Development and evaluation of C-ITS traffic incident management implementations using connected autonomous vehicles

Traffic incidents are unavoidable and impede traffic flow, reducing safety and efficiency. Different approaches can reduce these incidents' impact. Recently introduced methods include Cooperative Intelligent Transportation System scenarios and connected autonomous vehicle deployment in real-time traffic. This thesis integrates an incident management system with the Cooperative Intelligent Transportation System scenario "Slow or Stationary Vehicle Warning" using connected autonomous vehicles. The method reduces connected autonomous vehicle speeds during incidents to improve traffic safety and efficiency. 6 different connected autonomous vehicle ratios, 3 different incident durations, and 2 traffic demand levels are used in different simulation scenarios to evaluate the C-ITS incident management system using SUMO simulation software. The scenarios are evaluated on two networks. Network #1 is a three-lane, 10-kilometer continuous network without intersections or bottlenecks. A ramp and bottleneck exist at 20-kilometer Network #2. During the simulations of these scenarios, average speed, flow, and density data are collected through sensors. The collected data is visualized, and the implemented method is analyzed. It is observed that, in both networks, the C-ITS incident management method decreased average densities and increased average speeds, considering the whole network. However, analyzing the local incident regions, average densities, and speeds are both decreased in Network #1, whereas in Network #2, average densities are decreased, and average speeds are increased. At both networks, the C-ITS incident management method provided the best performance at a 60% connected autonomous vehicle ratio.