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 …

The operation of traffic signals is currently limited by the data available from traditional point
sensors. Point detectors can provide only limited vehicle information at a fixed location. The …

We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is
now beginning to revolutionize the driving experience and change how we think about …

A unified platoon-based mathematical formulation called PAMSCOD is presented to perform
arterial (network) traffic signal control while considering multiple travel modes in a vehicle-to …

We study how to estimate real time queue lengths at signalized intersections using
intersection travel times collected from mobile traffic sensors. The estimation is based on the …

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 …

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 …

Freeway traffic state estimation and prediction are central components in real-time traffic
management and information applications. Model-based traffic state estimators consist of a …