Determining the Effect of Self-Driving Vehicles on Capacity in Arterial Passages

The solution to congestion is the balance of demand and supply side. Increasing network capacity with road construction and more infrastructure is expensive and environmentally damaging. With better utilization of existing infrastructure, road capacity can be increased. Different traffic management methods have been proposed to address the rapid growth of travel demand, which seem to be promising. Due to the reduction of human error due to faster reaction, Autonomous adopt less distance between vehicles, which can eventually increase capacity by reducing the space between vehicles. The ability of connecting to each other can also further contribute to the formation of Platooning. Increasing the number of AVs on roads around the world causes uncertainty about their impact on the capacity of roads with AV and non-AV vehicles and mostly faces many challenges. Therefore, in this study, by applying the characteristics of AV and non-AV vehicle models in the road network of west Tehran, related southern arterial routes by various methods, total of 825 simulations were performed. These simulations take into account the models of car following, lane change and Platooning, when they participate in the network with different share values with %10 intervals that normal vehicles constitute the entire traffic flow and in Each stage of the simulation contributes to AV cars, showing the Autonomous Vehicles effects that have on the capacity of the network passages, The results of the thesis show that the full penetration of Autonomous vehicles in Platoon can contribute more than %50 in the capacity of the network as well as increasing the amount of time headway for the free driving and Leader of the platoon vehicles, which will have a positive effect on the interaction of AVs in low penetration, and these promise to experts to observe its effects in reality.