Getting committed: A new perspective on public transit market segmentation from two Canadian cities
Bibliographic record
Abstract
Traditionally transit market research has categorized riders into two distinct groups: captive and choice riders. While it is important for transit agencies to acknowledge the presence of these groups, market analyses that depend on such broad categories are likely to overlook important details about the needs and desires of their customer base. This study attempts to better understand the complexities of the different groups riding transit by using information from five years of customer satisfaction questionnaires collected by two Canadian transit providers in Montreal, Quebec, and Vancouver, British Columbia. Employing a series of clustering techniques, the analysis shows market segments that are present in both transit agencies. The segments are defined based on regularity of usage, choice, captivity and day of usage. The detailed analysis of these clusters reveals that the low cost of transit, convenience and efficiency of mode, as well as the service attributes are areas where policies are most likely to increase ridership among users, especially irregular ones. Since the findings have been consistent in two geographically distinct settings this research is expected to be replicable and applicable in other North American cities and is hoped to provide a new segmentation approach and policy framework that can help in the collective goal of increasing transit ridership.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 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".