Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling
Driving along any rural road within Western Australia involves some level of uncertainty about encountering an animal whether it is wildlife, farm stock or domestic. This level of uncertainty can vary depending on factors such as the surrounding land use, water source, geometry of the road, speed li...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
Taylor and Francis Ltd.
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/44505 |
| _version_ | 1848757020627304448 |
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| author | Xia, Jianhong (Cecilia) Murphy, A. |
| author_facet | Xia, Jianhong (Cecilia) Murphy, A. |
| author_sort | Xia, Jianhong (Cecilia) |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Driving along any rural road within Western Australia involves some level of uncertainty about encountering an animal whether it is wildlife, farm stock or domestic. This level of uncertainty can vary depending on factors such as the surrounding land use, water source, geometry of the road, speed limits and signage. This paper aims to model the risk of animal–vehicle crashes (AVCs) on a segmented highway. A hierarchical Bayesian model involving multivariate Poisson lognormal regression is used in establishing the relationship between AVCs and the contributing factors. Findings of this study show that farming on both sides of a road, a mixture of farming and forest roadside vegetation and roadside vegetation have significant positive effect on AVCs, while speed limits and horizontal curves indicate a negative effect. AVCs consist of both spatial- and segment-specific contributions, even though the spatial random error does not dominate model variability. Segment 15 is identified as the highest risk segment and its nearby segments also exhibit high risk. |
| first_indexed | 2025-11-14T09:21:27Z |
| format | Journal Article |
| id | curtin-20.500.11937-44505 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:21:27Z |
| publishDate | 2016 |
| publisher | Taylor and Francis Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-445052017-09-13T16:11:54Z Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling Xia, Jianhong (Cecilia) Murphy, A. Driving along any rural road within Western Australia involves some level of uncertainty about encountering an animal whether it is wildlife, farm stock or domestic. This level of uncertainty can vary depending on factors such as the surrounding land use, water source, geometry of the road, speed limits and signage. This paper aims to model the risk of animal–vehicle crashes (AVCs) on a segmented highway. A hierarchical Bayesian model involving multivariate Poisson lognormal regression is used in establishing the relationship between AVCs and the contributing factors. Findings of this study show that farming on both sides of a road, a mixture of farming and forest roadside vegetation and roadside vegetation have significant positive effect on AVCs, while speed limits and horizontal curves indicate a negative effect. AVCs consist of both spatial- and segment-specific contributions, even though the spatial random error does not dominate model variability. Segment 15 is identified as the highest risk segment and its nearby segments also exhibit high risk. 2016 Journal Article http://hdl.handle.net/20.500.11937/44505 10.1080/13588265.2016.1209823 Taylor and Francis Ltd. fulltext |
| spellingShingle | Xia, Jianhong (Cecilia) Murphy, A. Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling |
| title | Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling |
| title_full | Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling |
| title_fullStr | Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling |
| title_full_unstemmed | Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling |
| title_short | Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling |
| title_sort | risk analysis of animal–vehicle crashes: a hierarchical bayesian approach to spatial modelling |
| url | http://hdl.handle.net/20.500.11937/44505 |