## 6 Performance Evaluation
### Performance Evaluation Using Real Taxi Trajectories  
In order to further evaluate our proposal in real vehicular scenarios, we carry out experiments based on real taxi trajectories of 4600 taxis in Sanya City, Hainan Province, China, from 9 a.m. to 10 a.m. on 15 November 2016. The open access dataset is provided by Ministry of Transport of China [37]. The GPS data collection cycle is 10 s, but some vehicles have only one location entry in a minute.  
To simplify the analysis, we select a square area in the center of the city with longitude in [109.493, 109.506] and latitude in [18.237, 18.262], as shown in Figure 12a, and the time period is from 9:00:00 a.m. to 9:01:59 a.m. (the storage cycle is 2 min). In this scenario, after deleting incorrect trajectories, there are 259 vehicles traveling on the selected roads. For some missing GPS data, we insert estimated locations according to the existing prior and next entries as well as the velocities. Additionally, we set 48 roadside units, almost all of which are at intersections. The map of our selected area with roadside units is illustrated in Figure 12b.  
Since the encountering of vehicles and roadside units significantly affects the overall performances, we get statistics about the number of contacts. The distribution of the number of vehicles, which will encounter a specific number of roadside units, is shown in Figure 13a, while the distribution of the number of roadside units, which will meet a specific number of vehicles, is displayed in Figure 13b.  
Figure 11: Results with different numbers of contents using Poisson distribution. (**a**) access ratio; (**b**) average access delay; (**c**) number of replicas; (**d**) transmission overhead.  
As illustrated in Figure 13a, most of the vehicles encounter one or two roadside units during the two minutes, while two vehicles encounter 14 roadside units, which is the largest number of passing units. When a vehicle encounters more roadside units, the data it requested can be satisfied by any of these units, and hence the distribution solution has more flexibility to balance the requests from this vehicle and other vehicles in the network. Additionally, in Figure 13b, the majority of roadside units encounter less than 20 vehicles, while two units meet over 30 vehicles during the storage cycle. If a roadside unit has several encountering vehicles, its limited storage and bandwidth may be not enough to satisfy all of the requests from these vehicles. Therefore, an efficient distribution solution is highly required to improve the utilization of resources.  
In the real scenario experiments, the parameter configurations are the same as the simulated experiments. Data requests follow the Poisson distribution, and let the number of contents be 100, 200 and 300, respectively. The performances of the four schemes are shown in Figure 14. We see that our scheme DAS has the highest access ratios, although they are lower than those in simulated scenarios, as a result of the variety of mobility. Note that the number of replicas in Greedy is the smallest because most vehicles only meet one or two roadside units and the Greedy scheme results in no data distributed for some vehicles in these few roadside units. In addition, the latency of DAS is shorter than 45 s, which is acceptable compared with the storage cycle 120 s. Overall, DAS has a high delivery ratio with an acceptable delay.  
Figure 12: Real vehicular scenario. (**a**) Sanya City (China); (**b**) selected area.  
Figure 13: Encountering analysis. (**a**) distribution of the number of vehicles; (**b**) distribution of the number of roadside units.