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Articles

Investigating the formal countermeasures and informal strategies used to mitigate SPAD risk in train driving

, &
Pages 883-896 | Received 17 Aug 2014, Accepted 17 Dec 2014, Published online: 19 Jan 2015
 

Abstract

Various countermeasures are used to mitigate signal passed at danger (SPAD) events on railways, yet they continue. While risk factors that destabilise cognitive processes have been identified, less has been published on the relationship between these factors and the informal strategies that drivers themselves adopt to reduce individual SPAD risk. This study aimed to address this gap and used a participative approach to collect and thematically analyse data from 28 drivers across eight rail organisations in Australia and New Zealand. The results showed not all formal countermeasures were considered effective, and identified several informal strategies. These aimed to reduce task disruption, service distortion and maintain connectedness to signals. While some evidenced redundancies in the task and cab, others did not reduce baseline risk. This paper explores the relationship between the established risks and identified strategies towards evaluating the utility of formal and informal mitigations. The research has application to the investigation of collision risk in all transport domains.

Practitioner Summary: A participative approach was used to investigate SPAD mitigation techniques in train driving, and to explore risk-strategy relationship dynamics. Several informal strategies designed to reduce task disruption, service distortion and maintain signal connectedness were identified. While some evidenced redundancies in the task and cab, others did not reduce baseline risk.

Disclosure statement

The authors believe there are no conflicts of interest (financial or otherwise) that impinge on the quality or impartiality of the research in the paper.

Notes

1. Although 28 drivers participated in the study, 2 elected to generate an additional scenario.

Additional information

Funding

This work was supported by the CRC for Rail Innovation under Grant R2.116.

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