Excess Capacity and the Economics of Public Transit Investment: A Study of a Growing American City
Bibliographic record
Abstract
Declining ridership in public transport weakens the case for investments in expanded service or large investments in public transit infrastructures. Our study documents the decline in public transit ridership in Nashville, Tennessee, USA. Using data from Federal sources for 2002-2018 we document the influence of higher numbers of hours of bus service, employment, and, gasoline prices on public transit ridership. We find a surprising negative relationship between ridership and miles of bus service provided. Given the several control variables in the model, quadratic trend estimates inform us that peak ridership occurred in 2007 and the seasonally adjusted ridership might be falling since then. A second regression for the period after the great recession of 2008-09 gives a similar result regarding the declining ridership. Falling ridership in Nashville matches downward trends in other cities around the country. A major contribution of our study lies in the identification of separate roles for hours and miles of bus service. Using that insight, we decompose the time series while incorporating a quadratic trend to account for relative changes in the slope over time. Evidence of an underlying downward trend in ridership challenges the value of making large scale investments in transit capacity especially in the presence of increasing excess capacity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".