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

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 (...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1999-4893
1999-4893
DOI:10.3390/a15120447