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Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City

Author

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  • Arkadiusz Adam Drabicki

    (Department of Transportation Systems, Cracow University of Technology, 31-155 Kraków, Poland)

  • Md Faqhrul Islam

    (Transport Research Institute, Edinburgh Napier University, Edinburgh EH10 5DT, UK)

  • Andrzej Szarata

    (Department of Transportation Systems, Cracow University of Technology, 31-155 Kraków, Poland)

Abstract

Public transport (PT) service disruptions are common and unexpected events which often result in major impediment to passengers’ typical travel routines. However, attitudes and behavioural responses to unexpected PT disruptions are still not fully understood in state-of-the-art research. The objective of this study is to examine how PT users adapt their travel choices and what travel information sources they utilize once they encounter sudden PT service disruptions. To this end, we conduct a passenger survey among PT users in the city of Kraków (Poland), consisting of a series of stated- and revealed-preference questions. Results show that passengers’ reported choices during past PT disruptions mostly involve adjusting the current PT travel routine, exposing a certain bias with their stated choices (which tend to overestimate the probability of modal shifts). Factors influencing travel behaviour shifts include frequency and recency of PT disruption experience, as well as propensity to arrive on-time. With regards to travel information sources, staff announcement and personal experience play an important role in recognizing the emerging disruption, but real-time information (RTI) sources are the most useful in planning the onward journey afterwards. Based on these, we highlight the implications for future RTI policy during PT service disruptions; in particular, the provision of a reliable time estimate until normal service conditions are resumed. Such RTI content could foster passengers’ tendency to use PT services in uncertain conditions, especially as their stated wait time tolerance often matches the actual duration of PT disruptions.

Suggested Citation

  • Arkadiusz Adam Drabicki & Md Faqhrul Islam & Andrzej Szarata, 2021. "Investigating the Impact of Public Transport Service Disruptions upon Passenger Travel Behaviour—Results from Krakow City," Energies, MDPI, vol. 14(16), pages 1-14, August.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4889-:d:611924
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    References listed on IDEAS

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    1. David M. Levinson & Henry X. Liu & Michael Bell (ed.), 2012. "Network Reliability in Practice," Transportation Research, Economics and Policy, Springer, edition 1, number 978-1-4614-0947-2, June.
    2. Robert Ziółkowski & Zbigniew Dziejma, 2021. "Investigations of the Dynamic Travel Time Information Impact on Drivers’ Route Choice in an Urban Area—A Case Study Based on the City of Bialystok," Energies, MDPI, vol. 14(6), pages 1-14, March.
    3. Guiver, J.W., 2007. "Modal talk: Discourse analysis of how people talk about bus and car travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(3), pages 233-248, March.
    4. Shanjiang Zhu & David M. Levinson, 2012. "Disruptions to Transportation Networks: A Review," Transportation Research, Economics and Policy, in: David M. Levinson & Henry X. Liu & Michael Bell (ed.), Network Reliability in Practice, edition 1, chapter 0, pages 5-20, Springer.
    5. Greg Marsden & Jillian Anable & Jeremy Shires & Iain Docherty, 2016. "Travel Behaviour Response to Major Transport System Disruptions: Implications for Smarter Resilience Planning," International Transport Forum Discussion Papers 2016/09, OECD Publishing.
    6. Oded Cats & Erik Jenelius, 2014. "Dynamic Vulnerability Analysis of Public Transport Networks: Mitigation Effects of Real-Time Information," Networks and Spatial Economics, Springer, vol. 14(3), pages 435-463, December.
    7. David M. Levinson & Henry Liu & Michael G. H. Bell, 2012. "Introduction to Network Reliability in Practice," Transportation Research, Economics and Policy, in: David M. Levinson & Henry X. Liu & Michael Bell (ed.), Network Reliability in Practice, edition 1, chapter 0, pages 1-4, Springer.
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    Cited by:

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