Review on Estimation of Road Quality using Mobile Sensors & Machine Learning Techniques

Abstract

Machine learning techniques have become an integral part of realizing smart transportation Machine learning learns the latent patterns of historical data to model the behaviour of a system and to respond accordingly in order to automate the analytical model building. Using AI based Machine Learning Algorithms can detect the road anomalies, analyse them, share this information to the users while driving and also repair them by sending the relevant information to the road maintenance authorities. The availability of increased computational power and collection of the massive amount of data have redefined the value of the machine learning-based approaches for addressing the emerging demands and needs in transportation systems. The overall objective of this work is to develop an efficient machine learning techniques for mobile sensor data. As road maintenance is costly and authorities cannot go on each and every road network, such applications will be useful to provide the data to the higher authorities so that they can take the valuable actions towards it.Machine learning for sensors and signal data is becoming easier than ever: hardware is becoming smaller and sensors are getting cheaper. The paper presents brief information about the smart phone sensors and techniques used in Machine Learning how they are used for the road anomaly detection and the work done up till now in that domain