Assessing Mobility and Safety Impacts of a Variable Speed Limit Control Strategy
Bibliographic record
Abstract
With the recent advances in active transportation and demand management, variable speed limits (VSLs) have been identified as an active traffic management strategy for improving freeway mobility and safety. Several heuristic VSL strategies have been proposed and evaluated. This paper proposes a model predictive VSL control strategy and evaluates its safety and mobility impacts. The strategy uses second-order traffic flow models to predict the traffic state and to provide a speed for optimizing corridor operational performance. A sensitivity analysis of the VSL update frequency and the safety constraints for the VSL strategy was performed to determine the best scenario in terms of safety and mobility. A stretch of Whitemud Drive, an urban freeway corridor in Edmonton, Alberta, Canada, was selected as the study area. The proposed VSL strategy was implemented in the microsimulation platform with a special software module. A real-time collision prediction model was developed for the same study area by using a matched case-control logistic regression technique to estimate the collision probability for each scenario. The results indicated that the proposed VSL control strategy can improve safety by approximately 50% and mobility by approximately 30%. A VSL update frequency of 5 min and a maximum speed difference of 10 km/h between successive time steps yielded the best performances. This finding can be useful for field implementation of VSL control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".