Motive matters: How travel purpose interacts with predictors of individual driving behavior in greater Montreal
Bibliographic record
Abstract
Rising transport emissions represent a significant challenge for policymakers.Two principal options exist to reduce emissions: Make driving less polluting or reduce driving overall.Though cities have a role to play in both approaches, the levers that may influence the latter more squarely align with municipal competences regarding the urban form.This paper aims to refine our understanding of the relationship between urban form and driving behavior by exploring whether accessibility-the ease of reaching desired destinations-exerts a different influence on driving mode choice and total distance depending on travel purpose.We rely on disaggregate data from the 2013 Montreal (Quebec) Origin-Destination survey and employ a two-step "hurdle" approach with multilevel logistic and linear models.We find that both local and regional accessibility possess statistically significant and negative impacts on driving mode choice and vehicle distance driven by Montreal drivers.Regarding the decision to drive, regional accessibility, as defined by transit-accessible jobs, appears to exert a greater impact than local accessibility, as measured by Walk Score across all purposes.When considering total kilometers driven, however, the relative impact of both types of accessibility varies.Overall, and for work and school driving, regional accessibility is correlated with the greatest declines in distance driven.For healthcare and discretionary travel, local accessibility is correlated with a larger decline in total driving distance.Our findings also highlight the profound impact of other explanatory factors, particularly car ownership, suggesting additional policy approaches for municipal decision makers to reduce vehicle kilometers traveled (VKT).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".