Evidence map›Paper›PMID 33907339›Full record

ArticleTransportation2022

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis.

Daniel Hörcher, Ramandeep Singh, Daniel J Graham

Abstract read
In one paragraph

Article in Transportation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Preferences for using the London Underground during the COVID-19 pandemic.Transportation research. Part A, Policy and practice · 2022
    Article
  12. Article
  13. COVID-19 and suburban public transport in the conditions of the Czech Republic.Transportation research interdisciplinary perspectives · 2022
    Article
  14. Passenger Surveillance Using Deep Learning in Post-COVID-19 Intelligent Transportation System.Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology · 2022
    Article
  15. Article
  16. The impacts of the COVID-19 pandemic on transportation employment: A comparative analysis.Transportation research interdisciplinary perspectives · 2021
    Article
  17. Article
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.

Daniel HörcherTransport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, UK.ORCID 0000-0003-3070-8096
Ramandeep SinghTransport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, UK.
Daniel J GrahamTransport Strategy Centre, Department of Civil and Environmental Engineering, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

Indexed as

AuctioningCovid-19Demand managementInflow controlPricingPublic transportTradeable permit schemes

Identifiers

PMID33907339
PMCPMC8061464

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.