Department of Public Works Engineering, Faculty of Engineering, Tanta University, Tanta, 3111, Egypt.
Department of Architectural Engineering, Faculty of Engineering, Tanta University, Tanta, 31511, Egypt.
Environ Sci Pollut Res Int. 2023 Sep;30(41):94229-94241. doi: 10.1007/s11356-023-28934-7. Epub 2023 Aug 2.
Recently, several urban areas are trying to mitigate the environmental impacts of traffic, where noise pollution is one of the main consequences. Thus, studying the determinants of traffic-related noise generation and developing a model that predicts the level of noise by controlling the influencing factors are crucial for transportation planning purposes. This research aims at utilizing the response surface method (RSM) to develop a robust statistical prediction model of traffic-related noise levels and optimize different traffic characteristics' ranges to reduce the expected noise levels. The results indicate that the rate of L increase is higher at traffic flow values less than the 1204 veh/h. The interaction effect of flow-speed and flow-heavy vehicle percentage pairs shows that L has peak values around 45.8 km/h and 28.71%, respectively, with almost symmetric value distribution about those center points. The main effects study indicates a direct effect of traffic flow, speed, density, and traffic composition on roadside noise levels. The prediction model has good representativeness of observed noise levels by predicted noise levels as the model has a high coefficient of determination (R = 95.87% and R adj = 92.26%) with a significance level of 0.0036. Then, the research presents a methodology to perform an optimization of the roadside noise level by defining traffic characteristics that can keep the noise level below 65 dB(A) or minimize noise level. Decision-makers could use the proposed method to control the roadside noise level.
最近，一些城市地区正在努力减轻交通带来的环境影响，其中噪声污染是主要后果之一。因此，研究交通相关噪声产生的决定因素，并开发一种通过控制影响因素来预测噪声水平的模型，对于交通规划目的至关重要。本研究旨在利用响应面法（RSM）开发一个强大的交通相关噪声水平预测统计模型，并优化不同交通特性的范围，以降低预期噪声水平。结果表明，在交通流量值小于 1204 辆/小时的情况下，L 的增长率更高。流量-速度和流量-重车百分比对的交互效应表明，L 在大约 45.8 公里/小时和 28.71%处有峰值，这些中心值的分布几乎是对称的。主要效应研究表明，交通流量、速度、密度和交通组成对路边噪声水平有直接影响。预测模型通过预测噪声水平对观测噪声水平具有很好的代表性，因为模型具有很高的确定系数（R=95.87%和 Radj=92.26%），显著性水平为 0.0036。然后，研究提出了一种通过定义可以将噪声水平保持在 65dB(A)以下或最小化噪声水平的交通特性来优化路边噪声水平的方法。决策者可以使用所提出的方法来控制路边噪声水平。
