## 6 Conclusions  
In this study, an analysis and clustering methodology has been proposed to group RTN acoustic data by considering the spectro-temporal energy distribution of a set of recordings gathered simultaneously from an area of interest during a given period of time. The proposal has been evaluated in two different environments, either urban and suburban, using approximately 250 h of RTN data collected through operative WASNs of the DYNAMAP project over two different days (a weekday and weekend day). The obtained set of clusters have been analysed by experts, obtaining a similarity matrix that shows graphically the Euclidean distances between specific RTN STPs from the sensed locations.  
From the analyses conducted, it can be concluded that the weekday and weekend patterns shared many similarities, and even when certain locations were grouped differently, on both days (e.g., in the urban environment, up to three clusters were obtained on both days, differing only in 4 out of 23 sensor locations), which supports the idea of considering weekday and weekend to have different RTN behaviour at a spectro-temporal level. These findings could be considered to support decision-making by administrations or private entities supervising roads and traffic, to evaluate the effects of action plans in real time, and develop new policies to improve the quality of life of their citizens. It is of note that the clustering analysis in the suburban environment revealed more heterogeneity, i.e., a higher number of clusters was obtained (with up to five clusters in the weekday analysis), than in the urban environment (where partitions were quite highly correlated with the type of streets in terms of number of lanes). The higher homogeneity found in the urban environment could be harnessed to the detection of possible variations or anomalies in real-time through monitoring systems, including those provoking sudden changes in spectro-temporal patterns, e.g., those derived from the COVID19 pandemic.  
Future research will be developed to consider the viability of the proposed clustering and analysis methodology from the results found in this study. To do so, data derived from other sources of information (e.g., traffic monitoring systems) could also be considered to deepen the analyses performed to better understand the main reasons for the similarities and differences found between the set of clustered locations. This more precise knowledge will enable us to obtain, on the one hand, better RTN models that can be used for the development of more accurate ANED algorithms that can be trained specifically for each clustered RTN environment and, on the other hand, to enrich RTN monitoring systems with valuable information that could be used to support the development of specific actions for real-time traffic management in urban and suburban environments.  
Conceptualization, F.A.; methodology, J.C.S., F.A. and R.M.A.-P.; software, J.C.S.; validation, F.A.; data curation, J.C.S.; writing--original draft preparation, F.A. and J.C.S.; writing--review and editing, F.A., J.C.S. and R.M.A.-P.; visualization, J.C.S.; supervision, F.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANE & Anomalous noise event \\ ANED & Anomalous noise event detector \\ CNOSOS-EU & Common Noise Assessment Methods in Europe \\ DYNAMAP & Dynamic noise mapping \\ END & European Noise Directive \\ IR & Intermittency ratio \\ RTN & Road traffic noise \\ SNR & Signal-to-noise ratio \\ SPL & Sound pressure level \\ STP & Spectro-temporal profile \\ WASN & Wireless acoustic sensor network \\ \end{tabular}