Research on Monitoring Road Surface Anomalies Using an IoT-Based Automatic Detection System: Case Study in Taiwan
Abstract
Bad road quality brings many problems, such as putting drivers and passengers in danger and causing vehicle suspension system wear. Maintaining high-quality roads relies on regular inspections and repairs, but this is a time-consuming and labor-intensive task. To improve road quality and increase the efficiency of road repairs, an Internet of Things based anomaly detection system (ADS) is proposed to monitor road surfaces. A machine-learning method, support vector machine (SVM), is utilized to identify and classify different types of road surface anomalies. Other five classifiers are also examined using the same testing data. The high classification accuracies obtained from the proposed SVM model can be incorporated with a Google Map, so the road surface information can be easily browsed. With the proposed ADS system, it requires manpower and time that can be greatly reduced for examining surface conditions of roads and significantly improve the efficiency of road maintenance.
