Recent advancements in information technology have ushered a new wave of systems integrating Internet technology with sensing, wireless communication, and computational resources over existing infrastructures. As a result, myriad complex, non-traditional Cyber-Physical Systems (CPS) have emerged, characterized by interaction among people, physical facilities, and embedded sensors and computers, all generating vast amounts of complex data. Such a case is encountered within a contemporary airport hall setting: passengers roaming, information systems governing various functions, and data being generated and processed by cameras, phones, sensors, and other Internet of Things technology. This setting has considerable potential of contributing to goals entertained by the CPS operators, such as airlines, airport operators/owners, technicians, users, and more. We model the airport setting as an instance of such a complex, data-intensive CPS where multiple actors and data sources interact, and generalize a methodology to support it and other similar systems. Furthermore, this article instantiates the methodology and pipeline for predictive analytics for passenger flow, as a characteristic manifestation of such systems requiring a tailored approach. Our methodology also draws from DataOps principles, using multi-modal and real-life data to predict the underlying distribution of the passenger flow on a flight-level basis (improving existing day-level predictions), anticipating when and how the passengers enter the airport and move through the check-in and baggage drop-off process. This allows to plan airport resources more efficiently while improving customer experience by avoiding passenger clumping at check-in and security. We demonstrate results obtained over a case from a major international airport in the Netherlands, improving up to 60% upon predictions of daily passenger flow currently in place.

中文翻译：

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用于网络物理系统治理的 DataOps：机场客流案例

信息技术的最新进展带来了新的系统浪潮，将互联网技术与现有基础设施上的传感、无线通信和计算资源相结合。因此，出现了无数复杂的、非传统的网络物理系统 (CPS)，其特点是人、物理设施、嵌入式传感器和计算机之间的交互，所有这些都产生大量复杂的数据。在当代机场大厅环境中会遇到这种情况：乘客漫游、管理各种功能的信息系统以及由相机、电话、传感器和其他物联网技术生成和处理的数据。此设置具有相当大的潜力，有助于实现 CPS 运营商所接受的目标，例如航空公司、机场运营商/所有者、技术人员、用户、和更多。我们将机场设置建模为这样一个复杂、数据密集型 CPS 的实例，其中多个参与者和数据源交互，并概括了一种方法来支持它和其他类似系统。此外，本文将客流预测分析的方法和管道实例化为需要定制方法的此类系统的特征表现。我们的方法还借鉴了 DataOps 原则，使用多模式和现实生活数据来预测航班层面客流的潜在分布（改进现有的日层面预测），预测乘客何时以及如何进入机场并完成值机和行李托运流程。这允许更有效地规划机场资源，同时通过避免乘客在办理登机手续和安检时聚集来改善客户体验。我们展示了从荷兰一个主要国际机场的一个案例中获得的结果，在对当前每日客流量的预测基础上提高了 60%。