Critical Connections: Enhancing Aviation Safety Through Network Analysis

Yıl 2024, Cilt: 7 Sayı: 2, 263 - 285

Ayşe Aslı Yılmaz

https://doi.org/10.51513/jitsa.1524747

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

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

Kaynakça

• Al-Taie, M.Z., Kadry, S. (2017). Theoretical Concepts of Network Analysis. In: Python for Graph and Network Analysis. Advanced Information and Knowledge Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-53004-8_1
• Bazargan, M., Guzhva, V., & Raghavan, S. (2022). Human Factors in Aviation: Analysis of Pilot Errors in Different Flight Phases. Journal of Aviation Safety Research, 14(3), 245-259.
• Beers B., (2022) “Which Major Expenses Affect Airline Companies?,” Investopedia (Investopedia, October14, 2022), https://www.investopedia.com/ask/answers/ 040715/what-are-major-expenses-affect-companies-airline-industry.asp.
• Bellamy, W. (2017, April 3). ICAO is not calling for cockpit video cameras in new airplanes. Avionics. Available from http://www.aviationtoday.com/2017/03/28/icao-not-calling-cockpit-video-cameras-new-airplanes/
• Betweenness Centrality. (2022). Betweenness Centrality. Retrieved November 3, 2022, from https://www.sci.unich.it/~francesc/teaching/network/betweeness.html.
• Bishop, J. (2018, September 12). You'll never guess which country has the most female pilots. Forbes. Available from https://www.forbes.com/sites/bishopjordan.
• Blondel, V.& Guillaume, J., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics Theory and Experiment. 2008. 10.1088/1742-5468/2008/10/P10008.
• Borgatti, S. (2005). Centrality and Network Flow. Social Networks. 27. 55-71. 10.1016/j.socnet.2004.11.008.
• Borgatti, S.P & Foster, P.C. (2003). A network paradigm in organizational research: A review and typology. Journal of Management, 29 (6) :991-1013.
• Bounfour, A. (2016). Acceluction: Stakes, Opportunities and Risks. In: Digital Futures, Digital Transformation. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-23279-9_8
• Budriene, D. & Diskiene, D. (2020). Employee Engagement: Types, Levels and Relationship with Practice Of HRM. Malaysian E Commerce Journal. 4. 42-47. 10.26480/mecj.02.2020.42.47.
• Burt, S. R. (2011). Neighbor networks: competitive advantage local and personal. USA: Oxford University Press
• Causse, M., Dehais, F., Péran, P., & Pastor, J. (2013). The effects of emotion on pilot decision-making: A neuroergonomic approach to aviation safety. Transportation Research Part C: Emerging Technologies.
• Davies, A. (2014, July 10). Why pilots hate the idea of cameras watching them fly. Wired. Available from https://www.wired.com/2014/07/malaysia-370-cockpit-camera/
• Dingus, T. A., et al. (2006). The 100-Car naturalistic driving study: Phase II - Results of the 100-Car field experiment. PsycEXTRA Dataset, 28-60.
• Dismukes, K., & Nowinski, J. (2007). Prospective memory, concurrent task management, and pilot error. In Human Factors in Aviation Operations.
• Eyre, B., & Stanton, N. (2021). Assessing the Impact of Experience on Pilot Error Rates in Complex Flight Operations. Ergonomics in Aviation, 12(4), 331-342. Federal Register. (2014, February 12). Prohibition on personal use of electronic devices on the flight deck. Available from https://www.federalregister.gov/documents/2014/02/12/2014-02991/prohibition-on-personal-use-of-electronic-devices-on-the-flightdeck
• Federal Aviation Administration. (2024). Addendum: Human factors in aviation maintenance. FAA Safety Team https://www.faasafety.gov/files/gslac/courses/content/258/1097/AMT_Handbook_Addendum_Human_Factors.pdf
• Gatta, A. (2018). Rethinking human error in aviation accidents: Regulatory and standard practices in the Nigerian aviation industry. International Journal of Arts, Languages and Business Studies, 1(1), 187-202.
• Groom, V., Velazquez, M., & Wickens, C. D. (2023). The Influence of Automation on Pilot Situational Awareness and Error Likelihood. Human Factors in Aviation Automation, 15(2), 180-197.
• International Civil Aviation Organization. (2019). State of Global Aviation Safety 2019. ICAO Publications. International Civil Aviation Organization (2024). Aircraft Accidents. https://www.icao.int/safety/iStars/Pages/Accident-Statistics.aspx
• Ison, D. C. (2005). Top 10 pilot errors. Plane & Pilot Magazine. Available from http://www.planeandpilotmag.com/article/top-10-pilot-errors/#.WQcZEoWcFUl
• Leclerc, J. (2007). MEMS for aerospace navigation. IEEE Aerospace and Electronic Systems Magazine.
• Li, G., Grabowski, J., Baker, S. P., & Rebok, G. W. (2006). Pilot error in air carrier accidents: Does age matter? Aviation Space and Environmental Medicine.
• Li, W. C., & Harris, D. (2006). Pilot error and its relationship with higher organizational levels: HFACS analysis of 523 accidents. Aviation Space and Environmental Medicine.
• Liu, S. Y., Chi, C. F., & Li, W. C. (2013). The application of Human Factors Analysis and Classification System (HFACS) to investigate human errors in helicopter accidents. Proceedings of the 10th International Conference on Engineering Psychology and Cognitive Ergonomics: Applications and Services - Volume Part II.
• Molesworth, B. R. C., & Estival, D. (2015). Miscommunication in general aviation: The influence of external factors on communication errors. Safety Science.
• NetworkX — NetworkX documentation. (2022). NetworkX — NetworkX Documentation. Retrieved November 4, 2022, from https://networkx.org/
• Rashid, A., Cottenier, T., Greenwood, P., & Joosen, W. (2010). Aspect-oriented software development in practice: Tales from AOSD-Europe. Computer.
• Rebok, G. W., Qiang, Y., Baker, S. P., & Li, G. (2009). Pilot age and error in air taxi crashes. Aviation, Space, and Environmental Medicine, 80(7), 647-651.
• Shappell, S. A., & Wiegmann, D. A. (2004, November). HFACS analysis of military and civilian aviation accidents: A North American comparison. In Proceedings of the Annual Meeting of the International Society of Air Safety Investigators, Gold Coast, Australia.
• Shappell, S., Detwiler, C., Holcomb, K., & Wiegmann, D. A. (2007). Human error and commercial aviation accidents: An analysis using the Human Factors Analysis and Classification System. Human Factors: The Journal of the Human Factors and Ergonomics Society.
• Williams, P., & Jackson, R. (2022). The Effectiveness of Crew Resource Management (CRM) Training in Reducing Pilot Error Rates. Aviation Psychology Review, 10(1), 98-113.
• Winter, S. R., Rice, S., Capps, J., & Baugh, B. S. (2020). An analysis of a pilot’s adherence to their personal weather minimums. Safety Science.