Can link analysis be applied to identify behavioural patterns in train recorder data?

Ailsa Strathie, Guy H Walker

Research output: Contribution to journalArticle

4 Citations (Scopus)
70 Downloads (Pure)

Abstract

Objective: A proof of concept analysis was conducted to establish whether link analysis could be applied to data from on-train recorders to detect patterns of behaviour that could act as leading indicators of potential safety issues.

Background: On-train data recorders capture data about driving behaviour on thousands of routine journeys every day, and offer a source of untapped data that could be used to offer insights into human behaviour.

Method: Data from seventeen journeys undertaken by six drivers on the same route over a sixteen hour period were analysed using link analysis, and four key metrics were examined: Number of links, Network Density, Diameter, and Sociometric Status.

Results: The results established that link analysis can be usefully applied to data captured from on-vehicle recorders. The four metrics revealed key differences in normal driver behaviour. These differences have promising construct validity as leading indicators.

Conclusion: Link analysis is one method that could be usefully applied to exploit data routinely gathered by on-vehicle data recorders. It facilitates a proactive approach to safety based on leading indicators, offers a clearer understanding of what constitutes normal driving behaviour, and identifies trends at the interface of people and systems, which is currently a key area of strategic risk.

Application: These research findings have direct applications in the field of transport data monitoring. They offer a means of automatically detecting patterns in driver behaviour that could act as leading indicators of problems during operation, and which could be used in the pro-active monitoring of driver competence, risk management and even infrastructure design.
Original languageEnglish
Pages (from-to)205-217
Number of pages13
JournalHuman Factors
Volume58
Issue number2
Early online date11 Dec 2015
DOIs
Publication statusPublished - Mar 2016

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