Dash Cam videos on YouTube™ offer insights into factors related to moose-vehicle collisions

To gain a better understanding of the dynamics of moose-vehicle collisions, the authors analyzed 96 videos of moose-vehicle interactions recorded by vehicle dash-mounted cameras (Dash Cams) that had been posted to the video-sharing website YouTube™. Their objective was to determine the effects of road conditions, season and weather, moose behavior, and driver response to actual collisions compared to near misses when the collision was avoided. The authors identified 11 variables that were consistently observable in each video and that we hypothesized would help to explain a collision or near miss. The most parsimonious logistic regression model contained variables for number of moose, sight time, vehicle slows, and vehicle swerves (AICcw = 0.529). This model had good predictive accuracy (AUC = 0.860, SE = 0.041). The only statistically significant variable from this model that explained the difference between moose-vehicle collisions and near misses was ‘Vehicle slows’. The results provide no evidence that road surface conditions (dry, wet, ice or snow), roadside habitat type (forested or cleared), the extent to which roadside vegetation was cleared, natural light conditions (overcast, clear, twilight, dark), season (winter, spring and summer, fall), the presence of oncoming traffic, or the direction from which the moose entered the roadway had any influence on whether a motorist collided with a moose. Dash Cam videos posted to YouTube™ provide a unique source of data for road safety planners trying to understand what happens in the moments just before a moose-vehicle collision and how those factors may differ from moose-vehicle encounters that do not result in a collision.

• Record URL:
  https://doi.org/10.1016/j.aap.2018.02.020
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0001457518300824
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2018 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Rea, Roy V
  • Johnson, Chris J
  • Aitken, Daniel A
  • Child, Kenneth N
  • Hesse, Gayle
• Publication Date: 2018-9

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Photos; References; Tables;
• Pagination: pp 207-213
• Serial:
  • Accident Analysis & Prevention
  • Volume: 118
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Animal vehicle crashes; Crash analysis; Crash causes; Highway safety; Internet; Video
• Identifier Terms: YouTube
• Uncontrolled Terms: Moose-vehicle accidents
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01677050
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 30 2018 2:32PM