Title of Paper:Multi-model Fusion Dynamic Prediction Method of Enroute Congestion Situation with Considering the Correlation of Air Route Segment

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Affiliation of Author(s):民航学院

Journal:Jiaotong Yunshu Xitong Gongcheng Yu Xinxi J. Transp. Syst. Eng. Inf. Technol.

Abstract:This paper studies the dynamic real- time prediction of air route traffic congestion, which aims at providing scientific basis to alleviate air route traffic congestion and optimize control strategies. First, based on the theory of neural network, a traffic flow parameter prediction model is established, taking the correlation of air route segment into consideration. Then a multi-model fusion prediction algorithm is adopted to improve forecast accuracy, and air route segment congestion situation is predicted based on fuzzy C-means clustering algorithm and previous and predicted traffic flow parameters of air route segment. Finally, the model is verified by ATC radar data. The results demonstrate that this model takes into account the factors of both space and time, and the prediction accuracy of air route congestion is 82.29%. The model corresponds to reality and is feasible for air route traffic states prediction. Meanwhile, consideration of the correlation effects of air route segments and prediction using multi-model fusion algorithm can significantly improve forecast accuracy. Copyright © 2018 by Science Press.

ISSN No.:1009-6744

Translation or Not:no

Date of Publication:2018-02-01

Co-author:hmh

Correspondence Author:Lee