Chakraborty Meghna, Shakir Mahmud Md, Gates Timothy J, Sinha Subhrajit
Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI.
Pacific Northwest National Laboratory, Richland, WA.
Transp Res Rec. 2023 Apr;2677(4):380-395. doi: 10.1177/03611981211067794. Epub 2022 Jan 6.
Since the United States started grappling with the COVID-19 pandemic, with the highest number of confirmed cases and deaths in the world as of August 2020, most states have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the long-term implications of this crisis to mobility still remain uncertain. To this end, this study proposes an analytical framework that determines the most significant factors affecting human mobility in the United States during the early days of the pandemic. Particularly, the study uses least absolute shrinkage and selection operator (LASSO) regularization to identify the most significant variables influencing human mobility and uses linear regularization algorithms, including ridge, LASSO, and elastic net modeling techniques, to predict human mobility. State-level data were obtained from various sources from January 1, 2020 to June 13, 2020. The entire data set was divided into a training and a test data set, and the variables selected by LASSO were used to train models by the linear regularization algorithms, using the training data set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that several factors, including the number of new cases, social distancing, stay-at-home orders, domestic travel restrictions, mask-wearing policy, socioeconomic status, unemployment rate, transit mode share, percent of population working from home, and percent of older (60+ years) and African and Hispanic American populations, among others, significantly influence daily trips. Moreover, among all models, ridge regression provides the most superior performance with the least error, whereas both LASSO and elastic net performed better than the ordinary linear model.
自美国开始应对新冠疫情以来，截至2020年8月，其确诊病例数和死亡人数均位居世界首位，多数州实施了旅行限制，导致出行和旅行大幅减少。然而，这场危机对出行的长期影响仍不明朗。为此，本研究提出了一个分析框架，以确定在疫情初期影响美国人员流动的最重要因素。具体而言，该研究使用最小绝对收缩和选择算子（LASSO）正则化来识别影响人员流动的最重要变量，并使用线性正则化算法，包括岭回归、LASSO和弹性网络建模技术，来预测人员流动。州级数据是从2020年1月1日至2020年6月13日的各种来源获取的。整个数据集被分为训练数据集和测试数据集，使用LASSO选择的变量通过线性正则化算法，利用训练数据集来训练模型。最后，在测试数据上检验所开发模型的预测准确性。结果表明，包括新增病例数、社交距离、居家令、国内旅行限制、口罩佩戴政策、社会经济地位、失业率、交通方式份额、在家工作人口百分比以及老年（60岁及以上）人口以及非裔和西班牙裔美国人口百分比等多个因素，对日常出行有显著影响。此外，在所有模型中，岭回归的性能最为优越，误差最小，而LASSO和弹性网络的表现均优于普通线性模型。
