Full Bayesian Mixed-Effect Intervention Model for Before–After Speed Data Analysis
Bibliographic record
Abstract
The analysis of before–after speed data to evaluate the effectiveness of safety interventions is often limited to a non-model-based comparison of speed-related indicators. Moreover, modeling of speed data often does not take into account the nested nature of the data. The objective of the study was to quantify the effect of posted speed limit (PSL) reductions in an urban, residential context. The study employed a mixed-effect intervention model, which could address limitations of existing methods. To model mean free-flow speed and the probability of a speed being below or equal to various thresholds, mixed-effect normal regression and binomial logistic regression models were used. Use of a comprehensive, unique, and disaggregated data set enabled not only the before–after evaluation of the PSL reduction but also the exploration of the effects of various temporal, traffic, and road geometry factors on speed. Results demonstrated the appropriateness of using the mixed-effect model for speed data. Parameter estimations showed that nighttimes; weekends; a high proportion of vans, buses, and trucks; evening peak periods; collector roads; and low hourly traffic volumes were all associated with an increase in the mean free-flow speed and a decrease in the probability of speeds being below or equal to various thresholds. Evaluation results showed the mean free-flow speed was reduced by 3.85 km/h in the after period and that speeds below or equal to 50, 60, 70, and 80 km/h increased by 20.0%, 9.2%, 2.8%, and 0.9%, respectively. All improvements were statistically significant; this finding implied that the PSL reduction was effective at influencing vehicle speed.
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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 | 0.043 | 0.056 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".