Driving Factors behind Successful Carpool Formation and Use
Bibliographic record
Abstract
Sustainable transportation options are receiving increasing attention in cities across North America because of rising commute times, fluctuating fuel prices, and increased awareness of the environmental impacts of transportation choices. Carpooling represents one of many possible alternatives to single-occupancy vehicle use for work or school trips. Recent attempts to encourage carpool formation in Canada include web-based applications that facilitate connections between potential carpoolers. One such example is Carpool Zone, a service provided by Smart Commute in the Greater Toronto and Hamilton area. The service is coordinated regionally by the Smart Commute Team at Metrolinx (the regional transportation planning authority) and is free and open to the public. Data are used from Smart Commute to investigate the carpool formation and use process. Results from a logistic regression analysis of carpool use suggest that spatial accessibility to matches, household auto ownership, and sociodemographics influence carpooling more than do proximity to carpool infrastructure and personal attitudes (e.g., concern for the environment, cost). With respect to policy and planning, results suggest that increasing shared knowledge about commuting patterns at the home end of work trips could yield beneficial returns to the carpool formation and use process.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".