Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Abstract

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.

A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome

“…The is collected and filtered from PubMed to create a corpus and then clustered using the text mining approach proposed by [ 50 ]. Research by [ 10 ] classifies incident reports to improve aviation safety into two categories using an LSTM (Long Short-Term Memory) with attention. A total of 200,000 reports are preprocessed using NLTK, and word vectors are generated using ULMFiT (Universal Language Model Fine-tuning for Text Classification) [ 51 ].…”

“…Tool used NLTK [9][10][11][12][13], [14] Other [9,[15][16][17][18], [19][20][21], [13,[22][23][24], [25][26][27] Not declared [15,[28][29][30][31][32], [33][34][35][36][37], [38][39][40] Resources Scientific literature [12,15,28,29,41,42], [14,22,23,31], [24,43,44], [19][20][21]33,…”

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

“…AI and DL can streamline and automate customer services, analytics, machinery maintenance, and many other internal procedures and operations in the aviation industry. The most recent and relevant research contributions related to the use of DL techniques in the aviation industry field can be found in [127][128][129][130][131][132][133][134][135]. A comparison of prominent studies is presented in Table 7.…”

Identification of flight accidents causative factors base on SHELLO and improved entropy gray correlation method

“…Ni et al (2019) made civil aviation safety evaluation based on deep belief network and principal component analysis. Some scholars also use methods such as the establishment of structural equations, bayesian networks, machine learning, and bowtie models for aircraft fault safety diagnosis or safety assessment applications [ [16] , [17] , [18] , [19] , [20] ]. Many scholars have carried out a series of studies on new energy aircraft, human factors and text mining [ [21] , [22] , [23] ], found safety voice and safety listening during aviation accidents: cockpit voice recordings reveal that speaking-up to power is not enough [ 24 ].…”