Exploring the dynamic impacts of COVID-19 on intercity travel in China

被引：49

作者：

Li, Tao ^{[1
,2
]}

Wang, Jiaoe ^{[2
,3
]}

Huang, Jie ^{[2
]}

Yang, Wenyue ^{[4
]}

Chen, Zhuo ^{[2
]}

机构：

[1] Shaanxi Normal Univ, Northwest Land & Resources Res Ctr, Xian 710119, Shaanxi, Peoples R China

[2] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China

[3] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China

[4] South China Agr Univ, Coll Forestry & Landscape Architecture, Guangzhou 510642, Guangdong, Peoples R China

来源：

JOURNAL OF TRANSPORT GEOGRAPHY | 2021年 / 95卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Intercity travel; Mobility; COVID-19; GBDT model; Time-varying; LONG-DISTANCE TRAVEL; PUBLIC TRANSPORT; HOUSEHOLD TRAVEL; MOBILITY; NETWORK; DETERMINANTS; AUSTRALIA; LOCKDOWN; INSIGHTS; GERMANY;

D O I：

10.1016/j.jtrangeo.2021.103153

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Many studies have explored the effects of transportation and population movement on the spread of pandemics. However, little attention has been paid to the dynamic impact of pandemics on intercity travel and its recovery during a public health event period. Using intercity mobility and COVID-19 pandemic data, this study adopts the gradient boosting decision tree method to explore the dynamic effects of the COVID-19 on intercity travel in China. The influencing factors were classified into daily time-varying factors and time-invariant factors. The results show that China's intercity travel decreased on average by 51.35% from Jan 26 to Apr 7, 2020. Furtherly, the COVID-19 pandemic reduces intercity travel directly and indirectly by influencing industry development and transport connectivity. With the spread of COVID-19 and changes of control measures, the relationship between intercity travel and COVID-19, socio-economic development, transport is not linear. The relationship between intercity travel and secondary industry is illustrated by an inverted U-shaped curve from pre-pandemic to post-pandemic, whereas that with tertiary industry can be explained by a U-shaped curve. Meanwhile, this study highlights the dynamic effect of the COVID-19 on intercity mobility. These implications shed light on policies regarding the control measures during public health events that should include the dynamic impact of pandemics on intercity travel.

引用

页数：15