Moving Freight on Transit (FOT): Results of a Three Round Policy Delphi Study
Bibliographic record
Abstract
Freight on Transit (FOT) refers to any trip that uses public transit vehicles and/or infrastructure to move things other than people. It can mean moving goods alongside passengers on buses, attaching cargo trailers to transit vehicles, operating freight vehicles between transit trips on subway lines, etc. A three round Delphi Study was conducted to explore the costs, benefits, and challenges of FOT and assess potential FOT operations in Toronto. Thirty four transportation experts participated in the study and through the iterative survey process, it is revealed that practitioners are not opposed to the concept of FOT so long as operations are competitive with current delivery methods in terms of cost and time, there is sufficient capacity on transit networks to support goods movement, and the operations do not disrupt or degrade public transit service. The expert evaluation of potential FOT operating strategies in Toronto, formulated based on aggregate opinion obtained throughout the Delphi process, suggests that while current transit infrastructure in Toronto does not have the capacity to support additional movements, there may be opportunities to include freight service in future projects as a means of offsetting operating costs and reducing the environmental impacts of urban goods movements. The results support previous claims that the technical challenges of FOT may be easier to overcome than institutional barriers like securing financing and balancing the needs of multiple stakeholders; and that the challenges and risks associated with FOT may not be worth what are perceived as only marginal benefits.
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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 | 0.036 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".