Domino goes door-to-door!

AUTHOR: Damir Valput

As an attentive reader of this blog might already know, Domino’s main goal is to collect evidence on how various implementations of mechanisms such as 4D trajectory adjustments (including Dynamic Cost Indexing, DCI), Prioritisation of Flights (such as  User Driven Prioritisation Process (UDPP)) and Flight Arrival Coordination using Extended Arrival Manager (E-AMAN), could impact the relationships between the elements of the ATM system. To obtain a fuller picture, Domino takes into account the passengers’ perspective in addition to the more traditional, flight-centred point of view.

While the focus of Domino lies primarily in the network effects that emerge from observing the gate-to-gate phase of air travel, the Domino team is also keen on understanding better the influence of the studied ATM mechanisms on the overall passenger experience. After all, in Domino we focus on the commercial air travel, and ignoring the passengers' experience in this era of increasing desire for seamless travel experience could be costly (read more about it for example here).

Seamless travelling experience has become an ubiquitous phrase nowadays and it usually understands a travel experience with the absence of disruptions on the whole itinerary from point A to point B, personalised to the travelling needs of each passenger (group). It is a concept of growing importance, especially when placed in the context of the goals of the Flightpath2050 document, produced by The Advisory Council for Aviation Research and Innovation in Europe (ACARE). In it, they formulated, among other objectives, a very ambitious goal of 90% of the passengers, travelling inside Europe, executing their door-to-door travel in under 4 hours. On the Image 2 you can observe how time distributions for the total door-to-door travel time differ for two very diverse passenger groups: younger people and families. On average, younger people complete their whole door-to-door journey in 5 hours and 10 minutes, which is 46 minutes shorter than what it takes people who travel with their families. The graph is borrowed from the project Dataset2050, for more information click here!

Network effects (about which you can read more in the previous post on the network centrality metrics) can tell us only so much about passengers' travel experience and how far away are we from the 4 hours door-to-door goal. Domino already incorporates passenger itineraries and will consider how elements in the system are linked among them and could have different degrees of relevance depending if flight-centred or passenger-centred metrics are considered. Flights can propagate reactionary delay through the network but passengers can miss connections too! However, In order to fully integrate the flight perspective and the passenger perspective, Domino will consider going door-to-door! In other words, Domino is going to implement a module that will model passengers' needs and time processes during the door-to-gate and gate-to-door part of the trip as well.

Moreover, other actors in the ATM system (airports, airlines, etc.) could potentially benefit from seeing themselves through the eyes of a passenger and capturing phenomena that emerge from the complex interactions through this shift in perspective. Including the model of the passengers' behaviour during their "out-of-plane experiences" could lead to observing new interesting effects in the air-travel network. How do mechanism studied in Domino influence passengers' door-to-door times? How do the mechanisms affect the criticality of elements in the network from a passenger perspective. Is there any relationship between the time passengers spend on various airport processes and type of the airport characterised by the newly developed centrality metrics? Those are just some of the questions this extension of Domino could help us answer.

Are you interested in what Domino has to tell us about the convoluted relationship between passengers and the rest of ATM actors? Then stay tuned!

New network metrics for complex interactions!

Author: Silvia Zaoli

Air traffic can naturally be described as a networked system, where nodes are the different elements of the airspace, e.g. airports, airlines, or arrival and departure managers, and the links between those nodes describe the interaction between them. Network metrics capture relevant information from these network, as the interconnection of system’s elements and the causal relations between them, representing the spreading channel for delays and costs.

Figure 1: Network where nodes are airports and links are flights.

Delays and cancellations disrupt connections in the network, affecting the airports’ connectivity. This is why we considered centrality metrics, which measure the “importance” of the node of a network in terms of its role in getting the network connected, to evaluate the impact of delays in different scenarios. Existing centrality metrics are not suited to the ATM system, because they do not consider the temporal dynamics of the network, where links (flights) appear and disappear. Therefore, we developed the Trip centrality metric, accounting for the time ordering of connections, and showed that it is able to tell apart situations where delays affect the network connectivity from situations where they do not.

Figure 2: A temporal network changes in time, as links (black arrows) appear and disappear. A walk on this network (green arrow) must respect the time ordering.

Delays propagate through the network by means of the interactions between elements, e.g. through flights due to reactionary delays. We proposed to identify the channels of delay propagation by detecting causal relations (in the statistical sense) between the state of delay of airports. The network where nodes are airports and links are causal relations (named causal network) informs us on the patterns of delay propagation. The denser the network is, the more delay propagates. Therefore, we suggested the use of the density of links and feedback patterns in the causal network to assess the impact of innovations on the propagation of delay. Furthermore, to account for non-linearity in the propagation of delay, we proposed to use a method of causality detection which focuses on extreme delay events.

These new network metrics developed by Domino to assess the impact of innovations on the ATM system were presented at the SESAR Innovation Days in Salzburg! Download the paper and the presentation for more information about these exciting new metrics!

Follow our future updates to see the applications of the metrics to the results of the Agent Based Model in different scenarios!

Domino: The structure

Author: Luis Delgado

Domino’s project is structured in 6 workpackages as shown in the following image:

WP3 will analyse the current and future structure of the ATM system and define the mechanisms and the case studies that will be tested by Domino. These first case studies are the investigative case studies which will set the first set of scenarios to be tested. WP4 will develop an Agent Based Model (ABM) which will be able to execute the different scenarios. In Domino, we understand the different actors in the system as agents which try to optimise their utility functions subject to the system constraint and the environment. The system constraints are changed when different mechanism are implemented as different options arise; and the environment in ATM is subject to uncertainty that the actors need to manage.

The metrics generated by the ABM will cover the impact on both flight and passengers. These outcomes will be analysed by WP5 where a Complexity Science toolbox will be used in order to generate knowledge on the status of the system. Traditional and complex metrics will be generated but also specific network analysis to understand how the elements in the system are coupled and where the bottlenecks are generated. Once again this dual view flight an passenger perspective of the system is core in these analyses.

WP2 will provide support to the other technical packages in terms of data requirements, acquisition and preparation. Domino will model a past day of operations with new mechanisms applied to it.

Finally, Domino requires close collaboration and feedback from stakeholders and experts. This will be achieved with the interactions in WP6. The mechanisms will be subject to a consultation, the model developed in WP4 will be calibrated with the help of stakeholders and the results of the investigative case studies shared in a workshop (to be run in Spring 2019). This workshop be the forum where adaptive case studies will be selected. These case studies try to mitigate some of the network issues identified on the investigative case studies results. The adaptive case studies will be run again from WP3 to WP5 to develop the Domino's methodology: you have a new mechanism (technological or operational change) and you'd like to learn about its impact in the ATM system; this mechanism is modelled within the ABM framework; tested with the Complexity Science toolbox; and once hotspots are identified can be mitigated creating new scenarios to test!

Keep in touch to learn more or provide feedback to Domino and follow our updates regarding the preliminary results and the workshop!

See for more info on the project.

Domino: The knock-on effect

AUTHOR: Luis Delgado

The objective of Domino is to analyse the coupling of elements in the ATM system and how changes (for example, by implementing different mechanism) have an impact on the interrelationships between elements. In order to achieve this, Domino will develop a set of tools, a methodology and a platform to assess the coupling of ATM systems from a flight and a passenger perspective.

Different actors in the ATM system might have different views of its elements and their criticality. For this reason, Domino adds the passenger's view to the more classic flight-centred vision.

In Domino, the ATM system is seen as a set of elements that are related to each other by how the different actors (airlines, flights, passengers, airports, etc.) use them. The behaviour of these actors depend on the available rules of the system. These rules are defined, partially, by the mechanisms that are in place. Complexity Science tools will allow us to understand how the elements in the system are interconnected and how these connections change when the system is modified.

Domino will develop an Agent Based Modelling platform to capture the different systems' relations, and it will focus on three mechanism, implemented and deployed with different scope: Dynamic Cost Indexing (DCI), User Driven Prioritisation Process (UDPP) and Extended Arrival Manager (E-AMAN). Domino will provide a view of the effect of deploying solutions in different manners, e.g., harmonised vs. local/independent deployment.

If a piece in the system is knocked which others are going to be affected? Let Domino tell us!

See for more info on the project.

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