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20202023
most citedMacroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications

122 citations · 145 across the 5 of their papers we have counts for

collaborators

5 papers

math.OC2023★ 9 cited

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…

cs.LG2022★ 2 cited

A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation

Zhao Zhang, Ding Zhao, Xianfeng Terry Yang

Full-field traffic state information (i.e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways. However, inc…

stat.ML2020★ 10 cited

Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach

Yun Yuan, Qinzheng Wang, Xianfeng Terry Yang

Modeling stochastic traffic behaviors at the microscopic level, such as car-following and lane-changing, is a crucial task to understand the interactions between individual vehicle…

stat.ML2020★ 2 cited

Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations

Yun Yuan, Zhao Zhang, Xianfeng Terry Yang

Despite the success of classical traffic flow (e.g., second-order macroscopic) models and data-driven (e.g., Machine Learning - ML) approaches in traffic state estimation, those ap…

stat.ML2020★ 122 cited

Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications

Yun Yuan, Xianfeng Terry Yang, Zhao Zhang +1

Despite the wide implementation of machine learning (ML) techniques in traffic flow modeling recently, those data-driven approaches often fall short of accuracy in the cases with a…