research-article

Mobility Modeling and Prediction in Bike-Sharing Systems

Authors:

Thomas MoscibrodaAuthors Info & Claims

MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services

Pages 165 - 178

https://doi.org/10.1145/2906388.2906408

Published: 20 June 2016 Publication History

Abstract

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though providing convenient, low-cost and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems to both users and operators. The primary issue among these problems is the uneven distribution of bicycles caused by the ever-changing usage and (available) supply. This bicycle imbalance issue necessitates efficient bike re-balancing strategies, which depends highly on bicycle mobility modeling and prediction. In this paper, for the first time, we propose a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devise a traffic prediction mechanism on a per-station basis with sub-hour granularity. We extensively evaluated the performance of our design through a one-year dataset from the world's largest public bike-sharing system (BSS) with more than 2800 stations and over 103 million check in/out records. Evaluation results show an 85 percentile relative error of 0.6 for both check in and check out prediction. We believe this new mobility modeling and prediction approach can advance the bike re-balancing algorithm design and pave the way for the rapid deployment and adoption of bike-sharing systems across the globe.

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Cited By

Chen JHuang R(2024)Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands PredictionAlgorithms10.3390/a1709038417:9(384)Online publication date: 1-Sep-2024
https://doi.org/10.3390/a17090384
Feng JLiu H(2024)An Adaptive Spatial-Temporal Method Capturing for Short-Term Bike-Sharing PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340668225:11(16761-16774)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3406682
Lee DKim K(2024)AdaBoost.RDT: AdaBoost Integrated With Residual-Based Decision Tree for Demand Prediction of Bike Sharing Systems Under Extreme DemandsIEEE Access10.1109/ACCESS.2024.347401712(144316-144336)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3474017
Show More Cited By

Index Terms

Mobility Modeling and Prediction in Bike-Sharing Systems
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Networks
  1. Network properties
    1. Network mobility

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cover image ACM Conferences

MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services

June 2016

440 pages

ISBN:9781450342698

DOI:10.1145/2906388

General Chairs:
Rajesh Balan
Singapore Management University
,
Archan Misra
Singapore Management University
,
Program Chairs:
Sharad Agarwal
Microsoft
,
Cecilia Mascolo
University of Cambridge

Copyright © 2016 ACM.

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 20 June 2016

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Ministry of Science and Technology of the People's Republic of China
Organization Department of the CPC Central Committee

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MobiSys'16

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SIGMOBILE

MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services

June 26 - 30, 2016

Singapore, Singapore

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MobiSys '16 Paper Acceptance Rate 31 of 197 submissions, 16%;

Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

Chen JHuang R(2024)Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands PredictionAlgorithms10.3390/a1709038417:9(384)Online publication date: 1-Sep-2024
https://doi.org/10.3390/a17090384
Feng JLiu H(2024)An Adaptive Spatial-Temporal Method Capturing for Short-Term Bike-Sharing PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340668225:11(16761-16774)Online publication date: Nov-2024
https://doi.org/10.1109/TITS.2024.3406682
Lee DKim K(2024)AdaBoost.RDT: AdaBoost Integrated With Residual-Based Decision Tree for Demand Prediction of Bike Sharing Systems Under Extreme DemandsIEEE Access10.1109/ACCESS.2024.347401712(144316-144336)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3474017
Kim K(2024)A new soft clustering method for traffic prediction in bike-sharing systemsInternational Journal of Sustainable Transportation10.1080/15568318.2024.235614118:6(492-504)Online publication date: 11-Jun-2024
https://doi.org/10.1080/15568318.2024.2356141
Schuhmacher LKübler JWilkes GKagerbauer MVortisch P(2024)Comparing Implementation Strategies of Station-Based Bike Sharing in Agent-Based Travel Demand ModelsProcedia Computer Science10.1016/j.procs.2024.06.040238(396-403)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.040
Lanvin AMichel PCharléty JChasse A(2024)Weathering heights: An updated analytical model of the nonlinear effects of weather on bicycle trafficJournal of Cycling and Micromobility Research10.1016/j.jcmr.2024.1000312(100031)Online publication date: Dec-2024
https://doi.org/10.1016/j.jcmr.2024.100031
Behroozi AEdrisi A(2024)Predicting travel demand of a bike sharing system using graph convolutional neural networksPublic Transport10.1007/s12469-024-00371-wOnline publication date: 12-Nov-2024
https://doi.org/10.1007/s12469-024-00371-w
Gričar SLojanica NObradović SBojnec Š(2023)Unlocking Sustainable Commuting: Exploring the Nexus of Macroeconomic Factors, Environmental Impact, and Daily Travel PatternsEnergies10.3390/en1620708716:20(7087)Online publication date: 13-Oct-2023
https://doi.org/10.3390/en16207087
Ko JByun Y(2023)Analyzing Factors Affecting Micro-Mobility and Predicting Micro-Mobility Demand Using Ensemble Voting RegressorElectronics10.3390/electronics1221441012:21(4410)Online publication date: 25-Oct-2023
https://doi.org/10.3390/electronics12214410
Kania MMukku VKastner KAssmann T(2023)Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike FleetsApplied Sciences10.3390/app1401018014:1(180)Online publication date: 25-Dec-2023
https://doi.org/10.3390/app14010180
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