###### Abstract  
The introduced method is a proposal for detecting spaces (links) and times (90 s periods) where the average speed is below the desirable, and for selecting vehicles in those spaces and times so that vehicles are systematically and gradually reduced from one simulation to another until we get a simulation presenting the desirable average speed in all space and time. With our method can be detected the specific vehicles that can enter a street network so that the average speed on the network' links be always greater than a speed threshold. The speed on a segment is calculated from two perspectives: (1) the general speed (_vg_), calculated with measurements and estimates, used to estimate the links' travel times for selecting the vehicles routes, (2) the particular speed (_vp_), calculated without estimates and for segments with traffic light only with measurements performed during an interval of the green time, used to identify links and periods of unacceptable (low) speed. We test our method with different origin-destination (OD) tables, for each OD table we obtain the number of vehicles that can enter the network in 1 h so all links and periods present acceptable speed. Another result was, for each link, the change of the average (and of the standard deviation) of _VG_ (the vector containing the _vg_ of each period) between the final (after our method) and initial (the traffic conditions without our method) simulations, therefore the percentages of the links presenting a convenient change were evidenced. We did the same with _VP_ (the _vp_ of each period). number of vehicles; speed threshold; street network; sustainable speed; traffic metrics; traffic simulations 2023  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], F.S.C. completely forbids private cars in the central area, scenario 2 reduces by 50% the number of parking places, scenario 3 is the combination of scenarios 1 and 2; the average travel speed in scenario 3 is 43 km/h, in scenario 1 is 32 km/h, in scenario 2 is 22 km/h, and in scenario 0 is 16 km/h. The mode choice and the travel times in a city with two kinds of vehicle restrictions were analyzed in [4]. In the Restricted Traffic Zone (RTZ) taxis, public transport, motorcycles, and vehicles with a permission pass are allowed; in the Odd-Even Zone (OEZ) vehicles depending on their plate termination number are allowed. Simulations of 4-h, with different origin-destination (OD) tables and scaling demand patterns (incrementing accumulation from empty streets to congestion) were presented in [5]. Despite the chaotic behavior of individual links at the macroscopic level, the network presents a well-defined outflow vs. accumulation scatterplot. Furthermore, the data from different simulations form, approximately, the same pattern; by limiting the number of vehicles entering the reservoir (through reducing the traffic lights green time of the inbound directions, so accumulation does not exceed the network capacity) a greater number of completed trips is reached. An adaptive control approach to mitigate congestion and improve mobility is presented in [6]. The strategy to increase the rate of completed trips is to keep optimal accumulation in the neighborhoods with higher destinations; the systems presenting gridlock can be significantly improved by regulating the input rate and, if necessary, prohibiting vehicles from entering. As predicted in [6; 7], restricting the number of vehicles coming to congestion zones improves mobility for all vehicles. One way to prevent over-saturation in a part of a network (the protected part) is through gating [8], which consists in extend the traffic lights red phase of the selected intersections from where vehicles can enter the protected network. A perimeter flow control for a network with cars and buses was presented in [9], it was proposed a 3D macroscopic fundamental diagram (MFD) and it was found that the network's traffic composition affects the shape of the 3D-MFD since it modifies the maximum network's throughput.