Evidence map›Paper›PMID 34199900›Full record

ArticleInternational journal of environmental research and public health2021

Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase.

Yanbing Bai, Lu Sun, Haoyu Liu, Chao Xie

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Yanbing BaiCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China.ORCID 0000-0001-5223-9425
Lu SunSchool of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China.ORCID 0000-0002-3419-7994
Haoyu LiuCenter for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China.ORCID 0000-0002-0839-5460
Chao XieChina Transport Information Co., Ltd., Beijing 100007, China.

Funding

ResearchFundsfortheMajorInnovation Platform of Public Health and Disease Control and Prevention, Renmin University of China No
6 · The paper itself

Abstract

Large-scale population movements can turn local diseases into widespread epidemics. Grasping the characteristic of the population flow in the context of the COVID-19 is of great significance for providing information to epidemiology and formulating scientific and reasonable prevention and control policies. Especially in the post-COVID-19 phase, it is essential to maintain the achievement of the fight against the epidemic. Previous research focuses on flight and railway passenger travel behavior and patterns, but China also has numerous suburban residents with a not-high economic level; investigating their travel behaviors is significant for national stability. However, estimating the impacts of the COVID-19 for suburban residents' travel behaviors remains challenging because of lacking apposite data. Here we submit bus ticketing data including approximately 26,000,000 records from April 2020-August 2020 for 2705 stations. Our results indicate that Suburban residents in Chinese Southern regions are more likely to travel by bus, and travel frequency is higher. Associated with the economic level, we find that residents in the economically developed region more likely to travel or carry out various social activities. Considering from the perspective of the traveling crowd, we find that men and young people are easier to travel by bus; however, they are exactly the main workforce. The indication of our findings is that suburban residents' travel behavior is affected profoundly by economy and consistent with the inherent behavior patterns before the COVID-19 outbreak. We use typical regions as verification and it is indeed the case.

Indexed as

COVID-19AdolescentBig DataChinaHumansMaleSARS-CoV-2Travelbus ticketing big datachinese suburban residentspopulation flowpost-COVID-19 phasesspark distributed computing

Identifiers

PMID34199900
PMCPMC8200129

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.