Work

Predicting safety attitudes in aviation maintenance using machine learning: An exploratory study

Year: 2025

Type: article

Abstract: This study explores the application of machine learning techniques in predicting safety attitudes among aviation maintenance personnel. Personality traits and demographic information are used for this purpose, with data obtained from an online dataset. The Random Forest machine learning algorithm was utilised to identify the relationships and to enable predictions. The obtained results indicated that extraversion had the most positive influence, followed closely by openness. On the other hand, neuroticism had the most negative impact. Total years of experience and experience in the current role are, on the other hand, the most influential demographic information. Combining personality traits with demographic information can improve safety attitude predictions. Nevertheless, definitive causal inferences cannot be established, as further analysis is required to verify the suitability of the Random Forest algorithm relative to other machine learning algorithms.

Source: Transportation Research Interdisciplinary Perspectives

DOI: 10.1016/j.trip.2025.101596

Authors: Christos Emexidis, Anna V. Chatzi, Kyriakos I. Kourousis

Institutions: National and Kapodistrian University of Athens, University of Limerick

Language: English

Topic: Occupational Health and Safety Research

Subfield: Radiological and Ultrasound Technology

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,400