Public Transit Itinerary Choice Analysis Considering Various Incentives
Bibliographic record
Abstract
Previous research on public transport user preferences has primarily focused on mode choice, transfer penalties, and preferences related to service qualities. Individuals are generally found to not like waiting, transfers, or overcrowding. However, from an operation perspective, it could be important to convince users to change to a different itinerary to reduce overcrowding, which can cause problems such as bunching. This would most likely require convincing the user to voluntarily accept a longer wait or to take a different route potentially with a transfer. One means of doing so might be through incentives or offering a shared taxi. Incentivizing transit users to switch to a less-crowded itinerary, which could potentially be a way to manage bus overcrowding, is not well-studied. Through a discrete choice experiment, this study thus filled this research gap by testing people’s willingness to change routes or to take a shared taxi in exchange for one of three distinct types of incentives. A mixed binary logit model and a mixed nested logit model were used to investigate how different factors influence public transit user itinerary choice behavior. The results suggested that some people would be willing to change to the proposed alternative. Heterogeneity within the responses was seen as transit users were found to have distinct preferences for different incentives, and the value of the incentives influenced users differently. Our models and results could be helpful for public transit agencies to improve efficiency and reduce costs by better balancing the travel demand.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".