## 3 Methodology  
The essence of our study lies in the development and application of UTMOM, a meticulously crafted mathematical model designed to probe into urban traffic data. UTMOM serves as a crucial analytical framework specifically aimed at facilitating a detailed comparative analysis of traffic flows between two distinct datasets, thereby uncovering critical differences and patterns that inform traffic management strategies. Our primary aim with UTMOM is to identify and reduce any discrepancies between actual and predicted vehicle counts. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)** & **Focus** & **Methodology** & **Key Contributions** \\ \hline [PERSON] and [PERSON] [10] & Sector-based time variables in traffic optimization & Computational modeling and scenario analysis & Importance of time variables in traffic management strategies \\ [PERSON] et al. [11] & Traffic optimization decision system to alleviate congestion & Iterative traffic prediction model & diversion and reduced road traffic pressure \\ [PERSON] [12] & Urban traffic management with MAS using sensor data & Multi-agent system (MAS) & High accuracy in forecasting and efficient traffic control \\ [PERSON] et al. [13] & Spatial patterns and factors of traffic dominance analysis & Spatial data analysis & Understanding urban traffic flow \\ [PERSON] et al. [14] & Enhancing traffic prediction with deep learning & Deep learning & Key approach for navigating data complexity in ITS \\ [PERSON] et al. [15] & Traffic signal optimization in connected vehicle environments & Data from connected vehicles & Optimization of traffic signals and flow \\ [PERSON] et al. [16] & Review of ML and DL techniques for traffic flow prediction & Machine learning (ML) and deep learning (DL) & Detailed comparison of ML and DL techniques for traffic prediction \\ [PERSON] [17] & Integration of machine learning and geospatial deep learning & Machine learning and geospatial deep learning & Enhance urban management through \\ [PERSON] et al. [18] & Bus travel time prediction across networks & Deep learning and heuristic models & Importance of multi-city tested spatiotemporal data mining benchmarks \\ [PERSON] et al. [19] & Deep learning applications in trajectory data management & Survey and comprehensive review & Discusses challenges and advancements in traffic analysis using spatiotemporal data mining \\ [PERSON] et al. [20] & Managing urban traffic congestion & Federated learning and deep learning & Optimizes ride-hailing services through personalized clustering \\ [PERSON] et al. [21] & Analyzing urban rail transit passenger flow & Advanced data mining techniques & Significance of big data and machine learning in urban transit management \\ [PERSON] et al. [22] & Multi-source data for short-term passenger flow forecasting & Deep learning & Leverages deep learning methods to model temporal dependencies in time-series data \\ [PERSON] et al. [23] & Clustering of Isfahan’s census blocks & Neural gas networks & Application of unsupervised machine learning techniques in urban development planning and traffic management \\ [PERSON] et al. [24] & Traffic flow prediction model & Machine learning & Provides stakeholders with powerful tools for urban planning and transportation management \\ This Study & Comparative analysis of traffic datasets using a mathematical model & Data mining and mathematical modeling with UTMOM & Innovative comparison and deviation analysis of traffic data \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)This endeavor encompasses more than just a dataset comparison; it entails an in-depth examination of traffic patterns, uncovering valuable insights into the intricate dynamics of urban traffic flows. UTMOM is designed to delve into the dataset, identifying subtle patterns and variations that might typically remain hidden. Our ultimate ambition is to enhance our understanding of traffic behavior, thereby facilitating the formulation of more effective traffic management strategies.