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

Abbas, Montasir; Machiani, Sahar Ghanipoor; Garvey, Philip M.; Farkas, Andrew; Lord-Attivor, Rene; Virginia Polytechnic Institute and State University; Pennsylvania State University; Morgan State University

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

2014-04-01
By Abbas, Montasir ; Machiani, Sahar Ghanipoor ; Garvey, Philip M. ; ...

[PDF-2.00 MB]

English

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Creators:

Abbas, Montasir ; Machiani, Sahar Ghanipoor ; Garvey, Philip M. ; Farkas, Andrew ; Lord-Attivor, Rene

Abbas, Montasir ; Machiani, Sahar Ghanipoor ; Garvey, Philip M. ; Farkas, Andrew ; Lord-Attivor, Rene Less -
Corporate Creators:

Virginia Polytechnic Institute and State University ; Pennsylvania State University ; Morgan State University

Virginia Polytechnic Institute and State University ; Pennsylvania State University ; Morgan State University Less -
Corporate Contributors:

United States. Dept. of Transportation. Research and Innovative Technology Administration
Subject/TRT Terms:

[+]

Decision Making Dilemma Zone Drivers Driving Simulators Learning Perception Research Hub Signalized Intersections Surveys
Publication/ Report Number:

MAUTC-2012-04 ; LTI 2014-12

MAUTC-2012-04 ; LTI 2014-12 Less -
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:sha256:545e09ee90089c1e4f272326110f7cd68a634c0d286aa47b180db081f029ae23
Download URL:

https://rosap.ntl.bts.gov/view/dot/27315/dot_27315_DS1.pdf
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Modeling the Dynamics of Driver’s Dilemma Zone Perception Using Machine Learning Methods for Safer Intersection Control

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