A New GNB Model of Crash Frequency for Freeway Sharp Horizontal Curve Based on Interactive Influence of Explanatory Variables

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...

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Bibliographic Details
Published in:Journal of advanced transportation 2018-01, Vol.2018 (2018), p.1-9
Main Authors: Yan, Ying, Li, Xin-wei, Pu, HuaQiao, Wang, Xiao-fei, Yao, Jiangbei
Format: Article
Language:English
Subjects:
Alignment
Crashes
Elasticity
Flexibility
Highways
Influence
Investigations
Parameter estimation
Parameter sensitivity
Predictions
Production functions
Researchers
Road curves
Roads & highways
Segments
Sensitivity analysis
Statistical analysis
Traffic accidents & safety
Traffic flow
Traffic models
Traffic police
Traffic volume
Transportation
Variables
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Summary: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 R2. 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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2018/8973581