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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28254
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dc.contributor.authorLaube, Patrick-
dc.contributor.authorRatnaweera, Nils-
dc.contributor.authorWróbel, Anna-
dc.contributor.authorKaelin, Ivo-
dc.contributor.authorStephani, Annette-
dc.contributor.authorReifler-Baechtiger, Martina-
dc.contributor.authorGraf, Roland F.-
dc.contributor.authorSuter, Stefan-
dc.date.accessioned2023-07-13T11:50:17Z-
dc.date.available2023-07-13T11:50:17Z-
dc.date.issued2023-
dc.identifier.issn0921-2973de_CH
dc.identifier.issn1572-9761de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28254-
dc.descriptionErworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)de_CH
dc.description.abstractContext: Wildlife–vehicle collisions (WVCs) are a significant threat for many species, cause financial loss and pose a serious risk to motorist safety. Objectives: We used spatial data science on regional collision data from Switzerland with the objectives of identifying the key environmental collision risk factors and modelling WVC risk on a nationwide scale. Methods: We used 43,000 collision records with roe deer, red deer, wild boar, and chamois from 2010 to 2015 for both midlands and mountainous landscape types. We compared a fixed-length road segmentation approach with segments based on Kernel Density Estimation, a data-driven segmentation method. The segments’ environmental properties were derived from land-cover geodata using novel neighbourhood operations. Multivariate logistic regression and random forest classifiers were used to identify and rank the relevant environmental factors and to predict collision risk in areas without collision data. Results: The key factors for WVC hotspots are road sinuosity, and two composite factors for browsing/forage availability and traffic noise—a proxy for traffic flow. Our best models achieved sensitivities of 82.5% to 88.6%, with misclassifications of 20.14% and 27.03%, respectively. Our predictions were better in forested areas and revealed limitations in open landscape due to lack of up-to-date data on annual crop changes. Conclusions: We illustrate the added value of using fine-grained land-cover data for WVC modelling, and show how such detailed information can be annotated to road segments using spatial neighbourhood functions. Finally, we recommend the inclusion of annual crop data for improving WVC modelling.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofLandscape Ecologyde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectWildlife–vehicle collisionde_CH
dc.subjectKernel Density Estimationde_CH
dc.subjectNeighbourhood functionde_CH
dc.subjectSpatial data sciencede_CH
dc.subjectRandom forestde_CH
dc.subject.ddc333: Bodenwirtschaft und Ressourcende_CH
dc.subject.ddc380: Verkehrde_CH
dc.titleAnalysing and predicting wildlife–vehicle collision hotspots for the Swiss road networkde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.organisationalunitInstitut für Umwelt und Natürliche Ressourcen (IUNR)de_CH
dc.identifier.doi10.1007/s10980-023-01655-5de_CH
dc.identifier.doi10.21256/zhaw-28254-
zhaw.funding.euNode_CH
zhaw.issue7de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end1783de_CH
zhaw.pages.start1765de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume38de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBio-Inspired Methods & Neuromorphic Computingde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedGeoinformatikde_CH
zhaw.webfeedWildtiermanagementde_CH
zhaw.funding.zhawPrävention von Wildtierunfällen auf Verkehrsinfrastrukturende_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Laube, P., Ratnaweera, N., Wróbel, A., Kaelin, I., Stephani, A., Reifler-Baechtiger, M., Graf, R. F., & Suter, S. (2023). Analysing and predicting wildlife–vehicle collision hotspots for the Swiss road network. Landscape Ecology, 38(7), 1765–1783. https://doi.org/10.1007/s10980-023-01655-5
Laube, P. et al. (2023) ‘Analysing and predicting wildlife–vehicle collision hotspots for the Swiss road network’, Landscape Ecology, 38(7), pp. 1765–1783. Available at: https://doi.org/10.1007/s10980-023-01655-5.
P. Laube et al., “Analysing and predicting wildlife–vehicle collision hotspots for the Swiss road network,” Landscape Ecology, vol. 38, no. 7, pp. 1765–1783, 2023, doi: 10.1007/s10980-023-01655-5.
LAUBE, Patrick, Nils RATNAWEERA, Anna WRÓBEL, Ivo KAELIN, Annette STEPHANI, Martina REIFLER-BAECHTIGER, Roland F. GRAF und Stefan SUTER, 2023. Analysing and predicting wildlife–vehicle collision hotspots for the Swiss road network. Landscape Ecology. 2023. Bd. 38, Nr. 7, S. 1765–1783. DOI 10.1007/s10980-023-01655-5
Laube, Patrick, Nils Ratnaweera, Anna Wróbel, Ivo Kaelin, Annette Stephani, Martina Reifler-Baechtiger, Roland F. Graf, and Stefan Suter. 2023. “Analysing and Predicting Wildlife–Vehicle Collision Hotspots for the Swiss Road Network.” Landscape Ecology 38 (7): 1765–83. https://doi.org/10.1007/s10980-023-01655-5.
Laube, Patrick, et al. “Analysing and Predicting Wildlife–Vehicle Collision Hotspots for the Swiss Road Network.” Landscape Ecology, vol. 38, no. 7, 2023, pp. 1765–83, https://doi.org/10.1007/s10980-023-01655-5.


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