Characterizing the Impact of Autonomous Vehicles on Macroscopic Fundamental Diagrams

With the rapid development of autonomous driving, Autonomous Vehicles (AVs) have started to appear on public roads, which has inevitably affected current traffic conditions and the operations of Manual Vehicles (MVs). Current research on AVs’ influence has mainly been conducted at individual level of driving behaviors, while few studies have focused on the overall network level to consider the traffic flow pattern due to mixed traffic. In this work, considering varying signal control schemes and demand loading patterns, we conducted simulation experiments based on a grid network and a real-world network in Beijing using SUMO. Traffic flow with the mixture of MVs, low-level AVs (LAVs), and high-level AVs (HAVs) were emulated so to investigate how the network performs at various levels of mixed traffic. Driving behaviors between the three types of vehicles were calibrated using driving data drawn from OpenACC dataset, and Waymo Open Dataset. The capacity and critical accumulation of the Macroscopic Fundamental Diagram (MFD) were chosen as the key indicators of network performance. We found that AVs positively boost network capacity (up to 19.0% increase) but a negative influence on critical accumulation was also observed (up to 9.0% decrease). However, the positive impact of OpenACC and Waymo’s AVs on macroscopic traffic is still far from ideal since they may be too conservative. AVs can boost flow when traffic is in unsaturated or saturated states. However, when traffic flow is oversaturated, AVs can instead cause flow and average speed to drop faster than that in the MV-only scenario.