## 6 Conclusions  
The study detailed in this paper represents an important advancement in comprehending and analyzing urban traffic flow, facilitated by the deployment of UTMOM. Through the careful analysis of two diverse datasets, UTMOM sheds light on the dynamic and complex patterns of urban traffic, enriching the existing scholarly landscape in urban planning and traffic management with fresh insights. The innovation of our approach lies in its holistic examination of urban traffic phenomena. Moving beyond the scope of traditional studies that may focus narrowly on specific elements of traffic flow, UTMOM employs an extensive array of variables and data sources, offering a comprehensive perspective on urban mobility. However, in our current model, we considered the average numbers of vehicles such as cars, buses, trucks, and vans but did not include bicycles. The exclusion of these vehicle types in our initial model is due to limitations in data availability, the complexity of modeling, and the scope of the study. In future research, we aim to incorporate bicycles and other such transportation modes to enhance the accuracy of our model and provide a more comprehensive understanding of urban traffic dynamics. This will enable us to offer more effective solutions for traffic management and urban planning strategies. A key innovation introduced in our analysis is the strategic weighting system, an advanced technique designed to refine the accuracy of traffic predictions, ensuring a close alignment between UTMOM's forecasts and the actual observed data. This meticulous approach to model calibration underscores our study's dedication to precision and sets a new standard \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Polynomial Regression** & **Gaussian Process Regression** & **UTMOM** \\ \hline RMSE & 19.40 & 10.59 & 25.00 \\ MAE & 14.20 & 10.48 & 20.00 \\ Real-time Data Adaptation & No & No & Yes \\ Multidimensional Analysis & Limited & Moderate & Extensive \\ Strategic Planning Support & No & Limited & Yes \\ Data Integration & Single-source & Single-source & Multi-source \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of Optimized Model Performance. for subsequent research within this domain. Our contributions significantly enhance the scientific understanding of urban traffic dynamics, providing a solid foundation for future investigations. The methodological advancements presented in this work not only contribute to academic debate but also have tangible implications for urban planning and the development of traffic management policies. By laying a data-driven groundwork for traffic management strategies, our research supports the crafting of more sustainable, efficient, and adaptive urban mobility solutions. Acknowledging the limitations inherent in our study, we also recognize the significant impact of our findings on both the academic and practical realms of urban traffic management. Despite the ambitious scope of this study, certain limitations should be acknowledged. The collected data covered a five-day period within the vicinity of the LIMAK Cement Factory, which may not fully capture the broader traffic patterns of the entire city. Future studies incorporating broader datasets across different areas and longer periods are essential to fully understand Ankara's urban traffic dynamics. These results pave the way for future research endeavors aimed at deepening our understanding of urban traffic flows, thereby enhancing urban living standards and promoting environmental sustainability. UTMOM demonstrates the transformative potential of mathematical modeling in addressing complex urban challenges, marking an essential step towards creating more livable and manageable urban environments. However, this study's robust methodology and insightful conclusions are not without constraints. The reliance on data from specific intervals and locales may not fully capture the diverse scenarios encountered in urban traffic.