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Serviceability evaluation of highway tunnels based on data mining and machine learning: A case study of continental United States

Published in Tunneling and Underground Space Technology, 2023

This paper presents a data mining and machine learning approach for evaluating the serviceability of highway tunnels, using the continental United States as a case study.

Recommended citation: Ya-Dong Xue, Wei Zhang, Yi-Lin Wang*, et al. (2023). "Serviceability evaluation of highway tunnels based on data mining and machine learning: A case study of continental United States." Tunneling and Underground Space Technology, Volume 142, 105418.
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Dynamic Routing of Connected and Automated Vehicles for Improving Network Coverage

Published in Transportation Research Board Annual Meeting 2024, 2024

This paper formulates the CAV routing problem considering network coverage as an objective and proposes heuristic algorithms with greedy search to solve the multi-objective optimization.

Recommended citation: Yilin Wang, Yiheng Feng* (2024). "Dynamic Routing of Connected and Automated Vehicles for Improving Network Coverage." Transportation Research Board Annual Meeting 2024, Poster Presentation.

IDM-Follower: A Model-Informed Deep Learning Method for Car-Following Trajectory Prediction

Published in IEEE Transactions on Intelligent Vehicles, 2024

We introduce a physics-informed neural network (PINN) model that integrates the Intelligent Driving Model (IDM) into a deep learning framework for car-following trajectory prediction. The proposed model exhibits robustness against real-time GPS noise.

Recommended citation: Y. Wang and Y. Feng* (2024). "IDM-Follower: A Model-Informed Deep Learning Method for Car-Following Trajectory Prediction." IEEE Transactions on Intelligent Vehicles, vol. 9, no. 6, pp. 5014-5020.
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Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety

Published in Transportation Research Record, 2024

This paper presents a cooperative perception system connected by V2X between roadside LiDAR and CAV sensors, demonstrating safety benefits in detecting occluded vulnerable road users (VRUs).

Recommended citation: Hanlin Chen, Vamsi K Bandaru, Yilin Wang, Mario A Romero, Andrew Tarko, Yiheng Feng* (2024). "Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety." Transportation Research Record, 2678(12), 1498-1510.
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A Cooperative Perception Based Dynamic Vehicle Routing Framework for Urban Traffic Monitoring

Published in Transportation Research Board Annual Meeting 2026, 2025

We apply Cell Transmission Model (CTM) for traffic state prediction and propose a comprehensive MILP formulation for dynamic vehicle routing considering traffic monitoring performance. Also in submission to Transportation Research: Part B.

Recommended citation: Yilin Wang, Yiheng Feng* (2025). "A Cooperative Perception Based Dynamic Vehicle Routing Framework for Urban Traffic Monitoring." Transportation Research Board Annual Meeting 2026, Poster Presentation.

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