On reliable identification of factors influencing wildlife-vehicle collisions along roads

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F19%3A43915309" target="_blank" >RIV/62156489:43410/19:43915309 - isvavai.cz</a>

Alternative codes found

RIV/44994575:_____/19:N0000019

Result on the web

<a href="https://doi.org/10.1016/j.jenvman.2019.02.076" target="_blank" >https://doi.org/10.1016/j.jenvman.2019.02.076</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jenvman.2019.02.076" target="_blank" >10.1016/j.jenvman.2019.02.076</a>

Result language

angličtina

Original language name

On reliable identification of factors influencing wildlife-vehicle collisions along roads

Original language description

Wildlife-vehicle collisions (WVCs) pose a serious global issue. Factors influencing the occurrence of WVC along roads can be divided in general into two groups: spatially random and non-random. The latter group consists of local factors which act at specific places, whereas the former group consists of globally acting factors. We analyzed 27,142 WVC records (roe deer and wild boar), which took place between 2012 and 2016 on Czech roads. Statistically significant clusters of WVCs occurrence were identified using the clustering (KDE+) approach. Local factors were consequently measured for the 75 most important clusters as cases and the same number of single WVCs outside clusters as controls, and identified by the use of odds ratio, Bayesian inference and logistic regression. Subsequently, a simulation study randomly distributing WVC in clusters into case and control groups was performed to highlight the importance of the clustering approach. All statistically significant clusters with roe deer (wild boar) contained 34% (27%) of all records related to this species. The overall length of the respective clusters covered 0.982% (0.177%) of the analyzed road network. The results suggest that the most pronounced signal identifying the statistically significant local factors is achieved when WVCs were divided according to their occurrence in clusters and outside clusters. We conclude that application of a clustering approach should precede regression modeling in order to reliably identify the local factors influencing spatially non-random occurrence of WVCs along the transportation infrastructure.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Environmental Management

ISSN

0301-4797

e-ISSN

—

Volume of the periodical

237

Issue of the periodical within the volume

1 May

Country of publishing house

GB - UNITED KINGDOM

Number of pages

8

Pages from-to

297-304

UT code for WoS article

000465059900033

EID of the result in the Scopus database

2-s2.0-85061870444