Aviation is a complicated transportation system, and safety is of paramount importance because aircraft failure often involves casualties. Prevention is clearly the best strategy for aviation transportation safety. Learning from past incident data to prevent potential accidents from happening has proved to be a successful approach. To prevent potential safety hazards and make effective prevention plans, aviation safety experts identify primary and contributing factors from incident reports. However, safety experts’ review processes have become prohibitively expensive nowadays. The number of incident reports is increasing rapidly due to the acceleration of advances in information technologies and the growth of the commercial and private aviation transportation industries. Consequently, advanced text mining algorithms should be applied to help aviation safety experts facilitate the process of incident data extraction. This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. First, we prepare the data sets used for training, validation, and testing with approximately 200,000 qualified incident reports from the Aviation Safety Reporting System (ASRS). Then, we take an open-source natural language model, which is well trained with a large corpus of Wikipedia texts, as the baseline and fine-tune it with the texts in incident reports to make it more suited to our specific research task. Finally, we build and train an attention-based long short-term memory (LSTM) model to identify primary and contributing factors in each incident report. The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety.
Dong, T., Yang, Q., Ebadi, N., Luo, X. R., & Rad, P. (2021). Identifying incident causal factors to improve aviation transportation safety: Proposing a deep learning approach. Journal of Advanced Transportation, in press, Article 5540046. doi: 10.1155/2021/5540046
Journal of Advanced Transportation