Traffic incidents are a primary cause of traffic delays, which can cause severe economic losses. Effective traffic incident management requires integrating intelligent traffic systems, information dissemination, and the accurate prediction of incident duration. This study develops a clustering-based machine learning model to predict the incident duration. Unlike similar studies that train separate machine learning models for a fixed number of clusters, this study proposes an ensemble learning method based on multiple clustered individual models that can provide good and diverse prediction performance. The K-means clustering method is used in this study as a bootstrapping technique in the ensemble learning approach, with the individual models based on the artificial neural network model and random forest regression model. The models are tested using the incident data from Singapore, and the results show that the ensemble model outperforms both the traditional model with fixed clusters and the classical model without clustering. Additionally, this study attempted to determine the significance of different variables on traffic incident durations using the random forest feature importance function. The prediction of incident duration and the analysis of influence factors can contribute to several aspects of traffic management, such as improving traffic dissemination to mitigate traffic congestion caused by incidents.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Prediction of Traffic Incident Duration Using Clustering-Based Ensemble Learning Method


    Weitere Titelangaben:

    J. Transp. Eng., Part A: Systems


    Beteiligte:
    Zhao, Hui (Autor:in) / Gunardi, Willy (Autor:in) / Liu, Yang (Autor:in) / Kiew, Christabel (Autor:in) / Teng, Teck-Hou (Autor:in) / Yang, Xiao Bo (Autor:in)


    Erscheinungsdatum :

    01.07.2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Traffic Incident Duration Prediction Based on K-Nearest Neighbor

    Wen, Y. / Chen, S.Y. / Xiong, Q.Y. et al. | British Library Conference Proceedings | 2013


    Traffic Incident Duration Prediction Based on Support Vector Regression

    Wu, Wei-wei / Chen, Shu-yan / Zheng, Chang-jiang | ASCE | 2011


    Analysis of Regression Method on Traffic Incident Duration Prediction

    Wang, Xuanqiang / Chen, Shuyan / Zheng, Wenchang | ASCE | 2013


    Overview of traffic incident duration analysis and prediction

    Ruimin Li / Francisco C. Pereira / Moshe E. Ben-Akiva | DOAJ | 2018

    Freier Zugriff

    Overview of traffic incident duration analysis and prediction

    Li, Ruimin / Pereira, Francisco C. / Ben-Akiva, Moshe E. | Springer Verlag | 2018

    Freier Zugriff