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. 2016 Sep 26:5:2376.
doi: 10.12688/f1000research.9608.2. eCollection 2016.

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Ganesh M Babulal  1 , Cindy M Traub  2 , Mollie Webb  2 , Sarah H Stout  1 , Aaron Addison  2 , David B Carr  3 , Brian R Ott  4 , John C Morris  5 , Catherine M Roe  1
Affiliations

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Ganesh M Babulal et al. F1000Res. .

Abstract

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking Device TM was installed in each participant's vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven. Setting: The Knight Alzheimer's Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance. Participants were aged 65+ years and had normal cognition. Measurements: Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations. Temporal components included number of trips taken during different times of the day. Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods: Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS. Results: Primary locations were confirmed to be 100% accurate when compared to known addresses. Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns. Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night. Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.

Keywords: Alzheimer’s disease; geographic information system; global positioning data acquisition systems; in-vehicle technology; naturalistic driving; older adults.

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Conflict of interest statement

Competing interests: Ganesh Babulal, Cindy Traub, Molly Webb, Sarah Stout and Aaron Addison declare they have no competing interest.Brian Ott: Grants and funds: Eli Lily, Avid, Roche, TauRX, Merck, Univita, NIH/NIA; Honoria: NHTSA: Medscape; Consultant: Accera (DSMB).David Carr: Grants and funds: Missouri Department of Transportation; Honorarium: Harvard Speaker; Consultant: Traffic Injury Research Foundation, Advanced Drivers Education Products and Training; Medscape.John Morris: Grants/Funds: Healthy Aging and Senile Dementia, Antecedent Biomarkers for AD: The Adult Children Study, The Dominantly Inherited Alzheimer Network, and Alzheimer Disease Research Center. Honoraria: Cherkin Lecture, Chinese Society, 13 th Eibsee Meeting (Keynote Speaker), Korean Dementia Association, and DZNE research center Magdeburg Symposium. Consultant: Lilly USA; ISIS Pharmaceuticals; Charles Dana Foundation. Royalties: Blackwell Medical Publishers; Taylor & Francis. Board Member: Board of Directors American Academy of Neurology (AAN).Catherine Roe: Grants/Funds: NIH/NIA.

Figures

Figure 1.
Figure 1.. Data workflow required to generate driving metrics over different time scales.
Figure 2.
Figure 2.. Breadcrumbs and street centerline features used in near analysis.
Figure 3.
Figure 3.. A primary location polygon created using the aggregate points geoprocessing operation with a primary location point placed at its centroid.
Figure 4.
Figure 4.. Unique destination sample with multiple buffers.
Figure 5.
Figure 5.. Spatial profiles.
Figure 6.
Figure 6.. Area of overlap between Driving Areas from month to month.
Figure 7.
Figure 7.. Ratio of overlapping driving area to total driving area over the 5 month period.
Figure 8.
Figure 8.. Counts of unique destinations for 5 participants.
Figure 9.
Figure 9.. Number of trips driven during day and night.
Figure 10.
Figure 10.. Total number of alerts across the 5 months.
See this image and copyright information in PMC

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