Örüntü Tanıma ve Öznitelik Seçme Yöntemleri Kullanarak Kısa Zaman Sonraki Yol Trafik Hız Öngörüsü

Bu çalışmada İstanbul Büyükşehir Belediyesi’nin web sitesininden alınan RTMS (Remote Traffic Microwave Sensor) cihazlarının hız ölçüm değerleri kullanılarak ileriye yönelik trafik hızı tahmin edilmiştir. Örüntü tanıma yöntemi olarak k-En Yakın Komşu (kNN) ve Karar Destek Makinesi (SVM) kullanılmıştır. Bir sensöre ait hız verilerinin değişik zamanlarda alınarak yapılan trafik hızı öngörüsüne ek olarak bu sensöre yakın sensörlerin hız bilgileri alınarak ve yüksek bağıntıya sahip sensörlerin hız bilgileri alınarak hız öngörüsü yapılmıştır. Yapılan testler sonucunda genel olarak SVR metodunun KNN metodundan daha başarılı olduğu görülmüştür. Yakın konumlardaki veya yüksek bağıntılı sensör verisi kullanılarak yapılan tahminlerin ise bir sensör verisi kullanılarak yapılan tahminlerden daha iyi sonuç verdiği görülmüştür.

In this study we predict traffic speed on Istanbul roads using RTMS (Remote Traffic Microwave Sensor) speed measurements obtained from the Istanbul Municipality web site from 327 different sensor locations. We do speed predictions 5 minutes to an hour ahead and use SVM (Support Vector Machine) and kNN (k Nearest Neighbor) methods for speed prediction. We find out which other sensors could be used to predict the speed at a certain sensor location and show that especially for nearby/correlated sensors, it is possible to get better results using related sensor measurements in addition to the sensor being predicted.

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