Scientific Research projects Projects: [1] 2023-2025, PI, On-line Calibration of simulation-based Traffic Assignment models using SeMI-supervised Deep learning with automatic vehicle identification data incorporation (OCTA-SMID), Marie Skłodowska-Curie Fellowship. [2] 2022-2024, PI, Personalized Cooperative Automated Platooning using Safe Reinforcement Learning (PCAP-SARL), Technical University of Munich Global Postdoc Fellowship. [3] 2021-2023, PI, Mechanism-Design-Based Dynamic Road Side Unit Location Optimization, National Science Foundation of China. [4] 2019-2021, PI, Online Optimization of Edge Computation Resource Allocation, Shanghai Super Postdoctoral Research Project. [5] 2018-now, Participant, Freeway Bottleneck Control in Connected Autonomous Vehicle Environment, National Key Research and Development Program of China. [6] 2018-now, Participant, Strategy and Field Experiment for Mixed Traffic Flow Control, National Science Foundation of China.
Teaching Research projects Not applicable yet.
Journal Papers: [1] Xu, L., Wu, Z., Wang, Y., Tang, J., & Liang, Y.* (2024). Compressing Vehicle Trajectory Data Using Hybrid Coding With Kinematic Motion Prediction. IEEE Transactions on Intelligent Transportation Systems. [2] Liang, Y., Wu, Z., Yang, H., & Wang, Y. (2022). A Novel Framework for Road Side Unit Location Optimization for Origin-Destination Demand Estimation. IEEE Transactions on Intelligent Transportation Systems, 23(11), 21113-21126. [3] Liang, Y., Zhang, S., & Wang, Y. (2021). Data-driven road side unit location optimization for connected-autonomous-vehicle-based intersection control. Transportation Research Part C: Emerging Technologies, 128, 103169. [4] Liang, Y., Ma, N., Li, X., & Hu, J. (2021). Stochastic roadside unit location optimization for information propagation in the Internet of Vehicles. IEEE Internet of Things Journal, 8(17), 13316-13327. [5] Liang, Y., Wu, Z., & Hu, J. (2020). Road side unit location optimization for optimum link flow determination. Computer‐Aided Civil and Infrastructure Engineering, 35(1), 61-79. [6] Qin, W., Zhang, M., Li, W., & Liang, Y.* (2023). Spatiotemporal K-Nearest Neighbors Algorithm and Bayesian Approach for Estimating Urban Link Travel Time Distribution From Sparse GPS Trajectories. IEEE Intelligent Transportation Systems Magazine. [7] Chen, X., Wu, Z., & Liang, Y.* (2023). Modeling Mixed Traffic Flow with Connected Autonomous Vehicles and Human-Driven Vehicles in Off-Ramp Diverging Areas. Sustainability, 15(7), 5651. [8] Yang, W., Wu, Z., Tang, J., & Liang, Y.* (2023). Assessing the Effects of Modalities of Takeover Request, Lead Time of Takeover Request, and Traffic Conditions on Takeover Performance in Conditionally Automated Driving. Sustainability, 15(9), 7270. [9] Liang, Y., Tang, J., Wu, Z., & Jia, M. (2023). Influence of Psychological and Socioeconomic Factors on Purchase Likelihood for Autonomous Vehicles: A Hybrid Choice Modeling Approach. Sustainability, 15(21), 15452. [10] Liang, Y., Wu, Z., Li, J., Li, F., & Wang, Y. (2018). Shockwave-based queue length estimation method for pre-signals for bus priority. Journal of Transportation Engineering, Part A: Systems, 144(9), 04018057. [11] Liang, Y., Cui, Z., Tian, Y., Chen, H., & Wang, Y. (2018). A deep generative adversarial architecture for network-wide spatial-temporal traffic-state estimation. Transportation Research Record, 2672(45), 87-105. [12] Liang, Y., Wu, Z., Tian, Y., & Chen, H. (2018). Roadside unit location for information propagation promotion on two parallel roadways with a general headway distribution. IET Intelligent Transport Systems, 12(10), 1442-1454.
Conference Papers: [1] Liang, Y., Wu, Z., & Wang, Y. (2021). Actor-Critic Reinforcement Learning for Eco Cooperative Adaptive Cruise Control. The 101th TRB Annual Meeting [2] Chen, X., Wu, Z., & Liang, Y.* (2022). Modeling and Simulation Analysis of Mixed Traffic Flow in Off-ramp Diverging Areas. The 102th TRB Annual Meeting [3] Wang, D., Wu, Z., Liang, Y.*, Ma, G., & Gao, Z. (2022). Joint Optimization of Traffic Signal Timing and Speed Profile of Connected Autonomous Vehicles at Signalized Intersections. The 102th TRB Annual Meeting [4] Xu, L., Wu, Z., & Liang, Y.* (2022). Compression of Trajectory Data of Connected Autonomous Vehicles Using a Hybrid Coding Algorithm. The 102th TRB Annual Meeting [5] Li, C., Wu, Z., & Liang, Y.* (2023). Federated Multiagent Reinforcement Learning for On-Ramp Merging Control. The 103th TRB Annual Meeting [6] Xu, L., Wu, Z., & Liang, Y*. (2023). Diagnosis of Broken and Worn Traffic Markings Using a Hybrid Neural Network. The 103th TRB Annual Meeting [7] Ren, Y., Wang, Y., Wu, Z., & Liang, Y*. (2023). Few-shot Accurate Recogntion of Vehicle Lane-changing Intention in Connected Vehicle Environment: A Meta-Transformer Approach. The 103th TRB Annual Meeting [8] Liang, Y., Cui, Z., Tian, Y., Chen, H., & Wang, Y. (2017). A Deep Generative Adversarial Architecture for Spatial-Temporal Traffic State Estimation. Proceedings of the 97th TRB Annual Meeting [9] Wu, Z., Liang, Y.*, Lv, H., & Liu, J. (2016). Curved Roads Obstacle Avoidance Control Strategy for Autonomous Vehicles. Proceedings of the 96th TRB Annual Meeting [10] Wu, Z., Liang, Y.*, Fan, W., & Tian, Y. (2016). Comparison of Driving Performance under Different Positions of Smartphone Used for Navigation. Proceedings of the 96th TRB Annual Meeting
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