Variable Speed Limit Control for Individual Vehicles on Freeway Bottlenecks with Mixed Human and Automated Traffic Flows

Abstract

In rush hours, traffic congestion frequently happens in freeway bottleneck areas when traffic demands exceed bottlenecks' capacity. The traffic congestion in freeway bottlenecks leads to capacity drop phenomenon, which exacerbates traffic congestion and seriously hampers the performance of freeways. Variable speed limit (VSL) is an effective approach to mitigate capacity drop by adjusting speed limits posted on roadside VSL signs. However, the effectiveness of VSL is dependent on several crucial factors, such as the optimum placement of VSL signs, the coordination between consecutive VSL signs, the rate of speed limits changed on VSL signs, the driver compliance to the posted speed limits, and etc. In this paper, we propose a VSL control algorithm named Variable Speed Limit for Individual Vehicles (VSL-IV). By making use of vehicle communication technology, the VSL-IV algorithm can dynamically adjust the speed limits for individual vehicles not only based on the real-time traffic information at the macroscopic level (e.g., mean speed of vehicles, density of vehicles), but also based on the real-time traffic information at the microscopic level (e.g., acceleration, deceleration, speeds of individual vehicles). In addition, VSL-IV takes into account mixed human and automated traffic flows when determining speed limits for vehicles. Comprehensive experiments have been conducted to demonstrate the efficiency of the VSL-IV algorithm.