Effects of Autonomous Vehicle Behavior on Arterial and Freeway Networks

Abstract

Autonomous vehicles offer new traffic behaviors that could revolutionize transportation. Examples include reservation-based intersection control and reduced reaction times that result in greater road capacity. Most studies have used microsimulation models of those new technologies to study their impacts more realistically. However, microsimulation is not tractable for larger networks. Recent developments in simulating reservation-based controls and multiclass cell transmission models for autonomous vehicles in dynamic traffic assignment have allowed studies of larger networks. This paper presents analyses of several highly congested arterial and freeway networks to quantify how reservations and reduced reaction times affect travel times and congestion. Reservations were observed to improve over signals in most situations. However, signals outperformed reservations in a congested network with several close local road and arterial intersections because the capacity allocations of signals were more optimized for the network. Reservations also were less efficient than were traditional merges and diverges for on- and off-ramps. However, the increased capacity from reduced following headways resulted in significant improvements for both freeway and arterial networks. Finally, the authors studied a downtown network, including freeway, arterial, and local roads, and found that the combination of reservations and reduced following headways resulted in a 78% reduction in travel time.