This paper presents the development of freeway incident detection models based on the recently developed support vector machine (SVM) classifier. The overall framework, algorithm development, implementation and evaluation of this technique are discussed. Freeway traffic flow parameters measured by sensors, such as occupancy and volume are used by the SVM models to detect incidents. The performance of the developed algorithms is evaluated using the common criteria of detection rate (DR), false alarm rate (FAR), mean time to detection (MTTD), and misclassification rate (MCR). A performance index (PI) is then calculated by combining these performance criteria. Offline test results using real data collected at the I-880 Freeway in San Francisco Bay area. California have shown that the SVM models produce better PIs compared to the multi-layer neural network models.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Support vector machine models for freeway incident detection


    Beteiligte:
    Ruey Long Cheu, (Autor:in) / Srinivasan, D. (Autor:in) / Eng Tian Teh, (Autor:in)


    Erscheinungsdatum :

    2003-01-01


    Format / Umfang :

    331369 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Support Vector Machine Models in Freeway Incident Detection

    Cheu, R. L. / Srinivasan, D. / Teh, E. T. et al. | British Library Conference Proceedings | 2003



    Incident Occurrence Models for Freeway Incident Management

    Konduri, Sravanthi | Online Contents | 2003


    Incident Occurrence Models for Freeway Incident Management

    Konduri, Sravanthi / Labi, Samuel / Sinha, Kumares C. | Transportation Research Record | 2003


    Incident Occurrence Models for Freeway Incident Management

    Konduri, S. / Labi, S. / Sinha, K. C. et al. | British Library Conference Proceedings | 2003