Would You Wait? Bus Choice Behavior Analysis Considering Various Incentives
Bibliographic record
Abstract
During the peak hour, ridership is much higher, and this can lead to problems such as overcrowding for customers and vehicle bunching for the operator as dwell times increase for the lead bus. In this paper, we examined whether to avoid overcrowding people might be willing to wait for the next bus knowing there were seats and if they were offered an incentive. Three distinct types of incentive were offered, defined according to goal-framing theory. To obtain the choice data, a discrete choice experiment was developed and incorporated into an online survey that was distributed to public transport users. A binary logit model and a mixed multinomial logit model were used to investigate how different factors influence public transport users’ bus choice behavior. Results show several considerations that increase the likelihood of users agreeing to the request to wait. These include the weather being sunny/fine, not very cold, or both, the purpose being seeing a friend or shopping, longer in-vehicle time, shorter proposed bus waiting time, and the incentive. The results of the mixed multinomial logit model show variation among the respondents with regard to the incentives. We found that age, gender, work status, possession of a driver ID, and vehicle availability are significant predictors for incentive preference. In that our models target specific groups of users with tailored incentives, the results indicate how to persuade public transport users to avoid overcrowded buses.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".