Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation



Danassis, P, Sakota, M, Filos-Ratsikas, A ORCID: 0000-0001-7868-8114 and Faltings, B
(2022) Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation. Artificial Intelligence Review, 55 (7). pp. 5781-5844.

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Abstract

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to 50 % , and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics.

Item Type: Article
Uncontrolled Keywords: Ridesharing, Mobility-on-demand, Relocation, Transportation, Online matching, k-server, Coordination and cooperation, On-device
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 07 Mar 2022 08:57
Last Modified: 18 Jan 2023 21:11
DOI: 10.1007/s10462-022-10145-0
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3150119