## 7 Results
### Traffic Demand Scale 1  
The full traffic demand produced 143,815 vehicles during the first hour of the simulation. Figure 4 compares the fuel consumption of the C-EEDR application using IDEAL and C-V2X communication and contrasts them against the fuel consumption without using the C-EEDR application (base). The base fuel consumption is the average fuel consumption of all the vehicles that travelled through the road network based on the Frank-Wolfe [23] user-equilibrium traffic assignment for the entire simulation period. The C-EEDR application in general helped in decreasing the fuel consumption with the increasing market penetration rates. As more of the vehicles are connected and act as probe vehicles, more information are collected and thus more accurate routing decisions are made. The C-V2X C-EEDR application behavior follows the same behavior as the IDEAL C-EEDR application with slight higher fuel consumption due to the loss of information from the communication system. The fuel consumption increases at higher market penetration rates (>75%).  
Figure 4: Average network-wide Fuel consumption of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with traffic demand of scale factor 1.  
The average travel time and the average stopped delay exhibited a similar behavior as the fuel consumption up to a certain market penetration rate. At the beginning, the travel time decreased as the market penetration rate increased until it reached 25%, and after that, the travel time and stopped delay increased as the market penetration rate increased. The C-V2X showed the same behavior as the IDEAL C-EEDR application, as shown in Figures 5 and 6.  
An expected behavior of the C-EEDR application is that when we decrease the average system-wide fuel consumption, we may increase the average travel time and the stopped delay.  
Figure 5: Average network-wide Travel Time of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with traffic demand of scale factor 1.  
Figure 6: Average network-wide Stopped Delay of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with traffic demand of scale factor 1.  
It is worth noting that the C-V2X-enabled C-EEDR does not follow the same pattern as the IDEAL C-EEDR with market penetration rates below 10%. This could be due to limitations on the lack of information, which affect the quality of the routing decisions.  
We ran the two-tailed Student's \(t\)-Test (\(p\)-values ranged between [0.17-0.82]) between the IDEAL-based C-EEDR and C-V2X-based C-EEDR applications and found that there was no statistically significant difference between the corresponding travel time, stopped delay, and for market penetration rates less than 25% at a significance level of 0.05. For market penetration rates 25% and higher, there was a statistically significant difference between the IDEAL-based C-EEDR and C-V2X-based C-EEDR applications for the corresponding travel time, stopped delay, and the fuel consumption between the mean of the 10 different seed runs. Table 1 shows the percentage change in the fuel consumption, travel time, and stopped delay between the C-V2X and IDEAL-based C-EEDR applications and their corresponding \(p\)-value. The degradation in performance was less than 3.5% for fuel consumption and travel time and reached 15.6% for the stopped delay.  
Although there was a statistical significance between the performance of the C-V2X-based and the IDEAL-based C-EEDR applications at market penetration rates of 25% and higher, we still achieved fuel savings in both cases at all MPR levels. This result confirms the effect of the communication system on the traffic system performance and of the mutual interaction between both systems.