41
5.18
5.08
5.46
6.98

SCP
2.69
4.81
6.19
3.05
8.63
8.91
6.52
3.53
5.46
3.69
5.06
5.32
6.95
4.23
10.28
6.7
8.38
10.48
4.47
2.27
8.85
11.96
5.62
7.1

Com
2.97
4.95
5.36
2.98
7.44
8.04
4.45
3.38
4.6
3.95
4.48
2.59
5.26
4.03
8.48
10.41
5.61
4.96
5.04
2.9
7.45
9.12
4.75
5.66

Agglomeration Index (Euclidean Distance)
M
C
P
SM
SC
0.92
1.88
0.87
1.56
1.62
0.99
2.90
1.64
1.90
2.97
1.28
1.64
1.85
1.49
1.60
1.31
1.15
1.33
1.37
1.35
1.01
1.09
1.12
1.09
1.06
1.10
1.10
1.22
1.19
1.12
1.11
2.58
1.65
1.33
1.84
1.08
1.14
1.21
1.24
1.39
1.19
1.14
1.43
1.26
1.24
1.14
1.68
1.64
1.26
1.48
1.21
1.27
1.57
1.35
1.34
1.03
1.55
1.82
1.40
1.60
1.21
1.90
1.54
1.40
1.71
0.99
2.60
1.11
1.40
2.01
1.27
1.47
1.44
1.33
1.58
1.22
2.09
1.49
1.39
1.65
1.18
1.27
1.24
0.75
1.25
1.58
0.42
1.70
1.62
1.80
0.81
1.07
1.02
0.97
1.21
1.17
1.82
0.56
1.60
2.06
0.96
1.76
0.17
1.41
1.87
1.07
2.14
1.32
1.25
1.67

SCP
1.32
2.13
1.91
1.45
1.15
1.29
1.80
1.38
1.45
1.67
1.60
1.82
1.73
1.97
1.33
1.47
1.10
1.62
1.16
1.91
1.80
1.55

S
1.54
2.46
1.57
1.13
1.05
1.14
1.37
0.66
1.24
1.41
1.19
1.42
1.58
1.71
1.29
1.37
1.20
1.31
1.16
1.69
1.22
1.63

S
2.93
5.33
6.37
2.97
10.94
11.62
8.95
3.54
5.61
3.73
7.49
8.5
7.37
4.27
11.08
7.07
8.8
8.01
6.26
2.46
9.53
10.26
8.19
8.08

Average Pairwise Network Distance
M
C
P
SM
3.02
2.67
3.03
3.03
4.62
5.21
4.47
4.8
5.55
5.92
5.66
5.72
2.47
3.27
3.35
2.67
6.7
11.77
4.44
7.63
7.62
14.44
9.32
8.28
3.94
9.87
4.17
5.19
3.5
2.03
3.54
3.54
3.73
5.79
3.37
4.45
3.75
2.93
3.62
3.76
3.92
6.77
3.68
4.84
2.12
5.73
1.75
3.22
5.3
7.78
6.44
5.65
3.88
4.72
4.36
3.92
7.8
11.69
4.53
9.18
5.07
8.36
4.9
5.48
4.21
7.93
5.92
5.28
4.69
8.09
9.96
5.51
4.05
6.56
3.88
4.61
2.47
1.83
2.83
2.48
7.3
11.71
10.37
7.68
8.22
10.61
10.84
8.92
3.82
8.49
4.66
4.82
4.85
5.69
5.93
5.68

SC
3.1
5.4
6.38
2.93
11.25
11.96
9.16
3.48
5.75
3.64
7.4
7.95
7.54
4.4
11.41
14.16
8.62
8.21
6.06
2.57
11.95
12.37
8
7.64

SCP
3.04
4.88
5.94
3.3
9.2
9.93
6.07
3.54
5.99
3.67
5.39
6.18
6.83
4.36
9.7
6.66
8.42
9.48
4.64
2.76
10.21
11.86
5.26
7.63

Com
8.46
10.49
13.31
8.26
13.9
19.7
9.69
9.59
9.06
9.7
8.99
4.98
13.06
12.72
13.69
17.68
11.13
8.53
10.22
7.19
16.24
13.38
7.92
11.2

S
8.08
11.2
15.46
8.46
19.53
25.43
16.71
9.82
10.92
9.13
13.72
14.2
17.54
13.54
17.83
14.38
16.73
13.02
12.19
6.11
20.04
14.38
12.83
15.01

Average Pairwise Travel Distance
M
C
P
SM
8.47
7.75
8.69
8.48
9.88
10.81
9.69
10.22
13.66
13.99
14.16
14.05
7.07
8.96
9.74
7.65
12.76
20.31
9.17
14.26
18.71
29.72
22.23
19.97
8.81
17.75
10.12
10.84
9.23
5.89
9.97
9.39
7.6
11.03
6.95
8.89
9.19
7.51
9.05
9.22
8.09
12.16
7.97
9.58
4.24
9.96
3.78
5.98
13.19
18.45
15.64
13.94
12.28
14.57
13.74
12.44
12.56
18.48
7.79
14.78
10.81
16.59
11.41
11.57
8.52
14.97
11.75
10.46
8.12
12.48
15.64
9.35
8.49
12.54
7.59
9.5
6.22
4.35
7.11
6.23
15.89
23.33
21.8
16.62
11.88
14.88
14.68
12.73
6.82
13.33
8.55
8.2
9.59
11.21
11.23
11.03

SC
8.68
11.25
15.46
8.31
19.94
25.93
17.02
9.71
11.07
9
13.46
13.44
17.92
13.89
18.22
22.6
16.37
13.28
11.82
6.4
23.74
17.67
12.57
14.38

S
1.46
1.95
1.49
1.16
1.05
1.11
1.27
0.35
1.22
1.34
1.19
1.37
1.53
1.53
1.24
1.29
1.16
1.32
1.13
1.57
1.69
1.43

Agglomeration Index (Travel Distance)
M
C
P
SM
SC
0.89
1.68
0.99
1.38
1.55
0.93
1.85
1.29
1.23
1.94
1.18
1.45
1.64
1.23
1.48
1.08
1.17
1.14
1.07
1.16
0.98
1.05
1.04
0.98
1.06
1.05
1.01
1.16
1.06
1.10
1.03
1.59
1.44
1.07
1.57
0.94
0.98
1.09
0.98
1.12
1.12
0.98
1.31
1.14
1.23
1.05
1.39
1.41
1.06
1.35
1.07
1.21
1.31
1.09
1.20
1.12
1.39
1.48
1.12
1.36
1.10
1.61
1.46
1.14
1.55
0.95
1.83
0.98
1.09
1.67
1.03
1.17
1.23
1.05
1.25
1.01
1.46
1.25
1.06
1.32
1.07
1.09
1.12
1.09
1.15
1.16
0.37
1.34
1.18
1.32
0.90
0.98
1.10
0.94
1.13
0.98
1.33
0.59
1.19
1.47
0.92
1.43
0.23
1.07
1.39
0.99
1.96
1.17
1.07
1.53

SCP
1.22
1.54
1.57
1.15
1.05
1.15
1.41
1.10
1.30
1.41
1.28
1.44
1.47
1.44
1.24
1.27
1.13
1.33
1.11
1.40
1.35
1.32

Agglomeration Index
No

City

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

Nara
Otake
Fukuoka
Yokohama
Kawasaki
Kitakyushu
Matsuyama
Saitama
Kobe
Sendai
NagoyaSapporoHiroshima
Koriyama
Kumamoto
Utsunomiya
Chiba
Osaka
Tokorozawa
Yamanashi
Oyabe
Shizuoka

S
1.58
2.70
1.71
1.25
1.10
1.18
1.54
0.78
1.27
1.53
1.40
1.66
1.74
2.03
1.48
1.50
1.32
1.69
1.11
1.98
1.25
1.64

Agglomeration Index (Network Distance)
M
C
P
SM
SC
0.84
1.86
0.92
1.44
1.67
0.95
2.34
1.33
1.37
2.43
1.20
1.52
1.70
1.24
1.55
1.06
1.18
1.12
1.05
1.14
0.98
1.05
1.05
0.98
1.06
1.07
1.04
1.22
1.07
1.13
1.03
2.13
1.52
1.08
1.81
0.93
0.97
1.09
0.97
1.13
1.11
1.02
1.39
1.13
1.25
1.06
1.50
1.44
1.07
1.43
1.07
1.26
1.32
1.09
1.20
1.12
1.48
1.53
1.12
1.42
1.08
1.70
1.45
1.13
1.61
0.94
2.16
0.93
1.11
1.92
1.02
1.24
1.25
1.06
1.30
1.00
1.64
1.23
1.06
1.41
1.08
1.15
1.16
1.11
1.19
1.16
0.35
1.35
1.17
1.31
0.86
1.00
1.06
0.90
1.16
0.93
1.51
0.48
1.19
1.61
0.94
1.50
0.15
1.10
1.46
0.98
2.40
1.20
1.09
1.77

SCP
1.20
1.75
1.63
1.13
1.06
1.21
1.51
1.10
1.36
1.45
1.29
1.49
1.49
1.57
1.26
1.27
1.18
1.33
1.10
1.50
1.40
1.43

xxxi

SCP
8.65
10.39
14.72
9.55
16.83
23.1
12.83
9.94
11.56
9.13
10.55
10.63
16.45
13.76
15.67
14.06
16.02
15.04
9.09
6.94
21.44
16.74
9.3
14.33

Appendix 1 - Agglomeration Index of 69 Cities in Japan

No
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

City
Yokkaichi
Kyoto
MatsudoAkashi
Kochi
Takasaki
Ome
JoetsuKomatsu
Matsue
Gifu
Tsushima
Sakai
OdawaraTokushima
Kanazawa
YasugiMoriokaOmihachiman
Uji
Usuki
Iwata
Shiogama
Inagi
TorideKasugai
DazaifuIsahayaKagoshima
HirosakiIzumisanoNankokuTokai
Soja
Hitoyoshi
Toyohashi
Toyonaka
YuzawaImabari
Ina
Otaru
Kameyama
Urasoe
Kure
NagatoChitoseKainan

S
1.36
1.83
1.18
1.11
1.45
1.64
0.98
1.58
1.78
1.61
1.63
1.12
1.66
1.40
1.50
1.70
1.32
2.14
1.39
1.53
1.63
1.45
0.78
1.04
1.04
1.24
0.99
1.55
2.06
2.38
1.37
1.48
1.23
1.72
3.13
1.58
1.04
1.55
0.95
1.66
1.70
1.28
0.86
0.98
1.27
2.13
1.37

Agglomeration Index (Euclidean Distance)
M
C
P
SM
SC
1.20
1.67
1.44
1.39
1.66
1.35
1.89
1.81
1.58
1.75
0.13
1.10
1.12
1.23
2.49
1.18
1.14
1.03
1.19
1.22
0.95
2.22
1.52
1.20
1.88
1.13
1.35
1.13
1.36
1.49
1.03
1.33
0.82
1.06
1.14
1.16
1.98
0.66
1.38
1.78
1.10
1.59
1.69
1.48
1.86
1.05
1.79
1.38
1.30
0.83
1.29
1.59
1.61
1.46
1.74
0.75
0.97
0.42
1.05
1.21
1.12
1.00
1.21
1.32
1.40
1.02
0.80
1.58
1.28
1.52
0.92
1.64
1.52
1.21
1.66
1.33
1.70
1.66
1.50
1.85
0.91
1.80
1.57
1.25
1.59
1.27
3.22
1.57
1.48
2.15
1.02
1.57
0.56
1.16
1.58
1.29
1.19
1.16
1.55
1.63
0.98
1.89
0.10
1.28
1.81
1.16
1.49
0.85
1.41
1.58
0.43
0.23
1.19
0.86
0.79
1.18
1.01
0.87
1.23
1.20
0.94
1.05
0.73
1.05
1.16
0.98
1.04
1.19
1.22
1.33
0.88
0.97
0.89
2.34
1.06
0.90
1.68
0.64
1.18
1.52
1.40
2.63
1.60
1.59
2.02
0.87
2.31
0.83
1.45
2.33
1.17
0.75
1.41
1.26
1.42
1.05
1.56
0.51
1.31
1.66
1.36
0.92
1.12
1.37
1.28
0.88
1.54
0.69
1.24
1.82
0.83
2.50
0.65
1.47
3.31
1.16
1.67
1.35
1.31
1.73
0.96
1.30
1.04
1.06
1.16
1.16
1.40
0.62
1.41
1.40
0.67
1.13
0.61
0.80
1.70
0.87
1.50
1.14
1.10
1.72
0.91
1.60
1.66
1.29
1.45
0.69
1.20
0.81
0.97
1.32
0.71
0.59
1.10
1.02
0.95
0.81
1.25
1.08
0.88
1.15
1.00
1.15
1.04
1.16
1.43
0.99
1.91
0.99
0.63
2.21
0.90
1.06
1.04
1.11
1.42

SCP
1.59
1.93
1.26
1.22
1.62
1.42
1.01
1.42
1.86
1.46
1.75
1.13
1.36
1.62
1.68
1.82
1.74
1.93
1.34
1.47
1.76
1.32
1.17
1.19
1.03
1.25
1.25
1.39
1.79
1.66
1.45
1.71
1.31
1.33
2.62
1.56
1.13
1.51
0.97
1.68
2.16
0.99
1.07
1.09
1.53
1.39
1.45

S
1.31
1.49
1.08
1.13
1.38
1.43
1.08
1.35
1.46
1.48
1.42
1.10
1.34
1.29
1.34
1.50
1.21
1.65
1.24
1.28
1.44
1.15
0.93
0.99
1.08
1.19
1.00
1.47
1.44
2.01
1.05
1.22
0.94
1.67
3.29
1.40
1.06
1.31
0.68
1.57
1.62
1.24
0.85
1.28
1.13
1.72
1.43

Agglomeration Index (Network Distance)
M
C
P
SM
SC
1.05
1.43
1.28
1.10
1.35
1.12
1.39
1.39
1.18
1.50
0.98
0.88
1.03
1.00
2.37
1.07
1.17
1.10
1.08
1.14
0.94
2.09
1.36
1.01
1.67
1.03
1.16
1.02
1.10
1.37
1.03
1.28
0.82
1.04
1.14
1.00
1.74
0.54
1.11
1.59
0.97
1.29
1.40
1.11
1.42
1.00
1.62
1.23
1.10
0.70
1.12
1.38
1.43
1.16
1.41
0.93
0.98
0.49
0.96
1.11
1.01
0.84
1.04
1.01
1.10
0.99
0.72
1.38
1.04
1.33
0.93
1.50
1.45
0.99
1.40
1.12
1.47
1.41
1.18
1.50
0.78
1.58
1.44
0.98
1.44
0.93
2.54
1.30
1.06
1.74
0.88
1.43
0.61
0.97
1.31
1.12
0.91
1.08
1.15
1.26
0.86
1.62
0.10
1.07
1.50
1.00
1.17
0.78
1.03
1.17
0.76
0.33
1.14
0.80
0.80
1.02
0.90
1.02
1.02
1.04
0.93
1.05
0.90
0.97
1.09
1.03
1.10
1.06
1.07
1.19
0.83
1.10
1.13
0.90
0.99
0.90
1.58
0.60
1.02
1.51
0.95
1.80
1.16
1.03
1.49
0.89
2.22
0.94
1.17
2.06
1.04
0.60
1.05
1.05
1.03
0.81
1.26
0.73
0.97
1.25
0.95
0.74
0.92
0.95
0.92
0.87
1.51
0.82
1.08
1.65
0.82
2.22
0.68
1.24
3.07
1.01
1.48
1.22
1.07
1.43
0.96
1.17
1.08
0.97
1.09
0.92
1.38
0.53
1.08
1.35
0.49
0.80
0.47
0.53
1.36
0.75
1.41
1.05
0.94
1.54
0.95
1.63
2.01
1.11
1.66
0.80
1.30
0.77
0.92
1.20
0.85
0.63
0.98
0.85
0.89
0.98
1.57
1.39
1.03
1.61
0.90
1.16
1.19
0.98
1.36
0.80
1.79
0.98
1.01
1.69
0.86
1.01
1.05
1.00
1.35

SCP
1.30
1.44
1.05
1.13
1.43
1.19
0.96
1.36
1.43
1.44
1.42
1.04
1.05
1.38
1.43
1.45
1.44
1.42
1.11
1.13
1.45
0.98
1.10
1.02
0.99
1.11
1.11
1.24
1.23
1.36
1.05
1.30
0.93
1.20
2.39
1.30
1.08
1.14
0.64
1.50
1.91
0.92
0.95
1.37
1.30
1.11
1.35

S
1.20
1.41
1.07
1.12
1.33
1.32
1.06
1.32
1.39
1.41
1.34
1.11
1.63
1.24
1.30
1.41
1.19
1.46
1.19
1.18
1.42
1.13
0.96
0.96
1.07
1.16
1.02
1.40
1.29
1.73
1.02
1.21
0.94
1.53
2.85
1.34
1.06
1.30
0.81
1.50
1.53
1.19
0.85
1.23
1.07
1.62
1.34

Agglomeration Index (Travel Distance)
M
C
P
SM
SC
1.03
1.26
1.21
1.06
1.22
1.13
1.23
1.38
1.18
1.41
0.99
0.86
1.03
1.00
1.49
1.06
1.17
1.10
1.07
1.14
0.95
1.85
1.34
1.01
1.56
1.02
1.12
1.04
1.07
1.27
1.01
1.19
0.89
1.02
1.11
1.01
1.66
0.58
1.11
1.54
1.00
1.29
1.26
1.12
1.37
1.02
1.51
1.16
1.10
1.04
1.10
1.29
1.32
1.13
1.33
0.95
0.97
0.54
0.97
1.11
1.02
0.84
1.04
1.02
1.10
1.00
0.66
1.25
1.04
1.24
0.94
1.44
1.39
1.00
1.35
1.12
1.38
1.39
1.17
1.42
0.83
1.57
1.48
1.01
1.41
0.95
1.60
1.33
1.04
1.52
0.91
1.33
0.66
0.98
1.24
1.07
0.91
1.13
1.10
1.18
0.86
1.59
0.15
1.06
1.47
1.01
1.14
0.81
1.03
1.14
0.79
0.33
1.15
0.82
0.81
1.00
0.92
1.03
1.00
1.03
0.94
1.03
0.92
0.97
1.07
1.03
1.05
1.06
1.06
1.16
0.86
1.08
1.18
0.93
1.01
0.92
1.46
0.66
1.03
1.43
0.95
1.51
1.13
1.01
1.32
0.91
1.83
1.05
1.12
1.76
0.96
0.61
1.04
0.98
1.01
0.84
1.22
0.77
0.98
1.22
0.95
0.77
0.93
0.95
0.93
0.90
1.35
0.89
1.07
1.50
0.85
2.00
0.76
1.20
2.70
1.01
1.41
1.20
1.07
1.37
0.97
1.15
1.08
0.98
1.09
0.92
1.35
0.57
1.08
1.33
0.61
0.94
0.65
0.65
1.28
0.77
1.35
1.06
0.94
1.47
0.95
1.46
1.83
1.10
1.56
0.83
1.23
0.74
0.93
1.16
0.87
0.61
0.99
0.87
0.89
0.98
1.44
1.34
1.02
1.46
0.89
1.11
1.10
0.95
1.32
0.86
1.68
1.08
1.04
1.59
0.86
1.00
1.00
0.98
1.28

SCP
1.21
1.41
1.05
1.12
1.39
1.15
0.99
1.33
1.34
1.36
1.33
1.05
1.05
1.26
1.38
1.41
1.41
1.38
1.10
1.15
1.43
0.99
1.11
1.02
0.99
1.11
1.16
1.21
1.17
1.32
1.04
1.28
0.94
1.17
2.14
1.26
1.08
1.14
0.80
1.44
1.76
0.89
0.97
1.32
1.25
1.17
1.28

xxxii

Appendix 1 - Agglomeration Index of 69 Cities in Japan

Transportation-City Characteristics
No

City

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

Nara
Otake
Fukuoka
Yokohama
Kawasaki
Kitakyushu
Matsuyama
Saitama
Kobe
Sendai
NagoyaSapporoHiroshima
Koriyama
Kumamoto
Utsunomiya
Chiba
Osaka
Tokorozawa
Yamanashi
Oyabe
Shizuoka
Yokkaichi
Kyoto
MatsudoAkashi
Kochi
Takasaki
Ome
JoetsuKomatsu
Matsue
Gifu
Tsushima
Sakai
OdawaraTokushima
Kanazawa
YasugiMoriokaOmihachiman
Uji
Usuki
Iwata
Shiogama
Inagi
TorideKasugai
DazaifuIsahaya

Distance

Travel Time

11.9
14.37
10.87
12.42
12.83
13.03
11.18
12.34
17.93
11.86
10.7
10.58
14.41
11.17
9.82
12.43
15.49
14.71
12.68
10.58
13.22
10.27
11.81
9.41
14.22
14.06
8.39
11.58
11.5
12.59
11.42
12.32
10.56
11.25
12.07
14.11
9.04
10.59
11.43
12.16
12.55
11.9
15.4
10.88
10.63
14.39
13.62
10.18
12.97
11.6

31.56
27.64
28.05
33.41
34.38
28.37
24.45
32.14
33.15
29.2
29.61
26.9
29.21
27.26
25.82
26.52
33.28
31.37
32.38
23.85
23.86
25.01
26.83
27.86
35.8
30.01
21.73
26.3
28.8
24.03
22.37
22.59
25.68
27.51
29.5
30
22.24
23.38
22.63
25.83
26.65
28.82
25.53
26.17
27.79
33.84
30.63
27.64
28.95
25.6

Train
17.69
8.41
10.8
29.41
32.01
6.39
2.51
24.02
23.15
7.66
18.31
12.93
7.8
2.04
1.79
3.38
21.28
30.73
23.19
3.93
3.35
4.71
6.79
16.25
25.82
15.81
0.9
3.96
14.15
1.95
1.63
1.5
2.98
8.76
18.79
15.48
0.56
2.36
1.71
2.51
9.17
19.82
3.53
2.74
8.78
29.77
16.3
10.41
12.61
3.36

Bus
3.16
0.49
6.64
5.19
3.11
5.4
1.3
1.41
3.98
3.57
1.86
4.81
3.97
1.53
3.57
1.29
1.25
2.17
0.85
0.18
0.43
1.56
0.74
4.78
0.99
0.72
0.77
0.5
1.11
0.67
0.07
2.03
2.06
0.45
0.9
1.36
1.37
3.2
0.61
3.76
0.31
0.35
0.29
0.77
1
1.77
0.93
1.05
1.6
1.26

Modal Share (%)
Car
Motorbike
50.24
3.25
60.74
2.14
44.79
1.46
31.65
1.12
28.76
1.03
63.11
1.36
57.19
7.45
37.04
0.64
37.04
3.89
60.66
2.33
48.82
1.59
51.89
0.04
56.31
3.13
74.73
0.62
63.31
3.65
75.66
1.13
47.35
0.55
16.53
1.69
39.31
1.11
80.81
1.9
83.39
0.64
57.82
3.51
71.97
1.22
36.06
3.96
42.79
1.42
51.32
2.63
64.4
4.35
74.1
0.42
57.17
0.81
76.8
0.64
80.62
0.53
77.1
1.29
70.4
0.75
66.13
0.97
45.83
2.95
50.7
1.76
68.66
2.59
65.54
0.84
79.76
1.48
62.18
0.85
68.92
1.27
47.82
6.34
77.11
2.24
76.4
2.63
67.48
1.08
35.33
1.11
60.61
0.46
65.05
1.23
60.96
2.98
75.45
2.46

Bicycle
9.14
12.59
13.84
5.71
10.56
5.5
16.03
15.1
6.43
7.54
11.82
9.78
10.8
7.92
12.88
9.73
9.09
22.42
13.28
4.3
5.42
16.76
7.33
18.68
9.94
11.91
16.67
9.88
9.61
7.84
7.12
6.03
10.87
11.94
16.28
12
17.12
9.42
7.01
12
11.18
9.05
6.45
7.04
4.55
8.88
7.18
8.84
6.78
2.79

Walk
16.53
15.63
22.45
26.92
24.52
18.25
15.52
21.8
25.51
18.24
17.6
20.56
17.99
13.17
14.8
8.81
20.48
26.45
22.26
8.88
6.77
15.64
11.95
20.27
19.04
17.61
12.91
11.14
17.15
12.11
10.02
12.05
12.95
11.75
15.26
18.71
9.7
18.64
9.44
18.71
9.15
16.62
10.39
10.41
17.12
23.14
14.51
13.41
15.07
14.68

Population
364,969
28,4301,474,326
3,714,200
1,433,765
981,891
518,050
1,253,582
1,553,789
1,049,578
2,254,891
1,930,496
1,186,928
326,075
734,287
518,878960,051
2,667,830
342,925
37,116
31,669718,774
313,2031,420,719
485,962
297,057
338,909375,229
137,833
201,794108,980
206,404
416,625
65,114
849,107
196,493257,718
452,144
41,213295,680
82,429191,80241,486
170,960
56,256
86,169109,595
309,854
71,245141,011

Area Size

Population Density

277.9577.53
351.62436.88143.05503.61435.91217.26560.51785.67327.291120.42915.56756.17397.18416.57272.25225.4871.94
290.22134.341414.13207.45831.7761.22
49.52
312.01459.9
103.25973.25372.91579.04204.3125.35
150.63113.85
193.54469.54425.89885.7
180.1867.72
296.3
162.52
17.17
17.74
69.78
92.67
30.05
327.09

1313
367
4193
8502
10023
1950
1188
5770
2772
1336
6890
1723
1296
431
1849
1246
3526
11832
4767
128
236
508
1510
1708
7939
5998
1086
816
1335
207
292
356
2039
2568
5637
1726
1332
963
97
334
457
2832
140
1052
3276
4858
1570
3344
2371
431

Com
2327
7659
858
629
21627
5820
2254
14376
570
2254
849
419
1654
1584
1304
2489
7837
791
427
7090
2461
9122
11774
18184
4633
1391
4304
8493
2707
19813
3174
2715
5572
4105
548
32872
554
3092
2222
1074
921
52394
333
1782
393
10438
6982
18589
14226
822

Number of Facilities
M
S
C
422
57
44
1280
231
48
107
29
21
78
27
3
2570
300
150
695
112
51
291
80
15
1987
295
71
80
14
7
210
46
43
104
26
9
85
22
5
227
58
18
195
42
16
149
22
3
248
97
79
996
159
13
99
26
11
59
16
11
682
129
55
353
66
5
1748
229
13
1721
274
58
2587
359
11
538
102
38
134
46
18
437
116
89
1103
192
10
373
73
22
2614
395
5
569
82
4
318
77
36
763
117
47
500
102
14
49
20
9
3643
499
2
59
32
19
23
113
52
281
50
2
174
32
11
85
22
11
5934
583
10
43
11
10
179
52
3
34
12
16
1577
229
59
1250
183
7
2768
419
10
1525
268
62
71
10
3

P
281
881
156
111
1476
328
207
983
13
95
13
67
65
79
28
151
575
15
49
422
215
800
1469
1259
514
71
215
800
284
689
196
100
260
384
9
1254
24
401
106
76
28
900
45
86
19
714
907
2478
1474
36

GROUP
4
7
27
4
7
27
12
7
12
12
12
4
27
5
4
27
26
5
5
27
5
5
26
7
12
12
26
26
26
7
4
12
27
26
12
7
5
7
5
5
5
7
5
27
5
7
5
7
26
4

xxxiiiAppendix 1 - Agglomeration Index of 69 Cities in Japan

No

City

Distance

Travel Time

51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

Kagoshima
HirosakiIzumisanoNankokuTokai
Soja
Hitoyoshi
Toyohashi
Toyonaka
YuzawaImabari
Ina
Otaru
Kameyama
Urasoe
Kure
NagatoChitoseKainan

10.31
10.48
13.22
10.49
10.26
13.41
12.87
12.04
11.17
15.09
11.74
12.72
10.46
11.94
6.76
11.44
15.28
14.45
12.02

26.26
21.37
29.41
22.96
25.67
27.62
21.07
26.48
31.11
25.04
22.47
23.88
24.4
25.87
22.48
26.38
24.69
23.77
26.77

Train
2.23
1.43
16.72
2.09
9.5
5.31
1.03
6.59
26.73
1.22
1.13
1.26
4.51
3.34
0.8
6.05
0.89
5.19
5.69

Bus
6.03
1.67
0.25
0.32
0.17
0.19
0.31
0.42
2.34
0.4
0.5
0.7
9.7
0.18
2.5
4.26
1.36
1.39
0.31

Modal Share (%)
Car
Motorbike
63.25
3.99
73.9
0.89
52.5
3.74
74.95
3.75
69.23
0.94
77.81
0.83
76.68
2.09
68.47
1.29
26.98
2.01
82.34
0.31
71.25
3.91
84.38
0.63
59.84
0.51
80.32
1.97
71.53
5.88
58.17
3.12
82.22
1.36
72.56
0.39
68.91
5.14

Bicycle
5.07
11.38
13.86
8.16
5.76
7.05
8.9
9.82
16.36
6.98
12.51
2.9
2.07
4.07
2.05
8.47
5.28
7.26
9.77

Walk
19.44
10.73
12.92
10.73
14.4
8.8
10.99
13.41
25.58
8.74
10.7
10.12
23.36
10.11
17.24
19.93
8.89
13.2
10.17

Population
609,250
180,370
101,685
48,688112,310
67,76534,911
379,582
400,086
49,851
167,872
70,258127,224
50,073
114,217
238,046
37,384
95,481
54,838

Area Size

Population Density

558.86523.4756.92
127.1345.14
214.23215.6
261.3936.58
790.1
423.44668.1
243.34
191.5619.53
356.41365.08594.6
102.29

1090
345
1787
383
2488
316
162
1452
10938
63
396
105
523
261
5847
668
102
161
536

Com
10178
548
4665
856
2359
3766
840
4138
3006
772
1279
1211
493
6101
357
486
3424
27980
705

Number of Facilities
M
S
C
928
193
36
78
22
6
586
112
45
116
22
11
389
59
12
494
79
42
123
34
14
482
93
73
678
80
6
91
16
3
236
42
5
156
26
4
56
25
9
758
132
20
43
17
13
42
25
25
427
84
26
5137
697
5
59
26
14

P
414
47
233
45
152
98
110
400
368
9
128
78
11
669
19
26
308
2236
44

GROUP
27
26
26
4
5
5
5
26
4
4
5
4
27
26
27
12
26
7
12

xxxiv

Appendix 2 - Agglomeration Index of Jakarta

APPENDIX 2. AGGLOMERATION INDEX OF JAKARTA (MSTPs)
Area Name
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

Neighborhood
Cengkareng Barat
Cengkareng Timur
Duri Kosambi
Kapuk
Kedaung Kali Angke
Rawa Buaya
GrogolJelambar
Jelambar Baru
Tanjung Duren Selatan
Tanjung Duren Utara
TomangWijaya Kesuma
Kalideres
Kamal
Pegadungan
SemananTegal Alur
Duri Kepa
Kebon Jeruk
Kedoya Selatan
Kedoya Utara
Kelapa Dua
Sukabumi Selatan
Sukabumi Utara
Joglo
Kembangan Selatan
Kembangan Utara
Meruya Selatan
Meruya Utara
SrengsengJati Pulo
Kemanggisan
Kota Bambu Selatan
Kota Bambu Utara
PalmerahSlipi
GlodokKeagungan
KrukutMangga Besar
MapharPinangsia
Taman Sari
TangkiAngke
Duri Selatan

District
Cengkareng
Cengkareng
Cengkareng
Cengkareng
Cengkareng
Cengkareng
Grogol Petamburan
Grogol Petamburan
Grogol Petamburan
Grogol Petamburan
Grogol Petamburan
Grogol Petamburan
Grogol Petamburan
Kalideres
Kalideres
Kalideres
Kalideres
Kalideres
Kebon Jeruk
Kebon Jeruk
Kebon Jeruk
Kebon Jeruk
Kebon Jeruk
Kebon Jeruk
Kebon Jeruk
Kembangan
Kembangan
Kembangan
Kembangan
Kembangan
Kembangan
Palmerah
Palmerah
Palmerah
Palmerah
Palmerah
Palmerah
Taman Sari
Taman Sari
Taman Sari
Taman Sari
Taman Sari
Taman Sari
Taman Sari
Taman Sari
TamboraTambora

City

Zone
ID

% Commercial
Land use

Distance
to Central

West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta

226
225
221
224
223
222
198
199
201
196
195
197
200
228
231
229
227
230
186
185
187
188
184
182
183
176
180
181
178
179
177
194
191
193
192
189
190
219
218
213
217
214
220
215
216
208
203

0.18
0.19
0.17
0.09
0.09
0.1
0.2
0.12
0.15
0.24
0.22
0.16
0.21
0.21
0.19
0.11
0.14
0.36
0.15
0.17
0.2
0.17
0.08
0.15
0.13
0.09
0.16
0.08
0.07
0.15
0.09
0.09
0.13
0.24
0.03
0.12
0.25
0.24
0.18
0.17
0.51
0.34
0.58
0.33
0.26
0.14
0.09

11.49
10.21
10.81
7.98
7.38
9.17
3.66
4.58
4.79
3.12
3.92
2.72
5.79
13.6
14.67
13.9
12.73
12.61
4.79
4.89
5.91
6.42
5.76
6.08
4.86
9.24
7.57
8.17
9.35
9.23
6.83
1.75
2.72
1.36
1.42
3.27
1.76
1.95
2.3
2.52
1.48
2.28
1.6
2.01
1.28
3.78
3.12

Ratio
Online/
Dine
0.69
0.79
0.49
0.88
0.4
0.94
0.92
0.6
0.67
1.19
1.03
1.39
0.71
0.87
0.8
0.95
0.75
0.96
1.96
0.97
0.73
2.47
0.72
0.81
0.63
4.13
0.89
0.92
0.52
0.66
0.85
0.96
0.84
0.38
0.55
0.73
0.72
0.61
0.81
0.54
0.4
1.9
0.81
0.97
0.92
0.76
0.99

Index

Density

Food

OnlineDine-inCom

Food

OnlineDine-in

Com

1.14
1.42
0.94
1.95
0.55
0.73
1.16
0.96
1.14
0.98
1.17
1.12
1.26
0.97
4.08
1.13
0.79
2.68
1.15
0.9
0.95
1.25
1.3
0.84
0.97
1.46
0.78
1.14
0.89
0.99
1.09
5.66
0.92
5.67
3.42
1.14
1.82
2.51
1.11
0.83
3.26
2.04
3.5
2.77
3.3
1.68
1.46

3.31
3.39
3.04
1.92
6.16
0.57
1.09
1.43
1.22
0.73
1.09
1.01
1.3
2.63
4
3.52
4.56
4.2
1.19
1.06
0.92
1.19
1.32
0.98
1.81
1.33
0.79
1.48
4.33
1.51
1
2.52
0.93
6.74
4.43
1.43
2.93
2.76
0.89
1.76
4.25
2.14
2.13
3.58
3.11
1.6
1.2

0.84
0.86
0.51
1.68
2.45
0.54
1.5
1.35
1.12
1.37
1.62
2
1.53
0.81
1.68
1.16
2
2.3
2.93
1.33
1.27
2.95
1.26
0.8
1.65
2.53
0.7
1.37
2.25
1
0.84
3.22
1.09
2.11
1.74
1.64
1.24
1.25
1.02
1.25
1.21
3.42
1.51
1.26
1.43
1.71
1.8

1.06
1.08
1.05
1.84
1.22
1.23
1.65
1.08
1.42
1.32
1.47
1.41
1.46
1
0.53
1.09
0.51
1.76
1.55
1.3
1.51
1.47
1.17
1.34
1.47
1.4
1.19
1.19
3.48
0.93
1.46
3.23
1.74
2.83
4.52
1.52
2.76
3.16
2.06
1.14
3.75
1.84
2.9
2.68
3.47
1.28
2.09

1.01
2.41
3.8
1.57
1.34
0.8
4.93
1.17
14.1
20.04
10.48
18.89
4.75
3.21
0.54
1.33
10.05
0.54
1.36
1.86
77.1
32.16
86.86
12.62
6.7
11.35
3.37
1.53
5.59
2.15
1.85
10.77
10.8
6.81
16.52
13.72
13.19
16.34
174.1
7.14
9.02
145.45
9.92
12.04
367.3
7.36
15.46

2.53
4.16
3.42
2.54
2
0.27
10.85
1.17
14.8
20.04
10.48
8.34
7.6
1.96
0.36
2.39
7.67
0.54
0.27
1.59
36.47
31.58
49.73
17.52
4.69
7.94
1.78
0.51
6.58
1.91
2.47
14.36
5.4
10.21
13.52
9.74
8.12
21.78
75.82
8.92
19.85
87.91
13.88
12.04
238.74
2.45
25.76

0.25
0.22
1.14
0.39
0.33
0.27
1.97
0.58
2.11
2.97
2.99
3.89
0.47
0.36
0.18
0.13
1.28
0.18
0.27
0.27
5.73
2.34
11.21
2.8
0.67
1.36
0.2
0.25
0.66
0.24
0.41
2.39
0.98
1.7
1.5
0.89
1.01
2.72
19.66
1.78
1.8
19.18
0.99
1.51
26.24
1.23
2.58

5.32
8.32
5.50
10.57
2.00
0.27
20.71
2.33
35.95
43.80
26.19
7.78
12.34
2.50
0.54
2.39
17.54
1.44
5.18
1.33
60.95
20.76
75.65
25.93
2.68
9.08
5.54
1.53
4.28
5.26
6.57
25.14
15.21
23.83
67.58
14.17
6.09
27.23
165.6817.85
12.63
102.2916.86
12.04
291.21
4.91
41.22

xxxv

Appendix 2 - Agglomeration Index of Jakarta

Area Name
No
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98

Neighborhood

District

Duri Utara
Jembatan Besi
Jembatan Lima
Kalianyar
Kredang
PekojanRoa Malaka
TamboraTanah Sereal
Cempaka Putih Barat
Cempaka Putih Timur
Rawasari
CidengDuri Pulo
GambirKebon Kelapa
Petojo Selatan
Petojo Utara
Galur
Johar Baru
Kampung Rawa
Tanah Tinggi
Cempaka Baru
Gunung Sahari Selatan
Harapan Mulya
Kebon Kosong
Kemayoran
Serdang
Sumur Batu
Utan Panjang
Cikini
Gondangdia
Kebon Sirih
Menteng
Pegangsaan
Gunung Sahari Utara
Karang Anyar
Kartini
Mangga Dua Selatan
Pasar Baru
BungurKenari
KramatKwitang
PasebanSenen
Bendungan Hilir
GeloraKampung Bali
Karet Tengsin
Kebon Kacang

TamboraTamboraTamboraTamboraTamboraTamboraTamboraTamboraTamboraCempaka Putih
Cempaka Putih
Cempaka Putih
GambirGambirGambirGambirGambirGambirJohar Baru
Johar Baru
Johar Baru
Johar Baru
Kemayoran
Kemayoran
Kemayoran
Kemayoran
Kemayoran
Kemayoran
Kemayoran
Kemayoran
Menteng
Menteng
Menteng
Menteng
Menteng
Sawah Besar
Sawah Besar
Sawah Besar
Sawah Besar
Sawah Besar
Senen
Senen
Senen
Senen
Senen
Senen
Tanah Abang
Tanah Abang
Tanah Abang
Tanah Abang
Tanah Abang

City
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
West Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta
Central Jakarta

Zone
ID

% Commercial
Land use

Distance
to Central

205
207
209
202
206
212
211
210
204
156
155
154
170
175
172
173
171
174
153
150
151
152
158
164
157
162
163
160
159
161
141
142
143
139
140
166
168
167
169
165
149
144
146
147
145
148
133
132
138
134
137

0.21
0.21
0.27
0.12
0.29
0.24
0.72
0.19
0.12
0.14
0.2
0.21
0.31
0.16
0.13
0.62
0.36
0.52
0.16
0.08
0.1
0.07
0.1
0.38
0.07
0.05
0.3
0.11
0.29
0.13
0.33
0.25
0.35
0.09
0.1
0.23
0.22
0.1
0.52
0.32
0.11
0.16
0.25
0.19
0.15
0.32
0.27
0.46
0.49
0.54
0.21

3.29
3.82
2.89
3.74
3.06
2.68
2.08
2.35
2.87
3.34
3.48
3.07
1.75
2.84
0.11
1.35
1.23
1.79
2.64
3.05
3.05
2.42
2.17
0.79
2.05
0.69
1.43
1.37
2.56
1.46
1.45
0.92
1.13
0.54
1.19
1.38
2.01
1.9
0.7
1.33
2.09
2.05
2.23
1.62
2.12
1.34
2.86
3.47
0.31
1.62
0.39

Ratio
Online/
Dine
0.41
0.76
0.78
0.57
0.55
0.84
0.44
0.66
0.61
0.7
0.71
0.81
0.44
0.95
0.9
1.71
0.68
0.74
0.68
0.76
0.6
0.76
0.89
0.78
0.46
0.84
0.71
0.77
0.8
0.45
2.78
1.35
0.8
0.68
0.78
0.96
0.41
0.78
0.64
0.68
0.54
0.93
0.93
0.33
0.66
1.47
0.8
0.56
0.76
1.54
1

Index

Density

Food

OnlineDine-inCom

Food

OnlineDine-in

Com

0.98
1.72
1.23
0.77
1.34
1.02
1.24
1.09
0.8
2.39
0.89
0.78
2.06
0.51
3.24
2.91
2.26
1.99
1.05
0.77
1.4
0.69
1.28
2.83
3.02
3.17
2.5
2.97
0.95
4.17
3.1
3.39
3.03
3.71
2.93
3.14
2.23
1.89
2.82
3.18
1.67
0.76
6.24
2.12
0.89
4.33
0.97
1.71
2.77
3.34
2.81

3.44
0.97
0.58
0.96
1.31
0.81
1.67
0.81
1.02
1.51
1.12
0.87
3.17
0.54
3.83
2.93
2.48
2.04
1.02
1.04
2.12
0.89
1.11
2.86
4.38
2.91
2.52
2.89
1.27
4.4
2.83
3.24
2.92
2.93
2.67
3.03
4.1
1.68
3.31
3.66
1.9
1.7
1.16
6.82
1.54
4.46
0.94
1.15
2.87
2.35
2.8

1.9
1.04
0.75
0.84
1.22
1.18
1.34
1.13
1.13
1.56
1.4
1.01
0.72
0.81
1.25
2.05
1.13
1.37
1.19
1.38
1.57
0.97
1.13
0.97
1.66
1.24
0.81
1.41
1.61
1.54
3.18
2.72
1.34
1.7
1.03
1.19
1.38
1.03
1.51
1.59
1.53
0.95
0.91
2.24
1.61
4.41
1.34
0.95
1.57
2.84
1.6

4.72
2.96
1.11
1.5
1.19
1.84
3.42
1.2
1.5
1.86
1.57
1.47
3.09
1.24
2.93
2.83
2.49
2.88
4.02
1.91
3.15
1.74
1.46
2.86
3.72
3.55
3.11
2.44
1.95
3.46
2.91
3.62
2.74
3.77
3.04
3.59
3.1
3.33
2.9
3.53
2.47
3.42
1.41
3.3
1.1
4.05
1.58
1.24
3.5
2.91
2.78

21.68
3.74
117.646.61
127.62
2.49
26.83
14.14
21.01
15.11
2.73
25.48
1.56
20.45
2.81
21.42
95.7
32.85
10.77
9.35
33.3
12.7
139.04
5.58
43.3
3.95
10.12
45.22
1.78
7.46
35.24
9.52
18.8
15.17
16.23
10.58
63.84
36.48
14.44
3.29
9.47
24.6
66.36
32.62
4.83
2.37
3.52
22.51
84.54
1.31
5.46

16.26
7.48
124.06
3.31
88.12
8.7
16.1
14.14
17.78
18.29
1.36
18.53
2.35
13.15
2.81
12.6
85.86
32.85
17.95
3.4
59.93
17.47
104.02
2.79
21.65
3.95
11.81
23.22
0.89
7.46
17.62
7.62
10.12
14.35
16.23
4.07
48.82
21.12
9.12
3.84
15.79
19.01
38.71
44.27
7.25
8.31
2.69
11.26
53.68
0.65
2.73

2.71
1.87
12.83
3.31
6.08
1.24
3.58
3.54
3.23
5.57
0.45
2.32
0.78
2.92
1.21
1.26
9.84
3.55
3.59
0.85
6.66
3.18
10.61
0.46
5.9
0.99
1.69
9.78
0.89
1.86
5.03
1.9
1.45
1.64
2.03
0.81
7.51
3.84
3.04
0.55
1.58
5.59
17.97
13.98
1.21
1.19
0.83
2.81
5.37
0.65
1.37

27.09
11.23
205.34
9.92
188.40
6.21
17.89
17.68
35.56
21.47
3.64
24.71
2.35
45.29
4.82
54.18
67.08
51.50
21.54
26.36
83.24
44.46
148.60
6.97
47.24
5.92
13.50
28.11
2.67
27.97
61.68
23.49
13.02
31.16
28.40
13.84
71.35
49.92
8.36
11.52
15.79
34.66
67.74
93.20
12.08
15.43
4.14
30.96
99.31
1.31
19.11

xxxvi

Appendix 2 - Agglomeration Index of Jakarta

Area Name
No
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149

Neighborhood
Kebon Melati
Petamburan
Cilandak Barat
Cipete Selatan
Gandaria Selatan
Lebak Bulus
Pondok Labu
Ciganjur
CipedakJagakarsa
Lenteng Agung
Srengseng Sawah
Tanjung Barat
Cipete Utara
Gandaria Utara
GunungKramat Pela
Melawai
Petogogan
Pulo
Rawa Barat
SelongSenayanCipulir
Grogol Selatan
Grogol Utara
Kebayoran Lama Selatan
Kebayoran Lama Utara
Pondok Pinang
BangkaKuningan Barat
Mampang Prapatan
Pela Mampang
Tegal Parang
CikokoDuren Tiga
Kalibata
Pancoran
Pengadegan
Rawajati
Cilandak Timur
Jati Padang
Kebagusan
Pasar Minggu
Pejaten Barat
Pejaten Timur
RagunanBintaroPesanggrahan
Petukangan Selatan
Petukangan Utara

DistrictTanah Abang
Tanah Abang
Cilandak
Cilandak
Cilandak
Cilandak
Cilandak
Jagakarsa
Jagakarsa
Jagakarsa
Jagakarsa
Jagakarsa
Jagakarsa
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Baru
Kebayoran Lama
Kebayoran Lama
Kebayoran Lama
Kebayoran Lama
Kebayoran Lama
Kebayoran Lama
Mampang Prapatan
Mampang Prapatan
Mampang Prapatan
Mampang Prapatan
Mampang Prapatan
Pancoran
Pancoran
Pancoran
Pancoran
Pancoran
Pancoran
Pasar Minggu
Pasar Minggu
Pasar Minggu
Pasar Minggu
Pasar Minggu
Pasar Minggu
Pasar Minggu
Pesanggrahan
Pesanggrahan
Pesanggrahan
Pesanggrahan

City
Central Jakarta
Central Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta

Zone
ID

% Commercial
Land use

Distance
to Central

135
136
16
18
17
14
15
3
1
4
5
2
6
31
30
36
35
34
33
32
38
37
39
27
28
29
25
26
24
40
44
43
41
42
50
47
45
48
49
46
7
11
9
10
12
13
8
19
20
22
23

0.31
0.26
0.14
0.15
0.29
0.09
0.1
0.04
0.18
0.08
0.04
0.03
0.1
0.12
0.14
0.15
0.21
0.34
0.19
0.21
0.16
0.09
0.31
0.12
0.17
0.17
0.24
0.18
0.18
0.17
0.28
0.19
0.09
0.07
0.19
0.18
0.19
0.25
0.06
0.18
0.15
0.09
0.2
0.12
0.11
0.07
0.37
0.1
0.06
0.06
0.07

1.11
1.77
8.05
5.89
6.37
10.05
10.01
12.53
14.74
11.4
11.19
13.19
9.27
4.85
5.49
4.1
4.52
3.67
2.77
4.34
2.09
2.8
1.95
6.25
5.19
4.6
6.66
6
7.86
4.39
1.41
1.99
2.9
2.67
3.61
3.54
4.35
2.89
4.07
4.73
7.55
7.02
9.4
7.32
5.4
7.41
8.19
8.73
8.45
8.48
8.54

Ratio
Online/
Dine
0.92
0.97
1.08
1.11
1.44
1.43
1.49
0.89
0.85
0.82
0.74
0.88
0.81
0.8
0.54
1.02
1.18
1.46
1.03
2.23
1.27
1.09
0.91
1.31
0.78
0.78
0.75
0.27
0.86
0.64
0.64
0.83
0.7
0.71
0.84
3.25
0.83
0.72
0.81
0.74
0.78
1.63
0.92
0.69
0.82
0.79
1.64
0.77
0.66
0.95
0.77

Index

Density

Food

OnlineDine-inCom

Food

OnlineDine-in

Com

4.1
1.89
0.89
1.04
1.24
1.6
1.04
2.2
3.86
0.89
2.09
6
1.88
0.81
1.25
1.11
1.07
1.1
1.36
1.46
0.95
1.25
1.09
0.95
3.28
1.06
2.49
5.95
1.81
1.15
4.05
1.04
1.23
0.69
1.58
2.17
0.85
1.23
0.83
1.91
1.09
3.2
1.8
1.19
0.76
1.42
0.93
0.92
1.74
1.14
0.94

4.16
2.45
1.05
1.26
1.11
3.37
3.29
3.25
3.68
3.25
2.85
3.02
1.12
0.8
1.49
1.18
0.87
0.9
1.31
0.94
2.86
1.47
4.3
1.01
1.05
1.29
1.34
5.34
1.44
1.4
4.18
3.59
1.22
1.63
0.31
0.88
1.22
0.86
0.93
1.46
1.3
1.3
0.73
1.4
0.89
1.18
0.98
1.05
1.85
0.71
1.29

2.57
1.61
1.13
1.7
1.6
1.81
2.37
0.93
1.35
0.86
0.93
0.9
0.91
1.24
1.11
1.71
1.33
1.61
1.95
2.4
1.4
2.1
2.51
1.33
1.12
1.31
1
2.5
1.24
1.4
1.8
1.37
1.35
1.77
0.86
3.15
1.61
1.22
1.35
1.38
1
2.12
0.67
0.96
1.24
0.93
1.61
0.8
1.22
0.67
1

3.47
3.01
0.92
1.5
1.3
1.51
1.7
0.72
0.8
0.94
0.94
1.13
1.25
1.2
1.53
1.6
1.47
1.49
1.44
1.23
2.82
1.2
3.58
1.34
1.61
1.72
2.12
2.5
1.99
1.64
3.49
3.27
2.26
1.34
0.88
1.67
1.82
2.08
1.79
1.67
1.74
1.53
1.04
1.67
1.3
1.56
1.05
1
1.1
0.9
2.1

5.53
19.17
8.05
1.26
36.8
0.68
1.91
1.41
3.36
3.75
1.59
12.79
22.14
1.17
7.68
18.95
7.14
6.29
9.39
4.54
6.18
11.97
23.1
2.93
3.45
4.06
3.06
15.81
4.97
41.07
9.19
5.06
3.05
33.44
41.74
12.42
1.69
10.06
45.47
3.38
3.64
1.72
11.21
6.08
4.34
1.7
7.96
2.63
3.54
5.74
0.71

4.61
12.4
6.51
4.61
19.33
1.59
2.18
3.39
1.22
2.25
1.59
16.13
14.41
1.75
16.63
10.53
6.34
5.51
16.42
9.98
9.27
2.82
14
1.17
4.48
8.43
4.37
8.62
7.01
17.79
5.11
3.8
3.05
32.48
31.66
5.46
0.84
5.75
35.13
5.4
3.12
0.43
8.68
5.57
3.67
2.72
4.3
1.97
6.07
7.18
1.41

0.92
2.26
1.2
0.42
3.12
0.23
0.27
0.28
0.92
0.56
0.32
1.3
1.54
0.58
0.64
0.7
0.79
1.57
1.17
0.91
1.54
0.7
3.5
0.59
0.34
0.62
0.44
0.48
1.02
3.23
1.02
1.27
0.51
2.87
8.64
1.99
0.42
0.72
9.3
0.68
0.52
0.43
1.45
1.01
0.33
0.34
0.86
0.22
1.52
1.44
0.35

7.37
28.19
7.19
2.51
62.99
4.32
1.36
1.13
6.11
3.38
1.59
10.57
23.68
5.25
8.32
14.74
11.89
5.51
18.77
13.62
3.09
9.85
35.70
4.10
9.65
7.80
10.06
21.56
11.10
37.51
6.13
8.86
8.63
27.70
51.81
12.42
10.55
23.00
34.10
8.10
10.67
3.00
15.91
20.26
10.01
3.06
4.95
10.95
14.15
15.80
1.06

xxxviiAppendix 2 - Agglomeration Index of Jakarta

Area Name
No
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200

Neighborhood
UlujamiGunturKaret
Karet Kuningan
Karet Semanggi
Kuningan Timur
Menteng Atas
Pasar Manggis
Setia Budi
Bukit DuriKebon Baru
Manggarai
Manggarai Selatan
Menteng Dalam
Tebet Barat
Tebet Timur
Cakung Barat
Cakung Timur
Jatinegara
Penggilingan
Pulo Gebang
Rawa Terate
Ujung Menteng
Bambu Apus
Ceger
Cilangkap
Cipayung
Lubang Buaya
MunjulPondok Ranggon
Setu
Tmii
CibuburCiracas
Kelapa Dua Wetan
Rambutan
SusukanDuren Sawit
KlenderMalaka Jaya
Malaka Sari
Pondok Bambu
Pondok Kelapa
Pondok Kopi
Bali Mester
Bidara Cina
Cipinang Besar Selatan
Cipinang Besar Utara
Cipinang Cempedak
Cipinang Muara
Kampung Melayu

District
Pesanggrahan
Setia Budi
Setia Budi
Setia Budi
Setia Budi
Setia Budi
Setia Budi
Setia Budi
Setia Budi
Tebet
Tebet
Tebet
Tebet
Tebet
Tebet
Tebet
CakungCakungCakungCakungCakungCakungCakungCipayung
Cipayung
Cipayung
Cipayung
Cipayung
Cipayung
Cipayung
Cipayung
Cipayung
CiracasCiracasCiracasCiracasCiracasDuren Sawit
Duren Sawit
Duren Sawit
Duren Sawit
Duren Sawit
Duren Sawit
Duren Sawit
Jatinegara
Jatinegara
Jatinegara
Jatinegara
Jatinegara
Jatinegara
Jatinegara

City

Zone
ID

% Commercial
Land use

Distance
to Central

South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
South Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta

21
64
61
60
58
59
62
63
65
55
54
57
56
51
52
53
117
116
112
113
114
118
115
82
83
78
80
84
79
77
81
66
72
74
73
76
75
106
111
109
110
105
107
108
103
97
99
101
98
100
104

0.1
0.1
0.31
0.35
0.45
0.3
0.2
0.13
0.26
0.05
0.06
0.09
0.09
0.28
0.2
0.15
0.22
0.11
0.48
0.13
0.05
0.61
0.17
0.28
0.09
0.02
0.04
0.56
0.19
0.22
0.02
0.59
0.1
0.22
0.06
0.05
0.2
0.11
0.11
0.09
0.09
0.14
0.12
0.24
0.37
0.16
0.17
0.09
0.09
0.06
0.12

7.3
1.02
1.2
0.5
1.13
0.76
1.37
1.02
0.94
1.3
2.65
0.14
0.86
1.65
2.48
2.42
9.71
11.84
7.32
9.39
11.4
8.07
12.91
12.48
11.94
14.89
13.61
10.75
15.63
16.95
13.24
11.18
15.98
12.37
14.23
11.37
10.72
7.59
6.42
9.44
8.73
6.07
9.65
10.33
1.92
3.26
3.85
3.3
3.71
4.55
0.95

Ratio
Online/
Dine
0.76
0.58
0.44
0.59
0.72
0.68
0.92
0.58
0.51
1.89
0.65
0.47
0.85
0.31
0.78
0.73
0.99
0.28
0.86
0.82
0.84
0.83
0.7
0.91
0.79
0.64
0.9
0.69
0.59
0.75
0.84
0.41
0.8
0.96
0.74
0.79
0.39
0.93
0.45
0.56
0.9
1.16
0.57
0.75
0.93
0.63
0.89
0.5
0.89
0.74
0.67

Index

Density

Food

OnlineDine-inCom

Food

OnlineDine-in

Com

1.28
2.84
2.54
3.28
3.35
3.54
2.49
2.65
2.51
3.14
0.86
3.09
3.8
2.52
1.16
1.15
1.89
1.32
1.2
1.33
0.96
0.9
3.13
0.97
1.89
1.25
0.99
3.11
2.38
4.11
2.93
4.73
1.4
2.64
4.1
3.57
1.4
0.77
0.84
2.18
0.94
1.17
1.12
0.7
2.52
1.16
1.08
2.49
0.92
1.07
4.36

1.34
3.31
3.52
3.28
2.98
3.93
2.37
3.5
4.22
3.12
1.34
4.32
2.53
4.61
1.7
1.35
2.64
3.61
1.43
0.94
2.75
2.07
3.16
3.76
2.57
4.54
3.47
4.53
4.08
4.42
2.58
7.12
4.62
2.72
2.89
3.6
5.66
0.75
0.82
1.49
1.09
1.22
3.11
3.14
2.67
2.07
0.98
2.28
0.87
1.06
3.46

1.03
1.47
1.15
0.99
0.93
1.74
1.31
1.05
1.6
1.85
1.18
1.38
1.05
1.38
1.84
1.49
0.64
0.46
1.23
0.77
0.8
1.71
0.74
1.5
0.77
1.63
1.23
1.67
1.22
0.92
0.83
2.09
0.81
0.98
0.81
1.02
2.26
0.7
0.37
0.84
0.97
1.42
0.63
0.7
1.29
1.6
1.47
1.44
1.27
1.09
1.51

1.78
2.83
2.43
3.18
3.74
3.08
2.58
3.07
2.43
3.11
1.64
2.96
3.31
3.36
1.65
1.12
1.17
1.53
1.38
0.93
1.42
1.34
0.98
2.59
1.13
0.65
0.86
1.09
2.7
1.6
1.62
1.38
1.1
1.22
0.9
2.16
1.86
1.09
1.37
0.78
1.01
1.51
1.19
0.93
2.77
1.15
1.61
1.71
1.41
1.51
3.11

4.4
9.23
35.14
2.67
224.33
3.2
2.9
9.06
13.33
5.51
5.53
1.88
98.02
2.04
2.49
16
3.39
1.78
5.51
2.42
0.45
3.43
1.16
1.17
4.28
1.57
5.24
2.22
25.06
0.82
3.26
14.33
3.2
6.08
6.22
3.1
1.41
2.33
2.68
10.34
12.28
2.26
0.86
1.34
16.34
1.6
24.54
6.38
1.83
7.48
8.73

3.91
6.15
37.15
3.74
134.01
4.57
4.83
7.76
8.89
10.1
11.84
0.94
71.29
4.89
4.35
4.57
4.29
2.66
2.83
2.64
0.3
3.89
1.16
1.17
2.44
1.92
4.03
1.67
15.42
0.62
2.18
29.28
3.01
4.82
5.62
0.44
0.47
2.97
2.01
12.21
1.53
1.26
1.03
1.79
11.88
2.41
14.37
4.56
0.61
6.36
8.73

0.49
1.54
1
1.07
27.68
0.46
0.97
2.59
1.48
0.92
0.79
0.94
14.26
0.41
0.62
0.76
0.36
0.11
0.31
0.22
0.15
0.69
0.47
0.29
0.61
0.17
0.81
0.28
0.48
0.21
0.36
1.25
0.38
0.25
0.89
0.44
0.47
0.42
0.67
1.88
0.77
0.25
0.17
0.45
1.49
0.8
1.8
0.91
0.61
1.12
2.18

3.42
12.31
66.26
4.27
120.90
0.91
2.90
55.63
29.63
6.43
10.26
2.82
114.06
4.08
3.11
4.57
12.33
1.89
5.51
7.69
0.30
8.70
0.47
1.76
4.28
1.05
5.65
2.22
19.28
1.03
3.26
20.56
3.95
3.55
15.39
2.66
1.89
11.04
25.78
92.08
10.74
2.76
3.62
1.34
47.53
4.81
11.97
3.65
3.65
17.20
19.64

xxxviii

Appendix 2 - Agglomeration Index of Jakarta

Area Name
No
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251

Neighborhood
Rawa Bunga
Bale Kambang
Batu Ampar
CawangCililitan
Dukuh
Kampung Tengah
Kramat Jati
Cipinang Melayu
Halim Perdana Kusuma
Kebon Pala
MakasarPinang Ranti
Kayu Manis
Kebon Manggis
Pal Meriem
Pisangan Baru
Utan Kayu Selatan
Utan Kayu Utara
Baru
CijantungGedongKalisari
PekayonCipinang
Jati
Jatinegara Kaum
Kayu Putih
Pisangan Timur
Pulo Gadung
Rawamangun
Cilincing
Kali Baru
MarundaRorotanSemper Barat
Semper Timur
Suka Pura
Kelapa Gading Barat
Kelapa Gading Timur
Pegangsaan Dua
Koja
Lagoa
Rawabadak Selatan
Rawabadak Utara
Tugu Selatan
Tugu Utara
Ancol
Pademangan Barat
Pademangan Timur
Kamal Muara

District
Jatinegara
Kramat Jati
Kramat Jati
Kramat Jati
Kramat Jati
Kramat Jati
Kramat Jati
Kramat Jati
MakasarMakasarMakasarMakasarMakasarMatraman
Matraman
Matraman
Matraman
Matraman
Matraman
Pasar Rebo
Pasar Rebo
Pasar Rebo
Pasar Rebo
Pasar Rebo
Pulo Gadung
Pulo Gadung
Pulo Gadung
Pulo Gadung
Pulo Gadung
Pulo Gadung
Pulo Gadung
Cilincing
Cilincing
Cilincing
Cilincing
Cilincing
Cilincing
Cilincing
Kelapa Gading
Kelapa Gading
Kelapa Gading
Koja
Koja
Koja
Koja
Koja
Koja
Pademangan
Pademangan
Pademangan
Penjaringan

City

Zone
ID

% Commercial
Land use

Distance
to Central

East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
East Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta

102
90
91
96
95
93
92
94
89
88
87
86
85
129
126
127
128
130
131
69
70
71
68
67
120
122
121
124
119
125
123
259
262
258
257
261
260
256
253
254
255
252
250
247
251
248
249
239
237
238
232

0.24
0.05
0.09
0.15
0.08
0.05
0.12
0.15
0.06
0.31
0.13
0.11
0.44
0.09
0.13
0.21
0.07
0.07
0.26
0.03
0.03
0.09
0.02
0.18
0.05
0.13
0.19
0.21
0.14
0.13
0.32
0.28
0.33
0.11
0.05
0.1
0.3
0.19
0.2
0.23
0.25
0.14
0.13
0.35
0.08
0.1
0.1
0.44
0.2
0.27
0.34

2.45
6.86
7.04
4.61
5.72
9.63
8.36
7.17
7.94
7.57
5.66
8
9.5
1.69
0.65
1.35
2.05
2.27
2.72
11.21
11.2
9.29
12.53
13.94
4.54
5.52
5.65
4.71
3.31
6.27
3.99
11.41
8.95
13.03
11.85
8.8
9.9
8.59
4.99
6.14
7.23
6.91
7.94
5.89
6.41
6.9
7.54
3.69
0.96
1.36
10.61

Ratio
Online/
Dine
0.68
0.88
0.72
0.69
0.67
0.21
0.52
0.68
0.8
0.88
0.8
0.62
0.55
0.65
0.72
0.92
0.8
0.73
0.83
0.77
0.71
0.79
0.86
0.8
0.94
0.71
0.82
0.75
0.92
0.84
1.56
0.74
0.82
0.82
0.62
0.84
0.59
0.75
0.98
0.98
1.49
0.91
0.97
0.76
0.85
0.72
0.94
0.92
0.74
0.55
0.78

Index

Density

Food

OnlineDine-inCom

Food

OnlineDine-in

Com

0.67
0.92
0.99
1.6
1.43
1.18
0.43
1.19
0.98
0.78
1.36
2.3
2.5
2.82
3.05
3.2
0.73
1.44
0.66
2.16
0.76
1.38
2.77
2.94
0.73
0.81
1.66
1.4
1.35
1.23
1.07
2.69
2.71
2.72
2.19
0.49
1.5
0.24
1.49
1.13
1.04
1.53
0.43
1.53
0.59
0.56
1.07
1.88
3.67
2.92
2.66

0.86
1.13
1.19
1.77
1.63
6.48
4.07
1.32
1.03
0.8
1.08
1.17
2.36
2.41
3.76
3.42
2.55
1.32
0.76
2.8
2.86
0.95
3.69
3
0.76
1.58
1.13
1.12
1.38
0.93
1.23
4.15
2.22
4.5
4.91
1.39
3.47
0.73
1.38
1.09
0.89
1.62
0.58
0.63
0.59
1.37
0.98
2.32
3.26
4.97
4.88

1.18
0.99
0.86
1.83
1.6
1.79
2.1
0.9
0.82
0.7
1.46
0.72
1.31
1.23
1.62
1.81
0.94
1.35
0.93
0.54
0.82
0.75
1.19
0.64
1.22
1.72
1.23
1.34
1.88
0.78
2.21
1.67
1.83
2.05
1.81
1.17
1.1
0.55
1.85
1.08
1.32
1.48
0.57
0.98
0.5
0.99
0.93
2.72
1.21
2.13
2.4

1.74
1.74
1.44
1.54
2.28
0.74
0.87
1.46
1.35
1.42
1.32
1.44
1.63
4.26
3.32
3.49
2.51
2.14
1.22
1.06
0.84
1.15
1.57
0.76
1.72
1.89
1.9
1.52
1.46
1.53
1.45
2.78
2.36
0.74
2.13
1.09
1.29
0.67
1.67
1.38
1.38
1.95
1.26
2.19
0.99
1.56
1.38
3.48
3.13
2.94
2.09

84.28
6.56
5.12
8.8
1.69
1.17
5.42
4.7
3.81
0.46
16.85
13.76
3.65
54.16
7.68
7.79
13.69
34.48
7.19
1.56
1.93
3.69
1.16
4.71
7.32
3.33
1.56
7.01
5.97
2.29
5.78
2.81
1.61
4.84
0.7
6.52
1.04
0.63
0.69
3.83
0.36
11.89
1.29
11.78
3.94
3.18
3.38
2.3
6.77
7.61
1.39

53.41
4.77
7.48
4.95
2.82
5.84
2.47
2.68
4.96
0.23
12.1
7.57
1.83
29.88
5.12
10.91
9.58
17.24
6.16
3.12
2.7
9.68
0.77
2.83
4.66
5.71
7.78
3.12
3.8
6.28
5.78
1.32
0.4
3.41
0.3
5.9
3.12
0.63
0.69
5.42
0.9
5.1
5.16
3.53
2.36
4.24
2.11
1.33
3.38
6.57
0.74

13.06
1.19
0.79
1.65
0.56
0.58
0.99
1.34
1.14
0.08
1.3
1.38
0.91
9.34
1.28
1.56
1.37
5.17
1.03
0.52
0.77
1.38
0.39
1.26
2
0.48
0.78
0.26
1.09
0.57
0.77
0.17
0.4
0.77
0.1
0.31
0.21
0.32
0.14
1.28
0.18
1.7
0.65
0.59
0.79
0.53
0.42
0.15
1.35
0.35
0.19

103.27
15.50
13.78
25.84
3.95
14.03
9.37
3.35
10.68
0.31
37.60
45.42
3.65
91.52
5.12
21.82
47.91
57.75
5.14
8.31
1.93
20.73
3.09
10.36
15.98
8.56
16.35
2.86
13.02
16.00
6.17
2.98
6.05
6.05
0.20
8.70
2.29
1.42
0.14
6.38
1.44
5.10
11.61
18.85
2.36
7.42
3.80
3.41
7.45
12.45
5.18

xxxix

Appendix 2 - Agglomeration Index of Jakarta

Area Name
No
252
253
254
255
256
257
258
259
260
261
262

Neighborhood
Kapuk Muara
Pejagalan
Penjaringan
Pluit
Kebon Bawang
PapangoSungai Bambu
Sunter Agung
Sunter Jaya
Tanjung Priuk
WarakasDistrict
Penjaringan
Penjaringan
Penjaringan
Penjaringan
Tanjung Priok
Tanjung Priok
Tanjung Priok
Tanjung Priok
Tanjung Priok
Tanjung Priok
Tanjung Priok

City

Zone
ID

% Commercial
Land use

Distance
to Central

North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta
North Jakarta

233
234
235
236
245
242
244
240
241
246
243

0.17
0.29
0.33
0.09
0.14
0.26
0.55
0.21
0.32
0.12
0.06

7.33
4.79
3.48
5.26
5.65
3.76
4.52
2.02
2.72
4.96
4.57

Ratio
Online/
Dine
0.93
0.86
2.74
1.17
0.93
0.9
0.73
1.01
1.63
0.99
0.72

Index

Density

Food

OnlineDine-inCom

Food

OnlineDine-in

Com

1.18
1.11
1.08
1.32
1.05
0.97
1.56
2.81
1.64
5.5
1.08

1.53
1.24
0.65
1.34
0.86
0.93
1.55
2.91
1.36
0.66
0.99

1.43
1.57
2.27
2.16
1.3
1.44
1.74
1.32
2.72
1.25
1.31

1.77
1.42
1.32
1.79
1.38
1.42
1.66
2.91
2.05
2.4
1.69

2.44
3.76
1.13
2.07
39.96
8.69
30.67
2.46
1.14
19.53
11.09

1.71
2.42
1.36
2.66
28.37
8.35
25.02
1.32
1.14
6.51
5.54

0.12
0.54
0.45
0.44
4.63
1
2.02
0.19
0.19
0.93
0.92

3.90
1.34
1.58
7.68
31.85
6.68
55.29
2.84
0.57
15.81
9.24

xl

Appendix 3 – Attribute of Activities-Travel Diary Survey

APPENDIX 3. ATTRIBUTE OF ACTIVITY-TRAVEL DIARY SURVEY
No
1
2
3
4
5
6
7
8
9
10
11
12
13

Variablehhid
individ
access_code
hhmember_nameinterval_id
interval_type
start_time
end_time
mode_id
sid_interval
sid_stop
mode_source
original_mode_id

Definition
Household ID
Household person ID
Participant referral code
Household member name
Unique ID for each segment
Indicates if segment is stop, trip or data gap
Start time of the segment
End time of the segment
Final trip mode
Source of interval
Source of stop
Source of mode information
Original algorithm generated mode of the trip
before this stop
Original algorithm generated latitude of the stop
Original algorithm generated longitude of the stop
Unique ID for each user in MMM
If participant indicated that they were aprofessional driver
Original algorithm generated start time of thesegment

Questions

14
15
16
17

replaced_lat
replaced_lon
user_id
pro_driver

18

orginal_start_time

19

original_end_time

Original algorithm generated end time of thesegment

20
21
22

lat
lon
how_travelled

Latitude of the stop
Longitude of the stop
Text value of mode_id

23
24
25

other_mode
num_accomp
accomp_type

Specification of other modes
Number of other people in your traveling party
Relationship with the accompanied

Who was with you?

26
27

is_driverpark_type

Identification of driving activities
Type of parking area

Were you the driver?
Where did you park at the end of this trip?

28
29
30
31
32
33
34

park_fee
bus_type
car_type
car_type_other
fare_paid
is_motor_driver
moto_type

Money spend for parking fee
Bus type
Car type
Identification of other type of car ride
the fare of car ride service
identification of driving the motorbikeMotorbike ride hailing service

Paid amount (IDR)
What type of bus did you use?
Which of the following services did you use?
Please specify (If Car Mode = Other)
Paid amount (IDR)
Were you the driver?
Which of the following services did you use?

35
36
37

moto_type_othermoto_fareactivities

Other motorbike ride hailing service
Motorbike ride hailing service fare
All type activities in the segment

Please specify (If Motor Type = Other)
Paid amount (IDR)
Please select the type(s) of activity that best
describes what you did here

Options

3 (automatic generated; user generated; user changed)
3 (automatic generated; user generated; user changed)
3 (automatic generated; user generated; user changed)

Are you a professional driver?

How did you travel?

2 (Yes; No)

7 (walk; car; motorbike; public transport; ojek online; ridehailing; and others)
Please specify (If Travel Mode = Other)
3 (Household member only; non-household member only,
both household and non-household member)
2 (Yes; No)
4 (street; residential garage/driveaway; commercial or
public parking lot/garage; other)
How much did you pay for the parking fee?
4 (public bus; school bus, company bus; shuttle bus)
4 (GO-CAR; GREB-CAR; Taxi; Other)2 (Yes; No)
6 (GO-RIDE; GRAB-BIKE; Oke Jack; Ladyjek; Ojek
Pangkalan; Other)
6 (Home; Work; Eat-out; Shopping; Recreation; Other)

Note
Automatic filled
Automatic filled
Automatic filled
Automatic filled
Automatic filled
Automatic filled
Automatic filled
Automatic filled
Automatic filled
Flexible
Flexible
Flexible
Automatic filled
Automatic filled
Automatic filled
Automatic filled
Respondent choose
Only when the user changes thestart/stop times of the segment
during validation.
If the segment is validated and thesefields are empty, it means thesystem detected times are correct.
Automatic filled using GPS
Automatic filled using GPS
Respondent choose
Respondent fill
Respondent fill
Respondent choose
Respondent choose
Respondent choose
Respondent fill
Respondent choose
Respondent choose
Respondent fill
Respondent fill
Respondent choose
Respondent choose
Respondent fill
Respondent fill
Respondent choosexliAppendix 3 – Attribute of Activities-Travel Diary Survey

No
38

Variable
activities_otherDefinition
Other types of activities

Questions
Please select the type(s) of activity that best
describes what you did here
Which was your main activity? (If more than
one chosen)
Which of the following activities did you doat this stop?
Please indicate one or more activities that thepassenger(s) you were transporting engaged inat this stop.
Please select the type(s) of shop that best
describes what you did here
Please select the type(s) of grocery that best
describes what you did here

39

activities_main

Main activities

40

escort_type

Type of escort41

psgr_activ

Passenger activities

42

shop_typeType of shopping

43

groc_typeType of groceries

4445

groc_type_othernongroc_type

Other type of groceries
Type of non-groceries

46
47
48

nongroc_type_othergrocnongroc_amountfood_type

Other type of non-groceries
Shopping expenses
Food Type

Please specify (if groc_type=other)
Please select the type(s) of grocery that best
describes what you did here
Please specify (if non-groc_type = other)
Paid amount (IDR)
Food type

49
50
51

food_type_otherfood_amounte_activities

Other type of food
Food expenses
Online activity

Please specify (if food type = other)
Paid amount (IDR)
Type of online activities

52

e_activities_other

Other type of online activities

Please specify (if e-activity = other)

Options

Note
Respondent fill

6 (Home; Work; Eat-out; Shopping; Recreation; Other)

Respondent choose

3 (Dropped off passenger(s); Picked up passenger(s);Accompanied passenger(s) to his/her activity)
6 (Home; Work; Eat-out; Shopping; Recreation; Other)

Respondent choose

3 (Groceries; Non-groceries; Both (groceries and nongroceries))
7 (Fresh produce (fruit or vegetables); Meat, Chicken,
Seafood; Dairy, Eggs, Cheese, Tofu, Tempe; Dried orCanned food; Frozen food; Bakery products; Other)

Respondent choose

5 (Books/ Magazines; Electronics/ Home appliances;
Fashion goods; Household products; Other)
8 (Beverages; Snacks/Sweets; Fast food; Indonesian food;Western food; Eastern food; Bakso & Noodles; Other)
9 (Online food delivery order; Online grocery shopping;
Online non-grocery goods shopping; Online banking or
payments; Online meeting; Online class; Online
entertainment (e.g. games, movies); Other activities; Noneof the above (no e-activity))

Respondent choose

Respondent fill
Respondent choose
Respondent fill
Respondent fill
Respondent choose
Respondent fill
Respondent fill
Respondent choose

Respondent fillxliiAppendix 4 – Attraction Each Zone in Jakarta

APPENDIX 4. ATTRACTION EACH ZONE IN JAKARTA
No

Zone NameDistrict NameCity Name

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48

Ancol
Angke
Balekambang
Bali Mester
Bambu Apus
BangkaBaru
Batu Ampar
Bendungan Hilir
Bidara Cina
BintaroBukit Duri
BungurCakung Barat
Cakung Timur
Cawang
Ceger
Cempaka Baru
Cempaka Putih Barat
Cempaka Putih Timur
Cengkareng Barat
Cengkareng Timur
CibuburCidengCiganjur
CijantungCikini
CikokoCilandak Barat
Cilandak Timur
Cilangkap
Cililitan
Cilincing
Cipayung
Cipedak
Cipete Selatan
Cipete Utara
Cipinang
Cipinang Besar Selatan
Cipinang Besar Utara
Cipinang Cempedak
Cipinang Melayu
Cipinang Muara
Cipulir
CiracasDukuh
Duren Sawit
Duren Tiga

Pademangan
TamboraKramat Jati
Jatinegara
Cipayung
Mampang Prapatan
Pasar Rebo
Kramat Jati
Tanah Abang
Jatinegara
Pesanggrahan
Tebet
Senen
CakungCakungKramat Jati
Cipayung
Kemayoran
Cempaka Putih
Cempaka Putih
Cengkareng
Cengkareng
CiracasGambirJagakarsa
Pasar Rebo
Menteng
Pancoran
Cilandak
Pasar Minggu
Cipayung
Kramat Jati
Cilincing
Cipayung
Jagakarsa
Cilandak
Kebayoran Baru
Pulo Gadung
Jatinegara
Jatinegara
Jatinegara
MakasarJatinegara
Kebayoran Lama
CiracasKramat Jati
Duren Sawit
Pancoran

Jakarta Utara
Jakarta Barat
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Selatan
Jakarta Timur
Jakarta Timur
Jakarta Pusat
Jakarta Timur
Jakarta Selatan
Jakarta Selatan
Jakarta Pusat
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Pusat
Jakarta Pusat
Jakarta Pusat
Jakarta Barat
Jakarta Barat
Jakarta Timur
Jakarta Pusat
Jakarta Selatan
Jakarta Timur
Jakarta Pusat
Jakarta Selatan
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Timur
Jakarta Utara
Jakarta Timur
Jakarta Selatan
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Selatan
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Selatan

Population

Area Size (Km2)

Density

29570
35807
35466
11405
31585
25999
29403
58613
26314
44378
63416
41644
22626
72509
73107
40201
23012
41523
43913
28804
80056
99960
79698
18466
46319
50406
10199
12944
61331
31342
33247
49254
54779
31928
45954
31942
41311
47860
43128
58392
39036
51540
67193
48491
78854
30380
73679
33743

5.77
0.8
1.67
0.67
3.17
3.3
1.89
2.55
1.58
1.26
4.56
1.08
0.64
6.19
9.81
1.79
3.63
0.99
1.22
2.22
4.26
4.18
4.5
1.26
3.61
2.37
0.82
0.72
6.05
3.53
6.03
1.8
8.31
3.08
4.24
2.37
1.83
1.54
1.63
1.15
1.67
2.53
2.9
1.94
3.93
1.98
4.58
2.45

4981
43826
18012
17304
8400
7351
14133
19983
16018
35493
11808
38906
34463
10030
6464
21827
5492
38088
32561
12326
16409
20735
15686
14584
10334
18589
11711
17661
9546
8075
4396
26094
6326
8441
8473
12751
20664
29756
23417
48850
22917
18551
21525
22494
17648
13402
14100
12987

C_Food
32
7
12
12
5
128
4
14
132
18
20
8
3
7
23
13
5
7
7
20
29
21
17
17
8
5
30
3
56
12
5
18
3
6
6
48
15
10
23
4
18
14
21
12
4
3
35
12

Number of Facilities
O_FoodD_Food
21
4
5
3
11
4
11
3
7
3
58
12
9
3
22
4
101
12
16
6
26
9
10
5
6
3
6
3
14
5
12
3
7
3
14
3
13
3
27
4
17
6
24
5
27
3
12
5
7
3
13
3
25
8
7
3
61
8
22
3
9
3
19
4
6
3
15
3
10
4
41
9
15
4
14
3
20
7
8
3
11
3
13
4
17
3
7
5
14
3
6
3
29
6
10
5

Comm
48
6
28
34
8
118
18
37
142
22
29
14
8
10
26
52
8
9
12
71
51
53
19
49
9
23
38
8
98
40
12
23
5
6
7
44
43
8
33
9
20
16
34
22
8
11
40
26

Public
68
12
11
35
14
43
5
21
129
29
25
8
14
15
22
43
15
20
30
63
48
50
24
49
19
21
52
15
92
44
9
34
6
14
17
43
34
21
22
11
31
37
35
19
32
12
44
30

xliii

Appendix 4 – Attraction Each Zone in Jakarta

No

Zone NameDistrict NameCity Name

49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100

Duri Kepa
Duri Kosambi
Duri Pulo
Duri Selatan
Duri Utara
Galur
GambirGandaria Selatan
Gandaria Utara
Gedong
GeloraGlodokGondangdia
GrogolGrogol Selatan
Grogol Utara
GunturGunungGunung Sahari Selatan
Gunung Sahari Utara
Halim Perdana Kusuma
Harapan Mulya
Jagakarsa
Jati
Jati Padang
Jati Pulo
Jatinegara
Jatinegara Kaum
Jelambar
Jelambar Baru
Jembatan Besi
Jembatan Lima
Joglo
Johar Baru
Kali Baru
Kalianyar
Kalibata
Kalideres
Kalisari
Kamal
Kamal Muara
Kampung Bali
Kampung Melayu
Kampung Rawa
Kampung Tengah
Kapuk
Kapuk Muara
Karang Anyar
Karet
Karet Kuningan
Karet Semanggi
Karet Tengsin

Kebon Jeruk
Cengkareng
GambirTamboraTamboraJohar Baru
GambirCilandak
Kebayoran Baru
Pasar Rebo
Tanah Abang
Taman Sari
Menteng
Grogol Petamburan
Kebayoran Lama
Kebayoran Lama
Setia Budi
Kebayoran Baru
Kemayoran
Sawah Besar
MakasarKemayoran
Jagakarsa
Pulo Gadung
Pasar Minggu
Palmerah
CakungPulo Gadung
Grogol Petamburan
Grogol Petamburan
TamboraTamboraKembangan
Johar Baru
Cilincing
TamboraPancoran
Kalideres
Pasar Rebo
Kalideres
Penjaringan
Tanah Abang
Jatinegara
Johar Baru
Kramat Jati
Cengkareng
Penjaringan
Sawah Besar
Setia Budi
Setia Budi
Setia Budi
Tanah Abang

Jakarta Barat
Jakarta Barat
Jakarta Pusat
Jakarta Barat
Jakarta Barat
Jakarta Pusat
Jakarta Pusat
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Pusat
Jakarta Barat
Jakarta Pusat
Jakarta Barat
Jakarta Selatan
Jakarta Selatan
Jakarta Selatan
Jakarta Selatan
Jakarta Pusat
Jakarta Pusat
Jakarta Timur
Jakarta Pusat
Jakarta Selatan
Jakarta Timur
Jakarta Selatan
Jakarta Barat
Jakarta Timur
Jakarta Timur
Jakarta Barat
Jakarta Barat
Jakarta Barat
Jakarta Barat
Jakarta Barat
Jakarta Pusat
Jakarta Utara
Jakarta Barat
Jakarta Selatan
Jakarta Barat
Jakarta Timur
Jakarta Barat
Jakarta Utara
Jakarta Pusat
Jakarta Timur
Jakarta Pusat
Jakarta Timur
Jakarta Barat
Jakarta Utara
Jakarta Pusat
Jakarta Selatan
Jakarta Selatan
Jakarta Selatan
Jakarta Pusat

Population

Area Size (Km2)

Density

71926
95004
24997
17106
24143
23056
3066
26488
47546
43716
3883
8665
4666
20320
53392
52051
4445
10921
23768
19980
34989
28877
74998
38743
45448
35251
104837
30619
35928
46049
37228
25590
49365
45833
85725
30115
51167
88518
52193
65722
15170
14451
31479
27181
54252
167088
42848
32573
11581
19346
3214
23994

3.86
5.03
0.72
0.42
0.4
0.27
2.58
1.76
1.52
2.65
2.59
0.38
1.46
1.22
2.85
3.33
0.65
1.32
0.53
1.98
13.07
0.91
4.85
2.15
2.5
0.87
6.6
1.23
1.44
1.44
0.55
0.46
4.86
1.19
2.47
0.32
2.2
5.72
2.89
4.49
10.53
0.73
0.48
0.3
2.03
7.18
10.56
0.51
0.94
1.79
0.9
1.53

17148
15866
35628
41460
60175
79022
1350
13816
29247
14684
1450
24153
3194
16960
17009
14342
7174
8043
43858
9933
2546
29205
12940
17270
16524
38834
14709
21792
24624
30778
66447
54543
8812
35270
34278
94166
21353
13726
15517
12257
1144
19941
63973
86123
24281
20919
3243
63122
12948
10414
3202
13912

C_Food
60
42
7
3
8
4
16
11
21
6
6
26
155
25
3
12
6
21
15
7
9
4
13
14
9
9
11
7
28
13
14
4
21
8
3
5
10
36
3
3
51
12
5
3
5
19
20
4
30
77
16
64

Number of Facilities
O_FoodD_Food
34
7
27
5
11
3
4
3
8
3
4
3
15
5
16
5
20
5
14
4
5
3
14
6
95
21
22
3
13
3
17
4
4
3
21
8
12
4
13
3
9
3
8
3
29
3
30
4
24
5
7
3
16
3
7
3
18
3
9
3
8
3
6
3
15
5
15
3
4
3
4
3
15
4
21
4
13
3
5
3
38
8
7
3
4
3
4
3
5
3
14
4
14
4
5
3
19
7
65
7
11
4
43
6

Comm
103
22
12
5
6
8
39
30
48
23
9
27
85
16
26
54
21
31
33
18
12
8
15
27
47
13
30
10
23
17
19
6
20
20
4
9
23
37
23
6
42
33
17
5
27
16
28
10
51
139
58
76

Public
51
43
20
5
5
11
125
22
16
30
13
6
90
22
22
27
20
33
51
21
50
14
38
47
32
26
25
17
31
15
17
7
43
41
7
8
34
42
7
11
26
33
12
10
25
18
30
14
37
75
32
57

xliv

Appendix 4 – Attraction Each Zone in Jakarta

No
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152

Zone Name
KartiniKayu Manis
Kayu Putih
Keagungan
Kebagusan
Kebayoran Lama Selatan
Kebayoran Lama Utara
Kebon Baru
Kebon Bawang
Kebon Jeruk
Kebon Kacang
Kebon Kelapa
Kebon Kosong
Kebon Manggis
Kebon Melati
Kebon Pala
Kebon Sirih
Kedaung Kali Angke
Kedoya Selatan
Kedoya Utara
Kelapa Dua
Kelapa Dua Wetan
Kelapa Gading Barat
Kelapa Gading Timur
Kemanggisan
Kemayoran
Kembangan Selatan
Kembangan Utara
Kenari
Klender
Koja
Kota Bambu Selatan
Kota Bambu Utara
KramatKramat Jati
Kramat Pela
Kredang
KrukutKuningan Barat
Kuningan Timur
Kwitang
Lagoa
Lebak Bulus
Lenteng Agung
Lubang Buaya
MakasarMalaka Jaya
Malaka Sari
Mampang Prapatan
Mangga Besar
Mangga Dua Selatan
Manggarai

District NameCity Name

Sawah Besar
MatramanPulo Gadung
Taman Sari
Pasar Minggu
Kebayoran Lama
Kebayoran Lama
Tebet
Tanjung Priok
Kebon Jeruk
Tanah Abang
GambirKemayoran
Matraman
Tanah Abang
MakasarMenteng
Cengkareng
Kebon Jeruk
Kebon Jeruk
Kebon Jeruk
Ciracas
Kelapa Gading
Kelapa Gading
Palmerah
Kemayoran
Kembangan
Kembangan
Senen
Duren Sawit
Koja
Palmerah
Palmerah
Senen
Kramat Jati
Kebayoran Baru
TamboraTaman Sari
Mampang Prapatan
Setia Budi
Senen
Koja
Cilandak
Jagakarsa
Cipayung
MakasarDuren Sawit
Duren Sawit
Mampang Prapatan
Taman Sari
Sawah Besar
Tebet

Jakarta Pusat
Jakarta Timur
Jakarta Timur
Jakarta Barat
Jakarta Selatan
Jakarta Selatan
Jakarta Selatan
Jakarta Selatan
Jakarta Utara
Jakarta Barat
Jakarta Pusat
Jakarta Pusat
Jakarta Pusat
Jakarta Timur
Jakarta Pusat
Jakarta Timur
Jakarta Pusat
Jakarta Barat
Jakarta Barat
Jakarta Barat
Jakarta Barat
Jakarta Timur
Jakarta Utara
Jakarta Utara
Jakarta Barat
Jakarta Pusat
Jakarta Barat
Jakarta Barat
Jakarta Pusat
Jakarta Timur
Jakarta Utara
Jakarta Barat
Jakarta Barat
Jakarta Pusat
Jakarta Timur
Jakarta Selatan
Jakarta Barat
Jakarta Barat
Jakarta Selatan
Jakarta Selatan
Jakarta Pusat
Jakarta Utara
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Selatan
Jakarta Barat
Jakarta Pusat
Jakarta Selatan

Population

Area Size (Km2)

Density

27385
30764
48382
21068
54289
49670
52141
42930
63919
65382
26794
12486
37289
19822
40678
57560
15407
40507
38985
54802
28560
56286
41741
39073
38642
25623
32223
66417
11431
86157
35550
26522
31177
35760
41707
17366
24904
23375
15734
7155
19078
74371
44061
65486
77230
42454
37270
32994
22566
8875
34983
35007

0.55
0.57
4.37
0.32
2.26
2.57
1.78
1.3
1.73
2.69
0.71
0.78
1.13
0.78
1.26
2.3
0.83
2.61
2.28
3.14
1.5
3.37
4.53
5.31
2.33
0.55
3.6
3.65
0.91
3.08
3.27
0.61
0.63
0.71
1.52
1.23
0.32
0.55
0.98
2.15
0.45
1.58
4.41
2.28
3.72
1.85
0.99
1.38
0.78
0.51
1.29
0.95

49862
52740
11179
65800
20705
17598
27853
31580
35952
20957
35545
15890
28014
25601
30563
22647
18577
14438
15519
15976
17297
14203
8515
7089
16328
44202
7866
15703
11757
25456
10838
41082
46608
47630
25801
12872
74109
42111
15120
3335
40724
44105
8840
24703
18055
20695
36535
23592
27765
17882
26203
36246

C_Food
7
5
11
7
12
9
36
6
28
63
13
32
8
8
70
8
34
5
18
40
14
5
149
111
23
7
125
18
9
22
6
7
8
5
12
49
8
10
10
43
5
8
15
3
4
6
9
17
11
21
12
5

Number of Facilities
O_FoodD_Food
7
3
7
3
21
4
8
3
8
4
16
4
40
3
10
4
15
3
30
9
20
6
27
6
18
3
13
3
52
10
9
3
21
3
5
3
13
3
31
5
10
3
9
3
73
13
111
10
14
4
10
3
74
18
12
3
9
4
22
5
8
3
5
3
6
3
9
3
12
3
31
15
7
4
8
3
11
3
32
4
8
3
13
3
22
8
11
3
10
3
8
3
9
3
5
3
10
3
14
4
16
4
6
3

Comm
6
11
27
10
21
56
68
10
13
61
22
46
15
25
57
7
47
16
45
89
11
8
119
73
33
10
110
30
79
54
3
8
5
16
47
51
7
8
17
64
12
4
42
20
21
7
10
16
37
68
100
9

Public
10
9
51
5
17
29
34
16
21
81
25
18
17
13
49
23
34
17
28
48
23
17
80
49
43
11
80
20
82
37
5
26
26
31
45
47
7
11
23
79
16
12
39
29
27
15
15
8
20
14
42
17

xlv

Appendix 4 – Attraction Each Zone in Jakarta

No
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204

Zone Name
Manggarai Selatan
MapharMarundaMelawai
Menteng
Menteng Atas
Menteng Dalam
Meruya Selatan
Meruya Utara
MunjulPademangan Barat
Pademangan Timur
Pal Meriem
Palmerah
Pancoran
PapangoPasar Baru
Pasar Manggis
Pasar Minggu
PasebanPegadungan
Pegangsaan
Pegangsaan Dua
Pejagalan
Pejaten Barat
Pejaten Timur
PekayonPekojanPela Mampang
Pengadegan
Penggilingan
Penjaringan
Pesanggrahan
Petamburan
Petogogan
Petojo Selatan
Petojo Utara
Petukangan Selatan
Petukangan Utara
Pinang Ranti
Pinangsia
Pisangan Baru
Pisangan Timur
Pluit
Pondok Bambu
Pondok Kelapa
Pondok Kopi
Pondok Labu
Pondok Pinang
Pondok Ranggon
Pulo
Pulo Gadung

District NameCity Name

Tebet
Taman Sari
Cilincing
Kebayoran Baru
Menteng
Setia Budi
Tebet
Kembangan
Kembangan
Cipayung
Pademangan
Pademangan
Matraman
Palmerah
PancoranTanjung Priok
Sawah Besar
Setia Budi
Pasar Minggu
Senen
Kalideres
Menteng
Kelapa Gading
Penjaringan
Pasar Minggu
Pasar Minggu
Pasar Rebo
TamboraMampang Prapatan
Pancoran
CakungPenjaringan
Pesanggrahan
Tanah Abang
Kebayoran Baru
GambirGambirPesanggrahan
Pesanggrahan
MakasarTaman Sari
MatramanPulo Gadung
Penjaringan
Duren Sawit
Duren Sawit
Duren Sawit
Cilandak
Kebayoran Lama
Cipayung
Kebayoran Baru
Pulo Gadung

Jakarta Selatan
Jakarta Barat
Jakarta Utara
Jakarta Selatan
Jakarta Pusat
Jakarta Selatan
Jakarta Selatan
Jakarta Barat
Jakarta Barat
Jakarta Timur
Jakarta Utara
Jakarta Utara
Jakarta Timur
Jakarta Barat
Jakarta Selatan
Jakarta Utara
Jakarta Pusat
Jakarta Selatan
Jakarta Selatan
Jakarta Pusat
Jakarta Barat
Jakarta Pusat
Jakarta Utara
Jakarta Utara
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Barat
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Utara
Jakarta Selatan
Jakarta Pusat
Jakarta Selatan
Jakarta Pusat
Jakarta Pusat
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Barat
Jakarta Timur
Jakarta Timur
Jakarta Utara
Jakarta Timur
Jakarta Timur
Jakarta Timur
Jakarta Selatan
Jakarta Selatan
Jakarta Timur
Jakarta Selatan
Jakarta Timur

Population

Area Size (Km2)

Density

28023
19419
35644
3067
31108
33725
44336
39069
52740
28765
92393
44736
24060
76683
23693
48692
15236
32684
29594
29333
89902
28771
59011
88877
44922
70771
53257
27692
52827
25553
120095108579
33294
42817
13717
17969
21379
44882
65329
33297
12890
38531
49766
54858
74591
86150
43152
55099
66946
30535
6630
41862

0.51
0.59
7.92
1.26
2.44
0.9
2.58
2.85
4.76
1.9
3.53
2.61
0.65
2.11
1.24
2.8
1.89
0.78
2.79
0.71
8.89
0.98
6.28
3.23
2.9
2.88
3.14
0.78
1.62
0.95
4.48
3.95
2.1
0.9
0.86
1.14
1.12
2.11
2.99
1.89
0.96
0.68
1.8
7.71
5
5.72
2.06
3.61
6.84
3.66
1.27
1.29

52659
34693
3105
2528
11968
35511
16355
11142
9608
12734
24947
16245
36818
33544
17127
16353
8041
40395
9965
38400
8364
27151
8216
27856
14338
22624
14600
35333
30940
24076
22483
28071
13859
43262
15895
14465
18987
18260
19001
14793
13791
54913
26931
6344
13285
12673
18136
13349
8897
6772
5408
30260

C_Food
6
20
3
56
92
9
38
12
45
4
6
18
10
53
7
11
23
6
8
13
32
15
27
11
17
6
3
15
14
6
14
16
3
7
45
12
28
6
5
8
18
6
9
108
38
34
16
13
56
3
34
9

Number of Facilities
O_FoodD_Food
14
3
15
6
4
3
43
10
58
14
10
4
38
6
10
4
34
9
8
3
15
3
23
4
11
3
35
3
10
3
8
4
22
3
10
3
9
4
14
4
25
4
11
3
28
5
21
3
19
4
14
3
8
3
12
4
14
3
11
3
10
3
12
6
10
3
9
3
37
11
15
3
30
6
9
4
14
3
15
5
14
4
6
3
17
3
99
13
40
6
27
5
12
4
18
5
45
9
7
3
23
7
7
4

Comm
9
13
5
66
66
9
78
26
57
5
12
15
24
42
23
13
38
16
45
42
34
34
22
20
30
17
10
7
32
11
17
35
7
19
35
37
61
9
30
30
27
5
18
77
60
34
12
35
98
5
53
10

Public
12
11
9
58
102
24
37
22
48
6
21
39
15
57
25
17
88
27
39
31
47
32
49
25
33
29
12
19
21
10
22
34
14
29
27
49
39
19
17
22
35
5
32
56
45
37
19
35
68
6
34
21

xlvi

Appendix 4 – Attraction Each Zone in Jakarta

No

Zone NameDistrict NameCity Name

205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256

Pulo Gebang
RagunanRambutan
Rawa Barat
Rawa Buaya
Rawa Bunga
Rawa Terate
Rawabadak Selatan
Rawabadak Utara
Rawajati
Rawamangun
RawasariRoa Malaka
RorotanSelongSemananSemper Barat
Semper Timur
Senayan
Senen
Serdang
Setia Budi
Setu
Slipi
Srengseng
Srengseng Sawah
Suka Pura
Sukabumi Selatan
Sukabumi Utara
Sumur Batu
Sungai Bambu
Sunter Agung
Sunter Jaya
SusukanTaman Sari
TamboraTanah Sereal
Tanah Tinggi
TangkiTanjung Barat
Tanjung Duren Selatan
Tanjung Duren Utara
Tanjung Priuk
Tebet Barat
Tebet Timur
Tegal Alur
Tegal Parang
Tmii
Tomang
Tugu Selatan
Tugu Utara
Ujung Menteng

CakungPasar Minggu
CiracasKebayoran Baru
Cengkareng
Jatinegara
Cakung
Koja
Koja
Pancoran
Pulo Gadung
Cempaka Putih
TamboraCilincing
Kebayoran Baru
Kalideres
Cilincing
Cilincing
Kebayoran Baru
Senen
Kemayoran
Setia Budi
Cipayung
PalmerahKembangan
Jagakarsa
Cilincing
Kebon Jeruk
Kebon Jeruk
Kemayoran
Tanjung Priok
Tanjung Priok
Tanjung Priok
CiracasTaman Sari
TamboraTamboraJohar Baru
Taman Sari
Jagakarsa
Grogol Petamburan
Grogol Petamburan
Tanjung Priok
Tebet
Tebet
Kalideres
Mampang Prapatan
Cipayung
Grogol Petamburan
Koja
Koja
CakungJakarta Timur
Jakarta Selatan
Jakarta Timur
Jakarta Selatan
Jakarta Barat
Jakarta Timur
Jakarta Timur
Jakarta Utara
Jakarta Utara
Jakarta Selatan
Jakarta Timur
Jakarta Pusat
Jakarta Barat
Jakarta Utara
Jakarta Selatan
Jakarta Barat
Jakarta Utara
Jakarta Utara
Jakarta Selatan
Jakarta Pusat
Jakarta Pusat
Jakarta Selatan
Jakarta Timur
Jakarta Barat
Jakarta Barat
Jakarta Selatan
Jakarta Utara
Jakarta Barat
Jakarta Barat
Jakarta Pusat
Jakarta Utara
Jakarta Utara
Jakarta Utara
Jakarta Timur
Jakarta Barat
Jakarta Barat
Jakarta Barat
Jakarta Pusat
Jakarta Barat
Jakarta Selatan
Jakarta Barat
Jakarta Barat
Jakarta Utara
Jakarta Selatan
Jakarta Selatan
Jakarta Barat
Jakarta Selatan
Jakarta Timur
Jakarta Barat
Jakarta Utara
Jakarta Utara
Jakarta Timur

Population

Area Size (Km2)

Density116962
47768
45264
6506
77907
26241
31452
53387
43182
24745
44958
26730
3857
50715
3222
88410
85887
44793
3332
8515
37395
3603
24673
20244
54847
70267
69410
46192
47231
28508
37393
86152
77091
46367
16815
12499
31653
47611
15512
48015
30115
20192
43407
25015
21170
102989
39681
23012
35399
49245
85545
36549

6.86
5.05
2.09
0.69
4.67
0.88
4.1
1.33
1.33
0.67
2.6
1.25
0.53
10.64
1.4
5.98
4.44
3.17
1.53
0.81
0.82
0.94
3.25
0.97
4.92
6.75
5.61
1.57
1.6
1.15
2.36
6.65
4.68
2.19
0.68
0.28
0.62
0.62
0.37
3.65
1.76
1.11
5.59
1.72
1.39
4
1.06
3.93
1.88
1.86
2.37
4.43

13716
8681
19324
9778
14886
28478
7387
35441
31357
29915
16963
19945
8275
3859
3014
13037
18003
12725
2856
10158
41837
3852
6028
19980
9446
8912
11602
26532
26438
23271
15221
12234
14791
19019
26441
45375
49860
71177
43827
11460
15915
18227
7794
14080
14544
23429
34830
#N/A
18103
22869
34123
6972

C_Food
11
15
3
38
10
10
6
4
8
15
35
22
5
4
38
8
5
4
72
16
6
16
4
18
22
6
5
10
14
10
22
70
19
5
11
4
9
5
14
9
141
87
4
36
24
35
7
9
41
6
10
8

Number of Facilities
O_FoodD_Food
17
3
21
5
10
3
22
10
8
3
9
3
9
3
7
3
11
3
17
6
51
9
10
3
6
3
5
3
23
6
4
3
9
3
4
3
48
13
20
5
11
3
18
4
6
3
7
3
22
3
18
3
10
3
9
3
11
3
15
4
9
3
90
9
28
6
7
4
10
3
4
3
11
3
7
3
14
4
14
4
94
12
59
8
6
3
37
5
50
4
18
9
11
3
8
3
23
8
8
4
11
3
9
3

Comm
19
54
15
25
13
22
23
5
7
37
78
19
7
4
25
8
4
3
89
40
14
18
4
16
30
13
7
11
8
34
8
59
39
11
6
6
10
7
24
30
113
94
10
31
35
16
16
11
69
4
9
7

Public
27
56
23
20
16
21
8
12
23
22
64
35
8
9
56
17
12
6
60
47
26
20
11
39
48
27
19
25
11
26
16
72
28
13
9
6
16
24
2
27
66
46
18
52
31
16
18
22
57
15
33
8

xlvii

Appendix 4 – Attraction Each Zone in Jakarta

No
257
258
259
260
261
262

Zone Name
UlujamiUtan Kayu Selatan
Utan Kayu Utara
Utan Panjang
WarakasWijaya Kesuma

District NameCity Name

Pesanggrahan
Matraman
Matraman
Kemayoran
Tanjung Priok
Grogol Petamburan

Jakarta Selatan
Jakarta Timur
Jakarta Timur
Jakarta Pusat
Jakarta Utara
Jakarta Barat

Population

Area Size (Km2)

Density

49789
40218
34252
37137
56128
47778

1.71
1.12
1.05
1.05
1.09
2.61

26161
34434
31694
31889
49384
17092

C_Food
5
13
11
4
7
21

Number of Facilities
O_FoodD_Food
7
3
9
3
19
3
5
3
9
3
9
3

Comm
17
12
28
5
5
34

Public
12
20
30
10
8
29

xlviiiAppendix 5 – User’s Questionnaire

APPENDIX 5. USER’S QUESTIONNAIRESECTION 1. SOCIO-DEMOGRAPHIC
In this section, the respondent is asked to fill in some data related to self and family identity. The purpose of this section isto see whether individual attributes can influence habits and decisions in traveling and daily activities.
1. ID of respondent (filled by surveyor)
2. Gender:
01. Male
02. Female
3. Marital Status:
01. Married
02. Single
03. Divorce
04. Others (please specify), _____________
4. Phone Number:
5. Email Address:
6. Home Address:
01. Street name and number
02. RT/RW (block number)
03. District
04. Postal Code
7. Office Address:
01. Street name and number
02. RT/RW (block number)
03. District
04. Postal Code
8. Education Level:
01. Elementary School
02. Junior High School
03. Senior High School
04. Diploma
05. Bachelor
06. Master or Doctorate
07. Others (please specify), _____________
9. Occupancy:
01. Working (Full time, Gov’t Employee)
02. Working (Part time, Gov’t Employee)
03. Working (Full time, Private Employee)
04. Working (Part time, Private Employee)
05. Teacher or Lecturer
06. Entrepreneur
07. University Student
08. Others (please specify), _____________
10. Driving License Ownership: (multiple answer isallowed)01. SIM A
02. SIM B
03. SIM C
04. Do Not Have Any
05. Others (please specify), _____________
11. Household Vehicle(s) Ownership:
01. Car: _______ unit(s)02. Motorbike : _______ unit(s)03. Bicycle: _______ unit(s)04. Others (please specify): _______ unit(s)12. Household Member Data (Including Respondents,
please give a circle mark on yourself section)

Position in Family:
01. Head of Household
02. Wife/Spouse
03. Child
04. Daughter/Son in Law
05. Parents
06. Brother07. Sister
08. Grandchild
09. Housekeeper
10. Others
Social Activity:
01. Working (Full time, Gov’t Employee)
02. Working (Part time, Gov’t Employee)
03. Working (Full time, Private Employee)
04. Working (Part time, Private Employee)
05. Teacher or Lecturer
06. Entrepreneur
07. University Student
08. Students
09. Housewife
10. Retired
11. Do not work/Jobless12. Others
13. Average Household Monthly Income (Including all ofHousehold member’s income)
14. Average Household Monthly Expenses
15. Average Respondent’s Monthly Travel Expenses
16. Average Respondent’s Monthly Meal Expenses
17. Average Respondent’s Monthly Grocery Expenses
18. Average Respondent’s Monthly Non-Grocery
Expenses
The options for question number 13-18:
01. Under Rp. 1.000.000
02. Rp. 1.000.000-Rp. 1.499.999
03. Rp. 1.500.000-Rp. 1.999.999
04. Rp. 2.000.000-Rp. 2.999.999
05. Rp. 3.000.000-Rp. 3.999.999
06. Rp. 4.000.000-Rp. 4.999.999
07. Rp. 5.000.000-Rp. 5.999.999
08. Rp. 6.000.000-Rp. 7.999.999
09. Rp. 8.000.000-Rp. 9.999.999
10. Rp.10.000.000-Rp.12.499.999
11. Rp.12.500.000-Rp.14.999.999
12. Rp.15.000.000-Rp.17.499.999

xlixAppendix 5 – User’s Questionnaire

13. Rp.17.500.000-Rp.19.999.999
14. Rp.20.000.000-Rp.22.499.999
15. Rp.22.500.000-Rp.24.999.999
16. More than Rp.25.000.000
19. How many smartphone(s) did you have?
01. One02. Two
03. Three
04. More than Three
20. What type is your monthly plan?
01. Prabayar
02. Pascabayar
21. How many giga byte your monthly plan for internet on
smartphone(s)?
01. Under 500 MB
02. 1 GB03. 3 GB04. 5 GB05. 7 GB06. 10 GB
07. More than 10 GB
08. Unlimited
22. Did you have Wi-Fi in your home?
01. Yes
02. No23. Is your home’s Wi-Fi unlimited?
01. Yes
02. No24. Did you have access to Wi-Fi at your office?
01. Yes
02. No25. Is your office’s Wi-Fi unlimited?
01. Yes
02. No26. Do you have any requirement on the day that you need
to be at the office?
01. Yes
02. No (Go to question 28)
27. Do you go to the office every day? (Please select allthat apply)
MondayTuesday Never Go Never Go
 Mostly Not Go
 Mostly Not Go
 Mostly Go
 Mostly Go Always Go
 Always Go
WednesdayThursday Never Go Never Go
 Mostly Not Go
 Mostly Not Go
 Mostly Go
 Mostly Go Always Go
 Always Go
FridaySaturday Never Go Never Go
 Mostly Not Go
 Mostly Not Go
 Mostly Go
 Mostly Go Always Go
 Always Go
Sunday Never Go
 Mostly Not Go
 Mostly Go Always Go
28. Do you have any assigned working schedule?
01. Yes
02. No (Go to question 30)
29. When is the starting and ending time of your work
schedule? (Exact time)

Starting TimeHH (hours) MM (minutes)

a.m. / p.m.

Ending TimeHH (hours) MM (minutes)

a.m. / p.m.

※ Put dash (-) mark if no requirement
30. Do you have any minimum requirement of working
hour?
01. Yes
02. No (Go to question 32)
31. How many hours is the lunch break time in a day?
HH (hours)
MM (minutes)
32. Do you have any assigned lunch schedule?
01. Yes
02. No (Go to question 32)
33. When is the starting and ending time of your lunch
break? (Exact time)
34. Starting TimeHH (hours) MM (minutes) a.m. / p.m.

Ending TimeHH (hours) MM (minutes)

a.m. / p.m.

※ Put dash (-) mark if no requirement
35. How many hours is the lunch break time in a day?
HH (hours)
MM (minutes)※ Put dash (-) mark if no requirement
36. Do you have any assigned schedule of the arriving
time at home?
01. Yes
02. No37. How many hours is the lunch break time in a day?
HH (hours)
MM (minutes)※ Put dash (-) mark if no requirement
38. Does your workplace have designated place for lunch(e.g., Canteen or Café) inside the office?
01. Yes
02. No39. Does your workplace provide some free lunch for theemployee?
01. Yes
02. No40. How far is the distance of the nearby shops from yourworkplace?
01. <500 meter
02. 501 m – 1 Km
03. 1 – 2 Km
04. 2 – 3 Km
05. 3 – 4 Km
06. More than 4 Km
41. By which mode usually you went there?
01. Never went there
02. Walking
03. Your own Bicycle
04. Your own motorbike
05. Online OjeklAppendix 5 – User’s Questionnaire

06. Taxi
07. Busway

08. Others (please specify), _____________SECTION 2. TRAVEL DIARY AND ICT USAGE
Figure 1 - Trip-Activity Path
By using the travel diary survey application (provided by MMM@Singapore) we are trying to capture the real (actual) travelactivity behavior and the virtual activity behavior that happened
within 24 hours. In the detail we will colleting respondent’s:
1. Origin
2. Destination
3. Travel time
4. Travel purpose
5. Activity time (duration of activity)
6. Route choice
7. Mode choice
8. ICT usage on every activity
9. Follow-up questions:(1) Frequent place(2) Reason if did not make any trips on that day

3.
4.
5.
6.
7.

How does the application work?
1. In the beginning of the survey, the respondents will be asked
to fill the username and the password that will provided by MMM
Company.
2. Make sure to turn on the GPS.
Every day in 2-weeks (14 days) the respondent will be asked to open the MMM Application to make sure theapplication is running.
Let the application running in your background.
Verify the trips/activities in the end of trips/activities or in the end of the dat.
Answer the follow up question.
Finish.

The example interface of the application:

Travel time

Route ChoiceMode Choice

Trip PurposeliAppendix 5 – User’s Questionnaire

Follow up questions

Trip Diary

Detail of Trips

Verification

ICT Usage Questionnaire
The ICT usage question will be asked in the end of every trips/activities. By asking what kind of e-activities that they havedone along with the actual trips/activities, we tried to capture the virtual activities pattern that happened in the same time.

Figure 2. Time-space path for Actual and Virtual Travel-Activity
The questions are follows:“Did you do some e-activity within this activity? (multiply
answer allowed)”
The options are follows:
1. No
2. Order food from online food delivery services
3. Buying a grocery by online
4. Buying non-grocery goods by online
5. Online banking/online payment
6. Joining some online meeting

7. Joining some online class
8. Watch an online movie or playing an online game (eentertainment)
9. Other e-activities
Follow-up Questionnaire (attached to the application)
Standard travel diary questions:• Shopping trip purposeo Keep the shopping purposeo Insert the follow up question, asking:▪ Whether it was for:lii

Appendix 5 – User’s Questionnaire

1. Grocery,
2. Non-grocery,
3. Both (grocery and non-grocery).
▪ After choosing the category, there will be adetail category for each:
1. Grocery (multiple answer is allowed)
01. Fresh product (vegetable or fruit)
02. Meat, Chicken, Seafood
03. Diary, Eggs, Cheese, Tofu, Tempe
04. Dry and Canned Goods
05. Frozen Food
06. Bakery
07. Others (fill manually)
2. Non-grocery (multiple answer isallowed)01. Books/Magazine
02. Electronics & Home Appliance
03. Make Up & Skin Care
04. Fashion
05. Household Products
06. Others (fill manually)
3. If they choose both the above options
will be shown again▪ Price paid in total for the wholetransaction.
1. No need to distinguish or separate
the bills for every category of
shopping.
2. The cost in here is total amount thatthey paid in one-time activity.
• Eat-out trip purposeo Insert the follow up question, asking:▪ The type of food they ate or buy. The categoryof type food is:

1. Beverages
2. Snack/Sweets
3. Fast Food
4. Indonesian Food
5. Western Food
6. Eastern Food
7. Noodles & Meatballs
8. Others (fill manually)▪ How much they paid for the food they ordered?
• Mode Choiceo Online car trips would be included in the taxi/ridehailing. With the follow-up questions:▪ Which service was used?
1. GO-CAR
2. GRAB-CAR
3. Bluebird Taxi
4. Others (fill manually)▪ How much is the fare that they paid?
o Online motorcycle included in the Motorcycle
mode category. With the follow-up questions:▪ Asking if the user was the driver or passenger.
If Passenger, there will be another follow up
questions:▪ If any of the following services were used?”
1. GO-RIDE by GOJEK
2. GRAB-BIKE by GRAB
3. Oke Jack
4. LADYJEK
5. Ojek Pangkalan
6. Others (fill manually)▪ The fare paid if a service was used (that it’s
possible to be a passenger riding on the
motorcycle of a friend or family member
without using a service).

For the follow up questions, it will be better to put the Indonesian language. This following table is the complete questions
including the Indonesian translation.
SectionQuestion 1

Category for
shopping

Question 2
Question 1
Category forfood

Question 2

EnglishIndonesian
Shopping Trip Purpose
What did you buy?
Apakah yang anda beli?
Grocery
Bahan makanan
1.
Fresh product (vegetable or fruit)
1.
Produk segar (sayuran atau buah)
2.
Meat, Chicken, Seafood2.
Daging, ayam, ikan
3.
Diary, Eggs, Cheese, Tofu, Tempe
3.
Telur, susu, keju, tempe, tahu
4.
Dry and Canned Goods
4.
Makanan kering dan kalengan
5.
Frozen Food
5.
Makanan beku
6.
Bakery6.
Roti dan kue
7.
Others (fill manually)
7.
Lainnya (fill manually)
Non-Grocery
Bukan bahan makanan
1.
Books/Magazine
1.
Buku / Majalah2.
Electronics & Home Appliance2.
Elektronik & Perlengkapan Rumah Tangga
3.
Make Up & Skin Care
3.
Make Up & Skin Care
4.
Fashion4.
Fashion & Hijab
5.
Household Products
5.
Produk Rumah Tangga
6.
Others (fill manually)
6.
Lainnya (fill manually)
How much did you pay in this activity?
Berapakah total harga barang yang anda beli pada aktifitas ini?
Eat-out Purpose
What type of food did you eat?
Apakah jenis makanan yang anda pesan?
1.
Beverages
1.
Minuman2.
Snack/Sweets
2.
Snack/Sweets
3.
Fast Food
3.
Fast Food
4.
Indonesian Food
4.
Indonesian Food
5.
Western Food
5.
Western Food
6.
Eastern Food
6.
Eastern Food
7.
Noodles & Meatballs
7.
Mie & Bakso
8.
Others (fill manually)
8.
Lainnya (fill manually)
How much did you pay for the food?
Berapakah harga makanan yang anda pesan?

liiiAppendix 5 – User’s Questionnaire

SectionQuestion 1
Category for
taxi/ridehailing
Question 2
Question 1
Category for
onlinemotorcycle

Question 2

EnglishIndonesianMode Choice (the option for taxi/ride hailing)
Which service you used?
Apa layanan yang anda gunakan?
1.
GO-CAR
1.
GO-CAR
2.
GRAB-CAR
2.
GRAB-CAR
3.
Bluebird Taxi
3.
Bluebird Taxi
4.
Others (fill manually)
4.
Others (fill manually)
How much is the fare paid if a service was used in Berapa biaya yang anda keluarkan dalam menggunakan jasa
this travel activity?
transportasi tersebut?
Mode Choice (Online motorcycle)
Which service you used?
Apa layanan yang anda gunakan?
1.
GO-RIDE by GOJEK
1.
GO-RIDE by GOJEK
2.
GRAB-BIKE by GRAB
2.
GRAB-BIKE by GRAB
3.
Oke Jack3.
Oke Jack
4.
LADYJEK4.
LADYJEK
5.
Ojek Pangkalan
5.
Ojek Pangkalan
6.
Others_____________
6.
Others_____________How much is the fare paid if a service was used in Berapa biaya yang anda keluarkan dalam menggunakan jasa
this travel activity?
transportasi tersebut?

SECTION 3. STATED PREFERENCE
To capture the effects of such context-dependent factors, we designed a data collection process that is summarized in Figure
3.10. Upon successful recruitment, a respondent will first answer a web-based survey on her social-demographic
information. Following that, she will download an app onto her phone that will track her daily mobility activities
automatically. The user’s daily timeline will be displayed in the app to verify the activity and trips they made and answer
additional questions about trip and activity details and ICT usage. The tracking and verification process goes on for 14days, during which a customized stated-preference questionnaire will also be delivered to the user for selected eating-out
or shopping activities. Finally, at the end of the two weeks, participants will be required to take a short survey on theirattitude towards ICT.
Respondents

Sociodemographic
Questionnaire

Activity-travel
Diary Survey

Eat-out
ActivityType of food; Food
Price

Yes

No

Online Activity

YesType of Online
Activities

Stated Preference
Questionnaire

1 WeekActivity-travel
Diary Survey

Data Complete

Ask if want tocontinue

Continue

Ask to completed
the data

Yes

No

End

No

Continue Activitytravel Diary
Survey2 WeekActivity-travel
Diary Survey

Data CompleteNo

Ask to completed
the data

Yes
End

Survey FlowchartlivAppendix 5 – Questionnaire

The attribute in question will be based on data obtained from travel-activity dairy. We strive to make respondents remember
the actual conditions they felt at that time by indicating the date they were traveling. By showing the date when they took
the eat-out trip, it is hoped that the respondents will be able to remember all the activities and constraints they had at thattime. Next, we convert the respondent’s actual travel time into delivery time from online FD. Next, we calculate the travel
distance by calculating the travel route’s distance from origin and destination for the respondent’s eat-out trip. This travel
distance will then be multiplied by the based fare from online FD delivery in Jakarta, 6,000 IDR per Km, which is thenused as an attribute delivery fee. We also generated the food variance that we developed from the database of all foodcategories in the MSTPs apps, with seven types of food variance. We showed a random combination of seven variants withseveral different combinations for each question on each questionnaire. The last attribute we added was the food cost for
online FD. With the collaboration between MSTPs and the merchant, there might be differences in food prices between the
respondent eating directly on the spot and food price if ordering via FD online. We made a possible food price scenario
based on food prices that the respondent reported on to the activity-travel diary survey application.
Attributes and Level of SP Survey
Attributes
Delivery time for online FD

Delivery cost for online FD

Combinations of Food Types
1. Beverages
2. Snacks/Sweets
3. Fast food
4. Indonesian food
5. Western food
6. Eastern food
7. Bakso/Noodles
Food cost for online FD

1.
2.
3.
4.
5.
1.
2.
3.
4.
5.
1.
2.
3.
4.

Level
0.4*actual travel time
0.7*actual travel time
1.0*actual travel time
1.3*actual travel time
1.6*actual travel time
0.4*6,000 IDR*actual travel distance
0.7*6,000 IDR*actual travel distance
1.0*6,000 IDR*actual travel distance
1.3*6,000 IDR*actual travel distance
1.6*6,000 IDR*actual travel distanceOne food typeThree food types
Five food types
Seven food types

1. 0.8*actual food cost
2. 0.9*actual food cost
3. 1.0*actual food cost
4. 1.1*actual food cost
5. 1.2*actual food cost
Source: Author, 2020
Respondents will be asked about five different scenarios of online-based food delivery services. The respondents’ optionsare (1) continuing to conduct the eat-out trip or (2) shifting to the online-based food delivery services. The following
questions show hypothetical scenarios of an online food delivery service introduced in Jakarta and how residents would
use it.
By considering all the activities and constraints you have at that time if the following online food delivery
service is available, will you be shifting from eating out to ordering an online food delivery service?

Questionnaire Construction Flowlv

Appendix 5 – Questionnaire

Example of Questionnaire
We asked users if they would replace the eating-out with an online-based food delivery service if the service is available
with specified price and service quality parameters by pivoting the revealed preferences (e.g., Hensher and Greene, 2003).
In those five questions, the respondent will be provided by their actual eat-out data, shown by the pink table on the left sideand the purposed online-based food delivery service showed by the blue table on the right side. In each questionnaire, therespondents are required to choose the alternatives. This SP-pivoted RP survey will be using the actual eat-out activity fromthe first-week data of the activity-travel diary survey respondents’ data. This allows us to confirm whether ICT use
substituted a trip or not. Importantly, this pivoted stated preference design allows respondents to represent their preferences
given all context-dependent factors such as motivation and constraints they actually had at that time. This feature is ofparticular importance to capture the complex interdependencies between ICT use and travel, since their travel decisions
may come from not only extrinsic motivations (e.g., getting lunch meal), but also intrinsic motivations (e.g., interactingwith friends while traveling and having lunch). The respondent would not choose an online-based food delivery service,even when the service level is very high if they made a trip for intrinsic motivations.
SECTION 4. ATTITUDE TOWARDS ICT QUESTIONNAIRE
A. Food Delivery Services
Have you ever ordered food from restaurant’sdelivery services (Restaurant-to-Consumer (R2C)
Delivery)?
01. Yes
02. No2. Which Restaurant-to-Consumer (R-C) food delivery
service that has you tried? (Please select all that
apply)▪ Restaurant Sederhana▪ KFC▪ McDonald’s▪ Pizza Hut (PHD)▪ Domino’s Pizza▪ Hoka-hoka Bento▪ Bakmi GM▪ Others (please specify), _____________
3. On the average, how often you use Restaurant-toConsumer (R2C) food delivery service within amonth?
▪ 1-3 times▪ 4-6 times▪ 7-9 times▪ 10-12 times▪ 13-15 times▪ More than 15 times
4. For what purpose(s) do you usually use theRestaurant-to-Consumer (R-C) food delivery
service? (please select all that apply)▪ Breakfast▪ Lunch

▪
▪
▪
▪
▪
▪

1.

5.

6.

7.

8.

DinnerSnack/Sweets
Family Gathering
Company’s meeting
Party
Others (please specify), _____________
What kind of food that you have ordered from R-C
Food Delivery Service? (please select all that apply)▪ Beverages▪ Snacks/Sweets▪ Fast Food
▪ Indonesian Food
▪ Western Food
▪ Eastern Food
▪ Noodles & Meatballs▪ Others (please specify), _____________On the average, how much are your weekly expenses
for food Restaurant-to-Consumer (R2C) fooddelivery service?
▪ < Rp. 50.000▪ Rp. 50.000 – Rp. 150.000▪ Rp. 150.001 – Rp. 200.000▪ More than Rp. 200.000
Do weather conditions motivate you to use (R-C)
online-based food delivery services?
▪ Yes
▪ No
What weather conditions that the most motivate you
to use R2C food delivery services?
▪ Moderate Hot

lvi

Appendix 5 – Questionnaire

▪
▪
▪
▪
▪

9.

10.

11.

12.

13.

14.

15.

16.

Extreme Hot
Extreme Cold
Moderate Rain
Heavy Rain
Others (please specify), _____________Have you ever ordered food from Online-based Food
(Platform-to-Consumer (P-C) Food Delivery
Services)?
▪ Yes
▪ No
Which Platform-to-Consumer (P2C) food delivery
service that has you tried? (please select all that
apply)▪ Go-Food▪ Grab Food
▪ Uber Food
▪ Click-Eat▪ Deliveroo▪ Others (please specify), _____________On the average, how often you use Platform-toConsumer (P2C) food delivery service within amonth?
▪ 1-3 times▪ 4-6 times▪ 7-9 times▪ 10-12 times▪ 13-15 times▪ More than 15 times
For what purpose(s) do you usually use the Platformto-Consumer (P-C) food delivery service? (please
select all that apply)▪ Breakfast▪ Lunch▪ Dinner▪ Snack/Sweets▪ Family Gathering▪ Company’s meeting▪ Party▪ Others (please specify), _____________
What kind of food that you have ordered from P-C
Food Delivery Service? (please select all that apply)▪ Beverages▪ Snacks/Sweets▪ Fast Food
▪ Indonesian Food
▪ Western Food
▪ Eastern Food
▪ Noodles & Meatballs▪ Others (please specify), _____________On the average, how much are your weekly expenses
for food Platform-to-Consumer (P-C) food delivery
service?
▪ < Rp. 50.000▪ Rp. 50.000 – Rp. 150.000▪ Rp. 150.001 – Rp. 200.000▪ More than Rp. 200.000
Do you know there are many promotions/monetary
incentives on online-based food delivery services?
▪ Yes
▪ No
Where did you usually get information about thepromotion of online-based food delivery services?
(please select all that apply)

▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪
▪

Informed by friends/family
Advertisement
News
LINE
Recommendation from the apps
Instagram
Twitter
Platform application’s timeline
Facebook
YouTubeWhatsApp
Others (please specify), _____________
17. Have you ever ordered food from a restaurant thatprovides a delivery service (R-C) but using onlinebased (P-C) food delivery services? (e.g., Ordering
the KFC by using GO-FOOD)
▪ Yes
▪ No
18. Have you ever had an unpleasant experience while
using online-based food delivery services? (please
select all that apply)▪ I do not have any▪ Delivery time is too long
▪ The food is not fresh▪ Incorrect order▪ Food is damage▪ Others (please specify), _____________
19. Will you use the online-food delivery services (either
R-C or P-C) in the future?
▪ Yes
▪ No
20. Do weather conditions motivate you to use (P-C)
online-based food delivery services?
▪ Yes
▪ No
21. What weather conditions that the most motivate you
to use R2C food delivery services?
▪ Moderate Hot▪ Extreme Hot▪ Extreme Cold
▪ Moderate Rain
▪ Heavy Rain
▪ Others (please specify), _____________
B. Online Non-Grocery Shop
22. Have you ever ordered a goods from online delivery
services?
▪ Yes
▪ No
23. How often you do an online shop for non-groceries
online-based food delivery services within a month?
▪ 1-3 times▪ 4-6 times▪ 7-9 times▪ 10-12 times▪ 13-15 times▪ >15 times
24. How much do you typically spend on online shopping
per month for non-groceries?
▪ < Rp. 100.000▪ Rp. 100.000 – Rp. 250.000▪ Rp. 250.001 – Rp. 500.000lviiAppendix 5 – Questionnaire

▪
▪
▪
▪
▪

Rp. 500.001 – Rp. 750.000
Rp. 750.001 – Rp. 1.000.000
Rp. 1.000.001 – Rp. 1.500.000
Rp. 1.500.001 – Rp. 2.000.000
More than Rp. 2.000.000
25. Which platforms that have you tried? (Please selectthat all apply)▪ Go-Mart▪ Sdsd▪ Dsd▪ Sdsd26. What types of products do you usually buy online?
(please select that all apply)▪ Books/Magazine▪ Electronics & Home Appliance▪ Make Up & Skin Care
▪ Fashion▪ Household Products▪ Others (fill manually)
27. Please indicate which factors that affect yoursatisfaction most during your previous shop online
experience?
▪ Goods price▪ The speed of delivery▪ Quality of the goods▪ Delivery fee▪ Hospitality of merchants▪ Others▪
▪
▪
▪

Dry and Canned Goods
Frozen Food
BakeryOthers (fill manually)
33. Please indicate which factors that affect yoursatisfaction most during your previous shop online
experience?
▪ Goods price▪ The speed of delivery▪ Quality of the goods▪ Delivery fee▪ Hospitality of merchants▪ Others

C. Online Grocery Shop
28. Have you ever ordered groceries from online delivery
services?
▪ Yes
▪ No
29. How often do you shop online for the groceries withina month?
▪ 1-3 times▪ 4-6 times▪ 7-9 times▪ 10-12 times▪ 13-15 times▪ >15 times
30. How much do you typically spend on online shopping
per month for groceries?
▪ < Rp. 100.000▪ Rp. 100.000 – Rp. 250.000▪ Rp. 250.001 – Rp. 500.000▪ Rp. 500.001 – Rp. 750.000▪ Rp. 750.001 – Rp. 1.000.000▪ Rp. 1.000.001 – Rp. 1.500.000▪ Rp. 1.500.001 – Rp. 2.000.000▪ More than Rp. 2.000.000
31. Which platforms that have you tried? (please selectthat all apply)▪ Go-Mart▪ SayurBox▪ Fresh Box▪ TukangSayur
32. What types of products do you usually buy online?
(please select that all apply)▪ Fresh product (vegetable or fruit)▪ Meat, Chicken, Seafood▪ Diary, Eggs, Cheese, Tofu, Tempelviii

Appendix 6 – List of Variables

APPENDIX 6. LIST OF VARIABLES
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58

Notation𝐴𝒂𝑎𝑘ℂ𝑛
𝐶 𝑛 (𝑥𝑘 )𝐶
𝐶𝑚̃ (𝑡, 𝑙, 𝑑̃ )𝐶𝑑𝑒𝑙 (𝑙)𝑑̃𝑑𝑒𝑙𝐷𝑇𝐷𝑇𝑑𝑒𝑙 (𝑙)
𝑛
𝛿𝑚𝑜𝑑𝑒
∆𝑝 𝑡
𝜖
𝜖𝑘
𝜖𝑘 (𝑎𝑘 )𝐸𝑠
𝐸𝑉(𝑥𝑘 , 𝑎𝑘 )ℎ𝐼𝐼𝑓𝑜𝑜𝑑 (𝑙)𝐼𝑚𝑠𝑡𝑝 (𝑙)𝐼𝑐𝑜𝑚 (𝑙)
𝐽𝑛
𝑘
𝑘𝑛𝑗
𝐾
𝐿
𝑙
𝐿𝑛𝑎𝑐𝑡. (𝑝̃)
𝑛
𝑙𝑤𝑜𝑟𝑘
𝑙𝑘
𝑚
̃
𝑀
𝑀𝑛 (𝑚)
𝑚
𝑛
𝑁ℎ
𝑝̃
𝑃
𝑝
𝑃𝑟(𝑎𝑘 |𝑥𝑘 )𝜋𝜋(𝑠𝑘 )
𝑞𝑛 (𝑖)
𝑞𝑡
𝑞ℎ
𝒔
𝑠𝑘
𝑠𝑘+1
𝑆
𝑆𝑝
𝑡
𝑡𝑘
𝑡 (𝑘+1)𝑇𝑇𝑚̃ (𝑡 (𝑘) , 𝑙 (𝑘) , 𝑑̃ )𝜃𝑚̃Definition
Set of all actions
Sequence of actions
ActionChoice set
Conditional choice ser for an individual 𝑛 in specific states 𝑥𝑘
Variable cost
Travel Cost variable for the specific transportation mode
Delivery cost for online food delivery service (MSTP)
Destination (zones)
Delivery component
Variable delivery time
Delivery time for online food delivery service (MSTP)
Dummy variable for mode ownership
Total time to performing an activity
Random state vector
Random state vector at the specific kRandom state vector at the specific k taken in an action 𝑎𝑘
Respected to the stochasticity of 𝑠𝑘 given the decision rule 𝑎𝑘 = 𝜋(𝑠𝑘 )Expected value
Activity history
Agglomeration index
Agglomeration index for food merchants
Agglomeration index for online food merchants
Agglomeration index for commercial facilities
alternatives
Index (order of sequence states and actions
The number of times alternative 𝑗 appears in the choice set
Total number of stages (actions and states) during the time period
The set of locations
Current location
A set of location for an individual to performing specific purposes (actions)
Location for an individual to performing work activity
Location in specific k
The chosen mode of transport (when taking action 𝑎𝑘 )The set of modes of transportation
A set of modes for an individual
Previous mode of transport
Individual variable
Total number of activity history
Chosen activity purpose (the next activity when taking action 𝑎𝑘 )The set of activity purposes
Activity purpose of the previous act
Probability of path choice
PolicyPolicy at state 𝑠𝑘 )The probability that alternative 𝑗 ∈ ℂ𝑛
The transition of the state variables for time (𝑡), part of stochasticity
The transition of the state variables for activity history (ℎ), part of stochasticity
Sequence of states
The current state
The state that reached when taking action 𝑎𝑘 in the state 𝑠𝑘
Set of all states
The number of size variables for activity 𝑝
Time of the day (time-steps, depends on the interval (𝑡 = 𝑡𝑖𝑚𝑒 𝑝𝑒𝑟𝑖𝑜𝑑/𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙)
The current time steps
The next time steps
Travel time for a specific mode
Parameter for modeslixAppendix 6 – List of Variables

No
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98

Notation
𝜃𝑡𝑡
𝜃𝑐𝑜𝑠𝑡
𝜃𝐶,𝑒𝑎𝑡𝜃𝑒𝑎𝑡,𝑠𝑖𝑧𝑒𝜃𝑓𝑜𝑜𝑑
𝜃𝐼,𝑓𝑜𝑜𝑑
𝜃𝐶,𝑚𝑠𝑡𝑝𝜃𝑚𝑠𝑡𝑝,𝑠𝑖𝑧𝑒𝜃𝑚𝑠𝑡𝑝
𝜃𝐼,𝑚𝑠𝑡𝑝
𝜃𝑑𝑒𝑙,𝑐𝜃𝑑𝑒𝑙,𝑡𝜃𝐶,𝑜𝑡ℎ𝑒𝑟
𝜃𝑜𝑡ℎ𝑒𝑟,𝑠𝑖𝑧𝑒
𝜃𝑐𝑜𝑚𝜃𝐼,𝑐𝑜𝑚𝜃𝑤𝑜𝑟𝑘,𝑗𝜃𝑤𝑜𝑟𝑘,𝑗+1
𝑈(𝐬, 𝐚)𝑢𝑢𝑡𝑟𝑎𝑣𝑒𝑙 (t, 𝑙, 𝑑̃ , 𝑚̃)𝑢𝑎𝑐𝑡. (𝑡, 𝑙, 𝑝, 𝑝̃)
𝑢𝑝,𝑠𝑖𝑧𝑒 (𝑙, 𝑝)𝑢𝑤𝑜𝑟𝑘 (𝑡)𝑢𝑒𝑎𝑡,𝑠𝑖𝑧𝑒 (𝑙)𝑢𝑚𝑠𝑡𝑝,𝑠𝑖𝑧𝑒 (𝑙)𝑢𝑜𝑡ℎ𝑒𝑟,𝑠𝑖𝑧𝑒 (𝑙)𝑢(𝑠𝑘 , 𝑎𝑘 , 𝑠𝑘+1 )𝑢(𝑥𝑘 , 𝑎𝑘 )
𝑉
𝑉̅
𝑉(𝑠)
𝑉(𝑥𝑘 , 𝜖𝑘 )𝑉̅(𝑥𝑘 )
𝑥𝑘
𝑥𝑝,𝑙,𝑠 𝑒 𝜃𝑝,𝑠
𝑥𝑓𝑜𝑜𝑑 (𝑙)𝑥𝑚𝑠𝑡𝑝 (𝑙)𝑥𝑐𝑜𝑚 (𝑙)ξ

Definition
Parameter for travel time
Parameter for travel cost
Parameter for eating activity
Parameter for opportunity size to conduct eat activity
Parameter for food merchants
Parameter for agglomeration index of food merchants
Parameter for order online food delivery service activity
Parameter for opportunity size to order online food delivery service activity
Parameter for online food merchants
Parameter for agglomeration index of food merchants
Parameter for delivery cost for online food delivery service
Parameter for delivery time for online food delivery service
Parameter for other activities
Parameter for opportunity size to conduct other activities
Parameter for commercial facilities
Parameter for agglomeration of commercial facilities
Parameter for starting work activity
Parameter for ending work activity
Utility function
One-stage utility function
The utility of travel
The utility of performing some actions
The utility of opportunity size to performing some activity
The utility of working
The utility of opportunity size to performing eat activities
The utility of opportunity size to performing order online food delivery service
The utility of opportunity size to performing other activities
The utility function for activity-travel pattern
The utility of observed variables
Value function
Expected value function before the state variable 𝜖𝑘 have been observed
Value function at states
Value function in recursive form
the expected value of the value function before the state variables 𝜖𝑘 have been observed
The observable part of the state space (𝑠𝑘 = (𝑥𝑘 , 𝜖𝑘 ))The size variables
The number of food merchants at specific locations
The number of online food merchants at specific locations
The number of commercial facilities at specific locations
The need to perform activitieslxAppendix 7 – Presentation Handout

APPENDIX 7. PUBLIC HEARING PRESENTATION HANDOUT

lxi

Appendix 7 – Presentation Handout

lxii

Appendix 7 – Presentation Handout

lxiii

Appendix 7 – Presentation Handout

lxiv

Appendix 7 – Presentation Handout

lxv

Appendix 7 – Presentation Handout

lxvi