The proliferation of mobile devices and the emergence of the Internet of Things are leading to an unprecedented availability of operational data. In this article, we study how leveraging this data in conjunction with data science methods can help researchers and practitioners in the development and evaluation of new operational policies. Specifically, we introduce a two‐stage framework for exploratory data science consisting of a policy identification stage and an ex‐ante policy assessment stage. We apply the framework to the context of free‐floating carsharing—a novel mobility service that is an example of data‐rich smart city services. Through data exploration, we identify a novel preventive user‐based relocation policy and provide an ex‐ante assessment regarding the feasibility of its implementation. We discuss practical implications of our approach and results for shared‐mobility providers as well as the relationship between data science and operations management research.

中文翻译：

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探索性数据科学，用于发现和事先评估运营政策：车辆共享的见解

移动设备的激增和物联网的出现正导致空前的操作数据可用性。在本文中，我们研究如何将这些数据与数据科学方法结合使用，可以帮助研究人员和从业人员开发和评估新的运营政策。具体来说，我们为探索性数据科学引入了一个分为两个阶段的框架，其中包括一个策略识别阶段和一个事前政策评估阶段。我们将该框架应用于自由浮动汽车共享的背景下，这是一种新颖的移动服务，是数据丰富的智慧城市服务的一个示例。通过数据探索，我们确定了一种新颖的基于用户的预防性迁移策略，并对其实施的可行性进行了事前评估。