As the world is reopening from the unprecedented global pandemic, investigating how the intensity of transmission and responsive policies affect passenger air traffic demand is valuable for the aviation industry recovery and post-pandemic economic development. This paper investigates the effects of confirmed COVID-19 cases and state government policies at 28 hub airports in the United States from March 2020 to September 2021 by proposing an origin-destination (OD) spatial temporal econometric model. The investigation finds that (1) confirmed COVID-19 cases and state government policies had the highest effects on air traffic in the same month as these events occurred and the effects were diminishing in the following months; (2) The policy of internal movement restrictions in a given state generated a higher impact for trips arriving at this state, while confirmed COVID-19 cases and the testing policy generated a higher impact for trips departing from this state; (3) Reopening offices, lifting movement restrictions, maintaining flexibility in accessing COVID-19 tests, and using facial covering onboard are effective policies for aviation industry recovery. This paper aims to be a timely study on air travel demand when the domestic traffic has almost achieved the pre-pandemic level, offering insights into recovery of the aviation industry and preparation for future uncertainties. In addition, the proposed OD spatial temporal model which captures OD spatial dependences and temporal correlations simultaneously can equip spatial economists with an innovative and powerful tool.

随着世界正从前所未有的全球大流行中重新开放，研究传播强度和响应政策如何影响客运航空需求对于航空业复苏和大流行后经济发展具有重要意义。本文通过提出始发地-目的地（OD）时空计量经济模型，研究了 2020 年 3 月至 2021 年 9 月期间美国 28 个枢纽机场确诊的 COVID-19 病例和州政府政策的影响。调查发现 (1) 确诊的 COVID-19 病例和州政府政策在这些事件发生的当月对空中交通的影响最大，并且在接下来的几个月中影响逐渐减弱； (2) 某个州的内部流动限制政策对到达该州的旅行产生了更大的影响，而确诊的 COVID-19 病例和检测政策对离开该州的旅行产生了更大的影响； (3) 重新开放办公室、取消行动限制、保持接受 COVID-19 检测的灵活性以及在机上使用面部遮盖物是航空业复苏的有效政策。本文旨在及时研究国内交通量基本恢复到疫情前水平时的航空出行需求，为航空业的复苏提供见解，并为未来的不确定性做好准备。此外，所提出的 OD 时空模型同时捕获 OD 空间依赖性和时间相关性，可以为空间经济学家提供创新且强大的工具。