QiangQiang Shangguan

Visiting PhD student
Department of Civil & Environmental Engineering
Faculty of Engineering
University of Waterloo
Office: E2 2352C
E-mail: qiangqiang.shangguan@uwaterloo.ca

Past Research Activities

Qiangqiang Shangguan is a PhD candidate in transportation engineering at Tongji University, supervised by Prof. Shou’en Fang from 2017. He is currently a visiting PhD student at University of Waterloo under the supervision of Prof. Liping Fu. His research interests include traffic safety, driving simulation, and driving behavior.

Research Interests

Traffic safety, Driving behavior,
Naturalistic driving study,
Driving simulation

Links

Google Scholar: https://scholar.google.com/citations?user=DxicbPMAAAAJ

Journal Publications:

[1] Shangguan, Q., Fu, T.*, Wang, J., Fang, S. & Fu, L. A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns[J]. Accident Analysis & Prevention, 2022, 164: 106500. (SSCI, Q1, IF=4.993)
[2] Shangguan, Q., Wang, J., Fu, T.*, & Fang, S. E. Quantification of cut-in risk and analysis of its influencing factors: a study using random parameters ordered probit model[J]. Journal of Transportation Safety & Security, 2021, 2021: 1-26. (SSCI, Q3, IF=3.0)
[3] Shangguan, Q., Fu, T., Wang J.*, Luo, T. & Fang, S. An Integrated Methodology for Real-time Driving Risk Status Prediction using Naturalistic Driving Data[J]. Accident Analysis & Prevention, 2021, 156: 106122. (SSCI, Q1, IF=4.993)
[4] Shangguan, Q., Fu, T.*, Wang J., Jiang R. & Fang, S. Quantification of rear-end crash risk and analysis of its influencing factors based on a new surrogate safety measure[J]. Journal of Advanced Transportation, 2021, 2021: 5551273. (SCI, Q3, IF=2.419)
[5] Shangguan, Q., Fu, T.*, & Liu, S. Investigating Rear-end Collision Avoidance Behavior under Varied Foggy Weather Conditions: A Study using Advanced Driving Simulator and Survival Analysis[J]. Accident Analysis & Prevention, 2020, 139: 105499. (SSCI, Q1, IF=4.993)
[6] Fu T, Yu X, Xiong B, Jiang C.*, Wang, J.*, Shangguan, Q., & Xu, W. A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2022, 88: 37-53. (SSCI, Q2, IF=3.261)
[7] Wang, J., Xu, W., Fu, T.*, Gong, H., Shangguan, Q., & Anae, S. Modeling aggressive driving behavior based on graph construction[J]. Transportation Research Part C: Emerging Technologies, 2022, 138: 103654. (SCI, Q1, IF=8.795)
[8] Yu, G., Liu, S.*, & Shangguan, Q. Optimization and Evaluation of Platooning Car-Following Models in a Connected Vehicle Environment[J]. Sustainability, 2021, 13(6): 3474. (SCI, Q2, IF=3.251)
[9] Wang, J., Fu, T.*, Xue, J., Li, C., Song, H., Xu, W., & Shangguan, Q. Realtime Wide-area Vehicle Trajectory Tracking using Millimeter-wave Radar Sensors and the Open TJRD TS Dataset[J]. International Journal of Transportation Science and Technology, 2022.
[10] Zhu, W., Wu, J., Fu, T.*, Wang, J., Zhang, J., & Shangguan, Q. Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP[J]. Journal of Intelligent and Connected Vehicles, 2021.
[11] Wang, J., Song, H., Fu, T.*, Behan, M., Jie, L., He, Y., & Shangguan, Q. Crash prediction for freeway work zones in real time: A comparison between Convolutional Neural Network and Binary Logistic Regression model[J]. International Journal of Transportation Science and Technology, 2021.

Conference papers:

[1] Shangguan, Q., Fu, T., Wang, J.*, Fang, S., Fu, L. & Sobhani A. A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns[C]// the 101st Transportation Research Board Annual Meeting, January 9, 2022 – January 13, 2022, Washington, DC.
[2] Shangguan, Q., Fu, T.*, Jiang R. & Fang, S. Use of Naturalistic Driving Data to Quantify Influencing Factors of Driving Risk based on a New Surrogate Measure of Safety[C]// the 100th Transportation Research Board Annual Meeting, January 21, 2021 – January 25, 2021, Washington, DC.
[3] Shangguan, Q., Fu, T.*, & Liu, S. Investigating Rear-end Collision Avoidance Behavior and Safety on Freeways under Varied Foggy Weather Conditions: A Study using Advanced Driving Simulator[C]// the 99th Transportation Research Board Annual Meeting, January 12, 2020 – January 16, 2020, Washington, DC.
[4] Shangguan, Q., Wang X., Liu, S.*, & Wang, J. Car Following Behavior under Foggy Conditions with Different Road Alignments — A Driving Simulator Based Study[C]// the 5th International Conference on Transportation Information and Safety, July 14, 2019 – July 17, 2019, Liverpool, UK: 127–135. (EI)
[5] Luo, T., Fu, T., Wang, J.*, Shangguan, Q., & Fang, S. Risk Prediction of Cut-ins using Multi-driver Simulation Data and Machine Learning: A Comparison among Decision Tree, GBDT and LSTM[C]// the 101st Transportation Research Board Annual Meeting, January 9, 2022 – January 13, 2022, Washington, DC.