# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 4 Simulation Results and Discussion
### Simulation Results and Analysis  
Firstly, a simulation in which no instructions were given was carried out, see Table 1. This simulation shows the route situation and whether congestion would disappear by itself if no interference was taken. Secondly, a comparison between giving instructions with/without the filtering of drivers by SVM was made. In fact, if the goal is to reduce congestion on one route, the simplest way is to instruct all the newly arriving vehicles to change to other routes, but this is not efficient and effective since it will cause congestion on other routes. If new congestion appears on other routes, the system has to instruct all the newly arriving vehicles once again, which will not only extend the time for eliminating congestion but also confuse the drivers since contradictory instructions are given too often. Thus, since the surveillance takes place every 30 s and only one vehicle enters the scenario every second, it is sufficient to instruct 30 newly arriving vehicles to change routes during each surveillance. If SVM is applied to pre-filter the drivers and choose those obeying the instructions, since the system needs to inform 30 drivers in each surveillance and drivers are separated into 2 groups, the system has to implement the SVM classification for the 60 newly arriving drivers each time. Around 30 drivers will therefore be chosen to change routes. If SVM is not implemented and drivers are not pre-filtered, the system simply needs to instruct the next 30 newly arriving drivers. During the simulation, since real tests cannot be realized, two logics were proposed to simulate the driver's reaction when he or she receives instruction: (1) a combination of principal component analysis and \(k\)-means and (2) Fuzzy Logic. The driver data is first collected, and \(k\)-means or Fuzzy Logic is then applied. If the result is 1, that means this driver will follow the instruction, and a command to change the route for the vehicle will be sent to SUMO; otherwise, no command is sent, and the vehicle will continue to pass through the original route. The first simulation is the one with no instructions given by the system. The goal is to see what will happen on the road during congestion if no interference is made by the intelligent transportation system framework. At the beginning, the traffic lights created congestion on the road. The waiting queue became longer as vehicles kept entering the route. Although congestion was reduced a little bit when the traffic lights turned green, not all the vehicles in the waiting queue were able to exit the route before traffic lights turned red. The queue kept growing until the route was full of vehicles and the dynamic vehicle density reached its peak. Figure 1(a) shows the mean velocity of the vehicles on the route. From the graphs of the simulation, we can see that if no interference is taken, the congestion will not be eliminated by itself. The mean velocity reaches its peak at first, but when congestion happens, it goes down. Because of zero interference, the congestion keeps getting worse, and only a few vehicles can pass through the route when the traffic light is green. The original route was full of vehicles when the simulation ended. \begin{table}
\begin{tabular}{l} \hline \hline
**No Instruction** \\ \hline
8.075 \(\times\) 10\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Congestion durations without instruction. As depicted in Figure 2b, the number of stopped vehicles on the original route, contrary to the curve of the mean velocity of vehicles, has grown higher when the time step increases and reaches a peak because the whole route is filled with waiting vehicles. As explained previously, the vehicle flux is the mean number of vehicles that pass through the route per second; the higher the value, the better the route conditions. As displayed in Figure 2c, the vehicle flux decreases with the growing time steps. Furthermore, after simulating the same set of data five times, the mean congestion duration was extremely high.