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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.

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

European mobility for the future: strategic roadmaps and performance assessment

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 will be discussed at a 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 are 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).

The workshop will consist of two distinct sessions: the morning session on the Mobility4EU “Action Plan”and the afternoon one on the CAMERA “Progress towards EU mobility goals” topic. Both of these sessions will rotate through three parallel round-table discussions, and participants will actively contribute to the development of the Action Plan and to the identification of important aspects to be taken into consideration when designing Europe's future transport system.

Do you think you could bring something to these discussions? Are you interested in hearing what other experts think? Don’t miss this opportunity: take a look at the agenda and register for the workshop now! Admittance is free but places are limited.

EUROPEAN MOBILITY FOR THE FUTURE
STRATEGIC ROADMAPS AND PERFORMANCE ASSESSMENT

BRUSSELS, 15 JUNE 2018 Blue Point Brussels
Bvd. Auguste Reyers 80
1030 Brussels

www.h2020camera.eu
micol.biscotto@dblue.it
annika.paul@bauhaus-luftfahrt.net

www.mobility4EU.eu
marcia.urban@bauhaus-luftfahrt.net
beate.mueller@vdivde-it.de

SafeClouds Mid-term Review

 

On 11th of April, we had a successful mid-term review for our H2020 project, Safeclouds. The meeting was hosted by Eurocontrol in Brussels, with participants from all entities involved in the project.

Read Eurocontrol's post on the mid-term review here!

ANSPs, how changes on fuel price affect your airspace revenues?

AUTHOR: Luis Delgado

Vista allows to analyse complex scenarios with interactions between metrics of different stakeholders.

Flight plan generation and route selection

When airlines select their flight plans between a given origin and destination many different factors need to be considered, such as possible routes available, weather, aircraft performance or time required. Vista uses a data-driven approach analysing historical flight plans, routes between airports and aircraft performances to estimate the cost of operating those different routes.

As shown in the above diagram, the historical analysis of data allow us to generate a pool of two dimensional routes, probability distributions for cruise wind, speed and flight level request and length and duration of climb and descent phases. With this information, for each possible route we can estimate the 4D trajectories that the airline will plan and estimate the total operating cost of these possibilities.

A given flight will, of course, follow only one of the possibilities, so at pre-tactical level, the different flight plans options are prioritised considering their expected direct operating costs (as a function of flight time, fuel and en-route airspace charges). This selection is not deterministic as airlines not always will follow the apparent lest cost route and in Vista we are interested on reproducing realistic flight plan selections options, not the best option!

 

What if we change the cost of fuel?

Vista is a great tool to analyse the impact of changes of parameters such as fuel cost on the behaviour of the stakeholders in the system. In some areas of Europe, airlines face the possibility of selecting different routes which might incur on different airspace en-route charges and different fuel consumptions and flying time. This leads to trade-offs that can be captured by Vista. An example of one of those regions is western Europe and flights to-from the UK and the Canary Islands. As shown in this image, airlines can select more direct routes using the airspace of France, Spain and Portugal or operate longer routes which benefit from the low airspace usage cost of the Oceanic airspace.

The trade-offs between different metrics for the airlines can be explicitly computed by Vista as shown in the image below for different fuel price scenarios. With higher fuel cost, shorter routes tend to be selected leading to lower fuel usage but higher airspace en-route charges.

As Vista considers multiple stakeholders it is possible to assess the impact of these changes on the demand and expected revenue obtained by the different ANSPs as shown in the following images:

Expected revenue due to en-route charges variation for GCTS - EGKK flights

Expected revenue due to en-route charges variation for all of ECAC flights

The figure above shows the expected changes on revenues for the different ANSPs across Europe if changes of fuel price are produced. This illustrates how different parameters are interconnected for different stakeholders in subtle manners that can be captured by Vista: changes on fuel prices represent variations on routes preferences which might have an impact on airspace usage and revenues of the ANSPs!

SafeClouds presented in the EU-US workshop

Last January, a team of European and American entities organised a workshop on transatlantic research with the support of the European Commission. The event was hosted by the FAA in their facilities at the William J. Hughes Technical Center in Atlantic City. Those mostly in attendance were US and European companies interested in how the different research threads could be boosted through international cooperation.

Among the subjects discussed during the three day event, data analytics was mentioned several times as a interesting area with applicability to different areas in industrial research. Particularly, safety data analytics was covered in three presentations. First, the FAA presented their +10-year old programme ASIAS, which collects data from more than 40 carriers and has been leading the developments in this field for more than a decade. Second, EASA presented the Data4Safety programme, recently launched and in a proof-of-concept stage. Lastly, Innaxis presented the research programme SafeClouds.eu, including the latest technological developments and how they could complement the existing initiatives by providing and exploring new research avenues.

Infrastructure needed for Aviation Data Analytics

Author: Jens Krueger

Safety is key in aviation. To reach maximum safety, stakeholders are collecting a large amount of data for analytics. Ultimately, researchers want to not only evaluate the causal dependencies of safety critical events, but to also enhance operational efficiency.

Presently, such data is stored in isolated data silos. The goal of SafeClouds.eu is twofold: advance data-driven analytics for safety and efficiency and manipulate data outside of the silos to enable data sharing and merging between different stakeholders, including data owners. However, the infrastructure must ensure that personal or confidential data is not leaked to third parties; all while maintaining data sharing capabilities.
In order to address the requirements for data protection and analysis, the SafeClouds.eu infrastructure must enable the following data analysis paradigms:

  • Fusion of identified confidential data streams into a single de-identified data stream. Identified data is data that contains information that could be used to directly or indirectly (e.g. via linking attacks) expose personal data linked to a specific group of people or individuals.
  • Access to the de-identified data streams for SafeClouds.eu data analysis.
  • Information sharing of the analysis of restricted and confidential data from aviation stakeholders (airlines, ANSPs) for blind benchmarking.
  • Access governance should be in place, such specifics on data access (i.e. should be continuously monitored) and limitations.

The infrastructure architecture must reflect data protection requirements in order to guarantee the different data confidentiality levels. The physically-independent components are as follows:

Local system:
The local system sits at the premises of the participating companies (e.g. airlines and ANSPs) and stores raw datasets from different source systems. The data leverages other sources to comprise a 360-scenario dataset with enhanced informational context and processing. The global cloud system should provide such datasets. Finally, the dataset is de-identified and made accessible. Authorised third parties are allowed access only for data management and administrative tasks.

Dedicated private cloud:
Each participating party will be provided with a private segment of the cloud infrastructure that is logically and physically independent. It is used for de-identified data storage and analytics. Data scientists from SafeClouds.eu official partners will have access to the de-identified data under the data protection agreements.

Global cloud system:
The global cloud system is divided into two parts. The global storage will hold all open datasets (Meteo, ADS-B, SWIM, Radar). It will also ensure dataset quality and accessibility through pre-processing. In addition, it will grant access from the local systems and the dedicated private cloud. Note that the global processing infrastructure performs analytics on joint datasets from all dedicated private clouds. 

Figure 1: Hierarchical architecture of the SafeClouds.eu infrastructure

The SafeClouds.eu Cloud Infrastructure

The SafeClouds.eu cloud infrastructure is built on Amazon Web Services (AWS). One of the main advantages of AWS is that it consists of several datacenters located around the world. This enables SafeClouds.eu to reduce communication latencies by choosing the most appropriate datacenter locations. For example, each AWS datacenter is located within a region. Then, each region has several datacenters, or Availability Zones. Each Availability Zone is attached to a different part of the power grid, to mitigate a case of potential power outage damanage. Any distributed cloud application running in AWS must consider the tradeoff between fault-tolerance by placing nodes in different Availability Zones with keeping computational resources as close together as possible to enhance performance.  

For SafeClouds.eu, AWS enables the infrastructure to horizontally scale with an increasing number of stakeholders or increased processing or storage requirements.

To ensure security AWS Identitiy and Access Management (IAM) as well as virtual private clouds (VPC) and encryption for data in motion and at rest is used.

Remarks

The SafeClouds.eu infrastructure enables data protection, data sharing and flexibility. Data safety and security is key to gain trust from data providers; without it the overall project is at risk for success. This blog post stresses the importance of a distributed and secure infrastructure and gives a first look into how the overall infrastructure architecture is designed. However, alhough the base infrastructure technology supports scalability, security, and other factors, the most important challenge is to leverage and implement those technological capabilities. One of the main security threads is human failure, bugs, and wrong implementations. To account for user error, the infrastructure must be as automated as possible along with clearly defined and deterministic processes. In addition, each entry point must be defined and encapsulated while keeping accessibility and usability. SafeClouds.edu will be using this precise infrastructure for aviation data analytics, and will share those findings with the aviation and data science communities. 

Junior Researcher in Software modelling

Innaxis is currently seeking a software-modelling researcher (entry to junior level) to join its research team in Madrid, Spain. We look for a talented and highly motivated individual who wants to pursue a research career in the field of socio-technological systems modelling and simulation. Any individual with a great dose of imagination and problem-solving skills, along with algorithmic mind and passion are encouraged to apply.

As a software modeller, you will be developing algorithms to simulate the intricacies of socio-technological systems, such as the air transportation system and future concepts of European urban mobility. You will be applying several modelling techniques from agent based modelling, to event-driven simulation and stochastic modelling. You will also work with our Data Science team for hybrid approaches, eg. data-driven simulation tools and prescriptive analytics.

About

Innaxis is a private independent, non-profit, research institute focused on data science and its applications; most notably in aviation, air traffic management, and mobility. As an independent entity, Innaxis decides its own research agenda and has a decade of experience in European research programmes with more than 30 successfully executed research projects.

The Innaxis team consists of an interdisciplinary group of scientists, developers, engineers and programme managers. We work together with an extensive network of external partners and collaborators in Europe, including private companies, universities, public entities and other research institutes.

Skills

The ideal candidate complies with the following set of skills:

  • University degree on Computer Science, Mathematics, Physics and/or Engineering.
  • MSc or PhD not required but positively evaluated. Similarly, professional experience is positively evaluated but is not a requirement.
  • Proficient in Python and knowledge of other modern languages.
  • Understanding of different programming paradigms, eg. functional programming.
  • Expertise with the most common algorithm strategies for problem solving, eg. recursive, divide and conquer, dynamic programming, branch and bound, backtracking, greedy and heuristic algorithms.
  • Strong background in statistics.
  • Experience or knowledge of data science, eg. knowledge discovery in databases (artificial intelligence, machine learning), data visualisation.
  • Excellent English communication skills (written and oral), as it is the working language at Innaxis.
  • Great dose of imagination, problem solving skills and passion.

Knowledge of the European air transportation system is highly desirable.

Benefits

The successful candidate will be offered a position as a software-modelling researcher, including a unique set of benefits:

  • Become part of a young, dynamic, highly qualified, collaborative and heterogeneous international team.
  • Flexible working environment, schedule and location.
  • A horizontal hierarchy, small team of researchers working closely with both creativity and ownership.
  • Long-term and stable position; Innaxis has been steadily growing since its foundation ten years ago.
  • Salary adjusted to skills, experience and education.
  • The possibility to develop a unique career outside of mainstream: academics, private companies and consulting.
  • No outsourcing, all tasks will be performed at Innaxis offices.
  • Opportunity to travel in Europe following the research initiatives.
  • An agile working methodology; Innaxis recently implemented JIRA/Scrum and all the research is done on a collaborative wiki/Confluence.

Applying

IMPORTANT: Interested candidates should send their CV, along with an interest letter (around 400 words), and any other relevant information that supports their application to recruitment@innaxis.org. No applications will be considered otherwise.

If your application is accepted, you will be contacted and the interview process will start. We do not rely on a HR department and personally review and interview all candidates.

 

DATASET2050 goodbye

Dataset-post

_MG_9671

After the hundreds of days (36 months!) working hard in the project + corresponding proposal…

After the 3 successful events specifically organised by the project (London, Madrid, Belgrade)…

After the more than 30 DATASET2050 posts tackling mobility-related topics…

After the tens of scientific papers, deliverables and even a book chapter written around door-to-door and mobility topics…

After 3 always supportive European Commission project officers (Ivan, Mindaugas, Andreas)…

After the massive efforts dealing with the endless lists of mobility datasets reviewed, used and implemented in our model… (http://visual.innaxis.org/mobilityDataSETs/)

After hundreds of millions of passengers being modelled/measure in our door-to-door model (http://visual.innaxis.org/dataset2050/d2d-time-distribution/)

After interesting results about what is European door-to-door “reachability” in a certain amount of time (http://visual.innaxis.org/dataset2050/d2d-time-distribution/)

After interesting results in the “reachability” metric looking at the door-to-door price (http://visual.innaxis.org/dataset2050/d2d-price-map/)

our beloved CSA DATASET2050 have reached to its end!

But this is not the end! For future reference: our website with the public deliverables, presentations/videos during events, visualizations

and somehow a DATASET2050 continuation: H2020 CAMERA CSA kicked-off last month with a very similar consortium

PS: All the research done would not be feasible without the incredible team. In alphabetical order: Andrew, Annika, Dave, David, Gerald, Graham, Inés, Luis, Pete, Patricia, Paula, Samuel, Seddik, Ulrike and myself (Hector). Apologies for those missing in the pictures below!

_MG_9657

IMG-2687(1)

How long?

Dataset-post

With the imminent publication of the DATASET2050 project results, this seems an ideal moment to compare a recent trip with one of the key project outcomes, the average door-to-door travel time.

DATASET2050 modelling of passenger journeys within Europe has found the average door-to-door time to be 6 hours, some way off the Flightpath 2050 target of 90% of travellers being able to complete their journey within 4 hours. Of this 6 hour average, the time passengers spend at the departure airport is almost as long as the flight itself.

Out of interest, I timed each phase of a recent work trip between south London and central Madrid – from the front door of my home to the final destination. The journey took place on a weekday without undue disruption affecting any part of it.

Time taken for each phase journey:

  • Door-to-kerb: 64 minutes from my front door to the airport, travelling by bus and train, including walking and waiting time.
  • Kerb-to-gate: 78 minutes spent within the departure terminal, including check-in and security processes, plus walking, refreshments and waiting time.
  • Gate-to-gate: 175 minutes taken from aircraft boarding at Gatwick to alighting at Barajas. Of this, 109 minutes was in the air, the remaining 66 minutes on the ground (i.e. boarding, taxiing-out, taxiing-in and alighting).
  • Gate-to-kerb: 43 minutes taken from the arrival gate, through immigration and customs processes, plus walking time (note carry-on baggage only, so no waiting around to reclaim luggage).
  • Kerb-to-Door: 40 minutes from the airport to the hotel by metro, including walking and waiting time.

The overall door-to-door time comes out at 6 hours 40 minutes – worse than average! 27% of this time was spent in the air, with a further 34% spent at the departure airport (i.e. kerb-to-gate plus the ground portion of gate-to-gate at Gatwick). Admittedly some of the time spent in the departure terminal was unused door-to-kerb ‘buffer’ time (to allow for problems travelling to the airport), however a good proportion of the kerb-to-gate time was there ‘just in case’.

AUTHOR: GRAHAM TANNER

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