Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis
DOI: 10.1016/j.jsr.2020.07.001
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Summary
This study investigates the patterns and contributing factors associated with crashes involving pedestrians under the influence (PUI) of alcohol or drugs. While alcohol impairment is a well-documented factor in driver crashes, research specifically focusing on pedestrian impairment remains limited. The motivation for this research stems from a significant increase in pedestrian fatalities in Louisiana between 2010 and 2016, with a 62% overall rise and a 120% increase in alcohol-related PUI fatalities. In 2016, 34.4% of pedestrian fatalities in the state involved impaired pedestrians. The study aims to identify key attributes and association patterns linked to PUI crashes to inform effective mitigation strategies. The researchers analyzed seven years (2010–2016) of police-reported traffic crash data from the Louisiana Department of Transportation and Development. From a total of 11,386 pedestrian crashes, 1,231 were identified as PUI incidents. The study employed correspondence regression analysis, an innovative dimension reduction method, to examine the relationships between crash severity levels and various explanatory variables, including lighting conditions, roadway types, pedestrian actions, and environmental factors. Descriptive statistics and chi-squared tests were also used to assess significant differences across injury levels. The analysis revealed five distinct risk clusters for PUI crashes: intersection crashes at business/industrial locations; mid-block crashes on undivided roadways in residential or mixed areas; segment-related crashes involving pedestrians standing in the road; open country crashes occurring at night with no lighting; and crashes involving pedestrian violations on divided roadways. The correspondence regression identified specific attributes strongly associated with fatal and severe injuries. Notably, "dark with no lighting," "open country" roadways, and "non-intersection" locations were critical factors linked to higher severity outcomes. Descriptive data further indicated that PUI crashes occurred more frequently on weekends, on two-way undivided roadways, and involved male pedestrians more often than females. Additionally, crashes were most prevalent during clear weather conditions and when pedestrians were not crossing at intersections. The findings highlight the complex interplay between pedestrian impairment, environmental conditions, and roadway design. The identification of specific risk clusters and high-severity attributes, such as the lack of lighting in rural areas, provides actionable insights for traffic safety planning. The study concludes that innovative analytical methods like correspondence regression can uncover hidden patterns in crash data that traditional methods might miss. These results can guide the development of targeted countermeasures, such as improved lighting in high-risk areas and enhanced enforcement of pedestrian violations, to reduce the frequency and severity of PUI crashes.
Key finding
Correspondence regression analysis of Louisiana crash data identified five risk clusters for pedestrians under the influence, with fatal and severe crashes strongly associated with dark conditions lacking lighting, open country roadways, and non-intersection locations.
Methodology
dataset
Sample size: 1231
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | success | semantic_scholar | — | — | 4 | 2026-06-15 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 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.
- vru crash typology
- demographic disparities
- incidence prevalence
- pre crash contributing factors
- motorcycle crash typology
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