Evaluation of Bus Reliability Measures and Development of a New Composite Indicator
Bibliographic record
Abstract
Reliability is cited as a key aspect of service quality, but many of the indicators in use today do not measure reliability from the user's perspective. A review of earlier work on transit user behavior concluded that the traveler's perspective of reliability was driven by punctuality in arriving at the destination, short waiting times at the origin stop, and consistent wait and travel times. Twenty indicators were assessed, but none were well suited to capturing all of these elements of reliability. A new measure, journey time buffer index (JTBI), was therefore proposed; the index used estimates of wait times at bus stops while capturing variability in wait and travel times that tended to increase the disutility of transit travel. Alternative formulations were developed for short and long headway service, and the new indicator was applied to the London Transit Commission's bus network in London, Ontario, Canada. This procedure demonstrated that the JTBI was better suited to identifying the factors contributing to unreliable service than metrics that focused on a single component of reliability. A linear regression analysis also highlighted that route length, stop spacing, time of day, route orientation relative to the city center, and passenger load all influenced reliability although the low adjusted R 2 value of .298 showed that some major causes of reliability were not captured by the model.
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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 | 0.029 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".