Evidence map›Paper›PMID 41958943›Full record

ArticleJournal of transportation technologies2025

Classifying Cognitive Decline in Older Drivers from Behavior on Adverse Roads Detected Using Computer Vision.

Md Zahid Hasan, Guillermo Basulto-Elias, Shauna Hallmark, Jun Ha Chang, Anuj Sharma, Jeffrey D Dawson, Soumik Sarkar, Matthew Rizzo

Abstract read
In one paragraph

Article in Journal of transportation technologies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Md Zahid HasanInstitute for Transportation, Iowa State University, Ames, Iowa, USA.
Guillermo Basulto-EliasInstitute for Transportation, Iowa State University, Ames, Iowa, USA.
Shauna HallmarkInstitute for Transportation, Iowa State University, Ames, Iowa, USA.
Jun Ha ChangDepartment of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Anuj SharmaInstitute for Transportation, Iowa State University, Ames, Iowa, USA.
Jeffrey D DawsonDepartment of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA.
Soumik SarkarInstitute for Transportation, Iowa State University, Ames, Iowa, USA.
Matthew RizzoDepartment of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA.

Funding

Tracking and Evaluation CoreU54GM115458 · NIGMS · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI PHILIBERT, INGRID · 2016 to 2025
$42.8M
PREDICTIONS OF DRIVER SAFETY IN ADVANCING AGER01AG017177 · NIA · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI RIZZO, MATTHEW · 1999 to 2024
$9.9M
NIA NIH HHS R01 AG017177NIGMS NIH HHS U54 GM115458
6 · The paper itself

Abstract

As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as inclement weather, is especially challenging for older drivers due to their sensitivity to glare and reduced visibility. As a result, older drivers may adjust their behavior during adverse weather. This paper explores the differential impacts of weather on older drivers with cognitive decline compared to older drivers with normal cognitive function. Data were from a naturalistic driving study of older drivers in Omaha, Nebraska. Driver speed and weather data were extracted and the correlation between speed compliance, road weather conditions, and the cognitive/neurological status of the drivers was examined. Speed compliance was used as the surrogate safety measure since driving at lower speeds can indicate that the driver is challenged by roadway or environmental conditions and can therefore indicate a risk. The percentage of time during a trip when drivers were 16.1 kph under the speed limit was modeled as the dependent variable using beta regression. The variables that resulted in the best fit model were mild cognitive impairment (MCI), age group, traffic density, and weather. Results indicated that the youngest group of older drivers (young-old) spent less time driving at impeding speeds and had the least variability compared to the other two age groups. The middle group of older drivers (middle-old) had the highest amount of time driving at impeding speeds and had more variability than young-old drivers. The oldest group of older drivers (old-old) were the most likely to drive at impeding speeds and had the most variability. In general, older drivers were more likely to drive at impeding speeds during peak hours than during non-peak hours. Additionally, in most cases, older drivers spent less time below the speed limit when the weather was clear than in adverse conditions. Results indicate that older drivers are impacted by weather conditions, and distinct patterns were noted between older drivers who were cognitively impaired compared to drivers with normal cognition.

Indexed as

cognitive impairmentmachine learningolder driverspeedTraffic safety

Identifiers

PMID41958943
PMCPMC13060549

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.