Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15–17, 2023, Nanjing, China

Research Article

Research on Aircraft Runway Accident Investigation Report Based on LDA-Apriori Algorithm

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  • @INPROCEEDINGS{10.4108/eai.15-12-2023.2345370,
        author={Jianping  Bao},
        title={Research on Aircraft Runway Accident Investigation Report Based on LDA-Apriori Algorithm},
        proceedings={Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15--17, 2023, Nanjing, China},
        publisher={EAI},
        proceedings_a={PMBDA},
        year={2024},
        month={5},
        keywords={aircraft; accident investigation report; topic models (lda); association algorithm (apriori); cause of  the accident},
        doi={10.4108/eai.15-12-2023.2345370}
    }
    
  • Jianping Bao
    Year: 2024
    Research on Aircraft Runway Accident Investigation Report Based on LDA-Apriori Algorithm
    PMBDA
    EAI
    DOI: 10.4108/eai.15-12-2023.2345370
Jianping Bao1,*
  • 1: Civil Aviation Flight University of China
*Contact email: 821099895@qq.com

Abstract

In order to effectively prevent the occurrence of aircraft runway accidents, the improved text topic model (LDA) and association algorithm (Apriori) were used to dig out the causes of aircraft runway safety accidents and analyze the correlation between them. Firstly, 181 accident investigation reports were obtained after collecting nearly 20 years of aircraft runway accident investigation reports. Secondly, the collected text data was cleaned and the text was processed by the TF-IDF algorithm in the improved LDA model, and the ten topics of the accident text, the key feature words representing each topic and the probability distribution in the text were obtained. Finally, the association rules of each topic and subject term are analyzed, and the strong association rules are excavated to understand the key causal factors of aircraft runway accidents.