Comparative assessment of fairness in on-demand fleet management algorithms - Archive ouverte HAL Access content directly
Conference Papers Year : 2024

Comparative assessment of fairness in on-demand fleet management algorithms

Tarek Chouaki
Connectez-vous pour contacter l'auteur
Sebastian Hörl

Abstract

On-demand mobility systems in which a fleet of shared vehicles are increasingly tested and deployed. Their efficiency gains are partly due to central algorithms that control the movements and actions of vehicles and drivers. Existing assessments of the performance of such algorithms in large-scale simulation environments assume homogeneous users and vastly ignore special needs of vulnerable users. In this paper, we perform an assessment of two frequently used fleet management algorithms and compare their behaviour when working with heterogeneous customer demand. We show that requests for which higher interaction times at pick-up are anticipated are rejected with higher probability, propose measures to increase the fairness of these algorithms, and propose pathways for future research.
Fichier principal
Vignette du fichier
heart_2024___Inclusive_DRT.pdf (340.29 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04551002 , version 1 (20-06-2024)

Identifiers

  • HAL Id : hal-04551002 , version 1

Cite

Tarek Chouaki, Sebastian Hörl. Comparative assessment of fairness in on-demand fleet management algorithms. The 12th Symposium of the European Association for Research in Transportation (hEART), Jun 2024, Espoo, Finland. ⟨hal-04551002⟩
45 View
0 Download

Share

Gmail Mastodon Facebook X LinkedIn More