Preprints

• Tamaru, R., Li, P., & Ran, B. (2024). Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information.
• Gan, R., Shi, H., Li, P., Wu, K., An, B., Li, L., … & Ran, B. (2024). Goal-based Neural Physics Vehicle Trajectory Prediction Model.
• Wan, H., Li, P., & Kusari, A. (2024). Demystifying deep reinforcement learning-based autonomous vehicle decision-making.
• Wu, K., Li, P., Cheng, Y., Parker, S. T., Ran, B., Noyce, D. A., & Ye, X. (2024). A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors.
• You, J., Li, P., Cheng, Y., Wu, K., Gan, R., Parker, S. T., & Ran, B. (2024). Real-World Data Inspired Interactive Connected Traffic Scenario Generation.
• Ma, C., Li, H., Long, K., Zhou, H., Liang, Z., Li, P., Yu, H., Li, X. (2024). Real-Time Identification of Cooperative Perception Necessity in Road Traffic Scenarios.

Journal Papers

• Yin, H., Yue, L., Gong, Y., Li, P., & Huang, Y. (2024). Personalized lane departure warning based on non-stationary crossformer and kernel density estimation. Alexandria Engineering Journal, 109, 856-870.
• Li, P., Chen, S., Yue, L., Xu, Y. and Noyce, D.A., 2024. “Analyzing relationships between latent topics in autonomous vehicle crash narratives and crash severity using natural language processing techniques and explainable XGBoost”. Accident Analysis & Prevention, 203, p.107605.
• Liu, C., Sheng, Z., Li, P., Chen, S., Luo, X. and Ran, B., 2024. “A distributed deep reinforcement learning-based longitudinal control strategy for connected automated vehicles combining attention mechanism”. Transportation Letters, pp.1-17.
• Li, P., Wu, K., Cheng, Y., Parker, S. and Noyce, D.A., 2023. “How Does C-V2X Perform in Urban Environments? Results From Real-World Experiments on Urban Arterials”. IEEE Transactions on Intelligent Vehicles.
• Dong, J., Chen, S., Miralinaghi, M., Chen, T., Li, P. and Labi, S., 2023. “Why did the AI make that decision? Towards an explainable artificial intelligence (XAI) for autonomous driving systems”. Transportation research part C: emerging technologies, 156, p.104358.
• Li, P., Guo, H., Bao, S. and Kusari, A., 2023. “A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections”. IEEE Transactions on Intelligent Transportation Systems.
• Abdel-Aty, M., Zheng, O., Wu, Y., Abdelraouf, A., Rim, H. and Li, P., 2023. “Real-Time Big Data Analytics and Proactive Traffic Safety Management Visualization System”. Journal of Transportation Engineering, Part A: Systems, 149(8), p.04023064.
• Li, P., Abdel-Aty. M, 2022. “A Hybrid Machine Learning Model for Predicting Real-time Secondary Crash Likelihood”. Accident Analysis and Prevention, 165.
• Li, P., Abdel-Aty. M, 2022. “Real-Time Crash Likelihood Prediction Using Temporal Attention–Based Deep Learning and Trajectory Fusion”. Journal of Transportation Engineering, Part A: Systems, 148.
• Li, P., Abdel-Aty. M, Zhang, S., 2022. “Improving Spatiotemporal Transferability of Real-Time Crash Likelihood Prediction Models Using Transfer-Learning Approaches”. Transportation Research Record.
• Li, P., Abdel-Aty, M. and Islam, Z., 2021. “Driving Maneuvers Detection using Semi-Supervised Long Short-Term Memory and Smartphone Sensors”. Transportation Research Record, p.03611981211007483.
• Li, P., Abdel-Aty, M. and Yuan, J., 2020. “Using bus critical driving events as surrogate safety measures for pedestrian and bicycle crashes based on GPS trajectory data”. Accident Analysis & Prevention, 150.
• Li, P., Abdel-Aty. M, Cai, Q. and Islam, Z., 2020. “A Deep Learning Approach to Detect Real-Time Vehicle Maneuvers Based on Smartphone Sensors”. IEEE Transactions on Intelligent Transportation Systems.
• Zhang, S., Abdel-Aty, M., Cai, Q., Li, P. and Ugan, J., 2020. “Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network”. Accident Analysis & Prevention, 148.
• Li, P., Abdel-Aty, M., Cai, Q. and Yuan, C., 2020. “The application of novel connected vehicles emulated data on real-time crash potential prediction for arterials”. Accident Analysis & Prevention, 144.
• Zhang, S., Abdel-Aty, M., Yuan, J. and and Li, P., 2020. “Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network”. Transportation Research Record, p.0361198120912422.
• Li, P., Abdel-Aty, M. and Yuan, J., 2020. “Real-time crash risk prediction on arterials based on LSTM-CNN”. Accident Analysis & Prevention, 135, p.105371.