Kentsel Trafik Analizinde 3D/2D Hibrit CNN Mimarisi ve Dikkat Mekanizmalarının İncelenmesi

Abstract

Kentsel trafik yönetimi, artan nüfusa ve motorlu taşıt sayısına bağlı olarak her geçen gün daha da karmaşık hale gelmektedir. Trafik yoğunluğu veya yol durumlarının, zamanında ve doğru bir şekilde öngörülememesi durumunda; anlık değişen trafik olayları (kazalar, tıkanıklıklar vb.) tespit edilememekte ve müdahale süresinde gecikmeler yaşanmasına sebep olmaktadır. Bu durum ulaşımın güvenliği, çevre zararları ve ekonomik kayıplar açısından ciddi problemlerin doğmasına neden olmaktadır. Bu bağlamda klasik ve geleneksel yöntemler kullanılarak yapılan trafik analizleri yetersiz kalmaktadır. Ayrıca bu yöntemler kullanılarak büyük hacimli ve çok boyutlu veri setlerinin işlenmesi de mümkün olmamaktadır. Bu nedenle hem zamansal hem de mekânsal boyutlarda çıkarım yapabilen güvenilir ve güçlü yaklaşımlara ihtiyaç duyulmaktadır. Bu çalışma kapsamında, bluetooth sensörleri tarafından toplanan veriler kullanılarak 2D CNN, 3D CNN ve 3D/2D hibrit CNN mimarileri ile kentsel trafik yoğunluğu tahmini ve sınıflandırması yapıldı. 3D/2D hibrit CNN mimarisinin en yüksek açıklayıcılığa (R2=0.9286) sahip olduğu tespit edildi. Ayrıca 3D/2D hibrit CNN mimarilerinde dikkat mekanizmalarının kullanımı literatür temelli incelendi.

Keywords

• 2D CNN
• 3D CNN
• 3D/2D hibrit CNN
• dikkat mekanizmaları

Investigation of 3D/2D Hybrid CNN Architecture and Attention Mechanisms in Urban Traffic Analysis

Abstract

Urban 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 high-dimensional 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.

Keywords

• 2D CNN
• 3D CNN
• 3D/2D hybrid CNN
• attention mechanisms

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