Kablosuz manyetik sensörler kullanarak karar ağacı algoritma tabanlı araç sınıflandırmasının gerçekleştirilmesi

Abstract

Kablosuz sensör ağları kullanarak akıllı ulaşım sistemleri (Intelligent Transportation Systems, ITS) tasarlamak, hem maliyet hem de enerji verimliliği açısından avantajlı olup herhangi bir yolun trafiğini gözlemlemek, o yol hakkında trafik bilgisi edinmek veya sadece araçları tespit edip tipleri ve hızlarını saptamak son zamanlarda araştırmacıların ilgi odağı haline gelmiştir. Bu çalışmada sensör düğümü, manyetometre, güç kartı ve pilden oluşan ve diğer çalışmalarda kullanılan düğümlerden daha doğru ve anlaşılır veriler sunabilen bir sensör devresi kullanılmıştır. Bu sensör devreleri ile aracın tipini belirlemek için iki farklı yöntem sunulmuştur. İlk yöntemde, yoldan geçen araçlar, önerilen algoritma ve  (Manyetik İmza Uzunluğu) paremetresine göre otomobil, minibus, otobüs ve kamyon olarak sınıflandırılmıştır. Bu yöntemle elde edilen doğruluk payı %89 olmuştur. Diğer yöntemde ise araç sınıflandırması, makine öğrenmesi algoritması olan J48 kullanılarak yapılmış ve önerilen yöntem esas alınarak elde edilen sonuçların eniyilemesi yapılmıştır. Bir makine öğrenmesi yazılım paketi olan Weka'da uygulanan J48 sınıflandırma algoritmasını kullanır. Karar ağacı modeli, 3 eksenli HMC5983L manyetik sensöründen geçen araçlardan çıkarılan manyetik ham veri, ölçüm süresi gibi bir dizi özellikten oluşturulmuştur. Özellikler, çapraz geçerlilik temelinde değişen sınıflandırma oranları derecelerine sahip bir karar ağacı modeli üretmek için J48 eğitim algoritmasına doğru sınıflandırmalarla sağlanan niteliklerdir. Makine öğrenmesi algoritması olan J48 kullanımı araç sınıflandırmasında daha verimli ve doğru sonuçlar verdiği görülmüştür. İlk yöntemle elde edilen  değerleri hesaplama aşamasında zorluklar doğurmuştur. Ancak J48 algoritması kullanılarak daha belirgin ve hassas sınır ve eşik değerleri elde edilmiştir. Çalışmanın sonucu, araç sınıflandırma sisteminde önerilen algoritmanın eniyilemesiyle yaklaşık % 100 doğruluk payı ile etkili ve verimli olduğunu göstermektedir.

Keywords

• Kablosuz sensör ağları,Manyetik sensör,Araç sınıflandırması,Makine öğrenmesi algoritması,Manyetik imza uzunluğu

Implementation of the vehicle classification based-on decision tree algorithm using wireless magnetic sensors

Abstract

The design of Intelligent Transportation Systems (ITS) using wireless sensor networks to observe any road traffic, get road information, or just identify road vehicles has recently become an interesting and popular research topic because of its advantages in cost and energy efficiency. To perform this study, sensor circuit consisting of sensor node, magnetometer, power board and battery, is used. This sensor structure presents more accurate and intelligible results than sensor nodes used in other studies. Two different methods have been proposed to determine the type of vehicle with these sensor circuits. In the first method, vehicles passing by the road are classified as cars, minibuses, buses and trucks according to the proposed algorithm and  (Magnetic Signature Length) parameters. The accuracy achieved with this method was 89%. In the other method, vehicle classification was performed using machine learning algorithm J48 which is a machine learning decision tree extension and the obtained results were optimized based on the proposed method. It uses the J48 classification algorithm implemented in Weka, a machine learning software package. The Decision Tree model was built from a series of features like magnetic raw data, measurement time derived from vehicles passing through the 3-axis HMC5983L magnetic sensor. The properties are those provided by the correct classification into the J48 training algorithm to produce a decision tree model with grading ratios that vary on the basis of cross validity. The use of J48, a machine learning algorithm, has been shown to yield more efficient and accurate results in vehicle classification. The MSL values obtained by the first method have caused difficulties in the calculation process. However, by using the J48 algorithm, more specific and sensitive boundary and threshold values were obtained. The result of the study illustrates that the vehicle classification system is so effective and efficient with an accuracy rate of about 100% with optimization of the proposed system.

Keywords

• Wireless sensor networks,Magnetic sensor,Vehicle classification,Machine learning algorithm,Magnetic signature length

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