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

Panayiotis Danassis, Marija Sakota, Aris Filos-Ratsikas, Boi Faltings

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)5781-5844
Number of pages64
JournalArtificial Intelligence Review
Volume55
Issue number7
Early online date15 Feb 2022
DOIs
Publication statusPublished - 1 Oct 2022

Keywords

  • Ridesharing
  • Mobility-on-demand
  • Relocation
  • Transportation
  • Online matching
  • k-server
  • Coordination and cooperation
  • On-device

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