This research aims to understand temporal, regional, demographic, and policy factors that influenced travel reduction within the contiguous United States during the early period of the COVID-19 pandemic. Particularly, this research combines U.S. Census data, infection rates, and state-level mandates to determine their effects on daily, county-level vehicle miles traveled (VMT) estimations from March 1, 2020 to April 21, 2020. Specifically, this work generates metrics of VMT per capita, daily change in VMT, and VMT immediate reaction rates for every county in the U.S.A. and develops regression models to determine how these factors influence VMT rates over time. Results show that state-mandated orders were deployed in a pattern relative to their expected economic impact. Model results showed infection rates may have had a greater influence on forcing state policy adoption, ensuring reduced VMT, rather than the number of cases directly influencing individual travel to a significant degree. Additionally, counties with higher populations or labeled as urban counties saw a greater reduction in VMT across all three models compared with lower population and rural counties. Planners and policy makers in the future can utilize the results of this research to make better informed responses as well as to know the expected results of their actions.

本研究旨在了解在 COVID-19 大流行早期影响美国本土旅行减少的时间、区域、人口和政策因素。特别是，这项研究结合了美国人口普查数据、感染率和州级规定，以确定它们对 2020 年 3 月 1 日至 2020 年 4 月 21 日每日县级车辆行驶里程 (VMT) 估计的影响。具体来说，这项工作产生美国每个县的人均 VMT 指标、VMT 的每日变化和 VMT 即时反应率，并开发回归模型以确定这些因素如何随时间影响 VMT 率。结果表明，国家强制命令的部署模式与其预期的经济影响相关。模型结果显示，感染率可能对强制采用国家政策、确保减少 VMT 产生更大影响，而不是在很大程度上直接影响个人旅行的病例数量。此外，与人口较少的县和农村县相比，人口较多或标记为城市县的县在所有三种模型中的 VMT 下降幅度更大。未来的规划者和政策制定者可以利用这项研究的结果做出更明智的反应，并了解他们行动的预期结果。与人口较少的县和农村县相比，人口较多或标记为城市县的县在所有三种模型中的 VMT 下降幅度更大。未来的规划者和政策制定者可以利用这项研究的结果做出更明智的反应，并了解他们行动的预期结果。与人口较少的县和农村县相比，人口较多或标记为城市县的县在所有三种模型中的 VMT 下降幅度更大。未来的规划者和政策制定者可以利用这项研究的结果做出更明智的反应，并了解他们行动的预期结果。