A freight data repository as foundational pillar for urban freight research
Bibliographic record
Abstract
This research shows the key components and lessons learned from a freight data repository in Canada. The Freight Data Warehouse (FDW), hosted at the University of Toronto, was developed as part of the Smart Freight Centre, a collaboration between researchers from five Canadian universities and key stakeholders in the Greater Toronto and Hamilton Area (GTHA). The data repository exemplifies a successful collaboration between private, public, and academic sectors. This research presents the critical aspects of a data governance framework, a data policy, data classification, and data handling that was developed for the data repository. It shows a case study that computes the greenhouse gas (GHG) emissions on Highway 401 in the GTHA by using a data fusion approach. It discusses the potential policy impacts of the FDW for transportation professionals and policymakers. It also showcases a dashboard prototype to visualize GHG emissions and air contaminants on freeways in the GTHA. Lastly, it discusses vital insights the FDW team has learned over its six years of operations. Ultimately, this research intends to show practitioners and the scientific community the potential for freight data repositories to become foundational pillars for transportation research. • Shows the key components and lessons learned from a freight data repository in Canada. • Presents a data governance framework, a data policy, data classification, and data handling procedures. • Computes greenhouse gas emissions on Highway 401 in the Toronto Area using data fusion. • Discusses transportation policy applications of the data repository.
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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 | 0.049 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.025 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".