What drives sustainable student travel? Mode choice determinants in the Greater Toronto Area
Bibliographic record
Abstract
Examination of mode choice behavior is an important step in accurately predicting future travel demand. Despite having somewhat unique travel needs and challenges, there is a lack of knowledge in understanding the mode use behavior of university student population. The existing studies on university populations relied on a relatively smaller sample in investigating the behavior. Therefore, using world's largest university student's travel database, this study examines the factors affecting the mode choice behavior of a diverse university student population with student samples from four universities and their seven campuses located across the Greater Toronto Area (GTA) in Canada. Additionally, stratifying this diverse population using their attitudinal responses towards numerous travel modes, this study also estimates three additional mode choice models to obtain a more comprehensive understanding of how students in different markets, with different latent attitudes towards transportation, vary in terms of sustainable mode choice. A cluster analysis based on fourteen attitudinal responses, was conducted to stratify the sample whereas the popular multinomial logit approach was used to estimate the mode choice models. This study finds transit pass and bike ownership as important determinants that govern sustainable mode choice among the students in the region. The findings of this study could facilitate the sustainability offices at the four universities in making an informed policy decision in shifting the mode use behavior of students towards sustainable modes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".