Yel Acar, SumeyyeAri, Ali2026-04-042026-04-0420251302-09002147-9429https://doi.org/10.2339/politeknik.1752555https://hdl.handle.net/11616/108786Urban traffic management is becoming increasingly complex due to the growing population and the rising number of motor vehicles. When traffic density or road conditions cannot be predicted accurately and in a timely manner, dynamic traffic events (e.g., accidents, congestion, etc.) cannot be detected, which leads to delays in response times. This situation causes serious problems in terms of transportation safety, environmental harm, and economic losses. In this context, traffic analyses based on conventional and classical methods have become inadequate. Moreover, these methods are not capable of processing large-scale and highdimensional datasets. Therefore, there is a growing need for reliable and robust approaches that can make inferences in both temporal and spatial dimensions. In this study, urban traffic density prediction and classification were performed using data collected from Bluetooth sensors and employing 2D CNN, 3D CNN, and 3D/2D hybrid CNN architectures. Among these models, the 3D/2D hybrid CNN architecture achieved the highest explanatory power (R2 = 0.9286). In addition, the use of attention mechanisms in 3D/2D hybrid CNN architectures was examined based on a literature review.trinfo:eu-repo/semantics/openAccess2D CNN3D CNN3D/2D hybrid CNNattention mechanismsArticle10.2339/politeknik.1752555WOS:001607876700001Q40000-0002-5071-6790