Rolling-noise-relevant classification of pavement based on opportunistic sound and vibration monitoring in cars

As car and truck engines are becoming quieter due to noise emission regulations and new propulsion systems, rolling noise is becoming the dominant contribution of traffic noise. The interaction of tires and pavement causes rolling noise; thus mitigation is possible in both domains. In Europe, quiet tires are promoted at the EU level, amongst others by careful labelling. Pavement choice and maintenance remains the responsibility of local authorities. Typically, the information available on the acoustic quality of these pavements is scarce. Hence, we designed an opportunistic sound and vibration monitoring approach that allows to monitor pavements continuously. Several cars that drive regularly on the roads are equipped with a low-cost sensor box that collects noise, acceleration, and GPS data. Data analytics of the large datasets thus collected allows to classify and label pavements in a way that is relevant for rolling noise production. The classification method combines a set of carefully chosen sound and vibration features using blind clustering algorithms. Spatial connectivity is added to the clustering to represent the higher probability for similar pavements to be found on adjacent road segments. Action plans based on rolling noise labelling of pavements could become an important traffic noise mitigation approach.