Saving Time and Making Cents: A Blueprint for Building Transit Better
Bibliographic record
Abstract
Cities, states, and metropolitan areas across the United States are looking to invest in a range of public transit projects in order to connect people to jobs and economic opportunity, reduce greenhouse gas emissions from vehicles, and shape development patterns. According to one estimate, the United States invested about $50 billion in new transit projects in just the last decade.1 These include underground subways in Los Angeles, commuter rail lines along the Front Range near Denver, a streetcar in downtown Atlanta, light rail lines in suburban Phoenix, and bus rapid transit in Richmond, Virginia, among many others.While these projects are as diverse as the country itself, they all have one thing in common: increased scrutiny over their costs and timelines to build. A few very visible projects have reinforced the narrative that rail transit investments have systemic issues that are endemic to the United States.This all begs the questions: Is this true? If so, why? And what should we do about it?These are precisely the questions Eno set out to answer through this research, policy, and communications project to analyze current and historical trends in public transit project delivery. We convened a set of advisors and conducted in-depth interviews with key stakeholders to understand the drivers behind mass transit construction, cost, and delivery in the United States. A comprehensive database of rail transit projects was created and curated to compare costs and timelines among U.S. cities and peer metropolitan areas in Western Europe and Canada. Through this quantitative and qualitative approach, we developed actionable recommendations for policy changes at all levels of government as well as best practices for the public and private sectors.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".