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Social Media Use during Unplanned Transit Network Disruptions: A Review of Literature

Author

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  • Brendan Pender
  • Graham Currie
  • Alexa Delbosc
  • Nirajan Shiwakoti

Abstract

This paper explores social media's role in managing unplanned transit networks disruptions. Although literature exists more broadly on the use of social media in transit, this paper presents the first literature review in this setting. When disruptions occur, commuters require reliable, up-to-date information. Its provision reduces anxiety and allows informed choices. Social media is beneficial given it provides real-time information but it can only supplement (not replace) conventional approaches. Information reliability was critical. Research in the field of disaster management illustrates the importance of publicly contributed information. Known as "crowdsourcing", it is part of the emerging field of crisis informatics which for the first time was linked to unplanned transit disruption management. The results highlight that social media's real-time nature can reduce disrupted travel demand; however, its utilisation can be resource-intensive. A framework presented illustrates how social media utilisation varies according to the operational characteristics of a disrupted network. Social media use as an information delivery tool is still in its infancy and an unwillingness to embrace it is an impediment to sustained growth. Crowdsourcing is one approach that could resolve the issue of transit agency resourcing whilst satisfying the increased demand and expectation for real-time information.

Suggested Citation

  • Brendan Pender & Graham Currie & Alexa Delbosc & Nirajan Shiwakoti, 2014. "Social Media Use during Unplanned Transit Network Disruptions: A Review of Literature," Transport Reviews, Taylor & Francis Journals, vol. 34(4), pages 501-521, July.
  • Handle: RePEc:taf:transr:v:34:y:2014:i:4:p:501-521
    DOI: 10.1080/01441647.2014.915442
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    Cited by:

    1. Sarker, Rumana Islam & Kaplan, Sigal & Mailer, Markus & Timmermans, Harry J.P., 2019. "Applying affective event theory to explain transit users’ reactions to service disruptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 593-605.
    2. Li, Binbin & Yao, Enjian & Yamamoto, Toshiyuki & Tang, Ying & Liu, Shasha, 2020. "Exploring behavioral heterogeneities of metro passenger’s travel plan choice under unplanned service disruption with uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 294-306.
    3. Pascal Un & Sonia Adelé & Flore Vallet & Jean-Marie Burkhardt, 2022. "How Does My Train Line Run? Elicitation of Six Information-Seeking Profiles of Regular Suburban Train Users," Sustainability, MDPI, vol. 14(5), pages 1-22, February.
    4. Han Dong & Cinzia Cirillo & Marco Diana, 2018. "Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset," Transportation, Springer, vol. 45(2), pages 429-449, March.
    5. Maria Attard & Muki Haklay & Cristina Capineri, 2016. "The Potential of Volunteered Geographic Information (VGI) in Future Transport Systems," Urban Planning, Cogitatio Press, vol. 1(4), pages 6-19.
    6. Wenwen Zhang & Camille Barchers & Janille Smith-Colin, 2023. "Transit communication via Twitter during the COVID-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1244-1261, June.
    7. Andre Carrel & Raja Sengupta & Joan L. Walker, 2017. "The San Francisco Travel Quality Study: tracking trials and tribulations of a transit taker," Transportation, Springer, vol. 44(4), pages 643-679, July.
    8. Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
    9. Leng, Nuannuan & Corman, Francesco, 2020. "The role of information availability to passengers in public transport disruptions: An agent-based simulation approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 214-236.
    10. Wen Hua & Ghim Ping Ong, 2018. "Effect of information contagion during train service disruption for an integrated rail-bus transit system," Public Transport, Springer, vol. 10(3), pages 571-594, December.
    11. Mehmann, Jens & Frehe, Volker & Teuteberg, Frank, 2015. "Crowd Logistics − A Literature Review and Maturity Model," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg Internationa, volume 20, pages 117-145, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    12. Lorelei Schmitt & Alexa Delbosc & Graham Currie, 2019. "Learning to use transit services: adapting to unfamiliar transit travel," Transportation, Springer, vol. 46(3), pages 1033-1049, June.
    13. Zhu, Chunxiao & Shou, Minghuan & Zhou, Yitong & Li, Wenrui, 2023. "Modeling the effect of social media on older adults’ usage intention of public transport," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 239-250.
    14. Piera Centobelli & Roberto Cerchione & Emilio Esposito & Shashi, 2020. "Evaluating environmental sustainability strategies in freight transport and logistics industry," Business Strategy and the Environment, Wiley Blackwell, vol. 29(3), pages 1563-1574, March.

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