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Advances in Data Science permeate every field of Transportation Science andEngineering, resulting in developments in the transportation sector that {are}data-driven. Nowadays, Intelligent Transportation Systems (ITS) could bearguably approached as a ``story'' intensively producing and consuming largeamounts of data. A~diversity of sensing devices densely spread over theinfrastructure, vehicles or the travelers' personal devices act as sources ofdata flows that are eventually fed {into} software running on automaticdevices, actuators or control systems producing, in~turn, complex informationflows {among} users, traffic managers, data analysts, traffic modelingscientists, etc. These~information flows provide enormous opportunities toimprove model development and decision-making. This work aims to describe howdata, coming from diverse ITS sources, can be used to learn and adaptdata-driven models for efficiently operating ITS assets, systems and processes;in~other words, for data-based models to fully become \emph{actionable}.Grounded in this described data modeling pipeline for ITS, we~define thecharacteristics, engineering requisites and challenges intrinsic to its threecompounding stages, namely, data fusion, adaptive learning and modelevaluation. We~deliberately generalize model learning to be adaptive, since,in~the core of our paper is the firm conviction that most learners will have toadapt to the ever-changing phenomenon scenario underlying the majority of ITSapplications. Finally, we~provide a prospect of current research lines withinData Science that can bring notable advances to data-based ITS modeling, whichwill eventually bridge the gap towards the practicality and actionability ofsuch models.

From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability