Bulanık Mantık Destekli Trafik Yoğunluğu Tahmini

Abstract

Trafik yoğunluğu, modern şehirlerde bireysel seyahat sürelerini uzatan, çevresel kirliliği artıran ve ekonomik kayıplara yol açan önemli bir sorundur. İstanbul gibi büyük metropollerde ortalama trafik yoğunluğu %70'in üzerinde seyrederken, zirve saatlerde %85'e kadar çıkmaktadır. Artan nüfus ve araç sayısı, yalnızca günlük yaşamı zorlaştırmakla kalmayıp, şehir altyapısını da zorlamaktadır. Bu nedenle, trafik yoğunluğunu etkin bir şekilde yönetmek ve tahmin edebilmek, sürdürülebilir ulaşım ve akıllı şehir yönetimi açısından büyük önem taşımaktadır. Ancak, trafiği etkileyen faktörlerin dinamik ve belirsiz yapısı, geleneksel yöntemlerle yeterince doğru tahmin edilememektedir. Bu çalışmada, İstanbul Büyükşehir Belediyesi’nin (İBB) sağladığı 2022 yılına ait saatlik trafik yoğunluğu verileri ve Meteostat kaynaklı hava durumu verileri kullanılarak bulanık mantık tabanlı bir trafik yoğunluğu tahmin modeli geliştirilmiştir. Model, trafik yoğunluğunu etkileyen temel değişkenler olan araç sayısı, ortalama hız, sıcaklık ve yağış durumunu dikkate alarak Mamdani bulanık çıkarım yöntemi ile tasarlanmıştır. Geliştirilen model, doğrusal regresyon ve çoklu doğrusal regresyon gibi geleneksel istatistiksel yöntemlerle karşılaştırılmış ve daha düşük hata oranları ile bağımlı değişkendeki değişimi yüksek oranda açıklamıştır. Bu sonuçlar, bulanık mantık tabanlı sistemlerin belirsizlikleri daha iyi yönetebildiğini ve karmaşık trafik senaryolarında daha esnek ve gerçekçi tahminler üretebildiğini göstermektedir.

Keywords

• Trafik Yoğunluğu Tahmini
• Bulanık Mantık
• Akıllı Ulaşım Sistemleri

Fuzzy Logic-Based Traffic Congestion Prediction

Abstract

Traffic congestion is a major issue in modern cities, leading to longer individual travel times, increased environmental pollution, and economic losses. In large metropolitan areas like Istanbul, the average traffic congestion level exceeds 70%, reaching up to 85% during peak hours. The growing population and number of vehicles not only complicate daily life but also strain urban infrastructure. Therefore, effectively managing and predicting traffic congestion is crucial for sustainable transportation and smart city management. However, the dynamic and uncertain nature of factors influencing traffic makes it difficult to achieve accurate predictions using traditional methods. In this study, a fuzzy logic-based traffic congestion prediction model was developed using hourly traffic congestion data from the Istanbul Metropolitan Municipality (İBB) for 2022 and weather data from Meteostat. The model was designed using the Mamdani fuzzy inference method, considering key variables affecting traffic congestion, including vehicle count, average speed, temperature, and precipitation. The developed model was compared with traditional statistical methods such as linear regression and multiple linear regression and demonstrated a higher ability to explain variations in the dependent variable with lower error rates. These results indicate that fuzzy logic-based systems can better manage uncertainties and generate more flexible and realistic predictions in complex traffic scenarios.

Keywords

• Traffic Congestion Prediction
• Fuzzy Logic
• Intelligent Transportation Systems

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