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
Association for Computing Machinery
Drwal, Maciej
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Gerding, Enrico
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Stein, Sebastian
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Hayakawa, Keiichiro
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Kitaoka, Hironobu
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May 2017
Drwal, Maciej
0facb879-8c21-4221-85de-c474bed3d735
Gerding, Enrico
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Stein, Sebastian
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Hayakawa, Keiichiro
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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 AAMAS '17 Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems.
Association for Computing Machinery.
.
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, WTC São Paulo, São Paulo, Brazil, 2017-05-08 - 2017-05-12
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 405176
URI: http://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: 16 Mar 2024 03:57
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Contributors
Author:
Maciej Drwal
Author:
Enrico Gerding
Author:
Sebastian Stein
Author:
Keiichiro Hayakawa
Author:
Hironobu Kitaoka
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