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Derin Öğrenme Tabanlı Trafik Yoğunluğu Tahmini: İstanbul İçin Bir Vaka Çalışması

Trafik yoğunluk tahmini, kullanıcıların daha iyi seyahat kararları verebilmeleri, trafik sıkışıklığının hafifletilmesi, zaman ve yakıt tasarrufu sağlanması ile trafik işlem verimliliğinin arttırılması açısından önemlidir. Akıllı ulaşım sistemlerinin gelişmesi ve yaygınlaşmasıyla birlikte trafik yoğunluğunun tahmin edilmesi giderek daha fazla ilgi görmeye başladı. Trafik yoğunluk tahmini, büyük ölçüde geçmiş ve gerçek zamanlı trafik verilerine bağlıdır. Sensörler, kameralar, mobil cihazlar ve sosyal medya gibi kaynaklarından anlık olarak büyük miktarlarda trafik verileri elde edilmektedir. Giderek artan trafik verileri, trafik yönetimi sorununu çözebilmek amacıyla yapay zekâ teknolojilerinin kullanımını ön plana çıkarmaktadır. Bu çalışmada, trafik yoğunluk tahminine yönelik LSTM tabanlı bir tahmin modeli geliştirilmiştir. Geliştirilen tahmin modeli LR, RF, SVM, MLP, CNN ve Recurrent Neural Network (RNN) ile İstanbul’un trafik verileri kullanılarak test edilmiştir. Deneysel sonuçlar, geliştirilen LSTM tabanlı modelin karşılaştırılan modellere göre daha başarılı sonuçlar ürettiğini ve kavşaktan geçen araç sayısı tahmininde 0,897 R2 değerine, kavşaktan geçen araçların ortalama hızlarının tahmininde ise 0,883 R2 değerine sahip olduğunu göstermiştir.

Anahtar Kelimeler:

Trafik yoğunluk tahmini, Makine öğrenmesi, Derin öğrenme, LSTM

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