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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".