A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation

TL;DR

This paper introduces an enhanced physics-informed deep learning approach, PIDL+FDL, for traffic state estimation that simultaneously learns traffic models, parameters, and fundamental diagrams, showing improved accuracy and data efficiency.

Contribution

The paper presents PIDL+FDL, a novel hybrid deep learning framework that integrates machine learning into traffic models to improve estimation and learn fundamental diagrams.

Findings

PIDL+FDL outperforms baseline methods in accuracy.

It effectively learns unknown fundamental diagrams.

Demonstrates robustness on real traffic data.

Abstract

Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e.g., machine learning, ML), while each suffers from either deficient physics or small data. To mitigate these limitations, recent studies introduced a hybrid paradigm, physics-informed deep learning (PIDL), which contains both model-driven and data-driven components. This paper contributes an improved version, called physics-informed deep learning with a fundamental diagram learner (PIDL+FDL), which integrates ML terms into the model-driven component to learn a functional form of a fundamental diagram (FD), i.e., a mapping from traffic density to flow or velocity. The proposed PIDL+FDL has the advantages of performing the TSE learning, model parameter identification, and FD estimation simultaneously. We demonstrate the use of PIDL+FDL to solve popular first-order and second-order traffic flow models and reconstruct the FD relation as well as model parameters that are outside the FD terms. We then evaluate the PIDL+FDL-based TSE using the Next Generation SIMulation (NGSIM) dataset. The experimental results show the superiority of the PIDL+FDL in terms of improved estimation accuracy and data efficiency over advanced baseline TSE methods, and additionally, the capacity to properly learn the unknown underlying FD relation.