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 …

This study proposes a framework for human-like autonomous car-following planning based
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …

This paper proposes a recurrent neural network based microscopic car following model that
is able to accurately capture and predict traffic oscillation. Neural network models have …

This paper proposes a new dynamic driving risk potential field model under the connected
and automated vehicles environment that fully considers the dynamic effect of the vehicle's …

Abundant real-time vehicle trajectory information provides an important guarantee for the
driving safety and drivers' decision-making in the intelligent transportation system (ITS) …

Car-following behavior modeling is of great importance for traffic simulation and analysis.
Considering the multi-steps decision-making process in human driving, we propose a …

Electric vehicles (EVs) are deemed to be a solution for reducing air pollution and
greenhouse gas emissions. As a result, the market share has increased exponentially in …

Car-following is a control process in which a following vehicle adjusts its acceleration to
keep a safe distance from the lead vehicle. Recently, there has been a booming of data …

The focus of car-following models is to analyze the microscopic characteristics of traffic
flows, with particular attention given to the interaction between adjacent vehicles. This paper …