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).
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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.",
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",
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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

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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