Bibliographic record
Abstract
Ensuring that customers are satisfied with public transit is important, and traditionally transit agencies have assessed customer satisfaction by using questionnaires designed to collect information about users’ personal characteristics and perceptions of service. However, these questionnaires assess only individuals’ perceptions of transit services, without accounting for the service that users actually experienced. With that in mind, the purpose of this paper is to analyze the drivers of public transit satisfaction for users on the basis of an analysis of customer satisfaction questionnaires, as well as operations data obtained from automatic vehicle location and automatic passenger counter systems for an express bus route in Vancouver, British Columbia, Canada. The goal of the paper is to understand what the main factors influencing customer satisfaction in this context are. The paper questions whether using operations data in parallel with passengers’ perception data is useful in understanding customer satisfaction. With a series of logit models, it is found that actual crowding and users’ reported satisfaction with crowding are associated with how transit users perceive overall satisfaction with the bus service. Furthermore, the models reveal that car access, age, past use, and users’ perceptions of frequency, onboard safety, and cleanliness are also positively associated with overall satisfaction. This study could be useful for public transit planners as it provides new insight into how data derived from customer satisfaction surveys and bus operations can be used to identify which modifiable components of the service can be prioritized to effectively increase riders’ overall satisfaction.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".