Citation

Cooper J, Strayer DL. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2008; 52(23): 1920-1924.

Copyright

(Copyright © 2008, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1177/154193120805202319

PMID

unavailable

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.

Language: en