Sustainability of rapid-transit expansion in large metropolitan areas: Gaps and solutions for decision-making improvement
Bibliographic record
Abstract
Focusing on sustainability, this paper explores the decision-making process adopted in the Greater Toronto Area (GTA) for delivery of rapid transit projects. The GTA represents the largest Canadian metropolitan area operating under democratic principles similar to other democracies across the globe. The term “public sector” in this context includes all government levels—municipal, provincial, and federal—and their agencies, which collectively utilize taxpayer funding to deliver essential public services. This study consolidates the evolution and outcomes of a 30-year decision-making process by compiling open-source internet data into two specialized databases. The analysis indicates that, over the last three decades, rapid transit projects have experienced steadily increasing delivery timelines and costs. These trends correlate with a slow expansion of rapid transit networks, averaging only 0.7–1.6 km/year across Canada. Notably, in the GTA, the average project delivery timeline has extended from 7 years to 12 years since the year 2000, while costs have escalated fourfold. Additionally, construction costs for water and wastewater pipelines and tunnels have risen by up to a factor of 20 relative to historical values. In response to these unsustainable trends, this study proposes a novel decision-making framework to evaluate the sustainability of rapid transit alternatives. The framework addresses the absence of comprehensive project planning tools that compare horizontal routes, vertical alignments, and feasible construction methods. By offering a systematic approach to select the most sustainable rapid transit solutions, this framework is particularly beneficial for large metropolitan areas experiencing rapid urbanization and a pressing need for long-term sustainable infrastructure. Furthermore, its adoption modernizes an outdated decision-making paradigm rooted in the 20th century, advancing toward a more sustainable, integrated approach for each public sector decision.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".