Roadside accident frequency prediction model on expressway(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2017年04期
- Page:
- 119-126
- Research Field:
- Publishing date:
Info
- Title:
- Roadside accident frequency prediction model on expressway
- Author(s):
- MA Zhuang-lin; ZHANG Hong-lu; ZHANG Yi-yi; WANG Jin
- 1. School of Automobile, Chang’an University, Xi’an 710064, Shaanxi, China; 2. Deppon Logistics Co. Ltd., Shanghai 201702, China; 3. Yunnan Science Research Institute of Communication & Transportation, Kunming 650011, Yunnan, China
- Keywords:
- traffic engineering; road side accident frequency prediction; negative binomial regression model; zero-inflated negative binomial regression model; elastic analysis
- PACS:
- U491.3
- DOI:
- -
- Abstract:
- To identify the main induction factors of roadside accident and analyze the relationship between roadside accident frequency and main influence factors such as road geometry, traffic conditions of expressway, based on 178 cases of roadside accidents occurred in Eastern Guangzhou to Zhuhai Expressway in three years, this paper used the fixed-length method and homogeneous longitudinal grade method to divide the study section, selected eleven independent variables from the aspects of road geometry and traffic conditions, and adopted zero-inflated negative binomial regression model to establish roadside accident frequency prediction model. Three indicators including Vuong test statistics, log likelihood value, and Akaike’s information criterion were used to test the goodness-of-fit of the model. Two indicators including relative error and cumulative residual were used to test the prediction accuracy of the model. To further judge the better model, negative binomial regression model and zero-inflated negative binomial regression model were compared by the goodness-of-fit and prediction accuracy. Elastic analysis was used to determine the influence degree of the independent variables on the dependent variable in the better model. The results show that the roadside accident frequency prediction model based on zero-inflated negative binomial regression model is better than that of negative binomial regression model both for fixed-length segmentation method and homogeneous longitudinal grade segmentation methods. For roadside accident frequency prediction model based on zero-inflated negative binomial regression model, the prediction accuracy of fixed-length segmentation method is better than that of homogeneous longitudinal grade segmentation method. For fixed-length segmentation method, five independent variables in roadside accident frequency prediction model based on zero-inflated negative binomial regression model have significant impact on roadside accident frequency, and the influence degree in descending order is number of lane, curvature change rate, curve ratio, curve degree, and average longitudinal gradient grade.
Last Update: 2017-07-17