Compliance and Surrogate Safety Measures for Uncontrolled Crosswalk Crossings in Oregon
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Summary
This study addresses the limitations of relying solely on historical crash data for highway safety analysis, which is reactive, slow, and often incomplete due to low exposure rates. The primary objective was to determine if readily observable field measurements and geometric data could serve as reliable predictors of relative safety performance at unsignalized marked crosswalks in Oregon. Specifically, the research sought to identify macro-level surrogate safety measures that could help transportation agencies evaluate the need for crosswalk improvements without waiting for crash occurrences. The methodology combined a comprehensive literature review with empirical data collection. Researchers first analyzed statewide pedestrian crash data from 2007 to 2014 to identify significant predictors of crashes, including road characteristics, traffic conditions, and land use. Based on these findings, nine sites were selected for detailed field data collection. At these locations, video and radar equipment were deployed to capture pedestrian and vehicle interactions. The data collection process was designed to be efficient, requiring limited staff hours and portable equipment. Qualitative and quantitative analyses were conducted on the recorded interactions to assess driver compliance (such as stopping behavior) and pedestrian-vehicle conflicts. The analysis revealed that traditional crash-based metrics were insufficient for proactive safety management. Through the examination of field data, the study identified two specific metrics as effective macro-level surrogate measures: Normalized Entering Volume (NEV) and No-Stop Percentage (NSP). NEV accounts for the volume of vehicles entering the crosswalk area relative to pedestrian exposure, while NSP measures the proportion of vehicles that fail to stop for pedestrians. The results demonstrated that these metrics are strongly associated with pedestrian-vehicle conflicts. The study found that higher no-stop percentages and specific entering volume patterns correlated with increased dangerous interactions, providing a quantifiable basis for assessing risk at unsignalized crossings. The significance of this research lies in its provision of a proactive, data-driven tool for safety prioritization. By recommending NEV and NSP as standardized surrogate metrics, the study enables transportation agencies to identify high-risk crosswalks before crashes occur. These measures are based on observable field data and geometric information, making them practical for routine use by staff with limited resources. This approach shifts safety management from a reactive model dependent on historical crash records to a predictive model capable of identifying hazards through real-time behavioral and traffic data, ultimately supporting more effective allocation of safety improvement resources.
Key finding
Normalized entering volume and no-stop percentage are strongly associated with pedestrian-vehicle conflicts and are recommended as macro-level surrogate metrics for analyzing pedestrian safety at unsignalized marked crosswalks.
Methodology
field_study
Sample size: 9
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.
- pedestrian behavior perception
- rail grade crossings
- incidence prevalence
- driver vru interaction
- child pedestrian
- roadway lighting effects
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