Canadian Transportation Demand Management Impact Measurement Guidelines
Bibliographic record
Abstract
Transportation demand management (TDM) has become an increasingly common component of transportation master plans, neighbourhood plans, and transportation studies for development approvals. However, to date, the evidence of the impacts and effectiveness of TDM initiatives has been poorly quantified. This means that cost-benefit analysis, program assessment and an understanding of who is using TDM and why all have been difficult to measure. The need for impact quantification is important because, as this field expands, approval and funding bodies will increasingly require that the impacts of TDM investment be measured. The Transport Canada project Development of Standard TDM Impact Measurement Guidelines combined international best practices with an understanding of the needs of Canadian TDM practitioners. The project developed an evaluation system that provides a flexible framework in which to measure all types of TDM initiatives against their intended goals. The evaluation system recommends indicators and measurements for the various assessment levels and is supported by guidelines for data collection and calculations. The Canadian TDM Impact Measurement Guidelines provide a standardized process for TDM impact measurement and guidance for TDM practitioners, municipalities, and funding bodies. This paper provides a brief overview of the Guideline, including the evaluation process, assessment levels, indicators, data collection techniques, and evaluation procedures.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".