# Stabilization Time of Running Equivalent Level \(\mathbf{L_{Aeq}}\) for Urban Road Traffic Noise
## 3 Results and Discussion  
Figure 3a reports the histogram of the 6426 hourly \(L_{Aug,1h}\) considered, together with some descriptive statistics. The range is rather wide (\(\cong\)47 dB(A)), and the dataset is likely representative of the road traffic noise in the urban area. This is also reasonable for the standard deviation \(\sigma_{1h}\) of the \(L_{Aug,1s}\) level within each hour, the distribution of which is given in Figure 3b, where the histogram varies from \(\sigma_{1h}\) = 1 to 12 dB(A). Figure 4 shows the median of \(ST\) values for each hour and the three preset uncertainty intervals \(\varepsilon\) of the \(L_{Aug,1h}\) estimate plotted versus the median hourly road traffic flow \(Q_{h}\), as well as the regression lines according to the equation  
\[ST=a\cdot\left(Q_{h}\right)^{b}[\min], \tag{5}\]  
where \(a\) and \(b\) are constants, the values of which are obtained by least squares data fitting. The \(ST\) values decrease with increases in the uncertainty \(\varepsilon\) and with increases in the road traffic flow \(Q_{h}\). The trend is rather similar to that reported in [21]. The green dashed line corresponds to the measurement time \(t\) = 15 min, often used in common practice for road traffic noise measurement. It is clear that this approach, which uses a measurement time selected regardless of the road traffic flow, can lead to low accuracy in the \(L_{Aug,1h}\) estimate, as reported in the first row in Table 2.  
Figure 3: The distribution of **(a)** the 6426 hourly \(L_{Aug,1h}\) considered in the study; **(b)** the standard deviation \(\sigma_{1h}\) of 1 s short \(L_{Aug,1s}\) in each hour. Figure 2: The spatial distribution of road types in the urban area of Milan, Italy. The stratification of the dataset by road type leads to more meaningful outcomes of the statistical analysis, as pointed out in some studies [26; 30]. Table 2 reports the percentage of the observed \(ST\) values \(\leq 15\) min for each road type and uncertainty interval \(\varepsilon\). As expected, these percentages decrease from road types A to F and increase with increasing of the uncertainty \(\varepsilon\). This outcome points out that the stabilization time \(ST\) could be an efficient tool to guide in selecting the measurement time \(t\) adequate to the SPL time variability and, therefore, for increasing the accuracy of the \(L_{Avg,lh}\) estimate. The Kruskal-Wallis test applied to the \(ST\) values obtained for each uncertainty \(\varepsilon\) showed significant differences at 95% confidence levels for the road types and the Dunn test detected these significant differences for all six pairwise comparisons. This outcome confirms the importance of the dataset stratification by road type. Moreover, the Kolmogorov-Smirnov test showed a non-normal distribution of the \(ST\) values for all the combinations road type, hours and uncertainty \(\varepsilon\). This result is in agreement with that observed in [22], suggesting the use of the percentiles instead of the mean to summarize the \(ST\) values in each group. In the present study, adopting a precautionary approach, the third quartile of the \(ST\) values in each group was considered, corresponding to the 75 th percentile; that is, 75% of the \(ST\) values were shorter than the third quartile. These values for each of the uncertainty intervals \(\varepsilon\) are plotted in Figure 5 for each road type versus the median value of the hourly road traffic flow \(Q_{h}\) together with regression lines according to Equation (5). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Road Type**} & \multicolumn{3}{c}{**Uncertainty \(\varepsilon\) of the \(L_{Avg,lh}\) Estimate [dB(A)]**} \\ \cline{2-4}  & \(\pm\)**0.5** & \(\pm\)**1.0** & \(\pm\)**1.5** \\ \hline All & 17 & 46 & 64 \\ A (motorways) & 46 & 77 & 93 \\ D (thoroughfare roads) & 26 & 60 & 74 \\ E (urban district roads) & 21 & 52 & 67 \\ F (urban local roads) & 10 & 38 & 58 \\ \hline \hline \end{tabular}
\end{table}