Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method
Spatial correlation is a critical factor in establishing accurate traffic accident analysis models, with the choice of measurement method significantly influencing the results. Despite the central role of roads as the primary conduit for traffic flow and a direct exposure variable in accidents, thei...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/1923889 |
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| _version_ | 1832586821749440512 |
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| author | Jing Gan Qing Su Linheng Li Yanni Ju Linchao Li |
| author_facet | Jing Gan Qing Su Linheng Li Yanni Ju Linchao Li |
| author_sort | Jing Gan |
| collection | DOAJ |
| description | Spatial correlation is a critical factor in establishing accurate traffic accident analysis models, with the choice of measurement method significantly influencing the results. Despite the central role of roads as the primary conduit for traffic flow and a direct exposure variable in accidents, their impact on spatial correlation in accident analysis has not been fully explored. This study introduces an innovative spatial correlation matrix, termed the road matrix, which incorporates shared road lengths between grids to enhance accident prediction accuracy. The model examines the relationship between traffic accidents and various predictor variables, including land use, road networks, and public transportation facilities. Compared to traditional spatial correlation methods such as the rook and queen matrices, the road matrix provides a more precise characterization of spatial dependencies and significantly improves accident frequency estimation. Notably, the application of the road matrix within a conditional autoregressive (CAR) model uncovers additional significant contributors to traffic accidents, such as the number of interchanges and the length of nonexpress arterial roads. These findings offer new insights and practical recommendations for urban planning and traffic safety management. The study provides a valuable reference for future research on traffic accident frequencies and offers guidance for the design of more effective traffic safety measures. |
| format | Article |
| id | doaj-art-d02542d53ef34c318a7f10d1df8f2761 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d02542d53ef34c318a7f10d1df8f27612025-01-25T05:00:02ZengWileyJournal of Advanced Transportation2042-31952025-01-01202510.1155/atr/1923889Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction MethodJing Gan0Qing Su1Linheng Li2Yanni Ju3Linchao Li4School of Modern PostsCollege of Civil and Transportation EngineeringSchool of TransportationDepartment of Road Traffic ManagementCollege of Civil and Transportation EngineeringSpatial correlation is a critical factor in establishing accurate traffic accident analysis models, with the choice of measurement method significantly influencing the results. Despite the central role of roads as the primary conduit for traffic flow and a direct exposure variable in accidents, their impact on spatial correlation in accident analysis has not been fully explored. This study introduces an innovative spatial correlation matrix, termed the road matrix, which incorporates shared road lengths between grids to enhance accident prediction accuracy. The model examines the relationship between traffic accidents and various predictor variables, including land use, road networks, and public transportation facilities. Compared to traditional spatial correlation methods such as the rook and queen matrices, the road matrix provides a more precise characterization of spatial dependencies and significantly improves accident frequency estimation. Notably, the application of the road matrix within a conditional autoregressive (CAR) model uncovers additional significant contributors to traffic accidents, such as the number of interchanges and the length of nonexpress arterial roads. These findings offer new insights and practical recommendations for urban planning and traffic safety management. The study provides a valuable reference for future research on traffic accident frequencies and offers guidance for the design of more effective traffic safety measures.http://dx.doi.org/10.1155/atr/1923889 |
| spellingShingle | Jing Gan Qing Su Linheng Li Yanni Ju Linchao Li Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method Journal of Advanced Transportation |
| title | Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method |
| title_full | Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method |
| title_fullStr | Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method |
| title_full_unstemmed | Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method |
| title_short | Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method |
| title_sort | urban traffic accident frequency modeling an improved spatial matrix construction method |
| url | http://dx.doi.org/10.1155/atr/1923889 |
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