Motor Vehicle Occupant Fatality Risk Based on Person-Time Exposed: Age, Sex, and Period of Week
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Summary
This study addresses the methodological challenges in calculating motor vehicle occupant fatality risk, arguing that traditional exposure denominators (such as population size or vehicle distance) confound risk with covariates like speed and occupancy. The author proposes using "person-time exposed" (occupant hours of travel) as the appropriate denominator to accurately identify high-risk constituents. The research analyzes fatality risk as a function of age, sex, and period of the week using 2009 U.S. data, aiming to reveal specific demographic and temporal patterns obscured by aggregate statistics. The methodology combines data from four primary sources: the 2009 National Household Travel Survey (NHTS) for occupant travel hours; the Fatality Analysis Reporting System (FARS) for fatalities; the National Electronic Injury Surveillance System (NEISS-AIP) for injury data; and U.S. Census data for population estimates. Occupants under five years old, motorcyclists, and bus/truck occupants were excluded due to data limitations. Risk was defined as fatalities or injuries per million occupant hours of travel. Statistical analysis employed hierarchical quasi-likelihood generalized linear models to assess the significance of age, sex, day of the week, and time of day, while controlling for interactions. The results demonstrate that fatality risk exhibits strong circadian periodicities, with the highest risk occurring during late evening and early morning hours. This risk is significantly elevated on weekends, particularly Friday–Saturday and Saturday–Sunday. Young male occupants exhibit the highest fatality rates and risk levels. For instance, 15–19-year-old males faced a peak risk of 20.0 fatalities per million hours (fpm) on Sunday mornings, while 20–29-year-old males peaked at 16.6 fpm on Saturday mornings. Risk was consistently higher for males than females across all age groups. Furthermore, the study found that both drunk-driver-related and nondrunk-driver-related fatalities follow circadian patterns, though out of phase; drunk-driver fatalities peak just after midnight, while nondrunk-driver fatalities peak after midday. The circadian variation in risk persists across all age groups and suggests that drowsiness acts alone or synergistically with alcohol to impair driver performance. The significance of this work lies in its validation of person-time as a superior metric for transportation safety risk analysis. By disaggregating data by person-time, the study reveals that high-risk periods constitute a small fraction of total exposure but account for a disproportionate share of fatalities. This finding implies that safety interventions should target specific high-risk windows, such as late-night weekend hours for young males, rather than relying on broad aggregate averages. The results underscore the critical role of drowsiness and alcohol in fatal crashes, providing evidence-based support for targeted countermeasures.
Key finding
Young male occupants face the highest fatality risk, which peaks approximately 19 times above average during the 12:00-3:00 am period on Saturday mornings.
Methodology
dataset
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.
- sex gender
- demographic disparities
- fatality injury trends
- induced exposure
- exposure measurement
- incidence prevalence
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