Who benefits from new transportation infrastructure? Using accessibility measures to evaluate social equity in public transport provision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Who benefits from new transportation infrastructure? Using accessibility measures to evaluate social equity in transit provisionTransit provision has the potential to address several important societal goals: reducing GHG emissions, cutting traffic congestion, spurring economic development, creating jobs, as well as giving access to destinations regardless of car ownership.Understanding who benefits from new transit projects is a key factor in analyzing the sustainability of a system.This article explores the potential effects of proposed transit infrastructure projects in the 2007 Montreal transportation plan on residents of socially disadvantaged neighborhoods in Montreal, Canada.A social disadvantage index is used to identify neighborhoods in need of attention.We then model accessibility and travel time changes as a result of proposed transit infrastructure.These two measures are used to quantify the benefits at the regional and personal scales.Based on our analysis, the Montreal transportation plan is relatively equitable, though some areas benefit much more than others at the regional scale as well as at the personal scale.Balancing economic, environmental, and equity goals of transit plans is a complex and challenging process.It is recommended that policy makers carefully consider who will benefit from transit improvements when prioritizing among projects, using accessibility measures at the regional scale, and traveltime improvements at the personal scale.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it