Zhan Zhao

Zhan Zhao

Assistant Professor @ HKU Transportation PhD @ MIT

Publications

You can also find my articles on my Google Scholar profile.

Note: “*” indicates corresponding authorship, and “†” indicates students/RAs/Postdocs who work under my supervision.

Journal Publications

  1. Jin, Y., Ma, J.*, Xiao, Z., Zhao, Z. and Ke, J. (2027). Evaluating the impact of Urban Air Mobility on ground traffic efficiency: A multi-scenario simulation study. Journal of Air Transport Management, 138, 103095.
  2. Wang, X.†, Zhao, Z.*, Wang, R.† and Xu, Y. (2026). Event-aware analysis of cross-city visitor flows using large language models and social media data. Transportation Research Part A: Policy and Practice, 209, 105023.
  3. Ding, F.†, Zhao, Z.*, Wang, Y.†, Namgung, M., Lee, J. and Li, T.† (2026). Origin-destination flow generation for metro network expansion using spatiotemporal gated graph neural networks. Transportation Research Part C: Emerging Technologies, 188, 105677.
  4. Li, T.†, Zhao, Z.* and Zhao, S. (2026). Disentangling metro passenger travel delays under extreme weather events: An analytical framework. Transportation Research Part D: Transport and Environment, 154, 105284.
  5. Zhou, M., Zhou, J.*, Zhou, J. and Zhao, Z. (2026). Unraveling node, place, and resilience during disasters: Evident from a typhoon in Hong Kong. International Journal of Disaster Risk Reduction, 136, 106076.
  6. Liang, Y.†, Wang, S.*, Yu, J., Zhao, Z., Zhao, J. and Pentland, S. (2026). Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning. Travel Behaviour and Society, 43, 101171.
  7. Wang, X.†, Zhao, Z., Zhang, H., Guo, X. and Zhao, J. (2026). Assessing the benefits of collaborative ridesharing across transportation network companies. Transportation Research Part C: Emerging Technologies, 183, 105475.
  8. Ding, F.†, Zhao, Z.*, Han, Y., Zhou, Y. and Xu, Y. (2026). Does e-shopping weaken the link between land use and neighborhood shopping behavior? Evidence from large-scale mobile phone data. Applied Geography, 186, 103809.
  9. Lin, Y., Zhang, K., Kondor, D., Zhao, Z., Ratti, C. and Xu, Y.* (2026). Exploring influential factors of fleet and parking management in shared autonomous vehicle systems: An agent-based simulation framework. Transportation Research Part A: Policy and Practice, 203, 104762.
  10. Tang, Y.†, Zhao, Z.*, Deng, W., Lei, S., Liang, Y.† and Ma, Z. (2025). RouteKG: A knowledge graph-based framework for route prediction on road networks. IEEE Transactions on Intelligent Transportation Systems, 26(12), 22277-22295.
  11. Zhao, L.†, Shen, S. and Zhao, Z.* (2025). Large-scale electric bus network transition planning via deep reinforcement learning. Transportation Research Part D: Transport and Environment, 146, 104899.
  12. Ding, F.†, Tang, Y., Wang, Y.† and Zhao, Z.* (2025). Unraveling the network effects in station ridership growth patterns under metro network expansion. Journal of Transport Geography, 125, 104205.
  13. Zhang, Q., Ma, Z.*, Ling, Y., Qin, Z., Zhang, P. and Zhao, Z. (2025). Causal graph discovery for urban bus operation delays: A case in Stockholm. Transportation Research Record: Journal of the Transportation Research Board, 2679(5), 256-272.
  14. Tang, Y.†, He, J., and Zhao, Z.* (2025). Activity-aware human mobility prediction with hierarchical graph attention recurrent network. IEEE Transactions on Intelligent Transportation Systems, 26(2), 1604-1616.
  15. Hu, Y.†, Zhao, M. and Zhao, Z.* (2024). Uncovering heterogeneous effects of link-level street environment on e-bike and e-scooter usage. Transportation Research Part D: Transport and Environment, 136, 104477.
  16. Fu, T., Li, X.*, Wang, J., Zhang L., Gong, H., Zhao, Z. and Sobhani, A. (2024). Trajectory prediction and risk assessment in car-following scenarios using a noise-enhanced generative adversarial network. IEEE Transactions on Intelligent Transportation Systems, early access.
  17. Liang, Y.†, Zhao, Z.* and Webster, C. (2024). Generating sparse origin-destination flows on shared mobility networks using probabilistic graph neural networks. Sustainable Cities and Society, 114, 105777.
  18. Liang, Y.†, Liu, Y., Wang, X.† and Zhao, Z.* (2024). Exploring large language models for human mobility prediction under public events. Computer, Environment and Urban Systems, 112, 102153. (Top 1% highly cited paper in the field of Social Sciences, general, 2025; based on data from Essential Science Indicators)
  19. Hu, Y.†, Chen, L. and Zhao, Z.* (2024). How does street environment affect pedestrian crash risks? A link-level analysis using street view image-based pedestrian exposure measurement. Accident Analysis & Prevention, 205, 107682.
  20. Yang, H., Jiang, J.*, Zhao, Z., Pan, R. and Tao, S. (2024). STVANet: A spatio-temporal visual attention framework with large kernel attention mechanism for citywide traffic dynamics prediction. Expert Systems with Applications, 254, 124466.
  21. Huang, G.†, Zhao, Z.* and Yeh, A.G.O. (2024). How shareable is your trip? A path-based analysis of ridesplitting trip shareability. Computer, Environment and Urban Systems, 110, 102120.
  22. Lin, Y., Xu, Y.*, Zhao, Z., Tu, W., Park, S. and Li, Q. (2024). Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework. Transportation Research Part A: Policy and Practice, 181, 104003.
  23. Liang, Y.†, Zhao, Z.*, Ding, F.†, Tang, Y.† and He, Z. (2024). Time-dependent trip generation for bike sharing planning: A multi-task memory-augmented graph neural network. Information Fusion, 106, 102294.
  24. Ding, F.†, Chen, S. and Zhao, Z.* (2024). Incorporating walking into ride-hailing: The potential benefits of flexible pick-up and drop-off. Transportation Research Part D: Transport and Environment, 127, 104064.
  25. Zhao, L.†, Shen, S. and Zhao, Z.* (2024). Planning decentralized battery-swapping recharging facilities for e-bike sharing systems. Sustainable Cities and Society, 101, 105118. (HKU Foundation Publication Award for Research Postgraduate Students, 2024)
  26. Liang, Y.†, Huang, G.† and Zhao, Z.* (2024). Cross-mode knowledge adaptation for bike sharing demand prediction using adversarial graph neural networks. IEEE Transactions on Intelligent Transportation Systems, 25(5), 3642-3653.
  27. Jiang, F., Ma, J.*, Webster, C.J., Chiaradia, A.J.F., Zhou, Y., Zhao, Z. and Zhang, X. (2024). Generative urban design: A systematic review on problem formulation, design generation, and decision-making. Progress in Planning, 180, 100795. (Top 1% highly cited paper in the field of Social Sciences, general, 2025; based on data from Essential Science Indicators)
  28. Liang, Y.†, Zhao, Z.* and Zhang, X. (2024). Modeling taxi cruising time based on multi-source data: A case study in Shanghai. Transportation, 51, 761–790.
  29. Zhou, J.†*, Zhou, M., Zhou, J. and Zhao, Z. (2023). Adapting node-place model to predict and monitor COVID-19 footprints and transmission risks. Communications in Transportation Research, 3, 100110.
  30. Huang, G.†, Liang, Y.† and Zhao, Z.* (2023). Understanding market competition among transportation network companies using big data. Transportation Research Part A: Policy and Practice, 178, 103861.
  31. Huang, G.†, Lian, T., Yeh, A.G.O. and Zhao, Z.* (2023). To share or not to share? Revealing determinants of individuals’ willingness to share rides through a big data approach. Transportation Research Part C: Emerging Technologies, 157, 104372.
  32. Liang, Y.†, Ding, F.†, Huang, G.† and Zhao, Z.* (2023). Deep trip generation with graph neural networks for bike sharing system expansion. Transportation Research Part C: Emerging Technologies, 154, 104241.
  33. Lin, Y., Xu, Y.*, Zhao, Z., Park, S., Su, S. and Ren, M. (2023). Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study. Travel Behaviour and Society, 100587.
  34. Zhao, Z.* and Liang, Y.† (2023). A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards. Transportation Research Part C: Emerging Technologies, 149, 104079.
  35. Zhou, M., Zhou, J.* †, Zhou, J., Lei, S. and Zhao, Z. (2023). Introducing social contacts into the node-place model: A case study of Hong Kong. Journal of Transport Geography, 107, 103532.
  36. Zhao, Z.*, Koutsopoulos, H. N. and Zhao, J. (2022). Identifying hidden visits from sparse call detail record data. Transactions in Urban Data, Science, and Technology, 1(3–4), 121–141.
  37. Liang, Y.†, Zhao, Z.* and Sun, L. (2022). Memory-augmented dynamic graph convolutional networks for traffic data imputation with diverse missing patterns. Transportation Research Part C: Emerging Technologies, 143, 103826. (HKU Foundation Publication Award for Research Postgraduate Students, 2023)
  38. 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, 103731.
  39. Li, J.† and 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, 106586.
  40. Bi, W., Lu, W.*, Zhao, Z. and Webster, C.J. (2022). Combinatorial optimization of construction waste collection and transportation: A case study of Hong Kong. Resources, Conservation & Recycling, 179, 106043.
  41. Liang, Y.† and Zhao, Z.* (2022). NetTraj: A network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms. IEEE Transactions on Intelligent Transportation Systems, 23(9), 14470-14481.
  42. Mo, B.†, Zhao, Z.*, Koutsopoulos, H. N. and Zhao, J. (2022). Individual mobility prediction in mass transit systems using smart card data: An interpretable activity-based hidden Markov approach. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12014-12026.
  43. 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.
  44. Zhao, Z. and Zhao, J.* (2020). Car pride and its behavioral implication: An exploration in Shanghai. Transportation, 47(2), 793-810.
  45. 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.
  46. 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.
  47. 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.
  48. Zhao, J.*, Frumin, M., Wilson, N. H. and Zhao, Z. (2013). Unified estimator for excess journey time under heterogeneous passenger incidence behavior using smartcard data. Transportation Research Part C: Emerging Technologies, 34, 70-88.
  49. Frumin, M., Zhao, J.*, Wilson, N. H. and Zhao, Z. (2013). Automatic data for applied railway management: Case study on the London Overground. Transportation Research Record: Journal of the Transportation Research Board, 2353, 47-56.
  50. Zhao, Z., Zhao, J.* and Shen, Q. (2013). Has transportation demand of Shanghai, China, passed its peak growth? Transportation Research Record: Journal of the Transportation Research Board, 2394, 85-92.

Conference Papers

  1. Ding, F.†, Zhao, Z.*, Li, Z., Guo, X., Zhang, N., Wang, Y.† and Tang, Y. (2026). PROB-EMOE: A probabilistic ensemble mixture-of-experts framework for metro network expansion forecasting. 35th International Joint Conference on Artificial Intelligence (IJCAI 2026): Special Track on AI for Social Good, Bremen, Germany.
  2. Wang, X.†, Zhao, Z.*, Zhao, L.†, Wu, L. (2025). Just-in-time deliveries: Managing uncertain target arrival times with adaptive routing. 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), pages 3763-3768, Gold Coast, Australia.
  3. Li, T.†, Zhao, Z.* and Liu, X. (2025). Adaptive fusion of decomposed traffic components: A heterogenized spatio-temporal attention for traffic forecasting. 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), pages 1079-1083, Gold Coast, Australia.
  4. Tang, Y.†, Qu, A., Wang, Z., Zhuang, D., Wu, Z., Ma, W., Wang, S., Zheng, Y., Zhao, Z. and Zhao, J.* (2025). Sparkle: Mastering basic spatial capabilities in vision language models elicits generalization to spatial reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4083–4103, Suzhou, China. (Also won MKLM’25 Best Paper Award)
  5. Tang, Y.†, Wang, Z., Qu, A., Yan, Y., Wu, Z., Zhuang, D., Kai, J., Hou, K., Guo, X., Zhao, J.*, Zhao, Z.* and Ma, W.* (2024). ITINERA: Integrating spatial optimization with large language models for open-domain urban itinerary planning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1413–1432, Miami FL, USA. (Also won UrbComp’24 Best Paper Award)
  6. Ding, F.†, Liang, Y.†, Wang, Y.†, Tang, Y., Zhou, Y., and Zhao, Z.* (2024). A graph deep learning model for station ridership prediction in expanding metro networks. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI (UrbanAI’24), pages 6-14, Atlanta, GA, USA.
  7. Liang, Y.†, Huang, G.† and Zhao, Z.* (2022). Bike sharing demand prediction based on knowledge sharing across modes: A graph-based deep learning approach. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pages 857-862, Macau, China.

Book Chapters

  1. Zhao, Z., Koutsopoulos, H. N. and Zhao, J. (2020). Uncovering Spatiotemporal Structures from Transit Smart Card Data for Individual Mobility Modeling. In Antoniou, C., Efthymiou, D. and Chaniotakis, E. (eds.), Demand for Emerging Transportation Systems: Modeling Adoption, Satisfaction, and Mobility Patterns. Elsevier, 123-149.