Poisson Distribution for Dynamic Passenger Management: A Cost-Effective Strategy for Airports

The rapid growth of global air traffic has made optimizing passenger flow in airports a critical challenge for the aviation industry. Efficient management of airport infrastructure is essential for minimizing wait times, enhancing passenger comfort, and improving operational efficiency. This paper explores mathematical-statistical modeling techniques and technological solutions, including the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, for optimizing passenger mobility in the landside area of airports. A key aspect of this optimization is the development of an algorithm for dynamic passenger traffic management, designed to analyze real-time data and facilitate proactive decision-making. The proposed algorithm, implemented in MATLAB, employs a Poisson distribution model to centralize, sort, and filter passenger data. By analyzing distinct time intervals, the system identifies congestion hotspots and enables dynamic resource allocation, reducing processing delays and preventing overcrowding. Real-world applications of similar methodologies, such as those implemented at Schiphol Airport and Hong Kong International Airport, have demonstrated significant improvements in operational efficiency, including reduced wait times and optimized baggage handling processes. The paper further examines global trends in airport digitalization, multimodal transportation integration, and the increasing role of AI-driven predictive analytics. Additionally, statistical methods such as ARIMA, SARIMA, and machine learning-based neural networks are evaluated for their effectiveness in forecasting passenger flow and optimizing airport resources. The findings highlight the economic and operational benefits of intelligent passenger flow management, including reduced operational costs, improved airport revenue from commercial areas, and enhanced passenger satisfaction. By integrating real-time monitoring with predictive modeling, airports can maximize infrastructure utilization and provide a seamless travel experience. This research contributes to the development of future-ready airports by proposing scalable solutions for managing increasing passenger volumes through advanced analytics and automation.