APOC Business Process Reengineering Big Data Study
Improving airport performance is at the heart of the SESAR’s Airport Operations Centre (APOC) solution. By providing access to real-time data from various data sources of different APOC stakeholders, airports can make accurate predictions about their operations, including passenger movements. In this study, we review APOC roles and responsibilities, identify the key APOC processes that could be enhanced by data-driven predictions and machine learning techniques (DDP&ML), and demonstrate a case study of how shared data and advanced analytics can be used to make predictions of passengers’ connection times. In the case study, a regression tree model is fitted to a large training set with 3.7 million passenger records. This predictive model is applied to generate forecasts (together with prediction intervals) of each passenger’s connection time and the passenger flows during an eight-hour live trial. The real-time predictions generated by the model could be used to inform Target-Off-Block Time (TOBT) adjustments and determine transfer security resourcing levels.