Civil Engineering Department, DCRUST, Murthal, Haryana, India.
Environ Sci Pollut Res Int. 2022 Aug;29(37):55568-55579. doi: 10.1007/s11356-022-21395-4. Epub 2022 Jun 15.
Noise has emerged as a leading environmental problem and is an underestimated threat. The most significant source of noise pollution is road traffic. Road traffic noise problem has reached alarming levels. This proves the severity and necessity of mitigating the traffic noise from every delicate corner possible. Noise monitoring is required to check the noise levels and effectiveness of control methods implemented. Road traffic noise control can be exercised with the help of prediction models. This paper presents the traffic noise status of developing countries and a quantitative review and comparison of traffic noise prediction models developed by researchers for various cities. Findings suggest that most of the researchers have used regression modelling and use of evolutionary computing methods like genetic algorithm, fuzzy systems, and neural networks to develop traffic noise prediction model is lacking. The effect of many important variables affecting traffic noise like pavement type, vegetation along roads, road surface roughness, and gradient still needs to be studied. Further, studies are required to measure in vehicle noise levels on same roads to compare the noise levels tolerated by residents, road users, and the commuters; this will help in formulating traffic noise regulations.
噪声已成为主要的环境问题之一，也是一个被低估的威胁。噪声污染的最大来源是道路交通。道路交通噪声问题已经达到了惊人的程度。这证明了尽可能从每个细微的角落减轻交通噪声的严重性和必要性。需要进行噪声监测，以检查实施的噪声控制方法的噪声水平和效果。可以借助预测模型来控制道路交通噪声。本文介绍了发展中国家的交通噪声状况，并对不同城市的研究人员开发的交通噪声预测模型进行了定量回顾和比较。研究结果表明，大多数研究人员已经使用回归建模，并且缺乏使用遗传算法、模糊系统和神经网络等进化计算方法来开发交通噪声预测模型。仍需要研究影响交通噪声的许多重要变量，如路面类型、道路两旁的植被、路面粗糙度和坡度。此外，还需要在同一路段测量车内噪声水平，以比较居民、道路使用者和通勤者所能承受的噪声水平；这将有助于制定交通噪声法规。
