Publications
You can also find my articles on my Google Scholar profile.
Note: “*” indicates corresponding authorship, and “†” indicates co-first authorship.
Journal Publications
Qiao, Q., Ren, C., Chen, S., Liang, Y., Lai, Y., Zhou, Y., Schuldenfrei, E.*, Sarkar, C., Webster, C. (2025). Architectural design and building-level infections during the early stage of COVID-19: A study of 2597 public housing in Hong Kong. Building and Environment, 276, 112853.
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.
Liang, Y., Liu, Y., Wang, X. and Zhao, Z.* (2024). Exploring large language models for human mobility prediction under public events. Computers, Environment and Urban Systems, 112, 102153.
Liang, Y., Zhao, Z.*, Webster, C. J. (2024). Generating sparse origin-destination flows on shared mobility networks using probabilistic graph neural networks. Sustainable Cities and Society, 114, 105777.
Liang, Y., Zhao, Z.* and Zhang, X. (2024). Modeling taxi cruising time based on multi-source data: A case study in Shanghai. Transportation, 51(3), 761-790.
Feng, J.*, Liang, Y., Hao, Q. and Xu, K., and Qiu, W. (2024). Comparing effectiveness of point-of-interest data and land use data in theft crime modelling: a case study in Beijing. Land Use Policy, 147, 107357.
Liang, Y., Huang, G. and Zhao, Z.*. (2023). Cross-mode knowledge adaptation for bike sharing demand prediction using domain-adversarial graph neural networks. IEEE Transactions on Intelligent Transportation Systems, 25(5), 3642-3653.
Huang, G., Liang, Y. and Zhao, Z.*. (2023). Understanding market competition between transportation network companies using big data. Transportation Research Part A: Policy and Practice, 178, 103861.
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.
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.
Liang, Y., Zhao, Z.* and Sun, L. (2022). Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns. Transportation Research Part C: Emerging Technologies, 143, 103826.
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.
Liang, Y. and Zhao, Z.* (2021). 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.
Huang, H.*, Liu, Y., Liang, Y., Vargas, D. and Zhang, L. (2020). Spatial perspectives on coworking spaces and related practices in Beijing. Built Environment, 46(1), 40-54.
Liang, Y. (2020). A comparative study on the spatial characteristics and influencing factors of co-working and traditional office rental prices. Beijing Planning and Construction (in Chinese), 01, 60-65.
Conference Papers
Liang, Y., Wang, S.*, Yu, J., Zhao, Z., Zhao, J. and Pentland, S. (2025). Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning. In 104th Transportation Research Board Annual Meeting (TRB), Washington, DC, USA.
Wang, Q., Liang, Y., Zheng, Y., Xu, K., Zhao, J., and Wang, S.* (2025). Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models. In 104th Transportation Research Board Annual Meeting (TRB), Washington, DC, USA.
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. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI (UrbanAI’24), Atlanta, GA.
Liang, Y., Ding, F., Tang, Y. and Zhao, Z.* (2023). Time-aware trip generation for bike sharing system planning. In 12th ACM SIGKDD International Workshop on Urban Computing (UrbComp’23), Long Beach, CA, USA.
Liang, Y., Huang, G. and Zhao, Z.* (2022). Bike sharing demand prediction based on knowledge sharing across modes: A graph-based deep learning approach. In IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 857-862). IEEE.
Feng, J.*, Liang, Y., Hao, Q., Xu, K. and Qiu, W. (2022). POI data versus land use data: Which are most effective in modelling theft crime. In 27th Annual Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Sydney, Australia.