IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v6y2021i11p121-d682603.html
   My bibliography  Save this article

Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals

Author

Listed:
  • Christian Werner

    (Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria)

  • Martin Loidl

    (Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria)

Abstract

Active mobility, especially cycling, is an essential building block for sustainable urban mobility. Public and private stakeholders are striving to improve conditions for cycling and subsequently increase its modal share. Data are regarded as key for different measures to become efficient and targeted. There is extensive evidence for an increasing amount of mobility data, availability of new data sources and potential usage scenarios for such data. However, little is known about the current use of these data in policy making, planning and related fields. To the best of our knowledge, it has not been investigated yet to which degree professionals in the broader field of cycling promotion benefit from an increasing amount of cycling-related data. Thus, we conducted a multi-lingual online survey among domain professionals and acquired data on their perspectives on current data availability, use and suitability as well as the potential they see for the use of cycling data in the future. In total, we received 325 complete responses from 32 countries, with the vast majority of 241 valid responses originating from Germany, Austria and Italy. Key findings are: 84% of domain professionals attribute high importance to data, and 89% state that they currently cannot or only partly solve their tasks with the data available to them. Results emphasize the need for making more and better suited data available to professionals in cycling-related positions, in both the private and public sector.

Suggested Citation

  • Christian Werner & Martin Loidl, 2021. "Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals," Data, MDPI, vol. 6(11), pages 1-11, November.
  • Handle: RePEc:gam:jdataj:v:6:y:2021:i:11:p:121-:d:682603
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/6/11/121/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/6/11/121/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cuauhtemoc Anda & Alexander Erath & Pieter Jacobus Fourie, 2017. "Transport modelling in the age of big data," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 19-42, August.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lorenz Beck & Simge Özdal Oktay, 2023. "Designing a Cycling Dashboard as a Way of Communicating Local Sustainability," Sustainability, MDPI, vol. 15(17), pages 1-17, August.

    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. Nadav Shalit & Michael Fire & Eran Ben-Elia, 2023. "A supervised machine learning model for imputing missing boarding stops in smart card data," Public Transport, Springer, vol. 15(2), pages 287-319, June.
    3. Rawad Choubassi & Lamia Abdelfattah, 2020. "How Big Data is Transforming the Way We Plan Our Cities," Briefs, Fondazione Eni Enrico Mattei, December.
    4. Laila Oubahman & Szabolcs Duleba, 2022. "A Comparative Analysis of Homogenous Groups’ Preferences by Using AIP and AIJ Group AHP-PROMETHEE Model," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    5. Pieroni, Caio & Giannotti, Mariana & Alves, Bianca B. & Arbex, Renato, 2021. "Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city," Journal of Transport Geography, Elsevier, vol. 96(C).
    6. 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).
    7. Gleb V. Savin, 2021. "The smart city transport and logistics system: Theory, methodology and practice," Upravlenets, Ural State University of Economics, vol. 12(6), pages 67-86, October.
    8. María Vega-Gonzalo & Panayotis Christidis, 2022. "Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    9. Kandt, Jens & Leak, Alistair, 2019. "Examining inclusive mobility through smartcard data: What shall we make of senior citizens' declining bus patronage in the West Midlands?," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    10. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    11. 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.
    12. Yuan Liao & Sonia Yeh & Jorge Gil, 2022. "Feasibility of estimating travel demand using geolocations of social media data," Transportation, Springer, vol. 49(1), pages 137-161, February.
    13. Marko Šoštarić & Krešimir Vidović & Marijan Jakovljević & Orsat Lale, 2021. "Data-Driven Methodology for Sustainable Urban Mobility Assessment and Improvement," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    14. Tae-Hyoung Tommy Gim, 2018. "Tourist Satisfaction, Image, and Loyalty from an Interregional Perspective: An Analysis of Neighboring Areas with Distinct Characteristics," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    15. Carolina Ajeng & Tae-Hyoung Tommy Gim, 2018. "Analyzing on-Street Parking Duration and Demand in a Metropolitan City of a Developing Country: A Case Study of Yogyakarta City, Indonesia," Sustainability, MDPI, vol. 10(3), pages 1-14, February.
    16. Johannes Müller & Markus Straub & Gerald Richter & Christian Rudloff, 2021. "Integration of Different Mobility Behaviors and Intermodal Trips in MATSim," Sustainability, MDPI, vol. 14(1), pages 1-18, December.
    17. Andrew Sudmant & Vincent Viguié & Quentin Lepetit & Lucy Oates & Abhijit Datey & Andy Gouldson & David Watling, 2021. "Fair weather forecasting? The shortcomings of big data for sustainable development, a case study from Hubballi‐Dharwad, India," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(6), pages 1237-1248, November.
    18. Michał Zawodny & Maciej Kruszyna, 2022. "Proposals for Using the Advanced Tools of Communication between Autonomous Vehicles and Infrastructure in Selected Cases," Energies, MDPI, vol. 15(18), pages 1-15, September.
    19. Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    20. Liu, Xu & Dijk, Marc, 2022. "How more data reinforces evidence-based transport policy in the Short and Long-Term: Evaluating a policy pilot in two Dutch cities," Transport Policy, Elsevier, vol. 128(C), pages 166-178.

    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:jdataj:v:6:y:2021:i:11:p:121-:d:682603. 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.