# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 4 Performance Evaluation
### Different Urban Maps  
For urban scenarios, two maps were created. The first map is a fragment of Manhattan Island in New York City, USA (Figure 6a), and the second map is the downtown area of the city of Clermont-Ferrand, France (Figure 6b). The ROI size of both two maps is \(1\times 1\) km\({}^{2}\). The map statistics, as shown in Table 2, indicate that the map of Clermont-Ferrand has more lanes, junctions and shorter street lengths than the Manhattan one, which means that the road traffic topology is more complex in Clermont-Ferrand than in Manhattan. As expected, the obtained simulation results show that the different urban maps significantly impact the wireless communication protocols because the scenarios affect the network topology and network link quality, respectively. According to the simulation results of the two scenarios, the Packet Delivery Ratio (PDR) of AMD is the worst in both the Clermont-Ferrand and Manhattan scenarios. Therefore, we compare firstly the performance of TrAD and UV-CAST on different maps. Figure 8: Results with a 95% confidence interval in Manhattan scenarios with the traffic route 1 (uniform): (**a**) PDR; (**b**) Number of Transmissions; (**c**) Delay. Figure 7a shows that the PDR of TrAD and UV-CAST increases rapidly from about 70% to over 90% when the vehicular density increases from 40 v/km\({}^{2}\) to 80 v/km\({}^{2}\), and then continues to gradually reach close to 100%. In case of the Manhattan map (Figure 8a), the PDR of TrAD and UV-CAST already achieves over 90% when the vehicular density equals 40 v/km\({}^{2}\), and then reaches close to 100% when the vehicular density is 80 v/km\({}^{2}\). Notice that the PDR of TrAD is better than the UV-CAST one (93.82% _vs._ 90.84%) at the starting point of vehicular densities (_i.e._, 40 v/km\({}^{2}\)). The reason for these observations lies in the different complexity of the two maps. The Manhattan map, a regular one, is relatively easy to handle with the protocols. On the contrary, the complex road traffic environment of the Clermont-Ferrand map, _i.e._, winding and short streets, T style intersections and irregular buildings, leads to intermittent connectivity and increases the complexity of the road topology recognition. In fact, UV-CAST does not consider the road topology to classify vehicles into different directional clusters. It assigns a high priority to the vehicles located at an intersection to rebroadcast the data messages and use a gift-wrapping algorithm to select the boundary vehicles to be the SCF-agent. As the authors of UV-CAST admitted, the gift-wrapping algorithm would over-select the SCF-agent vehicles. On one hand, it seems that UV-CAST cannot control appropriately the number of transmissions. Thus, the number of transmissions of UV-CAST is much higher than the one of TrAD (Figures 7b and 8b). On the other hand, TrAD uses the vector-angle-based classification mechanism to sort vehicles into respective directional cluster that matches the road topology. Moreover, TrAD also resorts to the traffic adaptive sorting technique to fairly classify vehicles into the priority list. Therefore, the redundant data message transmissions are canceled appropriately in each directional cluster and, at the same time, the data messages are disseminated more efficiently into every possible direction in the ROI. Figures 7c and 8c indicate that the delay of TrAD and UV-CAST in both the Clermont-Ferrand and Manhattan scenarios decreases with increasing vehicular density. Moreover, the delay in the Clermont-Ferrand scenario is higher than the one in the Manhattan scenario for the respective vehicular densities. This shows the side effect of the complex road topology of Clermont-Ferrand scenarios. We also observe that TrAD achieves a similar level of delay as UV-CAST in the Clermont-Ferrand scenarios and gains an advantage over UV-CAST in the Manhattan scenarios.