Mixed Logit, Latent Class, Data Organization of the Canadian Freight Market: A Microscopic Approach
Bibliographic record
Abstract
This paper analyzes the potential use of contemporary freight surveys as an emerging source of exploring data organization patterns for development and application of agent-based freight models. The Commercial Vehicle Survey (CVS), conducted in Canada, has been employed as a beta-site for the implementation of this framework in the Canadian context. To manage the complexity of transport-logistics activities of the freight system, the “transport-market” concept is introduced. Accordingly, the demand side of the freight transport market is disaggregated by commodity flows between economic sectors while the supply side is expressed in terms of independent market segments characterized by truck types, tour-types and possibly transshipment activities. Pragmatic marketing tools such as soft computing methods (Fuzzy Clustering and Fuzzy Logic) along with principles of Material Flow Analysis (MFA) have been applied to create a picture of the Canadian freight transport demand and supply systems. The findings demonstrate the segmentation of the Canadian freight market in terms of independent market clusters that reveal their distinctive functionalities. The findings also support previous positive experiences with a comparable approach that was utilized as a database of a behavioristic actor-based national freight transport model in Germany.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".