Influence of Seasonal and Chronobiological Factors on Driver Behavior and Traffic Accident Risk: A Scoping Review
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Summary
This scoping review investigates the link between seasonal patterns, chronobiological factors, and traffic accident risk, aiming to synthesize existing evidence and identify knowledge gaps. Motivated by the global burden of road traffic injuries, the study addresses how environmental variations, such as photoperiod changes and daylight saving time (DST), disrupt circadian rhythms and impair driver alertness. Adhering to the PRISMA-ScR framework, the authors searched PubMed/MEDLINE, Embase, Cochrane, and LILACS for studies published up to November 2023. From 1,758 records, 40 studies were reviewed, with nine providing specific evidence on the joint influence of seasonality and chronobiology. The analysis synthesized data on hormonal fluctuations, sleep patterns, and driving performance metrics. The review confirmed that seasonal variations significantly impact crash risk through biological and environmental mechanisms. Winter conditions, characterized by reduced photoperiods, lead to melatonin deficits and elevated cortisol levels, causing drowsiness, impaired cognitive control, and increased collision risk due to adverse weather. Conversely, summer months see higher accident frequencies linked to increased traffic volume and fatigue during holidays. DST transitions were found to increase driver fatigue and fatal-accident risk, with effects persisting weeks after the change. Additionally, seasonal sensitivity was linked to compromised psychological well-being. These findings highlight that biological disruptions following seasonal patterns are critical determinants of traffic safety. The study underscores the need for preventive strategies that account for circadian misalignment, sleep health, and seasonal vulnerabilities to mitigate the substantial human and economic costs of sleepy driving.
Key finding
Seasonal patterns in traffic crashes confirmed, with biological disruptions (melatonin deficit, DST transitions, winter conditions) following seasonal patterns that contribute to higher crash rates at certain times of year.
Methodology
review
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 canonical_url on 2026-05-03 (8 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-03 |
| archive | success | unpaywall | — | — | 3 | 2026-05-03 |
| extract | success | cached | — | — | 2 | 2026-06-07 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | openalex | — | — | 2 | 2026-06-01 |
| promote | success | — | — | — | 1 | 2026-05-03 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-07 |
| tag | success | vector_similarity | — | — | 18 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-05-08 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-07; verification: verified.
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Information type
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- Empirical Findings: physiological data, crash risk outcomes
- Theoretical Contribution: theory or model