Implementing the Safe System Approach for Speed Management in Utah

Schultz, Grant G.; Tripp, Kezia I.; Goss, Abbie L. · 2026 · ROSA P / Utah Department of Transportation

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Summary

This research addresses the critical need for effective speed management to reduce fatal and serious injury crashes in Utah. Speed is identified as a primary determinant of crash severity, with speed-related incidents accounting for 32% of fatal crashes in Utah between 2019 and 2024. The study investigates how the Safe System Approach—a paradigm that acknowledges human error and vulnerability and aims to design systems that minimize crash severity—can be applied specifically to speed management. The research was motivated by the limitations of traditional safety methods, which often rely on reactive measures and place sole responsibility on road users, whereas the Safe System Approach emphasizes proactive, shared responsibility and systemic risk reduction. The methodology involved a comprehensive literature review and a "compendium of practice" analysis. The researchers evaluated federal guidance, particularly the Federal Highway Administration’s Safe System Approach for Speed Management Framework, and reviewed existing policies within the Utah Department of Transportation (UDOT). Additionally, the study analyzed case studies from peer jurisdictions, including cities such as Fremont, New York City, and Portland, and state Departments of Transportation in Washington, Nevada, Massachusetts, Minnesota, and California. The research identified and categorized 32 specific countermeasures, ranging from policy-based programs and automated enforcement to roadway design treatments like road diets, roundabouts, curb extensions, and gateway features. The analysis focused on how these jurisdictions implemented coordinated strategies and utilized data to align speed limits with safety goals. The findings indicate that effective speed management under the Safe System Approach requires the coordinated implementation of multiple strategies rather than isolated interventions. Jurisdictions that successfully reduced severe crashes combined policy direction, roadway design, enforcement, education, and land use considerations. The study highlights the critical role of high-quality, context-sensitive data, noting that proactive use of operating speed data, crash trends, and land use context allows agencies to identify systemic risks before severe crashes occur. Conversely, the research identifies limitations in traditional speed study methods, which often reflect existing driver behavior rather than safe operating conditions. Furthermore, establishing a clear, shared vision embedded in formal documentation, such as Strategic Highway Safety Plans, was found to be essential for building consensus and aligning state and local partners. The significance of this research lies in its provision of a structured, evidence-based path for UDOT to advance safe speeds and strengthen its safety culture. The authors recommend that UDOT continue implementing countermeasures in speed limit setting policies, evaluate current policies regarding speed safety cameras, and incorporate Safe System Approach practices into its Strategic Highway Safety Plan. Additional recommendations include creating a dedicated speed management action plan and emphasizing community education. By leveraging available federal and state tools and adopting a proactive, data-driven framework, the study concludes that UDOT can better align roadway design and speed limits with human tolerance limits, thereby reducing fatal and serious injury crashes across the state’s roadway network.

Key finding

Effective Safe System Approach speed management depends on the coordinated implementation of multiple strategies, such as policy, design, and enforcement, rather than reliance on single countermeasures.

Methodology

review

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 (5 acquisition events logged).

StageOutcomeToolModelPromptAttemptsCompleted
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 18 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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