## 4 Results and Discussion
Regarding segment \(i=22\), it accumulates a noticeable quantity of vehicles during the red time that is not quickly discharged during the green time. Regarding segment \(i=23\), the next segment according to the travel direction (i.e., segment \(i=25\)) is short in length (\(\sim\)54.58 m) and at times accumulates vehicles to full capacity and the vehicles in segment \(i=23\) need to wait for empty spaces in segment \(i=25\). Regarding segment \(i=65\) (with a length of \(\sim\)53.72 m), it is common that during the red phase, there is no accumulation of vehicles, so in the green phase vehicles enter the segment with speed. The proposed method gradually selects vehicles from the segments and periods classified as low speed, these vehicles will be prevented from entering the network in the following simulations. The selection of vehicles continues until for all segments \(vp>v\_b\_\) at each period. For completeness, the method was tested with 10 OD tables (one table per experiment), each table associates in a different way vehicles and destinations, therefore the vehicles destinations and routes are different among experiments. The last two questions of the research questions have already been answered through the section Results and Discussion, in Table 3 in the third column we present the answer to the first question. Furthermore, the OD table with ID = 1 was the one used to conduct the analysis presented so far. What is changing from one experiment to another is the OD table; with the procedure for generating an OD table we looked for an equal distribution of the vehicles among the network links, however, the number of vehicles that were prohibited from circulating is slightly changing among experiments. Considering the data presented in column 2 (the number of vehicles prevented from circulating) of Table 3, the average is 368 veh (the \(\sim\)5.11% of \(nt\) = 7200 vehicles) and the standard deviation is \(\sim\)75.78 veh; it is worth \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ID of the OD Table**} & \multicolumn{2}{c}{**Number of Vehicles**} & **Number of Vehicles That the** & _Factor_ **Value at the Final** \\  & **Prevented from Circulating** & **Street Network Supports in 1 h** & **Simulation** \\ \hline
1 & 297 & 6903 & 0.5 \\
2 & 342 & 6858 & 0.5 \\
3 & 289 & 6911 & 0.5 \\
4 & 276 & 6924 & 0.5 \\
5 & 440 & 6760 & 0.55 \\
6 & 431 & 6769 & 0.5 \\
7 & 440 & 6760 & 0.5 \\
8 & 347 & 6853 & 0.5 \\
9 & 328 & 6872 & 0.85 \\
10 & 490 & 6710 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiments results. noticing that 490 vehicles (the case with the maximum number of vehicles prevented from circulating) represent the \(\sim\)6.8% of \(nt\) and that 276 (the minimum) represent the \(\sim\)3.83%. The speed threshold \(v\_b\) can be modified to a lower value to allow more vehicles in circulation with the effect of reducing the speed on links and to a higher value to reduce even more the number of vehicles in circulation with the effect of increasing the speed on links. An important parameter is _factor_, this was tuned by trial and error and determines how quickly the vehicles that will be prevented from circulating are selected, the lowest the value of _factor_ the slowest the vehicles are being selected. Table 3 shows that in some experiments was necessary to increase (this happens at increments of 0.05) the value of _factor_ in order to obtain the number of vehicles that can enter the street network in 1 h.