Managing Autonomous Mobility on Demand Systems for Better Passenger Experience

TL;DR

This paper introduces the Expand and Target algorithm for managing autonomous mobility on demand systems, improving passenger experience by reducing wait times and increasing trip success rates through simulation with real city data.

Contribution

It presents a novel algorithm for autonomous vehicle dispatching that can be integrated with multiple scheduling strategies, enhancing passenger experience.

Findings

Reduced passenger waiting time by up to 29.82%

Increased trip success rate by up to 7.65%

Validated with real-world NYC taxi data

Abstract

Autonomous mobility on demand systems, though still in their infancy, have very promising prospects in providing urban population with sustainable and safe personal mobility in the near future. While much research has been conducted on both autonomous vehicles and mobility on demand systems, to the best of our knowledge, this is the first work that shows how to manage autonomous mobility on demand systems for better passenger experience. We introduce the Expand and Target algorithm which can be easily integrated with three different scheduling strategies for dispatching autonomous vehicles. We implement an agent-based simulation platform and empirically evaluate the proposed approaches with the New York City taxi data. Experimental results demonstrate that the algorithm significantly improve passengers' experience by reducing the average passenger waiting time by up to 29.82% and increasing the trip success rate by up to 7.65%.