A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables. (14th November 2018)

• Record Type: Journal Article
• Authors: Wang, Xiaofei; Pu, HuaQiao; Li, Xinwei; Yan, Ying; Yao, Jiangbei
• Other Names: Comi Antonio Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2018(2018)
• Publication Date: 2018-11-14
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2018/8973581
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Crash prediction of the sharp horizontal curve segment of freeway is a key method in analyzing safety situation of freeway horizontal alignment. The target of this paper is to improve predicting accuracy after considering the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. In the paper, flexibility and elasticity are defined to express the elastic influence of explanatory variables and interaction of explanatory variables on crash rate prediction. Thus, we proposed 6 types of models to predict crash frequency. These 6 types of models include 2 NB models (models 1 and 2), 2 GNB models (models 3 and 4), one NB model (model 5), and one GNB model (model 6) with flexibility and variable elasticity considered. The alignment and crash report data of 88 sharp horizontal curve segments from different institutions were surveyed to build the crash models. Traffic volume, highway horizontal radius, and curve length have been assigned as explanatory variables. Subsequently, statistical analysis is performed to determine the model parameters and conducted sensitivity analysis by AIC, BIC, and Pseudo R 2 . The results demonstrated the effective use of flexibility and elasticity in analyzing explanatory variables and in predicting freeway sharp horizontal curve segments. In six models, the result of model 6 is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 sharp horizontal curve segments with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than the others.