Characterizing the Impact of Autonomous Vehicles on Macroscopic Fundamental Diagrams*

Abstract

With the rise of autonomous driving technology, Autonomous Vehicles (AVs) are becoming increasingly prevalent on public roads, influencing traffic conditions and the operation of Manual Vehicles (MVs). It is anticipated that a mixed traffic flow comprising both AVs and MVs will persist for an extended period. Current research concerning the impact of AVs often lacks a comprehensive focus on network-level effects and presents varying perspectives on network performance. Some studies have shown that AVs have a smaller headway and are therefore able to improve road capacity and reduce energy consumption and emissions. Some other scholars have found that existing commercial AVs may generate and spread oscillations, which can aggravate congestion in bottleneck areas if no coordination strategy is implemented. Such discrepancies may stem from differing application scenarios, datasets, and assumptions utilized in each study. Furthermore, these investigations have predominantly examined individual vehicles or fleets within localized road networks, such as single bottlenecks or small-scale networks, thus offering limited guidance at a macroscopic level. This paper seeks to elucidate the impact of AVs on macroscopic traffic patterns, particularly within mixed traffic scenarios, through simulation experiments conducted on both a grid network and a real-world network in Beijing.