## 4 Conclusions  
Using the proposed method, we were able to answer our second investigative question as follows: the precision of driving is a variable that affects the MFD, the vehicles' top speeds distribution is a variable that affects the MFD, the procedure for selecting routes is a variable that does not affect the MFD, and the procedure for selecting destinations is a variable that does not affect the MFD. It is important to say that the aforementioned results are valid for the configurations of variables that we tried, for the network under study, and considering the threshold values we set (which can be tuned as needed for convenience) to be compared with the metrics values. Nevertheless, the method we proposed can be used with other networks, with different variables than those we used, and with different configurations of the variables we used. We chose procedures for selecting routes based on the idea that in developing countries, vehicles (or the drivers themselves) are not always equipped with (or carrying) the proper technology for selecting routes. The idea behind the procedures for selecting destinations in S1 and S2 in experiment 4 is that a desirable traffic situation involves links with homogeneous densities, i.e., vehicles uniformly distributed on links, therefore vehicles reaching destinations all over the network increases the likelihood of links with homogeneous densities. On the contrary, the procedure for selecting destinations in S3 of experiment 4 caused non-uniform selection of destinations. In experiment 1, we discovered that driving imperfection is a variable that can modify the MFD. This conclusion was drawn because between S2 and S3, the compared capacities, and the compared critical densities were dissimilar, and between S1 and S3, the compared DS patterns, the compared capacities, and the compared critical densities were dissimilar. We found that the DS patterns between S1 and S2 and between S2 and S3 were similar, although the DS pattern of S2 (in which the driving imperfection was at 50%) was below the DS pattern of S1 (in which the driving imperfection was non-existent) and presented lower network speeds than S1 for the occurring network densities. Also, the DS pattern of S2 was longer to the right than the DS pattern of S1 (thus, S2 presents higher densities and lower speeds not reached by S1), the DS pattern of S3 (in which the imperfection for driving is at 100%) is below the DS pattern of S2 and presents lower network speeds than S2 for the occurring network densities, and the DS pattern of S3 is longer to the right than the DS pattern of S2 (thus, S3 presents higher densities and lower speeds than S2). When the input flow was 0.1 veh/s per entrance (during the periods range [1, 40]) and 0.125 veh/s per entrance (during the periods range [41, 80]), we observed comparable network flows and densities (throughout time) among simulations, with S1 presenting higher speeds, followed by S2 and then S3. When the input flow was 0.1666 veh/s per entrance (during the periods range [81, 200]) the difference in the network speed, density, and flow between S3 and the rest was notorious, highlighting that in S3, the critical density was reached and exceeded; traffic conditions with drivers driving with total imperfection are not sustainable, since the network gets congested. In experiment 2, we found that the variable generating the vehicles top speeds is able to modify the MFD. Between S1 and S3, the DS patterns and the critical densities are dissimilar; the DS pattern of S1 (in which a top speed is selected in the range [16, 66, 21.66]) is above the pattern of S2 (in which a top speed is selected in the range [11.66, 21.66]), and the pattern of S2 is above the pattern of S3 (in which a top speed is selected in the range [11.66, 16.66]); the range in which density operates is similar for the three simulations; in phase three, densities throughout time between S2 and S3 are comparable and above the densities of S1.