Research

The Lab's research is multi-disciplinary involving traffic flow theory, control theory, and Artificial Intelligence (AI). The research mainly focused on advancing knowledge of mixed traffic flow theory with different levels of vehicular connectivity and automation, vehicle and transportation control methods, and AI applications on the intelligent transportation system. He has published more than 50 peer-reviewed journal papers such as TR-Part B, TR-Part C, T-ITS and CACAIE. For more information, please find https://scholar.google.com/citations?user=0xCRTw0AAAAJ&hl=en

Connected Automated Vehicles/Transportation System Control

1. Zhou, Y., Zhong, X., Chen, Q., Ahn, S.*, Jiang, J., & Jafarsalehi, G. (2023). Data-driven analysis for disturbance amplification in car-following behavior of automated vehicles. Transportation Research Part B: Methodological, 174, 102768.

2. Zhou, Y., Ahn, S.*, Wang, M., & Hoogendoorn, S. (2020). Stabilizing mixed vehicular platoons with connected automated vehicles: An H-infinity approach. Transportation Research Part B: Methodological, 132, 152-170.

3. Zhou, Y., Wang, M., & Ahn, S.* (2019). Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability. Transportation Research Part B: Methodological, 128, 69-86.

4. Zhou, Y., & Ahn, S.* (2019). Robust local and string stability for a decentralized car following control strategy for connected automated vehicles. Transportation Research Part B: Methodological, 125, 175-196.

5. Zhou, Y., Ahn, S.*, Chitturi, M., & Noyce, D. A. (2017). Rolling horizon stochastic optimal control strategy for ACC and CACC under uncertainty. Transportation Research Part C: Emerging Technologies, 83, 61-76.

6. Chen, T., Wang, M., Gong, S., Zhou, Y.*& Ran, B. (2021). Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach. Transportation Research Part C: Emerging Technologies, 133, 103451. https://doi.org/10.1016/j.trc.2021.103451.

7. Shi, H., Zhou, Y*., Wu, K., Chen, S., & Ran, B. (2023). A physics‐informed deep reinforcement learning based integrated two-dimensional car-following control strategy for connected automated vehicles, Knowledge-Based Systems, 110485, DOI: https://doi.org/10.1016/j.knosys.2023.110485.

Microscopic Traffic Flow Modeling

1. Zhou, Y., Lin, Y., Ahn, S., Wang, P.,& Wang, X.* (2021). Trajectory Completion in a Mixed Traffic Environment under Stochastic and Complete Communication Loss. IEEE Transactions on Intelligent Transportation Systems. doi: 10.1109/TITS.2022.3148976.

2. Shi, H., Chen, D., Zheng, N., Wang, X., Zhou, Y*. & Ran, B. Distributed Connected Automated Vehicles Control under Real-time Aggregated Macroscopic Car-following Behavior Estimation, (Accepted), Transportation Research Part C: Emerging Technologies.

3. Shi, K., Wu, Y., Shi, H., Zhou, Y. *, & Ran, B. (2022). An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network. Physica A: Statistical Mechanics and its Applications, 599, 127303.

4. Zhou, Y., Zhong, X., Chen, Q., Ahn, S.*, Jiang, J., & Jafarsalehi, G. (2023). Data-driven analysis for disturbance amplification in car-following behavior of automated vehicles. Transportation Research Part B: Methodological, 174, 102768.

5. Chen, T., Gong, S., Wang, M., Wang, X., Zhou, Y.* & Ran, B. (2023). Stochastic capacity analysis for a distributed connected automated vehicle virtual car-following control strategy. Transportation Research Part C: Emerging Technologies, 152, 104176.

6. Kontar, W., Li, T., Srivastava, A., Zhou, Y., Chen, D., & Ahn, S. (2021). On multi-class automated vehicles: Car-following behavior and its implications for traffic dynamics. Transportation Research Part C: Emerging Technologies, 128, 103166.

7. Zhong, X., Zhou, Y., Ahn, S., & Chen, D. (2023). Understanding Heterogeneity of Automated Vehicles and Its Traffic-level Impact: A Stochastic Behavioral Perspective. arXiv preprint arXiv:2401.00355

Intelligent Transportation System

1. Wang, X., Zhou, Y.*, Mackenzie, D. & Ding, F. (2022). Predicted Network Equilibrium Model of Electric Vehicles with Stationary and Dynamic Charging Infrastructure on the Road Network, IEEE Intelligent Transportation Systems Magazine, DOI: 10.1109/MITS.2020.3014145.

2. Lin, Y., Wang, P., Zhou, Y.*, Ding, F., Wang, C. & H. Tan. (2020), Platoon trajectories generation: A unidirectional interconnected LSTM-based car-following model, IEEE Transactions on Intelligent Transportation Systems, 1-11, 3031282. doi: 10.1109/TITS.2020.3031282

3. Shi, H., Nie, Q., Fu, S., Wang, X., Zhou, Y.*, & Ran, B. (2021). A distributed deep reinforcement learning–based integrated dynamic bus control system in a connected environment. Computer‐Aided Civil and Infrastructure Engineering,1-17,12803. https://doi.org/10.1111/mice.12803

High-fidelity Traffic Safety Analysis

Li, S., Mohammd, A., Zhang, H., Zhou, Y.*, Lord, D.,& Ye, X., Beyond 1D and Oversimplified Kinematics: A Generic Analytical Framework for Surrogate Safety Measures,

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