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Biblio Author: Xiaohu Zhang

  • 2024

    Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for travel behavior analysis?

    • Qingyi Wang
    • Shenhao Wang
    • Yunhan Zheng
    • Hongzhou Lin
    • Xiaohu Zhang
    • Jinhua Zhao
    • Joan Walker
    Transportation Research Part B
    ,
    Volume
    179
    ,
    2024
  • 2021

    The Mobility Pattern of Dockless Bike Sharing: A Four-month Study in Singapore

    • Xiaohu Zhang
    • Yu Shen
    • Jinhua Zhao
    Transportation Research Part D
    ,
    2021
  • 2021

    E-scooter sharing to serve short-distance transit trips: a Singapore Case

    • Zhejing Cao
    • Xiaohu Zhang
    • Kelman Chua
    • Honghai Yu
    • Jinhua Zhao
    Transportation Research Part A
    ,
    2021
  • 2020

    Estimating the Potential for Shared Autonomous Scooters

    • Dániel Kondor
    • Xiaohu Zhang
    • Malika Meghjani
    • Paolo Santi
    • Jinhua Zhao
    • Carlo Ratti
    IEEE Transactions on Intelligent Transportation Systems
    ,
    2020
  • 2019

    Built Environment and Autonomous Vehicles Mode Choice: A First Mile Scenario in Singapore

    • Yu Shen
    • Baichuan Mo
    • Xiaohu Zhang
    • Jinhua Zhao
    Transportation Research Board 98th Annual Meeting
    ,
    2019
  • 2020

    Is Ridesourcing More Efficient than Taxis?

    • Hui Kong
    • Xiaohu Zhang
    • Jinhua Zhao
    Applied Geography
    ,
    2020
  • 2020

    How does Ridesourcing Substitute for Public Transit? A geospatial perspective in Chengdu, China

    • Hui Kong
    • Xiaohu Zhang
    • Jinhua Zhao
    Journal of Transport Geography
    ,
    2020
  • 2018

    Understanding the Usage of Stationless Bike Sharing in Singapore

    • Yu Shen
    • Xiaohu Zhang
    • Jinhua Zhao
    International Journal of Sustainable Transportation
    ,
    2018

Massachusetts Institute of Technology | Department of Urban Studies and Planning | Jinhua Zhao | Accessibility

77 Massachusetts Ave. MIT 9-523, Cambridge, MA, 02139 | jinhua@mit.edu | 617-324-7594