Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 8028714, 8 pages
https://doi.org/10.1155/2018/8028714
Research Article

A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

School of Transportation and Logistics, Southwest Jiaotong University, No. 111 North Second Ring Road, Chengdu, Sichuan 610031, China

Correspondence should be addressed to Lilei Wang; moc.361@ude_ielilgnaw

Received 31 May 2017; Revised 10 November 2017; Accepted 3 December 2017; Published 3 January 2018

Academic Editor: Hakim Naceur

Copyright © 2018 Yajun Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Susan, G. Stacey, and Z. Hua, “Bike Sharing in Europe, the Americas, and Asia,” Transportation Research Record, pp. 159–167, 2010. View at Google Scholar
  2. T. Yang, P. Haixiao, and S. Qing, “Bike-sharing systems in Beijing, Shanghai and Hangzhou and their impact on travel behaviour,” in Proceedings of the Transportation Research Board Annual Meeting, 2011.
  3. C. Kloimüllner, P. Papazek, B. Hu, and G. R. Raidl, “Balancing bicycle sharing systems: an approach for the dynamic case,” in Evolutionary computation in combinatorial optimization, vol. 8600 of Lecture Notes in Comput. Sci., pp. 73–84, Springer, Heidelberg, 2014. View at Google Scholar · View at MathSciNet
  4. X. N. Liu, J. J. Wang, and T. F. Zhang, “A method of bike sharing demand forecasting,” Applied Mechanics and Materials, vol. 587-589, pp. 1813–1816, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. W. El-Assi, M. Salah Mahmoud, and K. Nurul Habib, “Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto,” Transportation, vol. 44, no. 3, pp. 589–613, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Faghih-Imani, N. Eluru, A. M. El-Geneidy, M. Rabbat, and U. Haq, “How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal,” Journal of Transport Geography, vol. 41, pp. 306–314, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. T. D. Tran, N. Ovtracht, and B. F. D'Arcier, “Modeling bike sharing system using built environment factors,” in Proceedings of the 7th CIRP Industrial Product-Service Systems Conference, IPSS 2015, pp. 293–298, France, May 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Frade and A. Ribeiro, “Bicycle Sharing Systems Demand,” Procedia - Social and Behavioral Sciences, vol. 111, pp. 518–527, 2014. View at Publisher · View at Google Scholar
  9. Q. Chen and T. Sun, “A model for the layout of bike stations in public bike-sharing systems,” Journal of Advanced Transportation, vol. 49, no. 8, pp. 884–900, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Borgnat, P. Abry, P. Flandrin, C. Robardet, J.-B. Rouquier, and E. Fleury, “Shared bicycles in a city: A signal processing and data analysis perspective,” Advances in Complex Systems (ACS), vol. 14, no. 3, pp. 415–438, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Fanaee-T and J. Gama, “Event labeling combining ensemble detectors and background knowledge,” Progress in Artificial Intelligence, vol. 2, no. 2-3, pp. 113–127, 2014. View at Publisher · View at Google Scholar
  12. R. Giot and R. Cherrier, “Predicting bikeshare system usage up to one day ahead,” in Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, CIVTS 2014, pp. 22–29, USA, December 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. S. M. Salaken, M. A. Hosen, A. Khosravi, and S. Nahavandi, “Forecasting Bike Sharing Demand Using Fuzzy Inference Mechanism,” in Neural Information Processing, vol. 9491 of Lecture Notes in Computer Science, pp. 567–574, Springer International Publishing, Cham, 2015. View at Publisher · View at Google Scholar
  14. H. Xu, J. Ying, H. Wu, and F. Lin, “Public bicycle traffic flow prediction based on a hybrid model,” Applied Mathematics & Information Sciences, vol. 7, no. 2, pp. 667–674, 2013. View at Publisher · View at Google Scholar
  15. D. Singhvi, S. Singhvi, P. I. Frazier et al., “Predicting bike usage for New York city's bike sharing system,” in Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 110–114, usa, January 2015. View at Scopus
  16. R. Rixey, “Station-level forecasting of bikesharing ridership,” Transportation Research Record, no. 2387, pp. 46–55, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Kaltenbrunner, R. Meza, J. Grivolla, J. Codina, and R. Banchs, “Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system,” Pervasive and Mobile Computing, vol. 6, no. 4, pp. 455–466, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. J. W. Yoon, F. Pinelli, and F. Calabrese, “Cityride: A predictive bike sharing journey advisor,” in Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management, MDM 2012, pp. 306–311, India, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Cagliero, T. Cerquitelli, S. Chiusano, P. Garza, and X. Xiao, “Predicting critical conditions in bicycle sharing systems,” Computing, vol. 99, no. 1, pp. 39–57, 2017. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Froehlich, J. Neumann, and N. Oliver, “Sensing and predicting the pulse of the city through shared bicycling,” in Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI-09, pp. 1420–1426, usa, July 2009. View at Scopus
  21. N. Fournier, E. Christofa, and M. A. Knodler, “A sinusoidal model for seasonal bicycle demand estimation,” Transportation Research Part D: Transport and Environment, vol. 50, pp. 154–169, 2017. View at Publisher · View at Google Scholar · View at Scopus