Modeling the Dynamics of Driver’s Dilemma Zone Perception Using Machine Learning Methods for Safer Intersection Control

Details

• Creators:
  Abbas, Montasir ; Machiani, Sahar Ghanipoor ; Garvey, Philip M. ; Farkas, Andrew ; Lord-Attivor, Rene
• Corporate Creators:
  Virginia Polytechnic Institute and State University ; Pennsylvania State University ; Morgan State University
• Corporate Contributors:
  United States. Department of Transportation. Research and Innovative Technology Administration
• Subject/TRT Terms:
  Decision Making Dilemma Zone Drivers Research Hub Driving Simulators Learning Perception Signalized Intersections Surveys
• Publication/ Report Number:
  MAUTC-2012-04;LTI 2014-12
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Mid-Atlantic Universities Transportation Center
• Abstract:
  The "dilemma zone" (DZ) is defined as the area where drivers approaching a signalized intersection must decide to either proceed or stop at the onset of the yellow indication. Drivers that might perceive themselves to be too close to an intersection for a safe stop, and too far to proceed without violating traffic regulations, are said to be caught in DZ. Despite the vast body of related literature, there is a critical gap in research related to the "dynamic nature of drivers' decision" in dilemma zones. In order to identify and capture all significant factors beyond existing research, a driver survey was administered in the three states of Virginia, Pennsylvania, and Maryland. State-of-the-art techniques in human psychology, experimental design, and statistical analysis were used to design the survey and interpret the results. A driving simulator study was conducted to investigate the dynamic nature of driver perception of the dilemma zone and to assess significant factors affecting a driver's decision at the onset of yellow. In addition, the use of machine learning methods to capture the effect of a driver's learning/dynamic perception of DZ was investigated. Findings from this research suggest that drivers do learn from their experience and also that agent-based models can be used for modeling driver behavior in the dilemma zone more accurately than models that currently exist in the literature. The research team therefore recommends that agent-based modeling and simulation techniques should be used for assessing the impacts of dilemma zone mitigation strategies.
• Format:
  PDF
• Funding:
  DTRT12-G-UTC03
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:7fc4682c17f5c64174c49fdfb4b6d220d9c5601a2409fb0ba4eff7f09a39a25837c2dba05a141256ceb76d6782f8a4c816d8942853f3d59754615c5e34af22a8
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/27315/dot_27315_DS1.pdf
• File Type:
  [PDF - 2.00 MB ]