Abstract
Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent identification of system dynamics at the evaluation level, without dealing with the actual physical events of the system, transforms the analysis of time series into a system identification process, which ensures control of an unknown (or only partially known) system. In this paper, the requirements for mathematical modeling are presented in the form of a step-by-step framework, which can be derived from the formal process model of ATM. The framework is applied to representative datasets based on former experiments and publications, for whose prediction of boarding times and classification of flight delays with machine learning (ML) the framework presented here was used. While the training process of neural networks was described in detail there, the paper shown here focuses on the control options and optimization possibilities based on the trained models. Overall, the discussed framework represents a strict guideline for addressing data and machine learning (ML)-based analysis and metaheuristic optimization in ATM.
Highlights
Understanding and controlling the inner connections of complex systems, such as ATM, requires knowledge about all occurring relationships among the participating components (air traffic management (ATM) physical subsystems, e.g., runway) and users (air traffic management (ATM) stakeholders, e.g., airline)
They differ in their way of aggregating data, but agree in their general concept: the definition of key performance areas (KPAs) and their subdivision into key performance indicators (KPIs) and performance indicators (PIs) at a lower level of abstraction
The motivation behind defining a trained artificial neural network (ANN) as a separate virtual air traffic management model is thatm in the applications and experiments performed, optimization, prediction, and dynamics analysis always occur at a different level than the system specifies
Summary
Understanding and controlling the inner connections of complex systems, such as ATM, requires knowledge about all occurring relationships among the participating components (air traffic management (ATM) physical subsystems, e.g., runway) and users (air traffic management (ATM) stakeholders, e.g., airline). A data-based analysis based on these milestones is considered useful, as the optimization of turnaround sub-processes has a significant impact on individual milestones (e.g., optimized aircraft boarding procedures; see [3]) These applications are distinguished according to their focus in macroscopic (general ATM performance, e.g., delays at an airport [4,5,6]) and microscopic (e.g., boarding times [3]) use cases (cf Section 1.1.2). In ATM, various committees are responsible for defining benchmarking frameworks—in particular, the Eurocontrol (EC) [7] and International Civil Aviation Organization (ICAO) [8,9] They differ in their way of aggregating data, but agree in their general concept: the definition of key performance areas (KPAs) and their subdivision into key performance indicators (KPIs) and performance indicators (PIs) at a lower level of abstraction.
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