Public Transit Service Reliability Assessment using Two-Fluid Model
Bibliographic record
Abstract
This study introduces a traffic flow theory-based reliability indicator to evaluate the inter-city public transit service. The two-fluid theory parameter n that measures the road networks’ resilience to changing traffic is used as a new reliability indicator of public bus service. We compared the performance of the new indicator with the three existing reliability indicators, including on-time performance, with a total of 52 different GO Bus routes to ascertain the usability of the new indicator. The GO Bus service is an inter-city bus service operated by Metrolinx in the Greater Toronto and Hamilton areas in Ontario, Canada. We used 1 month of GO Bus GPS data collected in July 2017 to estimate various public transit reliability indictors. We also investigated the relationship between reliability indicators and selected roadway network characteristics, such as route length, freeway ratio, intersection density along a route, and so forth. The study applied a series of statistical analyses, including principal component analysis, correlation analysis, and z-test using Fisher’s z transformation. The results showed that the proposed two-fluid indicator n can be used as a supplementary reliability indicator for assessing road networks’ resilience in regards to changing traffic flow conditions. The new indicator provided additional insights for inter-city bus service operators for initiating communication with roadway governing agencies for the purpose of improving road networks to further improve public transit service reliability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".