Data Reduction of Electronic Measures in Driving Studies

• Activity Number: 651
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Jeffrey D. Dawson*+ and Amy M. Johnson and Elizabeth Dastrup Mills and Ergun Y. Uc and Matthew Rizzo
• Companies: University of Iowa and University of Iowa and University of Iowa and University of Iowa and University of Iowa
• Keywords: Cognition ; Neuropsychology ; Time series ; Driving simulators

Abstract

Our multi-disciplinary research team has been studying in how demographics, disease, and neurological mechanisms predict driving behaviors in simulators, instrumented vehicles, and naturalistic settings. Our goals are a) to find neuropsychological tests that could be effective screening tools for driving safety, b) to find personal and vehicular interventions that could improve driver safety, and c) to use modern technology to modify vehicles to be diagnostics devices for neurological disease and progression. In our simulators and instrumented vehicles, we often collect electronic measures at high capture rates, e.g., between 10 and 100 frames per second. A methodological challenge we face is how to reduce such data to a manageable number of parameters to use for between- and within-driver comparisons. In this presentation, we briefly illustrate several methods of data reduction that we have used, ranging from simple summary statistics (means and variances), to time-to-event survival techniques, to more complex time series methods (Dawson et al, 2010; Johnson et al, 2010). This work is supported by NIH awards AG017177, AG015071, and NS044930, and by Nissan Motor Company.