TY - GEN
T1 - Modeling Wildlife Accident Risk with Gaussian Mixture Models
AU - Praschl, Christoph
AU - Schedl, David C.
AU - Stöckl, Andreas
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/4/25
Y1 - 2025/4/25
N2 - Traffic accidents involving wildlife pose a widespread problem globally, harming both humans and nature. These incidents often result in heavy vehicle damage, leading to expensive repairs and insurance claims. To mitigate these accidents, efforts are underway to understand wildlife populations near high-risk roads better and implement preventive measures such as visual or audible wildlife warning devices. To prevent wildlife accidents, high-risk areas must be identified first. In this work, we propose a model that predicts dangerous areas based on animal sightings and apply it to two road segments in Austria.
AB - Traffic accidents involving wildlife pose a widespread problem globally, harming both humans and nature. These incidents often result in heavy vehicle damage, leading to expensive repairs and insurance claims. To mitigate these accidents, efforts are underway to understand wildlife populations near high-risk roads better and implement preventive measures such as visual or audible wildlife warning devices. To prevent wildlife accidents, high-risk areas must be identified first. In this work, we propose a model that predicts dangerous areas based on animal sightings and apply it to two road segments in Austria.
KW - Gaussian mixture modelling
KW - Wildlife modelling
UR - http://www.scopus.com/inward/record.url?scp=105004255538&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-82957-4_5
DO - 10.1007/978-3-031-82957-4_5
M3 - Conference contribution
SN - 9783031829598
VL - 15173
T3 - Lecture Notes in Computer Science
SP - 43
EP - 53
BT - Computer Aided Systems Theory – EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers
A2 - Quesada-Arencibia, Alexis
A2 - Affenzeller, Michael
A2 - Moreno-Díaz, Roberto
ER -
