Identification of Road Surface Anomalies Using Crowdsourced Smartphone Sensor Data

Road pavement anomalies can result in many negative effects such as damages to vehicles, poor ride quality, additional ride time and road traffic accidents. Therefore it is important to regularly monitor and maintain roads, according to the standards. The traditional methods of road anomaly detection are expensive, time-consuming, and require the supervision of experts. Crowdsourcing systems provide an inexpensive and robust solution to overcome these challenges in traditional approaches. This paper proposes a platform to identify and classify road anomalies from crowdsourced accelerometer smartphone data, adjusting to different vehicle speeds and other characteristics. The collected accelerometer data is preprocessed using noise filtering and reorientation techniques and the anomalies are identified through a fuzzy logic approach and further classified based on the anomaly severity using machine learning models. The results from the conducted experiments suggest that the proposed method is capable of successfully identifying and classifying anomalies from crowdsourced data.