Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah
Modeling the impact of COVID-19 on transportation at later stage of the pandemic: A case study of Utah
复制标题
DOI：
10.1080/15472450.2022.2157212
复制
发表时间：
2022-12-14
影响因子：
3.6
通讯作者：
Wang, Aaron
中科院分区：
文献类型：
--
作者：
Gong, Yaobang;Isom, Tanner;Wang, Aaron
全球新冠肺炎疫情对美国各地的交通运输造成了巨大影响。然而，缺乏调查大流行后期对车辆交通影响的研究。因此，本文研究了大流行后期犹他州两个大城市县高速公路交通模式的变化。我们发现，随着旅行限制的放松和COVID疫苗的接种，车辆流量已经恢复到疫情前的水平，如果不是超过的话。由于网上购物和按需送货的增长，卡车流量高于疫情前的水平。为了帮助响应机构为近期的交通模式做好准备，提出了一种基于机器学习和图论相结合的交通预测模型。评价结果表明，该预测模型具有较好的预测效果。不同辖区的平均绝对百分比预测误差在0.38%~1.74%之间。平均而言，该模型的预测误差均方根值比传统的长短期记忆模型高出31.20%。
The global COVID-19 pandemic has had a great impact on transportation across the United States. However, there is a lack of studies investigating the pandemic's impact on vehicular traffic at the later stage of the pandemic. Therefore, this paper studies the change of freeway traffic patterns in two metropolitan counties in the State of Utah at the latter stage of the pandemic. We found that with the relaxation of travel restriction and the COVID vaccine, vehicular traffic has recovered to equaling, if not exceeding, pre-pandemic levels. Truck traffic is higher than the pre-pandemic level due to the growth of online shopping and on-demand delivery. To help responsive agencies to prepare for the near-future traffic pattern, a traffic prediction model based on an innovative approach integrating machine learning with graph theory is proposed. The evaluation shows that the proposed prediction model has a desirable performance. The mean absolute percentage prediction error is between 0.38% and 1.74% for different jurisdictions. On average, the modal outperforms the traditional long short-term memory model by 31.20% in terms of root mean squared prediction error.