Norway has large number of areas with wildlife danger along roads where it is not cost-effective to build large fences or wildlife crossings. The presence of wildlife along open roads can produce traffic collisions with wildlife, creating stress and produce dangerous situations for the safety of both humans and animals. The severity of the situation is visible in the number of collisions with cervids. Despite attempts of different solutions, these have had little success and the number of traffic collisions with wildlife have increased by around 50% in the last 5-10 years. WILDETECT aims to collect both existing and new data to build knowledge on where the likelihood of collision accidents are highest, in order to warn road users and reduce the number of traffic collisions with wildlife. The project will explore different solutions that collect data about the number and/or movement of wildlife groups, such as: 1) statistical data, 2) sensor data and video observations with drones, and 3) unstructured data from the road users, for example social media. In addition, it will be explored how to bring data together (data fusion), to build knowledge on which type of data to combine and how, to improve statistical models for predicting collisions. The knowledge produced in WILDETECT will also be used to test different warning systems, ranging from mobile applications to variable speed signs that activates when the risk is high. These warning systems will be evaluated by road drivers. To test different warning solutions, WILDETECT will make use of Virtual Reality (VR) and a driving simulator to more effectively evaluate the drivers' behaviour when warned the risk. WILDETECT will also include actors involved in the transport system, including those who receive data, make the notifications and the road users. This will allow co-creation processes, where contributions of stakeholders and users will be considered to define the type of data that can be included in a solution.
WILDETECT explores and extends various statistical and technical solutions in investigating the integration potential of data and technology in answer to traffic collision avoidance between vehicles and animals. The project aims to develop knowledge-based outcomes for the development of a safe and smart traffic system that can detect wildlife and warn road authorities and drivers of increased collision risk. The project results will be a starting point for developing future technology systems that can be applicable to traffic planning, providing road users with real and dynamic traffic information, thus contributing to increased transport safety.