18-23

Promising advances in autonomous vehicle (AV) technology have fueled industry and research fields to dedicate significant efforts to the study of the integration of AVs into the traffic network. While most studies anticipate a beneficial role of AVs, contributing to improved traffic efficiency and roadway safety, the underlying assumptions on the interactions between AVs and human-driven vehicles (HDVs) are often cooperative in nature. The first portion of this study investigates the impact of aggressive human-driven vehicles’ (AHDVs) merging behaviors on traffic performance in a mixed environment that includes three vehicle types: AVs, HDVs, and AHDVs. This study is undertaken in an open-source microscopic traffic simulation model, Simulation of Urban Mobility (SUMO). AHDVs have been modeled in this study to show aggressive merging behaviors at a merge section of a freeway exit ramp by targeting the farthest reachable AV for lane change as well as forcing a merge immediately in front of the AV. Results show that the travel-time gains achieved by AHDVs were at the expense of AVs and HDVs, and the interaction of aggressive HDVs with cooperative AVs could negatively impact overall capacity. The second portion of this study developed an Excel-based tool exploring the impact of AVs on departure capacity from a signalized intersection. Through both portions of this study, it was seen that critical indicators of the impact of AVs on traffic performance are: (1) Is a rise in aggressive interactions witnessed? (2) What are the headways being adopted by AVs? And (3) What are the spacing and maximum-length platooning characteristics?