CQUniversity
Browse

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

Download (897.64 kB)
Version 2 2022-12-19, 04:15
Version 1 2022-02-15, 00:11
journal contribution
posted on 2022-12-19, 04:15 authored by Gregoire S Larue, Anjum NaweedAnjum Naweed
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.

Funding

Category 3 - Industry and Other Research Income

History

Volume

163

Start Page

1

End Page

11

Number of Pages

11

ISSN

0001-4575

Publisher

Elsevier

Language

en

Peer Reviewed

  • Yes

Open Access

  • No

Acceptance Date

2021-09-30

External Author Affiliations

Queensland University of Technology (QUT), Centre for Accident Research and Road Safety – Queensland, Brisbane

Author Research Institute

  • Appleton Institute

Era Eligible

  • Yes

Journal

Accident Analysis and Prevention

Article Number

106432