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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 paper instantiates the methodology and pipeline for predictive analytics for passengers 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.

DataOps for Cyber-Physical Systems Governance: The Airport Passenger Flow Case