The COVID-19 pandemic has had a substantial impact on the airline industry. Air travel in the United States declined in 2020 with significantly lower domestic and international flights. The dynamic change and uncertainty in the trend of COVID-19 have made it difficult to predict future air travel. This paper aims at developing and testing neural network models that predict domestic and international air travel in the medium and long term based on residents' daily trips by distance, economic condition, COVID-19 severity, and travel restrictions. Data in the United States from various sources were used to train and validate the neural network models, and Monte Carlo simulations were constructed to predict air travel under uncertainty of the pandemic and economic growth. The results show that weekly economic index (WEI) is the most important predictor for air travel. Additionally, daily trips by distance play a more important role in the prediction of domestic air travel than the international one, while travel restrictions seem to have an impact on both. Sensitivity analysis results for four different scenarios indicate that air travel in the future is more sensitive to the change in WEI than the changes in COVID-19 variables. Additionally, even in the best-case scenario, when the pandemic is over and the economy is back to normal, it still takes several years for air travel to return to normal, as before the pandemic. The findings have significant contributions to the literature in COVID-19's impact on air transportation and air travel prediction.

COVID-19 大流行对航空业产生了重大影响。 2020 年，美国的航空旅行有所下降，国内和国际航班大幅减少。 COVID-19 趋势的动态变化和不确定性使得预测未来的航空旅行变得困难。本文旨在开发和测试神经网络模型，根据居民的日常出行距离、经济状况、COVID-19 严重程度和旅行限制来预测中长期国内和国际航空旅行。美国各种来源的数据被用来训练和验证神经网络模型，并构建蒙特卡罗模拟来预测疫情和经济增长不确定性下的航空旅行。结果表明，每周经济指数 (WEI) 是航空旅行最重要的预测指标。此外，按距离计算的每日出行在国内航空旅行的预测中比国际航空旅行发挥更重要的作用，而旅行限制似乎对两者都有影响。四种不同情景的敏感性分析结果表明，未来航空旅行对 WEI 的变化比对 COVID-19 变量的变化更敏感。此外，即使在最好的情况下，当疫情结束、经济恢复正常时，航空旅行仍需要数年时间才能像疫情之前一样恢复正常。这些发现对有关 COVID-19 对航空运输和航空旅行预测的影响的文献做出了重大贡献。