Toplu taşımada varış süresi tahmini için karar ağaçları tabanlı makine öğrenmesi modellerinin karşılaştırması

Öz

Bu çalışma, kentsel toplu taşıma sistemlerinde otobüslerin duraklar arası tahmini varış sürelerini (Estimated Time of Arrival, ETA) daha yüksek doğrulukla öngörebilmek amacıyla karar ağaçları tabanlı makine öğrenmesi algoritmalarının kapsamlı bir karşılaştırmasını sunmaktadır. MBTA tarafından sağlanan “Bus Arrival/Departure Times (2024)” veri seti kullanılarak ham zaman damgaları, sefer kimlikleri ve durak sıraları çok aşamalı bir ön işleme sürecinden geçirilmiş; her seferdeki tüm i < j durak kombinasyonlarını kapsayan durak çifti oluşturulmuştur. Bu yaklaşım, hem kısa mesafeli hem de uzun mesafeli segmentlerin aynı çerçevede modellenmesine olanak tanıyarak seyahat süresi dinamiklerinin daha ayrıntılı temsil edilmesini sağlamıştır. Çalışmada Karar Ağacı, Rastgele Orman, XGBoost, LightGBM ve CatBoost algoritmaları aynı eğitim ve test ayrımı altında değerlendirilmiş; performansları MAE, RMSE ve R² metrikleri ile ölçülmüştür. Üç farklı hat üzerinde yapılan deneylerde CatBoost, tüm modeller içinde en yüksek doğruluk seviyesine ulaşmıştır. Hat 01’de model RMSE = 261.59, MAE = 163.65 ve R² = 0.88; Hat 111’de RMSE = 128.24, MAE = 100.27 ve R² = 0.85; Hat CT2’de ise RMSE = 289.88, MAE = 151.67 ve R² = 0.86 değerlerini elde etmiştir. Eğitim süresi XGBoost ve LightGBM’e kıyasla daha uzun olmakla birlikte, CatBoost’un doğruluk, istikrar ve kategorik değişken işleme kapasitesiyle ETA tahmininde uygulanabilir bir üst seviye performans sunduğu görülmüştür.

Anahtar Kelimeler

• Varış Süresi Tahmini
• Karar Ağaçları
• Rastgele Orman
• LightGBM
• CatBoost
• XGBoost

Comparison of decision tree-based machine learning models for arrival time prediction in public transportation

Öz

This study presents a comprehensive comparison of decision tree-based machine learning algorithms to predict bus arrival times (ETAs) between stops with higher accuracy in urban public transportation systems. Using the "Bus Arrival/Departure Times (2024)" dataset provided by the MBTA, raw timestamps, trip IDs, and stop sequences were subjected to a multi-stage preprocessing process. A stop-pair structure encompassing all stop combinations i < j on each trip was created. This approach allows for the modelling of both short-distance and long-distance segments within the same framework, providing a more detailed representation of travel time dynamics. In this study, the Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost algorithms were evaluated under the same training-testing scenario, and their performance was measured using MAE, RMSE, and R² metrics. In experiments conducted on three different lines, CatBoost achieved the highest accuracy among all models. In Line 01, the model obtained RMSE = 261.59, MAE = 163.65, and R² = 0.88; in Line 111, RMSE = 128.24, MAE = 100.27, and R² = 0.85; and in Line CT2, RMSE = 289.88, MAE = 151.67, and R² = 0.86. Although the training time is longer compared to XGBoost and LightGBM, CatBoost has been shown to offer a feasible high-level performance in ETA estimation with its accuracy, stability, and categorical variable processing capacity.

Anahtar Kelimeler

• Estimated Time of Arrival
• Decision Trees
• Random Forest
• LightGBM
• CatBoost
• XGBoost

Kaynakça

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