## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **k-Means Cluster** & **1** & **2** & **3** & **4** \\ \hline \multirow{4}{*}{**Road Type**} & **N. of sites (\%)** & & 27 (30.0) & 4 (4.4) & 37 (19.3) & 22 (24.3) \\ \cline{2-6}  & A (motorway) & 2 & 2 (100) & 0 (0) & 0 (0) & 0 (0) \\ \cline{2-6}  & D (thoroughfare roads) & 7 & 4 (57.1) & 0 (0) & 2 (28.6) & 1 (14.3) \\ \cline{2-6}  & E (urban district roads) & 29 & 13 (44.8) & 0 (0) & 15 (51.7) & 1 (3.5) \\ \cline{2-6}  & F (urban local roads) & 52 & 8 (15.3) & 4 (7.7) & 20 (38.5) & 20 (38.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Distribution of the 90 sites across type of road and four partitions obtained by k-means cluster analysis; percentage values are between ( ). Figure 8: Differences of the median value patterns of 8 and IR between the two clusters (CL1 = cluster 1, CL2 = cluster 2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster** & **1** & **2** \\ \hline
**Site** & **TanOvest (Motorway)** & **Semeria (Local Road)** \\ \hline \multirow{3}{*}{\(\text{L}_{\text{Aeqh}}\) max[dB(A)]} & 5.20 & 3.62 \\  & \(\text{L}_{\text{Aeqh}}\) max[dB(A)] & 72.7 & 69.7 \\ \cline{1-1} \cline{2-4}  & Hourly interval & 06:00–07:00 & 08:00–09:00 \\ \cline{1-1} \cline{2-4}  & IR max[\%] & 56.2 & 93.3 \\ \cline{1-1} \cline{2-4}  & Hourly interval & 02:00–03:00 & 03:00–04:00 \\ \cline{1-1} \cline{2-4}  & N. events max & 248 & 272 \\ \cline{1-1} \cline{2-4}  & Hourly interval & 05:00–06:00 & 08:00–09:00 \\ \cline{1-1} \cline{2-4} Sound exposure level (SEL) max[dB(A)] & 76.4 & 80.4 \\ \cline{1-1} \cline{2-4}  & Hourly interval & 06:00–07:00 & 00:00–01:00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Maximum values of the parameters examined and corresponding hourly interval. Regarding the output of PCA, the correlation plot, showing the contributions of the variables in accounting for the variability in the two first principal components, is reported in Figure 10. It can be seen that dimension 1 can be interpreted as describing the eventfulness of the noise as the most contribution comes from the IR metric, whereas dimension 2 is linked to the noise energy, with L\({}_{\text{Aeqh}}\) being the most contributing variables. Figure 10: Correlation plot of the principal component analysis (PCA). Component 1, describing 37.8% of the data variability, can be associated with the eventfulness of the noise (IR), whereas component 2, describing 13.7% of the data variability, can be associated with the noise energy (L\({}_{\text{Aeq}}\)). L\(\#\) stands for L\({}_{\text{Aeqh}}\) and IR\(\#\) for IR\({}_{\text{h}}\). Figure 9: Density plots of 24 hourly values of acoustic parameters L\({}_{\text{Aeqh}}\)**(a)**, IR (**b**), number of events (**c**), and sound exposure level (SEL) (**d**) for two sites belonging to different clusters (blue = cluster 1, red = cluster 2) and far away from each other in the cluster bi-plot (see Figure 2). Dotted lines show the mode of the density distributions. To give an example of application of the above data classifications, let us consider three new roads for which the hourly values of \(\delta\) and IR, dotted lines in Figure 11, as well as the non-acoustic parameter x, are known. According to the categorization based on x, because for all the three roads, the x value is less than 4.48, they should be classified into cluster 2, the centroid of which is also given in Figure 11, together with that of cluster 1. Running the k-NN algorithm with k = 1, all three roads are recognized to belong to cluster 2 as well. Thus, for these data, both classification criteria, based on clustering and the parameter x, agree in the cluster membership assignment.