论文标题
从数据到智能运输系统中的动作：模型可行的功能要求的处方
From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability
论文作者
论文摘要
数据科学的进步渗透到运输科学和工程领域的每个领域，从而导致{}是数据驱动的运输部门的发展。如今，可以说智能运输系统（ITS）可以说是一个``故事''的``故事''，从而产生和消耗大量数据。 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.这项工作旨在描述来自不同源的数据如何用于学习和调整数据驱动的模型，以有效地运行其资产，系统和流程；用〜其他话来说，使基于数据的模型完全成为\ emph {casubleable}。基于此描述的数据建模管道，我们〜定义了其三个复合阶段的特征，工程要求和挑战，即数据融合，自适应学习和模型评估。我们要刻意将模型学习成为适应性的概括，因为在我们论文的核心中，大多数学习者将必须适应其大多数应用程序所基于的不断变化的现象。最后，我们〜提供了数据科学中当前的研究线的前景，可以为基于数据的建模带来显着进步，这最终将弥合差距，以了解此类模型的实用性和可行性。
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.
