Skip to main content
eScholarship
Open Access Publications from the University of California

Animal-Vehicle Collision Data Collection Throughout the United States and Canada

  • Author(s): Huijser, Marcel P.
  • Wagner, Meredith E.
  • Hardy, Amanda
  • Clevenger, Anthony P.
  • Fuller, Julie A.
  • et al.
Abstract

Animal-vehicle collisions affect human safety, property and wildlife, and the number of animal-vehicle collisions has substantially increased across much of North America over the last decades. Systematically collected animal-vehicle collision data help estimate the magnitude of the problem and help record potential changes in animal-vehicle collisions over time. Such data also allow for the identification and prioritization of locations that may require mitigation. Furthermore, systematically collected animal-vehicle collision data allow for the evaluation of the effectiveness of mitigation measures in reducing the number of animal-vehicle collisions. In the United States and Canada, animal-vehicle collision data are typically collected and managed by transportation agencies, law enforcement agencies and/or natural resource management agencies. These activities result in two types of data: data from accident reports (AR data) and data based on animal carcass counts (AC data). Here we report on a survey that examined the extent to which AR and AC data are collected across the United States and Canada. While a substantial percentage of the DOTs and DNRs collect and manage AR and/or AC data, many of them do not. Furthermore, DOTs and DNRs that do collect or manage AR or AC data typically do this for different or only partly overlapping reasons. In addition, DOTs and DNRs use different reporting thresholds, have varying search and reporting effort, and only have partial overlap in the parameters recorded. These differences also occur between DOTs and between DNRs, and oftentimes one and the same organization collects inconsistent data as certain parameters may only be recorded ‘sometimes’. These differences and inconsistencies affect the comparability and ultimately the usefulness of the data. Before an AR or AC program is initiated or improved, it is important to illustrate the needs and benefits of such data collection. We list the most important needs and benefits and provide considerations for the initiation or improvement of AR and AC data collection programs.

Main Content
Current View