教育经历

2021.12 – 2022.11 加拿大滑铁卢大学工程学院联合培养博士

2017.09 – 2022.11 tyc8722太阳集团城博士

2013.09 – 2017.07 长安大学公路学院学士

工作经历

2024.07至今 tyc8722太阳集团城交通学院特聘研究员

2022.12 – 2024.04 加拿大滑铁卢大学工程学院博士后

科研项目

1.Natural Sciences and Engineering Research Council of Canada (NSERC), Advancing “Vision Zero” road safety with operational AI and smart city data, 2021-2023,参与

2.Transport Canada Research Project, Development of GradeX Cost-Benefit Analysis (CBA) Module for Evaluating Grade Crossing Safety Improvement Projects, 2023-2024,参与

3.City of Toronto Research Project, A pilot project for continuous video-based traffic analysis and safety monitoring, 2021-2022,参与

4.国家重点研发计划项目，港珠澳大桥智能化运维技术集成应用，2020-2023，参与

5.国家自然科学基金面上项目，跟驰和换道中危险驾驶行为的病理诊断、致因和转归机理，2022-2025，参与

6.国家自然科学基金面上项目，高速公路容错性车流状态识别与管理控制方法研究，2019-2022，参与

获奖情况

1.2023, TRB Best Young Researcher Paper Award, Transportation Research Board (ACS10)

2.2023,第二届数字交通与智慧出行学术论坛优秀论文一等奖，广西道路运输协会

3.2022, CARSP Best Paper Award (2^ndPlace), Canadian Association of Road Safety Professionals

4.2022,上海市普通高等学校优秀毕业生，上海市教育委员会

代表性学术论文

1.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).

2.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)

3.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)

4.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).

5.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).

6.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).

7.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).

8.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).

9.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)

10.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)

学术专著

王俊骅；方守恩；傅挺；徐文翔；上官强强；《危险驾驶行为机理解析》，tyc8722太阳集团城出版社，2023，ISBN 978-7-5765-0047-9

发明专利

1.宋昊；王俊骅；上官强强；一种基于交通数字孪生的交通运行风险平行仿真系统，2024，ZL202210450623.3

2.宋昊；王俊骅；上官强强；一种基于交互式自解释模型的高速公路事故风险优化方法，2024，ZL202310903318.X