Crash Injury Severity Analysis Using Bayesian Ordered Probit Models
Publication: Journal of Transportation Engineering
Volume 135, Issue 1
Abstract
Understanding the underlying relationship between crash injury severity and factors such as driver’s characteristics, vehicle type, and roadway conditions is very important for improving traffic safety. Most previous studies on this topic used traditional statistical models such as ordered probit (OP), multinomial logit, and nested logit models. This research introduces the Bayesian inference and investigates the application of a Bayesian ordered probit (BOP) model in driver’s injury severity analysis. The OP and BOP models are compared based on datasets with different sample sizes from the 2003 National Automotive Sampling System General Estimates System (NASSGES). The comparison results show that these two types of models produce similar results for large sample data. When the sample data size is small, with proper prior setting, the BOP model can produce more reasonable parameter estimations and better prediction performance than the OP model. This research also shows that the BOP model provides a flexible framework that can combine information contained in the data with the prior knowledge of the parameters to improve model performance.
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Acknowledgments
The writers would like to thank the anonymous reviewers for their valuable comments that substantially improve the quality of this paper.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 135 • Issue 1 • January 2009
Pages: 18 - 25
Copyright
© 2009 ASCE.
History
Received: May 30, 2007
Accepted: Jul 2, 2008
Published online: Jan 1, 2009
Published in print: Jan 2009
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