## 7 Discussion  
The findings of this study underscore the significant challenges posed by human uncertainty in the performance of 5G-enabled CPSS. Through simulations conducted in the InTAS scenario, it was observed that variations in driver behavior introduce considerable  
Figure 10: Distribution of results with 10% VFH vehicle load in alternative routing scenarios  
variability in traffic dynamics, complicating the efficient allocation of network resources. These challenges are particularly pronounced in high-activity areas, where fluctuations in demand and operating conditions are frequent.  
Our results indicate that the introduction of alternative routes, i.e., deviations from the predefined paths suggested by the VFH application, exacerbates resource management difficulties, leading to suboptimal network performance due to insufficient resource allocation per cell. Notably, although the HC2 period with a 5% VFH vehicle load showed a more favorable outcome, this exception does not counteract the overall trend of performance degradation. This suggests that human variability remains a critical factor that current methods struggle to mitigate effectively.  
The analysis of frequency allocation methods, e.g., ceiling rounding versus nearest integer rounding, demonstrated that while both methods aim to optimize resource utilization, neither fully addresses the inefficiencies caused by human behavior variability. The nearest integer rounding method managed to keep the CDR below the 1% threshold in optimal routing scenarios, yet this came at the cost of suboptimal resource distribution when human uncertainty was introduced. This underscores the need for more advanced and adaptive strategies in 5G network management to accommodate the unpredictability of human behavior.  
Furthermore, these results highlight the limitations of static resource allocation strategies in dynamic urban environments. The variability in frequency allocation requirements across different cells and congestion periods suggests that a one-size-fits-all approach is insufficient. Areas characterized by high commercial and educational activity demand more dynamic and adaptive resource management strategies, whereas more stable areas may benefit from predictable resource planning.  
The practical implications of our findings are significant. By integrating real-time traffic data from InTAS, real vehicular patterns were reproduced, allowing us to accurately predict traffic congestion and load fluctuations in the 5G network. 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.  
Addressing human uncertainty in CPSS requires the development of more sophisticated models and adaptive resource management techniques. Incorporating real-time data analytics, machine learning, and predictive modeling could enhance the responsiveness of 5G networks to human behavior variability. Such advancements are essential for the successful deployment and operation of 5G-enabled services in complex urban environments, ultimately leading to improved service reliability and user satisfaction.