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A Multiple Factor Bike Usage Prediction Model in Bike-Sharing System

  • Zengwei Zheng
  • Yanzhen Zhou
  • Lin SunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

Bike-sharing is becoming popular in the world, providing a convenient service for citizens. The system has to redistribute bikes among different stations frequently to solve the imbalance of spatial distribution. Real-time monitoring doesn’t solve this problem well, since it takes too much time to redistribute the bike and affects the user experience. In this paper, we first analyze the influence of factors such as time, weather, the location of stations. Then we cluster neighboring stations with similar usage pattern, and propose a lagged variable to simulate the effect of weather conditions in usage number. Finally, a multiple factor regression model with ARMA error (MFR-ARMA) is proposed to predict the check-out/in number of bikes in each cluster in a period of time. Evaluation dataset is from New York Bike Sharing System. The prediction results of the model are compared with four baseline methods. The experiments show a lower RMSLE and ER for check-out/in number prediction in our model.

Keywords

Bike sharing system Regression prediction model ARMA error Cluster-level 

Notes

Acknowledgment

This work is supported by Zhejiang Provincial Natural Science Foundation of China (No. LY17F020008).

References

  1. 1.
    Shaheen, S., Guzman, S., Zhang, H.: Bikesharing in Europe, the Americas, and Asia: past, present, and future. Transp. Res. Rec. J. Transp. Res. Board 2143, 159–167 (2010)CrossRefGoogle Scholar
  2. 2.
    Vogel, P., Greiser, T., Mattfeld, D.C.: Understanding bike-sharing systems using data mining: exploring activity patterns. Procedia Soc. Behav. Sci. 20, 514–523 (2011)CrossRefGoogle Scholar
  3. 3.
    Gast, N., Massonnet, G., Reijsbergen, D., et al.: Probabilistic forecasts of bike-sharing systems for journey planning. In: CIKM2015-Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, Australia, 18–23, October, pp. 703–712. ACM, New York (2015)Google Scholar
  4. 4.
    Singla, A., Santoni, M., Meenen, M., et al.: Incentivizing users for balancing bike sharing systems. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, 25–30 January, pp. 723–729. AAAI Press (2015)Google Scholar
  5. 5.
    Li, Y., Zheng, Y., Zhang, H., et al.: Traffic prediction in a bike-sharing system. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, Washington, 03–06 November, Article no. 33. ACM, New York (2015)Google Scholar
  6. 6.
    Schuijbroek, J., Hampshire, R.C., Hoeve, W.J.V.: Inventory rebalancing and vehicle routing in bike sharing systems. Eur. J. Oper. Res. 257(3), 992–1004 (2017)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Borgnat, P., Abry, P., Flandrin, P., et al.: Shared bicycles in a city: a signal processing and data analysis perspective. Adv. Complex Syst. 14(03), 415–438 (2011)CrossRefGoogle Scholar
  8. 8.
    Kaltenbrunner, A., Meza, R., Grivolla, J., et al.: Urban cycles and mobility patterns: exploring and predicting trends in a bicycle-based public transport system. Adv. Complex Syst. 6(4), 455–466 (2010)Google Scholar
  9. 9.
    Yoon, J.W., Pinelli, F., Calabrese, F.: Cityride: a predictive bike sharing journey advisor. In: IEEE 13th International Conference on Mobile Data Management (MDM), Karnataka, India, 23–26 July, pp. 306–311. IEEE (2012)Google Scholar
  10. 10.
    Yang, Z., Hu, J., Shu, Y., et al.: Mobility modeling and prediction in bike-sharing systems. In: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, Singapore, 26–30 June, pp. 165–178. ACM, New York (2016)Google Scholar
  11. 11.
    Chen, L., Zhang, D., Wang, L., et al.: Dynamic cluster-based over-demand prediction in bike sharing systems. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 12–16 September, pp. 841–852. ACM, New York (2016)Google Scholar
  12. 12.
    Singhvi, D., Singhvi, S., Frazier, P.I., et al.: Predicting bike usage for New York City’s bike sharing system. In: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence: Computational Sustainability (2015)Google Scholar
  13. 13.
    Gebhart, K., Noland, R.B.: The impact of weather conditions on bikeshare trips in Washington D.C. Transportation 41(6), 1205–1225 (2014)CrossRefGoogle Scholar
  14. 14.
    Reed, W.R.: On the practice of lagging variables to avoid simultaneity. Oxford Bull. Econ. Stat. 77(6), 897–905 (2015)CrossRefGoogle Scholar
  15. 15.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C., et al.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)zbMATHGoogle Scholar
  16. 16.
    Citi Bike: Citi Bike System Data (2014). http://www.citibikenyc.com/system-data
  17. 17.
    Weather Underground Inc.: Weather history (2014). https://www.wunderground.com/history/

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hangzhou Key Laboratory for IoT Technology and ApplicationZhejiang University City CollegeHangzhouChina
  2. 2.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina

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