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Wenwen Zhang

Assistant Professor, Urban Affairs and Planning (UAP)
Wenwen Zhang
140 Otey St.
Blacksburg, VA 24061
  • Ph.D., City and Regional Planning, Georgia Institute of Technology, 2017
  • Ph.D., City and Regional Planning, Zhejiang University (China), 2017
  • M.S., Computational Science & Engineering, Georgia Institute of Technology, 2017
  • M.S., City & Regional Planning, Georgia Institute of Technology, 2013
  • M.S., Civil Engineering, Georgia Institute of Technology, 2013
  • B.E., City & Regional Planning, Zhejiang University (China), 2011
  • Autonomous electric vehicles
  • UAP 3024: Urban and Regional Analysis
  • UAP 5114: Computer Application in Planning – Spatial Data Analytics and Visualization
  • UAP 5424: Metropolitan Planning Topics: Micromobility
  • UAP 5494: Advanced Quantitative Techniques for Urban Research

Articles

  • “Forecasting Long-term Reduction Trajectories of Parking Demand toward a Shared and Automated Future,” with Kaidi Wang, Land Use Policy (2019).
  •  “Land Use Regression Models for 60 Volatile Organic Compounds: Comparing Google Point of Interest (PoI) and City Permit Data,” with Tianjun Lu, Jennifer Lansing, Matthew J. Bechle, and Steve Hankey. Science of The Total Environment (2019).
  • “Residential Location Choice in the Era of Shared Autonomous Vehicles,” with Subhrajit Guhathakurta, Journal of Planning Education and Research (2018).
  • “Estimating Residential Energy Consumption in Metropolitan Areas: A Microsimulation Approach,” with Caleb Robinson, Subhrajit Guhathakurta, Venu M Garikapati, Bistra Dilkina, Marilyn A Brown, and Ram M Pendyala, Energy 155: 162-173 (2018).
  • “The Impact of Private Autonomous Vehicles on Vehicle Ownership and Unoccupied VMT Generation,” with Subhrajit Guhathakurta and Elias Khalil, Transportation Research Part C: Emerging Technologies 90: 156-165 (2018).

I am currently involved in multiple research projects aiming at modeling energy emissions of autonomous electric vehicles and advance existing long-term urban simulation model framework to understand the social and policy impacts of autonomous electric mobility. Additionally, I also engaged in research that utilizes innovative data sources, such as Google Point of Interests and Street View, and machine learning techniques to develop more accurate and transferrable measurements for urban environment to fuel critical planning inquiries, such as predicting air quality and active travel behavior.

  • Urban Land Use and Transportation Interaction
  • Innovative/Disruptive Transportation Technology
  • Urban Modeling/Simulation
  • Sustainable Development
  • Data Analytics & Visualization in Planning