Evidence map›Paper›PMID 34957387›Full record

ArticleTransportation research interdisciplinary perspectives2022

Integrating COVID-19 health risks into crowding costs for transit schedule planning.

K W Devasurendra, Saeid Saidi, S C Wirasinghe, Lina Kattan

Abstract read
In one paragraph

Article in Transportation research interdisciplinary perspectives, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

K W DevasurendraDepartment of Civil Engineering, University of Calgary, 622 Collegiate Pl NW, Calgary, Alberta T2N 4V8, Canada.
Saeid SaidiDepartment of Civil Engineering, University of Calgary, 622 Collegiate Pl NW, Calgary, Alberta T2N 4V8, Canada.
S C WirasingheDepartment of Civil Engineering, University of Calgary, 622 Collegiate Pl NW, Calgary, Alberta T2N 4V8, Canada.
Lina KattanDepartment of Civil Engineering, University of Calgary, 622 Collegiate Pl NW, Calgary, Alberta T2N 4V8, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The public transport sector worldwide experienced the worst impact in recent history, in terms of ridership loss, due to the COVID-19 pandemic. The pandemic negatively affected passengers' perceptions of public transport and is likely to make a lasting impact on ridership, trip patterns, and modal share. Without any supportive changes to transit operations, ridership is likely to decline. This study explores the setting of frequencies in transit lines and proposes a two-part methodology that addresses the changing perceptions of users, especially in a health-related context. The first part develops a mathematical model that expresses the pre-COVID-19 cost of passenger crowding as an integral part of user costs to determine the optimal headway that considers the trade-offs between user and operator costs. A continuum approximation for the demand of the bus line has been used in the derivation. The second part extends the developed model to include both the costs of the health risks associated with the COVID-19 pandemic and crowding. The developed models will help transit planners and operators to plan and adapt operations to changing health risks during the pandemic and post-pandemic. Several numerical examples are provided to describe the uses and applications of the analytical models using information obtained from the literature.

Indexed as

COVID-19 and transitOptimum headwayPandemic health risksTransit scheduling

Identifiers

PMID34957387
PMCPMC8686693

What OpenQuestion holds

Textmetadata
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.