## 5 Analytical Post-Processing in Office
### Conclusion of the Application  
In conclusion, the deployment of UTMOM marks a substantial advancement over traditional methods in the field of urban traffic management. This model, by harnessing data-driven analysis, significantly enhances our understanding of complex urban traffic patterns, particularly during predictable peak commute times such as morning and evening. To assess the prediction accuracy of UTMOM, we conducted a rigorous evaluation using graphical analysis (as shown in Figures 2 and 3) and a confirmatory ANOVA statistical test. Figure 2 includes a dual Y-axis system, where the left Y-axis represents vehicle counts for both observed and estimated data, and the right Y-axis represents the absolute differences between these counts. Observed vehicle counts are shown with blue solid lines and circle markers, estimated vehicle counts with orange dashed lines and square markers, and absolute differences with red dotted lines and triangle markers. The X-axis denotes different time intervals observed, covering morning and evening sessions over five days. Each element is clearly labeled in the legend to distinguish between observed counts, estimated counts, and absolute differences. This separation prevents scale overlap, making it easier to compare the data independently. Utilizing different colors and markers for each dataset ensures immediate visual distinction. The labels on the Y-axes and X-axis are precise, guiding the reader to interpret the data correctly. These enhancements address the reviewer's concerns, providing a clearer, more comprehensible visual representation of the comparison between observed and estimated vehicle counts. The ANOVA, chosen for its ability to compare means across multiple groups, was employed to validate whether the observed and the model-predicted traffic volumes during peak hours differ significantly. ANOVA results are given in Table 8. With an F-value of 3.115 and a \(p\)-value of 0.152, the results indicate no statistically significant differences, confirming the model's reliability in accurately predicting real traffic behavior during these critical periods. This statistical verification reinforces our confidence in the robustness and precision of UTMOM's projections, underscoring its effectiveness in urban traffic analysis and management. Our investigations have illuminated not only the model's strengths but also its potential areas for refinement. The detailed dissection of discrepancies between observed and estimated counts opens avenues for targeted enhancements, aiming to elevate the model's precision and, consequently, its practical utility in urban traffic planning and management. This balanced approach--integrating rigorous graphical analysis with statistical validation--enriches our understanding, offering a comprehensive evaluation of the model's efficacy in predicting traffic flows. The broader implications of our model's estimations and subsequent analysis are profound, extending well beyond academic curiosity. By delineating the variances and constants in traffic volumes across diverse times and contexts, the model emerges as a guiding light for urban planners. It encourages informed, strategic decision-making that could significantly augment the efficiency and effectiveness of traffic systems. Remarkably, the model's adaptability stands out as a key attribute, ensuring its relevance across varied urban landscapes and its capacity to meet the unique challenges presented by different cities globally. The application of UTMOM heralds a new era in traffic management, characterized by a proactive, rather than reactive, approach. Leveraging sophisticated mathematical modeling and comprehensive data analysis, we move beyond merely interpreting the current state of urban traffic to anticipating future trends. This forward-thinking methodology is pivotal in shifting from facilitating to optimizing urban mobility, thereby markedly enhancing urban life quality. This endeavor transcends traffic analysis to reimagine the future of urban mobility comprehensively.