Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. <i>L<...
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MDPI AG
2021-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/13/6030 |
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doaj-1ab779c481cb4c76972f8e98c234cbe72021-07-15T15:30:23ZengMDPI AGApplied Sciences2076-34172021-06-01116030603010.3390/app11136030Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise PredictionDaljeet Singh0Antonella B. Francavilla1Simona Mancini2Claudio Guarnaccia3Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, IndiaDepartment of Civil Engineering, University of Salerno, 84084 Fisciano, ItalyDepartment of Information Engineering, Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, ItalyDepartment of Civil Engineering, University of Salerno, 84084 Fisciano, ItalyA vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. <i>L<sub>eq</sub></i> A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.https://www.mdpi.com/2076-3417/11/13/6030road traffic noisehonkingmachine learningpredictionmodelling |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Daljeet Singh Antonella B. Francavilla Simona Mancini Claudio Guarnaccia |
| spellingShingle |
Daljeet Singh Antonella B. Francavilla Simona Mancini Claudio Guarnaccia Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction Applied Sciences road traffic noise honking machine learning prediction modelling |
| author_facet |
Daljeet Singh Antonella B. Francavilla Simona Mancini Claudio Guarnaccia |
| author_sort |
Daljeet Singh |
| title |
Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction |
| title_short |
Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction |
| title_full |
Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction |
| title_fullStr |
Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction |
| title_full_unstemmed |
Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction |
| title_sort |
application of machine learning to include honking effect in vehicular traffic noise prediction |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2021-06-01 |
| description |
A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. <i>L<sub>eq</sub></i> A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models. |
| topic |
road traffic noise honking machine learning prediction modelling |
| url |
https://www.mdpi.com/2076-3417/11/13/6030 |
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