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 …

Speed plays a significant role in evaluating the evolution of traffic status, and predicting
speed is one of the fundamental tasks for the intelligent transportation system. There exists a …

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 …

The objective of this paper is to present a new analytical tool that predicts highway
congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data …

Traffic data prediction offers a significant way to evaluate the future traffic congestion status;
many deep learning based approaches have been widely applied in this field. Most current …

This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates.
The source of uncertainty in the proposed model is parametric. Specifically, we assume that …

An on-board traffic prediction algorithm is proposed for connected vehicles traveling on
highways. The prediction is based on data received from other connected vehicles ahead in …

Traffic flow prediction is quite crucial for estimating the future traffic states, efficient and
accurate prediction models greatly contribute to the smooth traffic of road networks …

ABSTRACT Incorporation of Human Factors (HF) into the mathematical car-following (CF)
models has always been the research hotspot. Ignorance of such inclusion would inevitably …

This work gives introduction to traffic control by connected automated vehicles. The
influence of vehicle control on vehicular traffic and traffic control strategies are discussed …