Identification of similar scenes based on mRMR in terminal area

Xiaowen Lv; Ligang Yuan; Haiyan Chen; Xiaye Hou; Yang Zeng

doi:10.1117/12.2619686

22 December 2021 Identification of similar scenes based on mRMR in terminal area

Xiaowen Lv, Ligang Yuan, Haiyan Chen, Xiaye Hou, Yang Zeng

Proceedings Volume 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021); 120582G (2021) https://doi.org/10.1117/12.2619686
Event: 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 2021, Chongqing, China

Abstract

The uncertainty of traffic and weather causes air traffic management to face the challenge of decision-making effectiveness, and providing auxiliary support for current scenes based on the decision measures of historical similar scenes is an effective way to improve the decision-making ability. To address the problem of similar scenes identification, a similar scenes identification method based on minimal-redundancy-maximal-relevance criterion(mRMR) feature selection algorithm in terminal area is proposed. Firstly, we use the clustering algorithm to preliminarily classify the scenes based on the delay data. Secondly, we use the mRMR feature selection algorithm to filter the features with higher contribution to the classification of similar scenes, and construct the identification model. Finally, the new sample data is input into the model for similar scenes identification. Experiments were conducted on the real dataset in the Guangzhou terminal area. The results show that the mRMR feature selection algorithm has a certain contribution to the identification of similar scenes, and the model can effectively identify similar scenes.

Citation Download Citation

Xiaowen Lv, Ligang Yuan, Haiyan Chen, Xiaye Hou, and Yang Zeng "Identification of similar scenes based on mRMR in terminal area", Proc. SPIE 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 120582G (22 December 2021); https://doi.org/10.1117/12.2619686

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KEYWORDS

Data modeling

Feature selection

Expectation maximization algorithms

Statistical modeling

Meteorology

Atmospheric modeling

Clouds

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