If You Cut It Will They Ride? Longitudinal Examination of the Elasticity of Public Transport Ridership in the Post-Pandemic Era
Bibliographic record
Abstract
In the past two years, public transport ridership has declined because of COVID-19 pandemic health measures and new working from home policies. This decline in ridership has caused major financial stress on public transport agencies around the world. Several agencies have responded to this financial stress by reducing services. The extent to which these service cuts will affect transit ridership is unknown because of the changing operational environment in the post-pandemic world. Our study uses a longitudinal panel data from Montréal, Quebec, Canada, to explore the relationship between route-level ridership and operational factors over time. We find that public transport ridership demand at the route level is highly elastic when compared to trip frequency and has become more elastic after the COVID-19 pandemic. Our findings imply that agencies cutting service in the post-pandemic era run a much more significant risk of creating a "doom spiral," where service reductions spur greater declines in ridership, forcing further reductions. Demand was found to be most elastic on more frequent routes, so agencies should prioritize maintaining services on their core routes in the post-pandemic era. This study can be of use to public transit planners and policymakers considering making service changes to attract more riders or trying to respond to post-COVID-19 financial stress.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".