Evidence map›Paper›PMID 36407477›Full record

ArticleCase studies on transport policy2022

COVID-19 impacts on mobility and travel demand.

Lisa Shemer, Elham Shayanfar, Jonathan Avner, Roberto Miquel, Sabyasachee Mishra, Mark Radovic

Open access · greenAbstract read
In one paragraph

Article in Case studies on transport policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
7.1field-weighted citation impact, top 3% 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

5 citing papers in PubMed, 31 citations in OpenAlex.

  1. Uncovering the surge: dengue fever on the rise.Frontiers in epidemiology · 2025
    Article
  2. Article
  3. Article
  4. Article
  5. COVID-19, travel restrictions and environmental consequences.Environment, development and sustainability · 2023
    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 at 1 institution in 1 country.

Lisa ShemerMaryland State Highway Administration, Travel Forecasting and Analysis Division, Office of Planning and Preliminary Engineering, Baltimore, MD 21202, United States.
Elham ShayanfarItenology Inc., Columbia, MD 21045, United States.
Jonathan AvnerWhitman, Requardt & Associates, LLP., Austin, TX 78759, United States.
Roberto MiquelWhitman, Requardt & Associates, LLP., Raleigh, NC 27607, United States.
Sabyasachee MishraDepartment of Civil Engineering, University of Memphis, Memphis, TN 38152, United States.
Mark RadovicGannett Fleming, Inc., Baltimore, MD 21244, United States.
University of Memphis · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the beginning of the COVID-19 pandemic, many travel restriction policies were implemented to reduce further spread of the virus. These measures significantly affected travel demand to levels which could not have been anticipated by most planners in transportation agencies. As the pandemic has proven to have significant short-term impacts, it is anticipated that some of these impacts may translate to longer-term impacts on overall travel behavior and the movement of people and goods. Beyond the pandemic, the observed travel patterns during this period also provides a great opportunity for planners to assess policies such as work from home and remote learning as strategies to manage travel demand. This study provides a scenario analysis framework to re-evaluate travel demand forecasts under uncertain future conditions using the Maryland Statewide Transportation Model (MSTM). Model parameters associated with working from home, household income, changes in discretionary travel, distance learning, increased e-commerce, vehicle occupancy and mode choice were identified. Parameter values were assigned under the various scenarios using employer surveys on workforce teleworking and observed data on e-commerce growth and shopping behavior. The main findings of this study capture the sensitivities of systemwide vehicle miles travel, and vehicle hours travel under different scenarios and implications on future investment decisions. The study found that future investments under the scenarios remain beneficial to systemwide performance and therefore justified. Although this study focuses on the state of Maryland, the scenario framework and parameter definitions can be used in other states or agencies within a travel demand model environment.

Indexed as

COVID-19E-commerceLong-term impactTravel forecastingWork from home

Identifiers

PMID36407477
PMCPMC9661421
OpenAlexW4308979210

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.