Investigation into Human Factors in Rear-End Crashes of Commercial Trucks Based on Human Factors Analysis and Classification System with Bayesian Networks Fusion Method

Abstract

Rear-end crashes of commercial trucks (ReC-CTs) account for the main type of truck traffic crashes, and human factors are important influencing factors that cause ReC-CTs. This study aims to investigate systematic human factors involved in ReC-CTs and further explore relationships between human factors at all levels and induced paths of unsafe acts. In this study, a total of 320 in-depth investigation cases of ReC-CTs in China from 2015 to 2022 were collected, and a novel systematic approach integrating the Human Factors Analysis and Classification System (HFACS) with Bayesian networks (BN) was proposed to identify and quantitatively analyze the human factors of ReC-CTs. The analysis of results leads to the following conclusions: 1) An improved HFACS model was constructed to identify 38 human factors related to ReC-CTs and to conduct a classification analysis at a systemic level; 2) The new systems-based method that integrates HFACS with BN, which can highlight the interrelationships among causal categories at various levels, is an effective method to quantitatively analyze the human factors of ReC-CTs; and 3) The influence relationships between unsafe acts and factors at various levels were quantitatively analyzed at a systemic level; the important influencing factors of each level that lead to unsafe acts were identified, and the most likely induced path for each unsafe act was determined. The research results can provide important guidance for effectively controlling the significant human factors at all levels of HFACS and for the targeted formulation of preventive measures for ReC-CTs.