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 primary research interests lie in the domain of intelligent evacuation and crowd dynamics. He is particularly focused on developing intelligent evacuation guidance systems for large-scale facilities, leveraging agent-based simulation, machine learning, and large language models. He aims to solve complex decision-making problems in emergency scenarios to enhance safety and efficiency in transportation hubs and high-density buildings.

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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 Firefighting

• Intelligent Evacuation Guidance

• Pedestrian Movement Prediction

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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. 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.

2. 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.

3. Xie, C. Z. T., Chen, Q. H., Zhu, B., Lee, E. W. M., Tang, T. Q., Yin, X., Yuan, Z. L. & Zhang, B. T.* (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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. Tang, T., Zhang, B., & Wang, T. (2022). An improved optimization framework for evacuation planning in facilities considering pedestrian dynamics. Journal of Transportation Safety & Security, 14(4), 693-722.

11. Tang, T., Zhang, B., & Xie, C. (2019). Modeling and simulation of pedestrian flow in university canteen. Simulation Modelling Practice and Theory, 95:86-111.

12. Tang, T., Zhang, B., Zhang, J., & Wang, T. (2019). Statistical analysis and modeling of pedestrian flow in university canteen during peak period. Physica A Statistical Mechanics & Its Applications, 521:29-40.

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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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