Straight road noise mapping prediction using probabilistic approach

Noise pollution in urban areas, primarily stemming from road traffic, poses a significant challenge in environmental management. Current road traffic noise prediction models are unable to accurately handle noise mapping and complex traffic flows. Existing models rely on deterministic approaches, which cannot effectively predict complex traffic patterns. This study focuses on developing a model using a probabilistic approach for the accurate prediction and mapping of straight road traffic noise levels, a crucial step for effective noise control and mitigation strategies. This approach allows for a comprehensive evaluation of the model's effectiveness under varying traffic conditions. The methodology involves a series of seven case studies within the Klang Valley, Malaysia, including both one-way and two-way lane scenarios, to validate the accuracy of the probabilistic approach framework. The results of the study are promising, demonstrating a high degree of accuracy in noise level predictions. The predictions show a close alignment with actual field measurements, maintaining an absolute difference of less than 3.0 dBA across different scenarios, with an average difference of 1.7 dBA. This accuracy is further supported by comparative analyses and R-correlation assessments, which confirm the model's reliability even in the face of moderate variations during off-peak times, mainly due to human-made uncertainties in studied areas. The average size of the correlation for peak sessions is 0.8338, and for off-peak sessions, it is 0.8311, with both strength correlations being very high. However, while the model shows high effectiveness in straightforward road scenarios, it requires modifications for complex road layouts. In summary, this prediction model can be used to predict noise levels for straight roads, as it shows strong correlations when predicting peak and off-peak sessions.