Speeding on Weekends and Weekdays: Note on Policy Implications
Bibliographic record
Abstract
Speeding in urban areas is a critical safety issue because of the concentration of pedestrians and cyclists. Previous studies have indicated that holiday periods are generally more dangerous for road users in terms of road safety indicators such as crash frequency and speeding. In this regard, weekdays have been generally shown to be safer than weekends. This paper examines potential factors that vary between speeding during weekends and weekdays. To this end, Bayes estimates of a Binomial logistic model were used to obtain model coefficients and their associated odds ratios. Observations from a sample of local streets in Montreal, with speed limits of 40 km/h and 50 km/h, were used. The results showed that evening hours, midday hours, and number of sidewalks were more prevalent in speeding on weekends than on weekdays. Interestingly, night hours were less prevalent in speeding during weekends. This finding contradicts the mainstream belief that weekend nights are more risky with respect to speeding. The authors also found that one-way streets were less prevalent in speeding on weekends. Additionally, speeding on streets with a posted speed limit of 50 km/h was under-represented during weekends. Policy implications that follow from this study’s findings provide noteworthy guidelines for policy makers to plan traffic enforcement strategies more efficiently.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".