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WIREs Data Mining Knowl Discov
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A semantic‐based classification and regression tree approach for modelling complex spatial rules in motor vehicle crashes domain

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Innovative data mining and knowledge discovery approaches that take advantage of geospatial analysis can be useful for analysis of motor vehicle crashes on regional highway corridors. This study presents an Ontology‐based Classification and Regression Tree (OCART) approach to induct crash rules on regional highway corridors by adding ontological reasoning to Classification and Regression Tree (CART) decision rules. It develops an ontology‐driven geospatial framework to predict the motor vehicles crash severity through the proposed OCART approach. A system prototype has been developed and implemented on a regional highway corridor in order to illustrate and evaluate the proposed method. The results demonstrate the application of ontological reasoning and spatial computation significantly improves the performance of CART crash rules. The proposed method reveals new relationships among crash severities and contributing factors that have been kept implicit in CART decision rules. WIREs Data Mining Knowl Discov 2015, 5:181–194. doi: 10.1002/widm.1152 This article is categorized under: Algorithmic Development > Hierarchies and Trees Technologies > Classification Technologies > Machine Learning
Study corridor.
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Performance evaluation of crash severity prediction.
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Conceptual graphical model of the framework domain ontology.
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The vehicle crash severity level and its surrounding spatial features.
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Graphical interface of the framework.
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The system architecture.
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Overall structure of the framework.
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Technologies > Machine Learning
Technologies > Classification
Algorithmic Development > Hierarchies and Trees

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