Mode and Carrier Choice in the Quebec City - A Random Parameters Approach
Bibliographic record
Abstract
The Quebec City-Windsor corridor is the busiest and most important trade and transportation corridor in Canada. The transportation sector is the second largest contributor to greenhouse gas (GHG) emissions in the country. Governments around the world, including Canada, are considering increased mode share by rail as a way to reduce transportation emissions. Evaluating the potential of freight mode shift as a means to reduce transportation emissions requires rigorous analytical models that can predict the effect of government policy on mode split. This paper presents the findings of a random parameters mixed-logit model of shipper carrier choice in the Quebec City-Windsor Corridor. The model itself was developed using the results of a stated preference survey undertaken in the fall of 2005. The survey was designed explicitly to evaluate shipper preferences for the carriage of intercity consignments, and particularly for their preferences for carriers that contract the services of rail companies to carry these shipments via rail. A fixed parameters approach suggests that shippers are very mistrustful of using rail to move their consignments and suggests that increasing rails share of freight faces tremendous challenges. This result is not entirely consistent with shipper interviews conducted during survey development that suggested some shippers might prefer rail for environmental-public relations reasons. A random-parameters approach was adopted to test whether preference variation across respondents would be able to explain this inconsistency. This random-parameter analysis suggests that there is some variation in shippers preferences for the use of intermodal transportation. In particular, the model suggests that for 20% of shippers, knowledge of a carriers use of rail has a positive effect. This appears to be the first attempt at a random parameter approach in the freight choice literature.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".