A multi-objective optimization model for bike-sharing

Yu Shan; Dejun Xie; Rui Zhang

doi:10.1145/3377170.3377175

A multi-objective optimization model for bike-sharing

Yu Shan, Dejun Xie, Rui Zhang

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

The study proposes a multi-objective optimization model for bike-sharing industry by monitoring, with high accuracy, the user demand and providing the suitable number of bikes at selected stations. One of the key factors for designing an optimized bike sharing system is to balance the demand of pick-ups (drop-offs) around a given station and the number of available bikes (vacant lockers) in the station throughout the day. The model optimizes the location of bicycle stations and the number of parking slots that each station should have by taking account of the main contributing factors including the total budget of the bike sharing system, the popularity of riding in the city, and the expected proximity of the stations. A case study using the bike-sharing in New York was conducted to test theeffectiveness of themodel.

Original language	English
Title of host publication	ICIT 2019 - Proceedings of the 7th International Conference on Information Technology
Subtitle of host publication	IoT and Smart City
Publisher	Association for Computing Machinery
Pages	383-387
Number of pages	5
ISBN (Electronic)	9781450376631
DOIs	https://doi.org/10.1145/3377170.3377175
Publication status	Published - 20 Dec 2019
Event	7th International Conference on Information Technology: IoT and Smart City, ICIT 2019 - Shanghai, China Duration: 20 Dec 2019 → 23 Dec 2019

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	7th International Conference on Information Technology: IoT and Smart City, ICIT 2019
Country/Territory	China
City	Shanghai
Period	20/12/19 → 23/12/19

Keywords

Bike sharing systems
Parking slots
Sharing economy
Traveling cost
Unmet demand

Access to Document

10.1145/3377170.3377175

Cite this

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title = "A multi-objective optimization model for bike-sharing",

abstract = "The study proposes a multi-objective optimization model for bike-sharing industry by monitoring, with high accuracy, the user demand and providing the suitable number of bikes at selected stations. One of the key factors for designing an optimized bike sharing system is to balance the demand of pick-ups (drop-offs) around a given station and the number of available bikes (vacant lockers) in the station throughout the day. The model optimizes the location of bicycle stations and the number of parking slots that each station should have by taking account of the main contributing factors including the total budget of the bike sharing system, the popularity of riding in the city, and the expected proximity of the stations. A case study using the bike-sharing in New York was conducted to test theeffectiveness of themodel.",

keywords = "Bike sharing systems, Parking slots, Sharing economy, Traveling cost, Unmet demand",

author = "Yu Shan and Dejun Xie and Rui Zhang",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 7th International Conference on Information Technology: IoT and Smart City, ICIT 2019 ; Conference date: 20-12-2019 Through 23-12-2019",

year = "2019",

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day = "20",

doi = "10.1145/3377170.3377175",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "383--387",

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Shan, Y, Xie, D & Zhang, R 2019, A multi-objective optimization model for bike-sharing. in ICIT 2019 - Proceedings of the 7th International Conference on Information Technology: IoT and Smart City. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 383-387, 7th International Conference on Information Technology: IoT and Smart City, ICIT 2019, Shanghai, China, 20/12/19. https://doi.org/10.1145/3377170.3377175

A multi-objective optimization model for bike-sharing. / Shan, Yu; Xie, Dejun; Zhang, Rui.
ICIT 2019 - Proceedings of the 7th International Conference on Information Technology: IoT and Smart City. Association for Computing Machinery, 2019. p. 383-387 (ACM International Conference Proceeding Series).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

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AU - Shan, Yu

AU - Xie, Dejun

AU - Zhang, Rui

PY - 2019/12/20

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N2 - The study proposes a multi-objective optimization model for bike-sharing industry by monitoring, with high accuracy, the user demand and providing the suitable number of bikes at selected stations. One of the key factors for designing an optimized bike sharing system is to balance the demand of pick-ups (drop-offs) around a given station and the number of available bikes (vacant lockers) in the station throughout the day. The model optimizes the location of bicycle stations and the number of parking slots that each station should have by taking account of the main contributing factors including the total budget of the bike sharing system, the popularity of riding in the city, and the expected proximity of the stations. A case study using the bike-sharing in New York was conducted to test theeffectiveness of themodel.

AB - The study proposes a multi-objective optimization model for bike-sharing industry by monitoring, with high accuracy, the user demand and providing the suitable number of bikes at selected stations. One of the key factors for designing an optimized bike sharing system is to balance the demand of pick-ups (drop-offs) around a given station and the number of available bikes (vacant lockers) in the station throughout the day. The model optimizes the location of bicycle stations and the number of parking slots that each station should have by taking account of the main contributing factors including the total budget of the bike sharing system, the popularity of riding in the city, and the expected proximity of the stations. A case study using the bike-sharing in New York was conducted to test theeffectiveness of themodel.

KW - Bike sharing systems

KW - Parking slots

KW - Sharing economy

KW - Traveling cost

KW - Unmet demand

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ER -

A multi-objective optimization model for bike-sharing

Abstract

Publication series

Conference

Keywords

Access to Document

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