Assessing the Impact of Traffic Volume Changes and Other Confounding Factors in Observational Before–After Safety Studies: a “No Treatment” Evaluation During the COVID-19 Pandemic in Canada
Bibliographic record
Abstract
As motor vehicle accidents result in significant societal and economic costs, it is crucial to evaluate the impact of safety measures accurately. Therefore, this thesis examines the effects of COVID-19-induced traffic volume changes and other factors on current methods used to assess the effectiveness of road safety engineering countermeasures. The study analyzes four observational before-after (BA) study methodologies: comparison group (CG) method, empirical Bayes (EB) method, a combination of EB and CG (EB with CG) method, and full Bayes (FB) method, which are commonly used to isolate the effects of countermeasures from confounding factors such as regression-to-the-mean (RTM), traffic volume fluctuations (exposure), unrelated effects and maturation (time trends). To accomplish this goal, a hypothetical BA study was conducted on multiple untreated signalized intersections across different Canadian jurisdictions during the period 2017-2021, which included COVID-19 pandemic. When these methodologies effectively account for confounding factors, no changes in expected collision frequency should be observed from the pre-pandemic to the post-pandemic period, assuming no specific safety countermeasures were implemented at the study sites.\n\nThe findings of this research indicate that among all the BA study types, the crash modification factors (CMFs) consistently exhibited values of approximately 1.0 as treatment effectiveness outcomes (i.e., 0% change in collision frequency from the before to the after period). The results demonstrate that all the examined methods successfully detected the hypothetical treatment within the confidence intervals of the estimated CMFs. However, they exhibited varying levels of accuracy and precision. These findings hold significant importance for practitioners to make informed choices regarding the selection and implementation of assessment methodologies for evaluating countermeasures introduced during COVID-19 pandemic.
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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 | 0.103 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".