Kocaeli Akçaray Tramvay Sisteminin Bakım ve Onarım Modelinin Veri Madenciliği ile İyileştirilmesi

Abstract

Ulaşım birçok insanın günlük rutinleri arasında yer almakta ve insan hayatının büyük bir kısmı ulaşımda geçmektedir. Ulaşımın bu kadar önem kazandığı bir süreçte konforlu, güvenli ve hızlı ulaşım için araçların bakım ve onarımlarının düzenli olarak yapılması önem kazanmaktadır. Bu çalışmada Kocaeli Büyükşehir Belediyesi tramvay işletmeciliğinin bakım ve onarım sürelerinin iyileştirilmesi amaçlanmıştır. Bu nedenle Veri Madenciliği algoritmalarından K-NN (K-en yakın komşular) sınıflandırması, Karar Ağaçları ve Derin Öğrenme algoritmaları uygulanarak Bakım Onarım Veri Modeli tasarlanmıştır. Çalışmada kullanılan üç algoritmadan elde edilen sonuçlar karşılaştırılmış ve en iyi bakım süresi performansı K-NN algoritması ile elde edilmiştir. Böylece tramvayların garanti süreleri uzatılmakta ve arızalar arasındaki ortalama süre ve bakım çalışma periyotlarının uzunluğuna bakılarak tramvayların maksimum performansla çalışması sağlanmaktadır.

Keywords

• Veri Madenciliği
• K-NN
• Karar Ağaçları
• Derin Öğrenme
• Tramvay Bakım ve Onarımı

Improving the Maintenance and Repair Model of Kocaeli Akçaray Tram System with Data Mining

Abstract

Transportation is among the daily routines of many people and a large part of human life is spent in transportation. In a process where transportation has gained so much importance, it is important to regularly perform maintenance and repair of vehicles for comfortable, safe and fast transportation. In this study, it is aimed to improve the maintenance and repair times of Kocaeli Metropolitan Municipality tram operation. Therefore, Maintenance Repair Data Model is designed by applying K-NN (K-nearest neighbors) classification, Decision Trees and Deep Learning algorithms from Data Mining algorithms.Thus, the warranty periods of the trams are extended and the trams operate with maximum performance by looking at the mean time between failures and the length of maintenance work periods.Quantitative findings revealed that the K-NN algorithm achieved the highest performance with an accuracy rate of 87.6%, outperforming the Decision Tree 85.95% and Deep Learning 70.66% models. Moreover, the K-NN model demonstrated the most balanced classification performance, with a precision of 0.716, recall of 0.637, and an F1-score of 0.652. In contrast, the Deep Learning algorithm exhibited lower performance, with an F1-score of only 0.355, indicating its limited effectiveness when applied to structured maintenance data. These results suggest that in cases where the dataset is structured and relatively small in scale, simpler non-parametric models such as K-NN may offer more efficient and practical solutions for predicting maintenance durations.

Keywords

• Data Mining
• K-NN
• Decision Trees
• Deep Learning
• Tram Maintenance and Reapir

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