A Queueing Network Approach to the Analysis and Control of Mobility-On-Demand Systems

TL;DR

This paper models mobility-on-demand systems using queueing networks, proposing rebalancing strategies and control policies that optimize vehicle and driver distribution for urban transportation efficiency.

Contribution

It introduces a queueing network model for MoD systems, providing new methods for system analysis, rebalancing, and real-time control to improve urban mobility services.

Findings

Optimal vehicle-to-driver ratio is between 3 and 5.

Rebalancing techniques effectively balance the system.

Real-time control policies are stable under typical loads.

Abstract

This paper presents a queueing network approach to the analysis and control of mobility-on-demand (MoD) systems for urban personal transportation. A MoD system consists of a fleet of vehicles providing one-way car sharing service and a team of drivers to rebalance such vehicles. The drivers then rebalance themselves by driving select customers similar to a taxi service. We model the MoD system as two coupled closed Jackson networks with passenger loss. We show that the system can be approximately balanced by solving two decoupled linear programs and exactly balanced through nonlinear optimization. The rebalancing techniques are applied to a system sizing example using taxi data in three neighborhoods of Manhattan, which suggests that the optimal vehicle-to-driver ratio in a MoD system is between 3 and 5. Lastly, we formulate a real-time closed-loop rebalancing policy for drivers and demonstrate its stability (in terms of customer wait times) for typical system loads.