Trends in Toronto’s Subway Ridership Recovery: An Exploratory Analysis of Wi-Fi Records
Bibliographic record
Abstract
The COVID-19 pandemic has left major shifts in transit usage patterns on systems around the world in its aftermath. Unfortunately, the lack of detailed post-pandemic data on passenger travel habits has limited transit agencies’ ability to respond to trends and leverage new travel markets. The rollout of wireless fidelity (Wi-Fi) services at stations and onboard vehicles presents a potential solution, as Wi-Fi device connections can be used to provide very detailed information on customers’ origins, destinations, exact route, and travel time, which in turn can be aggregated by time and geography to reveal broader trends. This study presents an exploratory analysis based on such Wi-Fi data to investigate post-COVID ridership recovery trends on the Toronto subway system, demonstrating that Wi-Fi connections can be a credible proxy for overall ridership. The data show that downtown office commuting has been the slowest-recovering travel market, with local riders in suburban areas, off-peak riders, and discretionary riders returning to the subway system at higher rates. The data also confirm past research findings that less affluent and non-office workers were the fastest to return to transit.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".