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Road User Video Evidence of Road Traffic Offences: Preliminary Analysis of Operation Snap Data and Suggestions for a Research Agenda

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  • Farrell, Graham

    (University of Leeds)

  • Lovelace, Robin
  • O'Hern, Steve

Abstract

This study uses data from Operation Snap (OpSnap), the UK police’s national system to receive road users’ video evidence of road traffic offences. Data from one police force area for 39 months (January 2021 to March 2024) (N = 20,364 records) is analysed. Half were submitted by vehicle drivers (49.8%), a third by cyclists (34.7%), 7.2% by pedestrians, 2.2% by horse riders, 0.2% by motorcyclists, and 5.8% were unknown. We estimate that, relative to road distance travelled, cyclists were 20 times more likely to submit video evidence than vehicle drivers. The most common offences overall were driving ‘without reasonable consideration to others’ or ‘without due care and attention’. Half (53.5%) of reported cases resulted in the recommended disposal of an educational course, % no further action 12.6% conditional offer, and 1.6% resulted in court appearance. A research agenda using OpSnap data is outlined that could emerge if national datasets are compiled and responsibly opened-up and made available for research and policy-making: data-driven research should identify hotspot locations and other correlates of dangerous and antisocial road use at regional, and local levels; research projects should investigate disposal-related decision-making, video quality, and the role of supporting evidence; offence concentration (recidivism, repeat submitters of evidence, spatial hotspots) and case progression including court cases should be explored with reference to new video evidence. We conclude that datasets derived from publicly-uploaded video submission portals have the potential to transform evidence-based policy and practice locally, nationally and internationally.

Suggested Citation

  • Farrell, Graham & Lovelace, Robin & O'Hern, Steve, 2024. "Road User Video Evidence of Road Traffic Offences: Preliminary Analysis of Operation Snap Data and Suggestions for a Research Agenda," SocArXiv cgjmr, Center for Open Science.
  • Handle: RePEc:osf:socarx:cgjmr
    DOI: 10.31219/osf.io/cgjmr
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    References listed on IDEAS

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    1. Mohamed R. Ibrahim & James Haworth & Nicola Christie & Tao Cheng & Stephen Hailes, 2021. "Cycling near misses: a review of the current methods, challenges and the potential of an AI-embedded system," Transport Reviews, Taylor & Francis Journals, vol. 41(3), pages 304-328, May.
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