Segmenting transit ridership: From crisis to opportunity
Bibliographic record
Abstract
Crises are an opportunity to learn, and transportation is no exception. The dramatic reduction in mobility levels during COVID-19, the slow recovery of transit ridership and new trends such as remote working have raised essential questions for the future of public transport. Our work focuses on transit rider segmentation, understanding the heterogeneity of users based on their behaviour before, during, and coming out of the pandemic, and what that means for the economic and social sustainability of transit systems. We asked ourselves two main questions: (i) will people continue riding transit after COVID-19? and (ii) what are riders’ reasons behind increasing, maintaining, or decreasing public transport use? Using a two-wave survey conducted in 2020 and 2021, we assessed the motives behind future public transport use in two Canadian cities (Toronto and Vancouver). We used quantitative and qualitative methods, particularly latent class cluster analysis (LCCA), text mining, and qualitative content analysis. We identified six transit riders’ profiles, ranging from those experiencing transport poverty who rely on public transport to those more resourced users who will ride less since they can choose alternatives such as remote work, private modes, or active travel. We discuss the policy and practice implications of these results, focusing on what public transport decision-makers should prioritize to benefit disadvantaged groups and recover 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 | 0.005 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".