Understanding bike trip patterns leveraging bike sharing system open data

Abstract

Bike sharing systems are booming globally as a green and flexible transportationmode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and station management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips (i.e., origin-destination station pairs) is much larger than the number of stations, we define the trip inference as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data fromWashington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.

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References

  1. 1.

    Shaheen S, Guzman S, Zhang H. Bikesharing in Europe, the Americas, and Asia. Transportation Research Record: Journal of the Transportation Research Board, 2010, 2143(1): 159–167

    Article  Google Scholar 

  2. 2.

    LDA Consulting. 2013 Capital Bikeshare Member Survey Report. Washington, D.C.: Capital Bikeshare, 2013

  3. 3.

    Wang J Y, Gao F, Cui P, Li C, Xiong Z. Discovering urban spatiotemporal structure from time-evolving traffic networks. In: Proceedings of the 16th Asia-Pacific Web Conference on Web Technologies and Applications. 2014, 93–104

    Google Scholar 

  4. 4.

    Chemla D, Meunier F, Calvo R W. Bike sharing systems: solving the static rebalancing problem. Discrete Optimization, 2013, 10(2): 120–146

    MathSciNet  Article  MATH  Google Scholar 

  5. 5.

    Chen L B, Zhang D Q, Pan G, Ma X J, Yang D Q, Kushlev K, Zhang W S, Li S J. Bike sharing station placement leveraging heterogeneous urban open data. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2015, 571–575

    Google Scholar 

  6. 6.

    Ji R R, Gao Y, Liu W, Xie X, Tian Q, Li X L. When location meets social multimedia: a survey on vision-based recognition and mining for geo-social multimedia analytics. ACM Transactions on Intelligent Systems and Technology, 2015, 6(1): 1–18

    Article  Google Scholar 

  7. 7.

    Yu Z, Xu H, Yang Z, Guo B. Personalized travel package with multipoint- of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems, 2016, 46(1): 151–158

    Article  Google Scholar 

  8. 8.

    Chen L B, Yang D, Jakubowicz J, Pan G, Zhang D Q, Li S J. Sensing the pulse of urban activity centers leveraging bike sharing open data. In: Proceedings of the 12th IEEE International Conference on Ubiquitous Intelligence and Computing. 2015

    Google Scholar 

  9. 9.

    Garcia-Palomares J C, Gutierrez J, Latorre M. Optimizing the location of stations in bike-sharing programs: a GIS approach. Applied Geography, 2012, 35(1–2): 235–246

    Article  Google Scholar 

  10. 10.

    Contardo C, Morency C, Rousseau L M. Balancing a Dynamic Public Bike-Sharing System. Cirrelt, 2012

    Google Scholar 

  11. 11.

    Singla A, Santoni M, Bartók G, Mukerji P, Meenen M, Krause A. Incentivizing users for balancing bike sharing systems. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 723–729

    Google Scholar 

  12. 12.

    Tikhonov A N, Arsenin V J. Solutions of Ill-Posed Problems. Washinton, D. C.: V. H. Winston & Sons, 1977

    Google Scholar 

  13. 13.

    Engl HW, Hanke M, Neubauer A. Regularization of Inverse Problems. Springer Science & Business Media, 1996

    Google Scholar 

  14. 14.

    Meinshausen N, Bühlmann P. High-dimensional graphs and variable selection with the lasso. The Annals of Statistics, 2006, 1436–1462

    Google Scholar 

  15. 15.

    Boyd S, Vandenberghe L. Convex Optimization. New York: Cambridge University Press, 2004

    Google Scholar 

  16. 16.

    Guo B, Wang Z, Yu Z, Wang Y, Yen N Y, Huang R H, Zhou X S. Mobile crowd sensing and computing: the review of an emerging humanpowered sensing paradigm. ACM Computer Survey, 2015, 48(1)

  17. 17.

    Froehlich J, Neumann J, Oliver N. Sensing and predicting the pulse of the city through shared bicycling. In: Proceedings of the International Joint Conference on Artificial Intelligence. 2009, 1420–1426

    Google Scholar 

  18. 18.

    Zhao Y, Chen L, Teng C, Li S, Pan G. GreenBicycling: a smartphonebased public bicycle sharing system for healthy life. In: Proceedings of the IEEE International Conference on and IEEE Cyber, Physical and Social Computing. 2013, 1335–1340

    Google Scholar 

  19. 19.

    Randriamanamihaga A N, Côme E, Oukhellou L, Govaert G. Clustering the Vélib dynamic Origin/Destination flows using a family of Poisson mixture models. Neurocomputing, 2014, 141: 124–138

    Article  Google Scholar 

  20. 20.

    Combal B, Baret F, Weiss M, Trubuil A, Macé D, Pragnère A, Myneni R, Knyazikhin Y, Wang L. Retrieval of canopy biophysical variables from bidirectional reflectance: using prior information to solve the illposed inverse problem. Remote Sensing of Environment, 2003, 84(1): 1–15

    Article  Google Scholar 

  21. 21.

    Baraniuk R. Compressive sensing. IEEE Signal Processing Magazine, 2007, 24(4)

  22. 22.

    Vardi Y. Network tomography: estimating source-destination traffic intensities from link data. Journal of the American Statistical Association, 1996, 91(433): 365–377

    MathSciNet  Article  MATH  Google Scholar 

  23. 23.

    Wang L Y, Zhang D Q, Pathak A, Chen C, Xiong H Y, Yang D Q, Wang Y S. CCS-TA: quality-guaranteed online task allocation in compressive crowdsensing. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2015, 683–694

    Google Scholar 

  24. 24.

    Chawla S, Zheng Y, Hu J. Inferring the root cause in road traffic anomalies. In: Proceedings of the IEEE International Conference on Data Mining. 2012, 141–150

    Google Scholar 

  25. 25.

    Burden A M, Barth R. Bike-Share Opportunities in New York City. New York: Department of City Planning, 2009

    Google Scholar 

  26. 26.

    Zabreyko P P, Koshelev A I, Krasnosel’skii M A, Mikhlin S G, Rakovshchik L S, Stet’senko V Y. Integral Equations: A Reference Text. Leyden: Noordhoff International Publishing, 1975

    Google Scholar 

  27. 27.

    Candes E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489–509

    MathSciNet  Article  MATH  Google Scholar 

  28. 28.

    Grant M C, Boyd S P. Graph implementations for nonsmooth convex programs. In: Blondel V D, Boyd S P, Kimura H, eds. Recent Advances in Learning and Control. London: Springer, 2008, 95–110

    Google Scholar 

  29. 29.

    Powers D M W. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2011, 2(1): 37–63

    MathSciNet  Google Scholar 

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Authors

Corresponding author

Correspondence to Jérémie Jakubowicz.

Additional information

Longbiao Chen received the BS degree in computer science from Zhejiang University (ZJU), China in 2010. He is currently pursuing the PhD degree in Department of Computer Science, ZJU and visiting Institut Mines-TELECOM / TELECOM Sud- Paris in France. His research interests are mainly in urban computing, big data applications, and ubiquitous computing.

Xiaojuan Ma is an assistant professor of human-computer interaction (HCI) at the Department of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology (HKUST), China. She received the PhD degree in computer science at Princeton University, USA. She was a post-doctoral researcher at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University (CMU), USA and before that a research fellow in the Information Systems Department, National University of Singapore (NUS), Singapore. Before joining HKUST, she was a researcher of Human-Computer Interaction at Noah’s Ark Lab, Huawei Tech. Investment Co., Ltd. in Hong Kong, China.

Thi-Mai-Trang Nguyen is an associate professor at University Pierre and Marie Curie (Paris 6) and doing research at Laboratoire d’Informatique de Paris 6 (LIP6), France. She received the PhD degree in computer science from University of Paris 6, France in 2003. The PhD thesis was co-supervised and carried-out at Ecole Nationale Superieure des Telecommunications (ENST-Paris). From 2004 to 2006, She was a postdoctoral researcher at France Telecom in Rennes, France and at University of Lausanne, Switzerland. Her research interests include network architecture, network protocol design, and network data analytics.

Gang Pan received the BS and PhD degrees in computer science from Zhejiang University (ZJU), China in 1998 and 2004, respectively. He is currently a professor with the College of Computer Science and Technology, ZJU. He has published more than 90 refereed papers. He visited the University of California, USA during 2007 and 2008. His research interests include pervasive computing, computer vision, and pattern recognition.

Jérémie Jakubowicz received the MS and PhD degrees in applied mathematics from the Ecole Normale Supérieure de Cachan, France in 2004 and 2007, respectively. He was an assistant professor with Télécom ParisTech, France. Since 2011, he has been an assistant professor with Télécom Sud-Paris, Evry, France and an associate researcher with the CNRS, France. His current research interests include distributed statistical signal processing, image processing, and data mining.

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Chen, L., Ma, X., Nguyen, T. et al. Understanding bike trip patterns leveraging bike sharing system open data. Front. Comput. Sci. 11, 38–48 (2017). https://doi.org/10.1007/s11704-016-6006-4

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Keywords

  • bike sharing system
  • open data
  • ill-posed inverse problems
  • urban computing