You can find a complete list of my papers on my Google Scholar.

Publications

2025

• Gan, R., Shi, H., Li, P., Wu, K., An, B., You, J., Li, L., Ma, J., Ma, C. and Ran, B., 2025. “Goal-based neural physics vehicle trajectory prediction model”. Transportation Research Part C: Emerging Technologies, 179, p.105283.
• Long, K., Ma, C., Li, H., Li, Z., Huang, H., Shi, H., Huang, Z., Sheng, Z., Shi, L., Li, P., and Chen, S. (2025). “AI-Enabled Digital Twin Framework for Safe and Sustainable Intelligent Transportation”. Sustainability, 17(10), p.4391.
• Huang, Z., Sheng, Z., Wan, Z., Qu, Y., Luo, Y., Wang, B., Li, P., Chen, Y.J., Chen, J., Long, K., and Meng, J. (2025). “Sky-Drive: A Distributed Multi-Agent Simulation Platform for Human-AI Collaborative and Socially-Aware Future Transportation”. arXiv preprint.
• Gan, R., Li, P., Long, K., An, B., You, J., Wu, K., and Ran, B. (2025). “Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language Models”. arXiv preprint.
• Wu, K., Li, P., Zhou, Y., Gan, R., You, J., Cheng, Y., … & Tu, Z. (2025). “V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors”. arXiv preprint.
• Wu, K., Li, P., Cheng, Y., Parker, S. T., Ran, B., Noyce, D. A., & Ye, X. (2025). “A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors”. IEEE Transactions on Intelligent Transportation Systems.

2024

• Tamaru, R., Li, P., & Ran, B. (2024). “Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information”. arXiv preprint.
• Wan, H., Li, P., & Kusari, A. (2024). “Demystifying deep reinforcement learning-based autonomous vehicle decision-making”. arXiv preprint.
• 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”. arXiv preprint.
• 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”. SSRN.
• 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.

2023

• 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.

2022

• 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.

2021

• 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.

2020

• 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 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.