An Optimized Air Traffic Departure Sequence According to the Standard Instrument Departures
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| Publicado no: | International Journal of Advanced Computer Science and Applications vol. 15, no. 3 (2024) |
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| Autor principal: | |
| Publicado em: |
Science and Information (SAI) Organization Limited
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| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.14569/IJACSA.2024.01503133 |2 doi | |
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| 245 | 1 | |a An Optimized Air Traffic Departure Sequence According to the Standard Instrument Departures | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Sequencing efficiently the departure traffic remains among the critical parts of air traffic management these days. It not only reduces delays and congestion at hold points, but it also enhances airport operations, improves traffic planning, and increases capacity. This research paper proposes an approach, that employs a genetic algorithm (GA), to help air traffic con-trollers in organizing a sequence for the departure traffic based on the standard instrument departures (SIDs) configuration. A scenario with Randomly assigned types, SIDs, and departure times was applied to a set of aircraft in a terminal area with a four-SID configuration to assess the performance of the suggested GA. Subsequently, a comparison with the standard method of First Come First Served (FCFS) was conducted. The testing data revealed promising results in terms of the total spent time to reach a specified altitude after takeoff. | |
| 653 | |a Configuration management | ||
| 653 | |a Traffic congestion | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Air traffic management | ||
| 653 | |a Traffic planning | ||
| 653 | |a Critical components | ||
| 653 | |a Collaboration | ||
| 653 | |a Computer science | ||
| 653 | |a Optimization | ||
| 653 | |a Aviation | ||
| 653 | |a Traffic flow | ||
| 653 | |a Automation | ||
| 653 | |a Air traffic control | ||
| 653 | |a Efficiency | ||
| 653 | |a COVID-19 | ||
| 653 | |a Aircraft | ||
| 653 | |a Scheduling | ||
| 653 | |a Infrastructure | ||
| 653 | |a Decision making | ||
| 653 | |a Air transportation industry | ||
| 653 | |a Airports | ||
| 653 | |a Air traffic controllers | ||
| 653 | |a Linear programming | ||
| 653 | |a Air travel | ||
| 653 | |a Airlines | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 15, no. 3 (2024) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3046786349/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3046786349/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |