Simulation and queueing network model formulation of mixed automated and non-automated traffic in urban settings

Abstract

Automated driving is an emerging technology in the automotive industry which will likely lead to significant changes in transportation systems. As automated driving technology is still in early stages of implementation in vehicles, it is important yet difficult to understand the nature of these changes. Previous research indicates that autonomous vehicles offer numerous benefits to highway traffic, but their impact on traffic in urban scenarios with mixed autonomous and non-autonomous traffic is less understood. This research addresses this issue by using microscopic traffic simulation to develop understanding of how traffic dynamics change as autonomous vehicle penetration rate varies. Manually driven and autonomous vehicles are modeled in a simulation environment with different behavioral models obtained from the literature. Mixed traffic is simulated in a simple network featuring traffic flowing through an isolated signalized intersection. The green phase length, autonomous vehicle penetration rate, and demand rate are varied. We observe an increase in network capacity and a decrease in average delay as autonomous vehicle penetration rate is increased. Using the results of the simulation experiments, an existing analytical network queueing model is formulated to model mixed autonomous and non-autonomous urban traffic. Results from the analytical model are compared to those from simulation in the small network and the Lausanne city network, and they are found to be consistent.