The Impact of the Suburbanization of Employment on Transit Modal Share: A Toronto Region Case Study
Bibliographic record
Abstract
This paper examines the impact that the suburbanization of employment has on transit mode share by focusing on the case of the Greater Toronto Area (GTA). The regional transportation planning agency Metrolinx has outlined its objectives as ‘Vision in Numbers’ in the regional transportation plan The Big Move. It is their vision that by 2031, on average one third of trips to work will be taken by transit. According to 2006 Transportation Tomorrow Survey data, the transit modal share for work-related trips for the Region is 19.5%. A business-as-usual scenario developed using Growth Plan projection for employment growth and transit-modal shares for work-related trips, forecasts that in twenty-five years the transit-modal share for work-related trips will be 14.5%, down from the 2006 figure of 19.5%. Therefore, two alternative scenarios are presented to counteract the negative impact that the suburbanization of employment will have on the transit-modal share for work-related trips, which are: (1) implement policies to influence the location of future employment development; and (2) provide ‘rapid transit’ to existing areas of employment growth. Both scenarios will have a positive impact on the transit-modal share for work-related trips; however, these scenarios must be pursued in tandem, as neither is sufficient by itself to achieve the work-trip transit mode share of the Metrolinx vision.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".