How Active Modes Compete with Motorized Modes in High-Density Areas: A Case Study of Downtown Toronto
Bibliographic record
Abstract
This paper studies commuters’ mode choice behaviour in high-density areas. The study focuses on short distance commuting trips where active modes (i.e., bike and walk) truly compete with motorized modes. The downtown area of the City of Toronto as one of the most vibrant active neighbourhoods in North America is selected as a case study. Data from the 2011-2012 Transportation Tomorrow Survey (TTS) is used for the empirical analysis. A Nested Logit (NL) mode choice model with three nests, namely: auto driver, transit, and active modes is developed. In addition to personal and household attributes, the model includes variables that explain individuals’ active mode choice behaviour such as mode-specific travel distances and times, the surrounding built environment, and weather conditions. The empirical investigation reveals useful insights that are helpful in understanding how active modes compete with motorized modes in high-density areas. The built-environment and weather conditions have a strong effect on active mode shares. In addition, shorter distances to destinations and lower travel cost per unit distance contribute significantly to the increase of bike and walk mode shares. Next steps of this research include the application of the developed model for policy analysis. As such, the effect of different variables on short distance trips modal shares can quantified. In addition, future research may consider comparing short distance commuting trips in different planning districts of the GTHA. This will provide more insights on how the built environment affects commuters’ decisions in different parts of the region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".