Roadway Pavement Assessment Using Low-Cost Vibration Sensors, European GNSS Technology and Machine Learning

This study presents case applications of developed vibration-based technologies for evaluating roadway networks at both point and street level, using smartphone-grade sensors. The approach is designed to (1) provide a low-cost but reliable alternative to expensive specialized equipment for pavement assessment, and (2) enable continuous data collection through participatory sensing. In the first case study presented, a smartphone was employed, whereas in the second case study, a custom integrated sensor device was utilized. In both case studies, hybrid vehicles were deployed as probe cars. Case Study 1 involved approximately two hours of roadway surveying, yielding about 0.6 million data points, while Case Study 2 extended to 16 hours of surveying, and produced roughly 1.6 million data points. The local municipality leverages the resulting ride-quality and pavement anomaly maps, by use of a simplified pavement management system (PMS), to prioritize roadway operations and maintenance activities. Ongoing research integrates in-development low-cost GNSS sensors with cameras, machine learning and machine vision and PMS software in a low-cost yet high-accuracy pavement assessment and management platform.