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(3.3)
(4.1)
(4.2)
(5.1)

What factors that influence people to choose online activities?
How the presence of MSTP’s online food delivery service will affect people’s eating behavior?
What factors that affects MSTP’s service level?
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 the spatial distribution offacilities through agglomeration index analysis and its correlation with the city-level characteristics. We employ
the agglomeration analysis across the different scales of the area and different types of facilities. With regards
to analyzing the impact of MSTP on activity-travel behavior, both revealed preference (RP) and the probeperson (PP) data are collected to capture the current activity-travel behaviors and preference of using MSTPs.
A travel-activity 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 ofMSTPs usage, mainly, the online-based food delivery service. To achieve this research aim, we extend thedynamic discrete choice model by adding the component of MSTPs to assess the impact of MSTPs on activitytravel behavior.
Data
To analyze the impact of MSTPs on activity-travel behavior and eating preference, a travel-activity diarysurvey was conducted in Jakarta, Indonesia. Along with two additional questionnaires about the individualcharacteristics and their preference for MSTPs usage, mainly, the online-based food delivery service. The data
was collected from 272 individuals who complete 14 days (2 weeks) smartphone app-based travel-activity diarysurvey. Regarding the analysis of agglomeration, two location study has been chosen, including 69 cities inJapan and 5 cities in Jakarta, Indonesia. Using the data of commercial and public facilities to construct theagglomeration index and the city-level characteristics for the advance analysis of the relationship between theagglomeration index and transportation variable.
Main Findings
The impact of MSTPs on the urban form was analyzed through the agglomeration analysis. We found thatthe city's agglomeration effect will be smaller if they start to have an online food delivery service. The presenceof MSTPs may lead to some changes in the city's structure due to their tendency to be agglomerated to copewith the minimum number of users, and food merchants’ partners in some areas will attract the driver to comeand get around those areas to provide some delivery. The MSTPs may encourage people to be in the central
area to get more range of the service, and it also may encourage the food merchants to be agglomerated andgenerate new service zones anywhere. MSTPs service 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 foodmerchants. In the long run, this kind of service may lead to a less structured city due to their ability to be locatedanywhere if they can maintain enough supply and demand at a specific location.
The impact of MSTPs on the individual’s eating behavior was made by analyzing the preferences ofindividuals using the online food delivery service. We found that people still feel it is quite costly to use onlinebased food delivery services because of their delivery time. The longer delivery time also indicates longer
waiting time for customers' side, and people feel the cost for waiting for the food ordered by using MSTPs isalmost the same as the cost that they need to pay if they are conducting some trip (eat-out trip). In this case, thepresence of MSTPs for online-based food delivery services has a substitution effect for eating out trip. Theresults indicate that if the merchants decide to move to the suburban area, they will lose their previous numberof potential customers in the central area because the increase in distance will increase the delivery time.
The impact of MSTPs on activity-travel behavior was not wholly assessed due to the limitation of the study.
We have attempted to develop a modeling framework to comprehensively understand the impacts of MSTPs byadding the utility of using online food delivery services into the utility of staying. We introduce the model andemphasizing how the model works through the simple toy model simulation. In this simulation, all the parameteris given, while in the actual calculation, we will estimate all parameters for each action and states. Based on thesimulation, we can see how the model will work and what is the outcome can be produced. By having MSTPs
components in our activity-behavior model, it gives such a significant impact on the probability of the chosen
path and how the pattern would be developed. In the utility of staying, the number of facilities has played asignificant role as the attraction of the zones that emphasize the size of opportunities to perform an action in aparticular zone. In this case, the discussion of location attraction also related to the discussion about the benefitof agglomeration. When the agglomeration is happening, we will have a much larger capacity in the zone.

Significant Contributions
The concept of Multi-service Transport Platforms (MSTPs), the part of ICT innovation, is expanded in thefield of activity-travel behavior research. In particular, this study could successfully:(1) Establish the comprehensive survey framework to capture the individuals' activity-travel behavior
simultaneously with the usage of online activities and their preferences of using the online services.
(2) Propose the new approach in constructing the counterfactuals for agglomeration index analysis.
(3) Propose the new framework of activity-travel behavior modeling by expanding the dynamic discrete
choice model component with the addition of the MSTPs component.
(4) Using the SP-PP survey to capture the individuals' preference can reveal the actual condition ofrespondents by gathering the individual activity and travel information by integrated with the GPSto collect much higher resolution data in real-time.
(5) While the agglomeration analysis was done in the aggregate scale of the area, this study provides acomprehensive analysis of the agglomeration index across area scale and facility types.
(6) Using the location of public facilities as the counterfactual, we can produce a new approach in theagglomeration index calculation.
(7) Considering the analytical procedure results, we found that IPWT can handle the bias caused by thesampling error or the survey design in the preference survey and give a better estimation result.
備考

論文の要旨はＡ４判用紙を使用し，4,000字以内とする。ただし，英文の場合は1,500語以内とする。
Remark: The summary of the dissertation should be written on A4-size pages and should not exceed 4,000Japanese characters. When written in English, it should not exceed 1,500 words.