Analysis of flows and speeds of urban transit systems for consideration of modal transition in a corridor
Bibliographic record
Abstract
As a transit corridor evolves with time (over several decades) due to land use and other changes, the public transit mode (or mix of modes) that serves it may have to transition from one to another several times.Two of the significant characteristics that must be considered in the transition are the capacity and average speed of each mode, since they impact passenger waiting times and invehicle travel times, respectively, as well as operating costs.Data on stated, as well as observed, maximum flows and average speeds of routes have been collected from many sources and analysed.In addition to intrinsic variations, there is considerable scatter in the data caused, in part, by the lack of information about the differences in the transit systems for which data are available, e.g. the number of transit routes passing through a corridor.Various modes considered suitable for the south Calgary corridor are ranked in terms of line capacity and average speed.The thresholds are those at which a mode transition is essential.However, mode transitions may occur well in advance of such thresholds, if a new modal mix is optimal for the corridor in terms of minimizing the sum of the costs to the users and the operator.Some preliminary results on the optimal mix of regular and express bus services in a given corridor are discussed, including the travel demand estimates and transit system parameters under which a transition from the regular bus mode to an optimal mix is mandated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".