Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents
Minor safety incidents on the railways cause disruption, and may be indicators of more serious safety risks. The following paper aimed to gain an understanding of the relationship between active and latent factors, and particular causal paths for these types of incidents by using the Human Factors A...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/36266/ |
| _version_ | 1848795254712434688 |
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| author | Madigan, Ruth Golightly, David Madders, Richard |
| author_facet | Madigan, Ruth Golightly, David Madders, Richard |
| author_sort | Madigan, Ruth |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Minor safety incidents on the railways cause disruption, and may be indicators of more serious safety risks. The following paper aimed to gain an understanding of the relationship between active and latent factors, and particular causal paths for these types of incidents by using the Human Factors Analysis and Classification System (HFACS) to examine rail industry incident reports investigating such events. 78 reports across 5 types of incident were reviewed by two authors and cross-referenced for interrater reliability using the index of concordance. The results indicate that the reports were strongly focused on active failures, particularly those associated with work-related distraction and environmental factors. Few latent factors were presented in the reports. Different causal pathways emerged for memory failures for events such a failure to call at stations, and attentional failures which were more often associated with signals passed at danger. The study highlights a need for the rail industry to look more closely at latent factors at the supervisory and organisational levels when nvestigating minor safety of the line incidents. The results also strongly suggest the importance of a new factor – operational environment – that captures unexpected and non-routine operating conditions which have a risk of distracting the driver. Finally, the study is further demonstration of the utility of HFACS to the rail industry, and of the usefulness of the index of concordance measure of interrater reliability. |
| first_indexed | 2025-11-14T19:29:10Z |
| format | Article |
| id | nottingham-36266 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:29:10Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-362662020-05-04T18:12:49Z https://eprints.nottingham.ac.uk/36266/ Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents Madigan, Ruth Golightly, David Madders, Richard Minor safety incidents on the railways cause disruption, and may be indicators of more serious safety risks. The following paper aimed to gain an understanding of the relationship between active and latent factors, and particular causal paths for these types of incidents by using the Human Factors Analysis and Classification System (HFACS) to examine rail industry incident reports investigating such events. 78 reports across 5 types of incident were reviewed by two authors and cross-referenced for interrater reliability using the index of concordance. The results indicate that the reports were strongly focused on active failures, particularly those associated with work-related distraction and environmental factors. Few latent factors were presented in the reports. Different causal pathways emerged for memory failures for events such a failure to call at stations, and attentional failures which were more often associated with signals passed at danger. The study highlights a need for the rail industry to look more closely at latent factors at the supervisory and organisational levels when nvestigating minor safety of the line incidents. The results also strongly suggest the importance of a new factor – operational environment – that captures unexpected and non-routine operating conditions which have a risk of distracting the driver. Finally, the study is further demonstration of the utility of HFACS to the rail industry, and of the usefulness of the index of concordance measure of interrater reliability. Elsevier 2016-09-10 Article PeerReviewed Madigan, Ruth, Golightly, David and Madders, Richard (2016) Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents. Accident, Analysis and Prevention, 97 . pp. 122-131. ISSN 0001-4575 HFACS System Analysis Rail Accident Investigation http://www.sciencedirect.com/science/article/pii/S0001457516303098 doi:10.1016/j.aap.2016.08.023 doi:10.1016/j.aap.2016.08.023 |
| spellingShingle | HFACS System Analysis Rail Accident Investigation Madigan, Ruth Golightly, David Madders, Richard Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents |
| title | Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents |
| title_full | Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents |
| title_fullStr | Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents |
| title_full_unstemmed | Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents |
| title_short | Application of Human Factors Analysis and Classification System (HFACS) to UK rail safety of the line incidents |
| title_sort | application of human factors analysis and classification system (hfacs) to uk rail safety of the line incidents |
| topic | HFACS System Analysis Rail Accident Investigation |
| url | https://eprints.nottingham.ac.uk/36266/ https://eprints.nottingham.ac.uk/36266/ https://eprints.nottingham.ac.uk/36266/ |