###### Abstract  
This research introduces the Urban Traffic Mobility Optimization Model (UTMOM), a data-driven methodology for analyzing two distinctive urban traffic datasets through the integration of data mining and mathematical modeling. Designed to decode the complexities of urban mobility patterns, UTMOM meticulously evaluates daily traffic dynamics with a focus on reducing discrepancies and underscoring variations in traffic intensity, particularly during peak times. Our findings unveil pivotal insights into the differences across datasets, providing a substantial contribution to the realms of traffic management and urban planning. UTMOM delves into the intricacies of traffic flow variations, emphasizing the critical importance of comprehending fluctuations in traffic volume across diverse times and locations. By incorporating detailed graphical representations and statistical validations, including ANOVA analysis, our study delivers a comprehensive evaluation of UTMOM's precision in reflecting real-world traffic scenarios. These insights affirm the value of data-informed strategies in optimizing traffic flow and alleviating congestion. Positioned as a valuable asset for traffic engineers, data scientists, and urban planners, UTMOM advocates for advanced modeling techniques to improve urban mobility. Beyond enriching academic discourse on traffic analysis, UTMOM offers actionable intelligence for enhancing the efficiency and sustainability of urban transportation systems. Through this in-depth investigation, our aim is to catalyze the development of innovative solutions to traffic challenges, steering towards smoother and more sustainable urban environments. traffic data comparison; mathematical modeling; deviation analysis; traffic management; urban transportation; data mining +
Footnote †: journal: Medical Informatics  
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landscape is marked by a dynamic interplay of factors, including rapid population growth, expanding economic opportunities, and the relentless march of technological advancements. As Ankara extends its reach both horizontally, with sprawling suburban developments, and vertically, with the construction of towering skyscarepers, the city's transportation networks find themselves subjected to unprecedented demands and profound changes. The conventional transportation infrastructures of the past, designed to cater to a smaller and less interconnected urban population, are now grappling with the complexities of a sprawling metropolis. Consequently, addressing the multifaceted challenges posed by Ankara's evolving urban transportation landscape has become not only an imperative but also a catalyst for sustainable development and enhanced quality of life. This study embarks on a comprehensive exploration of Ankara's intricate traffic and transportation systems through the lens of UTMOM. Our goal is to illuminate the multifaceted challenges within the city's mobility landscape and offer innovative, data-driven solutions to enhance urban traffic management. UTMOM leverages advanced data mining and mathematical modeling to transcend the limitations of historical data and traditional methodologies. This research is driven by the belief that a nuanced understanding and effective addressing of Ankara's transportation complexities require integrating advanced data analytics and computational intelligence. At the core of our investigation is UTMOM, a sophisticated mathematical programming model designed to optimize transportation efficiency, alleviate congestion, and improve traffic flow in Ankara's urban environment. By conceptualizing transportation challenges as mathematical problems, UTMOM allows for a systematic analysis of various factors--traffic patterns, road capacities, urban development plans--to derive actionable, data-informed solutions.