Socioeconomic System
Advancing Urban and Transportation Data Science
Mechanism Design for Mobility Services
Co-evolutionary Models of Cities and Transportation
Technologies for sensing travel behavior and urban observations are rapidly expanding, and the ability to handle such data is increasingly in demand. To move beyond short-term forecasting and toward long-term predictions of urban activities and policy responses, it is essential to incorporate not only GPS-based mobility trajectories and urban/transportation network analysis but also decision-making models that capture human psychology. By doing so, we can develop data science methodologies that contribute to the design of urban behavior.
New mobility services are being continuously implemented in urban areas, including on-demand mobility, autonomous vehicles, the integration of traditional public transportation with emerging modes, and the unified operation of passenger and freight flows. These innovations require not only advancements in service levels and technologies but also institutional designs that take user behavior into account. From the perspective of behavioral mechanism design, we aim to develop implementation strategies for introducing new mobility services into cities.
Over the past few decades, lifestyles within cities have undergone significant changes, while certain aspects of human life have remained constant. We aim to capture the long-term mutual interaction between urban development and changes in travel behavior through co-evolutionary modeling. Based on such theoretical and empirical approaches, we seek to explore the future form of cities, anticipate emerging challenges, and realize effective urban transition management.