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Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-30
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The paper presents a comparison between two modeling techniques, Bayesian network and Regression models, by employing them in accident severity analysis.
Three severity indicators, that is, number of fatalities, number of injuries and property damage, are investigated with the two methods, and the major contribution factors and their effects are identified.
The results indicate that the goodness of fit of Bayesian network is higher than that of Regression models in accident severity modeling.
This finding facilitates the improvement of accuracy for accident severity prediction.
Study results can be applied to the prediction of accident severity, which is one of the essential steps in accident management process.
By recognizing the key influences, this research also provides suggestions for government to take effective measures to reduce accident impacts and improve traffic safety.
American Psychological Association (APA)
Zong, Fang& Xu, Hong-guo& Yu, Bo. 2013. Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031914
Modern Language Association (MLA)
Zong, Fang…[et al.]. Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models. Mathematical Problems in Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1031914
American Medical Association (AMA)
Zong, Fang& Xu, Hong-guo& Yu, Bo. Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031914
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1031914