IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v13y2021i3d10.1007_s12469-019-00220-1.html
   My bibliography  Save this article

Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application

Author

Listed:
  • Ulrik Berggren

    (Lund University
    K2 The Swedish Knowledge Centre for Public Transport)

  • Karin Brundell-Freij

    (Lund University
    K2 The Swedish Knowledge Centre for Public Transport
    WSP Advisory)

  • Helena Svensson

    (Lund University
    K2 The Swedish Knowledge Centre for Public Transport)

  • Anders Wretstrand

    (Lund University
    K2 The Swedish Knowledge Centre for Public Transport)

Abstract

Waiting times are important indicators of the degree of travel time optimisation and other behavioural traits among public transport (PT) passengers. As previous studies have shown, the level and usage of pre-trip information regarding schedule or real-time departures are important factors that influence the potential to realise travel time savings by enabling PT passengers to optimise waiting times. Most empirical evidence regarding the revealed PT travel behaviour concerning information levels is based on manual interviews or traditional travel surveys, in which there is a risk that the actual context of where and when the choice of departure time was made is not taken into account. This paper reports the results of a travel survey based on a dedicated smartphone application applied in a field study in a Swedish mid-size urban and regional context. Context-aware notification prompting was used to allow respondents to state their use of pre-trip information as well as whether they had pre-planned their trip and how contingent planning aids were used for time optimisation. The implications on passenger waiting times of the use of information regarding departure times by passengers were emphasised during analyses of the resulting data, along with personal characteristics, in which auxiliary sources such as timetable data and Automatic Vehicle Location were utilised to determine ground truth trip trajectories and trip-contextual factors. The results indicate the significance of having access to pre-trip information, especially for long trips above one hour’s duration, in order to pre-plan and thereby optimise waiting times. In addition, the use and source of pre-trip information differ among age and gender groups. Trip purpose and time of day to some extent determine waiting times and choice of trip optimisation strategy (arrival or departure time).

Suggested Citation

  • Ulrik Berggren & Karin Brundell-Freij & Helena Svensson & Anders Wretstrand, 2021. "Effects from usage of pre-trip information and passenger scheduling strategies on waiting times in public transport: an empirical survey based on a dedicated smartphone application," Public Transport, Springer, vol. 13(3), pages 503-531, October.
  • Handle: RePEc:spr:pubtra:v:13:y:2021:i:3:d:10.1007_s12469-019-00220-1
    DOI: 10.1007/s12469-019-00220-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-019-00220-1
    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/s12469-019-00220-1?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. Spiess, Heinz & Florian, Michael, 1989. "Optimal strategies: A new assignment model for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 83-102, April.
    2. Cats, Oded & West, Jens & Eliasson, Jonas, 2016. "A dynamic stochastic model for evaluating congestion and crowding effects in transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 43-57.
    3. Chorus, Caspar G. & Arentze, Theo A. & Molin, Eric J.E. & Timmermans, Harry J.P. & Van Wee, Bert, 2006. "The value of travel information: Decision strategy-specific conceptualizations and numerical examples," Transportation Research Part B: Methodological, Elsevier, vol. 40(6), pages 504-519, July.
    4. Thorhauge, Mikkel & Cherchi, Elisabetta & Rich, Jeppe, 2016. "How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 177-193.
    5. Mulley, Corinne & Clifton, Geoffrey Tilden & Balbontin, Camila & Ma, Liang, 2017. "Information for travelling: Awareness and usage of the various sources of information available to public transport users in NSW," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 111-132.
    6. Velaga, Nagendra R. & Beecroft, Mark & Nelson, John D. & Corsar, David & Edwards, Peter, 2012. "Transport poverty meets the digital divide: accessibility and connectivity in rural communities," Journal of Transport Geography, Elsevier, vol. 21(C), pages 102-112.
    7. Wardman, Mark & Chintakayala, V. Phani K. & de Jong, Gerard, 2016. "Values of travel time in Europe: Review and meta-analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 93-111.
    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. Codina, Esteve & Rosell, Francisca, 2017. "A heuristic method for a congested capacitated transit assignment model with strategies," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 293-320.
    2. Sun, S. & Szeto, W.Y., 2018. "Logit-based transit assignment: Approach-based formulation and paradox revisit," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 191-215.
    3. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
    4. Hänseler, Flurin S. & van den Heuvel, Jeroen P.A. & Cats, Oded & Daamen, Winnie & Hoogendoorn, Serge P., 2020. "A passenger-pedestrian model to assess platform and train usage from automated data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 948-968.
    5. Kai Lu & Tao Tang & Chunhai Gao, 2020. "The Depth-First Optimal Strategy Path Generation Algorithm for Passengers in a Metro Network," Sustainability, MDPI, vol. 12(13), pages 1-16, July.
    6. Tong, C.O. & Wong, S.C., 1998. "A stochastic transit assignment model using a dynamic schedule-based network," Transportation Research Part B: Methodological, Elsevier, vol. 33(2), pages 107-121, April.
    7. Xu, Zhandong & Xie, Jun & Liu, Xiaobo & Nie, Yu (Marco), 2020. "Hyperpath-based algorithms for the transit equilibrium assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    8. Ding Luo & Oded Cats & Hans Lint, 2020. "Can passenger flow distribution be estimated solely based on network properties in public transport systems?," Transportation, Springer, vol. 47(6), pages 2757-2776, December.
    9. E. Codina & A. Marín & F. López, 2013. "A model for setting services on auxiliary bus lines under congestion," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 48-83, April.
    10. Preston, John, 2008. "Competition in transit markets," Research in Transportation Economics, Elsevier, vol. 23(1), pages 75-84, January.
    11. Chorus, Caspar G., 2014. "Benefit of adding an alternative to one׳s choice set: A regret minimization perspective," Journal of choice modelling, Elsevier, vol. 13(C), pages 49-59.
    12. Olsen, Jonathan R. & Thornton, Lukar & Tregonning, Grant & Mitchell, Richard, 2022. "Nationwide equity assessment of the 20-min neighbourhood in the scottish context: A socio-spatial proximity analysis of residential locations," Social Science & Medicine, Elsevier, vol. 315(C).
    13. Ziliaskopoulos, Athanasios & Wardell, Whitney, 2000. "An intermodal optimum path algorithm for multimodal networks with dynamic arc travel times and switching delays," European Journal of Operational Research, Elsevier, vol. 125(3), pages 486-502, September.
    14. Wu, Di & Yin, Yafeng & Lawphongpanich, Siriphong, 2011. "Pareto-improving congestion pricing on multimodal transportation networks," European Journal of Operational Research, Elsevier, vol. 210(3), pages 660-669, May.
    15. Pyddoke, Roger, 2020. "Analysis of daily variation in bus occupancy rates for city-buses in Uppsala and optimal supply," Working Papers 2020:8, Swedish National Road & Transport Research Institute (VTI).
    16. Awaworyi Churchill, Sefa & Koomson, Isaac & Munyanyi, Musharavati Ephraim, 2023. "Transport poverty and obesity: The mediating roles of social capital and physical activity," Transport Policy, Elsevier, vol. 130(C), pages 155-166.
    17. Mounce, Richard & Beecroft, Mark & Nelson, John D., 2020. "On the role of frameworks and smart mobility in addressing the rural mobility problem," Research in Transportation Economics, Elsevier, vol. 83(C).
    18. Sanko, Nobuhiro, 2020. "Activity-end access/egress modal choices between stations and campuses located on a hillside," Research in Transportation Economics, Elsevier, vol. 83(C).
    19. Kent, Jennifer L. & Mulley, Corinne & Stevens, Nick, 2020. "Challenging policies that prohibit public transport use: Travelling with pets as a case study," Transport Policy, Elsevier, vol. 99(C), pages 86-94.
    20. Younes Hamdouch & Siriphong Lawphongpanich, 2010. "Congestion Pricing for Schedule-Based Transit Networks," Transportation Science, INFORMS, vol. 44(3), pages 350-366, August.

    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:13:y:2021:i:3:d:10.1007_s12469-019-00220-1. 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.