Exploratory data science for discovery and ex‐ante assessment of operational policies: Insights from vehicle sharing
提出一个两阶段探索性数据科学框架，用于从运营数据中识别新策略并事前评估其可行性，以自由浮动式汽车共享为例，发现了一种预防性用户调度策略。
Abstract 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.