Development of in-vehicle noise prediction models for Mumbai Metropolitan Region, India

Traffic noise modelling at intersections in mid-sized cities: an artificial neural network approach

Traffic noise has emerged as one major environmental concern, which is causing a severe impact on the health of urban dwellers. This issue becomes more critical near intersections in mid-sized cities due to poor planning and a lack of noise mitigation strategies. Therefore, the current study develops a precise intersection-specific traffic noise model for mid-sized cities to assess the traffic noise level and to investigate the effect of different noise-influencing variables. This study employs artificial neural network (ANN) approach and utilizes 342 h of field data collected at nineteen intersections of Kanpur, India, for model development. The sensitivity analysis illustrates that traffic volume, median width, carriageway width, honking, and receiver distance from the intersection stop line have a prominent effect on the traffic noise level. The study reveals that role of noise-influencing variables varies in the proximity of intersections. For instance, a wider median reduces the noise level at intersections, while the noise level increases within a 50-m distance from intersection stop line. In summary, the present study findings offer valuable insights, providing a foundation for developing an effective managerial action plan to combat traffic noise at intersections in mid-sized cities.

Random effect generalized linear model-based predictive modelling of traffic noise

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

Vehicular traffic noise modelling of urban area-a contouring and artificial neural network based approach

Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L₁₀, L₉₀, L_dn, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles, and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, speed, traffic flow, road gradient, pavement, road side carriageway distance factors were taken as input parameter, whereas L_Aeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59%, two wheelers and different vehicle specifications with varying speeds also affect driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and the highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicate high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 and .029 in training and 0.458 and 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ± 0.6 dB(A) and the R² linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

Traffic noise prediction model of an Indian road: an increased scenario of vehicles and honking

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.