IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v14y2022i2d10.1007_s12469-021-00280-2.html
   My bibliography  Save this article

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

Author

Listed:
  • Christian Martin Mützel

    (Technische Universität Dortmund)

  • Joachim Scheiner

    (Technische Universität Dortmund)

Abstract

Modern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

Suggested Citation

  • Christian Martin Mützel & Joachim Scheiner, 2022. "Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data," Public Transport, Springer, vol. 14(2), pages 343-366, June.
    • Handle: RePEc:spr:pubtra:v:14:y:2022:i:2:d:10.1007_s12469-021-00280-2
    DOI: 10.1007/s12469-021-00280-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-021-00280-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12469-021-00280-2?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. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    2. Beck, Matthew J. & Hensher, David A., 2020. "Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions," Transport Policy, Elsevier, vol. 96(C), pages 76-93.
    3. Beck, Matthew J. & Hensher, David A., 2020. "Insights into the impact of COVID-19 on household travel and activities in Australia – The early days of easing restrictions," Transport Policy, Elsevier, vol. 99(C), pages 95-119.
    4. Yanshuo Sun & Jungang Shi & Paul M. Schonfeld, 2016. "Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro," Public Transport, Springer, vol. 8(3), pages 341-363, December.
    5. Wei Yu & Hua Bai & Jun Chen & Xingchen Yan, 2019. "Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    6. Meina Zheng & Feng Liu & Xiucheng Guo & Xinyue Lei, 2019. "Assessing the Distribution of Commuting Trips and Jobs-Housing Balance Using Smart Card Data: A Case Study of Nanjing, China," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    7. Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
    8. Ed Manley & Chen Zhong & Michael Batty, 2018. "Spatiotemporal variation in travel regularity through transit user profiling," Transportation, Springer, vol. 45(3), pages 703-732, May.
    9. Mi-Kyeong Kim & Sangpil Kim & Hong-Gyoo Sohn, 2018. "Relationship between Spatio-Temporal Travel Patterns Derived from Smart-Card Data and Local Environmental Characteristics of Seoul, Korea," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    10. Singhal, Abhishek & Kamga, Camille & Yazici, Anil, 2014. "Impact of weather on urban transit ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 379-391.
    11. Qi-Li Gao & Qing-Quan Li & Yan Zhuang & Yang Yue & Zhen-Zhen Liu & Shui-Quan Li & Daniel Sui, 2019. "Urban commuting dynamics in response to public transit upgrades: A big data approach," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-18, October.
    12. Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Purim Srisawat & Wuyi Zhang & Kassara Sukpatch & Wachira Wichitphongsa, 2023. "Tourist Behavior and Sustainable Tourism Policy Planning in the COVID-19 Era: Insights from Thailand," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    2. Zhang, Yongping & Manley, Ed & Martens, Karel & Batty, Michael, 2024. "A metro smart card data-based analysis of group travel behaviour in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 114(C).
    3. Hanumantha Rao Sama & Long-Sheng Chen & Venkateswarlu Nalluri & Madhavaiah Chendragiri, 2023. "Enhancing service quality of rural public transport during the COVID-19 pandemic: a novel fuzzy approach," Public Transport, Springer, vol. 15(2), pages 479-501, June.
    4. Jiang, Shixiong & Cai, Canhuang, 2022. "Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China," Transport Policy, Elsevier, vol. 127(C), pages 158-170.
    5. S Srivatsa Srinivas, 2023. "To increase or to decrease the price? Managing public transport queues during COVID-19 in the presence of strategic commuters," Public Transport, Springer, vol. 15(1), pages 275-285, March.

    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. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    2. Kim, Suji & Lee, Sujin & Ko, Eunjeong & Jang, Kitae & Yeo, Jiho, 2021. "Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price," Journal of Transport Geography, Elsevier, vol. 96(C).
    3. Yuxin He & Yang Zhao & Kwok Leung Tsui, 2021. "An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership," Transportation, Springer, vol. 48(3), pages 1185-1216, June.
    4. Su, Shiliang & Zhao, Chong & Zhou, Hao & Li, Bozhao & Kang, Mengjun, 2022. "Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning," Journal of Transport Geography, Elsevier, vol. 100(C).
    5. Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    6. Tscharaktschiew, Stefan & Reimann, Felix, 2021. "On employer-paid parking and parking (cash-out) policy: A formal synthesis of different perspectives," Transport Policy, Elsevier, vol. 110(C), pages 499-516.
    7. Molloy, Joseph & Schatzmann, Thomas & Schoeman, Beaumont & Tchervenkov, Christopher & Hintermann, Beat & Axhausen, Kay W., 2021. "Observed impacts of the Covid-19 first wave on travel behaviour in Switzerland based on a large GPS panel," Transport Policy, Elsevier, vol. 104(C), pages 43-51.
    8. Bo Wan & Xudan Zhao & Yuhan Sun & Tao Yang, 2023. "Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)," Sustainability, MDPI, vol. 15(10), pages 1-27, May.
    9. Shuai Yu & Bin Li & Dongmei Liu, 2023. "Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing–Tianjin–Hebei Urban Agglomera," IJERPH, MDPI, vol. 20(2), pages 1-22, January.
    10. Ingvardson, Jesper Bláfoss & Nielsen, Otto Anker, 2018. "How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas," Journal of Transport Geography, Elsevier, vol. 72(C), pages 50-63.
    11. van Wee, Bert & Witlox, Frank, 2021. "COVID-19 and its long-term effects on activity participation and travel behaviour: A multiperspective view," Journal of Transport Geography, Elsevier, vol. 95(C).
    12. Carolina Silva Costa & Cira Souza Pitombo & Felipe Lobo Umbelino de Souza, 2022. "Travel Behavior before and during the COVID-19 Pandemic in Brazil: Mobility Changes and Transport Policies for a Sustainable Transportation System in the Post-Pandemic Period," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    13. Claudiu George Bocean & Luminita Popescu & Anca Antoaneta Varzaru & Costin Daniel Avram & Anica Iancu, 2023. "Work-Life Balance and Employee Satisfaction during COVID-19 Pandemic," Sustainability, MDPI, vol. 15(15), pages 1-15, July.
    14. Vergel-Tovar, C. Erik & Rodriguez, Daniel A., 2018. "The ridership performance of the built environment for BRT systems: Evidence from Latin America," Journal of Transport Geography, Elsevier, vol. 73(C), pages 172-184.
    15. Currie, Graham & Jain, Taru & Aston, Laura, 2021. "Evidence of a post-COVID change in travel behaviour – Self-reported expectations of commuting in Melbourne," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 218-234.
    16. Hensher, David A. & Wei, Edward & Beck, MatthewJ. & Balbontin, Camila, 2021. "The impact of COVID-19 on cost outlays for car and public transport commuting - The case of the Greater Sydney Metropolitan Area after three months of restrictions," Transport Policy, Elsevier, vol. 101(C), pages 71-80.
    17. Peikun Li & Quantao Yang & Wenbo Lu & Shu Xi & Hao Wang, 2024. "An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environ," Land, MDPI, vol. 13(11), pages 1-20, November.
    18. Feuillet, Thierry & Bulteau, Julie & Dantan, Sophie, 2021. "Modelling context-specific relationships between neighbourhood socioeconomic disadvantage and private car use," Journal of Transport Geography, Elsevier, vol. 93(C).
    19. Wang, Yiyuan & Shen, Qing & Abu Ashour, Lamis & Dannenberg, Andrew L., 2022. "Ensuring equitable transportation for the disadvantaged: Paratransit usage by persons with disabilities during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 84-95.
    20. Farrukh Baig & Konstantinos Kirytopoulos & Jaeyoung Lee & Evangelos Tsamilis & Ruizhi Mao & Panagiotis Ntzeremes, 2022. "Changes in People’s Mobility Behavior in Greece after the COVID-19 Outbreak," Sustainability, MDPI, vol. 14(6), pages 1-22, March.

    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:spr:pubtra:v:14:y:2022:i:2:d:10.1007_s12469-021-00280-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.