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The First CAMERA Mobility Report is out

The European Union designates significant funds for various research areas via framework programs, such as Horizon 2020, with aviation research being just one of them. As part of its coordinating activity, the EU is performing investigative actions across those areas to make sure the fund usage is optimal and properly addresses the needs of European citizens.

CAMERA 1st Mobility Report is a result of the research performed so far within the CAMERA H2020 project that aims at analysing research initiatives from the past decade that focus on the European air transport system and its integration with other transport modes. The focus of the report is to investigate 158 selected research initiatives in European mobility research to determine their coverage of mobility challenges, identify potential gaps and form recommendations for future research initiatives.

Initial exploratory analysis performed so far captured a set of 158 mobility projects. The analysis was performed on a macro-level, exploring the projects' goals and assessing them in two ways: against the challenges defined in the performance framework and through automatic clustering of projects into nine different groups. Automatic grouping of the projects revealed a fairly diverse coverage of various research areas in mobility. The selected projects cover a number of crucial topics for mobility, such as innovative technological concepts, socio-economic and environmental aspects of mobility, sustainable development, and so on. Initial findings indicate that research into the resilience of transport systems, that places a passenger at the centre of those systems, is a mostly unexplored area. Closely followed is the research that focuses on passenger demand. Lack of knowledge in that area can be flagged as a critical gap in light of the importance of the passenger experience in shaping the future of European air transport systems.

Finally, there is a need for more multidisciplinary research in mobility. A number of holistic research initiatives that simultaneously address various mobility issues should be increased in order to make Europe globally recognised as providing a high-level mobility experience and delivering excellent mobility research results.

The digital version of the 1st CAMERA Mobility Report is available! Download here and subscribe to our newsletter to receive many other information regarding the project. 

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!

PhD Candidate offer

Tadorea, in collaboration with the Universidad Politecnica de Madrid - Telecommunications Faculty, is currently seeking a PhD candidate to undertake his/her PhD programme in the Cryptography field as applied to aviation and, in particular, as applied to air traffic management (ATM).
Today's aviation operations utilize a set of large, heterogeneous, widely-distributed systems which are sometimes even composed of isolated sub-systems. These are highly complex and very difficult to model analytically, especially considering the interactions between them. Often, detailed data about these systems is needed to understand and benchmark their performance, set up targets, make policies or even plan shared network resources. In the last decade, access has improved to this type of data as well as the computing infrastructures required to store and perform complex calculations with such data. Some of those calculations are, for instance, machine learning algorithms which have also proven their usefulness. Aviation researchers are implementing solutions based on the latest deep learning techniques (DataScience.aero, 2018).
That said, large datasets are not as available to aviation data researchers compared to data availability in other fields. Data science researchers face challenges related to the diversity of inhomogeneous data sources and the large volume of information to be handled and represented. However, the confidentiality of the datasets has historically been the most difficult barrier to data accessibility as most data owners have refused to provide access to significantly large datasets.
In this proposal, a potential approach is presented through the use of state-of-the-art cryptography techniques in overcoming this barrier. By painting some air traffic management data science problems as cryptography systems, and utilizing novel crypto-based solutions, the confidentiality barrier can be overcome without breaking confidentiality requirements. Private data could be used in ATM procedures and systems.
The selected candidate will join TADOREA´s research and development team in Madrid, Spain. His/her PhD thesis will be supervised by Pr. Dr. Victor A. Villagrá from the Telematics Department at the UPM-ETSIT who will be driving the research plan in applying state-of-the-art cryptography techniques to overcome data sharing barriers in the aviation sector. The combination of skills from the TADOREA and UPM-ETSIT teams will offer the candidate an ideal environment to develop his/her professional career in an environment with strong ATM-domain expertise and state-of-the-art data science, cybersecurity and privacy-preserving techniques.
The PhD programme is framed under the SESAR-Engage Knowledge Transfer Network and co-funded by it. As part of this network, the selected candidate will enjoy unique opportunities to participate in summer schools and conferences with other students and researchers in the field. The candidate will have access to a variety of datasets: from airlines, airports, air navigation service providers to other aviation stakeholders.
Talented and highly motivated individuals with a great dose of imagination, problem-solving skills, resourceful and data-driven passion are encouraged to apply.

Scientific goals:

  • Scientific goal 1 - Data privacy in aviation and ATM and challenges to improvements in procedures and system design
    The first scientific goal will be to advance the state-of-the-art in understanding how information sharing, using private datasets, can enable new ATM paradigms in performance assessment, policy and regulation and use of shared resources. Establishing the limitations of current solutions and proposing new systems and procedures shall lead to increased performance of the ATM along several KPAs. This in turn helps justify overhead in investment in research and development. Three different concrete scenarios will be defined corresponding to one different prominent challenge scenario in each line of work. Those scenarios should be representative of the line of work and simultaneously show significant barriers and impact potential.
  • Scientific goal 2 - Design of cryptographic systems for ATM
    The PhD candidate should then identify and design cryptographic systems that provide the functionalities sought after each ATM challenge scenario. The second scientific goal will be achieving a cryptosystem that guarantees accurate and secure computation, performs under a concrete communication infrastructure and improves the ATM performance.

Requirements are as follows:

  • A university degree in any of the related fields (Mathematics, Physics, Engineering), provided strong skills in Mathematics can be proven.
  • Basic understanding of cryptographic systems goals and design.
  • Strong background and experience in programming.
  • Experience with extraction, acquisition, preparation of data.
  • Fluency in English. Only candidates fluent in English should apply, as the interviews might be carried out in English.
  • Above all, a strong motivation in developing skills in privacy-preserving data analytics.

Other skills that may be relevant in the evaluation

  • Passion for data science on top of current thinking and trends
  • Proficiency in Python 3 and data science toolkits knowledge.
  • Familiar with distributed data processing architectures, e.g. Spark
  • Knowledge or experience with the air transport field.

Tadorea offers a unique set of benefits:

  • Immediate start - Candidates are mandated to start the PhD during Q1 2019. Only available candidates should apply.
  • Training, internal and external, on the work-related different technologies.
  • Integration in a highly qualified and collaborative international team with innovative thinking and agile working methodology.
  • Flexibility and good working conditions.

Gross salary: 22.000€
Interested candidates should send the following information to discovery@tadorea.com:

  • An up-to-date and detailed CV in pdf format. References, academic records and proof might be requested afterwards but they are not necessary for initial application.
  • A research motivational letter, carefully explaining why she or he is the perfect candidate.
  • Sharing any professional Internet presence is highly recommended, such as GitHub and/or Stack Overflow profiles, website-blog, portfolio, LinkedIn account, etc.
  • Any other relevant information supporting the application.

Download the PDF version

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!

All layers come together

Author: Luis Delgado

And everything comes together: the final integration of the three layers has been achieved.

Vista is now able to model the full ATM phases from the strategic planning of flights, schedules and passenger flows to the tactical execution and tracking of individual flights and passenger itineraries, while considering pre-tactical flight plans, ATFM regulations and passengers itineraries generation. The model capabilities allow us to model Current, 2035 and 2050 scenarios with different system evolution (Background factors) and testing selected factors (Foreground factors).

Vista combines different modelling techniques (agent-based modelling, data mining techniques applied to historical data, stochastic modelling, event-driven simulation) to produce a holistic view of the ATM system across the different ATM phases and stakeholders.

Traffic evolution through Europe is captured by the model while key metrics identified and tracked across the different scenarios for different stakeholders (airlines, airports, ANSPs, passengers and environment) for the three ATM layers.

Vista has shown its capabilities not only to assess key metrics for a given scenario but to capture their evolution across the different ATM phases. Vista has extended current classical metrics suggesting and estimating new indicators such as cost of uncertainty.

The close collaboration between universities, research institutes and key stakeholders (ANSPs and airlines) has proven successful on addressing the challenging topic of analysing in a holistic manner the trade-offs that emerge from applying different factors on different stakeholders. Vista has shown how complex interactions can be modelled and captured to assess the full impact of new regulatory and technological changes in the ATM system.

But this is not the end of this exciting adventure: Vista website and the different deliverables produced will remain available, further dissemination of model details and key results will be carried out and do not miss our final deliverable (D5.2 Final Assessment Report) and the report on the final results (D1.2 Final Project Results Report).

When airlines and ANSPs come together

SCLRG_DSC06380

The SafeClouds.eu project team came together for the last Consortium Meeting on November 6th and 7th in Majorca. Big thanks to Air Europa who supported and hosted the meeting.

For two days, five airlines (namely Air Europa, Iberia, Norwegian, Pegasus and Vueling) met with the 3 ANSPs participating in the project (Austrocontrol, ENAIRE and LFV), along with Eurocontrol, AESA and EASA (Spanish and European Safety Authority respectively). The last meeting was to collaboratively discuss their broad experience in safety. The group combination of airspace users, including pilots, ATCOs, FDM safety analysts, and safety authorities representatives provided a very inspiring and clear overview of present-day aviation safety analysis, its challenges and opportunities on the transition from event-driven to data-driven safety intelligence. These meetings provide critical insight for data scientists and are key to support the users-driven approach adopted for the project since its conception. The users have defined relevant safety scenarios where data science and ML techniques can provide an added value over the incident-analysis tools they currently have. The scenarios, Runway performance, unstable approaches, group proximity, and airprox drive the descriptive and predictive analytics for SafeClouds.eu. The consortium meetings are an important to present results from the data analysis and discuss and capture their requirements (both individually and in groups) for future work. As the final users of the data analysis work performed within SafeClouds.eu, it is key to ensure this alignment so their visualization dashboards provides relevant and usable ML tools.

SafeClouds is currently immersed in running the analytics based on three years of FDM data, which is merged with traffic data from Eurocontrol, weather data and surface radar data, among other data sources as required by the use case. This comes after investing the first months of the project to develop the legal and technical framework for securely managing and protecting the data. Considering this, the DataBeacon development, a data infrastructure that through several security layers and applying innovative cryptographic techniques, enables the data protection and merging while preserving its confidentiality. This Aviation ML platform, and the different implemented features and applications, enables data analysts to perform their analysis over various aviation data sources without actually having access to the databases. In all, this provides the necessary level of trust to the users and data owners.

With these developments, SafeClouds.eu is going one step further by providing breakthrough analytics on safety precursors based on ML techniques. This analysis will combine airline FDM data with traffic, ADS-B and METEO data, providing improved information on the scenario that individual airspace users cannot otherwise access. This provides airlines, ANSPs and airports an enhanced understanding on the main causes that influence a safety incident which can support decision making for developing customized mitigation actions. Interested in more details on the techniques and results? A follow-up post will be published soon.

Call for entry level or junior Data Scientists

Innaxis Research and Foundation (www.innaxis.org) is currently seeking for exceptional Data Scientists to join its research and development team based in Madrid, Spain. The position is directed towards talented and highly motivated individuals who want to pursue and lead a career in Data Science and Big Data outside of the more mainstream, conventional alternatives such as consulting or academia. Individuals with a great dose of imagination, problem solving skills, ambition and passion are encouraged to apply.

As a Data Scientist, you will mainly assist the team to understand, analyse and mine data, but also to prepare and assess the quality of such. You will also develop methods for data fusion and anonymization. Ultimately your goal will be to extract the best knowledge and insights from data, overcoming technical limitations and committing with regulatory requirements. You will also work closely with data engineers, you will help the engineers team to define the requisites for the Big Data architectures; covering the whole process of data gathering, processing and delivery. You will always need to be ahead and use the latest technologies and solutions for the ultimate performance and data insight.

About Innaxis

If not unique, Innaxis is at most not conventional: it is a private independent non-profit research institute focused on Data Science and its applications: most notoriously in aviation, air traffic management and mobility, among other areas.

As an independent entity, Innaxis determines its own research agenda and has now a decade of experience in European research programs with more than 30 successfully executed projects. New projects and initiatives are evaluated continuously and open to new opportunities and ideas proposed within the team.

Our team consist on a very interdisciplinary group of scientists, developers, engineers and program managers, together with an extensive network of external partners and collaborators, from private companies to universities, public entities and other research institutes.

Wish lists

Our team members work very closely, so broader knowledge means a much better coordination. The following list of skill defines the whole Data Scientist team at Innaxis. No not hesitate to apply, even if you don’t fulfil all the skills below. Hardly any single person does.

  • University degree, MSc or PhD on Data Science or Computer Science, or related field provided sufficient experience.
  • No professional experience required, although it might be positively evaluated.
  • Proficient in a variety of programming languages, for instance: Python, Scala, Java, R or  C++ and up to date on the newest software libraries and APIs, e.g. Tensorflow, Theano.
  • Experience with acquisition, preparation, storage and delivery of data,  including concepts ranging from ETL to Data Lakes.
  • Knowledge of the most commonly used software stacks such as LAMP, LAPP, LEAP, OpenStack, SMACK and similar.
  • Familiar with some of the IaaS, PaaS and SaaS platforms currently available such as Amazon Web Services, Microsoft Azure, Google Cloud and similar.
  • Understanding of the most popular knowledge discovery and data mining problems and algorithms; predictive analytics, classification, map reduce, deep learning, random forest, support vector machines and such.
  • Hands-on experience on most common visualisation tools: Tableau, Qlik, QuickSight, etc.
  • Continuous interest for the latest technologies and developments, e.g. blockchain, Terraform,
  • Excellent English communication skills. It is the working language at Innaxis.
  • Availability and wiling to travel to Europe and engage with our research partners and stockholders.
  • And of course, great doses of imagination, problem solving skills, ambition and passion.

Your benefits

The successful candidate will be offered a Innaxis’ position as a Data Scientist, including a unique set of benefits:

  • Being part of a young, dynamic, highly qualified, collaborative and heterogeneous international team.
  • Great flexibility and most excellent working conditions.
  • Long term and stable position. Innaxis is steadily growing since its foundation ten years ago.
  • A fair salary according to the nature of the institute and adjusted to skills, experience and education.
  • Independence, as a non-profit and research-focused nature of Innaxis, the institute is driven by different forces than in the private sector, free of commercial and profit interests.
  • The possibility to develop a unique career outside of mainstream: academia, private companies and consulting.
  • No outsourcing whatsoever, all tasks will be performed at Innaxis offices.
  • Opportunity to get around Europe while visiting our extensive partner network.
  • An agile working methodology; Innaxis recently implemented JIRA/Scrum and all the research is done on a collaborative wiki/Confluence.

How to apply

Interested candidates should send an email to recruitment@innaxis.org containing:

  • An up-to-date and detailed CV in pdf, references, academic records and proofs might be requested afterwards but they are not necessary for applying
  • research motivational letter, explaining carefully why she or he is the perfect candidate.
  • It is highly recommended to include any professional Internet presence, such as GitHub and/or Stack Overflow profiles, website-blog, portfolio, LinkedIn account , etc.
  • Any other relevant information supporting the application

You will be contacted further and a personal selection process will start.

Research documents clustering for CAMERA

The Horizon 2020 Coordination and support action CAMERA evaluates the impact of European mobility-related projects. The CORDIS database presents a high volume of unclassified project data to which manual methodologies would be impossible to apply due to the high dimensionality of the dataset. Also, not all of the projects presented in the CORDIS database are related to mobility.

These problems show the necessity of using algorithms to detect patterns within the corpus of documents presented in the database. By using automated methodologies in non-classified databases, we can amplify the scope of the project. This implies looking at all texts – including those normally unaffiliated with the topic of mobility but that may present soft relation with mobility areas. Also, by developing a data-driven statistical model, more metrics regarding the projects can be designed and assessed.

The rise of statistical Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence that aims to make computers “understand”, interpret and manipulate human language. NLP combines different disciplines including computer science, computational linguistics and statistical models in its pursuit to fill the gap between human communication and computer automation. The main challenges faced by NLP are speech recognition, natural language understanding and natural language generation.

Since the so-called “statistical revolution” in the late 1980s, NLP research has relied heavily on machine learning. The machine learning methodology focuses on using statistical inference, or automatically learning the rules of natural language through the data analysis of a large corpora of real-world examples. In machine learning, a corpus is a set documents with human or computer annotations that can be used to generate a large set of “features” that “teach” algorithms to “understand” the relationships within the documents.

CAMERA’s challenges

In CAMERA, we analyze more than 40.000 projects from 2007 to 2020. The document corpus is composed by concatenating the “title” and “objective” of each project, yielding a combined average text length of 4200 words per document. The documents can’t easily be labeled by topic, so no prior information is known regarding its content or the amount of topics covered. This prevents the use of supervised machine learning techniques to classify the documents by topic, adding a new layer of difficulty.

Furthermore, the texts are expected to present complex topical distribution with soft links between subtopics and documents belonging to multiple topics. Among all the research areas covered in the European Union, only mobility is relevant in CAMERA. This means that we need to identify the right topical distributions in a very sparse space. In this context, traditional unsupervised machine learning algorithms (e.g. clustering) do not perform well because they give fixed classes to texts, limiting the possibilities of hybrid topic distributions. In this scenario, more complex methodologies are required, such as using probabilistic clustering to generate topic models.

Topic modelling in CAMERA

Topic modeling is a well-known tool for discovering hidden semantic structures in a corpus of documents. Topic models learn many related words from large corpora without any supervision. Based on the words used within a document, they mine topic level relationships by assuming that a single document covers a small set of concise topics. Furthermore, the output of the algorithm is a cluster of terms that identify a “topic”. The topic model can be very useful for quickly examining a large set of texts and automating the discovery of topical relationships between them.

The most popular topic modeling algorithm is Laten Dirichlet Allocation (LDA). LDA is a three-level hierarchical Bayesian model that fits words and documents over an underlying set of topics. The main particularity of this algorithm is that it is an unsupervised generative statistical model that allows sets of observations to be explained by unobserved groups that break down why some parts of the data are similar. In this case, the observations are words collected into documents and each document can be presented as mixture of a small number of topics.

The most important aspect of LDA, the most relevant for the CAMERA objective, is that it is a matrix factorization technique. Any collection of documents can be represented in a vector space as a document-term matrix. The document-term matrix gives the frequency count of a word (represented as columns) in a Document (represented as rows). LDA decomposes this document-term matrix into two lower dimensional matrices: the document-topics matrix and the topic-terms matrix with dimensions (N,K) and (K,M) respectively where K is the number of topics (a parameter fixed by the analyst), M is the number of documents and N is the number of distinct terms.

By using the LDA topic modeling approach, we can analyze the corpus of documents and iteratively extract the documents with higher probability of belonging to mobility-related topics. After we extract the most relevant documents we can run a topic modelling again and extract the distribution of mobility-related subtopics. This will give us quantitative metrics such as the grade of coverage in specific research areas, correlations between topics and similarity metrics to find similar projects.

But the methodology presents another problem: with LDA being an unsupervised algorithm, we cannot “choose” which topics are interesting and which not. This is a huge issue when looking for a certain topic or distribution of topics. How did we solve this problem? Stay tuned for my next post on how we turned the unsupervised LDA methodology to semi-supervised.

1st CAMERA Workshop

What are Europe's mobility goals and how can progress towards these goals be measured? What would make up a feasible set of key performance indicators (KPIs) for mobility? And which major aspects of the work towards creating Europe's future transport system are addressed in the Mobility4EU Action Plan?
These are some of the key questions that were discussed during the workshop organised by the EU-sponsored CAMERA and Mobility4EU projects on 15th June in Brussels.
The aims of the “European mobility for the future: strategic roadmaps and performance assessment” workshop were to acquire feedback from experts from different mobility sectors on the development of a strategic roadmap for the European transport system (Mobility4EU Action Plan) and to discuss the research requirements, gaps, and bottlenecks shown up by this roadmap (Progress towards EU mobility goals).
A key output was that it is very easy to define things that should happen and very difficult to decide exactly how to measure them! For more details on the workshop results visit www.h2020camera.eu.
WORKSHOP MATERIALS: Handout | Final results

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 http://www.domino-eu.com for more info on the project.

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