Pei Li, Ph.D.

Civil and Architectural Engineering and Construction Management

Assistant Professor

Contact Information

pei.li@uwyo.eduDr. Li's Personal Research page
Dr. Pei Li, wearing a light blue button-up shirt and dark blue jacket, stands outside on a sunny day. Blurry trees and part of a building are in the background.

Education

  • B.Eng, Logistics Engineering, Tongji University, 2015
  • M.Eng, Communication and Transportation Engineering, Tongji University, 2018
  • M.S., Smart Cities, University of Central Florida, 2020
  • Ph.D., Civil Engineering, University of Central Florida, 2021

 

Research Interests

Digital Twins, Artificial Intelligence, Transportation Safety, and Human Factors

 

Publications

  • 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.
  • Li, P., Chen, S., Yue, L., Xu, Y., & 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, 107605.
  • Liu, C., Sheng, Z., Li, P., Chen, S., Luo, X., & Ran, B. (2024). A distributed deep reinforcement learning-based longitudinal control strategy for connected automated vehicles combining attention mechanism. Transportation Letters, 1-17.
  • Li, P., Wu, K., Cheng, Y., Parker, S. T., & 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., & 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, 104358.
  • Li, P., Guo, H., Bao, S., & 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., & Li, P. (2023). Real-Time Big Data Analytics and Proactive Traffic Safety Management Visualization System. Journal of Transportation Engineering, Part A: Systems, 149(8), 04023064.
  • Li, P., & Abdel-Aty, M. (2022). A hybrid machine learning model for predicting real-time secondary crash likelihood. Accident Analysis & Prevention, 165, 106504.
  • 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(7), 04022043.
  • 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: Journal of the Transportation Research Board, 2676(11), 621–631.
  • Li, P., Abdel-Aty, M., & Islam, Z. (2021). Driving Maneuvers Detection using Semi-Supervised Long Short-Term Memory and Smartphone Sensors. Transportation Research Record: Journal of the Transportation Research Board, 2675(9), 1386–1397.
  • Li, P., Abdel-Aty, M., & Yuan, J. (2021). Using bus critical driving events as surrogate safety measures for pedestrian and bicycle crashes based on GPS trajectory data. Accident Analysis & Prevention, 150, 105924.
  • Li, P., Abdel-Aty, M., Cai, Q., & Islam, Z. (2020). A deep learning approach to detect real-time vehicle maneuvers based on smartphone sensors. IEEE Transactions on Intelligent Transportation Systems, 23(4), 3148–3157.
  • Li, P., Abdel-Aty, M., Cai, Q., & Yuan, C. (2020). The application of novel connected vehicles emulated data on real-time  crash potential prediction for arterials. Accident Analysis & Prevention, 144.
  • Li, P., Abdel-Aty, M., & Yuan, J. (2020). Real-time crash risk prediction on arterials based on LSTM-CNN. Accident Analysis & Prevention, 135, 105371.
  • Zhang, S., Abdel-Aty, M., Cai, Q., Li, P., & Ugan, J. (2020). Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network. Accident Analysis & Prevention, 148, 105799.
  • Zhang, S., Abdel-Aty, M., Yuan, J., & Li, P. (2020). Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network. Transportation Research Record: Journal of the Transportation Research Board, 2674(4), 57–65.