Toward An Interdisciplinary Methodology to Solve New (Old) Transportation Problems

Abstract

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. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing; Ubiquitous and mobile computing; Scenario-based design; • Information systems → Social networks; Geographic information systems.

Tweets on the Go: Gender Differences in Transport Perception and Its Discussion on Social Media

“…However, it is not clear how to actually transfer those insights to these stakeholders in a meaningful way. Visualization has been a successful medium to communicate between data scientists and transportation experts, particularly in scenarios of solving transportation problems with non-traditional data sources and methods [83]. Finally, social media data is not considered a representative source of population information [21].…”