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Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned
2020
Sustainability
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
doi:10.3390/su12156001
fatcat:szqab2agmvaefhbqel6q3pvy6e