The Model of Severity Prediction of Traffic Crash on the Curve
Joint Authors
Xi, Jian-feng
Zhao, Zhong-hao
Liu, Hai-zhu
Cheng, Wei
Ding, Tong-qiang
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-01-09
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
With the study of traffic crashes on curved road segments as the focus of research, a logistic regression based curve road crash severity prediction model was established based on a sample crash database of 20000 entries collected from 4 regions of China and 15 evaluation indicators involving driver, driving environment, and traffic environment factors.
Maximum Likelihood Estimation and step-back technique were deployed for data analysis, the conclusion of which is that the three main contributory factors on curve road crash severity are weather, roadside protection facility, and pavement structure.
Hosmer and Lemeshow tests were used to verify the reliability of the model, and the model variables were discussed to a certain degree as well.
American Psychological Association (APA)
Xi, Jian-feng& Liu, Hai-zhu& Cheng, Wei& Zhao, Zhong-hao& Ding, Tong-qiang. 2014. The Model of Severity Prediction of Traffic Crash on the Curve. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-501779
Modern Language Association (MLA)
Xi, Jian-feng…[et al.]. The Model of Severity Prediction of Traffic Crash on the Curve. Mathematical Problems in Engineering No. 2014 (2014), pp.1-5.
https://search.emarefa.net/detail/BIM-501779
American Medical Association (AMA)
Xi, Jian-feng& Liu, Hai-zhu& Cheng, Wei& Zhao, Zhong-hao& Ding, Tong-qiang. The Model of Severity Prediction of Traffic Crash on the Curve. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-501779
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-501779