Application of GLASSO in Variable Selection and Crash Prediction at Unsignalized Intersections
Publication: Journal of Transportation Engineering
Volume 138, Issue 7
Abstract
In this paper, a new promising variable screening technique is proposed to select important covariates and to improve crash prediction; the group least absolute shrinkage and selection operator (GLASSO). The GLASSO’s main power lies in its ability to deal with data sets havinga large number of categorical variables, the case in this study. Identifying the significant factors affecting the safety of unsignalized intersections was also an essential objective. Two applications of GLASSO were investigated: before fitting the negative binomial (NB) model, and before fitting the promising multivariate adaptive regression splines (MARS) technique using extensive data representing 2,475 unsignalized intersections. Regarding the NB models, GLASSO yielded close prediction capability to the backward deletion and random forest techniques. Also, MARS model fitting after using GLASSO relatively outperformed that after using random forest, with similar prediction performance. Because of its outstanding performance with categorical variables and its simplicity, GLASSO is recommended as a promising variable selection technique. Some significant predictors affecting unsignalized intersections’ safety were traffic volume on the major road, upstream and downstream distances to the nearest signalized intersection, and median type on major and minor approaches.
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Acknowledgments
The authors would like to thank the Florida DOT for providing sufficient resources for collecting the data used in this study.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 7 • July 2012
Pages: 949 - 960
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Nov 15, 2010
Accepted: Dec 20, 2011
Published online: Dec 23, 2011
Published in print: Jul 1, 2012
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