Aiming at the problem of low efficiency in obtaining flight boarding allowed nodes at large busy airports, the randomness and regularity of the flight from the actual time of arrival to the release of flight boarding allowed node is studied. After using Laplace feature map to reduce the data dimensionality, a prediction model of flight boarding allowed node is constructed based on support vector regression. The model analyzes and extracts the main factors that have an impact on the nodes that boarding allowed, and groups the daily data according to the airport's busyness to improve reliability. To improve the application effect, a historical database is established, and the purpose of dynamic prediction is achieved by matching historical data. The experimental results show that the accuracy of dynamic prediction is gradually improved. Within the error range of ±3min, the average maximum prediction accuracy can be up to 86.70%.
KEYWORDS: Information security, Computer security, Data modeling, Network security, Inspection, Neural networks, Statistical analysis, Data processing, Data analysis, Analytical research
In order to solve the problem of passenger arrival distribution prediction in airport security inspection area, a prediction algorithm based on flight planning and historical security data is proposed. Firstly, the curve of the arrival probability density distribution of single-flight passengers is fitted, and the second-order Gaussian mixture model is selected. Then the RBF-GMM prediction model is constructed, and the Gaussian fitting structure parameters are predicted by the RBF neural network. In the prediction analysis, it is found that the short-term passenger flow of the adjacent flight can be used as a new feature input, which can further improve the prediction accuracy. Finally, the MRBF-GMM model is constructed to predict the arrival distribution of multi-flight passengers in the same security area. Experiments show that compared with the results of the E value-GMM prediction model, the accuracy of the MRBF-GMM model is improved by nearly 10%, which proposes a new solution for the accurate prediction of passenger traffic in the security building of the terminal building.
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