Improving Aviation Incidents using Association Rule Mining Algorithm and Time Series Analysis

Abesamis, Pierre Pauline R.; Bulos, Remedios de Dios; Ching, Michelle Renee D.

doi:10.1088/1757-899x/946/1/012005

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Scholars have devoted a significant amount of attention to aviation accidents and incidents at the global [6], national [7][8][9][10][11][12], and operational [13][14][15] levels. Recommendations for managing aviation safety might be provided by analyzing trends or statistical patterns of events.…”

Section: Introductionmentioning

confidence: 99%

Trend Analysis of Civil Aviation Incidents Based on Causal Inference and Statistical Inference

He,

Sun

2023

Aerospace

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The efficient management of aviation safety requires the precise analysis of trends in incidents. While classical statistical models often rely on the autocorrelation of indicator sequences for trend fitting, significant room remains for performance improvement. To enhance the accuracy and interpretability of trend analyses for aviation incidents, we propose the Causal-ARIMA model, which is grounded in causal inference theory, and we employ four distinct modeling strategies to fit the trend of incidents in China’s civil aviation sector between 1994 and 2020. The objective is to validate the performance of the Causal-ARIMA model and identify optimal trend analysis strategies. The four modeling strategies account for causation factors, stationarity, and causality with operational volume, incorporating models like AR, ARMA, ARIMA, and Causal-ARIMA. Our findings reveal that ensemble techniques incorporating the Causal-ARIMA model (Strategy 2 and 3) outperform classical trend analysis methods (Strategy 1) in terms of model fit. Specifically, the causality-based binary fitting technique (Strategy 3) achieves the most uniformly dispersed fitting performance. When the premises for using the Causal-ARIMA model are relaxed, applying it to variables without Granger causal relationships results in uneven model performance (Strategy 4). According to our study, the Causal-ARIMA model can serve as a potent tool for the analysis of trends in the domain of aviation safety. Modeling strategies based on the Causal-ARIMA model provide valuable insights for aviation safety management.

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