## 6 Conclusions
Additionally, the innovative weighting system requires further validation across varied urban settings to confirm its broad applicability. The efficacy of UTMOM in real-time traffic prediction and management also needs additional investigation, presenting a challenge for its direct integration into urban planning initiatives. Future research should focus on enhancing UTMOM's utility by integrating real-time data analytics and incorporating additional variables such as weather conditions and special events, potentially improving the model's predictive power. Collaborative efforts with urban planners and policymakers will be crucial in translating UTMOM's insights into actionable strategies within real-world settings. H.U. : Data collecting, Investigation, Writing--review and editing, Validation. M.A.Y. : Methodology, Software, Investigation, Writing--original draft. K.Y. : Data collecting, Writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The datasets used during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to privacy. We extend our heartful gratitude to Gazi University's Graduate School of Natural and Applied Sciences/Department of Traffic Planning and Implementation for their invaluable academic support and insights. Special thanks to Limak Cement Factory for providing essential data and insights, which were pivotal to our research. We are also grateful to the project research team for their dedication and expertise, Ankara Governorship and the Provincial Directorate of Security for their collaboration, and the Ankara Metropolitan Municipality for their assistance. This research was supported by GPD Inc. under a joint service protocol with Gazi University, to whom we owe our gratitude for enabling this study on urban traffic flow optimization through the Urban Traffic Mobility Optimization Model. The authors declare no conflict of interest. References  
[PERSON]; [PERSON]; [PERSON] Network Traffic Big Data Prediction Model Based on Combinatorial Learning. In Proceedings of the 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), Newark, CA, USA, 4-9 April 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 252-256. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Daily long-term traffic flow forecasting based on a deep neural network. _Expert Syst. Appl._**2019**, _121_, 304-312. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Network-wide traffic flow estimation with insufficient volume detection and crowdsourcing data. _Transp. Res. Part C Emerg. Technol._**2020**, _121_, 102870. [CrossRef]
* [PERSON] (2021) [PERSON] Data-Driven Approaches for Traffic State and Emission Estimation. Linkoping Studies in Science and Technology. Ph.D. Dissertation, No. 2144. Linkoping University, Norrkoping, Sweden, 2021. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model. _Mathematics_**2023**, _11_, 4075. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns. _Sustainability_**2019**, _11_, 5452. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China. _Front. Sustain. Cities_**2021**, \(3\), 1-15. [CrossRef]
* [PERSON] and [PERSON] (2022) [PERSON]; [PERSON]; [PERSON] Research on highway traffic flow prediction model and decision-making method. _Sci. Rep._**2022**, _12_, 19919. [CrossRef] [PubMed]
* [PERSON] (2022) [PERSON] Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data. _Sensors_**2022**, _22_, 208. [CrossRef] [PubMed]
* [PERSON] et al.