A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems

TL;DR

This paper introduces a queueing network framework for modeling, analyzing, and controlling autonomous mobility-on-demand systems, enabling performance evaluation and scalable policy synthesis in complex urban networks.

Contribution

It presents a novel BCMP queueing network model for AMoD systems, offering analytical tools and scalable routing policy synthesis with performance guarantees.

Findings

Validated model on New York City case study

Provides performance metrics including vehicle availability and throughput

Enables analysis of higher-order moments of system performance

Abstract

In this paper we present a queueing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system into a closed, multi-class BCMP queueing network model. Second, we present analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities, and first and second order moments of vehicle throughput. Third, we propose a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes. Finally, we validate our theoretical results on a case study of New York City. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which subsumes earlier Jackson and network flow models, provides a quite large set of modeling options (e.g., the inclusion of road capacities and general travel time distributions), and allows the analysis of second and higher-order moments for the performance metrics.