IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p17055-d1008441.html
   My bibliography  Save this article

Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics

Author

Listed:
  • Martin Zajac

    (Department of Geoinformatics, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic)

  • Jiří Horák

    (Department of Geoinformatics, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic)

  • Joaquín Osorio-Arjona

    (Department of Population, Centro de Ciencias Humanas y Sociales CSIC, 28037 Madrid, Spain)

  • Pavel Kukuliač

    (Department of Geoinformatics, Faculty of Mining and Geology, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic)

  • James Haworth

    (Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK)

Abstract

Public transport requires constant feedback to improve and satisfy daily users. Twitter offers monitoring of user messages, discussion and emoticons addressed to official transport provider accounts. This information can be particularly useful in delicate situations such as management of transit operations during the COVID-19 pandemic. The behaviour of Twitter users in Madrid, London and Prague is analysed with the goal of recognising similar patterns and detecting differences in traffic related topics and temporal cycles. Topics in transit tweets were identified using the bag of words approach and pre-processing in R. COVID-19 is a dominant topic for both London and Madrid but a minor one for Prague, where Twitter serves mainly to deliver messages from politicians and stakeholders. COVID-19 interferes with the meaning of other topics, such as overcrowding or staff. Additionally, specific topics were discovered, such as air quality in Victoria Station, London, or racism in Madrid. For all cities, transit-related tweeting activity declines over weekends. However, London shows much less decline than Prague or Madrid. Weekday daily rhythms show major tweeting activity during the morning in all cities but with different start times. The spatial distribution of tweets for the busiest stations shows that the best-balanced tweeting activity is found in Madrid metro stations.

Suggested Citation

  • Martin Zajac & Jiří Horák & Joaquín Osorio-Arjona & Pavel Kukuliač & James Haworth, 2022. "Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:17055-:d:1008441
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/17055/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/24/17055/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ainhoa Serna & Tomas Ruiz & Jon Kepa Gerrikagoitia & Rosa Arroyo, 2019. "Identification of Enablers and Barriers for Public Bike Share System Adoption using Social Media and Statistical Models," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    2. Garcia-Martinez, Andres & Cascajo, Rocio & Jara-Diaz, Sergio R. & Chowdhury, Subeh & Monzon, Andres, 2018. "Transfer penalties in multimodal public transport networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PA), pages 52-66.
    3. Paweł Brzustewicz & Anupam Singh, 2021. "Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA," Energies, MDPI, vol. 14(18), pages 1-20, September.
    4. Bansal, Prateek & Kessels, Roselinde & Krueger, Rico & Graham, Daniel J., 2022. "Preferences for using the London Underground during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 45-60.
    5. Andrew Peplow & Justin Thomas & Aamna AlShehhi, 2021. "Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach," IJERPH, MDPI, vol. 18(4), pages 1-9, February.
    6. N. Nima Haghighi & Xiaoyue Cathy Liu & Ran Wei & Wenwen Li & Hu Shao, 2018. "Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service," Public Transport, Springer, vol. 10(2), pages 363-377, August.
    7. Xinghua Liu & Qian Ye & Ye Li & Jing Fan & Yue Tao, 2021. "Examining Public Concerns and Attitudes toward Unfair Events Involving Elderly Travelers during the COVID-19 Pandemic Using Weibo Data," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    8. Vickerman, Roger, 2021. "Will Covid-19 put the public back in public transport? A UK perspective," Transport Policy, Elsevier, vol. 103(C), pages 95-102.
    9. Guo, Zhan & Wilson, Nigel H.M., 2011. "Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 91-104, February.
    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. Jara-Diaz, Sergio & Monzon, Andres & Cascajo, Rocio & Garcia-Martinez, Andres, 2022. "An international time equivalency of the pure transfer penalty in urban transit trips: Closing the gap," Transport Policy, Elsevier, vol. 125(C), pages 48-55.
    2. Shaojie Liu & Jing Teng & Yue Gong, 2020. "Extraction Method and Integration Framework for Perception Features of Public Opinion in Transportation," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    3. Nielsen, Otto Anker & Eltved, Morten & Anderson, Marie Karen & Prato, Carlo Giacomo, 2021. "Relevance of detailed transfer attributes in large-scale multimodal route choice models for metropolitan public transport passengers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 76-92.
    4. Tomhave, Benjamin J. & Khani, Alireza, 2022. "Refined choice set generation and the investigation of multi-criteria transit route choice behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 484-500.
    5. Liu, Yang & Feng, Tao & Shi, Zhuangbin & He, Mingwei, 2022. "Understanding the route choice behaviour of metro-bikeshare users," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 460-475.
    6. Espino, Raquel & Román, Concepción, 2020. "Valuation of transfer for bus users: The case of Gran Canaria," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 131-144.
    7. Jiao Ye & Jun Chen & Hua Bai & Yifan Yue, 2018. "Analyzing Transfer Commuting Attitudes Using a Market Segmentation Approach," Sustainability, MDPI, vol. 10(7), pages 1-16, June.
    8. Lois, David & Monzón, Andrés & Hernández, Sara, 2018. "Analysis of satisfaction factors at urban transport interchanges: Measuring travellers’ attitudes to information, security and waiting," Transport Policy, Elsevier, vol. 67(C), pages 49-56.
    9. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    10. Humberto, Mateus & Moura, Filipe & Giannotti, Mariana, 2020. "Incorporating children's views and perceptions about urban mobility," OSF Preprints yjxfm, Center for Open Science.
    11. Luyu Liu & Harvey J Miller, 2021. "Measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3140-3156, November.
    12. van Wee, Bert & Bohte, Wendy & Molin, Eric & Arentze, Theo & Liao, Feixiong, 2014. "Policies for synchronization in the transport–land-use system," Transport Policy, Elsevier, vol. 31(C), pages 1-9.
    13. 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.
    14. Allard, Ryan F. & Moura, Filipe, 2018. "Effect of transport transfer quality on intercity passenger mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 89-107.
    15. Cao, Zhejing & Zhang, Xiaohu & Chua, Kelman & Yu, Honghai & Zhao, Jinhua, 2021. "E-scooter sharing to serve short-distance transit trips: A Singapore case," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 177-196.
    16. Wasserman, Jacob L. & Taylor, Brian D., 2023. "State of the BART: Analyzing the Determinants of Bay Area Rapid Transit Use in the 2010s," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    17. Oluwayemi-Oniya Aderibigbe & Trynos Gumbo, 2022. "Variations in Mode Choice of Residents Prior and during COVID-19: An Empirical Evidence from Johannesburg, South Africa," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    18. Yue Liu & Jun Chen & Weiguang Wu & Jiao Ye, 2019. "Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    19. Shiqi Zhang & Tianwei Sun & Yuan Lu, 2023. "The COVID-19 Pandemic and Tourists’ Risk Perceptions: Tourism Policies’ Mediating Role in Sustainable and Resilient Recovery in the New Normal," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    20. Chen, Enhui & Stathopoulos, Amanda & Nie, Yu (Marco), 2022. "Transfer station choice in a multimodal transit system: An empirical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 337-355.

    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:gam:jsusta:v:14:y:2022:i:24:p:17055-:d:1008441. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.