Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural Networks

TL;DR

This paper introduces a physics-informed deep learning framework that integrates a nonlocal LWR traffic flow model, improving traffic state estimation accuracy over traditional local models using real-world datasets.

Contribution

It develops a novel PIDL framework incorporating nonlocal LWR models with fixed and variable kernels, enhancing traffic flow representation in deep learning methods.

Findings

Improved traffic state estimation accuracy over baseline models

Effective use of nonlocal kernels in PIDL framework

Validated on NGSIM and CitySim datasets

Abstract

This research contributes to the advancement of traffic state estimation methods by leveraging the benefits of the nonlocal LWR model within a physics-informed deep learning framework. The classical LWR model, while useful, falls short of accurately representing real-world traffic flows. The nonlocal LWR model addresses this limitation by considering the speed as a weighted mean of the downstream traffic density. In this paper, we propose a novel PIDL framework that incorporates the nonlocal LWR model. We introduce both fixed-length and variable-length kernels and develop the required mathematics. The proposed PIDL framework undergoes a comprehensive evaluation, including various convolutional kernels and look-ahead windows, using data from the NGSIM and CitySim datasets. The results demonstrate improvements over the baseline PIDL approach using the local LWR model. The findings highlight the potential of the proposed approach to enhance the accuracy and reliability of traffic state estimation, enabling more effective traffic management strategies.