Effectiveness of Speed Management Methods in Work Zones

Brown, Henry; Edara, Praveen; Sun, Carlos; Zeng, Qingzhong; Qing, Zhu; Long, Suzanna; Engstrom, Slade; Patil, Shivraj · 2022 · ROSA P / Missouri. Department of Transportation. Construction and Materials Division

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Summary

This study investigates the effectiveness of various speed management countermeasures in highway work zones to reduce crash risks. Sponsored by the Missouri Department of Transportation (MoDOT) and conducted by the University of Missouri-Columbia, the research evaluates strategies including speed display trailers, trailers with red and blue lights, work vehicles with flashing lights, and both active and passive law enforcement. The motivation stems from the need to improve compliance with posted work zone speed limits, as speeding is a significant factor in work zone crashes. The methodology comprised a literature review, a field study, a driving simulator study, and two driver surveys. The field study was conducted over four weeks at a work zone on Interstate 270 in the St. Louis region. Researchers used radar sensors upstream and downstream of countermeasure locations to collect speed data during both daytime (6 a.m. to 6 p.m.) and nighttime (6 p.m. to 6 a.m.). Countermeasures tested included no treatment (baseline), standard speed trailers, speed trailers with flashing feedback or lights, passive law enforcement (stationed police vehicle), active law enforcement (officers issuing citations), and work vehicles with flashing lights (nighttime only). The simulator study involved 50 participants navigating 13 scenarios in a medium-fidelity simulator, allowing for the testing of combined strategies and the collection of eye-tracking data. Additionally, post-simulator and general online driver surveys gathered perceptions and self-reported behaviors from 108 respondents. Results from the field study indicated that all tested countermeasures achieved speed reductions, with active law enforcement proving the most effective for both daytime and nighttime conditions. Countermeasures generally performed better at night. The simulator study revealed that combining a speed display trailer with active law enforcement ("super law enforcement") was the most effective strategy for daytime speed reduction. For nighttime conditions, the speed display trailer alone was the most effective. Eye-tracking data showed that "super law enforcement" had better visibility during the day, while speed trailers were more visible at night. Survey results highlighted a discrepancy between preference and efficacy: drivers generally preferred speed display trailers over law enforcement, citing the latter as potentially distracting, yet admitted that law enforcement was the most likely factor to cause them to slow down. Respondents identified the presence of active work and visibility as the primary factors influencing their speed selection. The study concludes that while law enforcement is the most effective single strategy for reducing speeds, it may not be feasible for all sites due to resource constraints. Therefore, a multi-strategy approach tailored to specific project conditions—such as traffic volume, work type, and cost—is recommended. The authors suggest that deploying multiple countermeasures, such as placing a second device 250 to 500 feet downstream from the first, could help prevent drivers from accelerating after passing the initial countermeasure. The findings provide MoDOT and other agencies with evidence-based guidance for selecting and implementing speed management tools to enhance work zone safety.

Key finding

Active law enforcement was the most effective single countermeasure for reducing speeds in the field study, while combining a speed display trailer with active law enforcement yielded the greatest speed reductions in the simulator during daytime conditions.

Methodology

mixed_methods

Sample size: 50

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).

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clean success 1 2026-06-01
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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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