Investigating the predictability of crashes on different freeway segments using the real-time crash risk models

Improvement of the prediction efficiency of crash risks has attracted the attention of numerous studies. Nevertheless, one of the most important factors, crash precursors, were neglected. This study mainly focuses on identifying optimal crash precursors for different freeway section types, as well as providing a threshold selection method for real-time crash risk models. Freeway sections are divided into four types, i.e. basic sections, weaving areas, merging areas, and diverging areas. Bayesian logistic regression (BLR) models were established for each type of segment, and significant factors were distinguished. A threshold selection method was proposed based on cost-benefit theory, and the threshold is determined as the value when the number of proactive safety interventions to prevent a crash is 5000 in this study. BLR models with one, two and three optimal variables were developed. Then the sensitivity and false alarm rate of the models were obtained and compared. Comparison results show that the minimum amount of parameters which can achieve the ideal prediction effectiveness is two. In this situation, 25 %, 50 %, 20 % and 20 % of the crashes occurring at basic sections, weaving areas, merging areas and diverging areas can be accurately predicted respectively. Downstream average speed was recommended as the best crash precursor variable for all the segment types. Support Vector Machine and Random Forest were applied to confirm the conclusion. The conclusion of this paper has the possibility to help reduce crash risk to a relatively economical level in practical applications.