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Adaptive pricing mechanisms for on-demand mobility

Adaptive pricing mechanisms for on-demand mobility
Adaptive pricing mechanisms for on-demand mobility
We consider on-demand car rental systems for public transportation. In these systems, demands are often unbalanced across different parking stations, necessitating costly manual relocations of vehicles. To address this so-called "deadheading-effect" and maximise the operator's revenue, we propose two novel pricing mechanisms. These adaptively adjust the prices between origin and destination stations depending on their current occupancy, probabilistic information about the customers' valuations and estimated relocation costs. In so doing, the mechanisms incentivise drivers to help rebalance the system and place a premium on trips that lead to costly relocations. We evaluate the mechanisms in a series of experiments using real historical data from an existing on-demand mobility system in a French city. We show that our mechanisms achieve an up to 64% increase in revenue for the operator and at the same time up to 36% fewer relocations.
1017-1025
Drwal, Maciej
0facb879-8c21-4221-85de-c474bed3d735
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Hayakawa, Keiichiro
29e1e6b7-c964-44c2-85be-e4495188032b
Kitaoka, Hironobu
f6dd3447-a82a-4f2b-b8ad-303456dc168d
Drwal, Maciej
0facb879-8c21-4221-85de-c474bed3d735
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
Hayakawa, Keiichiro
29e1e6b7-c964-44c2-85be-e4495188032b
Kitaoka, Hironobu
f6dd3447-a82a-4f2b-b8ad-303456dc168d

Drwal, Maciej, Gerding, Enrico, Stein, Sebastian, Hayakawa, Keiichiro and Kitaoka, Hironobu (2017) Adaptive pricing mechanisms for on-demand mobility. In Proceedings of AAMAS 2017: Sixteenth International Conference on Autonomous Agents and Multiagent Systems. 9 pp, pp. 1017-1025.

Record type: Conference or Workshop Item (Paper)

Abstract

We consider on-demand car rental systems for public transportation. In these systems, demands are often unbalanced across different parking stations, necessitating costly manual relocations of vehicles. To address this so-called "deadheading-effect" and maximise the operator's revenue, we propose two novel pricing mechanisms. These adaptively adjust the prices between origin and destination stations depending on their current occupancy, probabilistic information about the customers' valuations and estimated relocation costs. In so doing, the mechanisms incentivise drivers to help rebalance the system and place a premium on trips that lead to costly relocations. We evaluate the mechanisms in a series of experiments using real historical data from an existing on-demand mobility system in a French city. We show that our mechanisms achieve an up to 64% increase in revenue for the operator and at the same time up to 36% fewer relocations.

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Accepted/In Press date: 25 February 2017
e-pub ahead of print date: May 2017
Published date: May 2017
Venue - Dates: AAMAS 2017: Sixteenth International Conference on Antonomous Agents and Multiagent Sytems, São Paulo, Brazil, 2017-05-08 - 2017-05-12
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 405176
URI: https://eprints.soton.ac.uk/id/eprint/405176
PURE UUID: 76b4aa9a-62fc-4eff-8e01-ce62a6e495bc

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Date deposited: 26 Jan 2017 17:00
Last modified: 25 Jul 2018 16:30

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