Factors Influencing Commuting Mode Choice: A Multilevel Analysis
Bibliographic record
Abstract
Introduction: Mitigating private vehicle use can lead to improvements in physical health and reduce traffic-related emissions. Thus, influencing travel patterns and behaviors of individuals has become a focus for policy makers and researchers. This research provides a multilevel analysis on individual characteristics as well as area-level climate parameters that may influence an individual’s commuting mode choices across multiple Canadian cities. Methods: The study used individual data from the 2011 National Household Survey conducted by Statistics Canada. The city-level climate data were collected from Environmental Canada’s 1981-2010 Normals & Averages collection. Different multilevel models were fitted using MLwiN version 2.35 to investigate relationship between socioeconomic and climate variables and commuting mode choice (private, public and walking/cycling), using private vehicle as a reference. Results: Older age and higher income groups tend to prefer taking private vehicle in comparison to other modes. Educational and gender differences were also a significant determinant of mode choices. The climate variable indicate an increase in annual temperature encourages walking/cycling (OR=1.24, 95% CI: 1.03-1.48), but an increase in annual average summer temperature is associated with higher private transport use (OR=0.80, 95% CI: 0.69-0.92). Higher average winter temperature was significantly associated with higher odds of taking private vehicle compared to public transport. Conclusions: Individual characteristics can allow policy makers to identify target groups for promoting more sustainable modes of transportation. The climate variables indicate that temperature can affect travel decisions differently between locales with distinct climate condition, thus policies should be local. As climate change occurs, transportation policies should also consider heat threshold in which more individuals may switch to private vehicle use for more comfortable traveling.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".