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An Estimator for Traffic Breakdown Probability Based on Classification of Transitional Breakdown Events

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  • Petter Arnesen

    (Department of Transport Research, SINTEF Technology and Society, NO-7465 Trondheim, Norway)

  • Odd A. Hjelkrem

    (Department of Transport Research, SINTEF Technology and Society, NO-7465 Trondheim, Norway)

Abstract

In this paper we propose a new estimator for calculating the probability of traffic breakdown as a function of traffic demand. Traffic breakdown is a well-studied phenomena within previous literature and is of great importance to traffic planners and controllers. The proposed estimator has an appealing intuition and is able to overcome several of the problems associated with previously proposed methodology. The input to the estimator is a set of aggregated (typically five minute) traffic observations classified to either a breakdown or nonbreakdown state, and a customized and fast algorithm for this purpose is proposed. Last, we apply the classification algorithm and breakdown probability estimator to a large data set consisting of several observation sites on the Norwegian road network, and we compare our estimator to a previously defined estimator.

Suggested Citation

  • Petter Arnesen & Odd A. Hjelkrem, 2018. "An Estimator for Traffic Breakdown Probability Based on Classification of Transitional Breakdown Events," Transportation Science, INFORMS, vol. 52(3), pages 593-602, June.
    • Handle: RePEc:inm:ortrsc:v:52:y:2018:i:3:p:593-602
    DOI: 10.1287/trsc.2017.0776
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    References listed on IDEAS

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    1. Davis, L.C., 2006. "Controlling traffic flow near the transition to the synchronous flow phase," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(2), pages 541-550.
    2. Shiomi, Yasuhiro & Yoshii, Toshio & Kitamura, Ryuichi, 2011. "Platoon-based traffic flow model for estimating breakdown probability at single-lane expressway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1314-1330.
    3. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    4. Xiqun (Michael) Chen & Zhiheng Li & Li Li & Qixin Shi, 2014. "A Traffic Breakdown Model Based on Queueing Theory," Networks and Spatial Economics, Springer, vol. 14(3), pages 485-504, December.
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    Cited by:

    1. Kathrin Goldmann & Gernot Sieg, 2020. "Quantifying the phantom jam externality: The case of an Autobahn section in Germany," Working Papers 30, Institute of Transport Economics, University of Muenster.
    2. Pedro Cesar Lopes Gerum & Andrew Reed Benton & Melike Baykal-Gürsoy, 2019. "Traffic density on corridors subject to incidents: models for long-term congestion management," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 795-831, December.

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