Lessons learned from the large-scale application of Driver Feedback Signs in an urban city
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The City of Edmonton has invested in the installation of Driver Feedback Signs (DFSs) throughout the city starting from 2011. DFSs are dynamic speed display signs aimed at providing positive guidance to drivers with the goal of improving compliance to posted speed limits. Given the city’s extensive history with DFS installation, the goal of this study is to evaluate the safety performance of DFSs and to identify factors that can help in determining the future DFS sites selection. A before-and-after evaluation with Empirical Bayes (EB) adjustment was used to account for regression-to-mean bias and other confounding factors. Local safety performance functions and yearly calibration factors were developed using data from a set of reference urban roads. The EB method analysis was utilized to investigate the effect of DFS on different road and intervention types. Results showed significant collision reductions in all scenarios ranging from 31.0% to 41.6%. DFSs were more effective in reducing collisions for arterials compared to collectors. Also, the combined use of DFS and mobile photo enforcement had a slightly higher effect on safety. Initial collision frequencies, traffic volumes, road lengths and the presence of shoulders were found to impact the reduction in collisions for most collision types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it