Exploring rear-end roadway crashes from the driver's perspective
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This 1998 pilot study by Kostyniuk and Eby investigates rear-end roadway crashes from the driver’s perspective to identify self-reported causes and evaluate the utility of this approach for developing countermeasures. Motivated by the persistence of rear-end collisions despite existing vehicle and roadway safety features, the research aimed to determine if understanding driver perceptions could yield innovative solutions. The study sought to identify reasons for crashes, relate these reasons to specific situations and locations, and assess drivers' recognition of hazard cues. The researchers employed a qualitative methodology involving focus groups and telephone interviews with 26 drivers in Michigan who had recently been involved in rear-end crashes as the striking vehicle. Participants were recruited from state crash records, stratified by age (19–24, 25–64, and 65+), and gender. Due to low participation rates in focus groups, data collection shifted to telephone interviews for many subjects. The study analyzed responses using two distinct questioning methods: "question-based factors," where drivers were directly asked for causes, and "explanation-based factors," where drivers narrated the events leading up to the crash. The results revealed significant discrepancies in reported causes depending on the questioning method. When asked directly, 49% of drivers attributed the crash to the actions of the other driver, while 31% cited personal inattention or distraction. However, narrative explanations highlighted cognitive issues as the primary drivers of crashes. Approximately 70% of explanation-based causes were attributed to problems with divided attention or incorrect assumptions about traffic movement. These incorrect assumptions were categorized as "normative" (based on standard driving norms, such as assuming a car will move at a green light) and "non-normative" (illogical inferences, such as assuming a car is moving because traffic in another lane is moving). Non-normative assumptions were more prevalent among younger drivers, while divided attention issues were more common among older drivers. Additionally, drivers reported few cues indicating an imminent crash, often stating the event occurred before they could react. The study concludes that the driver-perspective approach, particularly through narrative explanation, is valuable for conceptualizing countermeasures. The findings suggest that countermeasures should target cognitive failures, such as providing unambiguous signals for stopped vehicles to address normative assumption errors, or enhancing attention monitoring to mitigate divided attention issues. Drivers expressed positive attitudes toward Intelligent Transportation Systems (ITS) like headway-monitoring and sleep-monitoring devices, with an average willingness to pay approximately $500 and $400, respectively. The research validates the use of cognitive classification frameworks for designing future crash-prevention technologies.
Key finding
Drivers attributed rear-end crashes primarily to the actions of other drivers when asked directly, but explanation-based analysis revealed that divided attention and incorrect assumptions about traffic movement accounted for approximately 70 percent of the causes.
Methodology
mixed_methods
Sample size: 26
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.
- pre crash contributing factors
- crash typology
- driver post crash behavior
- causation analyses
- incidence prevalence
- crash reconstruction hf
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
- Theoretical Contribution: theory or model