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Hara Lab
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The lab designs urban and transportation systems using data on how people move and where they carry out activities.
AboutAbout the lab
Designing how cities work from how people behave.
Hara Lab focuses on people's activities and travel behavior in cities, and conducts research to solve social challenges facing cities and regions.
Using data such as GPS movement records of people and vehicles and social media, the lab develops methods for understanding activity in cities.
Using game theory, the lab designs the institutions and pricing needed to bring new forms of mobility, such as autonomous driving, sharing services, and MaaS, into cities.
The lab values both theoretical rigor and practical usability.
Key terms
- Mechanism design
- An approach to designing rules so that, even when each participant acts in their own self-interest, the overall outcome is desirable.
- MaaS (Mobility as a Service)
- An approach that combines modes of transportation such as trains, buses, taxis, and sharing services into a single service to make them easier to use.
- Causal inference
- Statistical methods for determining from data whether a given cause has truly produced a given effect.
NewsLab news
A master's student won the Outstanding Poster Award at the Spring Conference on Infrastructure Planning and Management
Associate Professor Hara took up the post and founded Hara Lab
ResearchResearch themes
The lab focuses on people's activities and travel behavior in cities, with the aim of solving social challenges facing cities and regions. Its research rests on two pillars: urban and transportation data science, which uses diverse data such as movement records, and mechanism design, which uses game theory to design institutions and pricing for new mobility.
*Illustrative imageCo-evolution of cities, transportation, and activities
Using long-term real-world data, the lab clarifies how the individual character of each city arises, as well as the mechanisms by which all cities come to resemble one another. It builds models that reproduce human activities and movement from historical data on travel behavior and facility locations.
*Illustrative imageInstitutional design for new mobility services
The lab studies ride-hailing services, shared rides in sparsely populated areas, and community-operated car sharing. The work spans theory on the conditions under which such services are viable and on allocation mechanisms, validation with real-world data, and implementation in society.
*Illustrative imageNew travel behavior models
Combining statistics, econometrics, and machine learning, the lab works on understanding human travel behavior and modeling complex decision-making. Topics include how people use their time over the course of a day and the relationship between online behavior and actual travel.
*Illustrative imageCausal inference on urban activities and travel behavior
The lab identifies the factors that influence the activities of various actors in cities, as well as travel behavior such as the choice of destinations and routes. It clarifies the causal relationships between policies and actual behavior, and applies them to better urban design.
MembersFaculty
The members of Hara Lab consist of faculty, project specialists, secretaries, and master's and undergraduate students.As of 2026
- Associate ProfessorYusuke HaraPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Cityscape of Tokyo, view from Tokyo Skytree ( by Takeishiwataru @Pixabay 1272392)” Takeishiwataru / CC0 (cropped)
- Co-evolution of cities, transportation, and activities: “Cityscape from Tokyo Skytree” Cheng-en Cheng / CC BY-SA 2.0 (cropped)
- Institutional design for new mobility services: “Orlando Bike Share bicycles in Lake Eola Park” Michael Rivera / CC BY-SA 4.0 (cropped)
- New travel behavior models: “Nagel-schreck rho=0.35 p=0.3 edit” Richard Sear / Public domain (cropped)
- Causal inference on urban activities and travel behavior: “Nagel-Schreckenberg model - plot of average velocity v as a function of density of cars rho” Richard Sear / CC BY-SA 3.0 (cropped)
- Related lab (坂田・森・浅谷・西本研究室): “Social Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Related lab (鳥海研究室): “Social Network Visualization” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Related lab (大澤研究室): “Wikidata in the Linked Open Data cloud” Thomas Shafee / CC BY 4.0 (cropped)
- Related lab (松尾・岩澤研究室): “Deep-dream-white-noise-0036” MartinThoma / CC0 (cropped)
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