Advertisement

An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time

  • Luis Moreira-Matias
  • João Gama
  • João Mendes-Moreira
  • Jorge Freire de Sousa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)

Abstract

In this paper, we presented a probabilistic framework to predict Bus Bunching (BB) occurrences in real-time. It uses both historical and real-time data to approximate the headway distributions on the further stops of a given route by employing both offline and online supervised learning techniques. Such approximations are incrementally calculated by reusing the latest prediction residuals to update the further ones. These update rules extend the Perceptron’s delta rule by assuming an adaptive beta value based on the current context. These distributions are then used to compute the likelihood of forming a bus platoon on a further stop - which may trigger an threshold-based BB alarm. This framework was evaluated using real-world data about the trips of 3 bus lines throughout an year running on the city of Porto, Portugal. The results are promising.

Keywords

supervised learning probabilistic reasoning online learning perceptron regression bus bunching travel time prediction headway prediction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Powell, J. W., Huang, Y., Bastani, F., Ji, M.: Towards reducing taxicab cruising time using spatio-temporal profitability maps. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 242–260. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Gershenson, C., Pineda, L.: Why does public transport not arrive on time? the pervasiveness of equal headway instability. PloS One 4(10), 72–92 (2009)CrossRefGoogle Scholar
  3. 3.
    Daganzo, C.: A headway-based approach to eliminate bus bunching. Transportation Research Part B 43(10), 913–921 (2009)CrossRefGoogle Scholar
  4. 4.
    Daganzo, C., Pilachowski, J.: Reducing bunching with bus-to-bus cooperation. Transportation Research Part B: Methodological 45(1), 267–277 (2011)CrossRefGoogle Scholar
  5. 5.
    Bellei, G., Gkoumas, K.: Transit vehicles’ headway distribution and service irregularity. Public Transport 2(4), 269–289 (2010)CrossRefGoogle Scholar
  6. 6.
    Moreira-Matias, L., Ferreira, C., Gama, J., Mendes-Moreira, J., de Sousa, J.F.: Bus bunching detection by mining sequences of headway deviations. In: Perner, P. (ed.) ICDM 2012. LNCS, vol. 7377, pp. 77–91. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Wang, F.: Toward intelligent transportation systems for the 2008 olympics. IEEE Intelligent Systems 18(6), 8–11 (2003)CrossRefGoogle Scholar
  8. 8.
    Mishalani, R., McCord, M., Wirtz, J.: Passenger wait time perceptions at bus stops: empirical results and impact on evaluating real-time bus arrival information. Journal of Public Transportation 9(2), 89 (2006)Google Scholar
  9. 9.
    Strathman, J., Kimpel, T., Dueker, K.: Transportation Northwest: Bus transit operations control: review and an experiment involving tri-met’s automated bus dispatching system. Technical report, Transportation Northwest, Department of Civil Engineering, University of Washington (2000)Google Scholar
  10. 10.
    Chen, G., Yang, X., An, J., Zhang, D.: Bus-arrival-time prediction models: Link-based and section-based. Journal of Transportation Engineering 138(1), 60–66 (2011)CrossRefGoogle Scholar
  11. 11.
    Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65(6), 386 (1958)MathSciNetCrossRefGoogle Scholar
  12. 12.
    D’Agostino, R.B.: Transformation to normality of the null distribution of g1. Biometrika, 679–681 (1970)Google Scholar
  13. 13.
    Mendes-Moreira, J., Jorge, A., de Sousa, J., 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
  14. 14.
    R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012)Google Scholar
  15. 15.
    Cappé, O., Godsill, S., Moulines, E.: An overview of existing methods and recent advances in sequential monte carlo. Proceedings of the IEEE 95(5), 899–924 (2007)CrossRefGoogle Scholar
  16. 16.
    Dawid, A.: Present position and potential developments: Some personal views: Statistical theory: The prequential approach. Journal of the Royal Statistical Society. Series A (General) 147, 278–292 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Powell, W., Sheffi, Y.: A probabilistic model of bus route performance. Transportation Science 17(4), 376–404 (1983)CrossRefGoogle Scholar
  18. 18.
    Delgado, F., Muñoz, J.C., Giesen, R., Cipriano, A.: Real-time control of buses in a transit corridor based on vehicle holding and boarding limits. Transportation Research Record: Journal of the Transportation Research Board 2090(1), 59–67 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luis Moreira-Matias
    • 1
    • 2
  • João Gama
    • 2
    • 4
  • João Mendes-Moreira
    • 2
    • 3
  • Jorge Freire de Sousa
    • 5
    • 6
  1. 1.Instituto de TelecomunicaçõesPortoPortugal
  2. 2.LIAAD-INESC TECPortoPortugal
  3. 3.DEI-FEUP, U. PortoPortoPortugal
  4. 4.Faculdade de Economia, U. PortoPortoPortugal
  5. 5.UGEI-INESC TEC, U. PortoPortoPortugal
  6. 6.DEGI-FEUP, U. PortoPortoPortugal

Personalised recommendations