Estimating Passenger Capacity of Ships with Linear Regression Models

Abstract

Accurate prediction of passenger ship capacity is essential for ship design, fleet management, and planning. In this study, verified technical and operational data from about 2,000 passenger ships were analyzed. The dataset included key variables such as passenger capacity (P), gross tonnage (GT), draft (T), length (L), beam (B), deadweight tonnage (DWT), and main engine power (EP). First, the distributions and correlations of all these variables were examined. Variables that most strongly affected passenger capacity and reduced the risk of multicollinearity were carefully selected for the model. As a result, both a simplified model (GT and T) and an extended model (GT, T, L, B, EP, DWT) were developed. Ordinary Least Squares (OLS) and robust regression methods were applied to both models. In the GT–T model, passenger capacity was predicted with R² ≈ 0.73, while in the extended model, the explanatory power improved to R² ≈ 0.75. The robust regression approach limited the influence of outliers, but overall results were very similar to those of the OLS model. Diagnostic tests confirmed that the assumptions of the models were met and that the error distributions were close to normal. These findings suggest that both simplified and extended regression models can serve as effective and reliable tools for passenger capacity estimation in engineering applications.

Keywords

• Passenger capacity estimation
• Linear regression models
• OLS regression
• Robust regression
• Maritime data analysis

Gemilerin Yolcu Kapasitesinin Doğrusal Regresyon Modelleri Kullanılarak Tahmini

Abstract

Yolcu gemisi kapasitesinin doğru bir biçimde öngörülmesi, gemi tasarımı, filo yönetimi ve planlama süreçleri açısından kritik öneme sahiptir. Bu çalışmada yaklaşık 2000 yolcu gemisine ait doğrulanmış teknik ve operasyonel veriler incelenmiştir. Veri seti, yolcu kapasitesi (P), gros tonaj (GT), draft (T), boy (L), en (B), deadweight tonaj (DWT) ve ana makine gücü (EP) gibi temel değişkenleri içermektedir. İlk aşamada tüm değişkenlerin dağılımları ve aralarındaki korelasyonlar analiz edilmiştir. Yolcu kapasitesini en güçlü biçimde etkileyen ve çoklu doğrusal bağlantı riskini azaltan değişkenler model geliştirme süreci için seçilmiştir. Bu doğrultuda, bir basitleştirilmiş model (GT ve T) ile bir genişletilmiş model (GT, T, L, B, EP, DWT) oluşturulmuştur. Her iki modelde de En Küçük Kareler (OLS) ve robust regresyon yöntemleri uygulanmıştır. GT–T modelinde yolcu kapasitesi yaklaşık R² ≈ 0,73 düzeyinde tahmin edilmiştir. Genişletilmiş modelde ise açıklayıcılık gücü R² ≈ 0.75’e ulaşmıştır. Robust regresyon yaklaşımı aykırı değerlerin etkisini sınırlamış, ancak genel sonuçlar OLS modeliyle büyük ölçüde paralellik göstermiştir. Tanısal testler, modellerin varsayımlarının karşılandığını ve hata dağılımlarının normale yakın olduğunu doğrulamıştır. Elde edilen bulgular, hem basitleştirilmiş hem de genişletilmiş regresyon modellerinin mühendislik uygulamalarında yolcu kapasitesinin tahmininde etkili ve güvenilir araçlar olarak kullanılabileceğini ortaya koymaktadır.

Keywords

• Yolcu kapasitesi tahmini
• Doğrusal regresyon modelleri
• OLS regresyonu
• Robust regresyon
• Denizcilik veri analizi

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