## 4 Results and Discussion
### Network Testing  
To evaluate the trained networks, simulations were conducted for 3600 s involving 2600 vehicles and 2000 pedestrians. The number of cars and pedestrians in the simulation was derived from a detailed analysis in which traffic phase cycles were dynamically defined [22,24]. * The environment scenario was optimized based on observed traffic patterns, and the number of vehicles and pedestrians was estimated under the condition of the longest traffic signal phase. * The intelligent system, powered by neural networks, was implemented and demonstrated that these levels of traffic flow could be efficiently managed. The evaluation metrics validate the optimized performance of the proposed algorithm in terms of traffic safety and efficiency. Additional analyses were performed to assess the effects of optimization on halting vehicles and pedestrians in both standard and rerouting scenarios. The results confirmed that these traffic densities were feasible when optimal decisions were made by the neural-network-based intelligent system. This systematic approach allowed for realistic simulation conditions that closely mirror peak-hour scenarios. Figure 11a-c depict the halting vehicles at junctions C0, C1, and C2, respectively. In the scenario with three horizontally arranged junctions, C1 is identified as the critical junction. Situated between C0 and C2, it receives traffic from both directions, subjecting it to significant pressure and increasing vehicle queues. By incorporating queue length thresholds, the system dynamically responds to congestion levels on key roads, such as those connecting C0 to C1 and C1 to C2. For example, vehicles traveling from west to east at junction C0 are informed of road conditions between C0 and C1 upon entering the system. If the vehicle count exceeds a set threshold, such as 25 cars (despite the road's 400 m capacity to hold more), vehicles are rerouted to turn right instead. This rerouting prevents additional congestion on the road between C0 and C1, as illustrated in Figure (a)a for the rerouting and asymmetric rerouting scenarios. Vehicles that reroute exit the system sooner, alleviating pressure at subsequent junctions. This leaves space for new vehicles to enter and prevents scenarios where cars are stalled at green lights due to blocked lanes ahead. Additionally, the system manages congestion by prioritizing traffic phases that direct fewer cars into critical lanes. For instance, instead of activating the west-to-east phase, which would funnel more cars into C1's critical lane, the system may activate a north-to-south phase or a pedestrian phase, allowing C1 to clear its critical lane. As shown in Figure (b)b, during the peak period between 800 and 2200 s, the standard scenario exhibits significantly higher congestion at C1 compared to the rerouting scenarios. C1, being the pivotal junction between C0 and C2, connects two critical roads in the network. Strict micro-control is necessary, particularly due to the disparity in road lengths: the road between C0 and C1 is 400 m long, while the road between C1 and C2 is only 200 m. This difference in capacity requires careful traffic management to prevent overwhelming C2. For example, vehicles traveling from C2 to C1 and heading toward C0 move more easily due to transitioning from the shorter 200 m road to the longer 400 m road. However, even in this case, the system ensures the 25-vehicle limit is respected to maintain traffic flow. In Figure (c)c, the halting vehicles at C2 are shown. The rerouting scenarios consistently reduce the number of stopped vehicles compared to the standard scenario, aligning with earlier observations. While congestion management at C2 is generally effective, it requires greater precision due to the interplay of route changes and phase activations. The asymmetric rerouting scenario focuses on managing the higher traffic flow in the west-to-east direction, depriitizing the east-to-west flow where traffic volumes are lower.