Spatial and temporal visualisation techniques for crash analysis
Understanding the underlying structure of single vehicle crashes (SVCs) is essential for improving safety on the roads. Past research has found that SVCs tend to cluster both spatially and temporally. However, limited research has been conducted to investigate the interaction between the location of...
| Main Authors: | , , |
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| Format: | Journal Article |
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Elsevier B.V.
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/49639 |
| _version_ | 1848758283898191872 |
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| author | Plug, C. Xia, Jianhong (Cecilia) Caulfield, C. |
| author_facet | Plug, C. Xia, Jianhong (Cecilia) Caulfield, C. |
| author_sort | Plug, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Understanding the underlying structure of single vehicle crashes (SVCs) is essential for improving safety on the roads. Past research has found that SVCs tend to cluster both spatially and temporally. However, limited research has been conducted to investigate the interaction between the location of SVCs and the time they occur, especially at different levels of scales or spatial extents.
This paper applied spatial, temporal and spatio-temporal techniques to investigate patterns of SVCs in Western Australia between 1999 to 2008, at different levels of scale. Spider graphs were adapted to identify temporal patterns of vehicle crashes at two different levels of scales: daily and weekly with respect to their causes. The spatial structures of vehicle crashes were analysed using Kernel Density Estimation analysis at three different scales: West Australia, Metropolitan area, and Perth Local Government Area (LGA). These are illustrated using spatial zooming theory. Comap was then used to demonstrate the spatio-temporal interaction effect on vehicle crashes. The results show significant differences in spatio-temporal patterns of SVCs for various crash causes. The techniques used here have the potential to help decision makers in developing effective road safety strategies. |
| first_indexed | 2025-11-14T09:41:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-49639 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:41:32Z |
| publishDate | 2011 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-496392018-04-17T05:25:39Z Spatial and temporal visualisation techniques for crash analysis Plug, C. Xia, Jianhong (Cecilia) Caulfield, C. Understanding the underlying structure of single vehicle crashes (SVCs) is essential for improving safety on the roads. Past research has found that SVCs tend to cluster both spatially and temporally. However, limited research has been conducted to investigate the interaction between the location of SVCs and the time they occur, especially at different levels of scales or spatial extents. This paper applied spatial, temporal and spatio-temporal techniques to investigate patterns of SVCs in Western Australia between 1999 to 2008, at different levels of scale. Spider graphs were adapted to identify temporal patterns of vehicle crashes at two different levels of scales: daily and weekly with respect to their causes. The spatial structures of vehicle crashes were analysed using Kernel Density Estimation analysis at three different scales: West Australia, Metropolitan area, and Perth Local Government Area (LGA). These are illustrated using spatial zooming theory. Comap was then used to demonstrate the spatio-temporal interaction effect on vehicle crashes. The results show significant differences in spatio-temporal patterns of SVCs for various crash causes. The techniques used here have the potential to help decision makers in developing effective road safety strategies. 2011 Journal Article http://hdl.handle.net/20.500.11937/49639 10.1016/j.aap.2011.05.007 Elsevier B.V. fulltext |
| spellingShingle | Plug, C. Xia, Jianhong (Cecilia) Caulfield, C. Spatial and temporal visualisation techniques for crash analysis |
| title | Spatial and temporal visualisation techniques for crash analysis |
| title_full | Spatial and temporal visualisation techniques for crash analysis |
| title_fullStr | Spatial and temporal visualisation techniques for crash analysis |
| title_full_unstemmed | Spatial and temporal visualisation techniques for crash analysis |
| title_short | Spatial and temporal visualisation techniques for crash analysis |
| title_sort | spatial and temporal visualisation techniques for crash analysis |
| url | http://hdl.handle.net/20.500.11937/49639 |