Abstract Wildlife-vehicle collisions (WVCs) are caused by the close interaction of human and wildlife habitats worldwide. The large number of globally distributed accidents and the variety of environmental impacts characterize WVCs as intricate and challenging to predict. However, numerous research studies have been conducted to understand the causal relationships between drivers, animals, and the environment. In this paper, 645 publications are reviewed to provide an overview and a wide-ranging knowledge about WVC research. The study gathers the influencing factors on WVCs, systematizes the approaches for data collection, and identifies the main developments in analysis and predicting methods for WVCs. Factors such as the proximity to forest, a gentle topography with sparsely curves, street width, and seasonal differences are common denominators for WVCs - independent of the species -, while traffic volume, the distance to urban areas, or road accompanying infrastructure are not clearly assignable influencing or non-influencing factors. Different ways of data collection are observed, which range from carcass surveys by ecologists or crowdsourcing for species conservation to nearly real-time official reporting by involved parties as a basis for driving safety. Data collection and quality are discussed for their applicability, in particular, regarding the currently used analysing approaches for WVCs. Additionally, the advantages of the rarely employed machine learning approaches are discussed in terms of dynamic WVC risk prediction - including large-scale and temporally unrestricted transferability. These approaches may be helpful for prospective warning and road safety management on a global scale.

中文翻译：

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野生动物与车辆碰撞 - 影响因素、数据收集和研究方法

摘要 野生动物-车辆碰撞（WVCs）是由世界范围内人类和野生动物栖息地的密切相互作用引起的。全球分布的大量事故和各种环境影响使 WVC 复杂且难以预测。然而，已经进行了大量研究以了解驾驶员、动物和环境之间的因果关系。本文回顾了 645 篇出版物，以提供有关 WVC 研究的概述和广泛的知识。该研究收集了 WVC 的影响因素，将数据收集方法系统化，并确定了 WVC 分析和预测方法的主要发展。诸如靠近森林、具有稀疏曲线的平缓地形、街道宽度、季节性差异是 WVC 的共同分母——与物种无关——而交通量、与城市地区的距离或道路配套基础设施并不是明确可分配的影响或非影响因素。观察到不同的数据收集方式，从生态学家的尸体调查或物种保护的众包到相关各方近乎实时的官方报告，作为驾驶安全的基础。讨论了数据收集和质量的适用性，特别是关于目前使用的 WVC 分析方法。此外，在动态 WVC 风险预测方面讨论了很少使用的机器学习方法的优势 - 包括大规模和时间上不受限制的可转移性。