Mikro Ölçekli Trafik Simülasyonu, Mikro Ölçekli Emisyon Modellemesi ve Görüntü İşleme Teknikleri Üzerine Bir Derleme

Abstract

Kentsel ölçekte araç emisyonlarının doğru tahmin edilmesi, etkili çevre yönetimi ve trafik planlaması için büyük önem taşımaktadır. Son yıllarda, mikroskobik trafik simülasyonu, mikroskobik emisyon modellemesi ve görüntü işleme tabanlı araç tespit yöntemlerini birleştiren entegre yaklaşımlar, tahmin doğruluğunu artırma potansiyelleri nedeniyle giderek daha fazla ilgi görmektedir. Bununla birlikte, her üç bileşeni bir çerçevede birleştiren çalışmalar literatürde oldukça sınırlı kalmaktadır. Bu derleme, araç tespiti için görüntü işleme tekniklerini (örn. Gerçek zamanlı nesne tanıma yöntemi olan You Only Look Once [YOLO]), Kalman Filtresi, optik akış), trafik akışı modellemesi için trafik simülasyon araçlarını (örn. Trafik mikro simülasyon yazılımı, Verkehr In Städten – Simulation [VISSIM]), kentsel hareketlilik simülasyonu (Simulation of Urban Mobility [SUMO]) ve karbondioksit (CO₂), karbonmonoksit (CO), azotoksit (NOₓ) ve partikül madde (PM) gibi emisyonların hesaplanması için çeşitli emisyon modellerini (örn. Passenger Car and Heavy Duty Emission Model (PHEM), Vehicle Emission Model [VERSIT+]) kullanan seçilmiş araştırmaları incelemektedir. Özellikle gerçek zamanlı uygulamalara ve farklı veri kaynaklarının bütünleştirilmesine odaklanmaktadır. Bu çalışma, mevcut hibrit yaklaşımların avantaj ve sınırlılıklarını ortaya koymakta ve trafik verilerini emisyon tahmin araçlarıyla bütünleştiren kapsamlı modellerin geliştirilmesine yönelik geleceğe dair öneriler sunmaktadır. Bununla birlikte, bu sistemlerin karar destek mekanizmalarına entegre edilmesiyle şehir yönetiminde etkinlik sağlanabilmesi konusunda fikirler sunmaktadır.

Keywords

• Trafik Simülasyonu
• Emisyon Modellemesi
• Görüntü İşleme
• Hava Kirliliği

A Review of Microscopic Traffic Simulation, Microscopic Emission Modeling, and Image Processing Techniques

Abstract

Accurately estimating vehicle emissions at the urban scale is crucial for effective environmental management and traffic planning. In recent years, integrated approaches that combine microscopic traffic simulation, microscopic emission modelling, and image processing-based vehicle detection techniques have gained significant attention due to their potential to enhance prediction accuracy. However, studies that holistically incorporate all three components within a single framework remain scarce in the literature. This review analyzes selected research that utilizes image processing methods (e.g., YOLO, Kalman Filter, Optical Flow) for vehicle detection, traffic simulation platforms (e.g., VISSIM, SUMO) for modelling traffic flow, and various emission models (e.g., PHEM, VERSIT+) for estimating pollutants such as CO₂, CO, NOₓ, and PM. The study particularly emphasizes real-time applications and the integration of heterogeneous data sources. By examining the strengths and limitations of existing hybrid approaches, the paper highlights current challenges and identifies future research opportunities for developing comprehensive, data-driven systems that integrate traffic dynamics with emission estimation tools. Furthermore, it explores how these integrated systems can be leveraged as part of intelligent decision-support mechanisms to enhance urban management and sustainability efforts.

Keywords

• Traffic Simulation
• Emission Modeling
• Image Processing
• Air Pollution

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