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Traffic noise is a significant source of noise pollution, disrupting urban environments with fluctuating sound. The existing research on traffic noise prediction predominantly focuses on statistical methods to identify significant predictors affecting noise levels. While these approaches offer valuable insights, they often lack the interpretability and adaptability needed for complex urban environments. The proposed framework is aimed at presenting the insights of explainable AI (XAI) for the regression analysis of traffic noise levels which is predicted with the help of advanced machine learning (ML) models such as K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), Long-Short Term Memory (LSTM) and Random Forest (RF). Statistical analysis of these models was tested with a performance matrix by utilizing a comprehensive traffic dataset of Dhanbad city that includes vehicle speed and categories of vehicle type. Notably, the RF model excelled over other models with an RMSE of 1.27 and of 0.94. The XAI model was developed with the base of RF regressor which records the highest score. The analysis revealed that the number of 2-wheeler vehicle categories is a key predictor of traffic noise levels. The finding of this study can act as an automated information system for the benefit of the urban planners and decision-making bodies to mitigate noise pollution effectively in mid-sized cities. It is worth mentioning that the primary purpose of employing multiple ML models (RF, XGBoost, KNN, LSTM) in this study is to conduct a comparative analysis and identify the most suitable algorithm for urban traffic noise prediction in a Tier-2 Indian city.

The study reports the level of traffic-induced noise pollution in the major schools, colleges and hospitals of the Jessore city of Bangladesh. The noise levels have been measured at 14 locations of the city from 7am to 7pm in the working days. The findings also indicated that traffic noise levels depend on distance from roadside and diurnal variation. Motorized traffic is the main source of noise pollution in this city. The study found that the most noise-polluted institution in the city was Mentor International School with measured L^sub eq^ of 80.37 dB and the least noise polluted institution in the city was Ad-din hospital with measured L^sub eq^ of 64.09 dB. The L^sub 10^ levels in all the institutions were higher than 75.62 dB and L^sub 90^ level was higher than 58.51 dB and there is a strong positive correlation between L^sub 10^ and L^sub 90^ level. Findings also indicate that in all of the institutions the TNI level was higher than 96.94 dB and the NC level was higher than 22.2 dB and also there is a strong positive relationship between TNI and NC. When the NC level increases then the TNI level also increases. It has been observed that at all the locations, the level of noise remains far above the acceptable limit for all the time.

Background The objective of the research work is to assess day time traffic noise level at curbside open-air microenvironment of Kolkata city, India under heterogeneous environmental conditions. Results Prevailing traffic noise level in terms of A-weighted equivalent noise level (L eq ) at the microenvironment was in excess of 12.6 ± 2.1 dB(A) from the day time standard of 65 dB(A) for commercial area recommended by the Central Pollution Control Board (CPCB) of India. Noise Climate and Traffic Noise Index of the microenvironment were accounted for 13 ± 1.8 dB(A) and 88.8 ± 6.1 dB(A) respectively. A correlation analysis explored that prevailing traffic noise level of the microenvironment had weak negative (−0.21; p  < 0.01) and very weak positive (0.19; p  < 0.01) correlation with air temperature and relative humidity. A Varimax rotated principal component analysis explored that motorized traffic volume had moderate positive loading with background noise component (L 90 , L 95 , L 99 ) and prevailing traffic noise level had very strong positive loading with peak noise component (L 1 , L 5 , L 10 ). Background and peak noise component cumulatively explained 80.98 % of variance in the data set. Conclusions Traffic noise level at curbside open-air microenvironment of Kolkata City was higher than the standard recommended by CPCB of India. It was highly annoying also. Air temperature and relative humidity had little influence and the peak noise component had the most significant influence on the prevailing traffic noise level at curbside open-air microenvironment. Therefore, traffic noise level at the microenvironment of the city can be reduced with careful honking and driving.

The standards for the environmental quality required by the European Community are very demanding in what concerns traffic noise. The interaction tire/road is undoubtedly one of the main sources of traffic noise. Nevertheless, standards do not account for the increase in the noise level caused by wet road surfaces. Therefore, the aim of this work is to study the effects of water on pass-by noise since the weather is rainy about 25 per cent of the year in Portugal. Thus, it addresses two currently used pavement surfaces, porous asphalt and dense asphalt, constructed in a motorway. A version of the Statistical Pass-By Method was used to assess noise levels with dry and wet surfaces, using a selected set of heavy and light vehicles. The results include analysis of the statistical pass-by index, maximum noise levels and noise spectrum. Noise levels increase considerably with the presence of water, shifting the overall noise by 4 dB(A). The benefits of porous asphalt are very limited for heavy vehicles, particularly at high speeds.

A model is demonstrated that describes street traffic-induced noise pollution in 2008 in Yazd, Iran. Sound levels were measured using a Bruel and Kjaer-2260 sound level meter on 10 streets across the city over this period during the morning rush hour and different vehicle types were counted simultaneously at various sampling points. Geographical Information System was used to generate, store and retrieve the spatial data and map the sound levels using an interpolation technique. The minimum and maximum sound levels appeared to be 70.9 dBA and 80.7 dBA, respectively and these values were above the national legislated norm. Cars and motorcycles were the most commonly used vehicle type in the city, comprising 61.2% and 23.7 % of the total traffic volume, respectively. These data were followed by trucks, buses and bicycles. A number of parameters which were assumed to impact on noise pollution were collected and considered, including geographical position, elevation, the distance to the nearest intersection, street geometry and the numbers of vehicles according to class. The modelling demonstrated that there is a significant relationship between the average sound level and traffic flow (R2 = 0.5). The results showed that although street traffichas increased between 2002 and 2008, the sound levels in the city decreased slightly and this has been attributed to advances in vehicle design.

In many developing countries with surging vehicular traffic and inadequate traffic management, excessive road traffic noise exposure poses substantial health concerns, linked to increased stress, insomnia and other metabolic disorders. This study aims to assess the linkage between sociodemographic factors, traffic noise levels in residential areas and health effects using a cross-sectional study analyzing respondents’ perceptions and reports. Noise levels were measured at 57 locations in Srinagar, India, using noise level meter. Sound PLAN software was employed to generate noise contour maps, enabling the visualization of noise monitoring locations and facilitating the assessment of noise levels along routes in proximity to residential areas. Correlation analysis showed a strong linear relationship between field-measured and modelled noise ( r 2  = 0.88). Further, a questionnaire-based survey was carried out near the sampling points to evaluate the association of ischemic heart disease with traffic noise. Residents exposed to noise levels (L den  > 60 dB(A)) were found to have a 2.24 times higher odds ratio. Compared to females, males reported a 16% higher prevalence of the disease. Multi-faceted policy strategies involving noise mapping initiatives, source noise standards, traffic flow urban mobility optimization, smart city initiatives and stringent litigatory measures could significantly reduce its detrimental impact on public health. Finally, this study envisions a region-specific strong regulatory framework for integrating noise pollution mitigation strategies into the public health action plans of developing nations.

Noise is considered as an underrated and underemphasized pollutant in contrast to other pollutants of the environment. Due to the non-acute response of health effects, people are not vigilant towards consequences regarding noise pollution. The expansion of the transportation industry is contributing towards the increment in the public and private vehicular volume which causes an increment in noise pollution. For evaluation of respective scenario, the research study has been conducted on one of the minor roads of Nagpur, India; for 2 years, viz., 2012 and 2019. The study concludes an increment of 5–6 dB(A) in noise level, 4–6 times in honking, and 1.7 times in traffic volume. The study confirms increment in sound pressure by 65.9% and 81.9% for the year 2012 and 2019 during morning and evening sessions, respectively. Noise prediction model has also been developed for the abovementioned years, using multiple regression analysis, considering traffic volume, honking, and speed against noise equivalent level. Honking has been further characterized into honk by light and medium category vehicles as acoustical properties of horns vary with respect to category of vehicle and introduced into the noise prediction model. Noise prediction model for 2019 has predicted the noise level in a range of − 1.7 to + 1.4 dB (Leq) with 84% of observations in the range of − 1 to + 1 dB (Leq), when compared with observed Leq on the field. For proper management of noise pollution, a noise prediction model is essentially needed so that the noise level can be anticipated, and accordingly, measures can be outlined and executed. This increased noise level has serious impacts on human hearing capacity and overall health. Accordingly, noise mitigation preventive measures are recommended to control traffic noise in the urban environment.

Faced with the accelerated growth of cities and the consequent increase in the number of motor vehicles, urban noise levels caused by vehicular traffic have increased considerably. To assess noise levels in cities and implement noise control measures or identify the problem’s location in different urban areas, it is necessary to obtain the noise levels to which people are exposed. Noise maps are tools that have applications as they are cartographic representations of the noise level distribution in an area and over a period of time. This article aims to identify, select, evaluate, and synthesize information, through a systematic literature review, on using different road noise prediction models, in sound mapping computer programs in countries that do not have a standard noise prediction model. The analysis period was from 2018 to 2022. From a previous analysis of articles, the choice of topic was based on identifying various models for predicting road noise in countries without a standardized sound mapping model. The papers compiled by a systematic literature review showed that studies concentrated in China, Brazil, and Ecuador, the most used traffic noise prediction models, were the RLS-90 and the NMPB, and the most used mapping programs were SoundPLAN and ArcGIS with a grid size of 10 × 10 m. Most measurements were carried out during a 15-min period at a height from the ground level of 1.5 m. In addition, it was observed that research on noise maps in countries that do not have a local model has been increasing over time.

Transportation noise is increasingly acknowledged as a cardiovascular risk factor, but the evidence base for an association with stroke is sparse. We aimed to investigate the association between transportation noise and stroke incidence in a large Scandinavian population. We harmonized and pooled data from nine Scandinavian cohorts (seven Swedish, two Danish), totaling 135,951 participants. We identified residential address history and estimated road, railway, and aircraft noise for all addresses. Information on stroke incidence was acquired through linkage to national patient and mortality registries. We analyzed data using Cox proportional hazards models, including socioeconomic and lifestyle confounders, and air pollution. During follow-up ( ), 11,056 stroke cases were identified. Road traffic noise ( ) was associated with risk of stroke, with a hazard ratio (HR) of 1.06 [95% confidence interval (CI): 1.03, 1.08] per 10-dB higher 5-y mean time-weighted exposure in analyses adjusted for individual- and area-level socioeconomic covariates. The association was approximately linear and persisted after adjustment for air pollution [particulate matter (PM) with an aerodynamic diameter of ( ) and ]. Stroke was associated with moderate levels of 5-y aircraft noise exposure (40-50 vs. ) ( ; 95% CI: 0.99, 1.27), but not with higher exposure ( , ; 95% CI: 0.79, 1.11). Railway noise was not associated with stroke. In this pooled study, road traffic noise was associated with a higher risk of stroke. This finding supports road traffic noise as an important cardiovascular risk factor that should be included when estimating the burden of disease due to traffic noise. https://doi.org/10.1289/EHP8949.

Road traffic noise exposures have been recognized as serious environmental health concerns, especially in most developing countries with arid climate conditions, rapid increase in vehicle population, and limited traffic management systems. The excessive noise exposure level is associated with increase in the incidence of cardiovascular diseases and anxiety, including annoyance. This study aimed at determining traffic noise levels in residential areas, including the assessment of its annoyance and health effects based on the people’s perception and reportage. To do so, field measurement and traffic noise modeling were carried out in six road points to estimate the current noise levels along various roads close to human inhabitants in Muscat Governorate, Sultanate of Oman. The detailed measured noise levels in urban residential areas across the selected roads showed that noise levels have exceeded the local and international threshold limits at all locations during the entire day. The high sound levels (48.0–56.3 dBA) were observed using the US Federal Highway Administration’s Traffic Noise Model (TNM, version 2.5) results, which were in agreement with the observed (56.3–60.4 dBA) data. To assess health implication to residents through interviews ( n = 208), annoyance at home was found to be little (32%), moderate (28%), and high (9%) in comparison with workplace settings of 42%, 43%, and 15%, respectively. Nineteen percent of the interviewees had difficulties in sleeping, while 19.8% experienced stress due to road traffic noise exposures. Moreover, a strong association ( p < 0.05) was established between the use and objection of noise barriers. The study revealed high noise levels and the prevalence of annoyance and health effects among the exposed population. Therefore, immediate action is required to tackle the current noise levels.