SMART COMMUTE INITIATIVE - GREATER TORONTO'S SHOWCASE FOR TRANSPORTATION DEMAND MANAGEMENT
Bibliographic record
Abstract
The Greater Toronto Area (GTA) covers approximately 8,500 square km. and houses some 5.6 million residents and 2.75 million jobs. The area continues to experience significant growth with an average annual rate of population increase of around 100,000. Disturbingly, trends indicate that the growth in vehicle trips will be even greater than the rate of population growth as people continue to suburbanize, own more cars and make more trips over longer average distances. Projections estimate that vehicle travel in the GTA could increase by as much as 55% over the next 25 years. The GTA comprises 30 regional and local municipalities. At the heart of the region is the recently (1998) amalgamated City of Toronto that has a population of 2.4 million and which is the most densely developed part of the urbanized area. Beyond the City are the more suburban, lower density urbanized parts of the Regions of Halton, Peel, York, Durham and the amalgamated City of Hamilton. Overall, GTA residents make 11.5 million daily trips, 9.2 million (or 80%) of them by auto as either driver or passenger. In the City of Toronto, where local transit use (TTC) is higher, the comparable auto share of daily trips falls to 68%. The City of Toronto has a density of population (c.3,700 persons per square km.) which falls somewhere between the higher densities of typical European cities and the lower densities characteristic of North American ones. However, there is continued pressure for urban sprawl in the GTA and over 600 square km. of land were urbanized between 1976 and 2001, with a further 1,000 square km. projected to be urbanized by the year 2031 if current trends persist. The average number of vehicles per household in the GTA is currently 1.39, though this number dips as low as 0.8 in the inner part of the City of Toronto. Daily trip generation rates (currently 2.5 trips per adult) and average trip lengths have been increasing over the 1986 - 2001 period, while transit modal shares throughout the region have remained stable or have marginally declined. The region's transportation system is already not keeping pace with growth and, unless measures can be undertaken to curb the demand for vehicular travel, the situation will only become more unsustainable. It is against this background that the GTA municipalities (local and regional) are seeking to seriously revive and expand the application of Travel Demand Management (TDM) strategies to address the growing transportation challenges they are facing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".