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.

Preprints and Working Papers

• Tang, Y.†, Wang, Z., Qu, A., Yan, Y., Hou, K., Zhuang, D., Guo, X., Zhao, J., Zhao, Z. and Ma, W.* (2024). Synergizing spatial optimization with large language models for open-domain urban itinerary planning.
• Ding, F.†, Zhao, Z.*, Han, Y. and Xu, Y. (2024). Does e-shopping mitigate the impact of land use on travel behavior? Evidence from large-scale mobile phone data in Shanghai.
• Liang, Y.†, Liu, Y., Wang, X.† and Zhao, Z.* (2023). Exploring large language models for human mobility prediction under public events.
• Tang, Y.†, Deng, W., Lei, S., Liang, Y.†, Ma, Z. and Zhao, Z.* (2023). RouteKG: A knowledge graph-based framework for route prediction on road networks.
• Wang, X.†, Zhao, Z.*, Zhang, H., Guo, X. and Zhao, J. (2023). Quantifying the uneven efficiency benefits from ridesharing market integration.
• Tang, Y.†, He, J., and Zhao, Z.* (2022). HGARN: Hierarchical graph attention recurrent network for human mobility prediction.
• Zhou, J.†, Zhao, Z* and Zhou, J. (2022). Quantifying COVID-19 transmission risk based on human mobility data: A personalized PageRank approach to efficient contact-tracing.

Journal Publications

1. 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, accepted in April 2024.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Liang, Y.†, Huang, G.† and Zhao, Z.* (2023). Cross-mode knowledge adaptation for bike sharing demand prediction using adversarial graph neural networks. IEEE Transactions on Intelligent Transportation Systems, accepted in October 2023.
9. 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.
10. 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.
11. Jiang, F., Ma, J.*, Webster, C.J., Chiaradia, A.J.F., Zhou, Y., Zhao, Z. and Zhang, X. (2023). Generative urban design: A systematic review on problem formulation, design generation, and decision-making. Progress in Planning, 100795.
12. 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.
13. 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.
14. 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.
15. Liang, Y.†, Zhao, Z.* and Zhang, X. (2022). Modeling taxi cruising time based on multi-source data: A case study in Shanghai. Transportation. https://doi.org/10.1007/s11116-022-10348-y
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. Zhao, Z. and Zhao, J.* (2020). Car pride and its behavioral implication: An exploration in Shanghai. Transportation, 47(2), 793-810.
25. 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.
26. 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.
27. 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.
28. 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.
29. 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.
30. 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. Liang, Y.†, Ding, F.†, Tang, Y.†, and Zhao, Z.* (2023). Time-aware trip generation for bike sharing system planning. The 12th ACM SIGKDD International Workshop on Urban Computing (UrbComp’23), Long Beach, CA, USA.
2. Liang, Y.†, Huang, G.† and Zhao, Z.* (2022). Bike sharing demand prediction based on knowledge sharing across modes: A graph-based deep learning approach. IEEE Intelligent Transportation Systems Conference, Macau, China.
3. Zhao, Z., Koutsopoulos, H. N. and Zhao, J. (2018). Discovering latent activity patterns from human mobility. The 7th ACM SIGKDD International Workshop on Urban Computing (UrbComp’18), London, UK.
4. Zhao, Z., Koutsopoulos, H. N. and Zhao, J. (2018). Detecting changes in individual travel behavior patterns. Transportation Research Board 97th Annual Meeting, Washington, DC.
5. Zhao, Z., Koutsopoulos, H. N. and Zhao, J. (2017). Mobility as a language: Predicting individual mobility in public transportation using n-gram models. Transportation Research Board 96th Annual Meeting, Washington, DC.
6. Zhao, Z., Zhao, J. and Koutsopoulos, H. N. (2016). Individual-level trip detection using sparse call detail record data based on supervised statistical learning. Transportation Research Board 95th Annual Meeting, Washington, DC.
7. Zhao, Z. and Zhao, J. (2015). Car pride: Psychological structure and behavioral implications. Transportation Research Board 94th Annual Meeting, Washington, DC.
8. Zhao, Z., Chua G. and Zhao, J. (2012). Evolution of trip chaining patterns in London from 1991 to 2010. Innovations in Travel Modelling Conference, Tampa, FL.
9. Kang, L., Lin, B., Zhao, Z. and Jin, L. (2010). The traffic control system at urban intersections during the phase transitions based on VII. 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, 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. Chapter 7 for Demand for Emerging Transportation Systems.