F-MIRAI Project

Title: Mathematical approaches for mobility services in suburban areas
Industrial Partner: F-MIRAI at University of Tsukuba
Toyota will lead the way to the future of mobility, enriching lives worldwide with the safest
and most responsible ways of moving people. In the near future, cars are expected toconnect with people and communities and to perform in new roles as part of human social
infrastructure. New domains of service such as AI, autonomous driving, robotics, andconnected cars are becoming especially important. Toyota aims to reach the ultimate goalof sustainable mobility, creating a mobile future society full of smiles. Toyota and theUniversity of Tsukuba have jointly established the R&D Center for Frontiers of MIRAI in
Policy and Technology (F-MIRAI), which advances toward Society 5.0 through thedevelopment of infrastructure for future communities and the formation of industrial centersthrough long-term collaborative action.
Industrial mentor
Takahiro NOGUCHI, F-MIRAI, University of Tsukuba

Background:
Rapid suburban sprawl in metropolitan areas in Japan has led to the rapid development ofperipheral satellite cities. High-speed railway networks and motorways have contributed
considerably to improving connections among established metropolitan areas and thesenew peripheral cities. Nevertheless, rapid population growth has led simultaneously toconstruction of suburban sprawls without any schematic road planning.
Consequently, key functions of cities for the daily life of residents, such as commerce,administration, and schools, have diffused to suburban areas on the basis of widening useof private cars. Such development is neither sustainable nor desirable as populations age
and as energy conservation becomes increasingly important. In principle, a higher quality oflife can be achieved if key city functions are aggregated in city centers around main railway

stations. This aggregation necessitates the adoption of better and more efficient public
transportation networks in conjunction with small devices used for personal mobility.
Mobility-as-a-Service (MaaS*) describes the integration of different transportation modes toachieve mobility solutions that are consumed as a service. Mobility innovation can beimplemented fully or partially as MaaS and Smart Cities. Its principles are often designatedas the following: C, Connected; A, Autonomous; S, Sharing; E, Electric (CASE). A wider
range of demonstration programs and commercial services is being provided to improvetransportation safety, efficiency, and convenience. In particular, “IoT vehicle information” isanticipated for application to widely various services because it aggregates various
information related to local transportation in an immediate, remote, and wide-area manner.
The benefits of using IoT vehicle information with AI to meet transportation challenges andsupport local economic growth are important.
* MaaS: integration of various forms of transport services into a single mobility service thatis accessible on demand. To meet a customer’s request, a MaaS operator provides adiverse menu of transport options, be they public transport, ride-, car- or bike-sharing, taxi
or car rental/lease, or some combination thereof. For the user, MaaS can offer added value
through use of a single application to provide access to mobility, with a single payment
channel instead of multiple ticketing and payment operations. For its users, MaaS isexpected to be the best value proposition, helping users meet their mobility needs andsolve the inconvenient aspects of individual journeys as well as the entire system ofmobility services.
Source: MaaS Alliance, https://maas-alliance.eu/homepage/what-is-maas/

Project and Expectations
Many studies and demonstration experiments have elucidated MaaS as an implementationof CASE, particularly for metropolitan areas where other multiple transportation modes
such as rails, metro, and buses are available. Nevertheless, other areas for which private
cars constitute the dominant transportation mode have not been investigated thoroughly.
Mobility services for non-metropolitan areas can differ from those for metropolitan areas
because of differences in population density/distribution, transportation demand, and public
transportation networks. As an example, Tsukuba city, a city with a population of 241,000

people, and located 60 km northeast of Tokyo, has a transportation system that isdependent on private cars. However, Tsukuba city was designed as a science city suchthat the University of Tsukuba, which also has a hospital, is positioned as the center of its
urban function. Therefore, MaaS centered on the university can be a good example of afuture transportation system in a non-metropolitan area. Furthermore, the scope includes
mobility services supporting the regional transportation network.
This project will design MaaS for practical cases for the campus of University of Tsukuba
through analyses of person-trips and other related datasets. The present majortransportation modes around the university are private cars, buses, bicycles, and walks.
The services accompanied with mobility around the campus are various, ranging fromeducation, health care, and nursing care, to childcare.
Participants are expected to estimate the overall transportation demand to optimize thoseservices through analyses of available datasets such as traveling bus probe data andpersonal mobile data in Tsukuba city and peripheral areas, and to build a mathematicalmodel as a system-of-systems based on some of the following perspectives. All can beconsidered with automated vehicles.
l Traffic prediction and forecasting and dynamic traffic control with information provision.
l Minimizing global energy consumption for travel of people.
l Optimal allocation of mobility services in the regional transportation network
Although several approaches for such problems already exist, participants are expected tofind completely new mathematical approaches, descriptors, formulations, solvers,visualizations, operation plans, etc. to accommodate and support our future society withhighly sophisticated modes of mobility (See Ref. [10]). Additionally, investigating the energy
expenditure and traffic flow of cars in terms of dynamical systems theory is possible byimplementing mathematical models into automated vehicles in a miniature traffic course on
a trial basis. (See video in Ref. [9].)Requirements

We welcome applications from motivated team-players who have knowledge and practicalskills related to one or more of the following.
•

Mathematical statistics•

Optimization•

Graph theory•

Dynamical systems theory•

Machine/ Deep learning•

Operations research•

Programming language (Python, C, or MATLAB)

Recommended Reading and References[1] Applied Mathematical Programming. http://web.mit.edu/15.053/www/AMP.htm[2] Nonlinear Dynamics and Chaos.
https://www.youtube.com/playlist?list=PLbN57C5Zdl6j_qJA-pARJnKsmROzPnO9V[3] Toyota Motor Corporation. Toyota e-Palette Concept Opening Video. 2018.
https://www.youtube.com/watch?v=-z09mlA2mLU[4] Toyota Motor Corporation. Toyota e-Palette Concept Basic Function Video. 2018.
https://www.youtube.com/watch?v=XmoPQuMlOYE[5] OECD/International Transport Forum. Shared Mobility: Innovation for Liveable City.
2016. https://www.itf-oecd.org/itf-work-shared-mobility[6] GRIPS-Sendai 2018. Toyota Group Final report: Data-Driven Models for Predictive
Control of Toyota’s e-Palette Mobility System. 2018. (For those who have decided toparticipate)[7] g-RIPS Sendai 2019. Toyota Group Final report: Implementation of TOYOTA’s e-PaletteMobility System to Develop Data-Driven Optimization of Transportation Networks.
2019. (For those who have decided to participate)[8] Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials IPAM
long program: http://www.ipam.ucla.edu/programs/workshops/mathematicalchallenges-and-opportunities-for-autonomous-vehicles-tutorials/?tab=schedule[9] Miniature traffic model by F-MIRAI in University of Tsukuba (in Japanese):https://youtu.be/NvHfrb0K6Fo[10] Modeling Public Transportation Networks with Queues
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3986717