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26650 RoADS: A road pavement monitoring system for anomaly detection using smart phones
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Seraj, F. and van der Zwaag, B.J. and Dilo, A. and Luarasi, T. and 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. Lecture Notes in Computer Science 9546. Springer Verlag, Berlin, pp. 128-146. ISSN 0302-9743 ISBN 978-3-319-29008-9

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Official URL: http://dx.doi.org/10.1007/978-3-319-29009-6_7

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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.

Item Type:Book Section
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-General
Research Project:COMMIT/SENSA: Sensor Networks for Public Safety
Additional Information: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
Uncontrolled Keywords:anomaly detection, machine learning
ID Code:26650
Status:Published
Deposited On:08 January 2016
Refereed:Yes
International:Yes
More Information:statisticsmetis

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