Exploring the impact of autonomous vehicles in urban networks and potential new capabilities for perimeter control

Abstract

Recently there has been an enormous effort towards the development and field deployment of autonomous vehicles (AV). These developments have a direct impact on road traffic characteristics, as AV can also be used as sensors, actuators, and generally as effective components of an online traffic management system. In this work, we explore through microsimulation how AV can affect the network performance on urban networks. It is demonstrated through simulation experiments, that for mixed traffic (i.e. AV and conventional vehicles), different penetration rates of AV can reduce the density (i.e. congestion) and increase the capacity (i.e. throughput) of the network. Furthermore, new emerging capabilities for perimeter control in urban regions are explored, by utilizing new sources of data that can become available. A new feedback-based regulator is developed, which uses AV speed measurements as the state-feedback variables. The regulator is demonstrated to work equivalently well with a well fine-tuned regulator that uses loop detectors occupancy measurements (i.e. accumulation estimates) from all the links of the network. The speed-based feedback perimeter flow regulator performs significantly well even for very low penetration rates of AV (e.g. 5%), resulting in reduced delays for the users and less congestion in the urban region. Although the regulator has sparse measurements only from AV it can achieve the same improvement with the accumulation-based regulator that has the whole picture of the network.

Investigation of the Impact on Throughput of Connected Autonomous Vehicles with Headway Based on the Leading Vehicle Type

“…The efficiency enhancement delivered by platoons on highways has been largely studied, however, there are not as many references detailing platoons in urban environments. Platoons on urban roads, with the support of communication for their formation and evolution, can reduce the saturation headway at signalized intersections ( 13 ). Lioris et al showed that the capacity can be analytically improved by the same factor as the increase in the saturation flow rate, with 100% CAVs ( 14 , 15 ).…”

Autonomous Vehicles in Urban Networks: A Simulation-Based Assessment

“…One approach to represent the network MFD, which is adopted here, considers the total number of vehicles passing through the network (completing their trip) versus the total number of vehicles inside the network (which equals the density divided by the sum of all link lengths of the road network), within a specified interval. Alternatively, the total network production (veh · distance traveled per unit time) and accumulation (veh) can be calculated from loop detectors on links, where flows and densities are weighted by the length of the link ( 12 , 17 ). The first approach is preferred in homogeneously congested networks, whereas the second can be more accurate for the estimation of the average network speeds and densities in less homogeneously congested networks.…”

“…Three networks were used as testbed to demonstrate the feasibility of the proposed framework. The first network has been used in an earlier study ( 17 ) and corresponds to a part of the Cental Business District of Barcelona in Spain (Figure 2 a ). It consists of 1,570 sections and 565 signalized intersections.…”

“…The concept of the network MFD or Network Fundamental Diagram is not new and has been used in various studies, such as in perimeter traffic control (14)(15)(16)(17), modeling and control of urban traffic emissions (18), and validation of microscopic simulation models (19). Although several studies have utilized the MFD to express the traffic dynamics of large-scale urban transport networks, only a few have applied it for the identification of capacity implications of AVs.…”

“…The capacities for (C)AVs and for mixed traffic composed of both AV and conventional vehicles can be easily derived from the network MFD, independently of the specific modeling assumptions of AVs and network characteristics. In Kouvelas et al microscopic simulation is used to investigate how AVs affect the network performance on urban networks ( 17 ). It is demonstrated, using the network MFD obtained from simulation experiments, that the network throughput increases and the traffic conditions improve as the penetration rate of AVs in the traffic demand increases.…”