STATISTICAL MODELLING OF TRAFFIC NOISE USING SMART PLS-SEM IN URBAN ROADS

Abstract

In urban locations across the globe, traffic noise pollution has emerged as a pervasive and detrimental environmental issue.  The objectives of this study are to assess the traffic noise levels in Shah Alam, Selangor, Malaysia, and to propose traffic noise modeling among influential traffic noise factors. There are five influential traffic noise factors (climate condition (temperature, wind speed, and relative humidity), road geometry, built environment, traffic flow, and equivalent traffic noise (LAeq)) were measured. The data were collected using a sound pressure level meter and were analyzed using PLS-SEM software (SmartPLS version 4). Statistical analysis was conducted, and the model was formulated based on data collected from 6 sampling points on two different urban roads. Using this PLS-SEM model, the relationship among all the attributes was analyzed statistically and regression analysis to determine the prediction validity and reliability of the model. Results of this study showed that high traffic noise levels on urban roads exceeded the maximum permissible limit of 60dBA during daytime, according to Malaysian guidelines. The integrated approach developed the traffic noise prediction model with specific indirect effects 0.389 built environment > climate conditions > traffic flow. This also provides evidence that the built environment, climate condition, and traffic flow are more related to the noise levels compared to road geometry and equivalent traffic noise. All validity and reliability criteria, including internal consistency, indicator, convergent, and discriminant validity, are satisfied in the measurement model assessment. Additionally, the three parameters of degree of significance, coefficient of determination (R2), and predictive relevance (Q2) are used to evaluate the structural portion of the PLS-SEM model. With this investigation the effort to design a model that can precisely forecast traffic noise levels is a crucial resource for effectively controlling noise pollution, improving the quality of life in cities, ensuring regulatory compliance, protecting public health, and assisting in decision-making.