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Journals / Anadolu İktisat ve İşletme Dergisi / 2026 / Cilt: 10 Sayı: 2

Predicting Ticket Prices Per Flight Kilometer in Airline Operations Using Machine Learning Algorithms

Havayolları İşletmelerinde Makine Öğrenmesi Algoritmaları ile Seferlerin Uçuş Kilometre Başına Bilet Fiyatlarının Tahmini

Ali Akbaba(ISTANBUL TOPKAPI ÜNİVERSİTESİ)0000-0003-1745-8029
Nil Konyalilar(İSTANBUL TOPKAPI ÜNİVERSİTESİ)0000-0001-2345-6789
Pages
109–125
Publication Date
2026-10-10
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results indicate that RFR model achieved the highest predictive performance under both data-splitting scenarios.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to predict price per kilometer (PPK) in the Turkish domestic airline market using machine learning methods and to compare the predictive performance of different algorithms.
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Key Findings & Data:It uses 1,014 flight observations from three airlines operating in the Turkish domestic market and employs linear regression (LR), support vector regression (SVR), and random forest regression (RFR) models.
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Conclusion & Impact:Results indicate that RFR model achieved the highest predictive performance under both data-splitting scenarios.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Havayolu fiyatlandırması · kilometre başına bilet fiyat · makine öğrenmesi

Abstract

This study aims to predict price per kilometer (PPK) in the Turkish domestic airline market using machine learning methods and to compare the predictive performance of different algorithms. It uses 1,014 flight observations from three airlines operating in the Turkish domestic market and employs linear regression (LR), support vector regression (SVR), and random forest regression (RFR) models. The models were evaluated under 80%-20% and 70%-30% training-test split scenarios using MSE, RMSE, MAE, and R² metrics. Results indicate that RFR model achieved the highest predictive performance under both data-splitting scenarios. Furthermore, Feature Importance and SHAP analyses revealed that special days, flight distance, and the number of days remaining until the flight were the most influential variables in PPK prediction. Findings demonstrate that PPK can serve as an alternative indicator for comparing routes with different flight distances and that machine learning-based PPK prediction can support airlines’ revenue management and decision-support processes.

Özet

Bu çalışmada, Türkiye iç hat havayolu pazarında kilometre başına bilet fiyatının (PPK) makine öğrenmesi yöntemleriyle tahmin edilmesi ve farklı algoritmaların performanslarının karşılaştırılması amaçlanmaktadır. Araştırmada, üç havayoluna ait 1014 uçuş gözlemi kullanılmış; LR (lineer regresyon), SVR (destek vektör regresyonu) ve RFR (rastgele orman regresyonu) modelleri uygulanmıştır. Modeller %80-%20 ve %70-%30 eğitim-test senaryolarında değerlendirilmiş ve MSE, RMSE, MAE ve R² ölçütleri kullanılmıştır. Bulgular, her iki veri bölünmesinde de RFR modelinin en yüksek tahmin performansını gösterdiğini ortaya koymuştur. Ayrıca Feature Importance ve SHAP analizleri, özel günler, uçuş mesafesi ve uçuş tarihine kalan gün sayısının PPK tahmininde göreli en önemli değişkenler olduğunu göstermiştir. Çalışma, PPK'nın farklı uzunluktaki rotaların karşılaştırılmasında kullanılabilecek alternatif bir gösterge olduğunu ve makine öğrenmesi tabanlı PPK tahmininin havayollarının gelir yönetimi ve karar destek süreçlerine katkı sağlayabileceğini ortaya koymaktadır.

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Cite This Article

Akbaba, A., Konyalilar, N. (2026). Predicting Ticket Prices Per Flight Kilometer in Airline Operations Using Machine Learning Algorithms. Anadolu İktisat ve İşletme Dergisi, Cilt: 10 Sayı: 2, 109–125. https://doi.org/10.59293/anadoluiid.2000767

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