Investigation of the effect of autonomous vehicles (AV) on the capacity of an urban transport network

In this paper, we assess the effects of different shares of autonomous vehicles (AVs) on the traffic flow and, in particular, on the maximum possible capacity at signal-controlled intersections. For this purpose, all signal-controlled nodes in the traffic network of the Düsseldorf metropolitan area were systematically simulated and evaluated using the microscopic traffic simulation tool SUMO.The analysis shows that defensively parameterized AVs – as envisaged in the umbrella project of this research – may decrease the maximum possible traffic at signal-controlled intersections. Moreover, the simulation runs indicate that capacity at these intersections decreases almost linearly with a growing share of AV. In a second part of this analysis, a freeway section was simulated with the same varying shares of CV and AV to investigate free-flow traffic. In this case, the simulation results of the maximum traffic flow can be approximated by a third-order polynomial fit. The minimum capacity is found for the uniform share of both vehicle types (i.e. 50 % AV and 50 % CV).The overall intent of this project is to provide an approach to determine system-wide and long-term effects of AVs from local microscopic observations. To this end, the SUMO microscopic traffic simulation will be utilized to derive realistic flow capacities for signal-controlled intersections. In a next step, these capacities will be transferred to a mesoscopic traffic simulation. Subsequently, flow capacities can be systematically adjusted in this network-wide mobility simulation to parameterize the influence of future infrastructure and vehicle technologies.