A Disaggregate Urban Shipment Size/Vehicle-Type Choice Model
Bibliographic record
Abstract
The public sector is looking to increase precision in its freight models to better evaluate public investments, policy and regulation in the transport sector. Recently, freight models have been developed that use agent-based simulation incorporating discrete choice models that get closer to the behavior behind freight decisions. This paper describes the development of a disaggregate shipment size/vehicle-type choice model based on data from shipper-based survey of goods and services movements conducted in the Region of Peel, located in the Greater Toronto Area, in 2006. The model presented is a discrete/continuous model with shipment size as the continuous variable and vehicle-type choice as the discrete variable. The discrete model shows that small vehicles are more likely for shipments with higher value per unit weight, time sensitive shipments, and services (as opposed to good shipments) and larger vehicles for long distance shipments. The continuous model shows that shipment size (in kg) decreases with the increase of density values of the commodities ($/kg) and expected fuel operating cost of the vehicle-type chose ($/km) and with the decrease of distance (in km).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".