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Modeling the Impact of Weather Conditions on Pedestrian Injury Counts Using LASSO-Based Poisson Model

机译:基于套索泊松模型模拟天气条件对行人损伤计数的影响

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摘要

Statistical models for measuring the impact of adverse weather conditions on pedestrian injuries are of great importance forenhancing road safety measures. The development of these models in the presence of high collinearity among the weatherconditions poses a real challenge in practice. The collinearity among these conditions may result in underestimation of theregression coefficients of the regression model, and hence inconsistency regarding the impact of the weather conditions onthe pedestrian injuries counts. This paper presents a methodology through which the penalization-based regression is appliedto model the impact of weather conditions on pedestrian injury in the presence of a high level of collinearity among theseconditions. More specifically, the methodology integrates both the least absolute shrinkage squared operator (Lasso) with thecross-validation approach. The statistical performance of the proposed methodology is assessed through an analytical comparisoninvolving the standard Poisson regression, Poisson generalized linear model (Poisson-GzLM), and Ridge penalizedregression model. The mean squared error (MSE) was used as a criterion of comparison. In terms of the MSE, the Lasso-basedPoisson generalized linear model (Lasso-GzLM) revealed an advantage over the other regression models. Moreover, the studyrevealed that weather conditions involved in this study are of insignificant impact on pedestrian injury counts.
机译:用于测量恶劣天气条件对行人伤害的影响的统计模型非常重要提高道路安全措施。这些模型在天气中的高度共同性存在下的发展条件在实践中提出了真正的挑战。这些条件中的共同性可能导致低估了回归模型的回归系数,因此对天气条件的影响不一致行人伤害计数。本文提出了一种方法,通过该方法应用了基于惩罚的回归模拟天气条件在这些中存在高水平的共同性存在下的行人伤害的影响使适应。更具体地,该方法与最少的绝对收缩方块运算符(套索)相结合交叉验证方法。通过分析比较评估所提出的方法的统计性能涉及标准泊松回归,泊松广义线性模型(泊松-GZLM)和脊柱处罚回归模型。平均平方误差(MSE)用作比较的标准。根据MSE,基于套索的泊松广义线性模型(Lasso-GZLM)揭示了其他回归模型的优势。而且,研究透露,涉及本研究的天气条件对行人伤害计数影响微不足道。

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