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OTOYOL TRAFİK AKIM KOŞULLARINI SINIFLAMADA K-ORTALAMALAR KÜMELEME YÖNTEMİ

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K-MEANS CLUSTERING METHOD TO CLASSIFY FREEWAY TRAFFIC FLOW PATTERNS

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Page Number-First: 
232
Page Number-Last: 
239

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Abstract (Original Language): 
Bu çalışmada; makroskobik bir trafik akım modeliyle oluşturulmuş akım koşulu farklılaşmalarını belirlemek amacıyla çok değişkenli kümeleme yöntemlerinin başarımları araştırılmıştır. Trafik verisindeki gürültüyü gidermek ve geniş saçılımı kabul edilebilir düzeye getirmek amacıyla, ham trafik değişkenleri modelleme öncesi filtrelenmiştir. Trafik akımı, iki fazlı bir temel eğriyi baz alarak hesap yapan hücre geçişi modeliyle benzetilmiştir. Seçilen otoyol kesimindeki akım dinamikleri, varolan akım koşullarını belirlemek amacıyla irdelenmiştir. Temel eğri üzerinde akım koşullarının sınıflanması, kesim yoğunluk değişkeni gözetilerek kümeleme yöntemleriyle aranmıştır. Hiyerarşik olmayan kümeleme yaklaşımları, örnek otoyol kesimi üzerindeki ani koşul değişimlerini tespit etmeye yarayan başarılı sınıflama sonuçları vermiştir. Çok değişkenli kümeleme yöntemlerince izlenen prosedür, sistematik olarak dinamiktir ve temel eğri üzerinde statik bölütleme yöntemiyle elde edilen kümelere oldukça yaklaşık kümeler oluşturabilmektedir. K-ortalamalar yöntemiyle elde edilen sonuçlar üzerinden hesaplanmış belirlenim katsayıları, elde edilen sonuçları istatistik yönden karşılaştırmalı olarak değerlendirmek amacıyla kullanılmıştır.
Abstract (2. Language): 
In this paper, performances of multivariate clustering methods in specifying flow pattern variations reconstructed by a macroscopic flow model are sought. In order to remove the noise in and the wide scatter of traffic data, raw flow measures are filtered prior to modeling process. Traffic flow is simulated by the cell transmission model adopting a two phase fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The classification of flow states over the fundamental diagram are sought utilizing the methods of partitional cluster analyses by considering the stretch density. The fundamental diagram of speed-density is plotted to specify the current corresponding flow state. Non-hierarchical or partitional clustering analysis returned promising results on state classification which in turn helps to capture sudden changes on test stretch flow states. The procedure followed by multivariate clustering methods is systematically dynamic that enables the partitions over the fundamental diagram match approximately with the flow patterns derived by the static partitioning method. The measure of determination coefficient calculated by using the K-means method is comparatively evaluated to statistically derive this conclusion.

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DOI: 
10.5505/pajes.2014.36449

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