Classification and Regression Tree Approach for Predicting Drivers’ Merging Behavior in Short-Term Work Zone Merging Areas
Publication: Journal of Transportation Engineering
Volume 138, Issue 8
Abstract
This study aims to use the classification and regression tree (CART) approach, one of the most powerful data mining techniques, to predict drivers’ merging behavior in a work zone merging area. On the basis of the eight factors affecting drivers’ merging behavior, a binary CART is built using the merging traffic data collected from a short-term work zone site in Singapore. The CART comprises 7 levels and 15 leaf nodes to predict drivers’ merging behavior in the work zone merging area. The results show that the CART provides much higher prediction accuracy than the conventional binary logit model. Traffic engineers can easily understand how drivers make merging/nonmerging decisions. This demonstrates that the CART approach is a good alternative for investigating drivers’ merging behavior in work zone merging areas.
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Acknowledgments
The authors thank the editor and four anonymous referees for their helpful comments and valuable suggestions, which considerably improved the exposition of this work.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 8 • August 2012
Pages: 1062 - 1070
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Jul 9, 2010
Accepted: Jan 6, 2012
Published online: Jan 9, 2012
Published in print: Aug 1, 2012
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