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Analysis of driver and passenger crash injury severity using partial proportional odds models

机译：使用部分比例赔率模型分析驾驶员和乘客碰撞伤害的严重程度

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The question of whether crash injury severity should be modeled using an ordinal response model or a non-ordered (multinomial) response model is persistent in traffic safety engineering. This paper proposes the use of the partial proportional odds (PPO) model as a statistical modeling technique that both bridges the gap between ordered and non-ordered response modeling, and avoids violating the key assumptions in the behavior of crash severity inherent in these two alternatives. The partial proportional odds model is a type of logistic regression that allows certain individual predictor variables to ignore the proportional odds assumption which normally forces predictor variables to affect each level of the response variable with the same magnitude, while other predictor variables retain this proportional odds assumption. This research looks at the effectiveness of this PPO technique in predicting vehicular crash severities on Connecticut state roads using data from 1995 to 2009. The PPO model is compared to ordinal and multinomial response models on the basis of adequacy of model fit, significance of covariates, and out-of-sample prediction accuracy. The results of this study show that the PPO model has adequate fit and performs best overall in terms of covariate significance and holdout prediction accuracy. Combined with the ability to accurately represent the theoretical process of crash injury severity prediction, this makes the PPO technique a favorable approach for crash injury severity modeling by adequately modeling and predicting the ordinal nature of the crash severity process and addressing the non-proportional contributions of some covariates.
机译：在交通安全工程中，是否应该使用序数响应模型或无序（多项式）响应模型来模拟碰撞伤害严重性的问题仍然存在。本文提出使用部分比例赔率（PPO）模型作为一种统计建模技术，该技术可以弥合有序和无序响应建模之间的差距，并避免违反这两种选择中固有的碰撞严重性行为的关键假设。偏比例赔率模型是一种Logistic回归，它允许某些单个预测变量忽略比例赔率假设，该假设通常会迫使预测变量以相同的幅度影响响应变量的每个级别，而其他预测变量则保留此比例赔率假设。这项研究使用1995年至2009年的数据，研究了该PPO技术在预测康涅狄格州道路上的车辆撞车严重性方面的有效性。根据模型拟合的充分性，协变量的重要性，和样本外预测精度。这项研究的结果表明，PPO模型具有足够的拟合度，并且在协变量显着性和保持预测准确度方面表现最佳。结合准确表示碰撞伤害严重性预测的理论过程的能力，这使得PPO技术成为碰撞碰撞严重性建模的理想方法，它可以通过适当地建模和预测碰撞严重性过程的序数性质并解决碰撞事故严重程度的非比例贡献。一些协变量。

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