Classification of Air Transport Passengers' Kinetic Behavior

Abstract

Purpose Meeting the needs of passengers will increasingly become a competitive factor for airports. To meet passengers' needs even better, airports need to employ a passenger-centric viewpoint. Real-time information about flights is already passed to passengers nowadays. A next step towards passenger-centric services will be to use real-time information about passenger movement and behavior for an improved operational airport management. The goal of this research is to estimate passenger flows, level of service, and resulting infrastructure utilization at an airport. Methodology In this paper we employ a combined approach. In the first part we consider, trajectory and velocities of each individual passenger which is assumed to be captured by means of a technology in place (e.\,g.\ camera or laser). We use the quantities walking speed, variability of the trajectory, and stopping ratio to categorize passengers. In the second part we define areas of the airport along with transfer probabilities. These are used to refine possible next waypoints identified in the first part. The transfer probabilities could be extracted from a survey undertaken at an airport during real operations. The approach can be applied at any airport and is compatible with various motion prediction techniques. Expected findings We use the quantities described above to divide passengers into different categories such as ``going straight'', ``finding the way'', and ``walking around''. The first category ``going straight'' specifies passengers with a goal and a clear idea of how to get there. The second category consists of passengers who need to find their next waypoint or the route to an already identified place. In the last category there are passengers who are waiting or strolling. The transfer probabilities together with the trajectory of a specific passenger in the category can provide an estimation of the goal of the passenger. The entirety of passengers and their goals yield a probability distribution throughout the airport area. Contribution We adapted the approach originally developed for pedestrians in a shopping mall to the situation of an airport. In this paper, we apply our approach to model movements of passengers and to movement data derived from a microscopic simulation. In contrast to passenger flow simulations, the approach can be applied to passenger movements in real-time, providing the possibility to improve operational airport management.