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A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation
Full-field traffic state information (i. e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways.
Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach
Leveraging a recently developed theory named physics regularized Gaussian process (PRGP), this study presents a stochastic microscopic traffic model that can capture the randomness and measure errors in the real world.
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations
This novel approach can encode physics models, i. e., classical traffic flow models, into the Gaussian process architecture and so as to regularize the ML training process.
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
To address this issue, this study presents a new modeling framework, named physics regularized machine learning (PRML), to encode classical traffic flow models (referred as physical models) into the ML architecture and to regularize the ML training process.
