Detection of road pavement quality using statistical clustering methods

Road owners are concerned with the state of the road surface and they try to reduce noise coming from the road as much as possible. Using sound level measuring equipment installed inside a car, we can indirectly measure the road pavement state. Noise inside a car is made up of rolling noise, engine noise and other confounding factors. Rolling noise is influenced by noise modifiers such as car speed, acceleration, temperature and road humidity. Engine noise is influenced by car speed, acceleration, and gear shifts. Techniques need to be developed which compensate for these modifying factors and filter out the confounding noise. This paper presents a hierarchical clustering method resulting in a mapping of the road pavement quality. We present the method using a dataset recorded in multiple cars under different circumstances. The data has been retrieved by placing a Raspberry Pi device within these cars and recording the sound and location during various trips at different times. The sound data of our dataset was then corrected for correlation with speed and acceleration. Furthermore, clustering techniques were used in order to estimate the type and condition of the pavement using this set of noise measurements. The algorithms were run on a small dataset and compared to a ground truth which was derived from visual observations. The results were best for a combination of Generalised Additive Model (GAM) correction on the data combined with hierarchical clustering. A connectivity matrix merging points close to each other further enhances the results for road pavement quality detection, and results in a road type detection rate around 90%