Traffic state estimation plays a fundamental role in traffic control and management. In the
connected vehicles (CVs) environment, more traffic‐related data perceived and interacted …

Traffic state estimation (TSE), which reconstructs the traffic variables (eg, speed, flow) on
road segments using partially observed data, plays an essential role in intelligent …

We present a physics-informed deep learning (PIDL) approach to tackle the challenge of
data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep …

Real-time traffic state (eg, speed) prediction is an essential component for traffic control and
management in an urban road network. How to build an effective large-scale traffic state …

Traffic state estimation (TSE) is a critical task for intelligent transportation systems. However,
it is extremely challenging because the traffic data quality is often affected by the installation …

Full-field traffic state information (ie, flow, speed, and density) is critical for the successful
operation of Intelligent Transportation Systems (ITS) on freeways. However, incomplete …

This paper proposes a estimation model based on three-layer stacking model to estimate
traffic flow. Compared with estimation models of single machine learning and a two-layer …

Large-scale and diversified traffic data resources strongly support research into estimating
urban traffic states and predicting traffic flow. There are many studies on traffic prediction, but …

Traffic state estimation (TSE) refers to the inference of macroscopic traffic states, including
density, speed, and flow, based on partially observed traffic data and some prior knowledge …

The challenge of traffic state estimation (TSE) lies in the sparsity of observed traffic data and
the sensor noise present in the data. This paper presents a new approach–physics informed …