Data Organization Pattern for Microscopic Freight Demand Models
Bibliographic record
Abstract
In response to the changes in the freight transportation systems today, there is a demanding need for generations of new freight transport models that overcome the distinction of modeling either the demand-side or the supply-side of the transport market. The understanding of the behavior of the individual actors is a primary condition for the successful construction of those novel models. This paper provides a framework for the organization of data patterns of freight transport models that allows investigating the overall dynamic of the freight market. From a microscopic perspective, the demand side of the transport market is expressed by microscopic economic flows. On the supply side, homogeneous market segments can be explicitly distinguished. Such a scheme has been empirically developed for the German freight market. Data from different statistics such as manufacturing, domestic trade and data from the monetary Input/Output Table have been integrated. Data from national vehicle survey and concepts from classical marketing such as fuzzy cluster analysis have been employed to establish the market segmentation task. The compilation shows that the inter-sectoral commodity flows and the overall quantity in the transport markets fit together, which allows the construction of a microscopic freight model disaggregated according to sectoral commodity exchange processes and transport markets. A similar data organization scheme has been developed for the Canadian freight market based on data from the Commercial Vehicle Survey. It allows to make a generalization how a freight demand model based on transport markets can be applied to other geographical regions as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".