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

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Advances in Intelligent Data Analysis XIII (IDA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8819))

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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.

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References

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  3. Daganzo, C.: A headway-based approach to eliminate bus bunching. Transportation Research Part B 43(10), 913–921 (2009)

    Article  Google Scholar 

  4. Daganzo, C., Pilachowski, J.: Reducing bunching with bus-to-bus cooperation. Transportation Research Part B: Methodological 45(1), 267–277 (2011)

    Article  Google Scholar 

  5. Bellei, G., Gkoumas, K.: Transit vehicles’ headway distribution and service irregularity. Public Transport 2(4), 269–289 (2010)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  7. Wang, F.: Toward intelligent transportation systems for the 2008 olympics. IEEE Intelligent Systems 18(6), 8–11 (2003)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  11. Rosenblatt, F.: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65(6), 386 (1958)

    Article  MathSciNet  Google Scholar 

  12. D’Agostino, R.B.: Transformation to normality of the null distribution of g1. Biometrika, 679–681 (1970)

    Google Scholar 

  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. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  17. Powell, W., Sheffi, Y.: A probabilistic model of bus route performance. Transportation Science 17(4), 376–404 (1983)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Moreira-Matias, L., Gama, J., Mendes-Moreira, J., Freire de Sousa, J. (2014). An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_20

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  • DOI: https://doi.org/10.1007/978-3-319-12571-8_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12570-1

  • Online ISBN: 978-3-319-12571-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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