Traffic congestion on motorways is a serious threat for the economic and social life of
modern society as well as for the environment, which calls for drastic and radical solutions …

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 …

We develop a Kalman filter for predicting traffic flow at urban arterials based on data
obtained from connected vehicles. The proposed algorithm is computationally efficient and …

This paper addresses real-time joint traffic state and model parameter estimation on
freeways using data from fixed sensors and connected vehicles. It investigates how the …

In this paper, we consider connected cruise control design in mixed traffic flow where most
vehicles are human-driven. We first propose a swee** least square method to estimate in …

This paper proposes an integrated freeway traffic flow control framework that aims to
minimize the total travel cost, improve greenness and safety for freeway traffic mixed with …

Connected and autonomous vehicles (CAVs) are expected to significantly improve traffic
efficiency and safety. However, the overall impacts of CAVs on mixed traffic have not been …

We propose a kinematic wave-based Deep Convolutional Neural Network (Deep CNN) to
estimate high-resolution traffic speed fields from sparse probe vehicle trajectories. We …

The development of technologies related to connected and automated vehicles (CAVs)
allows for a new approach to collect vehicle trajectory. However, trajectory data collected in …

Vehicle trajectories can provide a clear picture of traffic flow, which facilitates traffic state
estimation and signal control optimization at intersections. Connected and Automated …