Evidence map›Paper›PMID 35530724›Full record

ArticleCities (London, England)2022

Data-driven analysis of the impact of COVID-19 on Madrid's public transport during each phase of the pandemic.

Rubén Fernández Pozo, Mark Richard Wilby, Juan José Vinagre Díaz, Ana Belén Rodríguez González

Abstract read
In one paragraph

Article in Cities (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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  8. To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19.Stochastic environmental research and risk assessment : research journal · 2023
    Article
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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

4 authors.

Rubén Fernández PozoDepartment of Mathematics Applied to Information and Communication Technologies, Universidad Politécnica de Madrid, Avda. Complutense, 30, 28040 Madrid, Spain.
Mark Richard WilbyDepartment of Mathematics Applied to Information and Communication Technologies, Universidad Politécnica de Madrid, Avda. Complutense, 30, 28040 Madrid, Spain.
Juan José Vinagre DíazDepartment of Mathematics Applied to Information and Communication Technologies, Universidad Politécnica de Madrid, Avda. Complutense, 30, 28040 Madrid, Spain.
Ana Belén Rodríguez GonzálezDepartment of Mathematics Applied to Information and Communication Technologies, Universidad Politécnica de Madrid, Avda. Complutense, 30, 28040 Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has become a major global issue with large social-economic and health impacts, which led to important changes in people's behavior. One of these changes affected the way people use public transport. In this work we present a data-driven analysis of the impact of COVID-19 on public transport demand in the Community of Madrid, Spain, using data from ticket validations between February and September 2020. This period of time covers all stages of pandemic in Spain, including de-escalation phases. We find that ridership has dramatically decreased by 95% at the pandemic peak, recovering very slowly and reaching only half its pre-pandemic levels at the end of September. We analyze results for different transport modes, ticket types, and groups of users. Our work corroborates that low-income groups are the most reliant on public transportation, thus observing significantly lower decreases in their ridership during pandemic. This paper also shows different average daily patterns of public transit demand during each phase of the pandemic in Madrid. All these findings provide relevant information for transit agencies to design responses to an emergence situation like this pandemic, contributing to extend the global knowledge about COVID-19 impact on transport comparing results with other cities worldwide.

Indexed as

COVID-19MadridPublic transportRidershipTicket validations

Identifiers

PMID35530724
PMCPMC9057950

What OpenQuestion holds

Textmetadata
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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.