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Evolution mechanism and optimisation of traffic congestion

Published online by Cambridge University Press:  06 July 2023

Fanrong Sun
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District, Nanjing City, Jiangsu Province China
Xueji Xu*
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District, Nanjing City, Jiangsu Province China
Huimin Zhang
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District, Nanjing City, Jiangsu Province China
Di Shen
Affiliation:
Air Force Engineering University, Xian City, Shaanxi Province China
Yao Mu
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District, Nanjing City, Jiangsu Province China
Yujun Chen
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District, Nanjing City, Jiangsu Province China
*
Corresponding author: Xueji Xu; n_nuaa@163.com

Abstract

Air route networks can no longer meet operational efficiency requirements because of the rapid growth of complex traffic flows. Machine learning is employed to investigate the evolutionary mechanism of congestion in such networks in view of their high complexity and high density, and a reasonable network optimisation scheme is presented. First, deviations between nominal and actual routes are investigated with reference to radar track data, and a network reflecting actual route operations is constructed using adversarial neural networks. Second, flight time is used to characterise congestion in route networks. Actual network operations are considered, and congestion is defined from the perspective of road traffic engineering. The effects of the operational properties of traffic flows on flight times are analysed to establish various congestion indicators. A gradient boosting model is used to select indicator characteristics and analyse patterns in the variations of indicator values for each flight segment in distinct periods. The indicator–time relationship is leveraged to explore the evolutionary mechanism of congestion in the route network. Third, on the basis of this mechanism, a multiobjective optimisation model of congestion is formulated, and a particle swarm optimisation algorithm is executed to adjust the route passage structure, thereby solving the optimisation model. Finally, calculation validation is conducted using radar track data from the control sector of the Yunnan region. The average flight time in a route segment is 10% shorter in the optimised route network than in the nonoptimised route network, which confirms that the optimisation solution is practicable.

Type
Research Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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