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

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  • Li, Tao
  • Wang, Jiaoe
  • Huang, Jie
  • Yang, Wenyue
  • Chen, Zhuo

Abstract

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.

Suggested Citation

  • Li, Tao & Wang, Jiaoe & Huang, Jie & Yang, Wenyue & Chen, Zhuo, 2021. "Exploring the dynamic impacts of COVID-19 on intercity travel in China," Journal of Transport Geography, Elsevier, vol. 95(C).
  • Handle: RePEc:eee:jotrge:v:95:y:2021:i:c:s0966692321002064
    DOI: 10.1016/j.jtrangeo.2021.103153
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    2. Meng, Xin & Guo, Mingxue & Gao, Ziyou & Kang, Liujiang, 2023. "Interaction between travel restriction policies and the spread of COVID-19," Transport Policy, Elsevier, vol. 136(C), pages 209-227.
    3. Takahiro Yabe & Bernardo García Bulle Bueno & Xiaowen Dong & Alex Pentland & Esteban Moro, 2023. "Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Yan, Yingying & Zhong, Shiquan & Tian, Junfang & Jia, Ning, 2022. "An empirical study on consumer automobile purchase intentions influenced by the COVID-19 outbreak," Journal of Transport Geography, Elsevier, vol. 104(C).
    5. Cho, Jung-Hoon & Kim, Dong-Kyu & Kim, Eui-Jin, 2022. "Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    6. Li, Tao & Cui, Leibo & Wang, Jiaoe, 2022. "New equilibrium? Dynamics of intercity mobility in China during COVID-19 pandemic period," Journal of Transport Geography, Elsevier, vol. 105(C).
    7. Yang, Binxin & Wu, Guangdong, 2023. "Multi-criteria analysis of cross-regional railways interconnection under the post COVID-19 pandemic crisis: A hybrid BWM-FAISM-DFS evaluation framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).

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