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首页> 外文期刊>Accident Analysis & Prevention >Analysis of traffic injury severity: an application of non-parametric classification tree techniques.
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Analysis of traffic injury severity: an application of non-parametric classification tree techniques.

机译：交通伤害严重性分析：非参数分类树技术的应用。

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摘要

Statistical regression models, such as logit or ordered probit/logit models, have been widely employed to analyze injury severity of traffic accidents. However, most regression models have their own model assumptions and pre-defined underlying relationships between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimations of injury likelihood. The classification and regression tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study uses the 2001 accident data for Taipei, Taiwan. A CART model was developed to establish the relationship between injury severity and driver/vehicle characteristics, highway/environmental variables and accident variables. The results indicate that the most important variable associated with crash severity is the vehicle type. Pedestrians, motorcycle and bicycle riders are identified to have higher risks of being injured than other types of vehicle drivers in traffic accidents.
机译：统计回归模型，例如logit或有序的probit / logit模型，已被广泛用于分析交通事故的伤害严重性。但是，大多数回归模型都有自己的模型假设以及因变量和自变量之间的预定义基础关系。如果违反了这些假设，则该模型可能会导致对伤害可能性的错误估计。分类和回归树（CART）是应用最广泛的数据挖掘技术之一，已广泛用于企业管理，行业和工程领域。 CART在目标（因变量）和预测变量（因变量）之间不需要任何预定义的基础关系，并且已被证明是强大的工具，尤其是在处理预测和分类问题时。本研究使用台湾台北市2001年的事故数据。开发了一个CART模型以建立伤害严重程度与驾驶员/车辆特性，公路/环境变量和事故变量之间的关系。结果表明，与碰撞严重程度相关的最重要的变量是车辆类型。与交通事故中其他类型的驾驶员相比，行人，摩托车和自行车驾驶员受伤的风险更高。

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