Toward An Interdisciplinary Methodology to Solve New (Old) Transportation Problems
This work addresses adoption barriers for stakeholders in urban transportation, though it is incremental as it builds on existing interdisciplinary approaches.
The paper tackles the challenge of adopting data-driven solutions in urban transportation by proposing an interdisciplinary methodology that integrates Data Science and Transportation to create more interpretable and transparent models, as demonstrated in a case study inferring commuting trips and modes from mobile phone data.
The rising availability of digital traces provides a fertile ground for new solutions to both, new and old problems in cities. Even though a massive data set analyzed with Data Science methods may provide a powerful solution to a problem, its adoption by relevant stakeholders is not guaranteed, due to adoption blockers such as lack of interpretability and transparency. In this context, this paper proposes a preliminary methodology toward bridging two disciplines, Data Science and Transportation, to solve urban problems with methods that are suitable for adoption. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the building of a potentially adoptable solution. As case study, we describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data.