Skip to content

Atlas / Learn / Papers / 09148886c5c74f46fbdb6110523911b880bfe13a

Semantic Scholar · Article (IET Image Processing)

Bibliometric analysis of human factors in aviation accident using MKD

Published 2021-03-09 From IET Image Processing 4 authors

Attribution

This is the abstract and citation. Full text lives at Semantic Scholar — we link out rather than host. All credit to the authors and IET Image Processing.

Abstract

Verbatim from Semantic Scholar. Not paraphrased, not summarized.

This study aims to provide a better understanding of human factors and human performance mechanism in aviation accident analysis by visualisation and taxonomy analysis using mapping knowledge domain (MKD). An overview of aviation accident analysis involving human factors is firstly conducted, followed by an introduction of several conceptual models and human reliability analysis methods. Finally, a specific framework for risk assessment of human factors in aviation is proposed. The human factors analysis and classification system structure combined with the quantitative method is the most frequently used mode in aviation accident analysis. From a macro ergonomic perspective, systematic consideration has been emphasised frequently in existing research. Pilots or individuals are thought to be associated with the organisations and environment they located in. On the other hand, the micro ergonomic aspect prefers a quantitative and probabilistic way to reveal the individual's failure mode or error rate. Based on previous studies, the proposed framework provides a systematic view to help with the evaluation of human performance in aviation, taking into account human issues as well as management and environmental factors.

Authors

  • Ming Wan
  • Ying Liang
  • Lixin Yan
  • Tuqiang Zhou

Keywords

  • Computer Science
  • Engineering
  • Environmental Science

Citation: Ming Wan, Ying Liang, Lixin Yan , et al. (2021). Bibliometric analysis of human factors in aviation accident using MKD. IET Image Processing. Semantic Scholar ID 09148886c5c74f46fbdb6110523911b880bfe13a. https://doi.org/10.1049/IPR2.12167 ↗