IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p16319-d995537.html
   My bibliography  Save this article

Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches

Author

Listed:
  • Parisa Zare

    (School of Arts, Design & Architecture, University of New South Wales, Sydney, NSW 2052, Australia)

  • Christopher Pettit

    (School of Arts, Design & Architecture, University of New South Wales, Sydney, NSW 2052, Australia)

  • Simone Leao

    (School of Arts, Design & Architecture, University of New South Wales, Sydney, NSW 2052, Australia)

  • Ori Gudes

    (School of Population Health, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

To increase the amount of bicycling as a mode of transport, many countries are developing placed based bicycling plans and strategies. However, this approach necessitates considering a fine-scale mapping of bicycling patterns and a detailed description of urban spaces. The rise of new data and technologies offers much promise to planners and researchers to access diverse and richer sources of information to optimise the bicycling network design. This review aims to comprehensively examine the role of data and technology in bicycling planning, historical changes in using data-driven approaches, and current domains in the existing body of research in bicycling planning from 1990 to 2021. For this, a systematic literature review has been conducted according to PRISMA framework. A total number of 1022 studies was analysed and synthesised with the VOS Viewer and CiteSpace platforms. Upon completing the review, we extracted the most-used datasets, tools, and methodologies. The results of the systematic review reveal three evolutionary phases in using data-driven approaches from 1990 to 1999, 2000 to 2009, and 2010 to 2021. In addition, we identified six knowledge domains in using data-driven approaches in bicycling planning that is (i) smart city, (ii) infrastructure, (iii) built environment, (iv) decision making, (v) people, and (vi) safety.

Suggested Citation

  • Parisa Zare & Christopher Pettit & Simone Leao & Ori Gudes, 2022. "Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches," Sustainability, MDPI, vol. 14(23), pages 1-20, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16319-:d:995537
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/16319/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/23/16319/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nankervis, Max, 1999. "The effect of weather and climate on bicycle commuting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(6), pages 417-431, August.
    2. Justin B. Hollander & Yaqi Shen, 2017. "Using Social Media Data to Infer Urban Attitudes About Bicycling: An Exploratory Case Study of Washington DC," Springer Optimization and Its Applications, in: Athanasia Karakitsiou & Athanasios Migdalas & Stamatina Th. Rassia & Panos M. Pardalos (ed.), City Networks, chapter 0, pages 79-97, Springer.
    3. Haozhi Pan & Stan Geertman & Brian Deal, 2020. "What does urban informatics add to planning support technology?," Environment and Planning B, , vol. 47(8), pages 1317-1325, October.
    4. Iacono, Michael & Krizek, Kevin J. & El-Geneidy, Ahmed, 2010. "Measuring non-motorized accessibility: issues, alternatives, and execution," Journal of Transport Geography, Elsevier, vol. 18(1), pages 133-140.
    5. Lugo, Adonia E., 2013. "CicLAvia and human infrastructure in Los Angeles: ethnographic experiments in equitable bike planning," Journal of Transport Geography, Elsevier, vol. 30(C), pages 202-207.
    6. Vitalii Naumov & Michał Pawluś, 2021. "Identifying the Optimal Packing and Routing to Improve Last-Mile Delivery Using Cargo Bicycles," Energies, MDPI, vol. 14(14), pages 1-15, July.
    7. Nees Jan van Eck & Ludo Waltman, 2009. "How to normalize cooccurrence data? An analysis of some well‐known similarity measures," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1635-1651, August.
    8. Levine, Jonathan & Merlin, Louis & Grengs, Joe, 2017. "Project-level accessibility analysis for land-use planning," Transport Policy, Elsevier, vol. 53(C), pages 107-119.
    9. Zhang, Dapeng & Magalhães, David José Ahouagi Vaz & Wang, Xiaokun (Cara), 2014. "Prioritizing bicycle paths in Belo Horizonte City, Brazil: Analysis based on user preferences and willingness considering individual heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 67(C), pages 268-278.
    10. Shaheen, Susan & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present, and Future," Institute of Transportation Studies, Working Paper Series qt79v822k5, Institute of Transportation Studies, UC Davis.
    11. Greg P. Griffin & Junfeng Jiao, 2019. "Crowdsourcing Bike Share Station Locations," Journal of the American Planning Association, Taylor & Francis Journals, vol. 85(1), pages 35-48, January.
    12. Mateo-Babiano, Iderlina & Bean, Richard & Corcoran, Jonathan & Pojani, Dorina, 2016. "How does our natural and built environment affect the use of bicycle sharing?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 295-307.
    13. Shaheen, Susan A & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present and Future," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6qg8q6ft, Institute of Transportation Studies, UC Berkeley.
    14. Vitalii Naumov, 2021. "Substantiation of Loading Hub Location for Electric Cargo Bikes Servicing City Areas with Restricted Traffic," Energies, MDPI, vol. 14(4), pages 1-16, February.
    15. Arellana, Julián & Saltarín, María & Larrañaga, Ana Margarita & González, Virginia I. & Henao, César Augusto, 2020. "Developing an urban bikeability index for different types of cyclists as a tool to prioritise bicycle infrastructure investments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 310-334.
    16. Hochmair, Hartwig H. & Bardin, Eric & Ahmouda, Ahmed, 2019. "Estimating bicycle trip volume for Miami-Dade county from Strava tracking data," Journal of Transport Geography, Elsevier, vol. 75(C), pages 58-69.
    17. Leonidas G. Anthopoulos, 2015. "Understanding the Smart City Domain: A Literature Review," Public Administration and Information Technology, in: Manuel Pedro Rodríguez-Bolívar (ed.), Transforming City Governments for Successful Smart Cities, edition 127, pages 9-21, Springer.
    18. van Eck, N.J.P. & Waltman, L., 2009. "How to Normalize Co-Occurrence Data? An Analysis of Some Well-Known Similarity Measures," ERIM Report Series Research in Management ERS-2009-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    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. Todd, James & O'Brien, Oliver & Cheshire, James, 2021. "A global comparison of bicycle sharing systems," Journal of Transport Geography, Elsevier, vol. 94(C).
    2. Pucher, John & Buehler, Ralph & Seinen, Mark, 2011. "Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 451-475, July.
    3. Hyungkyoo Kim, 2020. "Seasonal Impacts of Particulate Matter Levels on Bike Sharing in Seoul, South Korea," IJERPH, MDPI, vol. 17(11), pages 1-17, June.
    4. Lidong Zhu & Mujahid Ali & Elżbieta Macioszek & Mahdi Aghaabbasi & Amin Jan, 2022. "Approaching Sustainable Bike-Sharing Development: A Systematic Review of the Influence of Built Environment Features on Bike-Sharing Ridership," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    5. Corcoran, Jonathan & Li, Tiebei & Rohde, David & Charles-Edwards, Elin & Mateo-Babiano, Derlie, 2014. "Spatio-temporal patterns of a Public Bicycle Sharing Program: the effect of weather and calendar events," Journal of Transport Geography, Elsevier, vol. 41(C), pages 292-305.
    6. Jinyi Zhou & Changyuan Jing & Xiangjun Hong & Tian Wu, 2019. "Winter Sabotage: The Three-Way Interactive Effect of Gender, Age, and Season on Public Bikesharing Usage," Sustainability, MDPI, vol. 11(11), pages 1-14, June.
    7. Umer Mansoor & Mohammad Tamim Kashifi & Fazal Rehman Safi & Syed Masiur Rahman, 2022. "A review of factors and benefits of non-motorized transport: a way forward for developing countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 1560-1582, February.
    8. Mix, Richard & Hurtubia, Ricardo & Raveau, Sebastián, 2022. "Optimal location of bike-sharing stations: A built environment and accessibility approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 126-142.
    9. Hamidi, Zahra & Camporeale, Rosalia & Caggiani, Leonardo, 2019. "Inequalities in access to bike-and-ride opportunities: Findings for the city of Malmö," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 673-688.
    10. Suzanne Maas & Paraskevas Nikolaou & Maria Attard & Loukas Dimitriou, 2021. "Heat, Hills and the High Season: A Model-Based Comparative Analysis of Spatio-Temporal Factors Affecting Shared Bicycle Use in Three Southern European Islands," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    11. Mora, Rodrigo & Miranda-Marquez, Sebastián & Truffello, Ricardo & Sadarangani, Kabir P., 2024. "Bikesharing and ordinary cyclists from Chile: Comparing trips, attitudes, and health-behaviours," Journal of Transport Geography, Elsevier, vol. 116(C).
    12. Wang, Xudong & Cheng, Zhanhong & Trépanier, Martin & Sun, Lijun, 2021. "Modeling bike-sharing demand using a regression model with spatially varying coefficients," Journal of Transport Geography, Elsevier, vol. 93(C).
    13. Kim, Kyoungok, 2023. "Investigation of modal integration of bike-sharing and public transit in Seoul for the holders of 365-day passes," Journal of Transport Geography, Elsevier, vol. 106(C).
    14. Morton, Craig & Kelley, Scott & Monsuur, Fredrik & Hui, Tianwen, 2021. "A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London," Journal of Transport Geography, Elsevier, vol. 94(C).
    15. Daozhi Zhao & Di Wang, 2019. "The Research of Tripartite Collaborative Governance on Disorderly Parking of Shared Bicycles Based on the Theory of Planned Behavior and Motivation Theories—A Case of Beijing, China," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    16. Maas, Suzanne & Nikolaou, Paraskevas & Attard, Maria & Dimitriou, Loukas, 2021. "Spatial and temporal analysis of shared bicycle use in Limassol, Cyprus," Journal of Transport Geography, Elsevier, vol. 93(C).
    17. Hu, Beibei & Zhong, Zhenfang & Zhang, Yanli & Sun, Yue & Jiang, Li & Dong, Xianlei & Sun, Huijun, 2022. "Understanding the influencing factors of bicycle-sharing demand based on residents’ trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    18. Maas, Suzanne & Attard, Maria & Caruana, Mark Anthony, 2020. "Assessing spatial and social dimensions of shared bicycle use in a Southern European island context: The case of Las Palmas de Gran Canaria," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 81-97.
    19. Niccolò Comerio & Fernanda Strozzi, 2019. "Tourism and its economic impact: A literature review using bibliometric tools," Tourism Economics, , vol. 25(1), pages 109-131, February.
    20. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).

    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:jsusta:v:14:y:2022:i:23:p:16319-:d:995537. 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.