IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v176y2023ics0965856423002252.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856423002252
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2023.103805?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. 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.
    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. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Mengyue Yuan & Tong Liu & Chao Yang, 2022. "Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan," IJERPH, MDPI, vol. 19(11), pages 1-22, May.
    3. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    4. Liu, Shasha & Yamamoto, Toshiyuki, 2022. "Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 1-16.
    5. Sparks, Kevin & Moehl, Jessica & Weber, Eric & Brelsford, Christa & Rose, Amy, 2022. "Shifting temporal dynamics of human mobility in the United States," Journal of Transport Geography, Elsevier, vol. 99(C).
    6. Yuqi Chen & Zongyao Sun & Liangwa Cai, 2021. "Population Flow Mechanism Study of Beijing-Tianjin-Hebei Urban Agglomeration from Industrial Space Supply Perspective," Sustainability, MDPI, vol. 13(17), pages 1-15, September.
    7. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    8. Bart Roelofs & Dimitris Ballas & Hinke Haisma & Arjen Edzes, 2022. "Spatial mobility patterns and COVID‐19 incidence: A regional analysis of the second wave in the Netherlands," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S1), pages 21-40, November.
    9. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    10. Xiao-Bai Li & Jialun Qin, 2017. "Anonymizing and Sharing Medical Text Records," Information Systems Research, INFORMS, vol. 28(2), pages 332-352, June.
    11. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    12. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    13. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    14. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    15. Naiyang Guan & Lei Wei & Zhigang Luo & Dacheng Tao, 2013. "Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    16. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    17. Arroyo Arroyo,Fatima & Fernandez Gonzalez,Marta & Matekenya,Dunstan & Espinet Alegre,Xavier, 2021. "Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone," Policy Research Working Paper Series 9519, The World Bank.
    18. David Kofoed Wind & Piotr Sapiezynski & Magdalena Anna Furman & Sune Lehmann, 2016. "Inferring Stop-Locations from WiFi," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.
    19. Spelta, A. & Pecora, N. & Rovira Kaltwasser, P., 2019. "Identifying Systemically Important Banks: A temporal approach for macroprudential policies," Journal of Policy Modeling, Elsevier, vol. 41(1), pages 197-218.
    20. Wang, Peipei & Liu, Haiyan & Zheng, Xinqi & Ma, Ruifang, 2023. "A new method for spatio-temporal transmission prediction of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:176:y:2023:i:c:s0965856423002252. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.