In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …

In current years, the improvement of deep learning has brought about tremendous changes:
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …

The vehicle dispatching system is one of the most critical problems in online ride-hailing
platforms, which requires adapting the operation and management strategy to the dynamics …

Traffic incident management in metropolitan areas is crucial for the recovery of road systems
from accidents as well as the mobility and safety of the community. With the continuous …

In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …

Purpose-On-ramp merging areas are typical bottlenecks in the freeway network since
merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and …

Solving optimal control problems serves as the basic demand of industrial control tasks.
Existing methods like model predictive control often suffer from heavy online computational …

Purpose-This paper aims to review the studies on intersection control with connected and
automated vehicles (CAVs). Design/methodology/approach-The most seminal and recent …

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 …

Lane change is a common-yet-challenging driving behavior for automated vehicles. To
improve the safety and efficiency of automated vehicles, researchers have proposed various …