Staggered Routing in Autonomous Mobility-on-Demand Systems

TL;DR

This paper explores staggered routing in autonomous mobility-on-demand systems, demonstrating that delaying trip departures can significantly reduce congestion with minimal delay, using mixed integer linear programming and heuristics.

Contribution

It introduces a formal planning model for staggered routing and develops a scalable matheuristic for large-scale real-world instances.

Findings

94% congestion mitigation in low-congestion scenarios

90% congestion mitigation in online rolling horizon scenarios

Up to six-minute trip departure shifts achieve significant congestion reduction

Abstract

In autonomous mobility-on-demand systems, effectively managing vehicle flows to mitigate induced congestion and ensure efficient operations is imperative for system performance and positive customer experience. Against this background, we study the potential of staggered routing, i.e., purposely delaying trip departures from a system perspective, in order to reduce congestion and ensure efficient operations while still meeting customer time windows. We formalize the underlying planning problem and show how to efficiently model it as a mixed integer linear program. Moreover, we present a matheuristic that allows us to efficiently solve large-scale real-world instances both in an offline full-information setting and its online rolling horizon counterpart. We conduct a numerical study for Manhattan, New York City, focusing on low- and highly-congested scenarios. Our results show that in low-congestion scenarios, staggering trip departures allows mitigating, on average, 94% of the induced congestion in a full information setting. In a rolling horizon setting, our algorithm allows us to reduce 90% of the induced congestion. In high-congestion scenarios, we observe an average reduction of 66% as the full information bound and an average reduction of 56% in our online setting. Surprisingly, we show that these reductions can be reached by shifting trip departures by a maximum of six minutes in both the low and high-congestion scenarios.