## 5 Discussion and Conclusions  
In this paper, the authors mainly dealt with analysing the correlations between spatial properties and lighting levels in order to obtain useful information for the early design stage of lighting systems in urban context. At the present status (Scenario 1), for all the correlations, the \(R^{2}\) values are not significant. In other words, there is no relationship linking traffic conditions expressed by the \(I\) index to the lighting levels in the analysed roads. It should be noted that, at the present status, many lighting systems of the roads are not in compliance with the recommended minimum lighting requirements and all the lighting systems are characterised by poor energy performance. At the design status (Scenario 2), an increase of the \(R^{2}\) values in the correlations has been observed. The chosen design options, following the step-by-step methodology proposed in recent researches by the authors [11; 59; 60], guarantee that the lighting parameters are adjusted on the real traffic conditions. Therefore, with road lighting systems that are well-designed in more trafficked and central roads with more restrictive lighting requirements are expected higher illuminance levels than peripheral and less trafficked roads. For all the correlations of Scenario 2, \(R^{2}\) values increase with respect to the cases of the Scenario 1 and the correlations between spatial properties and lighting levels is stronger. For example (see Table 11), the \(R^{2}\) values vary as follows: from a negligible value to 0.63 for luminance correlations on carriageways; from 0.16 to 0.48 for the average horizontal illuminance correlations on sidewalks, on the same side of luminaires; from 0.08 to 0.43 for the average horizontal illuminance correlations on sidewalks, on the opposite side of luminaires; similar increments of \(R^{2}\) values can be observed for the  
Figure 19: Scenario 3 (ideal case): correlations between \(I\) and \(E_{h,m,rf}\) (black circles and a continuous line for the regression) and between \(I\) and \(E_{h,min,rf}\) (white circles and a dotted line for the regression) on the sidewalks on the opposite side of the luminaires. Figure 18: Scenario 3 (ideal case): correlation between \(I\) index and \(L_{m,rf}\). minimum maintained horizontal illuminance. In other words, traffic volumes assessed by the \(I\) index are able to influence the lighting levels. At the ideal case (Scenario 3) the highest \(R^{2}\) values have been found. In fact, the correlations show \(R^{2}\) values of 0.67 for luminance values and 0.53 for illuminance values (see Table 11). The \(R^{2}\) values for luminance correlations on carriageways increases of the 4% with respect to the Scenario 2 and of the 67% with respect to the Scenario 1. The \(R^{2}\) values for \(E_{h,att}\) correlations on sidewalks on the luminaire side increase by 5% with respect to Scenario 2 and by 37% with respect to Scenario 1 (10% with respect to Scenario 2 and 45% with respect to Scenario on sidewalks on the opposite luminaire side). Increments up to 20%, with respect to Scenario 2 and up to 53% with respect to Scenario 1, have been observed for the minimum maintained horizontal illuminance. It is important to note that in the Scenario 3 the roads are enlightened exactly with the values of minimum lighting requirements. The ideal case of Scenario 3, which is not always realisable in real cases, is characterised by the best energy performance. The higher values of \(R^{2}\) confirm that correlations become stronger as luminance and illuminance values tend to the recommended minimum lighting requirements and as the energy efficiency of the lighting systems increase. The presence of the discussed correlations lays the foundations for a change in the lighting design approaches on urban scale. In light of what has been verified by the authors, the preliminary phases of the lighting design could be placed side by side with the urban planning phases (not postponed after, as generally happens).