Traffic flow data collected by loop detectors have been widely used for traffic incident detection. As traffic flow data have strong spatial-temporal correlations, this study tries to detect traffic incidents using an unsupervised learning approach. In this paper, a novel automatic incident detection (AID) method based on Autoencoder (AE) is proposed to detect the occurrence time and the location of traffic incidents in both freeway and urban networks. AE is an unsupervised machine learning model, which extracts nonlinear features of traffic flow data. A statistic named Squared Prediction Error (SPE) is constructed for incident detection. Meanwhile, the contribution plot technique is applied for incident localization. The experiments are conducted via a microscopic simulation platform Vissim and the test results verify the efficiency and effectiveness of the proposed method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-time Traffic Incident Detection Using an Autoencoder Model


    Beteiligte:
    Yang, Huan (Autor:in) / Wang, Yu (Autor:in) / Zhao, Han (Autor:in) / Zhu, Jinlin (Autor:in) / Wang, Danwei (Autor:in)


    Erscheinungsdatum :

    2020-09-20


    Format / Umfang :

    2388026 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-Time Traffic Incident Detection with Classification Methods

    Li, Linchao / Zhang, Jian / Zheng, Yuan et al. | British Library Conference Proceedings | 2018


    Real-Time Traffic Incident Detection with Classification Methods

    Li, Linchao / Zhang, Jian / Zheng, Yuan et al. | Springer Verlag | 2017


    Real-time visual traffic monitoring and automatic incident detection

    Ali, A.T. / Dagless, E.L. | Tema Archiv | 1992


    Real-time traffic incident detection based on a hybrid deep learning model

    Li, Linchao / Lin, Yi / Du, Bowen et al. | Taylor & Francis Verlag | 2022


    Low complexity techniques for robust real-time traffic incident detection

    Garg, Kratika / Prakash, Alok / Srikanthan, Thambipillai | IEEE | 2017