@article{ref1,
title="New insights into driving using recurrence quantification analysis",
journal="Proceedings of the Human Factors and Ergonomic Society annual meeting",
year="2008",
author="Cooper, Joel and Strayer, David L.",
volume="52",
number="23",
pages="1920-1924",
abstract="Traditional measures of central tendency and dispersion, such as the mean and standard deviation, ignore ordering effects in time-series data. Hidden within the ordered regularity of time series' may lie unique human performance characteristics. Recurrence quantification analysis (RQA), a contemporary tool designed for the investigation of nonlinear-time-series data, is used to explore lateral driving movement in a simulated car-following task. This investigation assesses a previously published data set that contrasts baseline driving performance, with performance while legally intoxicated, and hands-free/hand-held cell phone conversation. A number of distinguishing lateral movement characteristics were found using RQA. Free from the constraints imposed by discrete driving measures, RQA has the potential to provide real-time measures of driver workload under a variety of conditions.<p /><p>Language: en</p>",
language="en",
issn="2169-5067",
doi="10.1177/154193120805202319",
url="http://dx.doi.org/10.1177/154193120805202319"
}