Noise on wheels: Decoding urban road traffic noise dynamics using a smartphone noise-based application

Abstract

In recent years, an alarming rise in noise pollution has been caused by increased vehicle volume on the road. Because traffic noise is a major social and physical health risk, periodic measurements are necessary to improve environmental noise level monitoring and management in our cities. This necessitates a noise monitoring tool that is accessible, affordable, and simple to use. In this study, the reliability and effectiveness of smartphones were evaluated as noise-level measuring tools. Furthermore, this study applied a multiple regression method to develop a statistical traffic noise model for the study locations in Kumasi using collected data on noise levels and other traffic parameters. The model featured the effect of vehicle class (light and heavy vehicles), average speed of vehicles, road class (arterial, collector and local roads) and associated vehicle honking on traffic noise levels. Holistically, findings revealed that speed, honking sound, heavy vehicles and collector roads are the most significant factors impacting an increase in road traffic noise. The traffic noise levels were compared to the Environmental Protection Agency's permissible limit and were found to be relatively higher. This implies that the roadside residents may be at risk of several adverse health effects posed by traffic noise pollution.