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出行指标:评估新冠疫情对出行行为的影响。

Metrics of Mobility: Assessing the Impact of COVID-19 on Travel Behavior.

作者信息

Panik Rachael Thompson, Watkins Kari, Ederer David

机构信息

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA.

出版信息

Transp Res Rec. 2023 Apr;2677(4):583-596. doi: 10.1177/03611981221131812. Epub 2022 Nov 14.

Abstract

The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis-initial post-disruption mobility index, changepoint on restoration of a "new normal," and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes.

摘要

新冠疫情扰乱了全球范围内的典型出行行为。在美国,政府机构采取行动,通过公共卫生信息来限制疫情传播,鼓励减少出行,从而降低病毒传播。然而,在各州应对新冠疫情的举措中,地方层面的反应各不相同。其中一些差异可能是由于个人对政府和健康信息的态度所致,但也可能部分是由于社区特征的影响。在本研究中,我们总结了已知会影响美国404个县出行行为的县级特征,并调查了2020年4月至9月期间出行流动性的相关因素。我们通过应用三种经变点分析得出的指标来进行研究,即初始疫情后出行流动性指数、恢复“新常态”的变点以及恢复后的出行流动性指数。我们发现,就业部门的变量具有显著相关性,并且在疫情期间对出行流动性有很大影响。州虚拟变量显著,这表明同一州内的县之间的行为比不同州的县之间更为相似。我们的研究结果表明,很少有通常与出行行为相关的出行特征与疫情期间的出行流动性有关,而且新冠病例数可能与出行结果无关。

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