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Data Mining Method for Incident duration Prediction

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Applied Informatics and Communication (ICAIC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 224))

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Abstract

Traffic incident management and information dissemination strategies will benefit from the prediction of incident duration in real time. This study investigates the development of an incident duration prediction model based on a detailed historical incident database. A data mining technique, namely the Bayesian Network was applied to develop the prediction models. The analysis results suggest that the Bayesian Network model is advantageous in terms of accurate prediction and the convenience of application.

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References

  1. Lindley, J.: Urban freeway congestion: quantification of the problem and effectiveness of potential solutions. ITE Journal 57, 27–32 (1987)

    Google Scholar 

  2. Giuliano, G.: Incident characteristics, frequency, and duration on a high volume urban freeway. Transportation Research Part A 23, 387–396 (1989)

    Article  Google Scholar 

  3. Ozbay, K., Kachroo, P.: Incident Management in Intelligent Transportation Systems. Artech House, Boston (1999)

    Google Scholar 

  4. Khattak, A.J., Schofer, J.L., Wang, M.: A simple time-sequential procedure for predicting freeway incident duration. IVHS Journal 2, 113–138 (1995)

    Google Scholar 

  5. Garib, A., Radwan, A.E., Al-Deek, H.: Estimating magnitude and duration of incident delays. Journal of Transportation Engineering, ASCE 123, 459–466 (1997)

    Article  Google Scholar 

  6. Nam, D., Mannering, F.: An exploratory hazard-based analysis of highway incident Duration. Transportation Research Part A 34(2), 85–102 (2000)

    Google Scholar 

  7. Qi, Y., Teng, H.: An information-based time sequential approach to online incident duration prediction. Journal of Intelligent Transportation Systems 12, 1–12 (2008)

    Article  MATH  Google Scholar 

  8. Smith, K., Smith, B.: Forecasting the clearance time of freeway accidents. Publication STL-2001-012. Center for Transportation Studies, University of Virginia (2002)

    Google Scholar 

  9. Stephen, B., David, F., Travis, W.S.: Naïve Bayesian Classifier for Incident Duration Prediction. In: Proceeding of Transportation Research Board 86th Annual Meeting, Transportation Research Board, Washington D.C (2007)

    Google Scholar 

  10. Jiyang, B., Zhang, X., Sun, L.: Traffic incident duration prediction grounded on Bayesian decision method-based tree algorithm. Journal of Tongji University (Natural Science) (2008)

    Google Scholar 

  11. Kim, W., Natarajan, S., Chang, G.: Empirical analysis and modeling of freeway incident duration. In: Proceeding of 11th International IEEE Conference on Intelligent Transportation System, Beijing, pp. 453–457 (2008)

    Google Scholar 

  12. Wei, C., Lee, Y.: Sequential forecast of incident duration using Artificial Neural Network models. Accident Analysis and Prevention 399, 44–954 (2007)

    Google Scholar 

  13. David, H.: A tutorial on learning with Bayesian networks. Report: MSR-TR-95-06, Microsoft Research (1995)

    Google Scholar 

  14. Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)

    MATH  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Shen, L., Huang, M. (2011). Data Mining Method for Incident duration Prediction. In: Zeng, D. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23214-5_64

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  • DOI: https://doi.org/10.1007/978-3-642-23214-5_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23213-8

  • Online ISBN: 978-3-642-23214-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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