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https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

PREDICTIVE MODELS FOR THE LOCATION OF ANIMAL-CAR ACCIDENTS AND THEIR APPLICABILITY TO MITIGATION

Many provinces in Spain have suffered an increasing number of animal-car collisions in recent years. For this reason, animal-car collisions have become one of the main issues to the official bodies with traffic and/or environmental responsibilities. In this context, the present study has been devoted to the analysis of the causes and potential solutions to the problem in the Province of Soria (Central Spain), where more than 50 percent of the reported car accidents in 2000 were related to the presence of wild animals on the road. The modeling has been carried out at two different spatial scales, a regional one focused on the definition of the areas with high accident rates, and one aimed at the search of factors determining the exact locations of accidents. The study was based on the database of car accidents provided by the Direccin General de Trfico with indication of date, hour and location of the accident (approximated to the nearest 0.1km post), and species involved in the crash. This database comprised a total of 2,067 accident locations corresponding to the 1988-2001 period. An initial analysis of the spatial contagion among accident locations lead to the definition a set of 41 "black sections" in roads, with 0.8 to 47.3km length each. These sectors embrace more than 70 percent of accident locations, though totalizing only a 7.7 percent of the road network of the province. A GIS-based analysis of the landscape features corresponding to these sectors was carried out in comparison with a set of 43 "white sections" interspersed among them. This task was based on the forest map of the province (1:50.000 scale) working with 1km radius circles centered on the midpoint of "black" and "white" sections. Nine land-use variables plus the length of ecotones and the diversity of substrata were used as input variables. The statistical analysis and the modeling showed the accident-prone areas to be characterized by their high forest cover, low presence of human structures, and a high diversity of vegetation types with some presence of crops. The analysis at the accident-point scale was carried out within a total of 18 "black sections" of roads, through a sampling of 12 points with accidents recorded and 12 free of them in each section. In each point 28 quantitative and qualitative variables were measured. The variables covered the most relevant features believed to be potentially related to accident rates, such as the road characteristics from the driver's point of view (distance to curve, signaling), habitat structure (land-uses, distance to trees), and local morphology (natural geomorphology plus human-made structures). The statistical analyses and modeling showed the accidents happening at points of animal corridors crossing the road, with vegetation, local morphology plus human structures forcing the animals to cross at predictable points.

Language

  • English

Media Info

  • Pagination: 1p

Subject/Index Terms

Filing Info

  • Accession Number: 00969091
  • Record Type: Publication
  • Files: TRIS, USDOT, STATEDOT
  • Created Date: Feb 5 2004 12:00AM