Development of a Red-Light Violation Index for Signalized Intersections in the District of Columbia
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Summary
This study addresses the lack of objective criteria for installing Red-Light Cameras (RLCs) in the District of Columbia (DC). Between 1999 and 2010, DC installed approximately 50 RLCs based on perceived violation rates, yet no formal threshold existed to determine when an intersection’s red-light violation (RLV) frequency exceeded normal expectations. The authors argue that using raw violation frequencies is flawed because it fails to account for traffic exposure and intersection geometry, potentially leading to inappropriate camera placement and unintended safety consequences. To resolve this, the research aims to develop a Red-Light Violation Index (RLVI), a probabilistic model that establishes a baseline "background" level of violations for urban intersections based solely on engineering properties, independent of crash records or citation data. The methodology involved a field study of 18 signalized intersections in DC. Researchers collected two-hour video recordings at each site to extract specific operational data, supplemented by field visits to gather geometric characteristics. The study defined a red-light violation as an event where a vehicle enters an intersection and fails to clear it before the onset of the red interval. A regression model was developed using five independent engineering variables: vehicles per hour green, lane configuration, intersection width (clearance distance), duration of green, and posted speed limit. The statistical analysis utilized Analysis of Variance (ANOVA) and Kolmogorov-Smirnov tests to validate the model’s fit and significance. The results demonstrated that the developed regression model was statistically significant at the 5% level, with an R-squared value of 81%, indicating that the five engineering variables explained 81% of the variance in red-light violations. The model successfully predicted the background potential for violations at these intersections. The findings suggest a strong correlation between engineering factors and RLV rates, implying that human factors—often cited in literature as equal contributors to violations—may be reasonably explained by these engineering variables or have minimal independent impact in the DC context. The study confirms that a reliable baseline for expected violations can be established without relying on historical crash or citation data. The significance of this work lies in providing the District Department of Transportation (DDOT) with a standardized, objective tool for prioritizing safety interventions. By establishing a baseline RLVI, engineers can determine if an intersection’s observed violation rate exceeds the expected potential derived from its physical and operational characteristics. This allows for more rational decision-making regarding the installation of RLCs or other countermeasures, ensuring that resources are targeted at locations with genuinely excessive violation rates rather than those with high volumes but normal behavior. The study concludes that such engineering-based indices are essential for mitigating the biases inherent in volume-based violation metrics.
Key finding
A regression model using five engineering variables explained 81% of the variance in red-light violations at signalized intersections.
Methodology
field_study
Sample size: 18
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.
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- Empirical Findings: crash risk outcomes