research-article

Effective Recycling Planning for Dockless Sharing Bikes

Authors:

Xianen LiAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 62 - 70

https://doi.org/10.1145/3347146.3359340

Published: 05 November 2019 Publication History

Abstract

Bike-sharing systems become more and more popular in the urban transportation system, because of their convenience in recent years. However, due to the high daily usage and lack of effective maintenance, the number of bikes in good condition decreases significantly, and vast piles of broken bikes appear in many big cities. As a result, it is more difficult for regular users to get a working bike, which causes problems both economically and environmentally. Therefore, building an effective broken bike prediction and recycling model becomes a crucial task to promote cycling behavior. In this paper, we propose a predictive model to detect the broken bikes and recommend an optimal recycling program based on the large scale real-world sharing bike data. We incorporate the realistic constraints to formulate our problem and introduce a flexible objective function to tune the trade-off between the broken probability and recycled numbers of the bikes. Finally, we provide extensive experimental results and case studies to demonstrate the effectiveness of our approach.

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Cited By

Zhang CWu FWang HZhang HMa HLiu Y(2024)A Deep Reinforcement Learning Model for a Two-Layer Scheduling Policy in Urban Public ResourcesIEEE Internet of Things Journal10.1109/JIOT.2023.329290311:2(2712-2727)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3292903
He XWang Q(2023)A location-routing model for free-floating shared bike collection considering manual gathering and truck transportationSocio-Economic Planning Sciences10.1016/j.seps.2023.10166788(101667)Online publication date: Aug-2023
https://doi.org/10.1016/j.seps.2023.101667
Zhang CWu FWang HTang BFan WLiu Y(2022)A Meta-Learning Algorithm for Rebalancing the Bike-Sharing System in IoT Smart CityIEEE Internet of Things Journal10.1109/JIOT.2022.31761459:21(21073-21085)Online publication date: 1-Nov-2022
https://doi.org/10.1109/JIOT.2022.3176145
Show More Cited By

Index Terms

Effective Recycling Planning for Dockless Sharing Bikes
1. Applied computing
  1. Operations research
    1. Forecasting
    2. Transportation
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 05 November 2019

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SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Zhang CWu FWang HZhang HMa HLiu Y(2024)A Deep Reinforcement Learning Model for a Two-Layer Scheduling Policy in Urban Public ResourcesIEEE Internet of Things Journal10.1109/JIOT.2023.329290311:2(2712-2727)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3292903
He XWang Q(2023)A location-routing model for free-floating shared bike collection considering manual gathering and truck transportationSocio-Economic Planning Sciences10.1016/j.seps.2023.10166788(101667)Online publication date: Aug-2023
https://doi.org/10.1016/j.seps.2023.101667
Zhang CWu FWang HTang BFan WLiu Y(2022)A Meta-Learning Algorithm for Rebalancing the Bike-Sharing System in IoT Smart CityIEEE Internet of Things Journal10.1109/JIOT.2022.31761459:21(21073-21085)Online publication date: 1-Nov-2022
https://doi.org/10.1109/JIOT.2022.3176145
Golpayegani FGueriau MLaharotte PGhanadbashi SGuo JGeraghty JWang S(2022)Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future DirectionIEEE Access10.1109/ACCESS.2022.316284810(35302-35320)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3162848
Pan MHuang WLi YZhou XLiu ZBao JZheng YLuo J(2020)Is Reinforcement Learning the Choice of Human Learners?Proceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422246(357-366)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422246
Liu CSun JJin HAi MLi QZhang CSheng KWu GQie XWang X(2020)Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing SystemsProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422212(227-238)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422212

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