Toward Sustainable Transportation on Campus: Analysis and Results of the 2020 University of Calgary Commuting Habits Survey
Bibliographic record
Abstract
The University of Calgary, comprising more than 33,000 students and 7,000 employees, contributes significantly to the city's transportation demand and needs for different transportation modes. Thus, it is important to enhance the sustainable transportation network and shift commuters' transportation demand to more sustainable modes. With this aim, the Ancillary Services and Office of Sustainability of the university, in partnership with a research group from the Civil Engineering Department of the university, started a project called "Toward Sustainable Transportation on Campus". In order to obtain the required data for this project, an online survey was designed and distributed among university members to capture their commuting behaviour and their attitude toward various aspects of transportation. We investigated the gathered data to shed light on commuters' current situation travel patterns to and from the University of Calgary campuses. We also identified barriers to use each transportation mode and examined the satisfaction level commuters have with their trips. Based on the results obtained from our analysis, a set of recommendations is provided that could increase the desirability of sustainable transportation modes and encourage commuters to switch from private cars to public transit and active modes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".