Evaluating the effects of automated monitoring on driver non-compliance at active railway level crossings

& Naweed, Anjum (2021) Evaluating the effects of automated monitoring on driver non-compliance at active railway level crossings. Accident Analysis & Prevention, 163, Article number: 106432.

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Description

Collisions between road users and trains at urban railway level crossings persist, despite active protection. The number of railway level crossings in most settings render their removal unfeasible. To effectively reduce or manage risk, alternative treatments are required. Increases in road and rail traffic invariably result in congestion issues at urban railway level crossings, which influences non-compliances by road users. Automated enforcement is one form of treatment that is being considered to reduce such non-compliances. This study conceptualised and adopted a before—after design to evaluate the effect of a conspicuous monitoring system on non-compliances by vehicular road users at an active level crossing. Baseline measurements of vehicle movements and level crossing status were recorded for two months. Conspicuous cameras and radar were subsequently installed, and a further month of data was recorded. Non-compliances with flashing lights were extracted and arranged into “must stop” and “should stop if safe to do so” categories, aligning with road rules at traffic lights. Non-compliances frequently occurred (N=1,086) with most (94%) of the latter category and ascribed to a lack of an advanced warning before crossing closure. Analysis with Generalised Linear Models revealed that non-compliances where drivers must stop reduced by 36% (from 13.4% to 8.6%) following the introduction of a conspicuous automated monitoring system, even though no actual enforcement was performed. This study suggests that non-compliances at railway level crossings have the potential to be reduced through the introduction of automated enforcement similar to the one used at traffic lights.

Impact and interest:

3 citations in Scopus
2 citations in Web of Science®
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ID Code: 213766
Item Type: Contribution to Journal (Journal Article)
Refereed: Yes
ORCID iD:
Larue, Gregoireorcid.org/0000-0001-8564-9084
Additional Information: Acknowledgements: While the authors received no financial support for the research, authorship and/or publication of this article, the authors are grateful to the Australasian Centre for Rail Innovation for funding the data collection used in this research (Project No. LC/1).
Measurements or Duration: 11 pages
Keywords: Railway level crossing, Human Factors, Automated monitoring, compliance, Safety
DOI: 10.1016/j.aap.2021.106432
ISSN: 0001-4575
Pure ID: 99426635
Divisions: Current > Research Centres > Centre for Future Mobility/CARRSQ
Current > QUT Faculties and Divisions > Faculty of Health
Current > Schools > School of Psychology & Counselling
Copyright Owner: 2021 Elsevier Ltd.
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Deposited On: 08 Oct 2021 15:16
Last Modified: 12 Nov 2026 04:07