Influence of Mobile Payment on Bus Boarding Service Time
Bibliographic record
Abstract
A new form of mobile payment, Quick Response (QR) code, has been a popular way of paying bus fares in China since 2017. Compared with conventional payment methods, cash or IC card, QR code shows a lot of differences in response time, recognition accuracy, and payment procedure, which significantly influences the boarding service time (BST) for passengers. However, no research has considered its efficiency. This study, therefore, tries to fill this gap and investigate its influence on BST. Sufficient ride-check data were collected, and the influence of the QR code payment method on BST was examined through a set of regression models. Passengers pay the bus fare with different payment methods as their first choice; nevertheless, when the payment fails, they may transfer among them. According to the payment choice, result, and process, we introduce the first-choice-based, the last-choice-based, and the choice-transfer-based models, respectively. The scenario with delays in calling out the QR code was considered in the choice-transfer-based model. The onboard crowdedness was regarded as a categorical variable to determine the regime of the boarding process in all models. We conduct empirical analysis in Wuhan, and this study can help to identify the influence of the QR code payment method on BST, consequently, improving bus service efficiency.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".