Human Factors Study to Understand Driver Behavior on Managed Lane Facilities
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Summary
This study investigates how different separation treatments between managed lanes (MLs) and general-purpose lanes (GPLs) influence driver behavior, aiming to inform safety and mobility improvements for transportation agencies like the Florida Department of Transportation (FDOT). The research addresses a gap in understanding the human factors associated with three primary separation types: pylons, buffer areas, and concrete barriers. To achieve this, the authors employed a dual-method approach combining a controlled driving simulator study and a naturalistic driving study using real-world data. The driving simulator study was conducted at the University of Central Florida using a miniSim™ simulator with eye-tracking technology. Sixty participants across three age groups (18–34, 35–64, and 65+) navigated a 6-mile simulated roadway featuring single- and two-lane ML sections. The experiment manipulated separation width (single vs. double solid lines) in both straight and curved segments and separation height (24-inch vs. 28-inch pylons) in curved segments. Performance metrics included deceleration, speed, lane deviation, steering angle, and visual attention. The naturalistic driving study analyzed real-world data from ML facilities in Florida and Washington State, sourced from the Regional Integrated Transportation Information System (RITIS) and the Second Strategic Highway Research Program (SHRP2). This analysis focused on lane utilization, travel speed, and lane deviation for facilities utilizing buffer, pylon, or concrete barrier separations. Results from the simulator study indicated that double solid lines were associated with higher mean speeds, particularly when combined with 28-inch pylons in curved segments, and resulted in shorter fixation durations. Conversely, double solid lines combined with 24-inch pylons led to greater lane deviation away from the separator. Over half of the participants reported that double solid lines and 28-inch pylons were more noticeable. In the naturalistic study, buffer-separated facilities exhibited the highest lane utilization for the leftmost GPL, while concrete barriers and pylons reduced utilization by 12.8% and 8.6%, respectively, compared to buffers. On the rightmost ML, pylon separations decreased utilization by 20% compared to buffers, whereas concrete barriers increased it by 2%. Buffer separations also correlated with higher average speeds on MLs. Regarding lane positioning, drivers in MLs adjacent to separators tended to drive away from the separator for all types, with the greatest magnitude of deviation observed in buffer-separated facilities. The findings provide critical evidence for transportation agencies to select appropriate separation treatments based on desired operational outcomes. By quantifying how specific physical separations affect speed, lane choice, and lateral positioning, the study enables agencies to implement designs that optimize safety and mobility. The research highlights that while buffers may encourage higher speeds and utilization, they also induce greater lateral deviation, suggesting a trade-off between throughput and lateral stability that must be considered in facility design.
Key finding
Buffer-separated managed lanes yield higher average travel speeds and lane utilization compared to pylon or concrete barrier separations, while double solid lines combined with 28-inch pylons in simulators produced higher speeds and shorter visual fixation durations.
Methodology
mixed_methods
Sample size: 60
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: behavioral performance data, observational prevalence
- Methodological Resource: dataset resource