An Integrated Approach to Managing Travel Demand in Downtown Calgary
Bibliographic record
Abstract
This paper describes how the City of Calgary, Canada has experienced rapid growth over the last decade, and ever increasing demand on our transportation infrastructure. Located in the energy-rich province of Alberta, Canada, the oil and gas industry has quite literally fueled a dramatic increase in employment and population throughout the city. In fact, Calgary has just welcomed our one-millionth citizen, up from 750,000 people only ten years ago! One of the greatest success stories for Calgary has been the continued strength of the downtown in both job and population growth. This subject has attracted numerous corporate head offices, resulting in 20,000 more jobs than ten years ago. The total number of trips into the downtown has subsequently increased by 25%. The success story is that all of this growth has been accommodated without the construction of any new roads into the downtown. Managing the increased travel demand has required a strategic and integrated approach, including: (1) Expansion and increased service levels on our centrally focused Light Rail Transit (LRT) system, resulting in some of the highest ridership levels in North America; (2) Strategic downtown parking policies, including a cash-in-lieu policy; (3) Transportation demand initiatives that actively promote alternatives to single occupant vehicles; and (4) Maximizing the efficiency of existing infrastructure for both automobiles and transit. This paper provides examples of these and other initiatives, and highlight how they have worked in concert to accommodate the incredible growth that Calgary continues to experience.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".