RoADS: A road pavement monitoring system for anomaly detection using smart phones

Fatjon Seraj, B.J. van der Zwaag, Arta Dilo, Tamara Luarasi, Paul J.M. Havinga

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    Monitoring the road pavement is a challenging task. Authorities spend time and finances to monitor the state and quality of the road pavement. This paper investigate road surface monitoring with smartphones equipped with GPS and inertial sensors: accelerometer and gyroscope. In this study we describe the conducted experiments with data from the time domain, frequency domain and wavelet transformation, and a method to reduce the effects of speed, slopes and drifts from sensor signals. A new audiovisual data labelling technique is proposed. Our system named RoADS, implements wavelet decomposition analysis for signal processing of inertial sensor signals and Support Vector Machine (SVM) for anomaly detection and classification. Using these methods we are able to build a real time multiclass road anomaly detector. We obtained a consistent accuracy of ≈90% on detecting severe anomalies regardless of vehicle type and road location. Local road authorities and communities can benefit from this system to evaluate the state of their road network pavement in real time.
    Original languageUndefined
    Title of host publicationBig Data Analytics in the Social and Ubiquitous Context
    EditorsMartin Atzmueller, Alvin Chin, Frederik Janssen, Immanuel Schweizer, Christoph Trattner
    Place of PublicationBerlin
    Number of pages16
    ISBN (Print)978-3-319-29008-9
    Publication statusPublished - 7 Jan 2016
    Event1st International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014 - Nancy, France
    Duration: 15 Sep 201415 Sep 2014

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Workshop1st International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014
    Other15 September 2014


    • EWI-26650
    • Anomaly Detection
    • METIS-315137
    • IR-98919
    • Machine Learning

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