Characteristics of Bus Transit Vehicles in the United States: How They Have Changed Over a Quarter Century
Bibliographic record
Abstract
In 2009, transit agencies in the United States spent nearly 60% of their bus transit capital funds on their revenue vehicles. Using 25 years of data from the National Transit Database (NTD), this paper examines the national trends of seven major characteristics associated specifically with bus revenue vehicles. These trends can provide important information on where the market might be heading and aid in the planning decision on transit investments. The characteristics examined include number of vehicles, spare ratio, average age, average capacity, ADA accessibility, vehicle reliability, and vehicle operations and maintenance expenses. Some findings from the trend data include: (1) a steady increase in the privatization of bus services; (2) the average spare ratios have consistently exceeded the maximum of 20%, as suggested by the Federal Transit Administration for systems operating with more than 50 vehicles; (3) vehicles operated by contractors tended to be significantly newer than those operated directly by transit agencies, although the gap has narrowed in recent years; (4) vehicles operated directly by transit agencies tended to have higher seating and standing capacities than those operated by contractors; (5) there was a tendency among transit agencies to trade seats for more standing room; (6) by 2006, nearly all bus vehicles were ADA-compliant, and increasingly vehicles with lifts were converted to vehicles with ramps/low-floor; (7) reliability of vehicles in terms of number mechanical failures per million revenue vehicle miles has significantly improved over the years; and (8) contractors spent less on average than transit agencies in operating and maintaining their vehicles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".