Advanced modelling and data analytics in aviation

• URL: http://arxiv.org/abs/2608.14746v1
• Date: Fri, 14 Aug 2026 00:12:41 GMT
• Title: Advanced modelling and data analytics in aviation
• Abstract summary: This research addresses the gap by applying machine learning (ML) and natural language processing (NLP) techniques to analyze aviation safety data.<n>It uncovers patterns contributing to safety related incidents such as accidents and near-misses.<n>The findings offer valuable insights for aviation stakeholders, including regulators, airlines, and policymakers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy [64.39232788946173]
  Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth.<n>This paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study.
  arXiv  Detail & Related papers  (2026-02-07T23:15:58Z)
• Utilizing AI for Aviation Post-Accident Analysis Classification [0.0]
  The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis.<n>This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data.<n>The findings demonstrate that both NLP and deep learning, as well as TM, can significantly improve the efficiency and accuracy of aviation safety analysis.
  arXiv  Detail & Related papers  (2025-05-30T19:15:04Z)
• Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports [0.0]
  This study employs Natural Language Processing (NLP) and Deep Learning models, including LSTM, CNN, Bidirectional LSTM (BLSTM), and simple Recurrent Neural Networks (sRNN) to classify flight phases in safety reports from the Australian Transport Safety Bureau (ATSB)<n>The models exhibited high accuracy, precision, recall, and F1 scores, with LSTM achieving the highest performance of 87%, 88%, 87%, and 88%, respectively.
  arXiv  Detail & Related papers  (2025-01-14T08:18:41Z)
• Applications of natural language processing in aviation safety: A review and qualitative analysis [0.0]
  This study explores using Natural Language Processing in aviation safety.<n>It focuses on machine learning algorithms to enhance safety measures.<n>There are currently 34 Scopus results from the keyword search natural language processing and aviation safety.
  arXiv  Detail & Related papers  (2025-01-03T07:36:10Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
  We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context.<n>We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
  arXiv  Detail & Related papers  (2024-07-31T17:59:24Z)
• Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models [0.0]
  This paper compares two prominent topic modeling techniques, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) LDA demonstrates higher topic coherence, indicating stronger semantic relevance among words within topics. NMF excelled in producing distinct and granular topics, enabling a more focused analysis of specific aspects of aviation accidents.
  arXiv  Detail & Related papers  (2024-03-04T01:41:07Z)
• Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis [63.532413807686524]
  This paper addresses the problem of maintaining safety during training in Reinforcement Learning (RL) We propose a new architecture that handles the trade-off between efficient progress and safety during exploration.
  arXiv  Detail & Related papers  (2023-12-18T16:09:43Z)
• Aviation Safety Risk Analysis and Flight Technology Assessment Issues [0.0]
  It focuses on two main areas: analyzing exceedance events and statistically evaluating non-exceedance data. The proposed solutions involve data preprocessing, reliability assessment, quantifying flight control using neural networks, exploratory data analysis, and establishing real-time automated warnings.
  arXiv  Detail & Related papers  (2023-08-10T14:13:49Z)
• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements [76.80453043969209]
  This survey presents a framework for safety research pertaining to large models. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models. We explore the strategies for enhancing large model safety from training to deployment.
  arXiv  Detail & Related papers  (2023-02-18T09:32:55Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.