Transit Benefit Index: A Comprehensive Index for Capturing Externalities in Transit Planning
Bibliographic record
Abstract
This research provides a methodology for estimating the total societal benefit generated from substituting private vehicle trips with public transportation trips. The external costs of private and public transportation were estimated using a base case travel demand model and then a mode shift was simulated to calculate the effects of shifting one full transit unit (e.g., bus) of demand from a private to public mode. This shift was performed for all origin–destination (O-D) pairs in a region to find the O-D pairs that resulted in the greatest net benefit. These benefits were then normalized using the total automobile vehicle kilometers traveled removed from the network to generate a “transit benefit index.” This methodology was applied to a case study of the city of Bogotá, Colombia. A total of 102 scenarios were simulated: a 2 in base case, and 10 total sensitivity analyses, each including two transit provision alternatives. The results were contrasted with the cost of a new transit unit—a new bus in this case—revealing that the total economic benefit derived from 1 year of increased transit ridership was larger than the financial cost of a new bus to the transit operator. These results suggest that the City of Bogotá should consider further subsidies to transit fares to increase ridership and mitigate externalities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".