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 research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control. Findings: It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies. Originality/value: In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions.

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 improvement in computation and communication technologies, connected and automated vehicles (CAVs) exhibit the potential to relieve incident-induced traffic degradation. To understand the benefits of CAVs on traffic incidents, this paper models the impacts of CAVs with joint consideration of microscopic CAV driving behaviors and macroscopic traffic assignment in mixed traffic environment comprising both CAVs and human-driven vehicles (HDVs). Firstly, a generic traffic assignment model with mixed traffic is proposed to analyze the mixed traffic process from the macroscopic perspective. Then, we incorporate the traffic assignment model with bottleneck delays and incident effects from the microscopic perspective, to model the dynamic road system with incident effects in mixed traffic environment. Furthermore, cooperating with the mixed traffic assignment model, dynamic signal control policies are presented according to different incident severities, and the conditions for equilibrium existence, uniqueness and stability of the road system are derived. The analytical results indicate that road system stability with incident effects is closely related to the incident severity, signal control policy as well as penetration rate and spatial distribution of CAVs. Finally, simulation results are conducted to demonstrate the effectiveness of our proposed incident management policy in improving the recovery rate and system stability of road networks.

This paper presents a cooperative traffic control strategy to increase the capacity of nonrecurrent bottlenecks such as work zones by making full use of the spatial resources upstream of work zones. The upstream area is divided into two zones: the regulation and the merging areas. The basic logic is that a large gap is more efficient in accommodating merging vehicles than several small and scattered gaps with the same total length. In the regulation area, a nonlinear programming model is developed to balance both traffic capacity improvements and safety risks. A two‐step solving algorithm is proposed for finding optimal solutions. In the merging area, the sorting algorithm is used to design lane‐changing trajectories based on the regulated platoons. A case study is conducted, and the results indicate that the proposed model is able to significantly improve work zone capacity with minor disturbances to the traffic.