Using Data Mining to Improve the Public Transport in Gran Canaria Island

  • Teresa Cristóbal
  • José J. Lorenzo
  • Carmelo R. García
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9520)


In this work Business Intelligence and Data Mining techniques have been used to extract useful knowledge for the main corporation of intercity public transportation on Gran Canaria island. The aim has been to find a pattern to predict the number of passengers who want to travel from one point to another. To achieve it, events files generated in the vehicles of the company and additional data have been used as information source: temporal (time of the trip, type of the day,month and season) and geographic and demographic (departure and destination bus stop, type of bus stop and zip codes of the origin and destination bus stop.


Data mining Intelligent transport systems Public transport management 


  1. 1.
    Frawley, W., Piatetsky-Shapiro, G.Y., Matheus, C.: Knowledge discovery in databases: an overview. AI Mag. 13(3), 213–228 (1992)Google Scholar
  2. 2.
    Arentze, T.A., Timmermans, H.: ALBATROSS - A Learning-Based Transportaion Oriented Simulation System. Urban Planning Group/EIRASS, Eindhoven University of Technology, Netherlands (2007)Google Scholar
  3. 3.
    Levner, E., Ceder, A., Elalouf, A., Hadas, Y., Shabtay, D.: Detection and improvement of deficiencies and failures in public-transportation networks using agent-enhanced distribution data mining. In: IEEE International Conference on Industrial Engineering and Engineering Management, art. no. 6118006, pp. 694–698 (2011)Google Scholar
  4. 4.
    Lathia, N., Froehlich, J., Capral, L.: Mining public transport usage for personalised intelligent transport systems. In: IEEE International Conference on Data Mining, pp. 887–892 (2010)Google Scholar
  5. 5.
    Lathia, N., Capra, L.: Mining mobility data to minimise travellers’spending on public transport. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1181–1189 (2011)Google Scholar
  6. 6.
    Lathia, N., Smith, C., Froehlich, J., Capra, L.: Individuals among commuters: building personalised transport information services from fare collection systems. Pervasive Mob. Comput. 9(5), 643–664 (2013)CrossRefGoogle Scholar
  7. 7.
    Du, B., Yang, Y., Lv, W.: Understand group travel behaviors in an urban area using mobility pattern mining. In: Proceedings of the IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013, art. no. 06726200, pp. 127–133 (2013)Google Scholar
  8. 8.
    Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehouse. 5(4), 13–23 (2000)Google Scholar
  9. 9.
    Pentaho. visited on 04 February 2014
  10. 10.
    Weka. The University of Waikato. visited on 15 June 2014
  11. 11.
    Manual CRISP\(\_\)DM de IBM SPSS Modeler. Copyright IBM Corporation 1994 (2012)Google Scholar
  12. 12.
    Roldán, M.C.: Pentaho Data Integration Beginner’s Guide. Packt Publishing (2003)Google Scholar
  13. 13.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, H.: The WEKA Data Mining Software: An Update. SIKDD Explor. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  14. 14.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)zbMATHGoogle Scholar
  15. 15.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  16. 16.
    Pavlov, Y.L.: Random Forest. VSP, Utrecht (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Teresa Cristóbal
    • 1
  • José J. Lorenzo
    • 2
  • Carmelo R. García
    • 1
  1. 1.Institute for Cybernetic Science and TechnologyUniversity of Las Palmas de Gran CanariaLas PalmasSpain
  2. 2.Institute of Intelligent Systems and Numerical Applications in EngineeringUniversity of Las Palmas de Gran CanariaLas PalmasSpain

Personalised recommendations