IDEAS home Printed from https://ideas.repec.org/p/zbw/itsb21/238005.html
   My bibliography  Save this paper

Crowdsourcing and Public Transportation: Barriers and Opportunities

Author

Listed:
  • Apanasevic, Tatjana
  • Rudmark, Daniel

Abstract

In the last decade, private companies have successfully used crowdsourcing to revolutionise mobility, while public transport companies are still mostly failing to utilise the benefits of crowdsourcing. The application of crowdsourcing in public transport is a new area of academic research, and research on crowdsourcing en route in real-time is missing. This research aims to address this gap, explore opportunities and challenges of this type of crowdsourcing, and conceptualise this phenomenon. The research is based on empirical data collected in five Northern European countries. Our research findings help identify areas where crowdsourcing en route can add value to public transport: new forms of communication, opportunities to communicate with third parties, and improved transit planning and optimisation. Identified challenges are related to behavioural change for users, a need to develop infrastructure to enable crowdsourcing en route, and financial rationalities.

Suggested Citation

  • Apanasevic, Tatjana & Rudmark, Daniel, 2021. "Crowdsourcing and Public Transportation: Barriers and Opportunities," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238005, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itsb21:238005
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/238005/1/Apanasevic-Rudmark.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    2. Adler, Niclas & Elmquist, Maria & Norrgren, Flemming, 2009. "The challenge of managing boundary-spanning research activities: Experiences from the Swedish context," Research Policy, Elsevier, vol. 38(7), pages 1136-1149, September.
    3. Jestico, Ben & Nelson, Trisalyn & Winters, Meghan, 2016. "Mapping ridership using crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 52(C), pages 90-97.
    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. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    2. Katharina Löhr & Christian Hochmuth & Frieder Graef & Jane Wambura & Stefan Sieber, 2017. "Conflict management programs in trans-disciplinary research projects: the case of a food security project in Tanzania," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(6), pages 1189-1201, December.
    3. Mário Meireles & Paulo J. G. Ribeiro, 2020. "Digital Platform/Mobile App to Boost Cycling for the Promotion of Sustainable Mobility in Mid-Sized Starter Cycling Cities," Sustainability, MDPI, vol. 12(5), pages 1-27, March.
    4. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    5. Roy, Avipsa & Fuller, Daniel & Nelson, Trisalyn & Kedron, Peter, 2022. "Assessing the role of geographic context in transportation mode detection from GPS data," Journal of Transport Geography, Elsevier, vol. 100(C).
    6. Hong, Jinhyun & Philip McArthur, David & Stewart, Joanna L., 2020. "Can providing safe cycling infrastructure encourage people to cycle more when it rains? The use of crowdsourced cycling data (Strava)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 109-121.
    7. Wang, Hwachyi & De Backer, Hans & Lauwers, Dirk & Chang, S.K.Jason, 2019. "A spatio-temporal mapping to assess bicycle collision risks on high-risk areas (Bridges) - A case study from Taipei (Taiwan)," Journal of Transport Geography, Elsevier, vol. 75(C), pages 94-109.
    8. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    9. Christine Keller & Felix Glück & Carl Friedrich Gerlach & Thomas Schlegel, 2022. "Investigating the Potential of Data Science Methods for Sustainable Public Transport," Sustainability, MDPI, vol. 14(7), pages 1-26, April.
    10. Ali Al-Ramini & Mohammad A Takallou & Daniel P Piatkowski & Fadi Alsaleem, 2022. "Quantifying changes in bicycle volumes using crowdsourced data," Environment and Planning B, , vol. 49(6), pages 1612-1630, July.
    11. Bantis, Thanos & Haworth, James, 2020. "Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach," Journal of Transport Geography, Elsevier, vol. 84(C).
    12. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    13. Masood Jafari Kang & Shervin Ataeian & S. M. Mahdi Amiripour, 2021. "A procedure for public transit OD matrix generation using smart card transaction data," Public Transport, Springer, vol. 13(1), pages 81-100, March.
    14. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    15. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.
    16. McArthur, David Philip & Hong, Jinhyun, 2019. "Visualising where commuting cyclists travel using crowdsourced data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 233-241.
    17. Eileen S. Johnson & Kathleen P. Bell & Jessica E. Leahy, 2018. "Managing the science-policy boundary: implications for river restoration," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 8(3), pages 281-289, September.
    18. Zhu, Yiwen & Koutsopoulos, Haris N. & Wilson, Nigel H.M., 2017. "A probabilistic Passenger-to-Train Assignment Model based on automated data," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 522-542.
    19. 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.
    20. Simon Bell & Francesca Benatti & Neil R. Edwards & Robin Laney & David R. Morse & Lara Piccolo & Oliver Zanetti, 2018. "Smart Cities and M3: Rapid Research, Meaningful Metrics and Co-Design," Systemic Practice and Action Research, Springer, vol. 31(1), pages 27-53, February.

    More about this item

    Keywords

    Future of transportation; public transportation; emerging technologies; automated vehicles; crowdsourcing;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:zbw:itsb21:238005. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: http://www.itsworld.org/ .

    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.