A review of hybrid physics-based machine learning approaches in traffic state estimation

Abstract Traffic state estimation (TSE) plays a significant role in traffic control and operations since it can provide accurate and high-resolution traffic estimations for locations without traffic states are measured with partially observed or flawed traffic data. Several comprehensive survey papers in recent years have summarized classical physics-based and pure data-driven approaches in TSE and found that both approaches …