## 6 Results
However, despite the application of both ceiling and nearest rounding methods for resource allocation, the CDR exceeds the established 1% limit in most scenarios. These results highlight the limitations of current resource allocation methods in maintaining service quality under unpredictable human behavior. Figure 9 depicts the distribution of frequency allocation results when considering alternative routes under a 5% VFH vehicle load across 10 selected cells during peak congestion hours. HC1 exhibits the greatest variability in frequency assignment, indicating a need to adapt to fluctuating demands and variable operating conditions commonly observed in scenarios involving high mobility, such as commercial activities or dynamic university schedules. Although HC1 shows slightly higher variability than HC2, the difference is minimal. HC2, despite sharing similar median values with HC1, displays fewer outliers and tends toward slightly higher frequency allocations in certain areas, particularly in cell C4, corresponding to mobility patterns linked to lunch hours in that area. Figure 8: CDR for alternative routing with 12 frequencies per cell during peak congestion periods. Figure 9: Distribution of results with 5% VFH vehicle load in alternative routing scenarios. In contrast, HC3 shows a more uniform and reduced frequency allocation, suggesting more stable network conditions and a response to more predictable demand patterns. This stability can be attributed to fixed work schedules and stricter operational constraints, reflected in lower variability. Institutional factors such as speed limits and overtaking restrictions, as incorporated in the model, also likely contribute to the more consistent behavior observed in HC3. Figure 10 displays the distribution of frequencies assigned to the cells under a 10% VFH vehicle load scenario. Cells C1, C2, and C3 exhibit greater variability in frequency allocation during HC1, with the dispersion in C3 being particularly notable. In contrast, cell C4 shows higher variability during HC2, suggesting less predictable behavior in this area compared to HC1. HC3 generally demonstrates more stable frequency allocations, with C1 showing higher, yet more consistent, allocations. Cells C5, C6, and C7 reveal distinct allocation patterns, with C6 experiencing significantly higher variability in HC3, while both HC1 and HC2 present more uniform distributions. In cells C8, C9, and C10, HC1 and HC2 maintain higher and more variable allocations than HC3, which exhibits a more concentrated and, in some instances, substantially lower frequency distribution, as observed in C9. These results suggest that areas characterized by high commercial and educational activity, such as cells C3 and C6, demand more dynamic and adaptive resource management strategies, whereas more stable areas, represented by cells such as C8 and C9, reflect a more predictable and manageable frequency demand, requiring less frequent adjustment. The simulation results obtained in this study are not limited to theoretical outcomes; rather, they provide practical insights that can be directly applied to real-world scenarios. By integrating real-time traffic data from the InTAS, actual vehicular patterns are replicated, enabling accurate predictions of traffic congestion and 5G network load fluctuations. Such preliminary studies are crucial prior to real-world implementations, as they offer valuable insights that can help avoid costs associated with potential errors by identifying and addressing issues through simulations. These results can inform traffic management strategies in urban environments, where human behavior and fluctuating demands complicate network performance. The model's ability to simulate these variables provides a valuable framework for future applications in smart city traffic management, VFH services, and dynamic resource allocation in urban networks.