Asfault: A low-cost system to evaluate pavement conditions in real-time using smartphones and machine learning

Modern smartphones have a large variety of built-in sensors that can measure different information about users and the environment around them. Given the increasing popularity of these devices, their high processing power, and the ability to transfer data over wireless networks, different smartphone-based applications have emerged in the last years to solve old problems with new approaches more efficiently and cheaply. One example is the assessment and monitoring of asphalt quality. This task has been done manually by experts since the 1930s, and with the help of expensive equipment since the 1960s. Currently, we are experiencing the emergence of next-generation tools to perform this monitoring with smartphones, significantly reducing costs, time, and effort of experts. However, there is a trade-off between the costs and precision of smartphone sensors, requiring the use of sophisticated software solutions. In this paper, we propose Asfault, a low-cost system to evaluate and monitor road pavement conditions in real-time using smartphone sensors and machine learning algorithms. The system is composed of an Android application responsible for doing automatic evaluations and a web application that aims to show the evaluations in an informative way. We propose to employ accelerometer sensors to measure the vehicle vibration while driving and use this data to evaluate the pavement conditions. Asfault achieves a classification performance superior to 90% in a 5-class problem considering the following road qualities: Good, Average, Fair, and Poor, as well the occurrence of obstacles in the road. Our system is publicly available for use and could be useful for practitioners responsible for urban and highway maintenance, as well for regular drivers in the planning of better routes based on the pavement quality and comfort of the travel. (C) 2018 Elsevier B.V. All rights reserved.