ASSESSING THE IMPACT OF ROADWAY CHARACTERISTICS ON OLDER DRIVERS USING NATURALISTIC DRIVING DATA. (11th November 2018)

• Record Type: Journal Article
• Authors: Wang, S; Sharma, A; Merickel, J; Rizzo, M
• Is Part Of: Innovation in aging. Volume 2(2018)Supplement 1
• Page Start: 830
• Page End: 830
• Publication Date: 2018-11-11
• Subjects: Aging -- Periodicals; Gerontology -- Periodicals; 612.67
• DOI: 10.1093/geroni/igy023.3090
• Languages: English
• ISSNs: 2399-5300

Abstract

Abstract: Our overarching goal is to measure the real-time effects of age-related cognitive dysfunction in safety-critical, real-world tasks like driving. Method: To tackle this problem of older driver safety, we recorded continuous data (3 months, 9, 206 drives) from sensors installed in 76 drivers (65 to 90 years' old, µ = 75) own vehicles. We tested the hypothesis that driver cognitive functioning predicts vehicle speed behavior in cognitively demanding speed transition zones (changes in posted speed limit). Cognitive function was measured via a composite score across 8 neuropsychological tests. Roadway characteristics were determined from Geospatial Information System databases (speed limit, roadway type, average traffic density). Results: Drivers with greater cognitive dysfunction were more likely to decrease their speed appropriately in speed transition zones (p = 0.02). Drivers were less likely to appropriately decrease speed on interstate or high traffic roadways, demonstrating the importance of environmental context in driver behavior models (p < 0.001). Discussion: Cognitive dysfunction is directly linked to driver behavior strategies on road. Relatively higher reduction speeds by certain drivers can increase the speed variance in these zones thus leading to higher overall crash risks. Results suggest that drivers with cognitive dysfunction may adopt compensatory driving strategies, like lower speed, to mitigate risk, but may fail to adequately apply these strategies in high-risk environments like interstate and high traffic roadways. Real-world sensor data coupled with detailed profiles of an older driver's functional abilities can inform patients, caregivers, interventions, policy, and the design of supportive in-vehicle technology for at-risk older drivers.