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- W2090977144 abstract "Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees (BRT), to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese 2004–2005 single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree (CART) models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes. The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. BRT models generally provide improved transferability than conventional logistic regression and CART models. This study also discusses the implications of the results for devising safety policies." @default.
- W2090977144 created "2016-06-24" @default.
- W2090977144 creator A5059383647 @default.
- W2090977144 date "2013-12-01" @default.
- W2090977144 modified "2023-10-07" @default.
- W2090977144 title "Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees" @default.
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- W2090977144 doi "https://doi.org/10.1016/j.aap.2012.08.015" @default.
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