# Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment
## 6 Discussion and Conclusions  
Evaluating the efficacy of low-noise surfaces has started to be a highly demanded activity since the publication of the European minimum environmental criteria. As a consequence, the measurement procedure involved deserves the highest degree of confidence and should be applicable in all contexts. Among the different methods, the Statistical Pass-By (SPB-ISO) method evaluates road traffic noise by means of microphone and speed acquisitions at the roadside in real traffic flow conditions. The original measurement setup does not allow its application in urban areas, which is where most inhabitants live and, thus, where the noise mitigation would have a larger effect. The present paper reported a method for on-field acquisitions, named the Urban Pass-By (U-SPB) method, which adapts the SPB-ISO to urban context. The event detection and their extraction are performed in lab, as well as the consequent analysis. The evolution the method represented allows the performance of measurements of vehicle pass-bys without the presence of operators, with a consequent saving of man hours. The method also uses the good achievements of previous attempts to adapt the method, such as:  
* The use of higher acoustic sensor position and the use of an SEL indicator instead of L\({}_{\text{Amax}}\), as suggested in the HARMONOISE/IMAGINE projects;
* The use of statistical data binning before fit, as in the LEOPOLDO project. The testing, validation and application of U-SPB were based on multiple measurement campaigns performed for different sites and road conditions. The results of the procedure were in good agreement with the standard ones, with the additional advantage of needing a reduced time to produce them. In fact, as an unattended methodology, U-SPB can use data acquired even during the night and then reduce the overall time required for monitoring. Furthermore, the availability of night noise and traffic data allows the estimation of long-term L\({}_{\text{den}}\) from long-term traffic counts or statistics, without the need for long noise measurement campaigns, and the evaluation of the presence of other sources influencing local disturbance. Such an estimation is more than a noise model as it is calibrated on site. It must be pointed out that the procedure can be used as before/after evaluation of mitigation measurement, allowing the comparison of levels measured in the same \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ante-Operam**} & \multicolumn{3}{c}{**Post-Operam**} \\ \hline
**Stretch** & \(\mathbf{L_{meas}}\) & \(\mathbf{L_{model}}\) & \(\mathbf{L_{meas}}-\mathbf{L_{model}}\) & \(\mathbf{L_{meas}}\) & \(\mathbf{L_{model}}\) & \(\mathbf{L_{meas}}-\mathbf{L_{model}}\) \\
1 & 67.8 & 66.5 & 1.3 & 64.8 & 64.5 & 0.3 \\
3 & 70.5 & 69.1 & 0.6 & 65.5 & 63.6 & 1.9 \\
5 & 72.7 & 72.4 & 0.3 & 69.4 & 68.5 & 0.9 \\
6 & 71.8 & 71.0 & 0.8 & 67.6 & 67.2 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: L\({}_{\text{den}}\) values ante- and post-operand in Massarosa. Levels in dB(A). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ante-Operam**} & \multicolumn{3}{c}{**Post-Operam**} \\ \hline
**Stretch** & \(\mathbf{L_{meas}}\) & \(\mathbf{L_{model}}\) & \(\mathbf{L_{meas}}-\mathbf{L_{model}}\) & \(\mathbf{L_{meas}}\) & \(\mathbf{L_{model}}\) & \(\mathbf{L_{meas}}-\mathbf{L_{model}}\) \\
1 & 59.4 & 58.1 & 1.3 & 56.7 & 56.7 & 0.0 \\
3 & 62.6 & 61.2 & 1.4 & 57.4 & 55.8 & 1.6 \\
5 & 64.8 & 64.1 & 0.7 & 61.1 & 60.1 & 1.0 \\
6 & 63.8 & 62.4 & 1.4 & 59.7 & 59.1 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: L\({}_{\text{n}}\) values ante- and post-operand in Massarosa. Levels in dB(A). place in different traffic conditions. However, attention should be paid to possible factors influencing results, such as long queues, which might cause category mismatching, and temperature [25] and, more generally, seasons. Indeed, the predominance of specific vehicle categories such as mopeds can influence results when looking only at SPBI.