Evidence map›Paper›PMID 35239712›Full record

ArticlePloS one2022

Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks.

Sanmay Shelat, Thijs van de Wiel, Eric Molin, J W C van Lint, Oded Cats

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
4.8field-weighted citation impact, top 5% of its field
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

3 citing papers in PubMed, 31 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Sanmay ShelatDepartment of Transport and Planning, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-5412-3933
Thijs van de WielDepartment of Transport and Planning, Delft University of Technology, Delft, Netherlands.ORCID 0000-0003-1954-5302
Eric MolinTransport and Logistics Group, Delft University of Technology, Delft, Netherlands.
J W C van LintDepartment of Transport and Planning, Delft University of Technology, Delft, Netherlands.
Oded CatsDepartment of Transport and Planning, Delft University of Technology, Delft, Netherlands.
Delft University of Technology · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionUnlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers' confidence it is critical to assess their risk determinants and trade-offs.

methodsTo this end, we survey train travellers in the Netherlands in order to: (i) quantify the impact of trip-specific, policy-based, and pandemic-related attributes on travellers' COVID-19 risk perceptions; and (ii) evaluate the trade-off between this risk perception and other travel attributes. Adopting the hierarchical information integration approach, in a two-stage stated preference experiment, respondents are asked to first rate how risky they perceive different travel situations to be, and then to choose between different travel options that include their own perceived risk rating as an attribute. Perceived risk ratings and choices between travel options are modelled using a linear regression and a mixed multinomial logit model, respectively.

resultsWe find that on-board crowding and infection rates are the most important factors for risk perception. Amongst personal characteristics, the vulnerability of family and friends has the largest impact-nearly twice that of personal health risk. The bridging choice experiment reveals that while values of time have remained similar to pre-pandemic estimates, travellers are significantly more likely to choose routes with less COVID-19 risk (e.g., due to lower crowding). Respondents making longer trips by train value risk four times as much as their shorter trip counterparts. By combining the two models, we also report willingness to pay for mitigating factors: reduced crowding, mask mandates, and increased sanitization.

conclusionSince we evaluate the impact of a large number of variables on route choice behaviour, we can use the estimated models to predict behaviour under detailed pandemic scenarios. Moreover, in addition to highlighting the importance of COVID-19 risk perceptions in public transport route choices, the results from this study provide valuable information regarding the mitigating impacts of various policies on perceived risk.

Indexed as

COVID-19AdolescentAdultAgedAged, 80 and overChoice BehaviorFemaleHumansMaleMiddle AgedNetherlandsPerceptionRisk AssessmentRisk FactorsRisk-TakingSARS-CoV-2

Identifiers

PMID35239712
PMCPMC8893614
OpenAlexW4214822125

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.