Stated Response Examination of Factors Influencing Commercial Movement Route Choice Behavior
Bibliographic record
Abstract
A stated response experiment was performed to examine how people in Montreal in Canada are influenced by specific route attributes in the selection of routes for commercial movements. The route attributes considered included the expected driving time, the roadway type (arterial or freeway), the magnitude of a toll and the toll collection method (if any), and the probability and associated magnitude of a possible delay. A total of 242 complete interviews were conducted in two separate surveys, one with commercial drivers and the other with trucking company operators. With multiple observations of choice behaviour obtained in each interview, a total of 1885 observations of the relevant route choice behaviour were obtained. Analysis of these observations indicates that all the included attributes other than toll collection type have significant effects on route choice in the situation being considered. They also indicate, among other things, that the probability of delay has a very large impact, that the sensitivity to time in delay is greater than the sensitivity to expected driving time, and that the implied value of driving time is impacted dramatically by the nature of the treatment of expected delay. Some of these results are consistent with findings in related work done by others, which is seen to add credence to the approach being used here. Some of these results are novel, with implications for other future work.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".