基于广义估计方程的时间相关性事故频次建模（英文）

Application of generalized estimating equations for crash frequency modeling with temporal correlation

研究目的:采用广义估计方程模型对存在时间相关性的事故频次数据进行建模,并与传统广义线性模型的估计效果进行对比。创新要点:通过广义估计方程来考虑事故频次建模中数据的时间相关性,从而提高参数估计准确度以及模型预测精度。研究方法:基于4年高速公路交通事故频次数据,建立考虑时间相关性的广义估计方程以及传统的广义线性模型,并采用统计指标对模型效果进行对比。重要结论:1.事故频次数据样本量对预测精度影响很大;2.广义估计方程能够有效考虑事故频次数据中存在的时间相关性;3.广义估计方程的参数估计比传统广义线性模型更准确,且精度更高。

Traditional crash frequency modeling uses crash frequency data averaged across multiple years. When data size is small, crash data in each year are used in the modeling to extend the size of the samples. The extension of sample size could create a temporal correlation among crash frequencies of the different years, which could affect the modeling accuracy. The primary objective of this study is to evaluate the application of the generalized estimating equation （GEE） procedures to account for the temporal correlation in the longitudinal crash frequency data. Four-year crash data at exit ramps on a freeway in China were collected for modeling. Based on the same data, traditional generalized linear models （GLMs） were estimated for model comparison. Results showed that traditional GLM underestimated the standard errors of coefficients for explanatory variables. The GEE procedure with an exchangeable correlation structure successively captured the temporal correlation among the crash frequencies of the different years. The GLM with GEE outperformed the traditional GLM in providing a good fit for the crash frequency data. Results of this study can help researchers better understand how various factors affect the crash frequencies at freeway divergent areas and propose effective countermeasures.