Physics Informed Deep Learning for Traffic State Estimation
Archie J. Huang, Shaurya Agarwal
Abstract
The challenge of traffic state estimation (TSE) lies in the sparsity of observed traffic data and the sensor noise present in the data. This paper presents a new approach - physics informed deep learning (PIDL) method - to tackle this problem. PIDL equips a deep learning neural network with the strength of the physical law governing traffic flow to better estimate traffic conditions. A case study is conducted where the accuracy and convergence-time of the algorithm are tested for varying levels of scarcely observed traffic density data - both in Lagrangian and Eulerian frames. The estimation results are encouraging and demonstrate the capability of PIDL in making accurate and prompt estimation of traffic states.