This study uncovers patterns contributing to safety related incidents such as accidents and near-misses by applying machine learning (ML) and natural language processing (NLP) techniques to analyze aviation safety data from Socrata, the Australian Transport Safety Bureau, the National Transportation Safety Board, and the Aviation Safety Network.
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.
Emerging technologies are reshaping safety performance across all transportation domains, transforming traditional risk models and redefining the interaction between humans, systems, and complex operational environments. From artificial intelligence and machine learning to advanced sensors, digital twins, predictive analytics, and autonomous platforms, these technologies offer unprecedented opportunities to enhance situational awareness, optimise decision-making, and strengthen organisational resilience. At the same time, their rapid integration introduces new socio-technical challenges, including algorithmic opacity, demands for human–machine coordination, cybersecurity vulnerabilities, and the need for a workforce capable of navigating increasingly intelligent systems. This paper examines the evolving role of emerging technologies in transportation safety, analysing how their adoption both augments and complicates human performance in safety-critical operations. The analysis begins by exploring how emerging technologies enable a shift from reactive to predictive and preventative safety paradigms. AI-driven data analytics now allow organisations to detect weak signals, forecast failure patterns, and identify precursors to incidents long before they manifest operationally. Advanced sensing technologies, such as real-time physiological monitoring, (Light Detection and Ranging) LiDAR, and high-fidelity environmental perception systems, enable continuous surveillance of operational risk conditions. Digital twins provide dynamic, real-world simulations that support scenario testing, training optimisation, and risk assessment. Together, these tools significantly expand system observability and provide a foundation for proactive risk management across aviation, rail, maritime, and surface transport. The paper then critically evaluates the human factors implications associated with this technological advancement. As systems become more autonomous and decision pathways increasingly algorithmic, human operators are required to manage higher levels of abstraction, supervise complex machine behaviour, and integrate diverse streams of automated information. These shifts can reduce manual proficiencies, intensify monitoring burdens, and create new forms of cognitive strain, especially during system anomalies or degraded modes. Trust calibration becomes central: both over-reliance and under-reliance on intelligent technologies pose risks to safety performance. Successful integration requires human-centred design, explainable AI interfaces, transparent communication architectures, and training that strengthens resilience, critical thinking, and understanding of system limitations. Organisational and regulatory implications are also analysed. Transportation safety frameworks, such as Safety Management Systems (SMS), Fatigue Risk Management Systems (FRMS), and competency-based training programmes, must evolve to incorporate technology-driven safety indicators, algorithmic risk assessments, and new classifications of human–machine interaction hazards. Regulatory bodies face the challenge of establishing performance requirements, certification pathways, and oversight mechanisms for systems that are no longer deterministic. Organisations must also invest in workforce capabilities that align with emerging roles in AI supervision, data-driven decision support, and autonomous operations management. The paper concludes by proposing a multi-layered safety model that integrates emerging technologies with human-centred practices, emphasising resilience engineering, adaptive training, transparent AI governance, and continuous learning across transportation ecosystems. It argues that technological innovation must be framed not as a replacement for human expertise but as an enabler of enhanced human performance. The long-term success of emerging technologies in transportation safety depends on designing systems, organisations, and regulatory structures that maintain human responsibility, support human cognitive strengths, and ensure safe coordination between people and intelligent machines. This paper contributes a cross-domain, resilience-oriented synthesis that positions emerging technologies as socio-technical amplifiers rather than deterministic safety solutions.
This study formally evaluates the effectiveness of AI models over multiple iterations of the models’ architecture for the domain-specific application of automated construction hazard assessment from multimodal inputs and introduces and validates high-fidelity, game engine-based synthetic images as a solution.
Trevor Neece, A. Fascetti· Journal of computing in civi...· 1 citation· ⚡1
This systematic review analyzes 21 peer-reviewed articles (2021–2025) from ScienceDirect, Elsevier, and IEEE Xplore to examine methodological advances in road safety research. Findings reveal a paradigm shift from retrospective crash analysis to proactive, data-driven approaches, with machine learning (ML) and deep learning (DL)—particularly ensemble methods such as Random Forest, XGBoost, and neural networks—achieving crash detection accuracies of 85–92%. Explainable AI (XAI) frameworks, especially SHAP, enhance model interpretability, while hybrid and ensemble models improve predictive stability. Real-time monitoring via IoT sensors, connected vehicles, and computer vision enables surrogate safety evaluations using conflict-based metrics. Despite these advances, challenges remain regarding data heterogeneity, model transferability, privacy, and computational demands. Future directions include integrating autonomous vehicles, implementing standardized data-sharing platforms, and deploying automated safety countermeasures to transition from prediction to proactive prevention.
Artificial intelligence, when responsibly implemented, represents a transformative adjunct to traditional safety practices – capable of significantly improving construction site safety performance globally – but it must be deployed in tandem with organizational commitment, worker training, and robust safety cultures.
Musaed M. Al-Thubaiti, Saeed S. Al-Shahrani, Ryan A. Alsaihaty· World Journal of Advanced En...· 0 citations
Job Safety Analysis (JSA) and pre-task planning can benefit from prior incident records, yet historical accident data is often stored as unstructured narratives that are difficult to consult at the point of planning. A novel framework centered on large language models (LLMs) for highway construction safety reporting and planning is proposed as a foundation for future agentic applications, prioritizing deterministic, local inferencing. The first aim is to enable classification and quality scoring of incident narratives for existing and future reporting purposes. The second is to evaluate retrieval of relevant historical accidents, related imagery, and trusted industry documents for incorporation into daily safety plans. Neural probes were trained to classify incidents along four multiclass and two binary Occupational Injury and Illness Classification System (OIICS) fields and to derive an overall quality score, evaluated on a test set of over 15,000 narratives and a held-out set of 100 author-labeled records, benchmarked against a majority-vote LLM ensemble. The retrieval of historical accidents, reference imagery, and industry documents was benchmarked across embedding models using standard information retrieval metrics. OIICS classification reached 75% held-out accuracy, though the two binary flags were degenerate. The quality score, while meaningful on one database, was distorted on out-of-distribution fatalities in the held-out dataset. Accident retrieval recovered relevant incidents far above chance, performing best on lexically distinct construction activities. On document question answering, an open-weight decoder embedding model surpassed proprietary models. Overall, this work provides a new framework rooted in local inferencing and text embedding models for future agentic applications, with emphasis on bridging external data to JSA reports.
M. Smetana, Trevor Neece, Lev Khazanovich· 0 citations
In civil engineering, Artificial Intelligence (AI) is a significant engineering tool that accelerates analysis, improves monitoring reliability, and enables more effective solutions for engineering decision-making throughout a building’s life cycle, particularly under conditions of nonlinear behavior and complex data. This review identifies the most significant AI achievements from 2023 to early 2026 and presents a task-oriented roadmap. The roadmap covers structural analysis, structural condition monitoring, damage detection, design optimization, and project management. It highlights how deep learning, graph models, physically-based learning, and surrogate modeling are making complex engineering workflows more efficient. Additionally, the review explores the relationship between optimization practices and multi-objective search using surrogate models, as well as the roles of large models in risk triage and evidence management. While summarizing these achievements, the article also identifies several implementation challenges, including data heterogeneity, limited generalizability of results across contexts, limited interpretability, and inconsistencies in standardization and regulatory requirements. Furthermore, priority areas for additional research and implementation are discussed.
Olena Hasii, G. Gasii, Serhii Hudz et al.· IOP Conference Series: Earth...· 0 citations