Advertisement

Analysis of Urban Bicycles’ Trip Behavior and Efficiency Optimization

  • Haoyu WenEmail author
  • Sheng Zhou
  • Zie Wang
  • Feier Qiu
  • Han Yu
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

Bicycle sharing systems are becoming more and more prevalent in urban environments. They provide a low environmental friendly transportation alternative city. The management of these systems brings many optimization problems. The most important of these problems is the individual maintenance of bicycle rebalancing and shared facilities, and the use of systems by creating requirements in asymmetrical patterns. In order to solve the problem of unbalanced use of bicycles, based on real data sets, a series of data mining is developed around these issues. By analyzing the characteristics of each site, the site is modeled from the perspective of individuals and clusters, through different models. The evaluation indicators to detect the accuracy of the results provide an effective method for predicting shared bicycles.

Keywords

Shared bicycles Data mining Clustering Forecasting 

References

  1. 1.
    Demaio, P.: Bike-sharing: history, impacts, models of provision, and future. J. Public Transp. 12(4) (2009)Google Scholar
  2. 2.
    Hu, J., Yang, Z., Shu, Y., Cheng, P., Chen, J.: Data-driven utilization-aware trip advisor for bike-sharing systems. In: 2017 IEEE International Conference on Data Mining (ICDM), vol. 00, pp. 167–176 (2018).  https://doi.org/10.1109/ICDM.2017.26
  3. 3.
    Fishman, E., Washington, S., Haworth, N.: Bike share: a synthesis of the literature. Urban Transp. China 33(2), 148–165 (2013)Google Scholar
  4. 4.
    Chemla, D., Meunier, F., Calvo, R.W.: Bike sharing systems: solving the static rebalancing problem. Discret. Optim. 10(2), 120–146 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. MONET 23, 368–375 (2018)Google Scholar
  6. 6.
    O’Mahony, E., Shmoys, D.B.: Data analysis and optimization for (citi)bike sharing. In: Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 687–694 (2015)Google Scholar
  7. 7.
    Froehlich, J., Neumann, J., Oliver, N.: Sensing and predicting the pulse of the city through shared bicycling. In: IJCAI 2009, Proceedings of the International Joint Conference on Artificial Intelligence, Pasadena, CA, pp. 1420–1426 (2009)Google Scholar
  8. 8.
    Yang, Z., Hu, J., Shu, Y., Cheng, P., Chen, J., Moscibroda, T.: Mobility Modeling and Prediction in Bike-Sharing Systems, pp. 165–178 (2016)Google Scholar
  9. 9.
    Rixey, R.A.: Station-level forecasting of bikesharing ridership. Transp. Res. Rec. J. Transp. Res. Board 2387(-1), 46–55 (2013)Google Scholar
  10. 10.
    Faghih-Imani, A., Eluru, N., El-Geneidy, A.M., Rabbat, M., Haq, U.: How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (bixi) in montreal. J. Transp. Geogr. 41, 306–314 (2014)CrossRefGoogle Scholar
  11. 11.
    Lin, L., He, Z., Peeta, S.: Predicting station-level hourly demands in a large-scale bike-sharing network: A graph convolutional neural network approach. Transp. Res. Part C: Emerg. Technol.97, 258–276 (2018)Google Scholar
  12. 12.
    Li, Y., Zheng, Y., Zhang, H., Chen, L.: Traffic prediction in a bike-sharing system. In: Sigspatial International Conference on Advances in Geographic Information Systems, pp. 1–10 (2015)Google Scholar
  13. 13.
    Chen, T., Guestrin, C.: Xgboost: A Scalable Tree Boosting System. arXiv:1603.02754
  14. 14.
    Prasad, A.M., Iverson, L.R., Liaw, A.: Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2), 181–199 (2006)CrossRefGoogle Scholar
  15. 15.
    Chai, T., Draxler, R.R.: Root mean square error (rmse) or mean absolute error (mae)?-arguments against avoiding rmse in the literature. Geosci. Model. Dev. Discuss. 7(3), 1247–1250 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Haoyu Wen
    • 1
    Email author
  • Sheng Zhou
    • 1
  • Zie Wang
    • 1
  • Feier Qiu
    • 1
  • Han Yu
    • 1
  1. 1.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina

Personalised recommendations