Physics-Informed Neural Networks (PINNs)-Based Traffic State Estimation: An Application to Traffic Network

• Published in: Algorithms 2022-12, Vol.15 (12), p.447
• Main Authors: Usama, Muhammad, Ma, Rui, Hart, Jason, Wojcik, Mikaela
• Format: Article
• Language: English
• Subjects: Communications traffic; Critical components; Decomposition; deep learning; Intelligent transportation systems; LWR; Machine learning; Neural networks; Partial differential equations; Physics; PINNs; State estimation; Traffic flow; traffic flow models; Traffic models; traffic state estimation (TSE); Transportation networks; Variables
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a15120447

Traffic state estimation (TSE) is a critical component of the efficient intelligent transportation systems (ITS) operations. In the literature, TSE methods are divided into model-driven methods and data-driven methods. Each approach has its limitations. The physics information-based neural network (PINN) framework emerges to mitigate the limitations of the traditional TSE methods, while the state-of-art of such a framework has focused on single road segments but can hardly deal with traffic networks. This paper introduces a PINN framework that can effectively make use of a small amount of observational speed data to obtain high-quality TSEs for a traffic network. Both model-driven and data-driven components are incorporated into PINNs to combine the advantages of both approaches and to overcome their disadvantages. Simulation data of simple traffic networks are used for studying the highway network TSE. This paper demonstrates how to solve the popular LWR physical traffic flow model with a PINN for a traffic network. Experimental results confirm that the proposed approach is promising for estimating network traffic accurately.