Sustainable transport safety : ComPASS case study of a community U-PASS in Kelowna, British Columbia
Bibliographic record
Abstract
One way to reduce the negative impacts of automobile-dependency is to encourage active transportation (AT), such as walking, biking, and transit, while reducing vehicle use. One initiative proven to reduce vehicle use is the Community U-Pass (ComPASS) concept demonstrated through Boulder, Colorado’s Neighbourhood Eco (NECO) Pass program. ComPASS is a universal community transportation pass (U-Pass) that would provide unlimited access transit passes and other possible components including recreation centre passes, bike tune-ups, merchant incentives, and emergency taxi rides home. The goal of providing a ComPASS to neighbourhoods is to provide an attractive alternative to encourage decreased personal vehicle use in favour of AT modes. This thesis explores the possibility of a ComPASS for the residents of the Glenmore neighbourhood in Kelowna, British Columbia. Two factors motivated this research: 1) an interest in sustainable communities and 2) sustainable transport safety (STS). The objectives of this research were to 1) compare Kelowna to other cities where similar ComPASS programs have been successful, 2) design a ComPASS that would compete with personal vehicle use, and 3) implement a ComPASS pilot program to test the potential of the program in Kelowna. Results suggest that ComPASS could significantly reduce personal vehicle use at a 93.7% confidence level and increase transit use at an 85.7% confidence level. Personal vehicle use could decrease between 6% and 12% amongst ComPASS holders which would translate to a reduction in vehicle kilometres travelled (VKT) per household, resulting in several community-wide benefits. Due to the potential benefits, ComPASS is a recommended tool for the City of Kelowna to implement in efforts to achieve their sustainability goals. Consequently, a three-year permanent ComPASS trial is recommended in the Phase 2 study area, along with transit improvements. Assuming a participation rate of 59%, 19 of the 32 piloted households would participate in a permanent ComPASS program. Over the three year trial period assuming 19 participating households, there could be 6,052 kg to 12,103 kg reduced greenhouse gas (GHG) emissions, 15 to 30 reduced road injuries, 0.06 to 0.11 reduced road fatalities, and social and government savings of $20,552.26 to $41,104.51.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".