Monitoring Strategy for Active Transportation Pilot Projects
Bibliographic record
Abstract
Transportation agencies recognize the growing interest in Active Transportation (AT) and implement design features to integrate AT within roadway network. There are some instances where many design features include innovative markings, lane configurations, signage and road geometry that vary from standard design approaches. Often these features are implemented on a trial basis to determine the effectiveness of the approach or design. The question remains, what are acceptable or unacceptable operations for these trial designs? In some cases the Ontario Ministry of Transportation has accepted and defined deviations from standard practice as 'pilot projects' initially as a trial feature at specific locations, conditional on monitoring these design options to evaluate the safety, operational efficiency and cost effectiveness. Monitoring methodologies have been identified as a basis for assessing pilot projects. These methods focus on addressing the following questions: Do the operations meet the objectives of the transportation solution? Are there net benefits comparing 'Before' and 'After' operational conditions? Is there positive public perception of the solution? The Ontario Ministry of Transportation has documented a wide range of progressive active transportation design measures and defined triggers that would necessitate the need for monitoring. The result of the Ontario Ministry of Transportation's efforts is a monitoring plan framework for consistent and technically sound evaluation of effectiveness of pedestrian and cycling facilities. It balances operational conditions for all road users. The process is inclusive of all operating departments through Value Engineering style workshops. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".