## 3 Results and Discussion
### Travels and Equivalent CO2 Emissions in Each VVM Point  
Figure 7 shows the travels per segment, considering the 11 VVMs, where it can be observed that the travels are distributed above and below the mean value (46,387,837 Km/yr), with a standard deviation of 16,404,516 km/yr.  
This variable is very important since it is directly related to the amount of fuel consumed and emissions generated (Table 1 and Figure 7).  
The main urban road under study was divided into segments from A through to J (Figure 7). Segments B, C, D, and E (associated with VVM points 2, 3, 4, and 5) present a positive slope due to a series of intersections in segments B and C that increase their densities, creating arterial road congestion [40]; in segment H, which is the peak of the slope, there are two important intersections characterized by heavy traffic, which, together with the length of the segments, increases the number of travels. Segments A, I, and J (associated with VVM points 1, 2, 9, 10 and 11) present a negative slope that indicates a decrease in the number of travels.  
Considering the different types of vehicles (cars, motorcycles, buses, and light and heavy cargo vehicles) and applying the weighted average GHG emission factors, CO2e emissions were obtained for each VVM point, since its variability in each one has a direct impact on the total amount of said parameter per VVM point (Figure 8). This same figure shows that point five presents the maximum values of CO2e, considering all types of vehicles (202,666 ton/year). This is associated with the high vehicular flow, which represents a high number of travels (636,244,430 km/year).  
Figure 6: Number of vehicles in each vehicle volume measurement (VVM) point.  
Subsequently, in VVM 7, travels and emissions decrease by 12%, with respect to maximum emission values; in VVM 8, the values increase to 572,469,060 km/year in travels and 182,351 ton/year in CO2e emissions due to traffic volume, although this remains below the highest values registered in VVM 5. In VVM 10, CO2e emissions decreased by 25% (151,568 ton/year) with respect to the maximum value; this point was located at the end of the urban road, where vehicular traffic is less intense.  
Emissions by private vehicles (considering that cars are the main form of transportation) are clearly different in each VVM (Figure 8), with an average of 128,152 and a standard deviation of 38,509 ton/year for vehicles; 3816 and 1147 ton/year for buses; 15,123 and 4544 ton/year for cargo vehicles; 880 and 265 ton/year for motorcycles. Private cars are the most significant category since it represents 87% of the total studied vehicles (Table 1). This is mainly due to the city's population, which is currently 1,049,792 inhabitants and grew  
Figure 8: CO2e emissions by type of vehicle in each VVM.  
Figure 7: Travels by segment.  
at a rate of 1.2% from 2015 to 2019, as reported by the National Institute of Statistics and Geography [29], as well as the 60.2% increase in the number of registered vehicles, which notably exceeded population growth. Clearly, the city must consider other transportation alternatives, such as intelligent transportation systems, electric and hybrid vehicles, or autonomous vehicles, among others [41].  
Finally, Table 2 presents the results of the main variables of the model for Tier 3 of the IPPC during the P0218-0119 period. Total distance traveled (464,536,891 km/year) and weighted performance for 2019 (7532 km/L) stand out. These two parameters are used to calculate fuel consumption, which was found to be 61,669 \(\times\) 10\({}^{6}\) L/year. Applying the corresponding emission factor, we obtained total CO\({}_{2}\)e emissions (147,971 tons/year). The content of this table was used to carry out the emissions projections, which are presented in the following section.