Finding Interesting Contexts for Explaining Deviations in Bus Trip Duration Using Distribution Rules

  • Alípio M. Jorge
  • João Mendes-Moreira
  • Jorge Freire de Sousa
  • Carlos Soares
  • Paulo J. Azevedo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)

Abstract

In this paper we study the deviation of bus trip duration and its causes. Deviations are obtained by comparing scheduled times against actual trip duration and are either delays or early arrivals. We use distribution rules, a kind of association rules that may have continuous distributions on the consequent. Distribution rules allow the systematic identification of particular conditions, which we call contexts, under which the distribution of trip time deviations differs significantly from the overall deviation distribution. After identifying specific causes of delay the bus company operational managers can make adjustments to the timetables increasing punctuality without disrupting the service.

Keywords

Bus trip duration deviations distribution rules 

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References

  1. 1.
    Azevedo, P.J.: CAREN - class project association rule engine (2008), http://www.di.uminho.pt/~pja/class/caren.html
  2. 2.
    Bowman, L.A., Turnquist, M.A.: Service frequency, schedule reliability and passenger wait times at transit stops. Transportation Research Part A 15, 465–471 (1981)CrossRefGoogle Scholar
  3. 3.
    Carey, M.: Optimizing scheduled times, allowing for behavioural response. Transportation Research Part B 32(5), 329–342 (1998)CrossRefGoogle Scholar
  4. 4.
    Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. John Wiley & Sons, New York (1999)Google Scholar
  5. 5.
    Duarte, E., Mendes-Moreira, J., Belo, O.: Exploração de técnicas de classificação associativa no planeamento de horários de transportes públicos (exploitation of associative classification techniques in the planning the schedules of public transports). In: 9 Conferência da Associação Portuguesa de Sistemas de Informação, Viseu - Portugal (2009)Google Scholar
  6. 6.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar
  7. 7.
    Hand, D., Manila, H., Smyth, P.: Principles of Data Mining. MIT Press (2001)Google Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer (August 2001)Google Scholar
  9. 9.
    Jorge, A.M., Azevedo, P.J., Pereira, F.: Distribution Rules with Numeric Attributes of Interest. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 247–258. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Kavšek, B., Lavrač, N., Jovanoski, V.: APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. In: Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 230–241. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Klősgen, W.: Explora: A multipattern and multistrategy discovery assistant. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park (1996)Google Scholar
  12. 12.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD 1998: Proceedings of the fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 80–86. ACM Press, New York (1998)Google Scholar
  13. 13.
    Mendes-Moreira, J., Duarte, E., Belo, O.: A decision support system for timetable adjustments. In: 13th EURO Working Group on Transportation Meeting (EWGT 2009), Padua - Italy (2009)Google Scholar
  14. 14.
    Mendes-Moreira, J., Jorge, A., de Sousa, J.F., Soares, C.: Comparing state-of-the-art regression methods for long term travel time prediction. Intelligent Data Analysis 16(3), 427–449 (2012)Google Scholar
  15. 15.
    Zhao, J., Dessouky, M., Bukkapatnam, S.: Optimal slack time for schedule-based transit operations. Transportation Science 40(4), 529–539 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alípio M. Jorge
    • 1
  • João Mendes-Moreira
    • 2
  • Jorge Freire de Sousa
    • 3
  • Carlos Soares
    • 4
  • Paulo J. Azevedo
    • 5
  1. 1.LIAAD-INESC TEC DCC-FCUPUniversidade do PortoPortugal
  2. 2.LIAAD-INESC TEC, DEI-FEUPUniversidade do PortoPortugal
  3. 3.UGEI-INESC TEC, DEGI-FEUPUniversidade do PortoPortugal
  4. 4.INESC TEC, FEPUniversidade do PortoPortugal
  5. 5.Haslab-INESC TECUniversidade do MinhoPortugal

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