IDEAS home Printed from https://ideas.repec.org/a/inm/ortrsc/v52y2018i3p593-602.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/trsc.2017.0776
    Download Restriction: no
    File URL: https://libkey.io/10.1287/trsc.2017.0776?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Cheng, Qixiu & Song, Qiyuan & Wang, Zelin & Lin, Yuqian & Liu, Zhiyuan, 2025. "Capturing traffic state variation process: An analytical modeling approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 198(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Han, Youngjun & Ahn, Soyoung, 2018. "Stochastic modeling of breakdown at freeway merge bottleneck and traffic control method using connected automated vehicle," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 146-166.
    2. Malte Willmes & Katherine M Ransom & Levi S Lewis & Christian T Denney & Justin J G Glessner & James A Hobbs, 2018. "IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
    3. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    4. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    5. Hui Zhang & Minna Väliranta & Graeme T. Swindles & Marco A. Aquino-López & Donal Mullan & Ning Tan & Matthew Amesbury & Kirill V. Babeshko & Kunshan Bao & Anatoly Bobrov & Viktor Chernyshov & Marissa , 2022. "Recent climate change has driven divergent hydrological shifts in high-latitude peatlands," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    6. Subhashis Chatterjee & Ankur Shukla, 2016. "Change point–based software reliability model under imperfect debugging with revised concept of fault dependency," Journal of Risk and Reliability, , vol. 230(6), pages 579-597, December.
    7. Dehler-Holland, Joris & Okoh, Marvin & Keles, Dogan, 2022. "Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    8. Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(C).
    9. Li, Boyan & Diao, Xundi, 2023. "Structural break in different stock index markets in China," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    10. Sheu, Jiuh-Biing & Wu, Hsi-Jen, 2015. "Driver perception uncertainty in perceived relative speed and reaction time in car following – A quantum optical flow perspective," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 257-274.
    11. Ibrar ul Hassan Akhtar, 2023. "Exploring Covid-19 Pandemic Initial 2020 Curve Based On Statistical Evaluation," Acta Scientifica Malaysia (ASM), Zibeline International Publishing, vol. 7(1), pages 08-16, February.
    12. repec:cdl:itsdav:qt5dz0j1gg is not listed on IDEAS
    13. Sevvandi Kandanaarachchi & Rob J Hyndman & Kate Smith-Miles, 2020. "Early classification of spatio-temporal events using partial information," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.
    14. Han, Youngjun & Chen, Danjue & Ahn, Soyoung, 2017. "Variable speed limit control at fixed freeway bottlenecks using connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 113-134.
    15. repec:osf:socarx:yndb7_v1 is not listed on IDEAS
    16. Brice B. Hanberry, 2021. "Timing of Tree Density Increases, Influence of Climate Change, and a Land Use Proxy for Tree Density Increases in the Eastern United States," Land, MDPI, vol. 10(11), pages 1-17, October.
    17. Li He & Yuàn Wang & Yi Wang & Ren-Gang Zhang & Yuán Wang & Elvira Hörandl & Tao Ma & Yan-Fei Mao & Judith E. Mank & Ray Ming, 2024. "Allopolyploidization from two dioecious ancestors leads to recurrent evolution of sex chromosomes," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    18. Hossein Hassani & Mohammad Reza Yeganegi & Rangan Gupta, 2020. "Historical Forecasting Of Interest Rate Mean And Volatility Of The United States: Is There A Role Of Uncertainty?," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-17, December.
    19. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    20. Nicola Nones, 2024. "The Greek crisis as a “morality tale†? An empirical assessment," European Union Politics, , vol. 25(2), pages 291-312, June.
    21. Ross, Gordon J., 2015. "Parametric and Nonparametric Sequential Change Detection in R: The cpm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i03).
    22. Mohammadi, Hossein & Challenor, Peter & Goodfellow, Marc, 2019. "Emulating dynamic non-linear simulators using Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 178-196.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ortrsc:v:52:y:2018:i:3:p:593-602. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.