Pothole Detection and Prediction using Sensors and Machine Learning.

In India, in metropolitan cities, a common problem seen is traffic jams or slowdown on traffic due to potholes. Most of the road accidents are due to potholes. In this paper, the authors have tried to ensure that the road surface quality and the potholes should be monitored continuously and must be repaired as necessary. The best possible distribution of resources could be attained when the real-time data is gathered from the mounted sensory system and displayed and visualized in a way (Google Maps) which is comprehensive and empirical. Different sensors would be mounted onto the public transport vehicles. The method mentioned in this paper uses the ultrasonic sensor and the accelerometer to send the data regarding the intensity of pothole straightaway to the cloud data storage, along with the location coordinates of the potholes and the time stamp. Different databases such as traffic density of the city, rain intensity of different areas is used to train different machine learning models to predict the intensity of potholes at the particular locations and get the best possible accuracy. The best-case accuracy attained was 35.5%. The pothole intensity data is visualized through a heat map application. This system helps in reducing the frequency of traffic jams in any city of India. The method suggested in the paper would help in reducing the number of accidents happening in India due to the potholes. [ABSTRACT FROM AUTHOR]