PEAK-PERIOD SERVICE SUPPLY VERSUS OBSERVED PASSENGER UTILIZATION FOR RAPID BUS AND RAPID RAIL MODES: ISSUES AND IMPLICATIONS
Bibliographic record
Abstract
Data from U.S. and Canadian rapid bus and rapid rail systems demonstrate a strong and consistently positive relationship between transit service supply and consumption during peak periods. Rail modes attract greater utilized capacity per unit of offered capacity during peak period than bus modes, and this aspect of consumer choice may be quantified by regression analysis. Data and observations fail to support alternative hypotheses to a consistent and observable consumer preference for rail. Observed consumer behavior suggests that peak service consumption may be determined from supply within a fairly broad demand range. The linear regression models might therefore be useful for supply-side verification of ridership forecasts. Peak consumption levels assumed by some previous studies were unrealistically high. Cost per passenger for various bus and rail projects were therefore higher, and ridership lower, than predicted during planning. Some potential consumers will choose not to ride if peak period service is inadequate, leading to increased costs per passenger. Crowding often discourages patronage in markets where consumers have competitive alternatives to public transit service. This occurs at crowding levels significantly below the capacity figures used by transit planners. These findings have important implications for planning and cost analysis, particularly when bus and rail modes are compared. A stronger case for rail transit might be made than some previous studies have found, but bus modes have significant advantages in certain situations.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".