Tang Li, Tang Chuanli, Luo Hao
Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chengdu, Sichuan, China.
School of Automobile and Transportation, Xihua University, Chengdu, Sichuan, China.
PLoS One. 2025 Jun 11;20(6):e0325118. doi: 10.1371/journal.pone.0325118. eCollection 2025.
COVID-19 has had a significant impact on global transportation. While extensive research has focused on its influence on public transit and shared travel, the changes in overall vehicle travel demand remain under - explored. This paper analyzes the magnitude, duration, and driving factors of the impact of COVID-19 on urban overall vehicle mobility. We introduced the Prophet time series model, a suitable tool for time - series prediction in this context, to predict vehicle mobility without the pandemic. By comparing the predicted and real values, the mobility loss was derived. Multiple linear regression was then applied to deeply explore the causes of this loss, with a particular focus on the interaction effects of the strictness of regulatory orders and public fear. A large-scale dataset with over 4 billion raw data from multiple sources was used for empirical analysis. Results indicate that 29.66% of urban vehicle mobility loss occurs during the national outbreak period. The factor representing nationwide confirmed cases has a positive lagging effect on travel mobility loss, with a lag time of around seven days. The loss of urban motorized travel demand is largely due to the interaction of perceived risks and control policies.
新冠疫情对全球交通运输产生了重大影响。尽管大量研究聚焦于其对公共交通和共享出行的影响,但整体车辆出行需求的变化仍未得到充分探索。本文分析了新冠疫情对城市整体车辆出行的影响程度、持续时间及驱动因素。我们引入了Prophet时间序列模型,这是在此背景下适用于时间序列预测的工具,用于预测无疫情时的车辆出行情况。通过比较预测值与实际值,得出出行损失情况。随后应用多元线性回归深入探究这种损失的原因,特别关注管控措施严格程度与公众恐惧的交互作用。使用了来自多个来源的超过40亿条原始数据的大规模数据集进行实证分析。结果表明,29.66%的城市车辆出行损失发生在全国疫情爆发期间。代表全国确诊病例的因素对出行损失有正向滞后效应,滞后时间约为七天。城市机动化出行需求的损失很大程度上归因于感知风险与管控政策的相互作用。