Freeway Accident Likelihood Prediction Using a Panel Data Analysis Approach
Publication: Journal of Transportation Engineering
Volume 133, Issue 3
Abstract
The ability to predict freeway accident likelihood promises significant benefits to freeway operations. However, the development of such prediction models has proven to be very challenging because of the random nature of accidents, as well as the impact of site-specific factors. In addition, accident data has a pronounced nature of discrete response—a preponderant portion of nonaccident cases. To address these challenges, this research investigates the use of a discrete response model designed for panel data—the random effects ordered probit model, in predicting freeway accident likelihood. Panel data refers to data sets that combine time series and cross section (i.e., from different individuals, groups, etc.) observations. The empirical results of this research illustrate that the random effects ordered probit model performs well in identifying factors associated with traffic accidents. In addition, when applied in a predictive setting, the model provides benefits in forecasting the likelihood of accidents based on both time-varying and site-specific parameters.
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Acknowledgments
The writers wish to extend their thanks and appreciation to the anonymous referees and editor whose constructive suggestions greatly improved this work. Nevertheless, any remaining errors and omissions remain the responsibility of the writers.
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© 2007 ASCE.
History
Received: Feb 24, 2005
Accepted: Jul 26, 2006
Published online: Mar 1, 2007
Published in print: Mar 2007
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