ArticleTravel behaviour & society2023
COVID-19, traffic demand, and activity restriction in China: A national assessment.
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.
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.
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.
Who cites it
6 citing papers in PubMed, 25 citations in OpenAlex.
- Short-term analysis of urban noise dynamics during covid-19 lockdown using a machine learning approach in Mashhad, Iran.Scientific reports · 2025Article
- Spatiotemporal Dynamics of COVID-19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing.GeoHealth · 2024Article
- The Impact of The COVID-19 Pandemic on Hospital Admissions Due to Road Traffic Crashes; a Systematic Review and Meta-Analysis.Archives of academic emergency medicine · 2024Review
- Estimating long-term and short-term impact of COVID-19 activity restriction on regional highway traffic demand: A case study in Zhejiang Province, China.International journal of disaster risk reduction : IJDRR · 2023Article
- Are the epidemic prevention facilities effective? How cities should choose epidemic prevention facilities: Taking Wuhan as an example.Frontiers in public health · 2023Article
- Unleashing the link between the relaxation of the COVID-19 control policy and residents' mental health in China: the mediating role of family tourism consumption.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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.