Safety impacts of reduced visibility in inclement weather.
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Summary
This study investigates the safety impacts of reduced visibility during inclement weather, addressing a gap in transportation research where visibility conditions are rarely documented with high precision in crash reports. Motivated by Federal Highway Administration data indicating that inclement weather contributes to over 13% of injury crashes and 10% of fatal crashes, the research aims to quantify the relationship between visibility levels and crash likelihood while identifying associated risk factors. The researchers employed a three-pronged methodological approach using data from the Strategic Highway Research Program 2 (SHRP2). First, they developed a parametric model using ordinal logistic regression on Florida SHRP2 Roadway Inventory Database (RID) crash data (2010–2012). This dataset was linked with real-time visibility scores from National Oceanic and Atmospheric Administration (NOAA) airport weather stations within a 5-mile buffer of crash sites. Periods of poor (0–0.5 mi) and medium (0.5–4.0 mi) visibility were matched with control periods of normal visibility (9–10 mi) from one week prior or subsequent to control for temporal and spatial variations. Second, a non-parametric analysis using multiple correspondence analysis (MCA) was conducted on Washington SHRP2 RID data to identify key factors associated with inclement weather crashes. Third, the team performed structural topic modeling and text mining on crash narratives from the SHRP2 InSight database to extract qualitative insights. The findings indicate that the likelihood of a crash increases during periods of low visibility, despite concurrent reductions in traffic volume and operating speeds. Descriptive statistics from the Florida dataset showed an increase in injury crashes from nearly 48% in excellent visibility to 50% in poor visibility, suggesting higher crash severity under low-visibility conditions. The MCA analysis identified several key associated factors for inclement weather crashes, including younger and older drivers, low-friction roadways, higher posted speeds, curved and undivided roadways, signalization, and the absence of lighting during dark conditions. The parametric modeling confirmed that variables such as driver age, skid number, and maximum speed significantly influence crash outcomes when visibility is reduced. The significance of this study lies in its ability to provide a direct, quantified relationship between visibility levels and safety outcomes, moving beyond general weather classifications. By identifying specific roadway and driver characteristics that exacerbate risk during low-visibility events, the research offers actionable insights for transportation planners and managers. These findings can inform targeted safety interventions, such as improved lighting, variable speed limits, and infrastructure modifications on high-risk road segments, ultimately enhancing crash prevention strategies in adverse weather conditions.
Key finding
The likelihood of a crash increases during periods of low visibility, with key associated factors including younger and older drivers, low friction roadways, higher posted speeds, curved and undivided roadways, signalization, and lack of lighting at night.
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 | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- weather rain fog snow
- incidence prevalence
- naturalistic crash near crash
- roadway lighting effects
- pre crash contributing factors
- visibility analysis litigation
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
- Methodological Resource: dataset resource