## 5 Simulation Results  
In this section, we compare the performance of the proposed algorithm with those of VeMAC, CFR MAC, and PTMAC. For the performance evaluation in diverse environments, we measure the mobility of a vehicle using GPS equipment called VBOX Sport. Figure 11 shows VBOX Sport and its management program. VBOX Sport records the actual vehicle path, speed, and location every 50 ms via GPS and is used to derive the actual mobility model of the vehicle in this paper. In order to collect measurement data in various environments, we collected data repeatedly from actual highways and intersections, taking into account dozens of moving cases such as acceleration/deceleration, stopping, high speed and low speed. Figures 12 and 13 show road scenarios for an intersection with traffic lights and a circuit intersection without traffic lights, respectively. It is assumed that the signal of the traffic lights on the road changes every 10 s. At an intersection, the vehicle's direction of travel can be a left  
Regarding the performance metrics, we evaluate merging collision rate and access collision rate as a function of the number of vehicles in a network. Merging and access collision rates are defined as the average number of merging collisions per frame and the average number of access collisions per frame, respectively. Figures 14-16 show the results for the average merging collision rate as a function of the number of vehicles in various environments. Figure 14 shows the merging collision rate on the highway. The result shows that the proposed algorithm decreases merging collision rate by up to 55.1%, 40.8% and 11.7% compared to VeMAC, CFR MAC, and PTMAC, respectively, which shows that the vector-based prediction model outperforms slot partitioning-based algorithms. Because PTMAC and the proposed algorithm do not use the slot partitioning method, merging collisions can occur between vehicles moving in different directions. However, the result also verifies that the proposed algorithm prevents merging collision more effectively than PTMAC. Figure 15 shows the merging collision rate at an intersection with traffic lights. In this scenario, the proposed algorithm reduces the merging collision rate by up to 63.3%, 46.9%, and 25.2%, compared to VeMAC, CFR MAC, and PTMAC, respectively. When the traffic light is red, there are many vehicles stopping, resulting in fewer merging collisions. On the other hand, when the traffic light is green and many vehicles pass through the intersection, there is a high possibility that resource collision will occur due to high mobility and the various moving directions of vehicles. As a result, the merging collision rate increases in VeMAC and CFR MAC using \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Highway** & **Intersection** \\ \hline Vehicles speed & 13.8\(\sim\)41.6 m/s & 0\(\sim\)41.6 m/s \\ Road width & 80 m & 80 m \\ Road length & 2000 m & 1000 m \\ The number of vehicles & 80\(\sim\)300 (step = 20) & 80\(\sim\)300 (step = 20) \\ Traffic light & - & 10 s \\ Frame size & 100 ms & 100 ms \\ The number of control time-slot & 100 & 100 \\ Time-slot duration & 1 ms & 1 ms \\ Time rate of change & 0.1 s & 0.1 s \\ Transmission range & 150 m & 150 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameter. Figure 13: Circuit intersection without traffic lights. the slot partitioning method. On the other hand, in PTMAC, the merging collision rate increases because each vehicle cannot accurately predict the moving direction of neighbor vehicles. Figure 16 shows the merging collision rate at a circuit intersection.