Cezalandırılmış Coğrafi Ağırlıklı Regresyon Modellerini Kullanarak Yolcu, Uçak Talebi ve Yük Miktarını Etkileyen Bölgesel Faktörlerin Analizi

Yıl 2024, , 19 - 30, 31.12.2024

Semra Türkan

https://doi.org/10.26650/JGEOG2024-1426209

Öz

Havayolu yolcu talebi, uçak talebi ve yük hacmi, havayolu şirketlerinin ve havalimanı yönetiminin ekonomik karar alma süreçlerinde en kritik faktörler arasında yer almaktadır. Havayolu yolcu talebini, uçak talebini ve yük hacmini etkileyen bölgesel faktörler iki ana gruba ayrılabilir: sosyo-ekonomik faktörler ve havayolu taşımacılığı faktörleri. Bu çalışmanın iki temel amacı vardır: Birincisi, bölgesel veriler olduğunda ve değişkenler arasında local çoklu bağlantı ile karşılaşıldığında cezalandırılmış modellerin kullanılmasının daha uygun olduğunu vurgulamak; ikinci olarak yolcu sayısı, uçak sayısı ve yük hacminin bölgesel sosyo-ekonomik göstergelerle ilişkili olup olmadığını araştırmak. Bu amaçla TÜİK'ten bölgesel göstergeler kullanılarak 48 şehire ilişkin havayolu istatistikleri ve ekonomik göstergeler elde edilmiştir. Model performans kriterlerine göre Coğrafi Ağırlıklı Lasso Regresyonu, verilerin analizi için en uygun model olarak belirlenmiştir. Bu çalışmanın bulguları, yolcu talebini, uçak talebini ve yük hacmini etkileyen en önemli faktörün bölgesel ekonomik büyümenin bir göstergesi olan ihracaatın olduğunu ortaya koymaktadır.

Anahtar Kelimeler

Cezalandırılmış coğrafi ağırlıklı regresyon, lokal çoklu bağlantı, havayolu istatistikleri

Analyzing Regional Factors Influencing Passenger, Aircraft Demand, and Freight Demand Using Penalized Geographically Weighted Regression Models

Öz

Airline passenger demand, aircraft demand, and cargo volume are among the most critical factors in the economic decision-making processes of airlines and airport management. Regional factors affecting airline passenger demand, aircraft demand and cargo volume can be divided into two main groups: socio-economic factors and air transport-related factors. This study has two main objectives: First, it emphasizes that when dealing with regional data and encountering local multicollinearity between variables, penalized models are more appropriate; second, to investigate whether the number of passengers, number of aircraft, and cargo volume are related to regional socioeconomic indicators. For this purpose, regional indicators from 48 provinces were obtained from TÜİK (Turkish Statistical Institute), including statistics on air transport and economic indicators. Based on the model performance criteria, Geographically Weighted Lasso Regression was determined as the most suitable model for data analysis. The findings reveal that the most important factor affecting passenger, aircraft, and cargo demand is exports, which is an indicator of regional economic growth.

Anahtar Kelimeler

Penalized geographically weighted regression, local multicollinearity, airline statistics

Kaynakça

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