Application of Airport Big Data Platform in Passenger Transfer Service Optimization Algorithm

This paper proposes an intelligent algorithm framework for optimizing passenger transfer services. First, a comprehensive transfer service monitoring system is constructed through the big data platform. The system can collect and analyze multi-dimensional data such as flights, passenger flow, and resource allocation in real time to provide data support for transfer services. Then, this paper designs an optimization framework based on Dijkstra algorithm and genetic algorithm, in which Dijkstra algorithm is used for shortest path calculation and genetic algorithm is used for global optimization of transfer paths and resource scheduling. The frame can decrease the passenger's transfer time and optimize the airport resource allocation. The experiment section uses the actual data of a big international airport, including the flight schedule, the passenger flow, and the layout of the airport facilities. The simulation results show that the proposed algorithm has achieved significant advantages over traditional optimization methods in optimizing transfer time and improving resource utilization efficiency. The optimized transfer time is reduced by an average of 15%, while the resource scheduling efficiency is improved by 12%. In addition, the algorithm has shown good adaptability in airports of different sizes and can handle dynamic changes in complex scenarios.