Factor Complexity of Accident Occurrence: an Empirical Demonstration Using Boosted Regression Trees
نویسنده
چکیده
Factor complexity is regarded as a typical characteristic of traffic accidents. This paper proposes a novel method, named boosted regression trees (BRTs), which is particularly appropriate for investigating complicated and nonlinear relationships in high-variance traffic accident data. The Taiwan 2004–2005 single-motorcycle accident data are adopted to demonstrate the usefulness of BRTs. Traditional logistic regression and classification and regression tree (CART) models are also developed to compare their estimation results and predictive performance. Both the insample cross-validation and out-of-sample validation results show that the increase of tree complexity provides better but declining improvement on the predictive performance, indicating a limited factor complexity of single-motorcycle accidents. While a certain portion of fatal accidents can be explained by the main effects of crucial variables including geographical, time, and socio-demographic factors, the relatively unique fatal accidents are better approximated by interactive terms, especially the combinations of behavioral factors. The BRTs models generally provide better transferability than logistic and CART models. The implications of analysis results for devising safety policies are also provided.
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