Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145863
Title: A Braess’s Paradox inspired method for enhancing the robustness of air traffic networks
Authors: Cai, Qing
Alam, Sameer
Ang, Haojie
Duong, Vu
Keywords: Engineering::Aeronautical engineering
Issue Date: 2020
Source: Cai, Q., Alam, S., Ang, H., & Duong, V. (2020). A Braess’s Paradox inspired method for enhancing the robustness of air traffic networks. Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence. doi:10.1109/SSCI47803.2020.9308452
metadata.dc.contributor.conference: 2020 IEEE Symposium Series on Computational Intelligence
Abstract: Air traffic networks (ATNs) play an important role in air transport. It is of practical application values to improve the robustness of ATNs. Here we propose a counter-intuitive idea with the inspiration comes from the Braess’s Paradox phenomenon. To be specific, we propose to delete edges from an ATN to improve its corresponding robustness. To achieve this goal, we formulate a bi-objective optimization problem which aims to maximize the robustness of the focal ATN as well as to minimize the number of edges to be removed. In order to address the developed optimization model, we introduce the nondominated sorting genetic algorithm (NSGA-II) and modify its algorithm operators to make it fit for the established model. To check if the research idea proposed works or not, we conduct experiments on nine real-world ATNs. In the experiments, NSGAII has been compared against its successor–NSGA-III, and another state-of-the-art optimization algorithm named MODPSO. Experiments indicate that NSGA-II performs better than the rest two algorithms on the tested ATNs. For the tested ATNs, three networks have their robustness improved by 100% by removing less than six edges while the remaining six get an improvement of around 10%. This work provides aviation decision makers with a new perspective on ATNs design and management.
URI: https://hdl.handle.net/10356/145863
ISBN: 978-1-7281-2547-3
DOI: 10.1109/SSCI47803.2020.9308452
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Air Traffic Management Research Institute 
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/SSCI47803.2020.9308452
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers

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