Dr. Qiangqiang Shangguan is a Research Professor with a Ph.D. from a joint program between Tongji University and the University of Waterloo, Canada. He also completed a postdoctoral fellowship at the University of Waterloo. Dr. Shangguan was selected for the 2024 Shanghai Magnolia Talent Program (Youth Project).
His research focuses on road safety design, analysis of risky driving behavior mechanisms, and proactive prevention and control of road operation risks. Over the past five years, he has published more than 30 papers in road traffic safety journals and conferences, including 10 SCI-indexed journal papers as the first or corresponding author, with 5 in top-tier journals (Q1). He co-edited the academic monographAnalysis of Risky Driving Behavior Mechanismsand holds two national invention patents. Dr. Shangguan was awarded the Best Young Researcher Paper Award by the Transportation Research Board (TRB) in 2023 and second place for Best Paper by the Canadian Association of Road Safety Professionals (CARSP) in 2022. He has contributed to over ten research projects, including those funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), Transport Canada, the City of Toronto’s Vision Zero initiative, the National Key Research and Development Program of China, and the National Natural Science Foundation of China (NSFC). Dr. Shangguan also serves as a Young Editorial Board Member for journals such as the Journal of Traffic and Transportation Engineering (ESCI, IF: 7.4), Digital Transportation and Safety, and the Journal of Chang'an University (Natural Science Edition). In addition, he reviews papers for over ten journals and conferences, including Analytic Methods in Accident Research, Transportation Research Part C: Emerging Technologies, and Accident Analysis & Prevention.
Education
Dec. 2021 – Nov. 2022, Visiting Ph.D., iTSS Lab, Department of Civil and Environmental Engineering, University of Waterloo, Ontario, Canada.
Sep. 2017 – Nov. 2022, Ph.D., Transportation Engineering (Road Safety and Environmental Engineering), College of Transportation Engineering, Tongji University, Shanghai, China
Sep. 2013 – Jul. 2017, Bachelor, Traffic Engineering (Road and Airport Engineering), School of Highway, Chang’an University, Xi’an, China.
Experience
Jul. 2024 - Present, Research Professor, TJRD Lab, College of Transportation Engineering, Tongji University, Shanghai, China.
Dec. 2022 – Apr. 2024, Postdoctoral Fellow, iTSS Lab, Department of Civil and Environmental Engineering, University of Waterloo, Ontario, Canada.
Research interests
Road Safety
Road Geometric Design
Risky Driving Behavior
Traffic Conflict Techniques
Driving Simulation
Machine Learning Algorithms
Naturalistic Driving Study
Honors & Awards
2023 TRB Best Young Researcher Paper Award, Transportation Research Board (ACS10), 2023
Best Paper Award (First Prize), Academic Forum on Digital Transportation and Smart Mobility, 2023
Provincial-level Outstanding Graduates, Shanghai Municipal Education Commission, 2022
2022 CARSP Best Paper Award, Canadian Association of Road Safety Professionals, 2022
China Scholarship Council (CSC: 202106260118), 2021
University-level Outstanding Student, Tongji University, 2019
Publications
Shangguan, Q., Wang, Y., & Fu, L. (2024). Quantifying the effectiveness of an active treatment in improving highway-railway grade crossing safety in Canada: an empirical Bayes observational before–after study.Canadian Journal of Civil Engineering, 2024. (SCI).
Shangguan, Q., Wang J.*, Fu, T.*, Fang, S., & Fu, L. (2023). An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances.Analytic Methods in Accident Research, 100265. (SSCI, Q1, IF=12.5)
Shangguan, Q., Keung J., Fu, L.*, Samara, L., Wang J., & Fu, T. (2023). Do Traffic Countermeasures Improve the Safety of Vulnerable Road Users at Signalized Intersections? A Combination of Case-Control and Cross-Sectional Studies Using Video-Based Traffic Conflicts.Transportation Research Record, 03611981231172748. (SCI)
Shangguan, Q., Fu, T.*, Wang, J., Fang, S., & Fu, L. (2022). A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns.Accident Analysis & Prevention, 164, 106500. (SSCI, Q1, IF=5.7).
Shangguan, Q., Fu, T., Wang, J.*, Luo, T., & Fang, S. (2021). An integrated methodology for real-time driving risk status prediction using naturalistic driving data.Accident Analysis & Prevention, 156, 106122. (SSCI, Q1, IF=5.7).
Shangguan, Q., Wang, J., Fu, T.*, & Fang, S. (2021). Quantification of cut-in risk and analysis of its influencing factors: a study using random parameters ordered probit model.Journal of Transportation Safety & Security, 1-26. (SSCI).
Shangguan, Q., Fu, T.*, Wang, J., Jiang, R., & Fang, S. (2021). Quantification of rear-end crash risk and analysis of its influencing factors based on a new surrogate safety measure.Journal of Advanced Transportation, 2021, 5551273. (SCI).
Shangguan, Q., Fu, T.*, & Liu, S. (2020). Investigating Rear-end Collision Avoidance Behavior under Varied Foggy Weather Conditions: A Study using Advanced Driving Simulator and Survival Analysis.Accident Analysis & Prevention, 139, 105499. (SSCI, Q1, IF=5.7).
Wang J., Fu, T.*, &Shangguan, Q.*. (2023). Wide-area Vehicle Trajectory Data based on Advanced Tracking and Trajectory Splicing Technologies: Potentials in Transportation Research.Accident Analysis & Prevention, 186, 107044. (SSCI, Q1, IF=5.7)
Lei, C., Ji, Y.*,Shangguan, Q.*, Du, Y., & Samuel, S. (2024). Vehicle group identification and evolutionary analysis using vehicle trajectory data.Physica A: Statistical Mechanics and its Applications, 639, 129656. (SCI)