# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
## 4 Discussion  
The results reported in Section 3 inspire interesting discussions and allow to draw some preliminary conclusions. Looking at the scatter plot reported in Figure 4, it is easy to see that, apart from decision trees (DT), some points underestimate the measured \(L_{eq}\) and all the models' results are gathered almost close to the bisector, meaning a general effectiveness of the proposed models. When the measured values of \(L_{eq}\) are around 70 dBA (site 2), the models have the best performance. This site is characterized by the absence of heavy vehicles and honking occurrences lower than in the other sites. Data above 78 dBA are also associated with good performances of the models. These points are related to site 4, in which the traffic volumes are higher than in the other sites. Finally, the larger dispersion is observed in the middle range of noise levels. In this range basically all the models produce results that are different with respect to the observed noise levels. There is not a clear pattern of overestimation or underestimation, meaning that these errors are basically random for all the models. Only the data comprised in the range 77-78 dBA seems to show a general underestimation of the models. These points belong to site 5, in which the highest mean honking occurrences has been observed. In order to highlight the importance of the honking inclusion in the models, a simulation without the honking parameter has been performed on the same dataset, both in training and testing phases. Results of the performance metrics without honking are reported in Tables 5 and 6. Figure 6: A ten-fold cross validation for \(\tau\), R\({}^{2}\), MSE and accuracy for the generalized linear model (GLM). Comparing with results reported in Tables 3 and 4, it can be noticed that the inclusion of honking in the modeling leads to an overall improvement of the performance metrics. The DT model seems to be not sensible to honking, since the metrics are basically constant with and without honking inclusion, both in training and testing phases. RF in testing dataset keeps r and R\({}^{2}\) basically constant, the accuracy has a little increase from 60.0% to 66.7%, but MSE increases from 0.797 dBA to 0.809 dBA. The other two models, namely GLM and ANN, converge to similar predictions and, consequently, to equal performance metrics. They exhibit a worsening of all the selected metrics when honking is not considered. Since GLM was the best performing model in the training dataset with honking, it is evident that the inclusion of this parameter leads to a better prediction of noise levels in the presented application. When honking is not included, the MSE of GLM increases from 0.666 dBA to 1.291 dBA and the GLM accuracy gets worse, lowering from 80% to 46.7%. Regarding the cross validation of GLM, the ten-fold cross validation results reported in Figure 6 shows a general stability. This confirms the goodness of the model and the suitability to be used in such applications, on noise data that includes a relevant contribution from honking. Although the dataset is sufficient for the present study, a bigger dataset with a greater spread across different times of the year and more sites can be prepared and utilized in a future study. A limitation of the present study, in fact, is that the models are region-specific, since they are based on the data collected at the specific sites identified for the assessment. The number of input parameters is limited to three in the present work (i.e., traffic volume Q, percentage of heavy vehicles P and total honking occurrences H). In a future work, this number can be expanded including more parameters like speed of vehicles, different types of vehicles (e.g., two-wheelers, three-wheelers etc.) and acceleration-deceleration of vehicles, etc. Furthermore, an assessment of the actual increase in the noise level (in dBA) due to honking can be made and the effect of the duration of the different types of horns used on different vehicles needs to be studied.