## 4 Numerical Results and Discussion
This result is due to the optimum and suitable choice of the vehicle position to communicate with other vehicles or to relay information between source-vehicle and infrastructure which increases the link reliability, retransmission time and consequently decreases the average delivery latency. For performance assessment under varying vehicle velocity, in order to imitate real multi-vehicle multi-speed roads, the proposed model has been investigated once again in terms of _S, PDR_ and _PLR_. The road vehicles are classified into three groups, corresponding to three speed-limited lanes, with different velocities. Each group has a velocity with \(\pm\delta\) of the road average velocity, where \(\delta\) is the variation in vehicle velocity. Based on the new consideration, the attested vehicles' velocity is classified as 50 km/h, 60 km/h and 70 km/h for the first, second and third lane, respectively, to emulate the realistic road traffic that has multi-lanes with vehicles moving at variable velocities. The throughput has been examined once again versus the number of vehicles as shown in Figure 13. As illustrated, even at this approach of imitating practical roads, the proposed technique reveals superior performance compared with the compared model in [12] under varying vehicles' velocity. This result shows how the proposed model has an efficient and reliable communication in the realistic road traffic. Additionally, the PDR has been studied under the same assumed conditions as illustrated in Figure 3:0 It can be shown  
Figure 3:1 Packet delivery ratio vs. number of vehicles. Figure 3:2 Average delivery latency (DL) vs. speed of the vehicles. that the proposed model has much higher PDR compared with the model presented in [12]. The results demonstrated in these figures comply with the results shown in Figure 3:3 Additionally, the results can be justified due to the optimum and suitable choice of the vehicle position offered by the proposed technique. These optimum vehicle positions are to communicate with other vehicles or to relay information between source-vehicle and infrastructure, which increases the link reliability. Finally, the proposed model has been inspected for the same conditions in terms of PLR. As can be observed from Figure 15, the proposed model has a noticeably lower PLR than the model presented in [12], as the proposed model succeeds in increasing the link reliability either between V2V or V2I, and consequently decreases the PLR. It can be concluded from the three previous figures that the proposed model is efficient and reliable, even when the realistic road traffic that has multi-lanes with vehicles moving at variable velocities is considered. Figure 3:4 Packet delivery ratio (PDR) vs. number of vehicles at variable vehicles’ velocity. Figure 3:5 System throughput (S) vs. number of vehicles at variable vehicles’ velocity. The performance evaluation presented in Figures 3-15, based on comparison with other models available in literature, shows how the proposed model increases the reliability and the effectiveness of the connectivity between V2V and V2I. Additionally, it was noted that there are many factors affecting the connection between V2V and V2I such as path loss, channel condition, vehicle mobility, density and vehicle speed. Then, it was shown that one of the suitable and adequate solutions is the proposed model, by finding the best inter-vehicle position either to communicate with other vehicles or to relay the information to the infrastructure. Based on the vehicle position, transmission power (\(P_{V}\)) and the required system QoS, the optimum transmission distance between V2V and V2I has been identified to improve the link reliability and reduce the number of retransmissions by the reduction of the probability of link failure. Moreover, it helps improve the overall system performance in terms of increasing throughput, increasing packet delivery ratio, decreasing packet loss rate and decreasing average delivery latency.