Kentsel Trafik Tahminine Yönelik Derin Öğrenme Tabanlı Verimli Bir Hibrit Model

Year 2023, , 107 - 117, 30.04.2023

Anıl Utku

https://doi.org/10.17671/gazibtd.1167140

Cited By: 1

Abstract

Trafik yoğunluğu problemi, kentsel hayatın en önemli sorunlarından biri haline gelmiştir. Trafik yoğunluğu sebebiyle harcanan zaman ve yakıt, araç kullanıcıları ve ülkeler için önemli bir kayıptır. Trafikte geçen zamanı azaltmak amacı ile geliştirilen uygulamalar, uzun vadeli trafik yoğunluğu hakkında başarılı tahminlerde bulunamamaktadır. Kameralar, sensörler ve mobil cihazlar üzerinden elde edilen trafik verileri, trafik yönetimi sorununu çözebilmek amacıyla yapay zekâ teknolojilerinin kullanımını ön plana çıkarmaktadır. Bu çalışmada, trafik yoğunluk tahminine yönelik Convolutional Neural Network (CNN) ve Recurrent Neural Network (RNN) modelleri kullanılarak hibrit bir tahmin modeli geliştirilmiştir. Çalışmada, CNN ve RNN'in öne çıkan özelliklerinden faydalanmak amaçlanmıştır. CNN, özellik çıkarma aşamasında, RNN ise sıralı zaman serisi verileri üzerinde öğrenme ve tahmin için etkili bir modeldir. Bu yöntemler hibrit bir şekilde kullanılarak tahmin doğruluğunun arttırılması amaçlanmıştır. İstanbul Büyükşehir Belediyesi tarafından sunulan saatlik trafik yoğunluğu veri seti kullanılmıştır. Kullanılan veriseti 2321 farklı nokta için 2020 Ocak ile 2020 Aralık tarihleri arasındaki trafik yoğunluk bilgisini içermektedir. Geçen araç sayısı, Bağcılar Avrupa Otoyolu kavşağında daha yüksek olduğu için bu konum deneysel çalışmalarda kullanılmıştır. Seçilen konum için 9379 satır araç bilgisi bulunmaktadır. Geliştirilen hibrit model Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), CNN, RNN ve Long-Short Term Memory (LSTM) ile İstanbul’un 2020 yılına ait trafik verileri kullanılarak test edilmiştir. Deneysel sonuçlar, önerilen hibrit modelin karşılaştırılan modellere göre daha başarılı sonuçlara sahip olduğunu göstermiştir. Önerilen model kavşaktan geçen araç sayısı tahmininde 0,929 R2 değerine, kavşaktan geçen araçların ortalama hızlarının tahmininde ise 0,934 R-Squared (R2) değerine sahip olmuştur.

Keywords

trafik tahmini, makine öğrenmesi, derin öğrenme, CNN, RNN

Deep Learning Based an Efficient Hybrid Model for Urban Traffic Prediction

Abstract

The traffic density problem has become one of the most important problems of urban life. The time and fuel spent due to traffic density is a significant loss for vehicle users and countries. Applications developed to reduce the time spent in traffic cannot make successful predictions about long-term traffic density. Traffic data obtained from cameras, sensors and mobile devices highlights the use of artificial intelligence technologies in order to solve the traffic management problem. In this study, a hybrid prediction model has been proposed by using CNN and RNN models for traffic density prediction. The proposed hybrid model has been tested using LR, RF, SVM, MLP, CNN, RNN and LSTM and Istanbul's traffic data for 2020. Experimental results showed that the proposed hybrid model has more successful results than the compared models. The proposed model has 0.929 R2 in the prediction of the number of vehicles passing through the junction, and 0.934 R2 in the prediction of the average speed of the vehicles passing through the junction.

Keywords

traffic prediction, machine learning, deep learning, CNN, RNN

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