Evidence map›Paper›PMID 36407119›Full record

ArticleTravel behaviour & society2023

COVID-19, traffic demand, and activity restriction in China: A national assessment.

Zhao Zhang, Daocheng Fu, Feng Liu, Jinghua Wang, Kai Xiao, Brian Wolshon

Open access · greenAbstract read
In one paragraph

Article in Travel behaviour & society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

6 citing papers in PubMed, 25 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

6 authors at 3 institutions in 2 countries.

Zhao ZhangSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Daocheng FuSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Feng LiuSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Jinghua WangSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Kai XiaoSchool of Foreign Languages and Cultures, Chengdu University of Technology, Chengdu 610059, China.
Brian WolshonDepartment of Civil and Infrastructure Engineering, Louisiana State University, Baton Rouge, USA.
Beihang University · CNChengdu University of Technology · CNLouisiana State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global COVID pandemic of 2020, affected travel patterns across the world. The level of impact was influenced not only by the virus itself, but also by the nature, extent, and duration of governmental restriction on commerce and personal activity to limit its spread. This paper focuses on the interaction between COVID-19 transmission and traffic volume and further explores the impact of traffic control policies on the interaction. Roadway traffic volume was used to quantify and assess the Chinese response to the pandemic; specifically, the relationship between government restrictions, travel activity, and COVID-19 progression across 29 provinces. Space and time distributions of traffic volume across China during the first half of 2020, were used to quantity the response and recovery of travel during the critical initial onset period of the virus. Most revealing of these trends were the impact of the Chinese restriction policies on both travel and the virus as well as the relationship of traffic trends during the closure period with the speed and extent of the recovery "bounce" across individual provinces based on location, economic activity, and restriction policy. These suggest that the most significant and rapid declines in traffic volume during the restriction period resulted in the most pronounced returns to normal (or more) demand levels. Based on these trends a Susceptible Infection Recovery model was created to simulate a range of outbreak and restriction policies to examine the relationship between COVID-19 spread and traffic volume in China.

Indexed as

COVID-19 pandemicHighway traffic volumeResilience metricSIR model

Identifiers

PMID36407119
PMCPMC9640390
OpenAlexW4308460925

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.