## Materials and Methods
### Data Collection and Study Area  
The data acquisition stage, which is one of the most important inputs of the research, was carried out in three different provinces at intersections with different infrastructures. Within the scope of the study, field studies were first conducted on the intersections selected from Istanbul and Kocaeli provinces in Turkey. Later, this scope was extended with the intersections selected from Konya. The working principle of the intersections in Konya province differs from the others due to the sensors used. While loop detectors and wireless sensors are mainly used in Istanbul and Kocaeli, the vehicle detection systems in Konya work with camera sensors. The main difference here is that sensors positioned on or below asphalt are positioned \begin{table}
\begin{tabular}{p{227.6 pt}} \hline \hline
**Input: \(Q_{kl}\), Arrival Rate; \(Q_{sl}\), Service Rate; \(\rho\), Flow Intensity; \(C\), Cycle Time; \(\varphi_{i}\), the Maximum Value of Intensity; \(\phi\), Assigned as a Critical Value of Highest Intensity; \(G_{i}^{k}\), the Green Time for Each Phase; \(T_{L}\) Loss Time; \(Y_{t}\), Yellow Time** \\ \hline \hline \multicolumn{2}{p{227.6 pt}}{Output: \(G_{e}\), the effective green time for each phase} \\ \hline \hline Steps. \\ 1. Find the number of vehicles arriving per unit time for each approach branch of the intersection, Arrival Rate \(Q_{kl}\), and find the number of vehicles served per unit time for each approach branch of the intersection, Service Rate \(Q_{sl}\). \\ 2. Calculate flow intensity \(p=Q_{kl}/Q_{sl}\). \\ 3. Calculate the intensity for each phase \(\varphi_{i}\). \\ 4. Assign the maximum value of \(\varphi_{i}\) obtained at the end of each cycle as critical value \(\phi\). \\ 5. Find cycle time, \(C\). \\ 6. Calculate the green time for each phase using the circuit duration, intensity value, and critical value (\(k\)). \\ \(G_{i}^{k}=\frac{C}{\sum_{i=1}^{k}\frac{C}{\varphi_{i}}}\), \(G_{i}=G_{i}^{k}\times p_{i}\) \\ 7. Calculate effective green time. \\ \(T_{l}=Loss\) Time, \(Y_{t}=Yellow\) Time \\ \(G_{e}=G_{i}+T_{l}-Y_{t}\) \\ 8. At the end of each cycle, find the critical rate again and compare it with the previous critical value. If (\(\varphi_{i}>\varphi_{i}\)) go to step 4, else go to step 6. \\ 9. Find effective green time for each phase. \\ 10. End. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Process steps for calculation of effective green time. at certain distances on the junction link arms, while the cameras are positioned close to the intersection center to detect vehicles passing through the intersection endpoint. This fundamental difference in sensor systems require performance values to be calculated with different analysis methods. For example, while the tail lengths on the junction approach arms can be calculated more accurately with the loop detectors, this could be calculated with a slightly more complex method with the camera sensors. However, this is not a measure of the need to use a camera as a vehicle sensor at intersections. As mentioned in Section 2.2, since both systems may have pros and cons concerning each in general terms, the preference of the system to be used may vary according to the parameters prioritized by the practitioners and city managers. Figure 8 shows the image of the intersection equipped with a camera system and examined in this study. While the selected intersections in Istanbul and Kocaeli are isolated, that is, the intersections do not have any connection or influence with any intersection near them, the intersections in Konya covering both situations were examined. The main purpose here was to see the relationship between two close intersections and to examine the interaction between them. However, the interaction between close intersections is planned to be conducted as a different research subject; hence, it is not within the scope of this study. In this study, only isolated intersections were evaluated.