Modeling the Satisfaction of Bus Traffic Transfer Service Quality at a High-Speed Railway Station
Bibliographic record
Abstract
Bus transit is one of the main transfer modes at high-speed railway (HSR) stations. Performing a scientific and reasonable evaluation of the present bus traffic transfer service is highly significant for improving the efficiency of the HSR and increasing the use of the system. This paper focuses on the passengers’ transfer experience and proposes a methodology to evaluate current bus traffic transfer service. Factors that affect passenger satisfaction during the bus transfer process at HSR stations based on the passengers’ perceptions are analyzed by convenience, comfort, safety, service, and economy. A structural equation model (SEM) is developed as an evaluation approach to explore the correlations of bus transfer service, passenger perceived value, and passenger satisfaction. To calibrate the model, a questionnaire survey of passengers transferring to a bus was conducted at Xi’anbei Railway Station. This paper analyzes the relationships between observed variables and latent variables in the measured model, the influences of exogenous variables on endogenous variables in the structural model, and the impact of the passengers’ socioeconomic attributes on passenger satisfaction. Analysis results of the SEM show that economy and convenience are the critical influential indicators of passenger satisfaction, among which bus fare preferential policy and transfer distance are the most significant factors. The findings can provide helpful information for planners and managers to improve the services of existing HSR stations and to guide the planning of new ones.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".