Jinhua Zhao

About

Jinhua Zhao is the Edward and Joyce Linde Associate Professor of City and Transportation Planning at MIT. His research focuses on shaping travel behavior, designing mobility systems, and reforming urban policies by integrating behavioral science and transportation technology. He develops methods to sense, predict, nudge, and regulate travel behavior and designs multimodal mobility systems that incorporate automated and shared mobility with public transport. Zhao sees transportation as a language to describe a person, characterize a city, and understand an institution, aiming to establish the behavioral foundation for transportation systems and policies. He directs the JTL Urban Mobility Lab and Transit Lab at MIT and leads long-term research collaborations with major transportation authorities and operators worldwide, including London, Chicago, Hong Kong, and Singapore. Zhao is also the co-director of the Mobility Systems Center of the MIT Energy Initiative and the director of the MIT Mobility Initiative. He enjoys working with students and is actively involved in advancing urban transportation planning and policy through his research and leadership roles.

Research topics

• Computer Science
• Data Mining
• Engineering
• Transport engineering
• Economics
• Computer Security
• Geography
• Artificial Intelligence
• Business
• Machine Learning

Selected publications

• Impacts of transportation network companies on urban mobility

  Nature Sustainability · 2021 · 294 citations

  Senior authorCorresponding
  DOI

• E-scooter sharing to serve short-distance transit trips: A Singapore case

  Transportation Research Part A Policy and Practice · 2021 · 127 citations

  Senior authorCorresponding
  DOI

• Dynamic Origin-Destination Prediction in Urban Rail Systems: A Multi-Resolution Spatio-Temporal Deep Learning Approach

  IEEE Transactions on Intelligent Transportation Systems · 2021 · 108 citations

  Senior authorCorresponding

  Short-term demand predictions, typically defined as less than an hour into the future, are essential for implementing dynamic control strategies and providing useful customer information in transit applications. Knowing the expected demand enables transit operators to deploy real-time control strategies in advance of the demand surge, and minimize the impact of abnormalities on the service quality and passenger experience. One of the most useful applications of demand prediction models in transi…

  DOI

• Modeling epidemic spreading through public transit using time-varying encounter network

  Transportation Research Part C Emerging Technologies · 2020 · 100 citations

  Senior authorCorresponding
  DOI

• Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model

  Transportation Research Part C Emerging Technologies · 2020 · 63 citations

  Senior authorCorresponding
  DOI

Frequent coauthors

• Shenhao Wang

  56 shared

• Haris N. Koutsopoulos

  53 shared

• Baichuan Mo

  41 shared

• Yu Shen

  Tongji University

  25 shared

• Yunhan Zheng

  25 shared

• Zhan Zhao

  23 shared

• Qingyi Wang

  21 shared

• Joanna Moody

  21 shared

Education

• PhD, Urban Studies and Planning

  Massachusetts Institute of Technology

  2009

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