Control of Robotic Mobility-On-Demand Systems: a Queueing-Theoretical Perspective

TL;DR

This paper develops a queueing-theoretical model for autonomous mobility-on-demand systems, proposing optimal rebalancing algorithms, analyzing congestion impacts, and demonstrating potential fleet size reductions in urban environments like New York City.

Contribution

It introduces a novel queueing-theoretical framework for autonomous MOD systems, including real-time rebalancing algorithms and congestion analysis, advancing system-wide coordination understanding.

Findings

Optimal rebalancing minimizes vehicle numbers using linear programming.

Approximately 8,000 robotic vehicles can meet Manhattan's current taxi demand.

Rebalancing vehicles impact overall congestion and system sustainability.

Abstract

In this paper we present and analyze a queueing-theoretical model for autonomous mobility-on-demand (MOD) systems where robotic, self-driving vehicles transport customers within an urban environment and rebalance themselves to ensure acceptable quality of service throughout the entire network. We cast an autonomous MOD system within a closed Jackson network model with passenger loss. It is shown that an optimal rebalancing algorithm minimizing the number of (autonomously) rebalancing vehicles and keeping vehicles availabilities balanced throughout the network can be found by solving a linear program. The theoretical insights are used to design a robust, real-time rebalancing algorithm, which is applied to a case study of New York City. The case study shows that the current taxi demand in Manhattan can be met with about 8,000 robotic vehicles (roughly 60% of the size of the current taxi fleet). Finally, we extend our queueing-theoretical setup to include congestion effects, and we study the impact of autonomously rebalancing vehicles on overall congestion. Collectively, this paper provides a rigorous approach to the problem of system-wide coordination of autonomously driving vehicles, and provides one of the first characterizations of the sustainability benefits of robotic transportation networks.