## 3 Results and Discussion
Likewise, while the delay in the first phase of the morning hours is 7.69 min, this delay is 0.36 min in the 4 th phase. Such fluctuations are not expected in a dynamically controlled intersection system. This result shows us that due to the extra time given to one phase, other phases are wasted unnecessarily. As mentioned in the operating principles of the intersection, signalized intersections allocate the cycle time to the connected arms, flows, or phases that it serves in certain orders and makes traffic more efficient for the vehicles. Thanks to the outputs of the queue analysis used to measure the general service level of the intersection, the phase sequences and effective green times calculation model mentioned in Sections 2.5.2 and 2.6 are proposed. It is aimed to complete the existing processes without exceeding the maximum service times determined by the Priority Scheduling method, which is recommended and adapted for the sequential phase model. In the graph below, the proposed phase sequence model is compared with other dynamic models. The proposed model is defined as M1. The results of other working dynamic models are taken from the intersection control devices of the related intersections. The delays in all branches of the three intersections selected from Istanbul and Konya were evaluated. By comparing M1 with the existing adaptive systems, M2 and M3 models, the delay data in 2 h intervals are revealed. Although there is not a significant difference between the proposed model according to dynamic working principles, it can be said that it is quite good according to the results obtained from the intersection with a fixed operating principle. We can say that the M1 model causes less delay than other models in the heavy traffic time zones of the day. Another important issue in planning and managing intersections is the efficient use of green times. Green periods used efficiently are extremely effective in reducing intensity on intersection roads. Figure 12 shows the change in intensity values of five phases in different periods in a day obtained with improved green times when the proposed model is applied. We can say that the intensities decreased significantly with improved green times. In addition, Figure 13 describes the number of vehicles in the queues before and after model suggested in Section 2.6. Figure 11: The changing average vehicle delays during different periods of a day. Figure 12: Density comparison of phases with improved green times. Based on the performance values obtained by the methods offered by the queuing theory, it was revealed that the intensity of some intersections was high and the efficiency of the intersections needed improvement. In detailed examinations, it is conspicuous that some currents are intense, especially at certain periods of the day, and that an improvement can be achieved in topics such as saving time, fuel, and energy. Based on the results obtained, a dynamic phase sequence model that allows dynamic managing of the intersection phase sequences is proposed. This model aims to prevent queuing by taking into account the intensity of the intersection arms by assigning priority to these branches in the change of phases. The model was tested in four intersections according to old and new operating principles. When compared to static and dynamic models, as can be seen in Figure 14, the intensity and waiting times, especially at peak hours of the day, were lower. Another issue that was encountered during field studies was the selection of the location and types of sensors to be used. It proved necessary to place cameras inside the intersections and intrusive sensors at certain distances on the arms to obtain the correct lengths of the waiting queues and to make improvements accordingly. Since both sensor  
Table 3.0 Spent time of the vehicles with control model M1 and comparison with another phase control models. Table 3.1 The change in the number of vehicles in the queues before and after the proposed model.