A Study Of Predicting Arrival Patterns Of Airport Passengers To The Counters On The Basis Of International Terminal

Abstract

AI-based passenger arrival predictions to the processing points are essential to ensure efficient management of many intertwined operational processes in the airport ecosystem. For example, to be able to analyze the number of ground service personnel that will be required in the following hours, days in different parts of the airport and for different types of operations, it is essential to predict how many passengers will come to the airport in the following time zones. Moreover, density-driven intelligent energy management and dynamic price offering options in different services could only be generated with accurate passenger arrival predictions. Passenger arrivals can be detected with various technologies such as computer vision, IoT, lidar, and radar. However, passenger boarding pass printing event messages from the CUPPS solution, which is implemented in İzmir Adnan Menderes Airport International Terminal, is used as the data source in this study. Also, Linear regression, FEDOT, LSTM, and hybrid methods are configured and compared to predict passenger arrival counts to the counters of the international terminal in the specified time slots.