Modeling Driver Compliance to VSL and Quantifying Impacts of Compliance Levels and Control Strategy on Mobility and Safety
Bibliographic record
Abstract
Variable speed limits (VSL) aim to improve freeway mobility and safety by influencing collective behaviors of drivers. Thus, VSL benefits should be positively correlated with the VSL compliance level (CL). Surprisingly, a number of heuristic VSL control strategies have shown that VSL with increased CLs can, in fact, increase travel time. However, it has yet to be analyzed whether or not that outcome is because of the control strategy design or the CL. Some recent studies have shown that, regardless of CL, a proactive optimal VSL control provides mobility benefits; however, no evidence has been found to indicate which CL is most achievable in practice, nor has a description been found for the distribution of speed of a given VSL. The objective of this paper is to quantify the relative contribution of CLs with a proactive optimal VSL control toward improving mobility and safety. In this study, several CL-to-VSL strategies have been modeled after real-world driver behavior. To quantify the impact of CLs only, speed distributions are altered with the static speed limit. Then, the benefits are quantified by implementing a proactive optimal VSL control strategy with CLs. The simulation evaluation shows that both VSL mobility and safety benefits are positively correlated with increasing CLs. Specifically, the travel time, throughput, and collision probability are improved in the CL ranges of 5–15%, 6–8%, and 50–60%, respectively. The study findings will help guide transportation agencies in deploying VSL control by considering CL, so as to achieve maximum mobility and safety benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".