Study of the factors affecting road roughness measurement using smartphones

Guanyu Wang, Michael Burrow, Gurmel Ghataora

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Abstract

The measurement of road roughness is important for the management of economic road maintenance. Not only is it an indicator of road condition and ride quality, but it is also used to determine road use costs, including travel time, fuel consumption and vehicle maintenance.

Because of the importance of roughness for road asset management decision making, road agencies spend considerable resources in trying to measure road roughness in a repeatable and reproducible manner. However, many road agencies with large road networks are unable to record the condition of the entire network on a sufficiently frequent basis to determine adequately road condition to make informed preventative maintenance decisions.

To address this, research has been carried out to develop low cost smartphone based technologies fitted inside vehicles to measure road condition. The trial of these systems has met with varying degrees of success.

This paper presents an in depth parametric study carried out using state of the art vehicle dynamics software, informed by a review of the literature, to appreciate how and to what degree various influencing variables might affect roughness measurements using a smartphone fitted to a moving vehicle. These variables included the type and position of the smartphone, the type, speed, mass, dynamic response, suspension system and tyre pressure of the vehicle in which the smartphone is fitted and the longitudinal road profile. The results of the parametric analysis were used to build multivariate linear regression and machine learning algorithms which predict road roughness from a measure of a vehicle’s vertical acceleration taking into account the predominant influencing variables. It was found that the multivariate linear regression equations could be used to predict road roughness to a similar degree of accuracy to that which might be expected from a visual inspection. The machine learning algorithms, suitably trained, on the other hand were found to be able to estimate reliably road roughness on an integer based rating scale at a level of detail which is suitable for strategic road asset management, provided that the vehicle type and speed and the type of smartphone are taken into account.
Original languageEnglish
Article number04020020
Number of pages15
JournalJournal of Infrastructure Systems
Volume26
Issue number3
Early online date11 May 2020
DOIs
Publication statusPublished - Sep 2020

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