Road traffic accidents are a social and public challenge. Various spatial concentration detection methods have been proposed to discover the concentration patterns of traffic accidents. However, current methods treat each traffic accident location as a point without consideration of the severity level, and the final traffic accident risk map for the whole study area ignores the users' requirements. In this paper, we propose an ontology-based traffic accident risk mapping framework. In the framework, the ontology represents the domain knowledge related to the traffic accidents and supports the data retrieval based on users' requirements. A new spatial clustering method that takes into account the numbers and severity levels of accidents is proposed for risk mapping. To demonstrate the framework, a system prototype has been implemented. A case study in the city of Calgary is also discussed. © 2011 Springer-Verlag.
CITATION STYLE
Wang, J., & Wang, X. (2011). An ontology-based traffic accident risk mapping framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6849 LNCS, pp. 21–38). https://doi.org/10.1007/978-3-642-22922-0_3
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