Animal-vehicle Collisions Research Articles

Real-Time Road Safety: A Web and Mobile Application Approach to Mitigating Animal-Vehicle Conflicts in Sri Lanka

This research paper presents a pioneering web and mobile application aimed at mitigating the rising issue of animal-vehicle collisions in Sri Lanka, a concern that poses significant risks to both human safety and wildlife preservation. The application leverages a combination of knowledge-based case studies and crowd- sourcing techniques to enhance real-time road safety by identifying animal habitats and behaviors in proximity to users' locations. Through the web platform, users can input data on observed wildlife interactions and animal- vehicle conflicts, contributing to a comprehensive database for statistical analysis and predictive modelling. This information is used to generate real-time alerts and feedback, enabling drivers to take precautionary measures. The mobile application, utilizing geofencing and geotargeting technologies, provides real-time alerts, facilitates user feedback, and fosters community participation in ensuring safer roads. In addition to mitigating accidents, the application serves as an educational tool, offering guidelines and raising public awareness about the importance of road safety in areas rich in wildlife. By fostering a collaborative effort among users, this solution aims to reduce the frequency of animal-vehicle collisions, thereby promoting safer roads for both humans and animals in Sri Lanka.

Using animal–vehicle collision data for wildlife population monitoring

Using animal–vehicle collision data for wildlife population monitoring

Prediction of Vehicle–Live Animal Crashes in Britain: Contact and Noncontact Incidents

<div>Animal–vehicle collisions (AVCs) can result in devastating injuries to both humans and animals. Despite significant advances in crash prediction models, there is still a significant gap when it comes to injury severity prediction models in AVCs, especially concerning small animals. It is no secret that large mammals can pose a significant threat to road safety; however, researchers tend to overlook the impact of domestic and small animals wandering along the roads. In this study, STATS19 road safety data was used containing any type of live animal, and a radial basis function (RBF) model was used to predict different severities of injury regardless of whether the animal was hit, or not. As a means of better understanding the factors contributing to severities, regression trees were used to identify and retain only the most useful predictors, removing the less useful ones. A comparison was made between the performance of the trees across a range of severity classes, and the model-fitting results were discussed. Initially, the study was unable to generate satisfactory predictions, but the optimization of the key predictors and the combination of severity classes significantly improved their accuracy. Research findings revealed factors contributing to the severities, which were discussed accordingly. Particular attention was drawn to the pressing safety issue posed by animals crossing A-class single carriageways in rural site clusters. Animals being present on those carriageways without direct vehicular contact significantly contributed to the severity of the injuries sustained. Although the majority of contributing factors were related to human behavior, no evidence of road safety education, training, or publicity interventions specifically targeting AVCs was found in the literature.</div>

Targeted recreational hunting can reduce animal-vehicle collisions and generate substantial revenue for wildlife management agencies

Reduction of conflicts arising from human-wildlife interactions is necessary for coexistence. Collisions between animals and automobiles cost the world's economy billions of dollars, and wildlife management agencies often are responsible for reducing wildlife-vehicle collisions. But wildlife agencies have few proven options for reducing wildlife-vehicle collisions that are effective and financially feasible at large spatiotemporal scales germane to management. Recreational hunting by humans is a primary population management tool available for use with abundant wild ungulates that often collide with automobiles. Therefore, we tested how well policies designed to increase human hunting of deer (longer hunting seasons and increased harvest limits) reduced collisions between white-tailed deer and automobiles along 618 km of high-risk roadways in Indiana, USA. We used a 20-y dataset that compiled >300,000 deer-vehicle collisions. Targeted recreational hunting decreased deer-vehicle collisions by 21.12 % and saved society up to $653,756 (95 % CIs = $286,063–$1,154,118) in economic damages from 2018 to 2022. Potential savings was up to $1,265,694 (95 % CIs = $579,108–$2,402,813) during the same 5-y span if relaxed hunting regulations occurred along all high-risk roadways. Moreover, license sales from targeted hunting generated $206,268 in revenue for wildlife management. Targeted hunting is likely effective in other systems where ungulate-vehicle collisions are prevalent, as behavioral changes in response to human hunting has been documented in many ungulate species across several continents. Our methods are attractive for management agencies with limited funds, as relaxed hunting regulations are relatively inexpensive to implement and may generate substantial additional revenue.

What next? Some practical suggestions for future studies on fence ecology

Practical gaps in knowledge of fence ecology or its specific requirements still exist despite numerous recommendations scattered throughout various articles. We recently carried out two different studies on fences in a French context whose results corroborate this observation, and highlight the need to synthetise and prioritise scientific and technical research. The first study was an exploratory research project on escape devices used to allow ungulates to exit fenced transport infrastructure right‐of‐way. The second study entailed drafting a guide on the mitigation of the fence's ecological impacts, using solar energy plants as a case study. Both were based on different types of information gathering, ranging from bibliographical research to interviews with governments officials, operating companies, engineering offices, naturalists and hunters' associations.Based on the similarities between these two studies, the urgency of certain needs, and the clear potential for these approaches to extend beyond France's borders, we have identified two key categories for future actions. The first category is a series of six research priorities: Diversifying monitoring of exclusion fencing; Developing practice‐oriented fence mapping tools; Further studying the impact of fencing on animal vehicle collisions; Considering animal behaviour in monitoring protocol; Ex‐situ testing of devices' effectiveness; and Studying large‐scale effects of fencing. The second category comprises six method recommendations: Accurately documenting fences; Early planning of scientific monitoring; Contextualising fencing requirements; Defining effectiveness goals and criteria; Taking advantage of opportunistic Animal‐Fence Event observations; and Developing artificial intelligence and computer vision to map fences. All these items are supported by examples drawn from one study or the other.Our conclusions indicate that, in the context of major expansion of fences, such recommendations must be integrated at all stages of development work. If this is not implemented, the free movement of animal species will not be adequately protected and the erosion of biodiversity will be further exacerbated.

The impact of RS–040 highway on wildlife roadkill patterns, Porto Alegre, Southern Brazil.

Road infrastructure has caused severe impacts on the environment and wildlife. Understanding and mitigating these effects are essential conservation measures. This study aimed to evaluate wildlife mortality due to animal-vehicle collisions along an 80-km stretch of the Tapir Rocha Highway (RS–040). The field inventory was carried out between August 2015 and March 2019. Once a week, two observers drove along the highway at an average speed of 40 km/h and all the vertebrate roadkill spotted from the car were assessed to record the species, carcass condition, and the roadkill locations along the road. In total, 2,371 dead animals were recorded and categorized into 137 species. The most affected classes were mammals (n = 1,223), and birds (n = 704). The most affected wild species were white-eared opossums (Didelphis albiventris, n = 559), black-and-white tegus (Salvator merianae, n = 129), and coypus (Myocastor coypus, n = 102). Carcass exposure assessments indicated that many (n = 637) of the animal deaths occurred the night before our arrival, which may suggest that nocturnal species were among the most vulnerable to mortality. Roadkill sighting peaked in the spring (34%) and summer (25%), probably due to reproductive activity and juvenile dispersal. Evident seasonal variations included a 50% reduction in reptile roadkill rates in autumn-winter compared to spring-summer, and a 20-percentage-point increase in the number of mammal road deaths in the winter. The reduced activity that reptiles display at low temperatures, and seasonal changes in carnivore food habits may explain these respective findings. While most of the animal victims of roadkill were common and widely distributed species with abundant populations, decreasing abundance of these species is a growing conservation concern.

Wild Animal Intrusion Detection

Animal Vehicle Collision, commonly called as roadkill, is an emerging threat to humans and wild animals with increasing fatalities every year. Amid Vehicular crashes, animal actions (i.e. deer) are unpredictable and erratic on roadways. This paper unveils a newer dimension for wild animals’ auto-detection during active nocturnal hours using thermal image processing over camera car mount in the vehicle. To implement effective hot spot and moving object detection, obtained radiometric images are transformed and processed by an intelligent system. This intelligent system extracts the features of the image and subsequently detects the existence of an object of interest. The main technique to extract the features of wild animals is the Histogram of Oriented Gradient transform. The features are detected by normalizing the radiometric image and then processed by finding the magnitude and gradient of a pixel. As human populations expand and encroach upon natural habitats, conflicts between humans and wildlife become increasingly common. To mitigate the risks associated with wild animal intrusions into human settlements, an intelligent and proactive intrusion detection system is essential. This study proposes a novel approach to wild animal intrusion detection using deep learning techniques. The proposed system leverages Convolutional Neural Networks (CNNs) to analyze images captured by surveillance cameras placed in strategic locations. The deep learning model is trained on a diverse dataset of wildlife images to enable accurate identification and classification of different species. Transfer learning is employed to fine-tune the model for specific regions and scenarios, enhancing its adaptability and performance. Furthermore, the system integrates real-time image processing and edge computing to minimize latency and enable swift response to potential intrusions. The use of edge devices ensures that the intrusion detection system operates autonomously, reducing the need for constant human intervention. The effectiveness of the proposed approach is evaluated through extensive testing in real-world environments, including wildlife reserves, suburban areas, and agricultural landscapes. Performance metrics such as accuracy, precision, recall, and F1 score are employed to assess the system's ability to reliably detect and classify wild animal intrusions. The findings of this study demonstrate the feasibility and effectiveness of employing deep learning for wild animal intrusion detection. The proposed system not only enhances the safety of human populations but also contributes to wildlife conservation efforts by minimizing negative interactions between humans and animals. The scalability and adaptability of the system make it suitable for deployment in various geographic regions, thereby addressing the global challenge of human-wildlife conflicts.

Temporal and spatial patterns of small vertebrate roadkill in a supercity of eastern China.

An assessment of animal roadkill can help develop road mitigation measures. This article is the first to report data on animal-vehicle collisions (AVCs) in Nanjing, a supercity in eastern China. The research was conducted on a 224.27 km stretch of nine roads in Nanjing. In the period, between November 2020 and October 2021, 26 fortnightly monitoring missions were conducted to gather roadkill carcasses so that we could analyze their temporal and spatial distribution patterns. A total of 259 carcasses were collected, comprising 22 different species, of which 46.42% were mammals and 48.81% were birds. Cats and dogs are the most roadkill mammals, and blackbirds and sparrows are the most roadkill birds. The temporal analysis demonstrated that the peak of vertebrate roadkill occurred from May to July. Spatial analysis showed that the distribution patterns of vertebrate roadkill on different roads varied with a generally non-random distribution and aggregation. By mapping accidents using kernel density analysis, we were able to pinpoint locations that were at high risk for roadkill. Due to the fortnightly survey, our results would underestimate the casualties, even if, our study suggests that the problem of car accidents due to animals should be a cause for concern, and the results of the analysis of temporal and spatial patterns contribute to the establishment of mitigation measures.

Predictive Modeling of Ungulate-Vehicle Collision in the Republic of Korea.

Animal-vehicle collisions (AVC) threaten animals as well as human life and property. AVC with ungulates, called ungulate-vehicle collision (UVC), often seriously endangers human safety because of the considerable body size of ungulates. In the Republic of Korea, three ungulate species, Capreolus pygargus, Hydropotes inermis, and Sus scrofa, account for a large proportion of AVC. This study aimed to understand the characteristics of UVC by examining various parameters related to habitat, traffic, and seasonality using MaxEnt. The results showed that the peak UVC seasons coincided with the most active seasonal behaviors of the studied ungulates. For the modeling results, in C. pygargus, habitat variables are most important for models across seasons, and UVC events are most likely to occur in high mountain chains. In H. inermis, habitat and traffic variables are most important for models across seasons. Although the important habitat for the models were different across seasons for S. scrofa, the maximum speed was consistently critical for models across all seasons. Factors critical to UVC in the Republic of Korea were different for the three ungulate species and across seasons, indicating that seasonal behavior should be considered along with landscape and traffic characteristics to mitigate UVC.

ASSESSMENT OF AUTONOMOUS EMERGENCY BRAKING (AEB) PEDESTRIAN SYSTEM IMPACT ON 2016 – 2020 MALAYSIAN ANIMAL-CROSSINGS ACCIDENTS DATA

The trend of animal-vehicle collision (AVCs) occurrences over the past years demonstrates increasing numbers, and this call for a proper mitigation plan by appropriate authority bodies. Autonomous Emergency Braking (AEB) pedestrian system - proven effective in collision prevention and mitigation for vehicle-pedestrian collision – can potentially expand its original functionality to AVCs avoidance. This study presents a new data assessment method to predict the impact of AEB pedestrian system implementation on vehicles to reduce AVCs cases from 2016 to 2020. In general, a new scoring system is introduced whereby fitment rating points of 1, 0.5 and 0 are given to describe successful crash avoidance, crash mitigation with reduced damage and failed crash avoidance. Several noteworthy findings were discovered in assessing impact data from five significant AEB-AVCs. The effectiveness of AEB is found to be correlated with camera detection, system working speed range, frequent collision time, human casualties, and heavy vehicles. In general, the results indicate overall positive consequences of AEB implementation to reduce AVCs, providing concrete reasoning for standardising AEB pedestrian systems in all manufactured road-legal vehicles for upcoming years.

First report on animal-vehicle collisions impact on wild and domestic animals in northern Algeria

ABSTRACT Animal-vehicle collisions (AVCs) are considered a major cause of wildlife mortality. This study identified carcasses of AVC-killed animals along a road network located south of Algiers. The results showed that different types of wild and domestic animals were killed by vehicles, mostly mammals. In terms of diversity, wild animals are more subjected to AVC than domestic ones. But, dogs were the most commonly reported. Hyaena hyaena, declared a threatened species, was also reported in this AVC study. Moreover, the road types’ diversity and seasonal variation also affected the AVC-affected animals. More collaboration between fauna conservationists and road authorities seems to be urgent to reduce AVC in Algeria.

Animal Detection for Vehicle

Abstract: A severe challenge with which all established countries are now dealing seems to be the deaths and harm resulting from traffic accidents. Animal-vehicle collisions are an increasing concern for transportation organizations worldwide since they result in thousands of deaths each year for both humans and animals. As more roads are developed, the places where animals live are decreasing, resulting in more collisions between vehicles and animals. The human and animal deaths and injuries, as well as the material expenses of these accidents, indicate the necessity of a solution for this issue. Deep learning algorithms to prevent animal collisions will be developed using data. The method may be enhanced by adding additional characteristics necessary to boost productivity on the datasets. The recommended strategy has the ability to improve public safety by reducing or avoiding animal or human incidents.

Better safe than sorry – Understanding the attitude and habits of drivers can help mitigating animal-vehicle collisions

The rapidly growing global road networks put serious pressures on terrestrial ecosystems and increase the number and severity of human-wildlife conflicts, which in most cases manifest in animal-vehicle collisions (AVCs). AVCs pose serious problems both for biodiversity conservation and traffic safety: each year, millions of vertebrates are roadkilled globally and the related economic damage is also substantial. For a comprehensive understanding of factors influencing AVC it is essential to explore the human factor, that is, the habits and attitude of drivers; however, to date, comprehensive surveys are lacking on this topic. Here we addressed this knowledge gap and surveyed the habits of drivers and their experience and attitude towards AVCs by a comprehensive questionnaire covering a large geographical area and involving a large number of respondents (1942 completed questionnaires). We aimed to reveal how driving habits affect the chance of AVC, and explored the attitude of the drivers regarding AVC. We found that the number of lifetime AVC cases was higher for male drivers, for those who drove longer distances per year, had more driven years, used country roads or drove large vehicles. Our results showed that almost half of the drivers surveyed had experienced at least one AVC in their lifetime. Drivers’ attitudes towards the importance of nature conservation or traffic safety in the aspect of AVC, and fear of collision showed a significant correlation with experienced AVC cases. Drivers' opinions indicated that the most trusted and desired AVC prevention measures were physical objects such as fences and wildlife crossings. Our research provides guidelines for developing targeted initiatives in the future to increase awareness about the significance of AVC and target those drivers who are most vulnerable to AVC.

Where to invest in road mitigation? A comparison of multiscale wildlife data to inform roadway prioritization

Roads and associated traffic have significant impacts on wildlife, from direct mortality caused by vehicle collisions to indirect effects when wildlife avoid roads, restricting access to important resources. Road mitigation measures such as constructing wildlife passages over or under the road with directional fencing have proven effective at reducing wildlife vehicle collisions while also enabling wildlife to safely cross the road. Highway mitigation projects are led by transportation agencies with a primary purpose of improving motorist safety. More recently, through the discipline of road ecology, considerations have included safe wildlife passage through transportation corridors. To prioritize road sections for mitigation, data sources include animal vehicle collision data collected by transportation agencies and connectivity models generated by wildlife professionals. We used a third data source, pronghorn observations collected by citizen scientists, and demonstrated its value to prioritize potential wildlife mitigation sites. Our results clearly demonstrate a misalignment of road mitigation sites using animal-vehicle collision data and those of rarer species of interest.

Traffic education campaigns and animal-vehicle collisions in Brazil.

Educational traffic campaigns aim to alert the population about adoption of behaviors that bring safety and quality in traffic. Animal-vehicle collisions are one of the most visible impacts on roads, and the planning of measures to reduce these collisions is justified for at least three reasons: animal welfare, financial cost, and drivers and passengers' safety. We evaluated whether the impact of animal-vehicle collisions was present in educational traffic campaigns and if campaigns showed any other environmental issues. We analyzed 205 campaigns from state and national traffic departments in Brazil. Although "collision" was the most frequent subject, animal-vehicle collisions only appeared on two campaigns, both portraying domestic animals. The identified environmental issues were "animal-vehicle collision" and "garbage", and most campaigns did not have an environmental focus. We highlighted some directions to include the subject of wildlife-vehicle collisions in traffic campaigns that could raise public awareness and, ultimately, reduce the impact on humans and animals involved in collisions.

Installation and monitoring of a canopy bridge connecting Atlantic Forest fragments containing endangered species in Northeastern Brazil

Abstract In 2019, a canopy bridge was built connecting two fragments of the Atlantic Forest separated by the Pacatuba unpaved road within the Pacatuba-Gargaú Corridor. This bridge was built as part of the Brazilian National Action Plan for the Conservation of Northeastern Primates (Plano de Ação Nacional para Conservação dos Primatas do Nordeste – PAN PRINE). The forest fragments contain a variety of mammal species, including the endangered primate species Alouatta belzebul and Sapajus flavius. Between September 2019 and March 2020, we monitored the canopy bridge continuously with a Bushnell camera trap; for a total of 204 days of sampling, resulting in evidence of 113 independent passage events. During the monitoring period, four mammal species used the canopy bridge, the Endangered Sapajus flavius (blond capuchin monkey), Callithrix jacchus (common marmoset), Marmosa demerarae (woolly mouse opossum), and Marmosa murina (murine mouse opossum). The common marmoset (Callithrix jacchus) used the bridge most frequently, representing 50.44% of the passage events (57), while the blond capuchin monkey (S. flavius) crossed the bridge in 30 events (26.5% of total crossings). All other mammals used the bridge less frequently than the primates (23% of crossings were of opossums). There was no record of crossings by the red-handed howler monkey (Alouatta belzebul) during the monitoring period. The construction of the canopy bridge within the Pacatuba-Gargaú forest corridor promoted structural and functional connectivity of the forest habitats, affecting the movement of the Endangered blond capuchin monkey and the common marmoset and benefiting other arboreal mammal species. The results indicate that monitoring the use of this canopy bridge provided important insights into the behaviour and ecology of the species studied (e.g., activity patterns, social interactions, group sizes, and reproduction, among many others). In addition, the use of the bridge decreases the risks of animal-vehicle collisions and electrocutions. Considering our results, we strongly recommend constructing and implementing canopy bridges as a strategy for the conservation of arboreal species living in fragmented forest areas.

A reviewof the application of canopy bridges in the conservation of primates and other arboreal animals across Brazil

Abstract Brazil is known as a high biodiversity country, but at the same time, it has an extensive road network that threatens its wildlife and ecosystems. The impacts of roads and railways on vertebrates have been documented extensively, and the discussion concerning the implementation of mitigation measures for terrestrial wildlife has increased in the last decade. Arboreal animals are especially affected by the direct loss of individuals due to animal-vehicle collisions and by the barrier effect, because most arboreal species, especially the strictly arboreal ones, avoid going down to the ground to move across the landscape. Here we summarize and review information on existing canopy bridges across Brazil, considering artificial and natural canopy bridge initiatives implemented mainly on road and railway projects. A total of 151 canopy bridges were identified across the country, 112 of which are human-made structures of different materials, while the remaining 39 are natural canopy bridges. We found canopy bridges in three of the six biomes, with higher numbers in the Atlantic Forest and Amazon, the most forested biomes. Most of the canopy bridges are in protected areas (76%) and primates are the most common target taxa for canopy bridge implementation. Our study is the first biogeographic mapping and review of canopy bridges for arboreal wildlife conservation in a megadiverse country. We synthesize the available knowledge concerning canopy bridges in Brazil and highlight gaps that should be addressed by future research and monitoring projects.

“The Road is the Biggest Killer:” Animal–Vehicle Collisions in Switzerland

“The Road is the Biggest Killer:” Animal–Vehicle Collisions in Switzerland

A Cow Crossing Detection Alert System

Artificial intelligence is rapidly growing in recent years and has derived several branches of studies such as object detection and sound recognition. Object detection is a computer vision technique that allows the identification and location of objects in an image or video. On the other hand, proper recognition is the ability of a machine or program to receive and interpret dictation or to understand and carry out direct commands. This paper presents a cow crossing detection alert system with object detection and sound recognition capabilities. The proposed system aims to protect the driver from animal-vehicle collision. A data-driven deep learning approach is used for cow detection. Consequently, the cow detection module is integrated with a Raspberry Pi device to perform real time monitoring. The proposed cow crossing detection alert system will send an alert message to relevant units like the road transport department to take further actions if a potential animal-vehicle collision is detected on the road. Experimental results show that the proposed cow detection approach yields a mean average precision 0.5 (mAP 0.5) of 99% in object detection and 100% accuracy in sound recognition, demonstrating the system's practical feasibility.

Improving Road Safety during Nocturnal Hours by Characterizing Animal Poses Utilizing CNN-Based Analysis of Thermal Images

Animal–vehicle collision is a common danger on highways, especially during nighttime driving. Its likelihood is affected not only by the low visibility during nighttime hours, but also by the unpredictability of animals’ actions when a vehicle is nearby. Extensive research has shown that the lack of visibility during nighttime hours can be addressed using thermal imaging. However, to our knowledge, little research has been undertaken on predicting animal action through an animal’s specific poses while a vehicle is moving. This paper proposes a new system that couples the use of a two-dimensional convolutional neural network (2D-CNN) and thermal image input, to determine the risk imposed by an animal in a specific pose to a passing automobile during nighttime hours. The proposed system was tested using a set of thermal images presenting real-life scenarios of animals in specific poses on the roadside and was found to classify animal poses accurately in 82% of cases. Overall, it provides a valuable basis for implementing an automotive tool to minimize animal–vehicle collisions during nighttime hours.