TY  - JOUR
PY  - 2024//
TI  - Analysing the impact of human error on the severity of truck accidents through HFACS and Bayesian network models
JO  - Safety (Basel)
A1  - Waskito, Dwitya Harits
A1  - Bowo, Ludfi Pratiwi
A1  - Kurnia, Siti Hidayanti Mutiara
A1  - Kurniawan, Indra
A1  - Nugroho, Sinung
A1  - Irawati, Novi
A1  - Mutharuddin, 
A1  - Mardiana, Tetty Sulastry
A1  - Subaryata, 
SP  - e8
EP  - e8
VL  - 10
IS  - 1
N2  - Truck accidents are a prevalent global issue resulting in substantial economic losses and human lives. One of the principal contributing factors to these accidents is driver error. While analysing human error, it is important to thoroughly examine the truck's condition, the drivers, external circumstances, the trucking company, and regulatory factors. Therefore, this study aimed to illustrate the application of HFACS (Human Factor Classification System) to examine the causal factors behind the unsafe behaviors of drivers and the resulting accident consequences. Bayesian Network (BN) analysis was adopted to discern the relationships between failure modes within the HFACS framework. The result showed that driver violations had the most significant influence on fatalities and multiple-vehicle accidents. Furthermore, the backward inference with BN showed that the mechanical system malfunction significantly impacts driver operating error. The result of this analysis is valuable for regulators and trucking companies striving to mitigate the occurrence of truck accidents proactively.<p />  <p>Language: en</p>
LA  - en
SN  - 2313-576X
UR  - http://dx.doi.org/10.3390/safety10010008
ID  - ref1
ER  -