AAA Longitudinal Research on Aging Drivers (LongROAD) Data User Guide
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Summary
The AAA Longitudinal Research on Aging Drivers (LongROAD) Data User Guide documents the design, methods, and data structure of a large-scale prospective cohort study aimed at understanding the natural history and determinants of driving behavior, safety, and mobility among older adults. Motivated by the aging U.S. population and the limitations of retrospective studies, the project sought to address five primary research questions: identifying risks and protective factors for driving safety; assessing the effects of medication classes on driving outcomes; understanding mechanisms of self-regulation in response to functional decline; evaluating the use and impact of new vehicle technologies; and determining the drivers and health impacts of driving cessation. The study was sponsored by the AAA Foundation for Traffic Safety and conducted by a collaborative team including the University of Michigan, Columbia University, and the Urban Institute. The LongROAD study enrolled 2,990 participants aged 65 to 79 at baseline across five geographically diverse sites: Ann Arbor, MI; Baltimore, MD; Cooperstown, NY; Denver, CO; and La Jolla, CA. Eligible participants were recruited from health systems, required to hold a valid license, drive at least weekly, and possess a vehicle with an accessible OBD-II port. Data collection occurred annually over five years, alternating between in-person and telephone assessments. Objective driving data were initially collected using Danlaw dataloggers installed in vehicles, which recorded speed, acceleration, GPS location, and trip metrics. Bluetooth low-energy tags identified the driver. In the third year, collection shifted to a Tourmo smartphone app, which used kinematic algorithms to define trips and identify drivers based on unique driving signatures. Additional data sources included vehicle inspections, self-report questionnaires on health and driving behaviors, in-person functional assessments, medication reviews, and archival crash records. The guide details the comprehensive data architecture, describing 36 distinct data files that capture baseline characteristics, annual follow-ups, objective driving metrics, and derived variables. It outlines procedures for data cleaning, management, and quality control, including protocols for handling missing data and flagged events. The document also records study modifications over time, such as the transition from dataloggers to smartphone apps, changes to visual acuity charts, and protocol adjustments in response to the COVID-19 pandemic. Specific instructions are provided for importing data into statistical software, merging files, and interpreting skip patterns and derived scales. This resource serves as a critical tool for researchers utilizing the LongROAD dataset to investigate older driver safety and mobility. By providing a detailed account of the study’s methodology, data structure, and evolution, the guide ensures accurate interpretation and analysis of the longitudinal data. The extensive dataset supports future research into the complex relationships between aging, health, medication, technology, and driving outcomes, ultimately aiming to enhance safe mobility and inform policies for older adults transitioning from driving to non-driving modes of transportation.
Key finding
The LongROAD study established a comprehensive longitudinal dataset comprising 2,990 older drivers, utilizing mixed methods including objective telematics, self-reports, and clinical assessments to track driving safety, health functioning, and mobility transitions over time.
Methodology
naturalistic
Sample size: 2990
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_aaa_foundation on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | aaa_foundation | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
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- exposure measurement
- older drivers
- older driver retraining
- mci dementia driving
- incidence prevalence
- dbq psychometrics
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: observational prevalence
- Methodological Resource: dataset resource, tool software