Abstract
Wildlife–vehicle collisions (WVCs) cause significant road mortality of wildlife and have led to the installation of protective measures along streets. Until now, it has been difficult to determine the impact of roadside infrastructure that might act as a barrier for animals. The main deficits are the lack of geodata for roadside infrastructure and georeferenced accidents recorded for a larger area. We analyzed 113 km of road network of the district Freyung-Grafenau, Germany, and 1571 WVCs, examining correlations between the appearance of WVCs, the presence or absence of roadside infrastructure, particularly crash barriers and fences, and the relevance of the blocking effect for individual species. To receive infrastructure data on a larger scale, we analyzed 5596 road inspection images with a neural network for barrier recognition and a GIS for a complete spatial inventory. This data was combined with the data of WVCs in GIS to evaluate the infrastructure’s impact on accidents. The results show that crash barriers have an effect on WVCs, as collisions are lower on roads with crash barriers. In particular, smaller animals have a lower collision share. The risk reduction at fenced sections could not be proven as fenced sections are only available at 3% of the analyzed roads. Thus, especially the fence dataset must be validated by a larger sample number. However, these preliminary results indicate that the combination of artificial intelligence and GIS may be used to analyze and better allocate protective barriers or to apply it in alternative measures, such as dynamic WVC risk-warning.
Highlights
Heterogeneous parameters of the road environment contribute, to a greater or lesser extent, to the cause of wildlife–vehicle collisions (WVCs)
The Wildlife–vehicle collisions (WVCs) distribution by species for the three secondary road sections shows that the vast majority of accidents is caused by deer, followed by rabbits and foxes (Table 1)
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Summary
Heterogeneous parameters of the road environment contribute, to a greater or lesser extent, to the cause of wildlife–vehicle collisions (WVCs). One aspect which has been marginally researched is the road accompanying structure This opens the question up as to what extent the roadside infrastructure, such as crash barriers and fences, affect the local occurrence of WVCs. This opens the question up as to what extent the roadside infrastructure, such as crash barriers and fences, affect the local occurrence of WVCs Analyses in this context have been restricted in the past by data availability for WVCs, on the one hand, and georeferenced and digital documentation of roadside infrastructures on the other hand. While camera data is georeferenced, we posed the question whether computer vision, in combination with GIS-based approaches, would be able to derive a georeferenced infrastructure inventory from these images. This information could provide knowledge about different risk types in the road environment, e.g., factors influencing the risk for wildlife crossings
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