Advertisement

Station Status Forecasting Module for a Multi-agent Proposal to Improve Efficiency on Bike-Sharing Usage

  • C. Diez
  • V. Sanchez-Anguix
  • J. Palanca
  • V. Julian
  • A. Giret
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10767)

Abstract

Urban transportation involves a number of common problems: air and acoustic pollution, traffic jams, and so forth. This has become an important topic of study due to the interest in solving these issues in different areas (economical, social, ecological, etc.). Nowadays, one of the most popular urban transport systems are the shared vehicles systems. Among these systems there are the shared bicycle systems which have an special interest due to its characteristics. While solving some of the problems mentioned above, these systems also arise new problems such as the distribution of bicycles over time and space. Traditional approaches rely on the service provider to balancing the system, thus generating extra costs. Our proposal consists on an multi-agent system that includes user actions as a balancing mechanism, taking advantage of their trips to optimize the overall balance of the system. With this goal in mind the user is persuaded to deviate slightly from its origin/destination by providing appropriate arguments and incentives. This article presents the prediction module that will enable us to create such persuasive system. This module allow us to predict the demand for bicycles in the stations, forecasting the number of available parking spots (or available bikes). With this information the multi-agent system is capable of scoring alternative stations and routes and making offers to balance bikes across the stations. In order to achieve this, the most proper offers for the user will be predicted and used to persuade her.

Keywords

Multi-agent systems Vehicle sharing systems 

References

  1. 1.
    Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Proc. Lett. Rev. 11(10), 203–224 (2007)Google Scholar
  2. 2.
    Bast, H., et al.: Route planning in transportation networks. In: Kliemann, L., Sanders, P. (eds.) Algorithm Engineering. LNCS, vol. 9220, pp. 19–80. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49487-6_2CrossRefGoogle Scholar
  3. 3.
    Bazzan, A.L., Klügl, F.: A review on agent-based technology for traffic and transportation. Knowl. Eng. Rev. 29(03), 375–403 (2014)CrossRefGoogle Scholar
  4. 4.
    Billhardt, H., et al.: Towards smart open dynamic fleets. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS/AT -2015. LNCS (LNAI), vol. 9571, pp. 410–424. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-33509-4_32CrossRefGoogle Scholar
  5. 5.
    Costa, A., Heras, S., Palanca, J., Jordán, J., Novais, P., Julián, V.: Argumentation schemes for events suggestion in an e-Health platform. In: de Vries, P.W., Oinas-Kukkonen, H., Siemons, L., Beerlage-de Jong, N., van Gemert-Pijnen, L. (eds.) PERSUASIVE 2017. LNCS, vol. 10171, pp. 17–30. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55134-0_2CrossRefGoogle Scholar
  6. 6.
    Diez, C., Sanchez-Anguix, V., Palanca, J., Julian, V., Giret, A.: A multi-agent proposal for efficient bike-sharing usage. In: An, B., Bazzan, A., Leite, J., Villata, S., van der Torre, L. (eds.) PRIMA 2017. LNCS (LNAI), vol. 10621, pp. 468–476. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69131-2_29CrossRefGoogle Scholar
  7. 7.
    Farahani, R.Z., Miandoabchi, E., Szeto, W., Rashidi, H.: A review of urban transportation network design problems. Eur. J. Oper. Res. 229(2), 281–302 (2013). http://www.sciencedirect.com/science/article/pii/S0377221713000106MathSciNetCrossRefGoogle Scholar
  8. 8.
    Giret, A., Carrascosa, C., Julian, V., Rebollo, M.: A crowdsourcing approach for last mile delivery. Emerging Technologies, Submitted to Transportation Research Part C (2017)Google Scholar
  9. 9.
    Hernández, E., Sanchez-Anguix, V., Julian, V., Palanca, J., Duque, N.: Rainfall prediction: a deep learning approach. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds.) HAIS 2016. LNCS (LNAI), vol. 9648, pp. 151–162. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32034-2_13CrossRefGoogle Scholar
  10. 10.
    Kull, M., Ferri, C., Martínez-Usó, A.: Bike rental and weather data across dozens of cities. In: ICML 2015 Workshop on Demand Forecasting (2015)Google Scholar
  11. 11.
    Li, Y., Zheng, Y., Zhang, H., Chen, L.: Traffic prediction in a bike-sharing system. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 33. ACM (2015)Google Scholar
  12. 12.
    Ochando, L.C., Julián, C.I., Ochando, F.C., Ferri, C.: Airvlc: an application for real-time forecasting urban air pollution. In: Proceedings of the 2nd International Conference on Mining Urban Data, vol. 1392, pp. 72–79. CEUR-WS.org (2015)Google Scholar
  13. 13.
    O’Mahony, E., Shmoys, D.B.: Data analysis and optimization for (citi)bike sharing. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 687–694. AAAI Press (2015). http://dl.acm.org/citation.cfm?id=2887007.2887103
  14. 14.
    Rigas, E.S., Ramchurn, S.D., Bassiliades, N.: Managing electric vehicles in the smart grid using artificial intelligence: a survey. IEEE Trans. Intell. Transp. Syst. 16(4), 1619–1635 (2015)CrossRefGoogle Scholar
  15. 15.
    Sanchez-Anguix, V., Aydogan, R., Julian, V., Jonker, C.: Unanimously acceptable agreements for negotiation teams in unpredictable domains. Electron. Commer. Res. Appl. 13(4), 243–265 (2014). http://www.sciencedirect.com/science/article/pii/S1567422314000283CrossRefGoogle Scholar
  16. 16.
    Satunin, S., Babkin, E.: A multi-agent approach to intelligent transportation systems modeling with combinatorial auctions. Expert Syst. Appl. 41(15), 6622–6633 (2014)CrossRefGoogle Scholar
  17. 17.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  18. 18.
    Schuijbroek, J., Hampshire, R., van Hoeve, W.J.: Inventory rebalancing and vehicle routing in bike sharing systems. Eur. J. Oper. Res. 257(3), 992–1004 (2017)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ticknor, J.L.: A bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(14), 5501–5506 (2013)CrossRefGoogle Scholar
  20. 20.
    Yoon, J.W., Pinelli, F., Calabrese, F.: Cityride: a predictive bike sharing journey advisor. In: IEEE 13th International Conference on Mobile Data Management (MDM), pp. 306–311. IEEE (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Dpto. Sistemas Informáticos y ComputaciónUniversitat Politècnica de ValènciaValènciaSpain
  2. 2.School of Computing, Electronics, and MathsCoventry UniversityCoventryUK
  3. 3.Florida UniversitariaCatarrojaSpain
  4. 4.Universidad Isabel IBurgosSpain

Personalised recommendations