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Record W4382808809 · doi:10.2139/ssrn.4493231

The Effects of COVID-19 on Public Transit in the City of Calgary: Fiscal Impacts and Equity Considerations

2023· article· en· W4382808809 on OpenAlexaffabout
Gillian Petit, Lindsay M. Tedds, Wenshuang Yu

Bibliographic record

VenueSSRN Electronic Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Equity (law)Public transport2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Transit (satellite)BusinessPublic economicsEconomicsPolitical scienceMedicineTransport engineeringEngineeringVirology

Abstract

fetched live from OpenAlex

Using monthly public transit revenue data, we investigate the effect of the SARS-CoV-2 (COVID-19) pandemic on public transit revenues in the large urban municipality of Calgary in Alberta, Canada. Calgary provides a poignant environment in which to examine the effect of COVID-19 on transit fare revenues. First, City Council has set a general policy whereby 50% of Calgary Transit operating costs are to be recovered through operating revenue, including transit fare revenue. Second, Calgary Transit has a unique program—the fare entry program—that heavily discounts public transit fares to qualifying low-income Calgarians. The fact that these fare products exist at not only the level of the local transit authority but also that the revenues from these products are tracked separately by the transit authority allows for an equity analysis of the impact of COVID-19 on public transit use that would not be possible in other jurisdictions. As a result, we can consider the impact on different user groups over the pandemic, paying particular attention to the inequitable impacts on transit users. Finally, Calgary was hit hard by COVID-19 caseloads, spread, and public health orders that resulted in the immediate reduction of public transit use. We find that COVID had the largest (statistically significant) impact on adult transit fare revenue, a smaller impact on youth fares, and almost no impact on low-income fares suggesting that youth and low-income transit pass users were less able to substitute away from or forgo public transit during the COVID shock, unlike adults. Reductions in transit services that occurred at the same time were more likely borne by youth and low-income transit users. To minimize service reductions and their inequitable effects, we argue that given municipalities have little financial power and flexibility, higher orders of government should provide transit operating funding during times of transit fare shocks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.031
GPT teacher head0.337
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractno

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