Classification and Regression Tree Approach for Predicting Drivers' Merging Behavior in Short-Term Work Zone Merging Areas

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.

• Record URL:
  https://doi.org/10.1061/(ASCE)TE.1943-5436.0000412
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/8674831
• Supplemental Notes:
  • Copyright © 2012 American Society of Civil Engineers
• Authors:
  • Meng, Qiang
  • Weng, Jinxian
• Publication Date: 2012-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1062-1070
• Serial:
  • Journal of Transportation Engineering
  • Volume: 138
  • Issue Number: 8
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-947X
  • Serial URL: https://ascelibrary.org/journal/jtepbs

Subject/Index Terms

• TRT Terms: Behavior; Data mining; Decision trees; Drivers; Merging traffic; Work zones
• Geographic Terms: Singapore
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01449214
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Oct 15 2012 10:31AM