Bibliographic record
Abstract
Capitalizing on large-scale origin-destination travel surveys conducted in two large Canadian urban centers, Montreal and Toronto, this paper presents a comparative analysis of the travel behavior trends in relation to variables such as demography, car accessibility, home location, and employment status. With trip rate as the dependent variable, three disaggregate multivariate regression models were estimated to observe how behaviors have evolved over time and how individual features affect the way in which people travel. These multivariate models allow the observation of the similarities and differences between explanatory factors between regions and over time. Although both cities are facing similar trends, such as the aging of the population, increasing rates of motorization, declining household sizes, and urban sprawl, they are also the sites of different trends with respect to average trip rates, differences between genders, and the impacts of car access. The geographic and cultural differences found between the populations of Toronto and Montreal include a smaller gender impact on trip generation in Montreal and a smaller age impact in Toronto. The study identified changes in the magnitude of the influence of explanatory variables on trip generation over time, including the declining importance of age and gender. The most promising policy actions that could be taken to decrease trip rates would be those that affect a decrease in household automobile ownership, as determined on the basis of the analysis in this paper.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".