Evidence map›Paper›PMID 36630380›Full record

ArticlePloS one2023

Estimating road traffic impacts of commute mode shifts.

Yue Hu, William Barbour, Kun Qian, Christian Claudel, Samitha Samaranayake, Daniel B Work

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Managing public transit during a pandemic: The trade-off between safety and mobility.Transportation research. Part C, Emerging technologies · 2022
    Article
  4. 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

6 authors.

Yue HuVanderbilt University and Institute for Software Integrated Systems, Nashville, TN, United States of America.ORCID 0000-0001-6579-0646
William BarbourVanderbilt University and Institute for Software Integrated Systems, Nashville, TN, United States of America.ORCID 0000-0002-1368-3192
Kun QianThe University of Texas at Austin, Cockrell school of engineering, Austin, TX, United States of America.
Christian ClaudelThe University of Texas at Austin, Cockrell school of engineering, Austin, TX, United States of America.
Samitha SamaranayakeCornell University, School of Civil and Environmental Engineering, Ithaca, NY, United States of America.
Daniel B WorkVanderbilt University and Institute for Software Integrated Systems, Nashville, TN, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

Indexed as

TransportationTravelHumansNew YorkSan Francisco

Identifiers

PMID36630380
PMCPMC9833534

What OpenQuestion holds

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LicenceCC BY
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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.