Reducing the effects of incidents by their early detection is one of a crucial requirements for incident management. This paper presents automated incident detection model based on unsupervised approach that uses only traffic observations as a model inputs. First, a novel self-tuning statistic is introduced as a feature generation function to capture spatio-temporal relationship of traffic data in both, urban and freeway networks. Next, these features are used as input in the segment-based mixture model that learns complex data distributions and their parameters. Then, we use the Mahalanobis distance to determine whether the traffic observation corresponds to an incident or recurrent traffic state. The model performance is demonstrated for two network examples, freeway with real data and urban with simulated data. Results show that the developed method achieves high accuracy rates and early incident detection compared to widely used approaches, such as California algorithm series and their extensions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Unsupervised Incident Detection Model in Urban and Freeway Networks


    Beteiligte:


    Erscheinungsdatum :

    01.11.2018


    Format / Umfang :

    652084 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Artificial Neural Networks for Freeway Incident Detection

    Stephanedes, Yorgos J. | Online Contents | 1995


    Real Time Freeway Incident Detection

    M. Motamed / R. Machemehl | NTIS | 2014


    Freeway Incident Management

    D. H. Roper | NTIS | 1990


    Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection

    Adeli, H. / Karim, A. | British Library Online Contents | 2000


    Transferability of Freeway Incident Detection Algorithms

    Stephanedes, Yorgos / Hourdakis, John | Transportation Research Record | 1996