IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i15p6001-d389999.html
   My bibliography  Save this article

Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned

Author

Listed:
  • Eduardo Graells-Garrido

    (Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
    Data Science Institute, Faculty of Engineering, Universidad del Desarrollo, Santiago 7610658, Chile)

  • Vanessa Peña-Araya

    (LRI, CNRS, Inria, Université Paris-Saclay, 91190 Paris, France)

  • Loreto Bravo

    (Data Science Institute, Faculty of Engineering, Universidad del Desarrollo, Santiago 7610658, Chile)

Abstract

The rising availability of digital traces provides a fertile ground for data-driven solutions to problems in cities. However, even though a massive data set analyzed with data science methods may provide a powerful and cost-effective solution to a problem, its adoption by relevant stakeholders is not guaranteed due to adoption barriers such as lack of interpretability and interoperability. In this context, this paper proposes a methodology toward bridging two disciplines, data science and transportation, to identify, understand, and solve transportation planning problems with data-driven solutions that are suitable for adoption by urban planners and policy makers. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the development of a potentially adoptable solution with evaluated outputs. We describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data, and we report the lessons learned during the process.

Suggested Citation

  • Eduardo Graells-Garrido & Vanessa Peña-Araya & Loreto Bravo, 2020. "Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned," Sustainability, MDPI, vol. 12(15), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6001-:d:389999
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/15/6001/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/15/6001/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Changfeng Jing & Mingyi Du & Songnian Li & Siyuan Liu, 2019. "Geospatial Dashboards for Monitoring Smart City Performance," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
    2. Davide Castelvecchi, 2016. "Can we open the black box of AI?," Nature, Nature, vol. 538(7623), pages 20-23, October.
    3. Sarah Giest, 2017. "Big data for policymaking: fad or fasttrack?," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(3), pages 367-382, September.
    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. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
    2. Md Altab Hossin & Jie Du & Lei Mu & Isaac Owusu Asante, 2023. "Big Data-Driven Public Policy Decisions: Transformation Toward Smart Governance," SAGE Open, , vol. 13(4), pages 21582440231, December.
    3. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    4. Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    5. Justin Longo & Alan Rodney Dobell, 2018. "The Limits of Policy Analytics: Early Examples and the Emerging Boundary of Possibilities," Politics and Governance, Cogitatio Press, vol. 6(4), pages 5-17.
    6. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
    7. Canellas, Marc & Haga, Rachel, 2017. "Framing Human-Automation Regulation: A New Modus Operandi from Cognitive Engineering," LawArXiv yu2h3, Center for Open Science.
    8. Hui Zhang & Huiying Ding & Jianying Xiao, 2023. "How Organizational Agility Promotes Digital Transformation: An Empirical Study," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    9. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    10. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
    11. Pujin Wang & Jianzhuang Xiao & Ken’ichi Kawaguchi & Lichen Wang, 2022. "Automatic Ceiling Damage Detection in Large-Span Structures Based on Computer Vision and Deep Learning," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    12. Roel Heijlen & Joep Crompvoets & Geert Bouckaert & Maxim Chantillon, 2018. "Evolving Government Information Processes for Service Delivery: Identifying Types & Impact," Administrative Sciences, MDPI, vol. 8(2), pages 1-14, May.
    13. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    14. Brian G Booth & Eva Hoefnagels & Toon Huysmans & Jan Sijbers & Noël L W Keijsers, 2020. "PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
    15. Dominic Chalmers & Niall G. MacKenzie & Sara Carter, 2021. "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, , vol. 45(5), pages 1028-1053, September.
    16. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    17. Rodrigo Tapia-McClung, 2020. "Exploring the Use of a Spatio-Temporal City Dashboard to Study Criminal Incidence: A Case Study for the Mexican State of Aguascalientes," Sustainability, MDPI, vol. 12(6), pages 1-25, March.
    18. Pei-Chen Tsai & Tsung-Hua Lee & Kun-Chi Kuo & Fang-Yi Su & Tsung-Lu Michael Lee & Eliana Marostica & Tomotaka Ugai & Melissa Zhao & Mai Chan Lau & Juha P. Väyrynen & Marios Giannakis & Yasutoshi Takas, 2023. "Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    19. Abuljadail, Mohammad & Khalil, Ashraf & Talwar, Shalini & Kaur, Puneet, 2023. "Big data analytics and e-governance: Actors, opportunities, tensions, and applications," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    20. Hui Niu & Siyuan Li & Jian Li, 2022. "MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization," Papers 2210.01774, arXiv.org.

    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:12:y:2020:i:15:p:6001-:d:389999. 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.