This paper presents a simulation-driven method for assessing the safety and efficiency of traffic at roundabouts incorporating connected and automated vehicle (CAV) technology. Utilizing the newly proposed CAV-based factors specified by the Highway Capacity Manual (HCM) provided a practical framework for analyzing capacity dynamics across various traffic scenarios. Using microscopic traffic simulation on a roundabout model replicating real-world geometry and traffic attributes facilitated the identification of crucial behavioral parameters. This simulation spanned from smooth traffic scenarios to operational saturation, aiding in the study of mixed traffic scenarios during the transition to increasing CAV presence. Additionally, the study assessed the safety and traffic impact of a dedicated CAV lane using surrogate safety metrics. Aimsun software aided in model parameter calibration, which, combined with the Surrogate Safety Assessment Model (SSAM), supported safety analysis. Despite observed enhancements in roundabout performance with CAV integration, the benefits of a designated CAV lane highlighted the potential to reduce conflicts among vehicles. In conclusion, the paper emphasizes the overall performance enhancement achieved with CAVs at roundabouts while also providing insights for evaluating the potential of CAV technologies in future mobility management strategies.
Traditional traffic control systems based on traffic light have achieved a great success in reducing the average delay of vehicles or in improving the traffic capacity. The main idea of these systems is based on the optimization of the cycle time, the phase sequence, and the phase duration. The right-of-ways are assigned to vehicles of one or several movements for a specific time. With the emergence of cooperative driving, an innovative traffic control concept, Autonomous Intersection Management (AIM), has emerged. In the framework of AIM, the right-of-way is customized on the measurement of the vehicle state and the traffic control turns to determine the passing sequence of vehicles. Since each vehicle is considered individually, AIM faces a combinatorial optimization problem. This paper proposes a dynamic programming algorithm to find its optimal solution in polynomial time. Experimental results obtained by simulation show that the proper arrangement of the vehicle passing sequence can greatly improve traffic efficiency at intersections.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.