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• W3045187024 startingPage "6001" @default.
• W3045187024 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." @default.
• W3045187024 created "2020-07-29" @default.
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• W3045187024 date "2020-07-26" @default.
• W3045187024 modified "2023-10-14" @default.
• W3045187024 title "Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned" @default.
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• W3045187024 doi "https://doi.org/10.3390/su12156001" @default.
• W3045187024 hasPublicationYear "2020" @default.
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