Realization of the penetration rate for autonomous vehicles in multi-vehicle assignment models

Growing development in technologies that can lead to fully automated driving is at pace. This can result in an enormous change in traffic operations and network properties. However, there are uncertainties about the full deployment time of these autonomous vehicles on road networks. The transition period from vehicles with drivers to driverless will result in a mutual environment with an interaction between traditional (that is, manual) vehicles, automated vehicles and infrastructure. In this context, this research attempts to focus on the various factors of land use, user demographics and road network affecting the percentage of autonomous vehicles into the multi-vehicle assignment models and their subsequent impacts on the traffic network properties. This research aims to use a realistic approach to evaluate the percentage of autonomous vehicles to be injected into the traffic models via an indicator matrix and seven decision indices. A macroscopic traffic model is formulated for mixed traffic flow to which demand is assigned following a stochastic user equilibrium approach using the Frank Wolfe algorithm. The formulated model is applied to a real-world city network for a small part of the Italian city of Genoa. Results showed an effective improvement in traffic network properties with increment in capacities and flow speeds against the saturation grade for the given network.