## 5 Simulation Results
In this scenario, the proposed algorithm reduces the merging collision rate by up to 49%, 25.8%, and 14.9%, compared with VeMAC, CFR MAC, and PTMAC, respectively, and this result can be explained by the same reason as for the explanation of Figure 15. In addition, the proposed algorithm decreases the merging collision rate compared to VeMAC, CFR MAC, and PTMAC, and this performance gain is the largest for the highway and the smallest for the circuit intersection without traffic lights. To sum up, the proposed algorithm has a greater effect in decreasing merging collision rates when the traffic density is high and the mobility of the vehicle is high with rapidly changing travel direction. Figure 14: Merging collision rate for a highway. Figure 15: Merging collision rate for an intersection with traffic lights. Figures 17-19 show the results for the average access collision rates as a number of vehicles in various environments. In Figure 17, it can be seen that the proposed algorithm in the highway environment reduces the access collision by up to 60.5%, 44.6%, and 17.9%, compared to VeMAC, CFR MAC, and PTMAC, respectively. In Figure 18, we can see that the proposed algorithm in the intersection with traffic lights reduces access collisions by up to 60.8%, 44.6%, and 23.4%, compared with VeMAC, CFR MAC, and PTMAC, respectively. In Figure 19, the result shows that the proposed algorithm in the circuit intersection without traffic lights reduces access collisions by up to 61.1%, 45% and 23.9%, compared with VeMAC, CFR MAC, and PTMAC, respectively. It is noticed that access collision is incurred by vehicles that simultaneously enter the network and occupy the time-slot, or by vehicles that experience merging collision and try to re-occupy a time-slot. Similar to the results of the merging collision rate, the proposed algorithm decreases access collision rate compared to VeMAC, CFR MAC, and PTMAC and this performance gain is largest for the highway and smallest for the circuit intersection without traffic lights. As such, the same conclusion can be made as in the case of the merging collision: the proposed algorithm has a greater effect on decreasing the access collision rate as the traffic density is high and the mobility of vehicles is high with rapidly changing travel direction. Figures 20 and 21 show both the simulation and analysis results for the merging and access collision rates of the proposed algorithm in various environments. In these figures, we can verify that the analysis results coincide with the simulation results. Figure 20 shows that the differences between analysis and simulation results for merging collision rates are at most 1.24%, 0.79%, and 0.51% for a highway, an intersection with traffic lights, and a circuit intersection without traffic lights, respectively. From Figure 21, we can see that the differences between analysis and simulation results for merging collision rates are at most 0.87%, 0.64%, and 0.59% for a highway, an intersection with traffic lights, and a circuit intersection without traffic lights, respectively. From the results shown in Figures 14-19, we can verify the performance enhancement of the proposed algorithm over other protocols. It is noticed that the proposed algorithm is a prediction-based algorithm and the performance improvement of the proposed algorithm against PTMAC (another prediction-based algorithm) is greater under the circuit or intersection than under the two-way freeway road, which shows the effectiveness of the proposed algorithm on roads with great freedom of movement. Figure 19: Access collision rate of a circuit intersection without traffic lights. Figure 18: Access collision rate of an intersection with traffic lights.