Flight Data Monitoring (FDM) is the process by which data from on-board recorders, or so-called ‘black boxes’, is analysed after every journey to detect subtle trends which, if allowed to continue, would lead to an accident. An opportunity has been identified to advance the state of the art in FDM processes by coupling recorder data to established Human Factors methodologies so that issues arising from the strategically important human/machine-system interface can be better understood and diagnosed. The research has also identified a significantly underused source of recorder-data within the railway industry. Taking this data, the paper demonstrates how key areas of driver performance can be quantified using a simple behavioural cluster detection method coupled to sensitivity and response bias metrics. Faced with a class of operational accident that is increasingly human-centred, an underused source of data, and methods that can join it to established human performance concepts, the potential for detecting risks in advance of an accident are significant. This paper sets out to describe and demonstrate this potential.
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- School of Energy, Geoscience, Infrastructure and Society, Institute for Infrastructure & Environment - Professor
- School of Energy, Geoscience, Infrastructure and Society - Professor
Person: Academic (Research & Teaching)