Xiaowei (Tom) Shi

Assistant Professor, Civil & Environmental Engineering
Affiliate Assistant Professor, Electrical Engineering
Founder and Director, Automated, Connected & Electric Mobility Systems Lab

Dr. Xiaowei (Tom) Shi is an Assistant Professor in Civil and Environmental Engineering and is also affiliated with Electrical Engineering and the Institute for Physical Infrastructure and Transportation (IPIT). He is the director of the Automated, Connected & Electric Mobility Systems (ACEMS) Lab at UWM. He is also a co-chair of the IEEE Emerging Transportation Technology Testing Technical Committee.

Dr. Shi’s research focuses on evaluating existing emerging mobility technologies, such as automated, connected, and electric vehicles, and developing novel technologies through the utilization of field experiments and hardware-in-the-loop methodologies. His works have been published in top transportation journals, e.g., Transportation Science, Transportation Research Parts Series (A-F), and IEEE Transactions on ITS.

Dr. Shi is the area editor of Cleaner Logistics Supply Chain and an editorial board member of Transportation Research Today. Dr. Shi has led a series of external research grants funded by the US DOT, Wisconsin DOT, and industry companies.

View Dr. Shi's website

View Dr. Shi's Automated, Connected & Electric Mobility Systems (ACEMS) Lab website

Education

- PhD, Transportation, Civil and Environmental Engineering, 2021, University of South Florida
- MS, Transportation, Civil and Environmental Engineering, 2020, University of South Florida
- MS, Control Science and Engineering, Traffic and Transportation, 2018, Beijing Jiaotong University
- BE, Urban Rail Transit, Traffic and Transportation, 2015, Beijing Jiaotong University

Research interests

- Evaluation and Testing of Connected and Automated Vehicles (CAVs)
- Development of Vehicle Automation and Vehicle-to-everything (V2X) Communications Technologies
- Hardware-in-the-loop Testing for Cooperative Driving Automation (CDA) Systems

Selected Publications

Google Scholar Link

- Long, K., Shi, X.*, Li, Y., Chen, Z., Wang, Y. and Li, X.*, 2025. Before and after riding: changing comfort attitude towards autonomous shuttles from perspectives as riders, drivers, and pedestrians. Transportation Research Part A: Policy and Practice. https://doi.org/10.1016/j.tra.2025.104613
- Xu, J.*, Xu, D., Wu, J., and Shi, X.*, 2025. Modeling the Collective Behavior of Pedestrians with the Spontaneous Loose Leader-follower Structure in Public Spaces. Computer-Aided Civil and Infrastructure Engineering. https://doi.org/10.1111/mice.13429
- Long, K., Shi, X.*, and Li, X., 2024. Physics-Informed Neural Network for Cross-Dynamics Vehicle Trajectory Stitching. Transportation Research Part E: Logistics and Transportation Review.https://doi.org/10.1016/j.tre.2024.103799
- Yang, L., Shi, X.*, and Li, X., 2024. Effect of Riding Experience on Changing Opinions Toward Connected and Autonomous Vehicle Safety – Evidence from Field Experiments. Transportation Research Part F: Traffic Psychology and Behaviour. https://doi.org/10.1016/j.trf.2024.07.023.
- Shi, X. and Li, X.*, 2023. Trajectory Planning for an Autonomous Vehicle with Conflicting Moving Objects over a Fixed Path. Transportation Research Part B: Methodological. https://doi.org/10.1016/j.trb.2023.05.001
- Shi, X., Yao, H., Liang, Z. and Li, X.*, 2022. An Empirical Study on Fuel Consumption of Commercial Automated Vehicles. Transportation Research Part D: Transport and Environment. https://doi.org/10.1016/j.trd.2022.103253
- Shi, X. and Li, X.*, 2021. Operations Design of Modular Vehicles on an Oversaturated Corridor with First-in-first-out Passenger Queueing. Transportation Science. https://doi.org/10.1287/trsc.2021.1074
- Shi, X. and Li, X.*, 2021. Constructing Fundamental Diagram for Traffic Flow with Automated Vehicles: Methodology and Demonstration. Transportation Research Part B: Methodological. https://doi.org/10.1016/j.trb.2021.06.011
- Shi, X. and Li, X.*, 2021. Empirical Study on Car Following Characteristics of Automated Vehicles with Different Headway Settings. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2021.103134
- Shi, X., Zhao, D., Yao, H. and Li, X.*, 2021. Video-based Trajectory Generation for High-Granularity Highway Simulation (HIGH-Sim). Communications in Transportation Research. https://doi.org/10.1016/j.commtr.2021.100014
- Wang, Z., Shi, X.*, Zhao, X. and Li, X., 2021. Modeling Decentralized Mandatory Lane Change for Connected and Autonomous Vehicles. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2021.103441
- Shi, X., Wang, Z., Li, X.* and Pei, M., 2021. The Effect of Ride Experience on Changing Opinions Toward Autonomous Vehicle Safety. Communications in Transportation Research. https://doi.org/10.1016/j.commtr.2021.100003
- Shi, X., Chen, Z., Pei, M. and Li, X.*, 2020. Variable-Capacity Operations with Modular Transits for Shared-Use Corridors. Transportation Research Record. https://doi.org/10.1177/0361198120928077
- Shi, X.*, Liu, H., Wang, M., Li, X., Ciuffo, B., Work, D., and Kan, D., 2023. Inconsistency of AV Impacts on Traffic Flow: Predictions in Literature. Road Vehicle Automation 10. https://doi.org/10.1007/978-3-031-34757-3_13

Community Involvement

- American Society of Civil Engineers (ASCE) AI in Transportation Committee
- American Society of Civil Engineers (ASCE) Connected & Autonomous Vehicles (CAV) Impacts Committee
- IEEE Emerging Transportation Technology Testing (ET3) Technical Committee
- UWM Institute of Transportation Engineers (ITE) Faculty Advisor
- Wisconsin Automated Vehicle External Advisory Committee