RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones

Fatjon Seraj; Berend Jan van der Zwaag; Arta Dilo; Tamara Luarasi; Paul J. M. Havinga

doi:10.1007/978-3-319-29009-6_7

RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones

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

Research output: Chapter in Book/Report/Conference proceeding › Contribution to conference proceeding › Academic › peer-review

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 labeling 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 multi class 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 language	English
Title of host publication	Big Data Analytics in the Social and Ubiquitous Context
Subtitle of host publication	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
Publisher	Springer Nature
Pages	128-146
Number of pages	19
Edition	1
ISBN (Print)	9783319290089
DOIs	https://doi.org/10.1007/978-3-319-29009-6_7
Publication status	Published - 7 Jan 2016
Externally published	Yes

Keywords

monitoring systems
anomaly detection
smartphones

Access to Document

10.1007/978-3-319-29009-6_7

Cite this

Seraj, F., Zwaag, B. J. V. D., Dilo, A., Luarasi, T., & Havinga, P. J. M. (2016). RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones. In Big Data Analytics in the Social and Ubiquitous Context: 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 (1 ed., pp. 128-146). Springer Nature. https://doi.org/10.1007/978-3-319-29009-6_7

Seraj, Fatjon ; Zwaag, Berend Jan van der ; Dilo, Arta et al. / RoADS : A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones. Big Data Analytics in the Social and Ubiquitous Context: 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. 1. ed. Springer Nature, 2016. pp. 128-146

@inproceedings{7c3e070c30ac407696f826e74fe6640c,

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 labeling 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 multi class 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 = "monitoring systems, anomaly detection, smartphones, monitoringsystemen, detectie van afwijkingen, smartphones",

author = "Fatjon Seraj and Zwaag, {Berend Jan van der} and Arta Dilo and Tamara Luarasi and Havinga, {Paul J. M.}",

note = "DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

year = "2016",

month = jan,

day = "7",

doi = "10.1007/978-3-319-29009-6_7",

language = "English",

isbn = "9783319290089",

pages = "128--146",

booktitle = "Big Data Analytics in the Social and Ubiquitous Context",

publisher = "Springer Nature",

address = "United Kingdom",

edition = "1",

}

Seraj, F, Zwaag, BJVD, Dilo, A, Luarasi, T & Havinga, PJM 2016, RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones. in Big Data Analytics in the Social and Ubiquitous Context: 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. 1 edn, Springer Nature, pp. 128-146. 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; Zwaag, Berend Jan van der; Dilo, Arta et al.
Big Data Analytics in the Social and Ubiquitous Context: 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. 1. ed. Springer Nature, 2016. p. 128-146.

Research output: Chapter in Book/Report/Conference proceeding › Contribution to conference proceeding › Academic › peer-review

TY - GEN

T1 - RoADS

T2 - A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones

AU - Seraj, Fatjon

AU - Zwaag, Berend Jan van der

AU - Dilo, Arta

AU - Luarasi, Tamara

AU - Havinga, Paul J. M.

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

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 labeling 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 multi class 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 labeling 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 multi class 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 - monitoring systems

KW - anomaly detection

KW - smartphones

KW - monitoringsystemen

KW - detectie van afwijkingen

KW - smartphones

U2 - 10.1007/978-3-319-29009-6_7

DO - 10.1007/978-3-319-29009-6_7

M3 - Contribution to conference proceeding

SN - 9783319290089

SP - 128

EP - 146

BT - Big Data Analytics in the Social and Ubiquitous Context

PB - Springer Nature

ER -

Seraj F, Zwaag BJVD, Dilo A, Luarasi T, Havinga PJM. RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones. In Big Data Analytics in the Social and Ubiquitous Context: 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. 1 ed. Springer Nature. 2016. p. 128-146 doi: 10.1007/978-3-319-29009-6_7

RoADS: A Road Pavement Monitoring System for Anomaly Detection Using Smart Phones

Abstract

Keywords

Access to Document

Fingerprint

Cite this