University Bus Services: Responding to Students’ Travel Demands?
Bibliographic record
Abstract
University bus services provide fixed-route public transport for students to and from large universities. They are relatively affordable and resource-efficient, but must compete against private cars and taxis which tend to be more convenient and flexible than University bus services. Many students, particularly those with lower incomes, depend on them. University administrators must understand how to improve University bus service in order to treat this group fairly. The main contribution of this paper is to examine how University bus service can help achieve social equity and sustainability goals. It investigates this by reporting the results of a survey of 303 students at Shahid Bahonar University of Kerman in Iran concerning their travel demands and University bus service utilization. Grounded theory and structural equation modeling is used to analyze the daily commute preferences of university students influenced by their characteristics, fleet condition, fares, station condition, and time factors. This study identifies various steps that the university administration can take to make University bus service more efficient and attractive, such as updating the bus fleet, improving stations, increasing security, and improving pedestrian access to stations. Survey respondents reported the highest satisfaction levels with fare levels, the security in buses, and waiting times. Women reported feeling discriminated against in their daily commutes. Poor-quality University bus service contributes to a negative feedback loop that increases automobile use and reduces university travel sustainability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".