Aging Road User Survey and Crash Analysis to Identify Issues and Applicable Improvement Strategies for Kansas Conditions
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Summary
This study addresses the growing highway safety concerns associated with Kansas’s aging population, which is projected to increase significantly by 2030. The research was motivated by data showing that drivers aged 65 and older were involved in more than one in five fatalities in Kansas between 2010 and 2014, despite comprising only 14.6% of the population. Older drivers were disproportionately represented in fatal and incapacitating injury crashes compared to all drivers. The primary objective was to identify specific issues, concerns, and barriers related to travel and safety for older drivers in Kansas and to propose applicable improvement strategies. The researchers employed a mixed-methods approach using data from the Kansas Crash Analysis and Reporting System (KCARS) and a dedicated road-user survey. The crash analysis compared older drivers with all drivers and developed three binary logistic regression models to assess crash severity: Model A for single-vehicle crashes with only the older driver, Model B for single-vehicle crashes with an older driver and passengers, and Model C for multi-vehicle crashes involving at least one older driver. The road-user survey gathered direct input on habits, needs, and concerns, utilizing contingency table analysis to identify relationships among variables. Key findings from the crash data indicated that older drivers were more frequently involved in crashes at four-way intersections, on straight and level roads, during daylight hours, and at stop or yield signs. The severity models revealed distinct factors influencing outcomes. For Model A, left turns, day of the week, speed, accident class, and maneuver significantly affected severity. In Model B, accident class, surface type, and vehicle type were significant, while passenger attributes were not. For Model C, the number of vehicles, speed, collision type, maneuver, and two-lane roads were significant predictors. Across all models, safety equipment use, crash location, weather, driver ejection or entrapment, and light conditions distinguished crash severity. Driver gender was not significant in any model. The survey results showed that seatbelt use had the highest probability of occurrence among older drivers. However, driving in heavy traffic, merging, moving away from traffic, and judging gaps were dependent on age group. The study concludes that since over 85% of crash contributory causes were driver-related, infrastructure changes alone are insufficient. Recommended countermeasures include driver awareness programs, licensing restrictions, expanded public transportation options, and targeted law enforcement. These strategies aim to enhance safety and mobility for older drivers, addressing the specific risks identified in Kansas conditions. The findings provide a data-driven basis for policy and program development to mitigate the high severity of crashes involving older drivers.
Key finding
Older drivers were involved in more than one in five fatalities in Kansas from 2010 to 2014, with their percentage of fatal injuries being more than twice that of all drivers.
Methodology
mixed_methods
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.
- incidence prevalence
- sex gender
- demographic disparities
- older drivers
- induced exposure
- pre crash contributing factors
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