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 …

Despite the wide implementation of machine learning (ML) technique in traffic flow modeling
recently, those data-driven approaches often fall short of accuracy in the cases with a small …

Traffic light optimization is known to be a cost-effective method for reducing congestion and
energy consumption in urban areas without changing physical road infrastructure. However …

This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a
stochastic node model. It then formulates a network model by coupling the node model with …

Observability and controllability are two critical requirements for a partially observable
transportation system. This paper proposes a stepwise signal optimization framework with …

In car-following models, the driver reacts according to his physical and psychological
abilities which may change over time. However, most car-following models are deterministic …

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and
prediction, one requires a probabilistic model of traffic flow. The usual approach to …

With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to
understand transportation traffic patterns and their impact on wireless network designs and …

The stochastic cell transmission model (SCTM) was originally developed for stochastic
dynamic traffic state modeling under several assumptions, eg, the independent/uncorrelated …