Evidence map›Paper›PMID 36743259›Full record

ArticleTransportation research interdisciplinary perspectives2023

COVID-19 public transit precautions: Trade-offs between risk reduction and costs.

Lily Hanig, Corey D Harper, Destenie Nock

Abstract read
In one paragraph

Article in Transportation research interdisciplinary perspectives, 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

3 authors.

Lily HanigEngineering & Public Policy, Carnegie Mellon University, United States of America.
Corey D HarperCivil & Environmental Engineering, Carnegie Mellon University, United States of America.
Destenie NockEngineering & Public Policy, Carnegie Mellon University, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public transit has received scrutiny as a vector for spreading COVID-19 with much of the literature finding correlations between transit ridership and COVID-19 rates by assessing the role that transportation plays as a vector for human mobility in COVID-19 spread. However, most studies do not directly measure the risk of contracting COVID-19 inside the public transit vehicle. We fill a gap in the literature by comparing the risk and social costs across several modes of transportation. We develop a framework to estimate the spread of COVID-19 on transit using the bus system in Pittsburgh. We find that some trips have demand that exceed their COVID-19 passenger limit, where the driver must decide between: (1) leaving a passenger without a ride or (2) allowing them on the bus and increasing COVID-19 risk. We consider five alternatives for alleviating overcapacity: allow crowding, additional buses, longer buses as substitutes, Transportation Network Company (TNC) rides, or Autonomous Vehicles (AVs) for passed-by passengers. We use transit ridership and COVID-19 data from the spring of 2020 by combining transportation data and an epidemiological model of COVID-19 stochastically in a Monte Carlo Analysis. Our results show that 4% of county cases were contracted on the bus or from a bus rider, and a disproportionate amount (52%) were from overcapacity trips. The risk of contracting COVID-19 on the bus was low but worth mitigating. A cost-benefit analysis reveals that dispatching AVs or longer buses yield the lowest societal costs of $45 and $46 million, respectively compared to allowing crowding ($59 million).

Indexed as

Autonomous vehiclesCOVID-19Public transportationRidesharingUncertainty analysis

Identifiers

PMID36743259
PMCPMC9886664

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.