# Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections
## 6 Application
### Congestion Level Service  
The analysis of the information stored at the database is performed using a fuzzy algorithm developed with the aid of Matlab. We have trained an Adaptive Neuro-fuzzy Inference System (ANFIS) according to data collected from sensors and stored into the database.  
The fuzzy system has two input and one output variables. The input variables are the traffic density observed during a given period (traversingVehiclesRate, number of vehicles observed from a traffic light during the green period); and the number of vehicles awaiting the next traffic light cycle (#waitingGreen). The congestion level is the output variable. The output values vary on the range [1; 10] as described above. The membership degree of each variable is given by three fuzzy sets (low, medium and high) provided by the ANFIS. Table 5 briefly summarizes the decision rules.  
In this paper, in order to illustrate congestion level service operation we have deployed a simplified interface, where data collected is used to present the current status of the traffic in the city. However, such information is processed to augment the information at each point taking into account previous analysis.  
In our case, a first approach has been the evaluation of the status of each conflict area considering the congestion level at different days. We integrate on a graphical view the level of congestion during different periods of time. We group the week on labor days (LD) and holidays (HD). We average the congestion level for both groups, and provide it to the user (traffic manager). In Figure 20 it corresponds to the pie chart next to some traffic lights. The colors of the pie chart correspond to the congestion level at each location. Figure 23 shows some of the parameters and indicators considered (temperature, wind orientation and intensity, pollution degree... ). The average, maximum and minimum temperatures observed during the three first weeks of June 2016 are depicted in Figure 23b, while Figure 23a shows the variation of the temperature each hour during the same period. The graphs on the bottom show the pollution degree (CO, SO\({}_{2}\) and NO\({}_{2}\)) and the wind direction (bottom right). Traffic managers can use such information in order to temporarily modify traffic light policies, or to send human resources (police) to regulate the traffic. Currently we are evaluating a few parameters, but the sensors deployed at traffic light have an enormous potential to provide better services for citizens. Nowadays, we obtain situational awareness from temperature and pollution sensors and also from passive infrared sensor (PIR), which aid to detect the presence of pedestrians and vehicles, allowing the estimation of the degree of occupancy of both lanes and crosswalks. Figure 24a shows the relative humidity observed, while the graph on the right shows the percentage of operating time during which the infrared sensor detects pedestrians. The system here proposed provides complete and adequate situational awareness to municipal experts in order to allow the modification of the traffic light cycles according to the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Traversing Vehicles Rate**} \\ \cline{3-5}  & & **High** & **Medium** & **Low** \\ \hline \multirow{3}{*}{\#waitingGreen} & High & \multicolumn{1}{c}{CHN = 5} & \multicolumn{1}{c}{CHN = 7} & \multicolumn{1}{c}{CHN = 10} \\  & Medium & \multicolumn{1}{c}{CHN = 3} & \multicolumn{1}{c}{CHN = 5} & \multicolumn{1}{c}{CHN = 7} \\ \cline{1-1}  & Low & \multicolumn{1}{c}{CHN = 1} & \multicolumn{1}{c}{CHN = 2} & \multicolumn{1}{c}{CHN = 3} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Congestion level service rules.  
specific needs of each instant considering multiple environmental factors in addition to the traditional vehicle counting.