3 papers
cs.LG2025
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2
Physics-informed machine learning (PIML) is crucial in modern traffic flow modeling because it combines the benefits of both physics-based and data-driven approaches. In convention…
cs.LG2025
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2
This study investigates why physics-informed machine learning (PIML) can fail in macroscopic traffic flow modeling. We define failure as cases where a PIML model underperforms both…
math.OC2024
An optimization-free approximation Framework for Connected and Automated Vehicles Eco-Trajectory Planning Under limited computing capacity
Yuanzheng Lei, Yao Cheng, Xianfeng Terry Yang
The trajectory planning problem (TPP) has become increasingly crucial in the research of next-generation transportation systems, but it presents challenges due to the non-linearity…