Abstract
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.
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Reference56 articles.
1. Reconstructing the traffic state by fusion of heterogeneous data;Treiber;Comput.-Aided Civ. Infrastruct. Eng.,2010
2. Highway traffic state estimation with mixed connected and conventional vehicles;Roncoli;IEEE Trans. Intell. Transp. Syst.,2016
3. A Dynamic Network Modeling-based approach for traffic observability problem;Agarwal;IEEE Trans. Intell. Transp. Syst.,2016
4. Traffic State Estimation on Highway: A Comprehensive Survey;Seo;Annu. Rev. Control,2017
5. On Kinematic Waves II. A theory of traffic flow on long crowded roads;Lighthill;Proc. R. Soc. Lond. Ser. A. Math. Phys. Sci.,1955
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献