Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …

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 …

Recent advances in neural networks (NNs) have enabled automatic querying of large
volumes of video data with high accuracy. While these deep NNs can produce accurate …

Cell-phones equipped with a global positioning system (GPS) provide new opportunities for
location-based services and traffic estimation. When traveling on-board vehicles, these …

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the
road network and provide great opportunities for enhanced short-term traffic predictions …

Traffic state estimation is a challenging problem for the transportation community due to the
limited deployment of sensing infrastructure. However, recent trends in the mobile phone …

The paper proposes a first-order macroscopic stochastic dynamic traffic model, namely the
stochastic cell transmission model (SCTM), to model traffic flow density on freeway …

In recent years, rapid advances in information technology have led to various data collection
systems which are enriching the sources of empirical data for use in transport systems …

This paper formulates the problem of real-time estimation of traffic state in freeway networks
by means of the particle filtering framework. A particle filter (PF) is developed based on a …

Advanced information and communication technologies can be used to facilitate traffic
incident management. If an incident is detected and blocks a road link, in order to reduce the …