Prediction of passenger arrival distribution in security area based on MRBF-GMM model

Zhiwei Xing; Ruohong Ling; Chao Wang; Biao Li

doi:10.1117/12.2623837

6 February 2022 Prediction of passenger arrival distribution in security area based on MRBF-GMM model

Zhiwei Xing, Ruohong Ling, Chao Wang, Biao Li

Proceedings Volume 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021); 120813O (2022) https://doi.org/10.1117/12.2623837
Event: Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 2021, Chongqing, China

Abstract

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.

Citation Download Citation

Zhiwei Xing, Ruohong Ling, Chao Wang, and Biao Li "Prediction of passenger arrival distribution in security area based on MRBF-GMM model", Proc. SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 120813O (6 February 2022); https://doi.org/10.1117/12.2623837

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