Potholes vs. Speed Bumps: A Multivariate Time Series Classification Approach

ARIEL MONTESERIN

Londres

Workshop; KDD-UMCit 2018 1st Workshop on Knowledge Discovery and User Modelling for Smart Cities; 2018

In this work, we present a preliminary approach to distinguish potholes from speed bumps by analyzing the acceleration values sensed by a mobile device. The information of the accelerometers is gathered by an experimental mobile application developed to automatically detect potholes. A driver, who has previously installed the application in her smartphone, places the devicein a fixed position inside the vehicle. Thus, this application records the accelerometer oscillations and the place where the vehicle transits. Then, if the road is damaged, the vibrations produced in the vehicle can be captured by the accelerometers indicating the pothole. However, in a road there are other structures that can produce similar effects: speed bumps. In both potholes and street bumps, the accelerometers of the mobile device produce a sequence of oscillations in the three axis (X, Y and Z). We model these sequence as multivariate time series and then we classify these by using a temporal classification approach. The preliminary results were carried out with real-world data and showed a promising accuracy.