## 4 Results and Discussion  
With the proposed method a final simulation is reached in which \(vp>v\_b\) for all segments and periods. The effectiveness of the method was tested for 10 experiments, each with a particular OD table, with satisfactory results. We select an experiment for evidencing the analysis that can be conducted; for the first simulation (with 7200 vehicles in 1 h) and the final (without the selected vehicles in the SSs), for each link, we investigate the correlation between DG and VG, and between DP and VP. The total number of segments under analysis is \(sg\) = 69, of which 61 are SCSs and 8 are SNSs. We calculate the Pearson correlation coefficient (r) between \(DG_{i}^{IS}\) and \(VG_{i}^{IS}\), with \(i\) = 1... 69 being the index of the segments; the r average for SCSs is \(-\)0.8732, and for SNSs is \(-\)0.9479; the 95.08% of the SCSs present r <= -0.75, the rest of the SCSs, i.e., segment \(i\) = 7, 49 and 55 present an r between \(-\)0.7 and \(-\)0.75; the 100% of the SNSs present r <= -0.9. Furthermore, we calculate the r between \(DG_{i}^{FS}\) and \(VG_{i}^{FS}\); the r average for SCSs is \(-\)0.8605, and for SNSs is \(-\)0.9285; again, 95.08% of the SCSs present r <= -0.75, the remaining SCSs are \(i\) = 7, 51, and 55 and present an r between \(-\)0.7 and \(-\)0.75; the 87.5% of the SNSs present r <= -0.9, the remaining SNS is \(i\) = 67 with r = - 0.86. As expected, the relation between DG and VG is better reflected by SNSs, since for SCSs the traffic lights add complexity to that relationship. Segments \(i\) = 7 and 51 are similar in length, their travel direction is the same and their start and end positions along the \(x\)-axis (the horizontal according to the point of view in Figure 2) are similar, then there is a positional relationship between them and may be the cause for which both present a similar r value between \(DG_{i}^{FS}\) and \(VG_{i}^{FS}\). In the IS and the FS, the r average of SNSs is closer to \(-\)1 than the r average of the SCSs. FromIS to FS the r average of SCSs slightly changes, the same is true for the r average of SNSs. In addition, we calculate the r between \(DP_{i}^{IS}\) and \(VP_{i}^{IS}\); the r average of SCSs is \(-0.7937\), and for SNSs is \(-0.8391\); for the 80.33% of the SCSs the r is less than or equal to \(-0.7\), the same is true for 100% of the SNSs; the r value of SCS \(i=3\) is the closer to zero (\(\sim\)\(-0.1\)), followed by SCS \(i=50\) (\(\sim\)\(-0.47\)); segment \(i=3\) is short in length and is not frequently used, therefore there is no relationship between \(DP_{3}^{IS}\) and \(VP_{3}^{IS}\). Furthermore, we calculate the r between \(DP_{i}^{FS}\) and \(VP_{i}^{FS}\); the r average of SCSs is \(-0.7790\), and of SNSs is \(-0.7911\); for 83.61% of the SCSs the r is less than or equal to \(-0.7\), the same is true for the 87.5% of the SNSs; the r value of SCS \(i=3\) is the closest to zero again (\(\sim\)\(-0.18\)); the r value of SCS \(i=54\) is \(\sim\)\(-0.48\), this segment is the only one connected to two input segments (i.e., segments by which vehicles enter the network). In the IS and FS the r average of SNSs is more negative than of the SCSs; in the FS the r average of SCSs, and of SNSs, is slightly more positive than in the IS. We calculate the average (AVG) of the content in the vector \(VG_{i}^{FS}\) minus the average of the content in the vector \(VG_{i}^{IS}\), for \(i=1\)... \(69\). Segment \(i=22\) stands out since it has the higher value in the \(y\)-axis (\(\sim\)1.39 m/s), which means that for that segment the AVG of VG notoriously increased in the FS with respect to the IS. The second highest \(y\)-axis value (\(\sim\)0.82 m/s) is presented by segment \(i=11\). The segments for which \(\left(AVG\ of\ VG_{i}^{FS}-AVG\ of\ VG_{i}^{IS}\right)\geq 0\) represent the (\(46/sg\))\(\times\)100 \(\cong\)\(66.66\%\), the mean of the \(y\)-axis values for those segments is 0.2864 m/s.