Temporal evolution of aviation safety topics: comparing Sructural Topic Modeling and BERTopic on FAA Service Difficulty Reports
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Albertoli, Leslie
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Abstract
The aviation industry has achieved remarkable safety improvements despite increasing flight volumes and aircraft in service. With fatal accidents now extremely rare, commercial aviation stands as one of the safest transportation modes. However, the projected growth in air traffic presents an ongoing safety challenge. Indeed, even with current low incident rates, the sheer increase in flight numbers could lead to more incidents.
To maintain and further improve these safety standards, the industry increasingly relies on proactive risk analysis, which identifies and mitigates potential hazards before incidents occur. This approach leverages extensive textual incident data, including Federal Aviation Administration (FAA) Service Difficulty Reports (SDR), to learn from past events and implement preventive measures. Yet the manual analysis of millions of incident reports presents a significant challenge for safety analysts.
Natural Language Processing (NLP) has emerged as a powerful solution to this challenge, enabling automated processing and understanding of vast amounts of safety-related text data. Within the realm of NLP applications, topic modeling techniques, particularly Bidirectional Encoder Representations from Transformers (BERT) for topic modeling (BERTopic) and Structural Topic Model (STM), offer promising approaches for analyzing aviation safety data. More specifically, these methods can identify emerging safety trends and potential risks by tracking how incident report topics evolve over time. Building on these capabilities, the key objective of this research is to evaluate the effectiveness of STM and BERTopic in identifying safety-related topics within the SDR dataset and monitoring their temporal evolution. This analysis aims to enhance our understanding of emerging aviation safety patterns and potential risk factors.
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2024-12-06
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Text
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Thesis (Masters Degree)