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.

Keywords

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

References

[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

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

[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

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

[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

[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

[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

[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

[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

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

[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

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

[13] Engl H W, Hanke M, Neubauer A. Regularization of Inverse Problems. Springer Science & Business Media, 1996

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

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

[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] 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

[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

[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

[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

[21] Baraniuk R. Compressive sensing. IEEE signal processing magazine, 2007, 24(4)

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

[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

[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

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

[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

[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

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

[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

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg