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Pattern analysis of Japanese long-distance travel change under the COVID-19 pandemic

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  • Yamaguchi, Hiromichi
  • Nakayama, Shoichiro

Abstract

The Japanese government implemented a request for people to reduce movement to suppress the spatial spread of COVID-19. Individuals cancelled relatively unimportant activities according to their perceived risk of infection and the changing level of the request because it carried no penalties. Thus, what long-distance travel patterns were reduced in response to these messages and the situation? Mobile phone location data is a powerful tool to answer the question. However, mobile phone location data has a major disadvantage that it does not include information regarding the purpose of travel such as business or leisure. Therefore, this study proposes an approach that uses other survey data regarding travel purpose information in addition to mobile phone location data to analyze the effects of COVID-19 on long-distance travel behavior. In our approach, information regarding a large number of origin–destination pairs is explained by a small number of representative travel patterns that are explicitly related to travel purpose. The results of this approach show that travel behavioral changes due to the COVID-19 pandemic differed for each travel pattern. Certain patterns were highly sensitive, and many people cancelled unimportant activities for a period longer than the government’s request period. In contrast, sensitivity was relatively low for short-distance travel.

Suggested Citation

  • Yamaguchi, Hiromichi & Nakayama, Shoichiro, 2023. "Pattern analysis of Japanese long-distance travel change under the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:transa:v:176:y:2023:i:c:s0965856423002252
    DOI: 10.1016/j.tra.2023.103805
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Yamaguchi, Hiromichi & Nakayama, Shoichiro, 2020. "Detection of base travel groups with different sensitivities to new high-speed rail services: Non-negative tensor decomposition approach," Transport Policy, Elsevier, vol. 97(C), pages 37-46.
    3. Yao, Enjian & Morikawa, Takayuki, 2005. "A study of on integrated intercity travel demand model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(4), pages 367-381, May.
    4. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    5. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
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