Reducing the threat of wildlife-vehicle collisions during peak tourism periods using a Roadside Animal Detection System

Wildlife-vehicle collisions: The disproportionate risk of injury faced by motorcyclists

We applied a generalized linear mixed-effects model to determine the factors leading to injuries from wildlife-vehicle collisions (WVCs). We used the Police database representing WVCs which took place on the Czech road network between 2009 and 2022. The majority of WVCs in Czechia are with roe deer, followed by wild boar, i.e., both relatively small ungulates. Less than 2 % of these encounters ends with an injury to the motor vehicle occupants. We found that the probability of sustaining injury was systematically higher for motorcyclists than for car occupants. The odds of sustaining an injury during WVC were roughly 1600 times higher for motorcyclists than for car occupants. When applying an evading manoeuvre, the odds of sustaining an injury were approximately 68 times higher for car occupants while only 2.3 times higher for motorcyclists compared to a direct hit to an animal. The lack of helmets (for motorcyclists) and missing seat belts (for car occupants) were additional factors which made the outcomes worse for WVCs. While the acceptance of a direct hit (preceded by braking) seems to be a reasonable strategy for car drivers, WVC awareness (including maintaining a lower speed during critical times and places) should be raised among motorcyclists as both manoeuvres are almost comparably dangerous for them.

Spatiotemporal Analysis of Road Crashes with Animals in Poland

This article describes the issue of the influences of the time factor and wildlife populations on road animal–vehicle crashes. The article presents problems associated with animal–vehicle crashes in Poland. They are the subject of many court disputes. For the purposes of the study, data on animal–vehicle road crashes were obtained from the SEWiK database, as well as data on the numbers of animals in hunting districts from 2016–2020. The relationship between the number of road crashes and time was analysed, and the relationships between the numbers of road crashes and the animal populations, as well as the locations, types, and categories of the hunting districts, were analysed as well. The factors related to changes in the wildlife populations, road crashes in previous years, and the lengths of the road networks were also analysed. The research shows no relationship between the abundance of a particular species and the number of road crashes. Instead, there is a correlation between the number of crashes in previous years and the road network length.

Active advanced arousal system to alert and avoid the crepuscular animal based vehicle collision

Animal Vehicle Collision (AVC) is relatively an evolving source of fatality resulting in the deficit of wildlife conservancy along with carnage. It’s a globally distressing and disturbing experience that causes monetary damage, injury, and human-animal mortality. Roadkill has always been atop the research domain and serendipitously provided heterogeneous solutions for collision mitigation and prevention. Despite the abundant solution availability, this research throws a new spotlight on wildlife-vehicle collision mitigation using highly efficient artificial intelligence during nighttime hours. This study focuses mainly on arousal mechanisms of the “Histogram of Oriented Gradients (HOG)” intelligent system with extracted thermography image features, which are then processed by a trained, convolutional neural network (1D-CNN). The above computer vision – deep learning-based alert system has an accuracy between 94%, and 96% on the arousal mechanisms with the empowered real-time data set utilization.

Toward Best Management Practices for Ecological Corridors

Ecological corridors are one of the best, and possibly only viable, management tools to maintain biodiversity at large scales and to allow species, and ecological processes, to track climate change. This document has been assembled as a summary of the best available information about managing these systems. Our aim with this paper is to provide managers with a convenient guidance document and tool to assist in applying scientific management principles to management of corridors. We do not cover issues related to corridor design or political buy in, but focus on how a corridor should be managed once it has been established. The first part of our paper outlines the history and value of ecological corridors. We next describe our methodologies for developing this guidance document. We then summarize the information about the impacts of linear features on corridors and strategies for dealing with them—specifically, we focus on the effects of roads, canals, security fences, and transmission lines. Following the description of effects, we provide a summary of the best practices for managing the impacts of linear barriers. Globally, many corridors are established in the flood plains of stream and rivers and occur in riparian areas associated with surface waters. Therefore, we next provide guidance on how to manage corridors that occur in riparian areas. We then segue into corridors and the urban/suburban environment, and summarize strategies for dealing with urban development within corridors. The final major anthropic land use that may affect corridor management is cultivation and grazing agriculture. We end this review by identifying gaps in knowledge pertaining to how best to manage corridors.

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

A new roadway eventual obstacle detection system based on computer vision is described and evaluated. This system uses low-cost hardware and open-source software to detect and classify moving elements in roads using infra-red and colour video images as input data. This solution represents an important advancement to prevent road accidents due to eventual obstacles which have considerably increased in the past decades, mainly with wildlife. The experimental evaluation of the system demonstrated that the proposed solution detects and classifies correctly different types of moving obstacles on roads, working robustly under different weather and illumination conditions.

Animal-vehicle collisions in Texas: How to protect travelers and animals on roadways

Animal-vehicle collisions (AVCs) are a growing problem in the United States, resulting in countless loss of animal life and considerable human injury and death every year, especially to motorcyclists. Due to underreporting, collision data generally provide a very low (highly biased) estimate of actual AVC counts and often lack key details, such as the species of animals involved. However, AVC reports cover entire states and nations, and can illuminate differences in wild versus domestic animal-vehicle collisions through statistical and spatial analysis. 51,522 animal-related crashes were reported to Texas police from 2010 through 2016, at a total cost over $1.3 billion annually to Texas motorists - not including the value of lost animal lives. AVC reports jump twice a day: between 5 and 8 AM and between 5 and 10 PM. Motorists are also significantly more likely to collide with a wild animal during the months of October, November, and December. Wildlife-vehicle collisions (WVC) are 64% of total reports, events involving domestic animals (like dogs and cattle) are 31%, and the remaining 5% of reports are unspecified. Most AVCs in the state occur at night in unlit locations, usually on rural roads with very low traffic volumes. Using ordinary least-squares (OLS) regression analysis across Texas' n = 254 counties, this work finds that less densely populated counties, marked as rural, and those with fewer vehicle-miles traveled (VMT) per capita but more lane-miles per capita, tend to experience the greatest number of AVCs per VMT, after controlling for county average rainfall, share of VMT onsystem roadways, job densities, and vehicle ownership (vehicles per capita). Intervention options for the mitigation of animal-vehicle collisions are numerous and diverse. For wildlife collisions specifically, this work finds that large crossing structures (underpasses and overpasses) at the highway link level return benefit-to-cost ratios near 3.0, while their lower-cost counterparts (wildlife fencing and animal detection systems) deliver ratios up to 30.

Animal learning may contribute to both problems and solutions for wildlife-train collisions

Transportation infrastructure can cause an ecological trap if it attracts wildlife for foraging and travel opportunities, while increasing the risk of mortality from collisions. This situation occurs for a vulnerable population of grizzly bears (Ursus arctos) in Banff National Park, Canada, where train strikes have become a leading cause of mortality. We explored this problem with analyses of rail-associated food attractants, habitat use of GPS-collared bears and patterns of past mortality. Bears appeared to be attracted to grain spilled from rail cars, enhanced growth of adjacent vegetation and train-killed ungulates with rail use that increased in spring and autumn, and in areas where trains slowed, topography was rugged, and human density was low. However, areas with higher grain deposits or greater use by bears did not predict sites of past mortality. The onset of reported train strikes occurred amid several other interacting changes in this landscape, including the cessation of lethal bear management, changes in the distribution and abundance of ungulates, increasing human use and new anthropogenic features. We posit that rapid learning by bears is critical to their persistence in this landscape and that this capacity might be enhanced to prevent train strikes in future with simple warning devices, such as the one we invented, that signal approaching trains. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.

A Review of the Impacts of Roads on Wildlife in Semi-Arid Regions

Roads now penetrate even the most remote parts of much of the world, but the majority of research on the effects of roads on biota has been in less remote temperate environments. The impacts of roads in semi-arid and arid areas may differ from these results in a number of ways. Here, we review the research on the impacts of roads on biodiversity patterns and ecological and evolutionary processes in semi-arid regions. The most obvious effect of roads is mortality or injury through collision. A diversity of scavengers are killed whilst feeding on roadkill, a source of easily accessed food. Noise pollution from roads and traffic interferes with vocal communication by animals, and birds and frogs living along noisy roads compensate for traffic noise by increasing the amplitude or pitch of their calls. Artificial light along roads impacts certain species’ ability to navigate, as well as attracting invertebrates. Animals are in turn attracted to invertebrates at streetlights, and vulnerable to becoming roadkill themselves. Genetics research across taxa confirms a loss of genetic diversity in small populations isolated by roads, but the long-term impact on the fitness of affected populations through a reduction in genetic diversity is not yet clear. Roads may rapidly cause genetic effects, raising conservation concerns about rare and threatened species. We assess mitigation measures and collate methods to identify the impact of roads on wildlife populations and their associated ecosystems, with a particular focus on recent advances.

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, we 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™. Our 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. We 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 (AIC_cw = 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'. Our 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.