## 5 Conclusions  
The proposed method was applied to 10 OD tables with satisfactory results, i.e., it was possible to eliminate vehicles until no segment, at any period, presents \(vp<=v\_b\). In the simulations we consider 1 h of constant flow at each entrance and a simple control for traffic lights to generate representative traffic of the zone under study, nevertheless, we consider it adequate that drivers select with intelligence its route (as it happens in real life), therefore we developed a procedure for estimating the segments travel times and with this knowledge the vehicles routes are generated. We propose two traffic metrics, named \(vg\) and \(vp\). For the segments with traffic lights, a representative percentage of segments, \(\sim\) 95% in the IS and the same percentage in the FS present a negative correlation less than or equal to \(-\)0.75 between DG and VG. For 100% of the segments without traffic lights there is a negative correlation less than or equal to \(-\)0.9 between DG and VG in the IS, and the same is true for 87.5% of the SNSs in the FS. The \(vg\) was designed (i.e., the way that measurements for calculating \(vg\) were implemented) to give information about the convenience of traveling on a segment; in the case of SCSs, when there are no vehicles traveling on it (i.e., \(obs=0\) veh), a _speed_measurement_\(\cong\) (16.66) \(\times\) 0.95 m/s; in the case of SNSs when \(obs=0\) veh then _speed_measurement_\(\cong\) 16.66 m/s, a greater speed than the selected for SCSs so the convenience of a SNS over a SCS (in the specific case when \(obs=0\) veh) can be accounted when travel times are estimated and routes are generated (\(vg\) is used in the procedure to calculate the links travel times, and these for choosing the vehicles routes). In the IS a correlation between DP and VP less than or equal to \(-\)0.7 stands for 80.33% of the segments with traffic lights, and for 100% of the segments without traffic lights. In the FS the percentage of SCSs for which the r between DP and VP is less than or equal to \(-\)0.7 is 83.61%, and for the SNSs is 87.5%. The measurements to calculate \(vp\) were made only when \(obs\
eq 0\) veh and for the SCSs only in an interval of the green time (no measurements in the red and yellow times were made), then no forecasted measurements were included and measurements when the vehicles are likely in motion in did were included, in this way, a low \(vp\) can be attributed (mostly) to the lack of capacity of the segment, even more, to the lack of capacity of the street network to handle the number of vehicles on it. With the proposed method we compute the number of vehicles that the street network can handle in 1 h so that the \(vp\) of the network segments is (always) greater than the speed threshold, therefore we compare the output from the first (with 7200 vehicles generated in 1 h) and the final (where the selected vehicles are not allowed to enter the network) simulations. From the IS to the FS, we found that for the \(\sim\)66.66% of the segments there was an increase of AVG of VG, and for the \(\sim\)89.85% there was a decrease of STD of VG. For the \(\sim\)60.86% of the segments, there was an increase of AVG of VP, and for the \(\sim\)73.91% there was a decrease of STD of VP. Therefore, in the FS and for most of the segments the standard deviation of the speed (VG or VP) is lower, and the average of the speed (VG or VP) is higher than in the IS. In addition, for the \(\sim\)82.6% of the segments the STD of DG from IS to FS was reduced, the same applies for the \(\sim\)81.15% of the segments and the STD of DP. For the \(\sim\)72.46% of the segments the AVG of DG from IS to FS was reduced, the same applies for the \(\sim\)62.31% of the segments and the AVG of DP. Due to the selected vehicles that in the FS were not generated, we have segments for which the AVG of DG of the FS is lower than the AVG of DG of the IS. We can say the same regarding the AVG of DP.