Back to Search
Start Over
Classification and Regression Tree Approach for Predicting Drivers' Merging Behavior in Short-Term Work Zone Merging Areas.
- Source :
-
Journal of Transportation Engineering . Aug2011, Vol. 138 Issue 8, p1062-1070. 9p. - Publication Year :
- 2012
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0733947X
- Volume :
- 138
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- Journal of Transportation Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 93647001
- Full Text :
- https://doi.org/10.1061/(ASCE)TE.1943-5436.0000412