Urban road travel time estimation is of great importance for intelligent transportation
applications, such as the vehicle navigation. With the ubiquitous sensing data, eg, the …

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 is an important part of the traffic control process and aims to creates
an accurate understanding of the current situation in traffic system. Bayesian Filtering is a …

Freeway traffic state estimation is a vital component of traffic management and information
systems. Macroscopic-model-based traffic state estimation methods are widely used in this …

This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates.
The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific …

Modeling the relationship between vehicle speed and density on the road is a fundamental
problem in traffic flow theory. Recent research found that using the least-squares (LS) …

Real-time estimation of the traffic state in urban signalized links is valuable information for
modern traffic control and management. In recent years, with the development of in-vehicle …

This study focuses on how to use multiple data sources, including loop detector counts, AVI
Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic …

Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due
to limited sensor deployment and potential data corruption. In this study, we propose an …

Network traffic prediction based on probe vehicle data is important for traffic management
and route recommendation and has been intensively studied. Previous traffic prediction …