Analysis of Driver Merging Behavior at Lane Drops on Freeways

Lane changing assistance systems advise drivers on safe gaps for making mandatory lane changes at lane drops. In this study, such a system was developed using a Bayes classifier and a decision tree to model lane changes. Detailed vehicle trajectory data from the Next Generation Simulation (NGSIM) dataset were used for model development (US Highway 101) and testing (Interstate 80). The model predicted driver decisions regarding whether or not to merge as a function of certain input variables. The best results were obtained when both the Bayes and decision tree classifiers were combined into a single classifier using a majority voting principle. Predictive accuracy was 94.3% for non-merge events and 79.3% for merge events. In a lane change assistance system, the accuracy of non-merge events is more critical than accuracy for merge events. Misclassifying a non-merge event as a merge event could result in a crash, while misclassifying a merge event as a non-merge event would only result in a lost opportunity to merge. Sensitivity analysis performed by assigning a higher misclassification cost for non-merge events resulted in even higher accuracy for non-merge events, but lower accuracy for merge events.

• Record URL:
  • http://matc.unl.edu/assets/documents/matcfinal/Edara_AnalysisofDriverMergingBehaviouratLaneDropsonFreeway.pdf
• 
• Summary URL:
  • http://matc.unl.edu/research/research_projects.php?researchID=443
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851

  Research and Innovative Technology Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Edara, Praveen
  • Hou, Yi
  • Sun, Carlos
• Publication Date: 2013-12

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 41p

Subject/Index Terms

• TRT Terms: Accuracy; Bayes' theorem; Decision trees; Driver support systems; Gap acceptance; Lane changing; Lane drops; Merging traffic; Sensitivity analysis; Vehicle trajectories
• Identifier Terms: Next Generation Simulation program
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01502055
• Record Type: Publication
• Report/Paper Numbers: MATC-MU: 184, 25-1121-003-184
• Files: UTC, TRIS, RITA, ATRI, USDOT
• Created Date: Dec 23 2013 7:53AM