Kritik Bağlantılar: Ağ Analizi ile Havacılık Güvenliğini Artırmak

Öz

Bu çalışmanın amacı, Ağ Analizi kullanarak farklı uçak tiplerinde insan faktörlerinin havacılık kazalarına etkisini incelemektir. Bu araştırma, daha önceki çalışmaların insan faktörlerinin rolünü kabul etmesine rağmen, merkezilik metriklerini analiz ederek en etkili hataları belirleyip, bu hataların havacılık güvenliği üzerindeki etkisini daha ayrıntılı bir şekilde anlamayı hedeflemektedir. Çalışmada Python'un NetworkX kütüphanesi kullanılarak Ağ Analizi yapılmıştır. NTSB havacılık kazası veri tabanından elde edilen bir veri seti kullanılmıştır. Her uçak tipi için iki modlu ağlar oluşturulmuş, düğümler kazaları ve insan hatası kategorilerini temsil ederken, kenarlar bu ilişkileri göstermektedir. Analiz, pilot hatasının tüm uçak tiplerinde en yüksek derece merkeziliğine sahip olduğunu ve kazalarda sıkça yer aldığını ortaya koymuştur. Yakınlık merkeziliği, pilot hatasının ağ üzerindeki önemli etkisini vurgulayarak merkezi rolünü daha da öne çıkarmıştır. Bulgular, havacılık güvenliğini artırmak için pilot hatalarının ele alınmasının kritik önemini vurgulamaktadır. Mürettebat hataları ve diğer insan hataları daha az sıklıkta görülmesine rağmen, verilerdeki varlıkları kapsamlı bir güvenlik stratejisinin bu faktörleri de dikkate alması gerektiğini göstermektedir. Bu çalışmanın sonuçları, pilot hatasının çeşitli uçak tiplerinde havacılık kazalarındaki en etkili insan faktörü olduğunu göstermektedir. Hedeflenmiş müdahaleler, örneğin geliştirilmiş pilot eğitimi ve daha sıkı güvenlik protokolleri gibi uygulamalara odaklanarak, havacılık endüstrisi kaza oranlarını önemli ölçüde azaltabilir

Anahtar Kelimeler

• Havacılık Emniyeti
• İnsan Faktörleri
• Network Analizi
• Merkezilik Ölçüleri

Critical Connections: Enhancing Aviation Safety Through Network Analysis

Öz

The objective of this study is to explore the influence of human factors on aviation accidents across different aircraft types using Network Analysis. This research specifically analyses centrality metrics to pinpoint the most influential errors, providing a more detailed understanding of their impact on aviation safety, while prior studies have acknowledged the role of human factors. The study employed Network Analysis using Python's NetworkX library. A dataset derived from the NTSB aviation accident database is used. Bipartite networks were constructed for each aircraft type, with nodes representing accidents and human error categories and edges indicating their relationships. The analysis revealed that pilot error consistently showed the highest degree centrality across all aircraft types, indicating its frequent involvement in accidents. Closeness centrality further highlighted the central role of pilot error, showing its significant influence on the network. The findings emphasized the critical importance of addressing pilot errors to improve aviation safety. While crew errors and other human errors were less frequent, their presence in the data suggested that a comprehensive safety strategy must also consider these factors. The results of this study demonstrate that pilot error is the most influential human factor in aviation accidents across various aircraft types. By focusing on targeted interventions such as enhanced pilot training and stricter safety protocols, the aviation industry can significantly reduce accident rates.

Anahtar Kelimeler

• Aviation Safety
• Human Factors
• Network Analysis
• Centrality Metrics

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