Applying human factors analysis and classification system for commercial vehicles crashes investigation and critical...

This study aims to develop a reliable and valid Human Factors Analysis and Classification System (HFACS) and Bayesian Network (BN) methodology to understand the causal factors of commercial vehicles (CMV) involved traffic crashes. The HFACS-CMV method has been established using learnings from 100 multi casualty crashes involving at least 10 fatalities. The extreme nature of such crashes ensures the existence of in-depth investigation reports which are necessary to generate sufficient data for HFACS analysis. By analyzing 100 road traffic investigation reports across 28 provinces in China from 2001 to 2021, the research employs odds ratio and BN which is able to quantitatively examine the relationships among contributing factors. The study identifies the highest frequencies of failures in establishing/implementing safety production systems, wrong responses to emergencies, and over speeding across four levels, 12 categories, and 53 sub-subcategories' HAFCS-CMV framework. Numerous associations between the upper and adjacent lower levels are revealed, especially between poor company supervision and government oversight across various subcategories. The HFACS-CMV with BN model highlights a critical failure route: inadequate government oversight leading to poor company supervision or poorly planned operations, resulting in substandard operator conditions and unsafe acts. Identifying associations and failure routes is crucial for developing effective countermeasures. Specific outcomes are limited by the crash reports used which were not generated with HFACS in mind, future research using HFACS informed crash reporting systems would be beneficial. However, the successful application of the method demonstrates the efficiency and applicability of HFACS-CMV as a robust method for understanding causal factors of road crashes.