JSM 2005 Online Program

Abstract #303743

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	317
Type:	Topic Contributed
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	General Methodology
Abstract - #303743
Title:	Accounting for the Covariate Effects in Driving Simulator Studies
Author(s):	Linda N. Boyle and Birsen Donmez*+ and David M. Neyens and John D. Lee
Companies:	The University of Iowa and The University of Iowa and The University of Iowa and The University of Iowa
Address:	3131 Seamans Center, Iowa City, IA, 52242,
Keywords:	covariates ; driving simulator ; driver distraction ; braking events
Abstract:	A driver's response to a braking event is dependent on many factors that are difficult to control experimentally. Performance measures are interrelated and conclusions regarding performance should not be drawn solely on one measure. In driving simulator studies on forward collisions, lead vehicle braking events are common scenarios to test with the performance measure being brake response time. Because time headway is a factor that cannot be accurately controlled in simulator studies, the driver's brake response times will be impacted by this factor and need to be considered during the analysis. This study demonstrates how results are affected when the covariance is not considered. We analyzed the effects of driver distraction on driving performance with time headway included as a covariate and as not. The results with and without the covariate effects strongly contradict each other. Results obtained with the covariate analysis show distractions have detrimental effects on braking response, whereas distractions appear to improve performance when the covariate is not considered.

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