Analysis of Commercial Airplane Accidents Worldwide Using K-Means Clustering
Bibliographic record
Abstract
Despite the Bureau of Transportation Statistics affirming the relative safety of air travel, with the lowest annual accident rate among various transportation modes, the importance of analyzing and mitigating aviation accidents remains paramount for the sustained safety and comfort of air travelers.This study leverages data from the Bureau of Aircraft Accident Archives (BAAA-acro) website, transformed into a dataset that encapsulates commercial airplane accident data spanning the period from 1918 to 2020.The dataset, comprising 110 observations across four variables, was subjected to K-means clustering to categorize the causes of airplane accidents.The optimal number of clusters for this analysis was determined using the Silhouette index.The investigation focused on two accident groups within the dataset.The first cluster, consisting of 106 observations, demonstrated a considerable degree of heterogeneity, indicative of a broad distribution and significant variation.The second cluster, comparatively smaller, comprised only four observations.The clustering exercise underscored that technical factors predominantly contribute to commercial airplane accidents.The findings of this study thus suggest that future efforts by aviation regulatory bodies to decrease aviation accident occurrences could benefit significantly from a concerted focus on these technical factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".