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.

References

[1]

LLC MetroBike. The Bike Sharing World - 2014 -Year End Data. http://bike-sharing.blogspot.com/2015/01/the-bike-sharing-world-2014-year-end.html.

[2]

Wikipedia. List of Bicycle-sharing Systems. https://en.wikipedia.org/wiki/List_of_bicycle-sharing_systems.

[3]

Jon Froehlich, Joachim Neumann, and Nuria Oliver. Sensing and Predicting the Pulse of the City through Shared Bicycling. In IJCAI, 2009.

Digital Library

[4]

Andreas Kaltenbrunner, Rodrigo Meza, Jens Grivolla, Joan Codina, and Rafael Banchs. Urban Cycles and Mobility Patterns: Exploring and Predicting Trends in a Bicycle-based Public Transport System. Pervasive and Mobile Computing, 6(4):455--466, 2010.

Digital Library

[5]

Patrick Vogel and Dirk C. Mattfeld. Strategic and Operational Planning of Bike-Sharing Systems by Data Mining - A Case Study. In Computational Logistics, pages 127--141. 2011.

Digital Library

[6]

Pierre Borgnat, Eric Fleury, Céline Robardet, and Antoine Scherrer. Spatial Analysis of Dynamic Movements of Vélo'v, Lyon's Shared Bicycle Program. In European Conference on Complex Systems (ECCS), 2009.

[7]

Yexin Li, Yu Zheng, Huichu Zhang, and Lei Chen. Traffic Prediction in a Bike Sharing System. In ACM SIGSPATIAL, 2015.

Digital Library

[8]

Susan a. Shaheen, Hua Zhang, Elliot Martin, and Stacey Guzman. China's Hangzhou Public Bicycle. Transportation Research Record: Journal of the Transportation Research Board, 2247(1):33--41, 2011.

[9]

Wikipedia. Hangzhou Public Bicycle. https://en.wikipedia.org/wiki/Hangzhou_Public_Bicycle.

[10]

Eoin O Mahony and David B Shmoys. Data Analysis and Optimization for (Citi) Bike Sharing. In AAAI, 2015.

Digital Library

[11]

Nicolas Gast, Guillaume Massonnet, Daniël Reijsbergen, and Mirco Tribastone. Probabilistic forecasts of bike-sharing systems for journey planning. In ACM CIKM, 2015.

Digital Library

[12]

Leo Breiman. Random Forests. Machine Learning, 45(1):5--32, 2001.

Digital Library

[13]

Longbiao Chen, Daqing Zhang, Gang Pan, Xiaojuan Ma, Dingqi Yang, Kostadin Kushlev, Wangsheng Zhang, and Shijian Li. Bike Sharing Station Placement Leveraging Heterogeneous Urban Open Data. In ACM Ubicomp, 2015.

Digital Library

[14]

Citi Bike. New York City Bike-sharing System Data. https://www.citibikenyc.com/system-data.

[15]

Desheng Zhang, Jun Huang, Ye Li, Fan Zhang, Chengzhong Xu, and Tian He. Exploring Human Mobility with Multi-source Data at Extremely Large Metropolitan Scales. In ACM MobiCom, 2014.

Digital Library

[16]

Lihuan Zhang, Siyuan Tang, Zidong Yang, Ji Hu, Yuanchao Shu, Peng Cheng, and Jiming Chen. Demo: Data Analysis and Visualization in Bike-Sharing Systems. http://www.sensornet.cn/bikevis/.

[17]

Susan a. Shaheen, Stacey Guzman, and Hua Zhang. Bikesharing in Europe, the Americas, and Asia. Transportation Research Record: Journal of the Transportation Research Board, 2143:159--167, 2010.

[18]

Susan a. Shaheen, Adam P. Cohen, and Elliot W. Martin. Public Bikesharing in North America: Early Operator Understanding and Emerging Trends. Transportation Research Record: Journal of the Transportation Research Board, 2387:83--92, 2013.

[19]

Stephen D. Parkes, Greg Marsden, Susan A. Shaheen, and Adam P. Cohen. Understanding the Diffusion of Public Bikesharing Systems: Evidence from Europe and North America. Journal of Transport Geography, 31:94--103, 2013.

[20]

Rahul Nair, Elise Miller-Hooks, Robert C. Hampshire, and Ana Bušić. Large-Scale Vehicle Sharing Systems: Analysis of Vélib'. International Journal of Sustainable Transportation, 7(1):85--106, 2013.

[21]

LDA Consulting Washington. 2013 Capital Bikeshare Member Survey Report. Technical Report 202, 2013.

[22]

Elliot W. Martin and Susan A. Shaheen. Evaluating Public Transit Modal Shift Dynamics in Response to Bikesharing: A Tale of Two U.S. Cities. Journal of Transport Geography, 41:315--324, 2014.

[23]

Ahmadreza Faghih Imani, Naveen Eluru, Ahmed M. El-Geneidy, Michael Rabbat, and Usama Haq. How does Land-use and Urban Form Impact Bicycle Flows: Evidence from the Bicycle-sharing System (BIXI) in Montreal. Transport Geography, (February):1--20, 2014.

[24]

Paul DeMaio. Bike-sharing: History, Impacts, Models of Provision, and Future. Journal of Public Transportation, 12(DeMaio 2004):41--56, 2009.

[25]

Department for City Planning New York. Bike-Share. Opportunities in New York City. Technical report, 2009.

[26]

P. Borgnat, C. Robardet, P. Abry, P. Flandrin, J. Rouquier, and N. Tremblay. A Dynamical Network View of Lyon's Vélo'v Shared Bicycle System. In Dynamics On and Of Complex Networks, volume 2, chapter A Dynamical, pages 267--284. Springer Berlin Heidelberg, 2013.

[27]

C Ome and Oukhellou Latifa. Model-Based Count Series Clustering for Bike Sharing System Usage Mining : A Case Study with the Vélib System of Paris. ACM Transactions on Intelligent Systems and Technology, 5(3):1--21, 2014.

Digital Library

[28]

Ji Won Yoon, Fabio Pinelli, and Francesco Calabrese. Cityride: A Predictive Bike Sharing Journey Advisor. In IEEE ICMDM, 2012.

Digital Library

[29]

Juan Carlos García-Palomares, Javier Gutiénrrez, and Marta Latorre. Optimizing the Location of Stations in Bike-sharing Programs: A GIS Approach. Applied Geography, 35(1--2):235--246, 2012.

[30]

Juan P. Romero, Angel Ibeas, Jose L. Moura, Juan Benavente, and Borja Alonso. A Simulation-optimization Approach to Design Efficient Systems of Bike-sharing. Meeting of the EURO Working Group on Transportation, 54:646--655, 2012.

[31]

Jenn-Rong Lin and Ta-Hui Yang. Strategic Design of Public Bicycle Sharing Systems with Service Level Constraints. Transportation Research Part E: Logistics and Transportation Review, 47(2):284--294, 2011.

[32]

Tal Raviv, Michal Tzur, and IrisA. Forma. Static Repositioning in a Bike-sharing System: Models and Solution Approaches. EURO Journal on Transportation and Logistics, 2(3):187--229, 2013.

[33]

Jia Shu, Mabel C. Chou, Qizhang Liu, Chung-Piaw Teo, and I-Lin Wang. Models for Effective Deployment and Redistribution of Bicycles Within Public Bicycle-Sharing Systems. Operations Research, 61(6):1346--1359, 2013.

Digital Library

[34]

Contardo, Claudio, Catherine Morency, and Louis-Martin Rousseau. Balancing a Dynamic Public Bike-sharing System. Technical report, 2012.

[35]

Jasper Schuijbroek, Robert Hampshire, and Willem-Jan van Hoeve. Inventory Rebalancing and Vehicle Routing in Bike Sharing Systems. Technical report, 2013.

[36]

Raghu Ganti, Mudhakar Srivatsa, Anand Ranganathan, and Jiawei Han. Inferring Human Mobility Patterns from Taxicab Location Traces. In ACM UbiComp, 2013.

Digital Library

[37]

Sourav Bhattacharya, Santi Phithakkitnukoon, Petteri Nurmi, Arto Klami, Marco Veloso, and Carlos Bento. Gaussian Process-based Predictive Modeling for Bus Ridership. In ACM UbiComp, 2013.

Digital Library

[38]

Neal Lathia and Licia Capra. How Smart is Your Smartcard?: Measuring Travel Behaviours, Perceptions, and Incentives. In ACM UbiComp, 2011.

Digital Library

[39]

Fosca Giannotti, Mirco Nanni, Dino Pedreschi, Fabio Pinelli, Chiara Renso, Salvatore Rinzivillo, and Roberto Trasarti. Unveiling the Complexity of Human Mobility by Querying and Mining Massive Trajectory Data. The VLDB Journal, 20(5):695--719, October 2011.

Digital Library

[40]

Jungkeun Yoon, Brian D. Noble, Mingyan Liu, and Minkyong Kim. Building Realistic Mobility Models from Coarse-grained Traces. In ACM MobiSys, 2006.

Digital Library

[41]

Jennie Steshenko, Vasanta G. Chaganti, and James Kurose. Mobility in a Large-scale WiFi Network: From Syslog Events to Mobile User Sessions. In ACM MSWiM, 2014.

Digital Library

[42]

Sibren Isaacman, Richard Becker, Ramón Cáceres, Margaret Martonosi, James Rowland, Alexander Varshavsky, and Walter Willinger. Human Mobility Modeling at Metropolitan Scales. In ACM MobiSys, 2012.

Digital Library

[43]

Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: User movement in location-based social networks. In ACM KDD, 2011.

Digital Library

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
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
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.

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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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

Publication History

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
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
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
Wang SZhu XWang GLiu YHe TZhang D(2023)eShare+: A Data-Driven Balancing Mechanism for Bike Sharing Systems Considering Both Quality of Service and MaintenanceIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325372535:10(10497-10513)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3253725
Wang LChai DLiu XChen LChen K(2023)Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic FrameworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313076235:4(3870-3884)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TKDE.2021.3130762
Wang JMiwa TMorikawa T(2023)A Demand Truncation and Migration Poisson Model for Real Demand Inference in Free-Floating Bike-Sharing SystemIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.327508124:10(10525-10536)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3275081
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