Journal Papers

  1. Li, T., Halatsis, A., & Stern, R. (2023). RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality. Under Review. Presented at NeurIPS 2023 workshop on Machine Learning and the Physical Sciences
  2. Li, T., Shang, M., Wang, S., & Stern, R. (2023). Understanding and detecting malicious cyberattacks on adaptive cruise control vehicles: A machine learning approach. Under Review
  3. Li, T., and Stern, R. (2023). Car-following-response based vehicle classification via deep learning. ACM Journal on Autonomous Transportation Systems
  4. Li, T., Klavins, J., Xu, T., Zafri, N., & Stern, R. (2023). Understanding driver-pedestrian interactions to predict driver yielding: field experiments in Minnesota. Under Review. Dataset available at here
  5. Xu, T., Barman, S., Levin, M.W., Chen, R., & Li, T. (2022). Integrating public transit signal priority into max-pressure signal control: Methodology and simulation study on a downtown network. Transportation Research Part C: Emerging Technologies
  6. Li, T., Qi, G.J., & Stern, R. (2022). Taxi Utilization Rate Maximization by Dynamic Demand Prediction: A Case Study in the City of Chicago. Transportation Research Record: Journal of the Transportation Research Board.

Conference Proceedings

  1. Kiani, A., Li, T., & Stern, R. (2023, December). [Modeling the evolution of traffic dynamics as an epidemic spread process]. In the 62nd IEEE Conference on Decision and Control (CDC 2023), Submitted
  2. Li, T., Rosenblad, B., Wang, S., Shang, M., & Stern, R. (2023). Exploring Energy Impacts of Cyberattacks on Adaptive Cruise Control Vehicles. The IEEE Intelligent Vehicles Symposium (IV 2023). doi: 10.1109/IV55152.2023.10186730.
  3. Li, T., Iogansen, X. & Stern, R. (2023). Assessing the Impact of Disruptive Events on Urban Mobility: A Case Study of Chicago Taxis during COVID-19. In Proceedings of Cyber-Physical Systems and Internet of Things Week 2023(DI-CPS), pp. 141-145, ACM
  4. Li, T., Shang, M., Wang, S., Filippelli, M. & Stern, R. (2022, October). Detecting Stealthy Cyberattacks on Automated Vehicles via Generative Adversarial Networks. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 3632-3637, IEEE
  5. Li, T. & Stern, R. (2022, May). Robustness of vehicle identification via trajectory dynamics to noisy measurements and malicious attacks. In 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), pp. 36-39, IEEE
  6. Li, T. & Stern, R. (2021, September). Classification of adaptive cruise control vehicle type based on car following trajectories. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 1547-1552, IEEE
  7. Li, T., Cullom, J., & Stern, R. (2021, May). Leveraging video data to better understand driver-pedestrian interactions in a smart city environment. In Proceedings of the Workshop on Data-Driven and Intelligent Cyber-Physical Systems (DI-CPS), pp. 6-11, ACM
  8. Li, T., Wu, X., Ban, X., & Wang, Y. (2020, August). “Centralized” Taxi Services in Big Metropolitan Areas: Evidenced by Chicago Data. In International Conference on Transportation and Development 2020 (ICTD), pp. 287-299, American Society of Civil Engineers (ASCE)
  9. Li, T., Zhou, L., Du, W., Sun, Z., & Zhang, N. (2017, August). The conceptual discussion of the long-distance public passenger transportation system.” In 2017 4th International Conference on Transportation Information and Safety. In 2017 4th International Conference on Transportation Information and Safety (ICTIS), pp. 306-311, IEEE