# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 5 Simulation Analyses and Results
### Results  
After the simulation in SUMO interacting with Python using traCI, the RSU deployment is calculated. Figure 7 shows the RSU deployment under the transmission range of 500 m, where the yellow dots represent the t-RSUs and the red dot represents the real RSU. What can be obviously seen from Figure 7 is that the total number of RSUs (whether t-RSU or real RSUs) in (a) is larger than in (b) and (c), where half for t-RSUs and half for real RSUs. It seems unfair for the MVCP and RandDeploy under the circumstances without any assistance from t-RSUs. Therefore, it could be predicted that the t-RSUs would play a vital role in undertaking the responsibility of transmission in the near future. The vehicle coverage ratio given in Figure 8 describes the excellent performance with parked cars serving as t-RSUs in detail. Figure 7: RSU Deployment under 500 m transmission range. Figure 8a-c represent the vehicle coverage under the RSU information transmission range of 100 m, 300 m, and 500 m, respectively. It can be seen that due to the help of t-RSU, our algorithm significantly outperforms the MVCD and RandDeploy algorithms in terms of vehicle coverage under the three RSU information transmission ranges. Taking the RSU information transmission distance of 100 m as an example, when the vehicle density is 112.5 vehicles/km\({}^{2}\) and 187.5 vehicles/km\({}^{2}\), the vehicle coverage rate of the proposed hybrid layered roadside unit deployment algorithm for activating t-RSU is 22.40% and 24.66% higher than that of MVCD, respectively. Compared with RandDeploy, the vehicle coverage of the proposed hybrid layered roadside unit deployment algorithm for activating t-RSU is increased by 25.12% and 25.6%, respectively. In addition, three different vehicle densities in the simulation area represent typical low, medium, and high vehicle densities, respectively. Low density can represent the density of driving vehicles at night, while medium and high density can reflect the density of driving vehicles during usual and peak hours during the day, respectively. At the same time, for Figure 8a,b, when the RSU information transmission distance is 100 m and 300 m, respectively, the vehicle coverage of MVCD and RandDeploy decreases with the increase of vehicle density. This phenomenon is contrary to the intuitive impression because the number of RSUs deployed is insufficient to cover the given area. Specifically, when the vehicle density is low, coincidentally, most vehicles are located within the transmission range of the RSU of the RSU. When the vehicle density is medium and high, the situation begins to change because the road network is large (relative to the transmission range of 100 m and 300 m), and more vehicles exceed the information transmission coverage of the deployed RSU. With the help of t-RSU, this phenomenon is not obvious in the hybrid layered RSU deployment scheme. However, for the RSU information transmission coverage of 500 m, as the vehicle density increases, the vehicle coverage also increases. In this case, the transmission range is larger than the entire road area, so the deployed RSU can cover most of the area of a given simulation road. The RSU information transmission range is set to 100 m, 300 m, and 500 m, respectively. For the three algorithms, the connectivity ratio between vehicle points is further compared. The results are shown in Figure 9.  
Figure 8: Vehicle coverage ratio. Figure 9: Point-to-point connectivity. Figure 9a-c represent the vehicle point-to-point connectivity ratios of the three algorithms under different vehicle densities in the three RSU information transmission ranges of 100 m, 300 m, and 500 m, respectively. In general, as the vehicle density increases, the connectivity ratio also increases.