Advanced modelling and data analytics in aviation

A new study applies machine learning and natural language processing to aviation safety data from Socrata, the Australian Transport Safety Bureau, the National Transportation Safety Board, and the Aviation Safety Network, uncovering patterns behind accidents and near-misses. The research, posted on arXiv (2608.14746v1), uses deep learning, transformer-based architectures, topic modelling, causal inference, and interpretable AI to enhance incident analysis and decision-making for regulators, airlines, and policymakers.

arXiv:2608.14746v1 Announce Type: new Abstract: The aviation industry characterized by its stringent safety standards has seen a growing need for innovative approaches to enhance safety measures. Despite the vast accumulation of aviation safety data over time, its full potential in predicting and preventing incidents has not been fully realized. This research addresses this gap by applying machine learning ML and natural language processing NLP techniques to analyze aviation safety data from Socrata, the Australian Transport Safety Bureau ATSB , the National Transportation Safety Board NTSB , and the Aviation Safety Network ASN . By leveraging existing ML models, including deep learning and transformer-based architectures alongside NLP methods for mining aviation incident narratives, this study uncovers patterns contributing to safety related incidents such as accidents and near-misses. Additionally, it employs various topic modelling techniques to extract meaningful themes from unstructured safety reports, enhancing the interpretability of incident analysis. Causal inference techniques and interpretable AI frameworks are further explored to improve model transparency and trustworthiness. A key contribution of this work is the deployment of advanced ML methodologies in a structured aviation safety context, assessing their effectiveness and providing insights into their practical implementation. The findings offer valuable insights for aviation stakeholders, including regulators, airlines, and policymakers, by providing data-driven solutions that enhance incident analysis and decision making. Ultimately, this research supports the industry s ongoing efforts to minimize risks, improve passenger and crew security, and integrate AI driven methodologies into aviation safety management.