Naturalistic speeding data: Drivers aged 75 years and older
Anna Chevalier,
Aran John Chevalier,
Elizabeth Clarke,
John Wall,
Kristy Coxon,
Julie Brown,
Rebecca Ivers,
Lisa Keay
Affiliations
Anna Chevalier
The George Institute for Global Health, Sydney Medical School, The University of Sydney, GPO Box 5389, Sydney, NSW 2001, Australia; Corresponding author.
Aran John Chevalier
Safer Roads Consulting, 53 Lachlan St, Thirroul, NSW 2515, Australia
Elizabeth Clarke
Kolling Institute of Medical Research, Sydney Medical School, The University of Sydney, Level 10, Kolling Building 6, Royal North Shore Hospital, St Leonards, NSW 2065, Australia
John Wall
The Centre for Road Safety, Transport for NSW, Level 3, 84 Crown St, Wollongong, NSW 2500, Australia
Kristy Coxon
The George Institute for Global Health, Sydney Medical School, The University of Sydney, GPO Box 5389, Sydney, NSW 2001, Australia; School of Science and Health, Western Sydney University, Narellan Road Campbelltown, NSW 2560, Australia
Julie Brown
Neuroscience Research Australia (NeuRA), Margarete Ainsworth Building, Barker St, Randwick, NSW 2031, Australia
Rebecca Ivers
The George Institute for Global Health, Sydney Medical School, The University of Sydney, GPO Box 5389, Sydney, NSW 2001, Australia
Lisa Keay
The George Institute for Global Health, Sydney Medical School, The University of Sydney, GPO Box 5389, Sydney, NSW 2001, Australia
The data presented in this article are related to the research article entitled “A longitudinal investigation of the predictors of older drivers׳ speeding behavior” (Chevalier et al., 2016) [1], wherein these speed events were used to investigate older drivers speeding behavior and the influence of cognition, vision, functional decline, and self-reported citations and crashes on speeding behavior over a year of driving. Naturalistic speeding behavior data were collected for up to 52 weeks from volunteer drivers aged 75–94 years (median 80 years, 52% male) living in the suburban outskirts of Sydney. Driving data were collected using an in-vehicle monitoring device. Global Positioning System (GPS) data were recorded at each second and determined driving speed through triangulation of satellite collected location data. Driving speed data were linked with mapped speed zone data based on a service-provider database. To measure speeding behavior, speed events were defined as driving 1 km/h or more, with a 3% tolerance, above a single speed limit, averaged over 30 s. The data contains a row per 124,374 speed events. This article contains information about data processing and quality control. Keywords: Older drivers, Speed, Road safety, Naturalistic, In-vehicle monitoring, Device
