A traffic noise prediction method based on vehicle composition using genetic algorithms

Gündoğdu, Ömer; Gökdağ, Mahir; Yüksel, Fi̇kret

doi:10.1016/j.apacoust.2004.11.003

Cited by 67 publications

(33 citation statements)

References 9 publications

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“…Hence scientists attempt to discuss and study occupational noise exposure in different fields and the effects of noise pollution on human"s health. Several noise surveys treating the problem of noise pollution in many countries throughout the world have been conducted [11,12,13,14,15,16,17,18,19]. Most of these researches have been concentrated with the effect of noise on the auditory system, such as temporary or permanent hearing loss [20,21,22], and it is now well established that exposure to noise levels of relatively high degrees can lead to direct hearing loss and / or hearing impairment [23].…”

Section: Introductionmentioning

confidence: 99%

Noise level and annoyance of Industrial factories in Duhok city

Al-Dosky¹

2014

IOSRJESTFT

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Section: Introductionmentioning

confidence: 99%

Noise level and annoyance of Industrial factories in Duhok city

Al-Dosky¹

2014

IOSRJESTFT

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“…It was offered for speeds of 35±45 mph and distances greater than 20 feet [3]. Lam and Tam (1998) in Hong Kong developed a noise prediction tool using the Monte-Carlo technique Ref [4] taking into account traffic flow, traffic speed and traffic composition in terms of percentage of heavy vehicles.…”

Section: Different Prediction Modelsmentioning

confidence: 99%

“…Ref [2] Since noise measurement is complicated and timeconsuming, and moreover impossible at the design stage , traffic noise prediction models are required for the design of new highways and other roads or the redesign of traffic flow on existing roads in order to achieve comfortable traffic noise conditions. In the last few years, a number of prediction tools have been created to estimate noise levels in several countries [4]. …”

Section: Introductionmentioning

confidence: 99%

Evaluation and Modelling of Traffic Noise on the Asian Highway in Golestan National Park, Iran

et al. 2016

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Abstract. The increasing number of vehicles on Iran's highways and major roads has led to an increase in noise levels. As a result, traffic is now considered a main source of noise pollution. This paper reports on the modelling of traffic noise levels in Golestan National Park, Golestan using vehicle data and other environmental features. For the evaluation of noise and the recording of independent environmental variables, Sampling stations were selected using a systematic-random method at 76 points at various distances and between 0-250 meters from the road. At each sampling point, traffic flow (number and speed of vehicles, number of horn beeps) was measured for 15 minutes from 8 am to 8 pm. Simultaneously other environmental variables were assessed, including the geometry of the road surface and location conditions .The best multivariable regression based on the correlation coefficient (R) and the coefficient of determination (R2) was achieved. The R-square (73%) and the adjusted R-square (68%) of the regression equation were 73% and 68% respectively. The results of modelling show that the most important variables affecting noise pollution are distance from the road, roughness coefficient, speed of medium-weight vehicles, relative humidity, and height and number of light vehicles. There is a negative correlation with distance from the road and noise pollution.The accuracy of the model was found to be about ±5 dB. Therefore, the model is suggested for the prediction of traffic noise on the Asian Highway in Golestan National Park.

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“…Recently, several prediction models were developed using artificial neural networks (ANNs) (Genaro et al, 2010;Givargis and Karimi, 2010) and genetic algorithms (Gundogdu et al, 2005;Rahmani et al, 2011). This paper presents a research of application of soft computing techniques for improvement of conventional methodologies for traffic noise prediction.…”

Section: Introductionmentioning

confidence: 99%

Assessment of traffic noise levels in urban areas using different soft computing techniques

Tomić¹,

Bogojević²,

Pljakić³

et al. 2016

The Journal of the Acoustical Society of America

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Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

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A traffic noise prediction method based on vehicle composition using genetic algorithms

Cited by 67 publications

References 9 publications

Noise level and annoyance of Industrial factories in Duhok city

Noise level and annoyance of Industrial factories in Duhok city

Evaluation and Modelling of Traffic Noise on the Asian Highway in Golestan National Park, Iran

Assessment of traffic noise levels in urban areas using different soft computing techniques

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