Road Anomaly Detection and Classification Using Smartphones and Artificial Neural Networks
Ημερομηνία
2016Συγγραφέας
Kyriakou, Charalambos![ORCID logo](https://orcid.org/sites/default/files/images/orcid_16x16.png)
![ORCID logo](https://orcid.org/sites/default/files/images/orcid_16x16.png)
Transportation, Research Board
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Εμφάνιση πλήρους εγγραφήςΕπιτομή
The study presented herein explores the use of data, collected by sensors from smartphones and from automobiles’ on-board diagnostic (OBD-II) devices while vehicles are in movement, for the detection of roadway anomalies. The smartphone-based data collection is complimented with artificial neural network techniques for classifying detected roadway anomalies. Thirty-one factors are used for the detection (subsequently reduced to eleven, without loss of accuracy). The proposed method and system architecture are checked against three types of roadway anomalies, and validated against hundreds of roadway runs (relating to several thousands of data points) with above 90% accuracy rate. The study’s results confirm the value of smartphone sensors in the low-cost (and eventually crow-sourced) detection of roadway anomalies.