Who benefits from new transportation infrastructure? Using accessibility measures to evaluate social equity in public transport provision
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".