Developing a near real-time road surface anomaly detection approach for road surface monitoring

Abstract

Road surface hazards affect the driving safety and comfort of road users. Recently, smartphones and mobile devices equipped with motion sensors such as accelerometers and gyroscope sensors have attracted researchers’ attention for the development of low-cost approaches for road surface monitoring. However, processing smartphone sensors to monitor road surface conditions is technically challenging due to dissimilar sensor properties, different smartphone placement, and also different vehicle mechanical properties. This study aimed to develop a hybrid method using threshold based and Machine Learning approaches for near real-time detection and classification of road surface anomalies using smartphone sensor data with higher-level accuracy. The proposed algorithm has self-adapting and self-updating capabilities to adapt itself to any type of smartphone and the dynamic behaviors of various vehicles and road surface conditions. A prototype is developed using MATLAB and ArcGIS to perform sensor data analysis, geocoding, geo-visualizing, and data querying for performance evaluation.