Platooning and coordination are two implementation strategies that are frequently proposed
for traffic control of connected and autonomous vehicles (CAVs) at signal-free intersections …

As a cutting‐edge strategy to reduce travel delay and fuel consumption, platooning of
connected and autonomous vehicles (CAVs) at signal‐free intersections has become …

Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and
human-driven vehicles (HVs) at a signalized intersection has become a concern of …

With the growing traffic congestion problem, more and more deep reinforcement learning
(DRL) methods have been applied in traffic signals control (TSC). But researches show that …

Existing trajectory-based signal timing studies either assume time-invariant vehicle arrivals
or require high penetration rates of connected vehicles (CVs). However, vehicle arrivals at …

Traffic congestion has several causes, including insufficient road capacity, unrestricted
demand and improper scheduling of traffic signal phases. A great variety of efforts have …

Traffic accident management as an approach to improve public security and reduce
economic losses has received public attention for a long time, among which traffic accidents …

Multi-agent deep reinforcement learning (MDRL) is a popular choice for multi-intersection
traffic signal control, generating decentralized cooperative traffic signal strategies in specific …

Due to the nonlinearity and dynamics of transportation systems, traffic signal control (TSC) in
urban traffic networks has always been an important challenge. In recent years, model …

To investigate the issue of multi-entry bus priority at intersections, an intelligent priority
control method based on deep reinforcement learning was constructed in the bus network …