Using the New SHRP2 Naturalistic Driving Study Safety Databases to Examine Safety Concerns for Older Drivers
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Summary
This study investigates the safety concerns of older drivers (age 65 and over) in the New England region, who exhibit higher crash and fatality rates than middle-aged drivers, particularly during left turns at signalized intersections. Motivated by the aging U.S. population and the lack of naturalistic data on this specific demographic, the research aims to identify contributing factors to these crashes and assess the utility of the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) database for such analyses. The findings are intended to inform countermeasures and training programs to improve intersection safety. The researchers utilized SHRP2 NDS data, which includes video recordings, sensor data, and participant surveys from 3,400 drivers. The study focused on 884 trips involving 586 drivers, comprising all trips by drivers aged 65+ involving signalized intersections with crashes or near-crashes, a baseline sample of non-eventful trips for this age group, and a comparison sample of drivers aged 30–49. Data variables included driver health, cognitive and visual abilities, vehicle characteristics, and intersection details. Researchers scored dash-cam videos using a detailed rubric to code intersection geometry, traffic signals, and opposing traffic conditions. The data were analyzed using descriptive statistics and three supervised machine learning models: Logistic Regression, Support Vector Machines, and Random Forest algorithms to predict crash risk and identify significant variables. The results indicated that 81% of left-turn intersection crashes occurred at 4-way intersections and 15% at T-intersections. Most crashes involving older drivers were minor, with over 70% involving vehicles hitting a curb or leaving the roadway. The statistical analysis revealed that the most significant variables impacting crash likelihood were related to the drivers' health, specifically visual and cognitive factors. These impairments hindered the drivers' ability to monitor oncoming traffic and identify sufficient gaps for safe turns, particularly during permissive left turns. The study noted that training programs helping older drivers adjust to these age-related limitations have proven beneficial. However, the statistical significance of the findings was limited by the small number of crashes in the dataset. The study concludes that SHRP2 NDS data is a valuable tool for examining older driver safety, offering high ecological validity compared to simulator studies. The identification of visual and cognitive deficits as primary crash contributors suggests that targeted training and intersection design modifications could mitigate risks for this demographic. As the population of older drivers grows, leveraging naturalistic data to understand their specific behavioral challenges is critical for maintaining their driving independence and ensuring roadway safety for all users.
Key finding
Most crashes involving older drivers at signalized intersections were minor events hitting curbs or leaving the roadway, and statistical significance was driven by health, visual, and cognitive factors affecting gap acceptance and traffic monitoring.
Methodology
naturalistic
Sample size: 586
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_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 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
Ranked by relevance to this paper. Hover a topic for its definition.
- naturalistic crash near crash
- older drivers
- sex gender
- novice drivers
- incidence prevalence
- telematics crash prediction
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: crash risk outcomes, observational prevalence
- Methodological Resource: dataset resource