Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario

TL;DR

This paper introduces a novel physics-informed teacher-student ensemble learning framework for traffic state estimation under varying speed limit scenarios, improving accuracy by capturing traffic heterogeneity.

Contribution

It develops an innovative ensemble approach combining PIDL and MLP classifiers to adapt to changing traffic conditions with VSLs, addressing limitations of existing PIDL methods.

Findings

Ensemble framework outperforms baseline methods in traffic state estimation.

The approach effectively captures heterogeneity in VSL scenarios.

Case study shows significant reduction in relative L2 error.

Abstract

Physics-informed deep learning (PIDL) neural networks have shown their capability as a useful instrument for transportation practitioners in utilizing the underlying relationship between the state variables for traffic state estimation (TSE). Another efficient traffic management approach is implementing varying speed limits (VSLs) on transportation corridors to control traffic and mitigate congestion. However, the existing training architecture of PIDL in the literature cannot accommodate the changing traffic characteristics on a freeway with VSL. To tackle this challenge, we propose a novel framework integrating teacher-student ensemble training with PIDL neural networks for TSE under VSL scenarios. The physics of flow conservation law is encoded locally in the teacher models by PIDL, and the student model uses a multi-layer perceptron classifier (MLP) to identify traffic characteristics and selects the ensemble member of PIDL neural networks for TSE. This integrated framework provides a natural solution for capturing the heterogeneity of VSL and accurately addressing the TSE problem. The case study results validate the proposed ensemble approach, demonstrating its superior performance in TSE compared to other popular baseline methods, as indicated by relative L2 error.