Evidence map›Paper›PMID 38625353›Full record

ArticlePublic transport (Heidelberg, Germany)2023

Three's a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic.

Tianxing Dai, Brian D Taylor

Abstract read
In one paragraph

Article in Public transport (Heidelberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Tianxing DaiDepartment of Civil and Environmental Engineering, Northwestern University, Evanston, IL 60208 USA.ORCID 0000-0003-1984-1857
Brian D TaylorUCLA Institute of Transportation Studies, Los Angeles, CA 90095-1656 USA.ORCID 0000-0002-1037-2751

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic dramatically affected public transit systems around the globe. Because transit systems typically move many people closely together on buses and trains, public health guidance demanded that riders should keep a distance of about two meters to others changed the definition of "crowding" on transit in 2020. Accordingly, this research examines how U.S. public transit agencies responded to public health guidance that directly conflicted with their business model. To do this, we examined published crowding standards before the COVID-19 pandemic for a representative sample of 200 transit systems, including whether they started or changed their published standards during the pandemic, as well as the reasons whether agencies publicize such standards at all. We present both descriptive statistics and regression model results to shed light on the factors associated with agency crowding standards. We find that 56% of the agencies surveyed published crowding standards before the pandemic, while only 46% published COVID-19-specific crowding standards. Regression analyses suggest that larger agencies were more likely to publish crowding standards before and during the COVID-19 pandemic, likely because they are more apt to experience crowding. Pandemic-specific crowding standards, by contrast, were associated with a more complex set of factors. We conclude that the relative lack of pandemic standards reflects the uncertainty and fluidity of the public health crisis, inconsistent and at times conflicting with the guidance from public health officials, and, in the U.S., a lack national or transit industry consensus on appropriate crowding standards during the first year of the pandemic.

Indexed as

COVID-19CrowdingLoad factorsSocial distancingTransit

Identifiers

PMID38625353
PMCPMC9762872

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.