What makes travel ‘local’: Defining and understanding local travel behavior
Bibliographic record
Abstract
In recent years, land use and transportation planning priorities have shifted from issues of mobility to focus on the capacity of neighbourhoods to provide opportunities to live, work, shop, and socialize at the local scale.This research explores a sample of households from Montreal, Quebec, Canada, that engaged in multiple trip purposes on the same day and measures the effects of household, individual, and trip characteristics on their travel behavior, especially the localization of these trips.A new measure to understand the spatial dispersal of actual activity space of each household is proposed while controlling for distance traveled.The findings show that levels of regional and local accessibility have different effects on this new index.Furthermore, these effects vary with household size and sociodemographic factors.This study could help transportation professionals who are aiming to develop policies to localize household travel patterns through land use and transportation coordination at the neighborhood and regional scale.As wealthier car-owning households are seen to exhibit more dispersed travel behavior regardless of accessibility measures, implications for social equity and exclusion are also explored.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".