## 4 Numerical Results and Discussion
As it can be seen from the zoomed illustration in Figure 9, the proposed model has an enhanced of PDR when comparing it with the competitor model [12]. It is also mentioned that, in both models, PDR is affected by the change in vehicular density. Figure 7: Vehicle transmission power vs. overall system data rate. For both investigated models, PDR decreases gradually when the density of the vehicles increases, as the increment of the vehicular density means increasing the number of transmission vehicles which will cause the congestion and overhead in the network, and thus cause decreases in the PDR. The results obtained in this figure are justified by the results obtained in Figure 8, that choosing the best vehicle position to communicate with each other or to relay information to \(I\) is the main reason for the increment of PDR for the proposed model when comparing it with other models. Additionally, it justifies that the higher the information throughput, the higher the packet delivery ratio. The overall system packet loss rate (PLR) for the proposed model and the model in [12] versus the number of transmitting vehicles are evaluated and examined in Figure 10 for two average vehicles' velocities of 20 km/h and 60 km/h. It can be shown in Figure 10 that the proposed model has much lower PLR than the compared model [12], as choosing the best  
Figure 8: System throughput vs. number of vehicles. Figure 9: Packet delivery ratio (PDR) vs. number of vehicles. position of the intermediate vehicles leads to increasing the link reliability, either between V2V or V2I, and then decreasing the PLR. It is also mentioned that for the proposed model and the model suggested in [12], increasing the number of transmission vehicles affects the system performance in terms of PLR. Increasing the number of the transmission vehicles for both models increases the packet loss rate, which is caused by network congestion and physical channel conditions. The result obtained in Figure 10 is correlated with the corresponding performance in throughput and _PDR_, as shown in Figures 8 and 9, respectively. Furthermore, to show that the proposed algorithm is efficient and reliable, it has been evaluated with other models proposed in [22]. Stochastic dynamic programming (SDP) is employed as a tool to analyze and solve network resource constraint and allocation issues [22]. In addition, it suggested an alternative solution called model predictive control algorithm (MPCA) that used approximation as a way to allocate resources over time in response to information on data transmission completion status. These two methods are suggested to enhance the system performance in terms of PDR and average delivery latency. Then, it was compared with the geographic-based spray and relay routing scheme (GSaR) proposed in [36]. As illustrated in Figure 11, the packet delivery ratio (PDR) versus the speed of the vehicles is evaluated once again for the proposed model and compared with the model presented in [22]. As illustrated in Figure 11, when the vehicle's speed is 40 km/h, the PDR of the proposed model is 95.8075% and the PDR of SDP, MPCA and GSaR are 91%, 90% and 88%, respectively. Furthermore, the PDR of the proposed model is 96.6647% when the vehicle's speed is 70 km/h, while the PDR of SDP, MPCA and GSaR are 90%, 92% and 95%, respectively. In addition, it is shown that when the speed of vehicles increases, it does not significantly affect the system PDR, which means that the proposed model is suitable for any vehicle mobility and speed. Additionally, the average of delivery latency of the proposed model is compared with the schemes proposed in [22]. Figure 12 depicts the performance of the proposed model in terms of average delivery latency. As one can observe from Figure 12, the proposed model has much lower average delivery latency compared with the schemes presented in [22]. Figure 10: Packet loss rate (PLR) vs. number of vehicles.