Impacts of Connected and Autonomous Vehicles on Traffic Flow with Heterogeneous Drivers Spatially Distributed over Large-Scale Networks

Abstract

Connected and automated vehicle technologies are expected to significantly contribute in improving mobility and safety. As connected and autonomous vehicles have not been used in practice at large scale, there are still some uncertainties in relation to their applications. Therefore, researchers utilize traffic simulation tools to model the presence of these vehicles. There are several studies on the impacts of vehicle connectivity and automation at the segment level. However, only a few studies have investigated these impacts on traffic flow at the network level. Most of these studies consider a uniform distribution of connected or autonomous vehicles over the network. They also fail to consider the interactions between heterogeneous drivers, with and without connectivity, and autonomous vehicles at the network level. Therefore, this study aims to realistically observe the impacts of these emerging technologies on traffic flow at the network level by incorporating adaptive fundamental diagrams in a mesoscopic simulation tool. The adaptive fundamental diagram concept considers spatially and temporally varying distributions of different vehicle types with heterogeneous drivers. Furthermore, this study considers the intersection capacity variations and fundamental diagram adjustments for arterial links resulting from the presence of different vehicle types and driver classes. The proposed methodology is applied to a large-scale network of Chicago. The results compare network fundamental diagrams and hysteresis loop areas for different proportions of connected and autonomous vehicles. In addition to quantifying impacts of connected and autonomous vehicles, the results demonstrate the impacts of various factors associated with these vehicles on traffic flow at the network level.

Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

“…Liu et al (2017) also confirmed that the implementation of AVs significantly increases highway capacity [25]. Fakhrmoosavi et al (2020) concluded that CVs and AVs increase the intersection capacity [7].…”

“…This improvement is prominent with higher penetration rates of AVs. Fakhrmoosavi et al (2020) concluded that the implementation of CVs and AVs led to substantial improvements in traffic flow and faster recovery [7]. In contrast to the above findings, Makridis et al (2018) concluded that the low penetration rate of AV had a negative impact on traffic stability [43].…”

“…However, the penetration rates of 10% to 50% showed similar performance. Higher penetration rates of CVs and AVs result in slightly higher flow and improved stability [7]. F. Zheng et al (2020) concluded that the improvement of the traffic stability is more significant with high penetration rates (50%) of AVs [52].…”

“…The main rule of such systems is to improve driving comfort, reduce human driving errors, minimize the necessity for physical road signals and vehicle insurance, and improve safety [4][5][6]. Most of these are based on adaptive cruise control (ACC) systems, which are available now in a wide range of the existing models of the RVs [5,7]. By automatically controlling the throttle or the brake, the ACC system can control the car-following behavior, such as adjusting its speed and providing a specified distance, location, and acceleration from the trajectory data by using on-board sensors [2,5,[8][9][10].…”

“…The cooperative adaptive cruise control (CACC) is an updated and improved driver assistance system built upon the ACC system. It uses more information collected from the advanced V2V communication technologies [7] to automatically adjust the longitudinal control depending on the behavior of its leading and following vehicles [7,8,12]. With both ACC and CACC systems, the drivers are still responsible for actively steering the vehicle, keeping lanes, and monitoring traffic conditions.…”