Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned †
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Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
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Data Science Institute, Faculty of Engineering, Universidad del Desarrollo, Santiago 7610658, Chile
3
LRI, CNRS, Inria, Université Paris-Saclay, 91190 Paris, France
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Eduardo Graells-Garrido and Vanessa Peña-Araya. 2020. Toward An Interdisciplinary Methodology to Solve New (Old) Transportation Problems. In Companion Proceedings of the Web Conference 2020 (WWW ’20 Companion), Taipei, Taiwan, 20–24 April 2020.
Sustainability 2020, 12(15), 6001; https://doi.org/10.3390/su12156001
Received: 29 June 2020
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Revised: 21 July 2020
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Accepted: 22 July 2020
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Published: 26 July 2020
(This article belongs to the Special Issue Smart Cities, City Dashboards, Planning and Evaluation of Urban Performances)
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.
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Keywords:
transportation; urban mobility; data science; mobile phone data
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MDPI and ACS Style
Graells-Garrido, E.; Peña-Araya, V.; Bravo, L. Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned. Sustainability 2020, 12, 6001. https://doi.org/10.3390/su12156001
AMA Style
Graells-Garrido E, Peña-Araya V, Bravo L. Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned. Sustainability. 2020; 12(15):6001. https://doi.org/10.3390/su12156001
Chicago/Turabian StyleGraells-Garrido, Eduardo, Vanessa Peña-Araya, and Loreto Bravo. 2020. "Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned" Sustainability 12, no. 15: 6001. https://doi.org/10.3390/su12156001
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