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.
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.
AUTHOR: Luis Delgado
Vista allows to analyse complex scenarios with interactions between metrics of different stakeholders.
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!
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!
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.
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:
The infrastructure architecture must reflect data protection requirements in order to guarantee the different data confidentiality levels. The physically-independent components are as follows:
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.
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.
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.
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.
The ideal candidate complies with the following set of skills:
Knowledge of the European air transportation system is highly desirable.
The successful candidate will be offered a position as a software-modelling researcher, including a unique set of benefits:
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 email@example.com. 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.
Are EU research and initiatives on the right trajectory to reach long-term goals in the (air) mobility sector? How far is Europe from the mobility goals envisioned for the future? How can synergies with other transport domains be fostered? These are some of the questions that the CAMERA (Coordination and support Action for Mobility in Europe: Research and Assessment) project will address in the next few years.
The project is funded by the European Commission under the Horizon 2020 framework programme and presents an innovative solution for assessing and reporting (air) mobility research and pursues win-win opportunities with other research domains, while guaranteeing strategic support to the European Commission and ACARE WG1.
After the hundreds of days (36 months!) working hard in the project + corresponding proposal…
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!
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:
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
Author: Paula Lopez (INX)
Machine learning is producing outstanding results although we know it is still far from emulating human intelligence. Applying machine learning techniques, including multi-level artificial neural networks (deep learning) to, for example, speech or image recognition has been continuously resulting in improved results (e.g. digital assistants like Apple´s Siri or Amazon´s Echo). In spite of the significant progress achieved so far, there are still some challenges that need to be resolved in order to be applicable in most industries. On one hand, we face a fragmented ecosystem, meaning that there is a gap between the data scientists and the domain experts working in each particular sector. In order to be able to convert data into knowledge, collaboration among both expertises is required. On the other hand, challenges related to data management and data analysis need to be addressed prior to implementing machine learning techniques in most industries. These challenges, just to name a few, include heterogeneous and distributed data sources, data validation, distributed data architectures, data security, scalability, real-time analysis and decision-support or data visualization.
However, we cannot fall into the error of assuming that a machine learning problem can be addressed through a generic standard application of a set of algorithms and techniques. Machine learning problems are highly case-dependent and, therefore, the purpose of the analysis needs to be carefully defined in advance. This is what we (at Innaxis) call Purposeful Knowledge Discovery which also was the title of the keynote speech made by Innaxis President Carlos Alvarez Pereira at the SESAR Innovation Days 2017 in Belgrade. And this is, precisely, the approach we follow at Innaxis in our data science research projects, like SafeClouds.eu: an H2020 project aimed at enhancing aviation safety through the application of data science techniques.
SafeClouds.eu includes a team of 16 partners including data scientists and engineers from several research entities (Innaxis, Tadorea, Fraunhofer, TU Munich, Linköping University, TU Delft and CRIDA) and a group of airlines, ANSPs and safety authorities (Iberia, Air Europa, Vueling, Norwegian, Pegasus, LFV, Eurocontrol, AESA and EASA). This group of airspace stakeholders is the user group of the project, in other words, those defining the questions for which they need data for gaining answers. These questions can be of three types: descriptive (what happened?), predictive (what will happen?) or prescriptive (what to do for what we want to happen). Once the questions are defined (SafeClouds.eu use cases) the team of data scientists and engineers work together and collaborate with users covering the full cycle of data science techniques: data management, data processing architecture, deep analytics, data protection, pseudo- anonymization, advanced visualization and user experience. As previously mentioned, every step has its own challenges as there are no data science standard tools to be transferred automatically from one field to another. Below, we outline just two challenges: fusion of proprietary confidential data and benchmarking among these competing stakeholders.
These are just some examples of the challenges the SafeClouds.eu team is facing in the field of aviation safety data analysis. The solutions offered by these techniques make them ideal to be applied to other fields such as fuel consumption but, again, the purpose of the analysis will determine the following necessary steps.
The human footprint is increasing fast and will —if not reversed— eventually lead to a collapse of the global economy. So say the authors of the new book Come On! which proposes an overhaul in the way that governments, businesses, financial systems, innovators and families interact with our planet.
Now, in cooperation with more than 30 members from the Club of Rome, authors Ernst Ulrich von Weizsäcker and Anders Wijkman, co-presidents of the Club, suggest possible solutions to the global ecological and social crises. At the core is the suggestion to develop a new Enlightenment for a "Full World": we can no longer depend on a societal model that was developed for a world of less than one billion people.
Humans and farm animals constitute 97 percent of the bodyweight of all living land vertebrates on earth so it’s not surprising that the remaining 3 percent of wildlife struggles to compete for land and for survival. Alongside an environmental crisis are social, political and moral crises. Billions of people no longer put trust in their governments, poverty has deepened in many countries, in the US the middle-class is rapidly shrinking.
Measuring our success on GDP growth has proven inadequate to the task and it also masks a growth in inequality between rich and poor. New indicators such as a Genuine Progress Indicator could more accurately measure economic welfare.
The present model of development is seriously flawed. Profit maximization – under the principle of shareholder value first – and saving the planet are inherently in conflict. The new Enlightenment must be characterized by a vastly improved balance between humans and nature, between markets and the law, between private consumption and public goods, between short-term and long term thinking, between social justice and incentives for excellence.
Carlos Alvarez Pereira (President of Innaxis and member of the Club of Rome) contributed to the report with a chapter on the Digital Revolution, highlighting that advances in technology will be crucial in order to cope with environmental degradation. However technological disruption must be analyzed beyond the current hype that digitization is clean and exponentially opening up new possibilities. Instead the effects on resources depletion, climate change, and employment have to be carefully considered and addressed for a true sustainable and inclusive technological disruption.
This book comprises many practical examples, success stories and opportunities for the “Full World”. A move towards a circular economy can help overcome mineral scarcity, significantly lower carbon emissions and increase the number of jobs. Regenerative agriculture will help stop soil erosion, enhance yields and build carbon in the soil. Efforts have to be made to rein in the financial sector by increasing capital reserves and control of money creation. Some insights can come from the Hopi tradition in North America, which developed sustainable agriculture and maintained a stable population size while avoiding wars.
Civil society, the communities of investors, and the research and education communities should become strong players in the necessary transformation.