Physics Informed Deep Learning: Applications in Transportation
This work addresses traffic state estimation for transportation systems, showing incremental improvement by integrating physical laws into deep learning.
The paper tackles traffic state estimation by applying physics-informed deep learning (PIDL) to learn traffic conditions in unobserved areas, demonstrating that PIDL surpasses a regular deep learning neural network in convergence time and reconstruction accuracy for vehicle velocity estimation.
A recent development in machine learning - physics-informed deep learning (PIDL) - presents unique advantages in transportation applications such as traffic state estimation. Consolidating the benefits of deep learning (DL) and the governing physical equations, it shows the potential to complement traditional sensing methods in obtaining traffic states. In this paper, we first explain the conservation law from the traffic flow theory as ``physics'', then present the architecture of a PIDL neural network and demonstrate its effectiveness in learning traffic conditions of unobserved areas. In addition, we also exhibit the data collection scenario using fog computing infrastructure. A case study on estimating the vehicle velocity is presented and the result shows that PIDL surpasses the performance of a regular DL neural network with the same learning architecture, in terms of convergence time and reconstruction accuracy. The encouraging results showcase the broad potential of PIDL for real-time applications in transportation with a low amount of training data.