Systemwide Variations and Factors Affecting Mixture Transit Travel Time Distributions
Bibliographic record
Abstract
Understanding transit service reliability is essential for agencies to improve their operations and passenger experiences. Transit travel times that follow mixture distributions would create an additional layer of uncertainty when studying transit reliability. This paper tries to identify segments where transit travel times follow mixture distributions at different analysis levels, namely stop pair level, route timepoint level, and service pattern level. We then identify potential factors related to them. Hartigans’ Dip Test is applied to archived transit vehicle location data from Montreal to explore the presence of mixture distributions. The results contain mixture distributions at three analysis levels, and the proportion of mixture distributions varies temporally and spatially. Then we test several classification models to identify the potential factors that affect transit travel time distributions, where we found demand variations, traffic lights, service frequency, and segment lengths have a larger effect on the results. The findings will help transit planners to later pinpoint the issues causing transit travel time variations on each segment, then create strategies to reduce the transit travel time variations thus improving the reliability of our transit system.
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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 | 0.001 | 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 | 0.000 |
| 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".