Evolution of Personal Travel in Toronto Area and Policy Implications
Bibliographic record
Abstract
This paper presents a descriptive analysis of the historical evolution of personal travel behavior in the Greater Toronto Area (GTA) over the past 35 years. The analysis indicates that in many respects the GTA taken as a whole is similar to other cities within North America in terms of increasing auto ownership; increasing individual auto-drive trip rates; increasing suburbanization of population and employment into areas poorly served by transit; increasingly complex travel patterns; and transit, at best, maintaining a constant number of trips per capita but losing modal share. The analysis also highlights ways in which the GTA, particularly the city of Toronto, deviates from the North American “norm.” These include transit per capita ridership, overall mode splits, revenue-cost operating ratios are still extremely high by North American standards; the regional commuter rail system has been very successful in attracting increasing numbers of commuters from outside Toronto into the Toronto central area; the continuing strength of the Toronto central area has provided a strong, viable transit service; and more generally, the relatively high density and transit orientation of development throughout the city of Toronto is highly supportive of transit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".