Managing Road Traffic Speed: Challenges, Opportunities, and New Developments
Bibliographic record
Abstract
Nowadays, speeding has become a primary concern globally because of its significant impact on increasing the frequency and severity of road crashes, fuel consumption, and environmental pollution. These problems have created an urgent need for advancements in managing vehicle speeds to mitigate the negative impacts of speeding. Concerning this, strategies such as setting speed limits, traffic calming measures, police enforcement, and spot speed camera enforcement (SSCE) have been widely investigated for their suitability and impacts on speed management. Although such conventional measures are effective, depending on circumstances, in reducing vehicle speed in the vicinity of the interventions, studies have shown that their impact is limited in space, leading to the problem of event migration. The promising approaches to solving such limitations are the use of variable speed limits (VSLs), intelligent traffic calming devices, sectional speed enforcement systems (SSES), and intelligent speed adaptation (ISA) systems. Despite their limitations, conventional speed management measures are continuing to be implemented predominantly around the world because of their lower initial cost of installation and implementation. This paper provides an overview of the scientific evidence regarding the impact of state‐of‐the‐art speed management measures on speed‐related outcomes. Furthermore, it presents the current progress and prospects for advancing speed management strategies to improve road safety and environmental protection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".