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• W2061855265 abstract "Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules." @default.
• W2061855265 created "2016-06-24" @default.
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• W2061855265 date "2013-01-01" @default.
• W2061855265 modified "2023-10-10" @default.
• W2061855265 title "Extracting decision rules from police accident reports through decision trees" @default.
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• W2061855265 doi "https://doi.org/10.1016/j.aap.2012.09.006" @default.
• W2061855265 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23021419" @default.
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