This article analyses the most recent studies on urban traffic noise. About 67 relevant articles on urban road traffic noise and its mitigation strategies were preferred for a critical review. Only 5.97% of items describe how to monitor and record the noise measurement for urban roads, while 7.46% of articles enumerated urban traffic noise pollution exposure. 29.85% of articles proposed a model to evaluate noise reduction effects and predict the noise level. Also, many articles reported noise map generation and its analysis. 56.71% of articles described the noise mitigation strategies in detail, concerning noise control by green vegetation, land use planning, low noise tire and pavement material, noise reduction through façade shielding. Noise pollution standards are being breached in all areas. There is a need for the proper implementation of rules and regulations. Therefore, noise mitigation strategies such as designing noise barriers and other noise control materials are needed. Finally, it is summarized that economic and low-cost optimized noise pollution mitigation strategies like ingeniously made noise barriers, vegetation and landscaping are need of the hour for urban areas of developing countries.
In the issue of expanding noise levels the world over, road traffic noise is main contributor. The investigation of street traffic noise in urban communities is a significant issue. Ample opportunity has already passed to understand the significance of noise appraisal through prediction models with the goal that assurance against street traffic noise can be actualized. Noise predictions models are utilized in an increasing range of decision-making applications. This study’s main objective is to assess ambient noise levels at major arterial roads of Surat city, compare these with prescribed standards, and develop a noise prediction model for arterial roads using an Artificial Neural Network. The feed-forward back propagation method has been used to train the model. Models have been developed using the data of three roads separately, and one final model has also been developed using the data of all three roads. Among the prediction in three arterial roads, the predicted output result from the model of Adajan-Rander showed a better correlation with a mean squared error (MSE) of 0.789 and R2 value of 0.707. But with the combined model, there is a slight deterioration in mean squared value (MSE) 1.550, with R2 not getting changed much significantly, i.e., 0.755. However, the combined model’s prediction can be adopted due to the variety of data used in its training.
Urban traffic noise is emerging as a crucial problem in the 21st century. Variation in the level of noise from urban traffic causes several health-related issues. This study demonstrates the noise assessment and appraisal of morning peak time urban road traffic noise at selected locations of major arterial roads of Surat city. The noise is compared against the norms and standards given by the noise pollution (Regulation and Control) Rules, 2000. MoEF&CC has published the Ambient Air Quality Standards in respect of noise under Rule 3(1) and Rule 4(1) as per Schedule in the annexe. In this research work, noise levels were measured at four different locations from Athwa line chowk to Dumas Road of Surat city. Traffic count has been done by calculating the numbers of two wheelers, three-wheelers, four-wheelers, and heavy vehicles (bus & truck). The A-weighted sound level was 78.9 dB(A) near the urban road, which exceeds the standard value recommended by CPCB. The maximum equivalent noise level was 114.9dB at Sushrut hospital, while the minimum was 46.1 dB at Keval chowk. Finally, the study indicates an increment in noise levels with an increment in the count of vehicles. The factors causing the increased noise levels are traffic flow, horn honking, lane indiscipline, heterogeneous traffic condition, morning rush, etc.
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