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

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    Abstract

    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
    PublisherSpringer
    Pages128-146
    Number of pages16
    ISBN (Print)978-3-319-29008-9
    DOIs
    Publication statusPublished - 7 Jan 2016

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Number9546
    Volume9546
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Keywords

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

    Cite this

    Seraj, F., van der Zwaag, B. J., Dilo, A., Luarasi, T., & Havinga, P. J. M. (2016). RoADS: A road pavement monitoring system for anomaly detection using smart phones. In M. Atzmueller, A. Chin, F. Janssen, I. Schweizer, & C. Trattner (Eds.), Big Data Analytics in the Social and Ubiquitous Context (pp. 128-146). (Lecture Notes in Computer Science; Vol. 9546, No. 9546). Berlin: Springer. https://doi.org/10.1007/978-3-319-29009-6, https://doi.org/10.1007/978-3-319-29009-6_7
    Seraj, Fatjon ; van der Zwaag, B.J. ; Dilo, Arta ; Luarasi, Tamara ; Havinga, Paul J.M. / RoADS: A road pavement monitoring system for anomaly detection using smart phones. Big Data Analytics in the Social and Ubiquitous Context. editor / Martin Atzmueller ; Alvin Chin ; Frederik Janssen ; Immanuel Schweizer ; Christoph Trattner. Berlin : Springer, 2016. pp. 128-146 (Lecture Notes in Computer Science; 9546).
    @inbook{fdd6438946eb45e683034306ea376089,
    title = "RoADS: A road pavement monitoring system for anomaly detection using smart phones",
    abstract = "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.",
    keywords = "EWI-26650, Anomaly Detection, METIS-315137, IR-98919, Machine Learning",
    author = "Fatjon Seraj and {van der Zwaag}, B.J. and Arta Dilo and Tamara Luarasi and Havinga, {Paul J.M.}",
    note = "5th International Workshop on Modeling Social Media, MSM 2014, 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014, and First International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014, Revised Selected Papers",
    year = "2016",
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    Seraj, F, van der Zwaag, BJ, Dilo, A, Luarasi, T & Havinga, PJM 2016, RoADS: A road pavement monitoring system for anomaly detection using smart phones. in M Atzmueller, A Chin, F Janssen, I Schweizer & C Trattner (eds), Big Data Analytics in the Social and Ubiquitous Context. Lecture Notes in Computer Science, no. 9546, vol. 9546, Springer, Berlin, pp. 128-146. https://doi.org/10.1007/978-3-319-29009-6, https://doi.org/10.1007/978-3-319-29009-6_7

    RoADS: A road pavement monitoring system for anomaly detection using smart phones. / Seraj, Fatjon; van der Zwaag, B.J.; Dilo, Arta; Luarasi, Tamara; Havinga, Paul J.M.

    Big Data Analytics in the Social and Ubiquitous Context. ed. / Martin Atzmueller; Alvin Chin; Frederik Janssen; Immanuel Schweizer; Christoph Trattner. Berlin : Springer, 2016. p. 128-146 (Lecture Notes in Computer Science; Vol. 9546, No. 9546).

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    TY - CHAP

    T1 - RoADS: A road pavement monitoring system for anomaly detection using smart phones

    AU - Seraj, Fatjon

    AU - van der Zwaag, B.J.

    AU - Dilo, Arta

    AU - Luarasi, Tamara

    AU - Havinga, Paul J.M.

    N1 - 5th International Workshop on Modeling Social Media, MSM 2014, 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014, and First International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014, Revised Selected Papers

    PY - 2016/1/7

    Y1 - 2016/1/7

    N2 - 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.

    AB - 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.

    KW - EWI-26650

    KW - Anomaly Detection

    KW - METIS-315137

    KW - IR-98919

    KW - Machine Learning

    U2 - 10.1007/978-3-319-29009-6

    DO - 10.1007/978-3-319-29009-6

    M3 - Chapter

    SN - 978-3-319-29008-9

    T3 - Lecture Notes in Computer Science

    SP - 128

    EP - 146

    BT - Big Data Analytics in the Social and Ubiquitous Context

    A2 - Atzmueller, Martin

    A2 - Chin, Alvin

    A2 - Janssen, Frederik

    A2 - Schweizer, Immanuel

    A2 - Trattner, Christoph

    PB - Springer

    CY - Berlin

    ER -

    Seraj F, van der Zwaag BJ, Dilo A, Luarasi T, Havinga PJM. RoADS: A road pavement monitoring system for anomaly detection using smart phones. In Atzmueller M, Chin A, Janssen F, Schweizer I, Trattner C, editors, Big Data Analytics in the Social and Ubiquitous Context. Berlin: Springer. 2016. p. 128-146. (Lecture Notes in Computer Science; 9546). https://doi.org/10.1007/978-3-319-29009-6, https://doi.org/10.1007/978-3-319-29009-6_7