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

• URL: http://arxiv.org/abs/2002.02210v3
• Date: Mon, 8 Feb 2021 15:17:27 GMT
• Title: From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability
• Authors: Ibai Lana, Javier J. Sanchez-Medina, Eleni I. Vlahogianni, Javier Del Ser
• Abstract summary: This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes. Grounded in this described data modeling pipeline for ITS, wedefine the characteristics, engineering requisites and intrinsic challenges to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
• Score: 10.27718355111707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that {are} data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a ``story'' intensively producing and consuming large amounts of data. A~diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed {into} software running on automatic devices, actuators or control systems producing, in~turn, complex information flows {among} users, traffic managers, data analysts, traffic modeling scientists, etc. These~information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-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 the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We~deliberately generalize model learning to be adaptive, since, in~the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we~provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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