Section I. Behavioral Foundation of Urban Mobility

 

Section II. Mobility System: Design with Behavioral Perspective

 

Section III. Mobility Policy: FORMULATE, IMPLEMENT and EVALUATE


 

 

Section IV: Machine Learning for Transportation

Public Transit: Driving the future of mass transport.

   
 
          
 

The JTL Urban Mobility Lab at MIT brings behavioral science and transportation technology together to shape travel behavior, design mobility systems, and improve transportation policies. We apply this framework to managing automobile ownership and usage, optimizing public transit planning and operation, promoting active modes of walking and cycling, governing autonomous vehicles and shared mobility services, and designing multimodal urban transportation systems.

We see transportation as a language: to describe a person, to characterize a city, and to understand an institution. We organize our research into four themes, considering the behavioral foundation of urban mobility, the design of mobility systems, the development of policies related to transportation, and the use of machine learning to develop new tools for mobility analysis.

JTL is directed by Prof. Jinhua Zhao, the Edward H. and Joyce Linde Associate Professor of City and Transportation Planning at MIT, working with an enthusiastic group of transportation researchers. Our inter-disciplinary lab includes social scientists, data wranglers, designers, computer scientists, transport engineers, and planners of all stripes.