Development of A Behavioral-Based National Freight Demand Model and an Innovative Freight Data Collection Method : [fact sheet]
Bibliographic record
Abstract
Understanding how freight moves now and predicting how it might flow in the future are essential to transportation planning that meets the need for efficient, safe, and economical freight movement. The Federal Highway Administration (FHWA) Exploratory Advanced Research (EAR) Program is supporting freight transportation research that will develop tools to accurately model future freight demands and devise new methods of freight data collection. Resource Systems Group, Inc. (RSG), with research partners at the University of Washington and the University of Toronto, launched the “National Freight Demand Model” project. It will allow decision-makers to understand better the factors that influence freight movement on scales ranging from interurban commercial deliveries to regional and national infrastructure needs. In a related FHWA-supported project, “Future Freight and Logistics Survey,” the Massachusetts Institute of Technology (MIT) is evaluating the use of innovative communication technologies to collect high-resolution and high-frequency data that accurately describe the behavior of choices that underpin freight movement. Together, these EAR Program-funded projects will significantly enhance transportation managers’ ability to anticipate and plan freight movement capacity, operation, and infrastructure investment.\n
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".