Biography

Dr. Zhang obtained his Ph.D. from the Department of Architecture and Civil Engineering, City University of Hong Kong. Prior to this, he received his B.Eng. and M.Eng. degrees from the School of Transportation Science and Engineering at Beihang University. His research focuses on smart evacuation and crowd dynamics, with particular interests in intelligent evacuation guidance and decision support for large-scale and complex facilities. His work integrates agent-based simulation, machine learning, and large language models to address decision-making challenges in emergency scenarios, with the goal of improving evacuation safety, efficiency, and resilience in the built environment. 

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Background of Education

• 2021-2025, Ph.D. in Dept. of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong.

• 2018-2021, M.Eng. in Civil Engineering (Traffic and Transportation), Beihang University.

• 2014-2018, B.Eng. in Civil Engineering (Traffic and Transportation), Beihang University.

 

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Research Areas

• Smart Evacuation

• Crowd Dynamics

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Awards & Honours

• Best Paper Award on the 15th International Workshop on Computational Transportation Science (2024)

• Outstanding Academic Performance Award for Research Degree Students, City University of Hong Kong (2024)

• Research Postgraduate Scholarship, City University of Hong Kong (2021-2025).

• Excellent Graduate of Beijing, Beijing Municipal Commission of Education (2021)

• National Scholarship, Ministry of Education, China (2020)

• Merit Student of Beihang University (2019, 2020)

• The First Prize Scholarship, Beihang University (2019, 2020)

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Journal Publications

1. Zhang, B., Pan, S., Chen, Q.*, Lu, S., Tang, T. Q., Zhang, Y., Chen, H., & Xie, C. Z. T. (2026). Enhancing fire evacuation safety in metro railway stations: multi-agent reinforcement learning-guided dynamic signage coordination. Computers & Industrial Engineering, 112024. 

2. Huang, H., Tsou, J. Y., Zheng, H., Ma, R., Shi, H., & Zhang, B.* (2026). Modeling hiking speed via interpretable machine learning: Uncovering nonlinear impacts of multidimensional environmental factors. Applied Geography, 192, 104052. 

3. Zhang, B., Dai, Z.*, Ma, R., Tang, T., & Lo, S. (2026). What determines a step: Observations from pedestrian movement evaluation at intersections with machine learning methods. IEEE Transactions on Intelligent Transportation Systems, 27(3). 

4. Yang, X., Wan, J., Li, Y., Xie, C., & Zhang, B.* (2025). A knowledge–data dual-driven framework for intelligent flood evacuation in subway stations. Physica A: Statistical Mechanics and its Applications, 2025: 130924. 

5. Zhang, B., Yan, Y.*, Xie, W., Luo, X., Lee, W., & Deng, X. (2025). The Halo effect in airport terminals: how wayfinding experiences influence emergency preparedness through perceived reliability. Accident Analysis & Prevention, 220, 108149. 

6. Xie, C. Z. T., Chen, Q., Zhu, B., Lee, E. W. M., Tang, T. Q., Yin, X., Yuan, Z. & Zhang, B.* (2025). Coordinating dynamic signage for evacuation guidance: A multi-agent reinforcement learning approach integrating mesoscopic crowd modeling and fire propagation. Chaos, Solitons & Fractals, 194, 116246. 

7. Zhang, B., Xu, J., Lo, S., Zhu, B., Tang, T. Q., Xie, C. Z.*, & Tian, Y. (2024). Forecaster as a Simulator: Simulating Multi-directional Pedestrian Flow with Knowledge-guided Graph Neural Networks. Computers & Industrial Engineering, 198, 110668. 

8. Xie, C. Z. T., Xu, J., Zhu, B., Tang, T. Q., Lo, S., Zhang, B.*, & Tian, Y.* (2024). Advancing crowd forecasting with graphs across microscopic trajectory to macroscopic dynamics. Information Fusion, 106, 102275. 

9. Zhang, B., Lo, J. T.*, Fang, H., Xie, C., Tang, T., & Lo, S. (2024). Directed rooted forest based direction setting method: A step toward automated dynamic exit signs. Journal of Building Engineering, 85, 108504. 

10. Zhang, B., Lo, J. T.*, Fang, H., Xie, C., Tang, T., & Lo, S. (2024). Coupled simulation-optimization model for pedestrian evacuation guidance planning. Simulation Modelling Practice and Theory, 134, 102922. 

11. Dai, Z., & Zhang, B.* (2023). Electric vehicles as a sustainable energy Technology: Observations from travel survey data and evaluation of adoption with Machine learning method. Sustainable Energy Technologies and Assessments, 57, 103267. 

12. Fang, H.*, Xu, M., Zhang, B., & Lo, S. M. (2023). Enabling fire source localization in building fire emergencies with a machine learning-based inverse modeling approach. Journal of Building Engineering, 78, 107605. 

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Conference

1. Zhang, B., Dai, Z. ∗ , & Lee, E. W. M. (2025). Observation from Crossing Pedestrian Flow with Data-Driven Movement Prediction. In EPJ Web of Conferences (Vol. 334, p. 04013). EDP Sciences.

2. Zhang, B. ∗ , Lo, S. (2023, March). A Computationally Efficient Method for Simulation-Based Evacuation Guidance Optimization. In International Civil Engineering and Architecture Conference (pp. 949-960). Singapore: Springer Nature Singapore.

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(last updated on 11/Aug/2026)