Developing and Applying Fluidity Performance Indicators in Canada to Evaluate International and Multimodal Freight System Efficiency
Bibliographic record
Abstract
As part of Transport Canada’s Gateways and Trade Corridors Initiative, the Directorate of Economic Analysis was interested in developing freight performance measurements for goods using Canada’s international gateways and traveling along its freight transportation corridors. These performance indicators—termed “fluidity” measures—will assist Transport Canada in painting a clear picture of system efficiency for their freight significant corridors. The indicators will ultimately aid Transport Canada in identifying to what extent the Government of Canada’s policies and investment in infrastructure are being leveraged and operated to support trade and economic prosperity. Transport Canada contracted with the Texas Transportation Institute (TTI) to develop and apply the indicators for measuring freight system performance. Researchers created two “fluidity indicators” using an index-approach. One indicator captures average conditions (Fluidity Index), while the other indicator captures daily variation in travel time (Planning Time Index). Because freight moves according to both travel time and delivery requirement schedules, and because travel time varies according to mode, the performance measures use a normalizing concept to allow comparisons within a mode and across an entire supply chain. This paper describes the development and application of the measures. The paper includes two applications. One application demonstrates how the fluidity measures are computed and presented for truck shipments. In the second application, researchers demonstrate the use of the fluidity measures for monitoring freight system performance for an international and multimodal corridor from China to Canada. The measures, application, and findings documented in this paper are valuable for practitioners and freight movement stakeholders interested in monitoring freight system efficiency.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 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".