Zhao, Zhan 趙展

BEng (Tongji); MS (UBC); PhD (MIT)

Dr. Zhan Zhao is an Assistant Professor in Urban Data Science based in the Faculty of Architecture at the University of Hong Kong. His research interests lie in the intersection of data science, human behavior, and urban mobility. In particular, his research contributes to the general field of urban science through integrating advances in machine learning and artificial intelligence into spatiotemporal data analysis for analyzing human behavior, characterizing urban dynamics, and designing new mobility solutions.

Prior to joining HKU, Zhan was a senior data scientist at Via Transportation Inc., a transportation network company providing on-demand transit services. He holds a PhD degree in Transportation from MIT, where he was a research assistant at Transit Lab and Urban Mobility Lab. Over the years, he worked at, and collaborated with, several research institutes and government agencies around the world, including Singapore-MIT Alliance for Research and Technology (SMART), IBM Research – Ireland, Transport for London, TransLink (British Columbia), and Energy Foundation China.

Fields of Interest:

  • Urban Computing and Spatiotemporal Data Mining
  • Human Mobility and Travel Behavior
  • Public Transit and Shared Mobility on Demand Systems

Recent Publications:

  • Liang, Y., Huang, G., and Zhao, Z. (2022). Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach. Transportation Research Part C: Emerging Technologies, 140. DOI: 10.1016/j.trc.2022.103731
  • Liang, Y., & Zhao, Z. (2021). NetTraj: A network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms. IEEE Transactions on Intelligent Transportation Systems, 1-12. DOI: 10.1109/TITS.2021.3129588
  • Li, J., & Zhao, Z. (2022). Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City. Accident Analysis & Prevention, 167. DOI: 10.1016/j.aap.2022.106586
  • Mo, B., Zhao, Z., Koutsopoulos, H.N., & Zhao, J. (2021). Individual mobility prediction in mass transit systems using smart card data: An interpretable activity-based hidden Markov Approach. IEEE Transactions on Intelligent Transportation Systems, 1-13. DOI: 10.1109/TITS.2021.3109428
  • Zhao, Z., Koutsopoulos, H. N., and Zhao, J. (2020). Discovering latent activity patterns from transit smart card data: A spatiotemporal topic model. Transportation Research Part C: Emerging Technologies, 116, 102627
  • Zhao, Z., Koutsopoulos, H. N., and Zhao, J. (2018). Detecting pattern changes in individual travel behavior: A Bayesian approach. Transportation Research Part B: Methodological, 112, 73-88
  • Zhao, Z., Koutsopoulos, H. N., and Zhao, J. (2018). Individual mobility prediction using transit smart card data. Transportation Research Part C: Emerging Technologies, 89, 19-34.
  • Zhao, Z., and Zhao, J. (2018). Car pride and its behavioral implication: An exploration in Shanghai. Transportation, 1-10.
  • Goulet-Langlois, G., Koutsopoulos, H. N., Zhao, Z., and Zhao, J. (2018). Measuring regularity of individual travel patterns. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1583-1592.