Effects of Fare Payment Types and Crowding on Dwell Time
Bibliographic record
Abstract
Dwell time, the time a transit vehicle spends stopped to serve passengers, contributes to the total reliability of transit service. Dwell time is affected by factors such as passenger activity, bus crowding, fare collection method, driver experience, and time of day. The types of effects crowding can have on dwell time are debatable because of its interaction with passenger activity and inaccuracies in its calculation. Different payment methods also have an effect on dwell time. This debate can be linked to the absence of appropriate data that can actually capture the real effects of these variables. This research attempts to determine the influence of crowding and fare payment on dwell time through manual data collection. The study was conducted along three heavily used bus routes in the Trans-Link system in Vancouver, British Columbia, Canada. Multiple regression dwell time models are performed by using a traditional model and a new expanded model with the additional details that manually collected data provide. The traditional model overestimated dwell times because of a lack of detail in fare payment and crowding, while the expanded model showed that crowding significantly increased dwell time after approximately 60% of bus capacity was surpassed. Fare payment methods had various positive effects on dwell time, with different magnitudes. This research can help public transit planners and operators develop better guidelines for fare payment methods as well as policies associated with crowding.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".