Virtually all ITS applications rely on accurate traffic data. Identification of faulty detectors is thus vital for their reliability and efficiency. Most existing approaches solely use current and historical data of single or adjacent detectors and are based on empirical thresholds. We present a method for fault detection using Floating-Car Data (FCD) as independent source of information which allows to distinguish changed traffic conditions from sensor faults. Fault detection is based on residuals of a nonlinear regression model fitted to detector readings and FCD traffic speeds. Instead of applying rule-of-thumb thresholds we employ a statistical test, where thresholds result naturally from historical data, sample sizes and required fault detection accuracy. We provide a theoretical framework for fault detectability analysis and empirically evaluate the fault detection capability of our approach using data obtained from a microscopic traffic simulation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Identifying faulty traffic detectors with Floating Car Data


    Contributors:
    Widhalm, P. (author) / Koller, H. (author) / Ponweiser, W. (author)


    Publication date :

    2011-06-01


    Size :

    388308 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Predicting Traffic Flow on Faulty Traffic Detectors Using Machine Learning Techniques

    Bagabaldo, Alben Rome B. / González, Marta C. | TIBKAT | 2022


    RAIM algorithm for identifying simultaneous two-faulty satellites

    Qiang, Z. / Xiaolin, Z. / Xiaoming, C. | British Library Online Contents | 2008




    Short distance traffic planning-progress or faulty design

    Vaubel, Ulrich O. | Online Contents | 1996