Implementation of Artificial intelligence (AI) in Transport Accident Investigations

Abstract

Transport accident investigations are crucial for understanding causal factors, improving system safety, and preventing future incidents. Traditionally, these investigations rely on a multidisciplinary process involving human expertise, manual data analysis, and narrative reconstruction. However, with the growing complexity of transportation systems and the increasing volume of operational data—from flight data recorders, cockpit voice recordings, sensor logs, to surveillance systems—the limitations of manual analysis are becoming evident. This paper explores the emerging role and potential of Artificial Intelligence (AI) in augmenting and transforming transport accident investigations across aviation, maritime, rail, and roadway domains.AI technologies such as machine learning, natural language processing (NLP), and computer vision are proving to be powerful tools in extracting patterns, identifying anomalies, and drawing correlations from large datasets that are otherwise time-consuming and error-prone for human analysts. This paper examines several case studies and research projects where AI-assisted tools have been piloted or implemented in post-accident analysis. These include automated speech recognition for cockpit voice recordings, anomaly detection in flight trajectories, and sentiment analysis of maintenance logs. Findings indicate that AI can significantly reduce investigation timeframes, increase objectivity in evidence evaluation, and uncover hidden contributing factors—particularly in cases involving complex system interactions or human-machine interface failures.Despite its promise, the implementation of AI in accident investigations is not without challenges. One critical concern is transparency and explainability. Unlike traditional analytical methods, AI models—especially deep learning systems—can function as "black boxes," making it difficult for investigators, regulators, and courts to interpret how a conclusion was reached. This raises questions about the admissibility of AI-generated evidence and its alignment with legal and ethical standards in safety investigations. The paper emphasizes the need for human-in-the-loop approaches where AI augments, rather than replaces, expert judgment. Human oversight remains essential in contextual interpretation, ethical reasoning, and final decision-making.Furthermore, the integration of AI into accident investigation agencies requires cultural and organizational shifts. Investigators need training not only in technical AI tools but also in data literacy, interdisciplinary collaboration, and understanding the biases that AI models may inherit from their training data. This paper proposes a roadmap for implementation, including phased adoption, validation protocols, inter-agency cooperation, and regulatory support.In conclusion, AI has the potential to revolutionize transport accident investigations by enhancing speed, depth, and predictive capability. However, its integration must be guided by principles of transparency, accountability, and collaboration between technologists and human factors experts. As transportation systems evolve toward greater automation and data dependence, leveraging AI in accident investigations is not only beneficial but essential for ensuring the continued integrity and learning capacity of safety-critical systems.

Keywords: Artificial Intelligence, Accident Investigation, Human-in-the-Loop, Transportation Safety, Explainable AI, Predictive Analytics

DOI: 10.54941/ahfe1006922