Rebalancing Shared Mobility Systems by User Incentive Schemes: State-Action Representation Design and Analysis

Matthew Schofield; Ning Wang; Shen Shyang Ho

doi:10.1145/3605098.3636130

Rebalancing Shared Mobility Systems by User Incentive Schemes: State-Action Representation Design and Analysis

Matthew Schofield
, Ning Wang
, Shen Shyang Ho

Computer Science

Rowan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In shared mobility systems research, there is increasing interest in the use of Reinforcement Learning (RL) techniques to both manage the resources and improve the service level (i.e., percentage of users served). The goal of using RL is to learn an effective data-driven user incentivization policy to encourage users of a shared mobility system (e.g. bikeshare systems) to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve its profitability. We present our study on the effects of state-action representation for a shared mobility system on performance for user incentivization, in terms of service level.

Original language	English (US)
Title of host publication	39th Annual ACM Symposium on Applied Computing, SAC 2024
Publisher	Association for Computing Machinery
Pages	1045-1047
Number of pages	3
ISBN (Electronic)	9798400702433
DOIs	https://doi.org/10.1145/3605098.3636130
State	Published - Apr 8 2024
Event	39th Annual ACM Symposium on Applied Computing, SAC 2024 - Avila, Spain Duration: Apr 8 2024 → Apr 12 2024

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Conference

Conference	39th Annual ACM Symposium on Applied Computing, SAC 2024
Country/Territory	Spain
City	Avila
Period	4/8/24 → 4/12/24

ASJC Scopus subject areas

Software

Access to Document

10.1145/3605098.3636130

Cite this

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title = "Rebalancing Shared Mobility Systems by User Incentive Schemes: State-Action Representation Design and Analysis",

abstract = "In shared mobility systems research, there is increasing interest in the use of Reinforcement Learning (RL) techniques to both manage the resources and improve the service level (i.e., percentage of users served). The goal of using RL is to learn an effective data-driven user incentivization policy to encourage users of a shared mobility system (e.g. bikeshare systems) to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve its profitability. We present our study on the effects of state-action representation for a shared mobility system on performance for user incentivization, in terms of service level.",

author = "Matthew Schofield and Ning Wang and Ho, \{Shen Shyang\}",

note = "Publisher Copyright: {\textcopyright} 2024 Copyright held by the owner/author(s).; 39th Annual ACM Symposium on Applied Computing, SAC 2024 ; Conference date: 08-04-2024 Through 12-04-2024",

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doi = "10.1145/3605098.3636130",

language = "English (US)",

series = "Proceedings of the ACM Symposium on Applied Computing",

publisher = "Association for Computing Machinery",

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booktitle = "39th Annual ACM Symposium on Applied Computing, SAC 2024",

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Schofield, M, Wang, N & Ho, SS 2024, Rebalancing Shared Mobility Systems by User Incentive Schemes: State-Action Representation Design and Analysis. in 39th Annual ACM Symposium on Applied Computing, SAC 2024. Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, pp. 1045-1047, 39th Annual ACM Symposium on Applied Computing, SAC 2024, Avila, Spain, 4/8/24. https://doi.org/10.1145/3605098.3636130

Rebalancing Shared Mobility Systems by User Incentive Schemes: State-Action Representation Design and Analysis. / Schofield, Matthew; Wang, Ning; Ho, Shen Shyang.
39th Annual ACM Symposium on Applied Computing, SAC 2024. Association for Computing Machinery, 2024. p. 1045-1047 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In shared mobility systems research, there is increasing interest in the use of Reinforcement Learning (RL) techniques to both manage the resources and improve the service level (i.e., percentage of users served). The goal of using RL is to learn an effective data-driven user incentivization policy to encourage users of a shared mobility system (e.g. bikeshare systems) to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior are intended to over time increase the service level of the shared mobility system and improve its profitability. We present our study on the effects of state-action representation for a shared mobility system on performance for user incentivization, in terms of service level.

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Rebalancing Shared Mobility Systems by User Incentive Schemes: State-Action Representation Design and Analysis

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ASJC Scopus subject areas

Access to Document

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