Association of Selected Intersection Factors with Red-Light-Running Crashes

Mohamedshah, Yusuf M.; Chen, Li Wan; Council, Forrest M. · 2000 · ROSA P / Turner-Fairbank Highway Research Center

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Summary

This study investigates the relationship between specific intersection geometric and traffic characteristics and the frequency of red-light-running (RLR) crashes. Motivated by the significant safety burden of RLR incidents—which account for 16–20% of crashes at urban signalized intersections and result in higher injury rates than other urban crash types—the research aims to identify non-driver factors that contribute to RLR risk. The findings are intended to help traffic safety officials target intersections for enforcement measures, such as RLR cameras or heightened police presence. The analysis utilized data from the Highway Safety Information System (HSIS), specifically selecting California due to its comprehensive data linking crash records with intersection geometry and traffic volumes. The dataset covered a four-year period from 1993 to 1996, comprising 4,709 two-vehicle RLR crashes across 1,756 four-legged signalized urban intersections. Researchers filtered crash records using violation and fault variables to identify the specific vehicle that ran the red light and assigned it to a street approach. Two primary analytical methods were employed: contingency table analysis to compare RLR crashes with all urban signalized intersection crashes, and negative binomial regression models to quantify the effects of intersection characteristics on RLR crash frequencies. Separate models were developed for vehicles entering from high-volume "mainline" streets and low-volume "cross-streets." The results indicate that Average Daily Traffic (ADT), intersection width, and signal actuation type are significant predictors of RLR crashes, though their effects vary by street type. For both mainline and cross-street approaches, RLR crashes increased with higher ADT on the entering street. Additionally, fully actuated signals were associated with 35–39% more RLR crashes than pre-timed signals, potentially due to longer cycle lengths and unexpected red phases in high-speed, non-networked suburban areas. Regarding geometry, the number of lanes on the cross-street did not significantly affect mainline RLR crashes. However, for vehicles entering from the cross-street, each additional lane on the mainline increased RLR crash risk by 7%. Furthermore, higher ADT per lane on the cross-street increased RLR crashes for mainline vehicles, but mainline volume did not significantly impact cross-street RLR crashes. The study concludes that while these geometric and traffic flow variables are difficult to modify through design, they provide a basis for prioritizing intersections for enforcement. High-priority locations for countermeasures include intersections with high entering volumes, wide mainlines coupled with high minor-road volumes, and those equipped with fully actuated signals. These findings offer a data-driven approach to allocating limited enforcement resources to areas with the highest predicted risk of red-light-running violations.

Key finding

Intersections with fully actuated signals experienced 35 to 39 percent more red-light-running crashes than those with pre-timed signals, and higher traffic volumes on entering and crossing streets significantly increased crash frequencies.

Methodology

dataset

Sample size: 1756

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).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• roadway lighting effects
• incidence prevalence
• crash typology
• rail grade crossings

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