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.
Highlights
Cities are becoming increasingly complex, growing larger due to urbanization processes [1], and with increasing layers of interaction between their populations and their urban infrastructure [2]
In this work we describe each step of the methodology, including the stakeholders and concepts involved, as well as a case study of a method to infer the transportation mode share of the population in Santiago (Chile) using mobile phone data [17]
The final solution should consist in an evaluated data science model that addresses the requirements of the transportation problem and a set of visual representations of this model
Summary
Cities are becoming increasingly complex, growing larger due to urbanization processes [1], and with increasing layers of interaction between their populations and their urban infrastructure [2]. The discipline of transportation is affected by these issues, due to city growth and because of the arrival of new technologies and changes in urban behavior Another discipline, data science, has studied urban phenomena at previously unseen spatio-temporal granularity, mainly through the usage of mobile phone data [3]. Data science, has studied urban phenomena at previously unseen spatio-temporal granularity, mainly through the usage of mobile phone data [3] Both disciplines complement each other: data science may provide tools to transportation to identify, understand, evaluate, and solve problems; transportation may provide important domain problems to be solved with data-driven approaches. Due to this gap between both disciplines, relevant stakeholders, such as public institutions and transportation authorities/operators, do not take advantage of the scalability, readiness, and granularity of data science-based models
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