Modelling of Noise Pollution Due to Heterogeneous Highway Traffic in India

Aditya, Kamineni; Duda, Sunil Kumar; Chowdary, Venkaiah; Prasad, C. S. R. K.

doi:10.2478/ttj-2019-0003

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Effect of traffic volume on traffic noise are in accordance to studies reported by Halim and Abdullah [49]. Tandel and Macwan [50] and Kamineni et al [51] confirms heavy traffic highways have higher noise levels. Wei et al [52] in their study confirmed that increase in IRI increases the corresponding noise levels on roads.…”

Section: Traffic Noise Predictionsupporting

confidence: 89%

Application of M5P Model Tree and Artificial Neural Networks for Traffic Noise Prediction on Highways of India

mann,

Singh

2024

Civil and Environmental Engineering Reports

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Traffic noise prediction is the fastestgrowing development that reflects the rising concern of noise as environmental pollution. Prediction of noise exposure levels can help policy makers and government authorities to make early decisions and plan effective measures to mitigate noise pollution and protect human health. This study examines the application of M5P model tree and Artificial Neural Network (ANN) for prediction of traffic noise on Highways of Delhi. In total 865 data sets collected from 36 sampling stations were used for development of model. Effects of 13 independent variables were considered for prediction. Model selection criteria like determination coefficient (R2), root mean square error (RMSE), Mean absolute error (MSE) are used to judge the suitability of developed models. The work shows that both the models can predict traffic noise accurately, with R2 values of 0.922(M5P), 0.942(ANN) and RMSE of 2.17(M5P) ,1.95(ANN). The results indicate that machine learning approach provides better performance in complex areas, with heterogenous traffic patterns. M5p Model tree gives linear equations which are easy to comprehend and provides better insight, indicating that M5P model trees can be effectively used as an alternative to ANN for predicting traffic noise.

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Section: Traffic Noise Predictionsupporting

confidence: 89%

Application of M5P Model Tree and Artificial Neural Networks for Traffic Noise Prediction on Highways of India

mann,

Singh

2024

Civil and Environmental Engineering Reports

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“…Several studies have indicated that highway traffic is the major source of environmental noise (Murphy & King, 2007). In addition to traffic volume, speed and composition, the traffic-noise level is generally affected by the vehicle's propulsion system, aerodynamics and tire-pavement interaction (Abdur-Rouf & Shaaban, 2022; Kamineni et al, 2019;Murat & Ebru, 2016). However, vehicle-engine design and body-shape improvements reduce the noise generated by propulsion and aerodynamic effects (Yang et al, 2011;Bravo et al, 2012;Al-Masaeid & Fayyad, 2018).…”

Section: Introductionmentioning

confidence: 99%

Modeling of Traffic Noise along Urban Arterials in Irbid City of Jordan

R. Al-Masaeid

2024

JJCE

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Traffic-related noise pollution is a recurring problem in large and medium-sized cities. The objective of this study is to quantify traffic-noise levels along selected urban arterials and model the generated traffic noise based on traffic and pavement characteristics. Urban arterial and collector streets in Irbid city, Jordan, were taken as a case study. The city is considered an example of a medium-sized city. 65 urban arterial and collector sections were selected to achieve the stated objective. For each section, ten noise measurements were taken using a time interval of 2.5 minutes for each observation. The statistical pass-by method was used to measure 650 externalnoise observations. In addition, data on traffic, pavement and section geometric characteristics was obtained through field measurements. The collected traffic characteristics, including traffic flow, percentage of trucks and speed in each direction of travel, were obtained. In addition to the measurement of pavement macrotexture depth, an international roughness index was measured using a smartphone application. Investigation of the collected noise data indicated that urban streets experienced high noise levels, reaching maximum and average values of 82 and 77.2 dB(A), respectively. The results of the analyses showed that an increase in each of the included traffic characteristics resulted in significantly higher noise levels. For example, an increase in speed from 35 to 55 km/h would increase noise by 2.7 dB(A). In addition, the interaction term of roughness index and pavement macrotexture depth was found to increase the generated noise. Finally, the results of the analysis indicated that both multivariate linear -and exponential-regression models are suitable to model the generated traffic noise. Each model explained approximately 54% of the noise variability. Probably, traffic composition and vehicle-power type heterogeneity might reduce the level of explained variability. Keywords: Traffic noise, Urban arterials, Pass-by method, Noise modeling, Jordan.

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“…Various types of vehicles produce different amounts of noise. Studies have found that fluctuations in the ratio of cars cause changes in traffic noise levels(Kamineni et al, 2019) and (Agarwal and Swami, 2011). An increase in traffic volume makes the noise level higher.…”

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confidence: 99%

Traffic Noise in Arterial Streets: Case of Kirkuk City

Ali

Albayati²

2022

jcoeng

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Loud noise can be extremely harmful to the auditory system as well as to human health. Noise pollution is primarily caused by traffic noise. The study's goal was to determine how various vehicle types and speeds affected the amount of noise generated by traffic. The two factors were investigated at seven different arterial streets throughout Kirkuk city to measure the noise levels. The measurements were performed during peak hours to compare the result with WHO standards for noise specification. Traffic volume and vehicle speed are shown to be the key elements that determine an increase in noise level.

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Modelling of Noise Pollution Due to Heterogeneous Highway Traffic in India

Cited by 10 publications

References 9 publications

Application of M5P Model Tree and Artificial Neural Networks for Traffic Noise Prediction on Highways of India

Application of M5P Model Tree and Artificial Neural Networks for Traffic Noise Prediction on Highways of India

Modeling of Traffic Noise along Urban Arterials in Irbid City of Jordan

Traffic Noise in Arterial Streets: Case of Kirkuk City

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