Advertisement

Studying Solutions of the p-Median Problem for the Location of Public Bike Stations

  • Christian CintranoEmail author
  • Francisco Chicano
  • Thomas Stützle
  • Enrique Alba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11160)

Abstract

The use of bicycles as a means of transport is becoming more and more popular today, especially in urban areas, to avoid the disadvantages of individual car traffic. In fact, city managers react to this trend and actively promote the use of bicycles by providing a network of bicycles for public use and stations where they can be stored. Establishing such a network involves the task of finding best locations for stations, which is, however, not a trivial task. In this work, we examine models to determine the best location of bike stations so that citizens will travel the shortest distance possible to one of them. Based on real data from the city of Malaga, we formulate our problem as a p-median problem and solve it with a variable neighborhood search algorithm that was automatically configured with irace. We compare the locations proposed by the algorithm with the real ones used currently by the city council. We also study where new locations should be placed if the network grows.

Keywords

Bike station location p-median problem Variable neighborhood search 

Notes

Acknowledgements

This research was partially funded by the University of Málaga, Andalucía Tech, the Spanish MINECO and FEDER projects: TIN2014-57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO.

References

  1. 1.
    Avella, P., Boccia, M., Salerno, S., Vasilyev, I.: An aggregation heuristic for large scale p-median problem. Comput. Oper. Res. 39(7), 1625–1632 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen, L., et al.: Bike sharing station placement leveraging heterogeneous urban open data. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp 2015, pp. 571–575. ACM Press, NY (2015)Google Scholar
  3. 3.
    Chen, Q., Liu, M., Liu, X.: Bike fleet allocation models for repositioning in bike-sharing systems. IEEE Intell. Transp. Syst. Mag. 10(1), 19–29 (2018)CrossRefGoogle Scholar
  4. 4.
    Chen, Q., Sun, T.: A model for the layout of bike stations in public bike-sharing systems. J. Adv. Transp. 49(8), 884–900 (2015)CrossRefGoogle Scholar
  5. 5.
    Chira, C., Sedano, J., Villar, J.R., Cámara, M., Corchado, E.: Urban bicycles renting systems: modelling and optimization using nature-inspired search methods. Neurocomputing 135, 98–106 (2014)CrossRefGoogle Scholar
  6. 6.
    Dantrakul, S., Likasiri, C., Pongvuthithum, R.: Applied p-median and p-center algorithms for facility location problems. Expert Syst. Appl. 41(8), 3596–3604 (2014)CrossRefGoogle Scholar
  7. 7.
    Drezner, Z., Brimberg, J., Mladenović, N., Salhi, S.: New heuristic algorithms for solving the planar p-median problem. Comput. Oper. Res. 62, 296–304 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Drezner, Z., Brimberg, J., Mladenović, N., Salhi, S.: New local searches for solving the multi-source Weber problem. Ann. Oper. Res. 246(1–2), 181–203 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hu, S.R., Liu, C.T.: An optimal location model for a bicycle sharing program with truck dispatching consideration. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1775–1780. IEEE, October 2014Google Scholar
  10. 10.
    Kloimüllner, C., Raidl, G.R.: Hierarchical clustering and multilevel refinement for the bike-sharing station planning problem. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) LION 2017. LNCS, vol. 10556, pp. 150–165. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69404-7_11CrossRefGoogle Scholar
  11. 11.
    Lin, J.R., Yang, T.H., Chang, Y.C.: A hub location inventory model for bicycle sharing system design: formulation and solution. Comput. Ind. Eng. 65(1), 77–86 (2013)CrossRefGoogle Scholar
  12. 12.
    Liu, J., et al.: Station site optimization in bike sharing systems. In: 2015 IEEE International Conference on Data Mining, pp. 883–888. IEEE, November 2015Google Scholar
  13. 13.
    López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L.P., Birattari, M., Stützle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Megiddot, N., Supowits, K.J.: On the complexity of some common geometric location problems. SIAM J. Comput. 13(1), 182–196 (1984)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Mladenović, N., Brimberg, J., Hansen, P., Moreno-Pérez, J.A.: The p-median problem: a survey of metaheuristic approaches. Eur. J. Oper. Res. 179(3), 927–939 (2007)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Park, C., Sohn, S.Y.: An optimization approach for the placement of bicycle-sharing stations to reduce short car trips: an application to the city of Seoul. Transp. Res. Part A: Policy Pract. 105, 154–166 (2017)Google Scholar
  18. 18.
    Reese, J.: Methods for Solving the p-Median Problem: An Annotated Bibliography (2006)Google Scholar
  19. 19.
    Singhvi, D., et al.: Predicting Bike Usage for New York City’s Bike Sharing System (2015)Google Scholar
  20. 20.
    Whitaker, R.A.: A Fast algorithm for the greedy interchange for large-scale clustering and median location problems. INFOR: Inf. Syst. Oper. Res. 21(2), 95–108 (1983)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christian Cintrano
    • 1
    Email author
  • Francisco Chicano
    • 1
  • Thomas Stützle
    • 2
  • Enrique Alba
    • 1
  1. 1.E.T.S. Ingeniería InformáticaUniversity of Málaga Andalucía TechMálagaSpain
  2. 2.Universite Libre de Bruxelles, CoDEBrusselsBelgium

Personalised recommendations