This paper presents the development of a new model for predicting traffic incident duration using random forests (RFs), a data-driven machine learning technique. Utilizing an extensive dataset with over 140,000 incident records and 52 variables, the developed models were optimized by fine-tuning their parameters. The best-performing RF model achieved a mean absolute error (MAE) of 36.652 min, which is acceptable given the wide range of incident duration considered (1–1,440 min). Another set of models was developed using a short range of 5- to 120-minute incident duration. The performance of the best models for the short range improved significantly, i.e. the MAE decreased to 14.979 min (about a 40% reduction). In comparison, the ANN models developed using the same dataset slightly outperformed (only 0.24%) their RF counterparts; nevertheless, the RF models showed more stable results with a small-error range. Further analysis confirmed that the accuracy of the predictions could be slightly downgraded in return for a substantial reduction in the number of variables utilized.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predicting incident duration using random forests


    Beteiligte:
    Hamad, Khaled (Autor:in) / Al-Ruzouq, Rami (Autor:in) / Zeiada, Waleed (Autor:in) / Abu Dabous, Saleh (Autor:in) / Khalil, Mohamad Ali (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2020-01-01


    Format / Umfang :

    25 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Predicting Freeway Incident Duration Using Machine Learning

    Hamad, Khaled / Khalil, Mohamad Ali / Alozi, Abdul Razak | Springer Verlag | 2020


    Application of nonparametric regression in predicting traffic incident duration

    Shi Wang / Ruimin Li / Min Guo | DOAJ | 2018

    Freier Zugriff

    A SIMPLE TIME SEQUENTIAL PROCEDURE FOR PREDICTING FREEWAY INCIDENT DURATION

    Khattak, Asad J. / Schofer, Joseph L. / Wang, Mu-Han | Taylor & Francis Verlag | 1995


    Estimating incident duration

    HE QING / JINTANAKUL KLAYUT / KAMARIANAKIS IOANNIS et al. | Europäisches Patentamt | 2015

    Freier Zugriff

    Modeling Traffic Incident Duration Using Quantile Regression

    Khattak, Asad J. / Liu, Jun / Wali, Behram et al. | Transportation Research Record | 2016