Dr Jie Sun is currently a Tenured Associate Professor with Tongji University. He had been working as a Postdoctoral Research Fellow at The University of Queensland and The Hong Kong University of Science and Technology from 2019 to 2023. His research interests include traffic flow theory, traffic flow modelling and simulation, connected and automated vehicles, AI-based/data-driven modelling. He has co-authored 30+ papers in peer-reviewed journals and conferences, including 12 in Transportation Research Part B/C, IEEE T-ITS (the most prestigious journals in Transportation Engineering). He has been awarded the Excellent Young Scientists Fund (Overseas) of National Natural Science Foundation of China and Shanghai Outstanding Academic Leaders Plan (Overseas). He is also serving as a member of Artificial Intelligence Empowered Traffic Management and Service Technical Committee of WTC.
Education
Mar.2014-Jul.2019, Dr. Eng. Degree, Transportation Engineering, Tongji University, China
Oct.2016-Oct.2018, Visiting Ph.D Scholar, The University of Queensland, Australia
Sep.2012-Mar.2014, Master Candidate, Traffic Engineering, Tongji University, China
Sep.2008-Jul.2012, B.Eng. Degree, Computer Science & Technology (Traffic Information Engineering), Chang’an University, China
Experience
Feb.2024-present, Tenured Associate Professor, Tongji University, China.
Feb.2023-Dec.2023, Research Associate, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
Aug.2019-Jan.2023, Postdoctoral Research Fellow, The University of Queensland, Australia
Research interests
Traffic flow theory
Traffic flow modelling and simulation
Connected and automated vehicles
AI-based/data-driven modelling
Honors & Awards
Excellent Young Scientists Fund (Overseas) of National Natural Science Foundation of China, 2023
Shanghai Outstanding Academic Leaders Plan (Overseas), 2022
Outstanding Doctoral Dissertation Award, Tongji University, 2021
COTA Best Dissertation Award (second place), 2020
Outstanding PhD graduate, Tongji University, 2019
Best Paper Award (second place), China Journal of Highway and Transport, 2018
Selected publications
1.Jie Sun, Hai Yang, 2024. Learning two-dimensional merging behaviour from vehicle trajectories with imitation learning.Transportation Research Part C:Emerging Technologies160, 104530.
2.Xiaohui Zhang,Jie Sun*, Zuduo Zheng, Jian Sun*, 2024. On the string stability of neural network-based car-following models: A generic analysis framework.Transportation Research Part C:Emerging Technologies160, 104525.
3.Jie Sun, Jiwon Kim, 2023. Towards data-driven simulation of network-wide traffic: A multi-agent imitation learning approach using urban vehicle trajectory data.IEEE Transactions on Intelligent Transportation Systems.
4.Jie Sun, Zuduo Zheng, Anshuman Sharma, Jian Sun, 2023. Stability and extension of a car-following model for human-driven connected vehicles.Transportation Research Part C:Emerging Technologies155, 104317.
5.Jie Sun, Zuduo Zheng, Jian Sun, 2023. Stability evolution of car-following models considering asymmetric driving behaviour.Transportation Research Record, 2677(8), 361–371.
6.Jie Sun, Jiwon Kim, 2021. Joint prediction of next location and travel time from urban vehicle trajectories using long short-term memory neural networks.Transportation Research Part C: Emerging Technologies128, 103114.
7.Jie Sun, Zuduo Zheng, Jian Sun, 2020. The relationship between car following instability and traffic oscillations in finite-sized platoons and its use in easing traffic congestion with connected and automated vehicles.Transportation Research Part B: Methodological142, 58-83.
8.Jie Sun, Zuduo Zheng, Jian Sun, 2018. Stability analysis methods and their applicability to car-following models in conventional and connected environments.Transportation Research Part B: Methodological109, 212-237.
9.Jie Sun, Jian Sun, 2016. Real-time crash prediction on urban expressways: identification of key variables and a hybrid support vector machine model.IET Intelligent Transport Systems10(5), 331-337.
10.Jie Sun, Jian Sun, 2015. A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data.Transportation Research Part C: Emerging Technologies54, 176-186.
11.Jie Sun, Zhipeng Li, Jian Sun, 2015. Study on traffic characteristics for a typical expressway on-ramp bottleneck considering various merging behaviors.Physica A: Statistical Mechanics and its Applications440, 57-67.
12.Jian Sun,Jie Sun, Peng Chen, 2014. Use of support vector machine models for real-time crash risk prediction on urban expressways.Transportation Research Record2432, 91-98.