In this paper, we review trajectory data-based traffic flow studies that have been conducted
over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest …

A comprehensive literature review reveals that there exist lots of ambiguities, confusion and
even contradictions in setting a car-following calibration problem. In particular, confusion …

Car-following behavior has been extensively studied using physics-based models, such as
Intelligent Driving Model (IDM). These models successfully interpret traffic phenomena …

With the development of technology, platoons of intelligent and connected vehicles (ICVs)
will join regular vehicles on roads, and the characteristics of the traffic flow will change …

Jointly optimizing multi-vehicle trajectories is a critical task in the next-generation
transportation system with autonomous and connected vehicles. Based on a space-time …

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows
for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the …

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory
prediction contains many sources of uncertainty in data and modelling. A thorough …

This paper proposes a control strategy for a freeway merging bottleneck consisting of a
Connected and Automated Vehicle (CAV) exclusive lane and a human-driven vehicle (HDV) …

Reflecting different driving styles in Adaptive Cruise Control (ACC) is of great importance for
its market acceptance. A novel data-based method is presented for designing a …

Car-following behavior is the result of the interaction of various elements in the specific
driver-vehicle-environment aggregation. Under the intelligent and connected condition, the …