AdaBoost algoritmasını kullanarak demiryolu trafik yönetimi için seyir süresinin tahmini

Yıl 2022, Cilt: 24 Sayı: 1, 300 - 312, 05.01.2022

Mehmet Taciddin Akçay , Abdurrahim Akgundogdu , Hasan Tiryaki

https://doi.org/10.25092/baunfbed.937333

Cited By: 3

Öz

İstasyonlar arasında geçen seyir süresi belirlenirken bekleme süresi, hareket direnci, eğim, kurp, cer kuvveti, maximum hız, aracın kütlesi ve iki istasyon arası mesafe gibi bir takım tasarım parametreleri göz önünde bulundurulmaktadır. Bu parametreler aracın hareketine ait sistemin tanımının alt yapısını oluşturmaktadır. Ayrıca, hız profili oluşturulurken hat için tanımlanmış sefer sıklığının sağlanabilmesi için seyir süresine özellikle dikkat edilmelidir. Bu çalışmada şehiriçi metro sistemlerine ait istasyonlar arası seyir süresi değerinin makine öğrenmesi yöntemlerinden Adaptive Boosting yöntemi ile tahmini gerçekleştirilmiş ve iyi bilinen çeşitli yöntemler ile karşılaştırılmıştır. Kullanılan veriler çapraz doğrulama ve rastgele örnekleme yöntemleri ile önerilen modele uygulanmış ve belirleme katsayısı (R2) değerleri hesaplanmıştır.

Anahtar Kelimeler

AdaBoost, demiryolu, makine öğrenmesi, seyir süresi, sinyalizasyon.

Prediction of travel time for railway traffic management by using the AdaBoost algorithm

Öz

While determining the travel time between stations, a number of design parameters such as waiting time, motion resistance, slope, curve, traction force, maximum speed, vehicle mass, and distance between two stations are taken into consideration. These parameters form the infrastructure of the system definition of the motion of the vehicle. Furthermore, while creating the speed profile, special attention should be paid to the travel time in order to ensure the defined headway for the line. In this study, the travel time value between stations for intracity metro stations was predicted using the adaptive boosting method, which is one of the machine learning methods, and compared with various well-known methods. The data used were applied to the proposed model with the cross-validation and random sampling hold-out methods, and the values of the coefficient of determination (R2) were calculated.

Anahtar Kelimeler

AdaBoost, machine learning, railway, travel time, signalization

Kaynakça

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