Honking noise corrections for traffic noise prediction models in heterogeneous traffic conditions like India

Kalaiselvi, R.; Ramachandraiah, A.

doi:10.1016/j.apacoust.2016.04.003

Cited by 45 publications

(14 citation statements)

References 3 publications

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“…The inclusion of honking was done because it is the main characteristic of road traffic in developing countries, including India. Besides, some studies have also shown improvements in traffic noise modeling by including honking as a parameter (Kalaiselvi and Ramachandraiah 2016 ; Sharma et al 2014 ; Vijay et al 2014 , 2015 ). All the variables were incorporated in a matrix form by assigning the weights to the selected parameters, represented along the matrix's diagonal, and weights were assigned to the variables' interactions, represented by the off-diagonal elements.…”

Section: Discussionmentioning

confidence: 99%

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

Gilani

Mir

2021

Environ Sci Pollut Res

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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 (L eq,1h ), maximum noise level (L 10,1h ), and background noise level (L 90,1h ). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for L eq,1h , L 90,1h , and L 10,1h 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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Section: Discussionmentioning

confidence: 99%

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

Gilani

Mir

2021

Environ Sci Pollut Res

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“…Light motor vehicles have been found to be the major culprit in noise pollution. The recent investigations of Kalaiselvi [5] also accounted a horn noise component. It has been observed that horn noise occurs with a frequency of 16 events per minute and raises the L eq by 12 dB(A).…”

Section: Introductionmentioning

confidence: 94%

“…Since Delhi has been facing severe traffic-related problems, so a model based on Delhi's traffic noise would be sufficient to generalize this problem and can be used to predict the traffic noise in other parts of the country as well. The previous studies in Indian context are gener-B N. Garg ngarg@mail.nplindia.ernet.in 1 ally focused on a regression-based approach [1][2][3][4][5][6].Various models have been developed for different Indian cities in the past few years. Rao et al [1] developed a regression equation for modeling L A10 as a function of traffic density.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ANN and Analytical Models in Traffic Noise Modeling and Predictions

et al. 2015

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This paper demonstrates the applications of artificial neural networks to predict the equivalent continuous sound level (L Aeq ) and 10 Percentile exceeded sound level (L 10 ) generated due to traffic noise for various locations in Delhi. A Model based on back-propagation neural network was trained, validated, and tested using the measured data. The work shows that the model is able to produce accurate predictions of hourly traffic noise levels. A comparative study shows that neural networks out-perform the multiple linear regression models developed in terms of total traffic flow and equivalent traffic flow. The prediction model proposed in the study may serve as a vital tool for traffic noise forecasting and noise abatement measures to be undertaken for Delhi city.

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“…Honking has become a common event in India, which again is a function of un-authorized parking on the main road [18,19]. The equivalent noise level was increased 2 to 13 dB (A) compared to measured values due to horn honking events while using the standards such as RLS-90, FHWA, CoRTN, in SoundPLAN software [20][21][22]. These standards have limited applicability for heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Integrated road traffic noise mapping in urban Indian context

Sonaviya

Tandel

2020

Noise Mapping

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AbstractRoad 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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Honking noise corrections for traffic noise prediction models in heterogeneous traffic conditions like India

Cited by 45 publications

References 3 publications

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

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

Comparison of ANN and Analytical Models in Traffic Noise Modeling and Predictions

Integrated road traffic noise mapping in urban Indian context

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