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Time spent in processing zones at an airport are an important part of thepassenger's airport experience. It undercuts the time spent in the rest of theairport, and therefore the revenue that could be generated from shopping anddining. It can also result in passengers missing flights and connections, whichhas significant operational repercussions. Inadequate staffing levels are oftento blame for large congestion at an airport. In this paper, we present astochastic simulation that estimates the operational uncertainty in passengerprocessing at immigration. Congestion and delays are estimated on arrivals anddepartures based on scheduled flight departures and arrivals. We demonstratethe use of cellular tracking data in refining the model, and an approach tocontrolling congestion by adjusting staffing levels.

Passenger Flow Predictions at Sydney International Airport: A Data-Driven Queuing Approach