Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers “duration dependence” of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep survival analysis model for incident clearance time prediction


    Weitere Titelangaben:

    E.-J. Kim et al.
    Journal of Intelligent Transportation Systems


    Beteiligte:
    Kim, Eui-Jin (Autor:in) / Kang, Min-Ji (Autor:in) / Park, Shin Hyoung (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    04.05.2025


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Traffic Incident Clearance Time Prediction and Influencing Factor Analysis Using Extreme Gradient Boosting Model

    Jinjun Tang / Lanlan Zheng / Chunyang Han et al. | DOAJ | 2020

    Freier Zugriff

    Associating Incident Clearance Duration with Freeway Segment Types Using Hierarchical Bayesian Survival Model

    Salum, Jimoku H. / Kodi, John H. / Kidando, Emmanuel et al. | ASCE | 2023



    Incident dispatching, clearance and delay

    Hall, Randolph W. | Online Contents | 2002