Improving the Service Quality of Public Transit with Exclusive Bus Lanes: A Perspective from Passenger Satisfaction
Bibliographic record
Abstract
Exclusive bus lanes have been widely regarded as an effective way to promote bus priority and improve the service quality, while they have not attracted more passengers to travel by bus in China. Also, more attention is usually paid to the infrastructure and facilities of exclusive bus lanes, but not much attention is paid to the passenger satisfaction of public transit with them. Therefore, this paper studies how to improve the service quality of public transit with exclusive bus lanes from a perspective of passenger satisfaction. A Structural Equation Model (SEM) was developed to investigate the mechanism of relationships between passenger satisfaction and the factors influencing the service quality of public transit with exclusive bus lanes. A total of 2087 respondents from Shanghai, China, participated in this study. Also, a comparison model of different groups using public transit including captive riders, choice riders, and captive by choice riders was established. Results show that passengers were dissatisfied with the current service of public transit with bus lanes, while they would prefer to take buses in the near future. In addition, travel environment, facilities, and convenience, rather than operational efficiency, had significant effects on passenger satisfaction. Through the comparison model, it was found that choice and captive by choice users might prefer public transit with bus lanes and captive users might be unable to bear the financial burden of private motorized travel. “Crowdedness in the buses on bus lanes during peak hours” especially for choice riders and “driving stability” especially for captive riders were the improvement direction for attracting more passengers to travel by bus. The results and the proposed policies of this study can benefit for the planning and operations of exclusive bus lanes in Shanghai and other similar cities around the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".