Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms

TL;DR

This paper develops a network flow model for routing autonomous vehicles in congested networks, showing that proper coordination of rebalancing vehicles does not increase congestion and enabling efficient routing algorithms.

Contribution

It introduces a theoretical framework demonstrating that vehicle rebalancing need not worsen congestion, allowing decoupled and computationally efficient routing and rebalancing algorithms.

Findings

Rebalancing vehicles, when properly coordinated, do not increase congestion.

The proposed algorithm outperforms existing point-to-point methods.

Numerical experiments validate the theoretical insights.

Abstract

This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.