Article ; Online: The use of Bayesian Belief Networks (BBNs) to probe deeper into railway safety management systems - Two studies from Great Britain and Italy.
2023 Volume 109, Page(s) 103968
Abstract: The importance of Safety Management Systems (SMS) to the railway industry is underlined by the fact that all organisations operating on UK railways are required by law to have one. Analysing SMSs can provide a reliable systemic tool to identify hazards ... ...
| Abstract | The importance of Safety Management Systems (SMS) to the railway industry is underlined by the fact that all organisations operating on UK railways are required by law to have one. Analysing SMSs can provide a reliable systemic tool to identify hazards and weaknesses within complex systems like the railway, making it possible to significantly increase safety, reducing the odds of near misses and accidents. However, there is little empirical research evidence to determine the impact on safety of a structured SMS. The current paper describes two studies which use Bayesian Belief Networks (BBN) to conceptualise SMSs and their impact on front-line performance. The paper presents the usefulness of BBNs to compare complex systems and reconcile cultural differences within the railway industry, identifying factors that are deemed vital within Italy and Britain. The two studies allowed us to identify the most influential factors within a SMS and how they interact with each other, as well as the strength of the identified relationships. A BBN is particularly useful in estimating how changing some of the node states (e.g., by making safety leadership present) affected the other factors. The current study showed that safety leadership has an impact on the SMSs of the British and Italian railway industries. |
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| MeSH term(s) | Humans ; United Kingdom ; Bayes Theorem ; Accidents ; Safety Management ; Italy ; Railroads |
| Language | English |
| Publishing date | 2023-01-31 |
| Publishing country | England |
| Document type | Journal Article |
| ZDB-ID | 2003513-5 |
| ISSN | 1872-9126 ; 0003-6870 |
| ISSN (online) | 1872-9126 |
| ISSN | 0003-6870 |
| DOI | 10.1016/j.apergo.2023.103968 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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