Complex Adaptive Systems for the Optimization of Performance in ATM
The Challenge of Air Transport Management
The Air Transport System is a highly complex system of systems with a huge number of involved actors and many unpredictabile elements.
Understanding the cause-effect relationships between Air Traffic performance drivers and performance indicators constitutes a major, unresolved challenge. Policy makers have a very limited understanding of how the assignment of solutions and resources to respond to current and future ATM challenges will affect the network, and have little guidance in defining the right mechanisms to manage delay propagation, capacity limits, network congestion, and other phenomena.
As a single, globally connected, real-world system with millions of daily users, the Air Transport Network profoundly effects the competitiveness of economies, on businesses and on the passengers and delivery customers who rely on it. And it must be safe. Experimenting with approaches within the current system without having some understanding of potential outcomes is not practical. For this reason, establishing reliable and functional modeling and simulation is of paramount importance.
Complex Adaptative Systems for Optimisation of Performance in ATM
The CASSIOPEIA project was created to tackle this fundamentally important problem by putting at work the techniques of Complex Systems and paradigms in Computer Science to build up a theoretical framework and an effective software implementation tool. This software will enable a virtual laboratory to explore new concepts and/or regulations in ATM.
The theoretical framework of CASSIOPEIA explores the elements, or subsystems, composing the whole system and analyses their role in interactions within. Using this valuable input, an Agent Based Model is created and implemented into software.
In order to show the potential of the software platform and the modeling techniques, three Case Studies have been selected and will be explored once the software is completed. These Case Studies were selected with extensive consultation from ATM stakeholders based on their relevance and alignment with the project’s objectives.
The main goal of the CASSIOPEIA project is to develop a theoretical framework and a demonstrative software platform that helps to understand the cause-effect relationships between Air Traffic performance drivers and performance indicators. In pursuit of this goal, a number of complimentary results are derived:
1. Betterment of state-of-the-art in the field of Air Traffic System Performance Modeling: CASSIOPEIA will investigate how tools from Complexity Science may be adapted and combined to overcome the limitations of the existing tools, contributing to the development of credible and reliable performance models. This will allow different stakeholders to better understand the main drivers for an optimal trade-off between different performance indicators.
2. Assistance to policy makers in making more informed decisions. This is the hoped-for outcome of the modeling framework and the Case Studies evaluation in itself will not only serve to test this model but will help to evaluate and quantify the effects of different actual proposed regulations in Air Transport, investigating their impact at European level. This will ultimately contribute to the sustainability of the European Air Transport industry.
3.Betterment of state-of-the art in Complex Adaptive Systems. Though the Project is essentially an applied research effort, it is likely that some specific features of the ATM system make it necessary to develop new or adapt current formalisms, improving the state-of-the-art in the Complex System field.
4. The development of Complex System research in ATM. By implementing an open platform that will have be easily used and improved by other researchers and end users the CASSIOPEIA aims to foster further development in this critically important area.
Project Strategy and Current Status
In order to cope with its objectives, the CASSIOPEIA project has been structured into two main streams. First, a theoretical framework has been developed providing a high level specification for the next stage, which is the software development. The developing phase not only includes implementation, but also a careful analysis of requisites and design. Finally, once both the theoretical framework and the agent base simulation platform are ready a set of three Case Studies will be analyzed in detail to demonstrate the potential application of these new techniques.
About the Project’s Case Studies
At the final stage of the project three case studies will be conducted, with the three-fold purpose of guiding the modeling, providing a preliminary validation of the models, and a first assessment of the model capabilities. Each of the Case Studies will encompass different types of modeling challenges and techniques.
The three Case Studies have been selected according to an expert consultation process, via a Delphi survey and a workshop. A wide variety of stakeholders were present in the process, representing different groups from industry to regulators, and airspace users to service providers. Most of them are still involved with the project.
The Case Studies are as follows, each exploring the potential outcomes of a specific proposed regulation.
Case Study 1: Local Restrictions Limiting Airport and Terminal Airspace Capacity.
This case study analyses the impact and implications of airport environmental regulations on the airlines affected and on the rest of network, taking into account, particularly, the rescheduling of flights and a regionalization of night traffic and performing a cost-benefit analysis for quantifying the economic cost of each measure on the airline operators versus the environmental benefits (noise alleviation, reduction of emissions).
The case study analyses the impact that a curfew regulation would have on the air traffic network if it were applied to the 10 busiest European Airports. Airlines and airports are classified and modelled in such a way that each subcategory searches for its best solution, as they would in real life. In the regulated airports there would be a cumulative capacity demand just before and after curfew, and each airport would sell the slots to those airlines which have a hub at the airport, many flights, heavy aircraft, and other criteria to maximise their profit. The airlines also have some decision-making to do. In this case study, based on the slots offered by the airport, they will decide whether to stay at the airport, move to a regional airport or cancel the flight. The previous and next flights by the same aircraft are checked to ensure that a change in schedule will not overload the capacity at the connecting airports.
The model will analyse different indicators to measure different performance aspects of the agents involved. These metrics will allow the regulator to understand the economic, operational and social impact (noise levels reduction at populated areas) of the regulation imposed.
While the model is based on this regulation, the software platform allows the users to easily change the inputs of the regulations, such as changing gthe curfew, to optimise the regulation.
Case Study 2: ATFM Slot Allocations as a Tradable Commodity
The objective of this case study is to understand the effect of considering ATFM slot allocations as a tradable commodity, analyzing the impact and the cost advantages for airlines (taking into account the potentially higher cost of the traded slot versus savings in flight time, delay at the destination, fuel consumption, emissions reduction, etc.).
The cost of a delayed flight for an airline depends on many factors, like connecting passengers, missing airport slots, overtime of crew, etc. This case study proposes the ATFM Slots be considered a tradable commodity. This would allow users to auction their assigned ATFM slot in such a way that an airline can bid to obtain an earlier slot, thereby saving delay costs.
This new paradigm is expected to improve the economic efficiency of the system and reduce passenger delay at the same time.
Case Study 3: Dynamic Cost Indexing
This case study explores the impact of airlines modifying Cost Index to minimize airline cost. Depending on the airline type, the particular flight’s connecting passengers, the turnaround time required and other criteria, a function of delay cost is generated for each flight. Each airline, identifies the best cost index several times for each of its flights. This CI calculation will modify the airspeed of the aircraft, which may increase or decrease fuel consumption and emissions, but save on overall delay cost. This CI modifications extended over network airlines affect their flight plans, and may have tactical sector capacity demand implications.
This Case Study will research the potential economical gains of airlines, operational flexibility of the air transport network, and identify changes in behaviour of the different actors.
Completed deliverables of the project free for readers to download are provided here: