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Can Link Analysis Be Applied to Identify Behavioral Patterns in Train Recorder Data?

Strathie, Ailsa and Walker, Guy H. (2016). Can Link Analysis Be Applied to Identify Behavioral Patterns in Train Recorder Data? Human Factors: The Journal of the Human Factors and Ergonomics Society, 58(2) pp. 205–217.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1177/0018720815613183
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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 behavior that could act as leading indicators of potential safety issues.
Background: On-train data recorders capture data about driving behavior on thousands of routine journeys every day and offer a source of untapped data that could be used to offer insights into human behavior.
Method: Data from 17 journeys undertaken by six drivers on the same route over a 16-hr period were analyzed 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 behavior. 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 behavior, 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 behavior that could act as leading indicators of problems during operation and that could be used in the proactive monitoring of driver competence, risk management, and even infrastructure design.

Item Type: Journal Item
Copyright Holders: 2015 Human Factors and Ergonomics Society
ISSN: 0018-7208
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetEP/I036222/1EPSRC (Engineering and Physical Sciences Research Council)
Keywords: leading indicators; link analysis; graph theory; data recorders; driving
Academic Unit/School: Faculty of Arts and Social Sciences (FASS) > Psychology and Counselling > Psychology
Faculty of Arts and Social Sciences (FASS) > Psychology and Counselling
Faculty of Arts and Social Sciences (FASS)
Item ID: 46175
Depositing User: Ailsa Strathie
Date Deposited: 28 Apr 2016 12:46
Last Modified: 07 Aug 2019 04:38
URI: http://oro.open.ac.uk/id/eprint/46175
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