## 5 Discussion  
Following analysis of the results, there are several aspects to be discussed. For instance, it is worth noting that the described clustering and analysis methodology has been applied  
Figure 16: Dendrogram of the agglomerative hierarchical clustering for urban environment at the weekend. Clustering solutions for \(Q=2\) (in red) and \(Q=3\) (in green) are also highlighted. Figure 17: Similarity matrix (**5**) for urban environment at the weekend. White dashed lines separate three clusters of the \(Q=3\) clustering solution (marked as green rhomboids at bottom), and the \(Q=2\) solution (also marked on the left-side as red rectangles). Inter (\(i=j\)) and intra-cluster (\(i\
eq j\)) distances are highlighted in white and denoted as \(D_{ij}\). to acoustic data gathered from a WASN. However, it can also be applied to other kinds of raw acoustic data obtained from \(N\) locations, e.g., from field measurement campaigns conducted by experts, if it is collected simultaneously. Nevertheless, in this example it could be more difficult to gather data 24 h/7 than using a WASN. Moreover, the clustering results obtained in both environments (urban and suburban) reinforce the need to consider acoustic data from the locations of each cluster to adapt ANED for RTN monitoring systems, and to use data from different days, as considered in this study (weekday and weekend). These results helped us to acquire a better understanding of the characteristics of RTN, with the future possibility of customizing the training and testing of the ANED algorithm in each specific location at a spectro-temporal level. Regarding the number of clusters, they were larger in the suburban than in the urban environment. In this sense, it is worth mentioning that the sensor nodes of the Rome suburban WASN were installed on the portals along the A90 highway, considering different locations of specific road geometry, such as single roads, crossings, nearby railways and multiple connections [9, 35]. This fact can probably explain why a higher number of clusters were found within this environment than were found in the urban environment where the sensors were deployed in different types of streets, but all within the same district of Milan, with less differences among locations. The clustering analysis of the spectro-temporal patterns obtained from the RTN acoustic data can be linked to other information (e.g., related to the physical sensor location particularities) that could reinforce the STP clusters found. Regarding the suburban environment, the sensor locations can be seen in detail in [11], where it can be appreciated that all the sensors were located on the portals along the A90 highway surrounding the city of Rome in Italy, except hb104, hb153, hb155 and hb143, which were located at secondary road connections and, for the first three, at less than 500 m of the main highway crossing. From the analyses presented in Section 4.2, these three sensor locations belonged to one cluster during the weekend, while on the weekday this cluster was broken down into two groups: one composed of hb153 and hb104, and another containing only hb155. Hence, it seems that the acoustic ambient RTN presented a particular pattern, which was clearly different from the rest of the sensor locations (see large distances shown graphically in Figures 7 and 10). However, sensor locations hb111 and hb156, which were also grouped together on both monitored days, seem to show a particular pattern. After inspecting their STPs, they exhibited lower energy values for the majority of hours and frequency bands, a pattern that could have been due to lower traffic density. Considering the urban environment, the analyses conducted have shown that it is reasonable to group the sensor locations in three clusters of sensor locations on both weekdays and at weekends. In this environment, a contrast analysis can be performed considering the type of roads within the city, as previously considered in [21] to analyse the distribution of ANEs.