Academic Journal

Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models

التفاصيل البيبلوغرافية
العنوان: Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models
المؤلفون: Dwitya Harits Waskito, Ludfi Pratiwi Bowo, Siti Hidayanti Mutiara Kurnia, Indra Kurniawan, Sinung Nugroho, Novi Irawati, Mutharuddin, Tetty Sulastry Mardiana, Subaryata
المصدر: Safety, Vol 10, Iss 1, p 8 (2024)
بيانات النشر: MDPI AG
سنة النشر: 2024
المجموعة: Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية: truck accidents, accident analysis, HFACS, human error, bayesian network, drivers, Industrial safety. Industrial accident prevention, T55-55.3, Medicine (General), R5-920
الوصف: 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.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 2313-576X
Relation: https://www.mdpi.com/2313-576X/10/1/8; https://doaj.org/toc/2313-576X; https://doaj.org/article/53ef544eec9f4c3eb0768326d57711b5
DOI: 10.3390/safety10010008
الاتاحة: https://doi.org/10.3390/safety10010008
https://doaj.org/article/53ef544eec9f4c3eb0768326d57711b5
رقم الانضمام: edsbas.4C48AE40
قاعدة البيانات: BASE
الوصف
تدمد:2313576X
DOI:10.3390/safety10010008