# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
## 5 Conclusions  
A traffic noise prediction approach using machine learning methods has been presented, which considers the parameter of honking noise. Horn noise contributes significantly to the overall road traffic noise in Indian road traffic conditions as well as other regions in the Indian sub-continent. Therefore, there is a need to pay attention to this important parameter while developing traffic noise models. An approach to include the effect of honking noise by considering the honking occurrences as a parameter has been illustrated with the help of a case study of the Patiala city in India. A preliminary multivariate analysis of the dataset showed that the presence of honking cannot be neglected in the cases under study. A relevant correlation with the continuous equivalent level was found. This correlation was equal to the one found between noise levels and percentage of heavy vehicles, that is a parameter commonly implemented in standard road traffic noise models.  
The models developed were compared using the criteria of r, R\({}^{2}\), mean square error and accuracy. It is seen that the generalized linear model (GLM) and artificial neural networks (ANN) are the best performing models, with GLM doing slightly better than ANN on the considered criteria.  
A simulation without honking parameter inclusion has been performed, to assess its contribution on the models' performances. Results showed that including honking leads to an improvement of the prediction, in particular for GLM, i.e., the model that best performed in the testing phase.  
Also, a ten-fold cross validation has been done for the best performing model (GLM) to check the robustness of the developed model, showing an excellent stability of the technique.  
It can be concluded that the machine learning approach can be used to develop traffic noise prediction models, including honking as a parameter, with considerable accuracy. The effect of changing the traffic parameters on the overall traffic noise can be assessed with the help of these models, without the actual need of experiments using the sound level meter or other equipment. Thus, the policy makers and administration can take suitable steps for noise abatement, which would be beneficial for the health and well-being of all the involved stakeholders.  
Future developments of this research will be aimed at the calibration and testing of the presented models on more data, coming from different sites and with different traffic conditions, in order to enlarge the possible applications to other scenarios. Furthermore, the inclusion of further parameters, such as speed of vehicles, different types of vehicles (e.g., two-wheelers, three-wheelers etc.) and acceleration-deceleration regimes, will be a possible development of the methodology presented.  
Conceptualization, D.S.; methodology, D.S. and C.G.; software, D.S.; validation, D.S., A.B.F., S.M. and C.G.; formal analysis, D.S., A.B.F., S.M. and C.G.; investigation, D.S., A.B.F., S.M. and C.G.; resources, D.S., A.B.F., S.M. and C.G.; data curation, D.S., A.B.F., S.M. and C.G.; writing--original draft preparation, D.S.; writing--review and editing, D.S., A.B.F., S.M. and C.G.; visualization, A.B.F. and S.M.; supervision, D.S. and C.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available in Appendix A.  
The authors are thankful to the Noise and Vibration laboratory, Mechanical Engineering Department, Thapar Institute of Engineering and Technology, Patiala, for the equipment and other support during the execution of this work. Also, the authors are thankful to Civil Engineering Department, University of Salerno for providing infrastructure and computational facilities.  
The authors declare no conflict of interest.