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.

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

A traffic noise system involves several subsystems like road traffic subsystem, human subsystem, environment subsystem, traffic network subsystem, and urban prosperity subsystem. The study's main aim was to develop road traffic noise models using a graph theory approach involving the parameters related to road traffic subsystem. The road traffic subsystem variables selected for the modeling purposes included vehicular speed, traffic volume, carriageway width, number of heavy vehicles, and number of honking events. The interaction of the selected variables considered in the form of permanent noise function is given in the matrix form. Eigenvalues and corresponding eigenvectors are calculated for removing any human judgmental error. The permanent noise function matrix was then updated using the eigenvectors, which was ultimately utilized for obtaining the permanent noise index. Data regarding the selected variables were collected for three months, and the noise parameters included in the study were equivalent noise level (Leq,1h), maximum noise level (L10,1h), and background noise level (L90,1h). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for Leq,1h , L90,1h , and L10,1h respectively. The models were validated using the data collected from the same locations for nine months. The models were found to provide satisfactory results, although the results were somewhat overestimated. The method can prove beneficial for estimating future noise levels, given the expected changes in values for the independent variables considered in the study.

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Modelling road traffic Noise under heterogeneous traffic conditions using the graph-theoretic approach

Abstract Road traffic noise has been recognized as a serious issue that affects the urban regions. Due to urbanization and industrialization, transportation in urban areas has increased. Traffic noise characteristics in cities belonging to a developing country like India are highly varied compared to developed nations because of its heterogeneous conditions. The objective of the research study is to assess noise pollution due to heterogeneous traffic conditions and the impact of horn honking due to un-authorized parked vehicles on the main roadside. Noise mapping has been done using the computer simulation model by taking various noise sources and noise propagation to the receiver point. Traffic volume, vehicular speed, noise levels, road geometry, un-authorized parking, and horn honking were measured on tier-II city roads in Surat, India. The study showed not so significant correlation between traffic volume, road geometry, vehicular speed and equivalent noise due to heterogeneous road traffic conditions. Further, analysis of traffic noise showed that horn honking due to un-authorized parked vehicles contributed an additional up to 11 dB (A), which is quite significant. The prediction models such as U.K’s CoRTN, U.S’s TNM, Germany’s RLS-90 and their modified versions have limited applicability for heterogeneity. Hence, the noise prediction models, which can be used for homogeneous road traffic conditions are not successfully applicable in heterogeneous road traffic conditions. In this research, a new horn honking correction factor is introduced with respect to unauthorized parked vehicles. The horn honking correction values can be integrated into noise model RLS-90, while assessing heterogeneous traffic conditions.

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Integrated road traffic noise mapping in urban Indian context

Noise pollution has become a major concern of communities living in the vicinity of highways/road corridors, and intersections. The recognition of highway noise, as one of the main sources of environmental pollution led to design models that enable us to predict traffic noise level. Several models have been developed to predict traffic noise along the highways from fundamental variables such as noise descriptors. Experimental data are gathered for seven squares along the National Highway-316 (connecting Bhubaneswar to Jagannath Dham, Puri) in the present study. Noise pollution was analysed in seven different squares (road sections) during morning, afternoon and evening to assess the level of noise pollution along this road. A methodical comparison of simulated and experimental data is made, in order to test the perfection and behaviour of the models in different studied sites in an Indian road condition. Co-relation coefficient is applied to infer the level of significance.

Assessment of Highway Noise and Predictive Models along NH-316

Traffic noise is continuously rising alongside roadways, especially at intersections, due to rapid urbanization, eventually affecting acoustical climate and quality of life. This present study develops a traffic noise model for intersections with minimal evidence of interrelationships among influential traffic noise factors. An integrated Bayesian networks and Partial least squares structural equation modelling approach has been employed on 342-hour field measurement data collected from nineteen intersections in Kanpur, India. The integrated approach developed the traffic noise prediction model with 62.4% explanatory power and identified direct and indirect effects of five influential factors on traffic noise. For instance, Traffic flow attributes, i.e., traffic volume and honking, are the most crucial ones to degrade acoustical climate at intersections. Besides, Built environment and Climate conditions induce only indirect effects on traffic noise. Thus, this study provides a useful basis for planners to understand traffic noise relationships and deploy noise mitigation strategies accordingly. 

Modelling of traffic noise in the vicinity of urban road intersections

Noise is one of the environmental pollutant that is encountered in daily life. Noise pollution has become a major concern of communities living in the vicinity of major highway corridors. In view of the rapid development it is essential to study highway noise with respect to various causative factors. In the present paper a road traffic noise prediction model for Indian conditions is developed using regression analysis which is based on Calixto model. Data collected has been analyzed and compared with the values predicted by Calixto model. After comparison of results it was observed that Calixto Model could be satisfactorily applied for Indian conditions as they give accepted results with a good value.

K. Rawat

Papers

Mathematical modeling of road traffic noise prediction