# Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment
## 5 Results
### Testing  
The feasibility test provided results for SEL, L\({}_{\text{Aeq}}\) and L\({}_{\text{Amax}}\). The pros and cons of each of the metrics are briefly described:  
* SEL was chosen by the NORDTEST method, HARMONOISE, IMAGINE and the Italian LEOPOLDO projects for its representativeness in noise models and its ability to estimate the overall noise levels. A potential weakness is represented by its partial dependence on the 10 dB(A) cut made by the operator [41];
* On the contrary, L\({}_{\text{Aeq}}\) is weakly dependent on the 10 dB(A) cut, but it is representative of average vehicles only when their speed variation is small and when the time of passage is almost the same for all vehicles. These conditions are especially difficult to achieve at nighttime, which is when data are intended to be collected, because single-passage events are more frequent;
* L\({}_{\text{Amax}}\) is the ISO/DIS 11819-1 parameter and does not depend on cut, but it is strongly dependent on the local peculiarities of the road (such as potholes, bumps, etc. ), which lack homogeneity, and vehicle discrepancies. Thus, its results tend not to be stable even for the same vehicle and pavement. The data analysis revealed a relevant difference between fits performed by plotting speed against SEL, L\({}_{\text{Aeq}}\) (recomputed by the average spectrum) and L\({}_{\text{Amax}}\) (from spectrum on maximum) for the light vehicle category. Then, the model in Equation (4), which is a generalization of Equation (2), with f being one of the three tested parameters, was applied to the measured dataset:  
\[f=a+b\cdot log\Big{(}\frac{v}{50}\Big{)} \tag{5}\]  
Table 3 shows the results obtained in T0 (Capannori) for each lane and fit values for the light vehicle category. As mentioned in the previous chapter, the near lane direction was the lane closest to the noise monitoring station, and the far lane was the opposite one. Distances from microphones to center lanes were known, and data were corrected for distance using a linear divergence model in order to allow comparison with the standard requirements (1.2 m height, 7.5 m distant from center lane) and between lanes. Figure 4: Example of data collected in binning chunks (blue points with error bars) for the best fit between SEL and velocity. The 95% confidence interval from the fit is reported in yellow. The SEL slope results were lower than the other metrics, probably because they already considered time/speed effect. The near lane was noisier according to L\({}_{\rm{Amax}}\) and L\({}_{\rm{Aeq}}\), while it was not for SEL. The preliminary test also showed that pass-by parameters can be derived with good fit agreement in terms of \(\chi^{2}\) by starting from data acquired with the monitoring station, and the noisiness of a single vehicle category can be determined as a function of speed. Then, the testing measurement in T1 (Marina di Pisa) allowed a comparison of the SPB-L and the U-SPB applied over the same sample flows. The test intended to address whether there were differences due to the microphones' positions, regardless of the speed and sample variability. The three parameters were once again tested. U-SPB data were grouped into the morning common sample, which was intended to be the passages collected on the monitoring station at the same time as the attended SPB-L, and the night independent sample. Table 4 reports the results for the light vehicle category for each indicator in the first U-SPB dataset. Linear correction for distance was applied to the monitoring station and to the 3 m microphone to allow comparisons. Some relevant differences emerged for each microphone, with lower values for the near lane and higher values for the far lane from the 1.2 m SPB-L microphone. This might be due to the specific location, which presented a small, green hedge at the roadside that might have partially screened noise from the near lane at low heights.