Freight Demand Forecast for a Proposed Railway in Canada With New Approach to Freight Rail Assignment
Bibliographic record
Abstract
This research examined the freight demand forecast for a new short railway linking the Okanagan Valley in southern British Columbia to American railways in the South (Orville), and to Canadian railways in the North (Kamloops). An Origin-Destination (O-D) table including local, domestic and international demands for the Okanagan freight rail was developed based on available surveys and observed truck freight data. In the absence of data to derive utility functions, the current mode share for each commodity in the base year as well as current elasticities between truck and rail was used to forecast the mode share in the future year. Rail assignment techniques are among the forgotten problems of freight demand forecasting due to their complexities, including: 1) written and unwritten practices of the rail industry, and 2) cost functions that are classically employed in truck or auto assignments. In this study, a comprehensive review was conducted on the rail freight demand assignment techniques. A new assignment procedure was introduced by combining the available mathematical choice models and new initiatives of the Canadian government toward rail industry. Finally, the predicted share of freight rail was assigned to the rail network using three methods, which provided three independent freight demand forecasts. The mid-range forecast was selected as the freight demand for the Okanagan Valley while two others (low/high) were used for sensitivity analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".