Special Issue "TMA and Apron Operations"
Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 341
Interests: air transportation; data-driven and model-based environments; predictive analysis; integrated airspace and airport management
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Interests: air traffic management; airport operations; optimization; machine learning; data analytics
The airspace environment around the airport consists of a complex design for arrival and departure traffic flows. Climbing and descending traffic have to be efficiently managed while complying with safety regulations and sequencing demands. Limited capacities and resources at air and ground determine the system design, which benefits from integrated synchronization and optimization of decent/landing, apron (taxi and gate), and departure/climbing operations. Improved awareness of stakeholders (air traffic control, airliners, airport, and ground handlers) will allow for an appropriate response to the variable traffic demand over the day of operations.
This Special Issue invites innovative and disruptive contributions that address models, methods, optimization approaches, as well as improved operational procedures or design challenges to be solved for both the terminal maneuvering area (TMA) and the airport apron, e.g., the following topics:
- Optimization of runway/taxiway operations;
- Seamless optimized decent operations in complex airspace;
- Optimization by enhanced ground to air sharing;
- Management of variable traffic demands;
- Dynamic/synchronized arrival/departure management;
- Traffic optimization by minimizing additional ASMA time;
- Real-time predictions of operational capacities;
- Air traffic data analysis to understand airspace traffic flows in the TMA;
- Optimized airspace concept/structure, considering (new) SID and STARS;
- Simulation of airspace and airport operations;
- Stand/gate allocation;
- Coordination of planning and implementation requirements;
- Consideration of economic, ecological, and operational requirements for the aircraft, incorporating the requirements of aircrew and operator;
- Monitoring for TMA operations using operational data (e.g., ADS-B);
- Machine learning approaches to derive operational knowledge and provide improved awareness.
Dr. Michael Schultz
Dr. Sameer Alam
Dr. Cheng-Lung (Richard) Wu
Manuscript Submission Information
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