## 4 Results and Discussion
### Global Agent Decisions  
Figure 13 shows a comparison of trends over time for all of the active phases (agent actions) validated by the global agent at C0, C1, and C2 intersections in standard (left) versus rerouting symmetrical (middle) and asymmetrical (right) scenarios. At the top, the nine actions that the agent can take in the environment are displayed. These mini-figures highlight the differences in traffic management strategies across the scenarios under consideration. They represent how the neural network adapted its strategy during training, reflecting the learned traffic signal control patterns tailored to the observed traffic dynamics in each environment. Results show that the system diverges from a fixed phase sequence, characteristic of dynamic traffic control systems, by continuously adapting to real-time traffic conditions. Crucially, pedestrian phases are only triggered upon pedestrian request, optimizing phase usage by prioritizing vehicular movement unless a pedestrian need arises. When comparing the three scenarios, the allocated green times and phase sequences across intersections exhibit notable variations over time. During the first 1500 s, green times for Phase 1 (N > S) and Phase 5 (E > W) at intersections C0 and C1 in the standard scenario differ significantly from those at C2. In contrast, the rerouting scenarios show distinct adjustments: green times for Phase 1 at C0 and C1 nearly double, while green times for Phase 5 at C2 increase significantly in both rerouting configurations. Furthermore, the symmetric and asymmetric rerouting scenarios prioritize Phase 9 (pedestrians) and Phase 8 (left turns) during the initial period. This emphasis reflects a strategic focus on smoother rerouting and effective congestion management by prioritizing pedestrian and turning movements to support traffic flow redistribution. This adaptive system efficiently handles both pedestrian and vehicle flows under varying traffic conditions. * _Standard Scenario:_ The system aims to maximize green band efficiency for vehicles while minimizing pedestrian rule violations. Green lights are coordinated to reduce vehicle stops, while pedestrian crossing times are scheduled to prevent excessive wait times. * _Rerouting Scenarios:_ When arterial traffic demand exceeds system capacity due to incidents or severe congestion, the system activates rerouting mechanisms. Traffic light coordination is dynamically reconfigured to redistribute traffic flow. For example, in these scenarios, 25% of vehicles are directed along the more congested straight paths, while the remaining 75% are rerouted to turning movements. This adjustment alleviates congestion on main arteries and enhances overall flow efficiency. In the symmetric rerouting scenario, equal priority is given to both traffic directions, ensuring balanced flow across the network. Green times and phase sequences adapt  
Figure 13: Comparison of trends over time for the active phases (agent actions) at C0, C1, and C2 intersections in standard (**left**) versus rerouting symmetrical (**middle**) and asymmetrical (**right**) scenarios. The nine possible phases are indicated at the top. dynamically to accommodate redistributed traffic. In the asymmetric rerouting scenario, the system assigns priority based on traffic demand. For instance, when one direction experiences higher traffic volumes, green times for corresponding high-traffic lanes are extended. This asymmetry effectively reduces pressure on critical lanes while maintaining manageable pedestrian waiting zones. Table 3 presents the overall percentages of green times for intersections C0, C1, and C2 over a training segment for both scenarios. These values are also visualized in Figure 14 for comparison. As expected, the system prioritizes critical phases (P1, P5, P6, and P9) with green bands that depend on the scenario, adapting its strategy to reduce wait times and improve traffic flow. Figure 14 provides a comparison of green time trends across all active phases at intersections C0, C1, and C2 for the analyzed scenarios.