# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 3 Dynamap Approach
### The Dynamap Network for Z9: Implementation, Location, and Operation
#### 3.3.3 The Network Operation: Standard Method and the \(\beta\)-Model  
The Dynamap network is expected to work continuously [12], yielding integrated equivalent noise levels at intervals \(\tau=(5,15,60)\) min. Extensive hourly data have been downloaded and the 24 time series analyzed according to the clustering procedure discussed in Section 2.3.1. The corresponding mean normalized hourly cluster profiles, \(\overline{\delta_{k}}(h)\), \(k=1,2\) (see Equation (12)), are displayed in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sensor Code** & **Group \(g_{t}\)** & \(x\) & **Cluster** & **Sensor Code** & **Group \(g_{t}\)** & \(x\) & **Cluster** \\ \hline
135 & 1 & 2.89 & 1 & 108 & 2 & 3.06 & 2 \\
137 & 1 & 1.90 & 1 & 120 & 3 & 3.74 & 2 \\
139 & 1 & 1.13 & 1 & 121 & 4 & 4.06 & 2 \\
144 & 1 & 2.94 & 1 & 129 & 4 & 3.94 & 2 \\
124 & 2 & 3.50 & 1 & 106 & 5 & 3.90 & 2 \\
125 & 2 & 2.69 & 1 & 123 & 5 & 4.30 & 2 \\
145 & 2 & 3.42 & 1 & 136 & 5 & 4.21 & 2 \\
115 & 3 & 3.58 & 1 & 151 & 5 & 4.40 & 2 \\
116 & 3 & 3.60 & 1 & 109 & 6 & 4.75 & 2 \\
133 & 3 & 3.75 & 1 & 114 & 6 & 4.58 & 2 \\
127 & 4 & 3.90 & 1 & 117 & 6 & 4.85 & 2 \\
138 & 4 & 4.19 & 1 & 140 & 6 & 4.70 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monitoring sensors information: Code, group membership, non-acoustic parameter \(x=\log(\mathcal{T}_{T})\), and cluster membership. The cluster compositions are: (Cluster 1) 12 sensors; (Cluster 2) 12 sensors (91). Note that sensors in groups: \(\delta_{2}\), \(\delta_{3}\), \(\delta_{4}\), are shared among the two clusters.  
Figure 6: Calculated static map (basic noise map) for all roads belonging to Milan urban Zone 9 [86] (see Figure 5). The colors represent the _Leq_ values, according to the scale indicated in the inset, obtained during the ‘rush hour’ hour (8:00–9:00) a.m. The latter is the hour with the highest traffic flow in the day, where the basic noise map is most accurate. The black triangles give the position of the 24 Dynamap noise sensors (numbered in the range 106–151, see Tables 2 and 3), and the yellow circles the locations of the test measurements for validation (numbered 1–21, see Table 4).  
The cluster compositions are reported in Table 3, together with the sensor codes, groups \(g_{i}\), and values of the local non-acoustic parameter \(x\). As one can see from these results, Cluster 1 is built predominantly upon groups 1, 2, and 3, while Cluster 2 on groups 5 and 6. Stations located within group 4 are present with equal numbers in both clusters. This is a consequence of the partial overlap of the two main clusters, as we already found in Figure 4. The strong similarity of both results is noteworthy, Figures 3 and 7, suggesting that the 24 sensors yield an accurate representation of the two mean noise cluster behaviors. Once these mean variations are known, one can apply Equation (13) in order to obtain the mean group noise variations, \(\delta^{(\tau)}(t,g_{i})\). In principle, one can run the network based on the standard approach using Equation (11), which is also useful in the preliminary stages of operation.