Comparison of Smartphone-Based and Drone-Based Approaches for Assessing Road Roughness

Abstract

Continuous road-pavement condition monitoring is essential to maintain the effectiveness and performance of road infrastructure. It also helps to optimize resource allocation and prioritization for pavement rehabilitation programs. The traditional methods that have been used for assessing road pavement condition are high cost and time consuming. Contrary to conventional methods, low-cost and new emerging technologies such as smartphones and drones can be used. The study aims to investigate the merits of using two different types of device for assessing road roughness: the smartphone as a response-type and the drone as a non-contact-type device. To this end, firstly, linear vertical acceleration data is collected by using a smartphone that is mounted on a car dashboard. Then, the collected data is used for calculating vertical displacement data using a fast Fourier transform. The displacement data is used for calculating international roughness index (IRI) as a measure of road roughness. Secondly, a drone flies over the studied road to construct a 3D model, extract the longitudinal profile, and eventually calculate the accurate IRI values of the road. Finally, the results of the approaches above will be compared with the results of a road surface profiler (RSP) as a reference method. Although the results reveal a significant correlation (above 0.8) of both methods with the RSP method, from an operational point of view, the smartphone was more cost and time effective, and also an easy-to-use approach for the purpose.