中文摘要：

为了提高基本路段事故预测模型（SPF）的预测精度，收集了640个基本路段设计资料及事故资料，应用3个负二项回归模型（NB）和3个广义负二项（GNB）回归模型对收集的数据进行拟合，并分析了解释变量的交互影响．研究表明在上述6个模型中，其中考虑了年平均日交通量和路段长度交互影响的2个模型（一个为NB，另一个为GNB），其预测结果更为合理．进一步综合对比表明考虑交互影响时，NB模型和GNB模型的适用性几乎相同，而GNB略佳．

英文摘要：

In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.