IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v137y2020icp447-458.html
   My bibliography  Save this article

Combining environmental quality assessment of bicycle infrastructures with vertical acceleration measurements

Author

Listed:
  • Nuñez, Javier Yesid Mahecha
  • Bisconsini, Danilo Rinaldi
  • Rodrigues da Silva, Antônio Nélson

Abstract

Growing interest in zero emission transport modes, such as cycling, is currently generating motivation to construct new cycle paths. However, transportation planners and managers cannot always rely on practical methods for allocating the resources (often limited) needed for inventories and assessing cycling infrastructures. The aim of this study is to develop a method for classifying cycle paths in terms of roughness and general conditions of the pavement surface. Inventory data and information regarding the infrastructure conditions were collected on-site using video recordings taken by an action camera directly mounted on a bicycle. Georeferenced vertical acceleration data were collected using a smartphone. Acceleration data of three different pavement surfaces (asphalt, concrete and concrete bricks) were registered. The results showed the lowest acceleration values for concrete pavement and the highest values for interlocking concrete pavement. The proposed method can be a practical and efficient approach to evaluate cycling infrastructures in terms of pavement condition.

Suggested Citation

  • Nuñez, Javier Yesid Mahecha & Bisconsini, Danilo Rinaldi & Rodrigues da Silva, Antônio Nélson, 2020. "Combining environmental quality assessment of bicycle infrastructures with vertical acceleration measurements," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 447-458.
  • Handle: RePEc:eee:transa:v:137:y:2020:i:c:p:447-458
    DOI: 10.1016/j.tra.2018.10.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856417309217
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2018.10.032?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. Menghini, G. & Carrasco, N. & Schüssler, N. & Axhausen, K.W., 2010. "Route choice of cyclists in Zurich," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 754-765, November.
    2. John Pucher & Ralph Buehler, 2007. "Making Cycling Irresistible: Lessons from The Netherlands, Denmark and Germany," Transport Reviews, Taylor & Francis Journals, vol. 28(4), pages 495-528, November.
    3. Broach, Joseph & Dill, Jennifer & Gliebe, John, 2012. "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1730-1740.
    4. Calvey, J.C. & Shackleton, J.P. & Taylor, M.D. & Llewellyn, R., 2015. "Engineering condition assessment of cycling infrastructure: Cyclists’ perceptions of satisfaction and comfort," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 134-143.
    5. Joo, Shinhye & Oh, Cheol, 2013. "A novel method to monitor bicycling environments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 1-13.
    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. Martín López-Molina & David Llopis-Castelló & Ana María Pérez-Zuriaga & Carlos Alonso-Troyano & Alfredo García, 2022. "Skid Resistance Analysis of Urban Bike Lane Pavements for Safe Micromobility," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    2. Tufail Ahmed & Ali Pirdavani & Davy Janssens & Geert Wets, 2023. "Utilizing Intelligent Portable Bicycle Lights to Assess Urban Bicycle Infrastructure Surfaces," Sustainability, MDPI, vol. 15(5), pages 1-22, March.

    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. Stefan Flügel & Nina Hulleberg & Aslak Fyhri & Christian Weber & Gretar Ævarsson, 2019. "Empirical speed models for cycling in the Oslo road network," Transportation, Springer, vol. 46(4), pages 1395-1419, August.
    2. Levy, Nadav & Golani, Chen & Ben-Elia, Eran, 2019. "An exploratory study of spatial patterns of cycling in Tel Aviv using passively generated bike-sharing data," Journal of Transport Geography, Elsevier, vol. 76(C), pages 325-334.
    3. Anowar, Sabreena & Eluru, Naveen & Hatzopoulou, Marianne, 2017. "Quantifying the value of a clean ride: How far would you bicycle to avoid exposure to traffic-related air pollution?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 66-78.
    4. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    5. Ospina, Juan P. & Duque, Juan C. & Botero-Fernández, Verónica & Montoya, Alejandro, 2022. "The maximal covering bicycle network design problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 222-236.
    6. Paulsen, Mads & Rich, Jeppe, 2023. "Societally optimal expansion of bicycle networks," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    7. Meister, Adrian & Felder, Matteo & Schmid, Basil & Axhausen, Kay W., 2023. "Route choice modeling for cyclists on urban networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    8. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    9. 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.
    10. Wong, Melvin & Farooq, Bilal & Bilodeau, Guillaume-Alexandre, 2016. "Next Direction Route Choice Model for Cyclist Using Panel Data," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319265, Transportation Research Forum.
    11. Fitch, Dillon T. & Handy, Susan L., 2020. "Road environments and bicyclist route choice: The cases of Davis and San Francisco, CA," Journal of Transport Geography, Elsevier, vol. 85(C).
    12. Felipe González & Carlos Melo-Riquelme & Louis Grange, 2016. "A combined destination and route choice model for a bicycle sharing system," Transportation, Springer, vol. 43(3), pages 407-423, May.
    13. Scott, Darren M. & Lu, Wei & Brown, Matthew J., 2021. "Route choice of bike share users: Leveraging GPS data to derive choice sets," Journal of Transport Geography, Elsevier, vol. 90(C).
    14. van Oijen, Tim P. & Daamen, Winnie & Hoogendoorn, Serge P., 2020. "Estimation of a recursive link-based logit model and link flows in a sensor equipped network," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 262-281.
    15. Bhat, Chandra R. & Dubey, Subodh K. & Nagel, Kai, 2015. "Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 341-363.
    16. Liu, Shan & Jiang, Hai, 2022. "Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    17. Vedel, Suzanne Elizabeth & Jacobsen, Jette Bredahl & Skov-Petersen, Hans, 2017. "Bicyclists’ preferences for route characteristics and crowding in Copenhagen – A choice experiment study of commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 53-64.
    18. Seungkyu Ryu, 2020. "A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    19. Bram Boettge & Damon M. Hall & Thomas Crawford, 2017. "Assessing the Bicycle Network in St. Louis: A PlaceBased User-Centered Approach," Sustainability, MDPI, vol. 9(2), pages 1-18, February.
    20. Michael Hardinghaus & Simon Nieland & Marius Lehne & Jan Weschke, 2021. "More than Bike Lanes—A Multifactorial Index of Urban Bikeability," Sustainability, MDPI, vol. 13(21), pages 1-17, October.

    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:eee:transa:v:137:y:2020:i:c:p:447-458. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.