Physics Maths Engineering
Air traffic inefficiencies lead to excess fuel burn, emissions and air traffic controller (ATCo) workload. Various stakeholders have developed metrics to assess the operation performance. Most metrics compare the actual trajectories to some benchmark ones to calculate excess time or distance. This research is inspired by cellular automata (CA) and develops a combined time-distance lateral inefficiency and predictability metric using discrete space and time mapping on published flight routes. The analysis is focused on Tokyo International Airport, but uses only track data and published routes, which makes it easily applicable to any other hub airport worldwide. The mapping and velocity analyses are used to investigate when and where ATCos are most likely to intervene to provide save separation. A metric which can be adjusted to evaluate both traffic flow predictability and efficiency is proposed. This metric can be applied to better understand current traffic and enable future improvements towards seamless air traffic flow management.
The closed-form system of nonlinear equations introduced in this study provides a unified framework for analyzing aircraft manoeuvres. It integrates theoretical flight dynamics with practical applications, enabling more accurate and efficient simulations of aircraft behaviour during various manoeuvres. :contentReference[oaicite:1]{index=1}
The model validates its accuracy by recovering well-known short period and phugoid modes, which are fundamental to understanding aircraft stability and control. This validation demonstrates the model's capability to accurately represent the dynamic characteristics of fixed-wing aircraft. :contentReference[oaicite:2]{index=2}
Implementing model-based manoeuvre analysis in pilot training could lead to improved safety standards in both general and commercial aviation. It may also expedite theoretical course completion in air transport by providing a more comprehensive understanding of aircraft dynamics, thereby enhancing pilot proficiency and decision-making skills. :contentReference[oaicite:3]{index=3}
Show by month | Manuscript | Video Summary |
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2025 January | 36 | 36 |
2024 December | 47 | 47 |
2024 November | 62 | 62 |
2024 October | 16 | 16 |
Total | 161 | 161 |
Show by month | Manuscript | Video Summary |
---|---|---|
2025 January | 36 | 36 |
2024 December | 47 | 47 |
2024 November | 62 | 62 |
2024 October | 16 | 16 |
Total | 161 | 161 |