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

Airports constitute some of the most complex systems enabling mobility in today’s society. The various components inside the airport system each have specific requirements and include numerous systems, processes and stakeholders. Stakeholders are comprised of government entities (security and customs), private bodies (airport owner, airlines) and customers (passengers and cargo). An airport determines the traveler’s first and last impression of a city. A positive airport experience is beneficial to sales and influences future travel choices (cite airport council international customer service). Airports have taken steps to increase their customer focus. An improved customer experience relies on technologies supporting better service. Two examples of such technologies are Radio-Frequency Identification (RFID), which would enable airports to track passengers and bags effectively [1] [2], and Bluetooth [3] to support passenger tracking. To facilitate the needs of airport customers and operators, such technologies need to be part of the activities of airport users at the airport. While at the airport, passengers engage in processing and discretionary activities. Processing activities are enforced to conform to the legal and regulatory requirements for air travel. They correspond to : check in, departure paperwork fill out, going through identity and security checkpoints, boarding and deboarding a plane. Passengers actually spend a small portion of their time at airports engaging in processing activities, including time spend waiting to be processed [4]. A common feature of service systems is that the demand for service varies throughout the day. Air terminal queues [5] Staffing requirements are part of the design and management of the service system. In the long term planning horizon, managers set the system capacity. On the short term horizon, managers make agents scheduling decisions, indicating the number of agents working during specific hours, and breaking down the day in time intervals. The scheduling decision is often made based on the solution of an integer linear program [6], [7], [8]. In real time, managers may make additional adjustements (flexing decisions) to move agents on and off the line of duty. This can be achieved if there are additional agents on site working on other tasks or if more agents can be called on short notice. Robertson et al. [9] provided a detailed procedure to model passenger arrivals to estimate how many passengers arrived at the airport during each day and time of day. The raw passenger volume for each time interval was the final product and corresponded to the passenger arrival pattern. Further analysis provided access to passenger arrival patterns at different processing points (check-in, baggage security, security checkpoint...). The passenger arrival pattern for each checkpoint was computed using several inputs : passenger arrival behavior, flight schedules, aircraft capacity, load factors and transfer rates. In a system where congestion can build up at peak hours. The number of servers is dynamically adjusted according to queue length. If the queue reaches an upper threshold, additional servers are opened. If some servers are idle, they get closed. By choosing appropriate thresholds, the queue length can be controlled in a certain range with high probability. This staffing policy is called congestion-based staffing [10].