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Application of structural topic modeling to aviation safety data

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  • Rose, Rodrigo L.
  • Puranik, Tejas G.
  • Mavris, Dimitri N.
  • Rao, Arjun H.

Abstract

Data-driven frameworks for analyzing aviation safety data have recently gained traction. Text-based machine learning techniques often rely purely on word frequency analysis to eliminate the innate subjectivity of human language, but more refined techniques like structural topic modeling (STM) attempt to simulate text generation to identify the thematic undertones of text corpora. This paper presents an application of STM to two text-based sets of aviation safety data, the Aviation Safety Reporting System (ASRS) and accident and incident reports published by the National Transportation Safety Board (NTSB). A framework for cleaning and pre-processing the datasets is discussed, including a brief discussion of bag-of-words and TF–IDF representations of narratives. The methodology behind STM is described, including techniques for selecting the optimal number of topics. The results of the STM analysis on the ASRS and NTSB datasets are presented, with a focus on the clarity and specificity based on most common words associated with topics. A brief exploration of the correlation between pairs of topic labels is also undertaken, including a visualization of narratives in 2-dimensional space. STM is found to show promise in identifying themes within technical datasets, with model performance increasing for more specific corpora that use precise and unique language.

Suggested Citation

  • Rose, Rodrigo L. & Puranik, Tejas G. & Mavris, Dimitri N. & Rao, Arjun H., 2022. "Application of structural topic modeling to aviation safety data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022001776
    DOI: 10.1016/j.ress.2022.108522
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    References listed on IDEAS

    as
    1. Zhang, Xiaoge & Mahadevan, Sankaran, 2021. "Bayesian network modeling of accident investigation reports for aviation safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Asadayoobi, N. & Taghipour, S. & Jaber, M.Y., 2022. "Predicting human reliability based on probabilistic mission completion time using Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Hesabi, Hadis & Nourelfath, Mustapha & Hajji, Adnène, 2022. "A deep learning predictive model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Chen, Thomas Ying-Jeh & Guikema, Seth David, 2020. "Prediction of water main failures with the spatial clustering of breaks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Zhang, Xiaoge & Mahadevan, Sankaran & Deng, Xinyang, 2017. "Reliability analysis with linguistic data: An evidential network approach," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 111-121.
    7. Pan, Xing & Wang, Huixiong & You, Weijia & Zhang, Manli & Yang, Yuexiang, 2020. "Assessing the reliability of electronic products using customer knowledge discovery," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    8. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    9. Saleh, J.H. & Marais, K.B. & Bakolas, E. & Cowlagi, R.V., 2010. "Highlights from the literature on accident causation and system safety: Review of major ideas, recent contributions, and challenges," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1105-1116.
    10. Margaret Roberts & Brandon Stewart & Tingley, Dustin & Edoardo Airoldi, 2013. "The structural topic model and applied social science," Working Paper 132666, Harvard University OpenScholar.
    11. Gu, Shuang & Li, Keping & Feng, Tao & Yan, Dongyang & Liu, Yanyan, 2022. "The prediction of potential risk path in railway traffic events," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Suo, Weilan & Wang, Lin & Li, Jianping, 2021. "Probabilistic risk assessment for interdependent critical infrastructures: A scenario-driven dynamic stochastic model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Rao, Arjun H. & Marais, Karen, 2020. "A state-based approach to modeling general aviation accidents," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    14. Xu, Zhaoyi & Saleh, Joseph Homer & Subagia, Rachmat, 2020. "Machine learning for helicopter accident analysis using supervised classification: Inference, prediction, and implications," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    15. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    16. Bai, Xiwen & Zhang, Xiunian & Li, Kevin X. & Zhou, Yaoming & Yuen, Kum Fai, 2021. "Research topics and trends in the maritime transport: A structural topic model," Transport Policy, Elsevier, vol. 102(C), pages 11-24.
    17. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2020. "A novel method for maintenance record clustering and its application to a case study of maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    Full references (including those not matched with items on IDEAS)

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