Assessing the Effectiveness of an Integrated Speed Management Plan on Highways
Bibliographic record
Abstract
Manned speed enforcement has long been used as a safety measure to improve drivers' compliance with posted speed limits on highways. The sustainable presence of police squads at high-risk locations is key to the successful implementation of an enforcement program and is usually supported by other measures, such as educational campaigns, messages, and warnings. The goal of this study is to evaluate the effectiveness of an integrated speed management plan that is focused on manned traffic enforcement at three highway locations near the City of Leduc, Canada. Baseline speed data was collected and used to develop an enforcement deployment schedule. Following a public educational and engagement program, the enforcement plan was implemented. A detailed analysis was conducted for the speed data before, during, and after manned enforcement operations. To account for potential confounding factors, the evaluation method utilized a control site to correct for trends and other effects. The results showed that there was a statistically significant reduction in the average speed of vehicles that ranged from 1.14 to 8.96 km/h while the number of speed violations dropped by up to 25.5% at enforcement locations. Overall, the results from this study demonstrated that implementing an integrated speed management program, with manned enforcement at its core, has a high potential to improve safety by improving compliance, reducing the number of violations, and decreasing the average speeds on highways. The sustained manned enforcement is expected to increase drivers’ compliance with speed limits, which should eventually reduce collisions and improve safety. Keywords: Vehicular Speed, Manned Enforcement, Speed Violations, Safety Impacts, Speed Limit Compliance DOI: 10.7176/CER/13-1-04 Publication date: January 31 st 2021
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".