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Mobility metrics and indicators rethought

Performance is about comparing some output of a system with some level of expectations. The issue of setting the right level of expectations is certainly a major issue by itself, but choosing the right metrics to measure is probably even more difficult.

This difficulty comes from the fact that Key Performance Areas (KPAs) live in a different world than Key Performance Indicators (KPIs). KPAs live in a qualitative world, where general ideas are thought to be important for human beings. For instance, ‘safety’. KPIs on the other hand belong to a quantitative world of ‘cold values’ — floats, integers — observed on the real world. Matching these two worlds is like getting into Mordor: first you think that it will be obvious, then you think that it will be impossible, and you finally pick a way because it is pretty much the only one available.

Indeed, the potential KPIs that one could imagine are fortunately severely restrained by reality and what we can observe in the system. For instance, in DATASET2050 we were trying to define an indicator for the ‘seamlessness’ of a trip, something which is important for all travelers without a doubt. Important, ok, but what is it exactly?

Seamlessness is about the perception of travellers. As a consequence, it is highly subjective, which by definition cannot be part of an indicator, because an indicator is meant to be objective. So instead of a top-down approach where we use the question ‘What would be the best metrics to measure in order to represent seamlessness?’, we are left with a bottom-up approach consisting in ‘Among the ones I can measure, what are the metrics which would be related somehow to seamlessness?’.

So, what can we measure? For many years now, sociologists and psychologists use the ‘cognitive load’ to have a measure of the effort needed by a brain to accomplish a given task. Seamlessness is about being able to forget the trip itself and not actively be forced to take decisions or looking for information for the continuation of the journey. We thus defined a first indicator, which is the total cognitive load of a given trip for the passenger as a measure of seamlessness. Ok, but how do you measure cognitive load in reality?

Well, you don’t, as least not on a large scale. And here comes the second step of the search for a good indicator: can we find something easily measurable which is an approximation for what would be a perfect indicator?

In the case of seamlessness, we have to go back to how the travel unfolds. For instance, what is the difference between:

1) depart from home, take a taxi, take a train, take a taxi, arrive at destination.vs:

2) depart from home, take a taxi, take a train, take another train, take a taxi, arrive at destination.

Easy: there is one train more. Ok, but what makes you choose the first option over the second if both have the same travel time, price, etc.? Well, the first is easier, right? You do not have to think about getting off the train, find the next one, wait, get in train, possibly struggling to find a spot to seat, etc. So the idea that the first one is easier than the second one comes ultimately from the ‘continuation’ property of the actions you are taking, which is associated with a low cognitive load dedicated to the journey. In other words, taking different actions during a trip is more annoying that taking only one action.

Following this idea, DATASET2050 defined the journey as a series of ‘phases’ and ‘transitions’. ‘Phases’ are typically long with a low cognitive load dedicated to the journey, whereas ‘transitions’ are short and require the active participation of the passenger in order to continue the journey. A simple indicator can then be defined as the number of transitions taken in a single journey, which is trivial to compute for nearly any journey, with very little data input.

A slightly more advanced indicator is to consider the time spent within the transitions — for instance, queuing times — compared to the total travel time. For instance, a small 45 minutes trip where one has to take three buses is quite tiring compared to a single-bus journey. This indicator requires more data, as the specific times in each of the segments are required. However, it is largely feasible to compute it with modern methods of data collection (e.g. GPS tracking). Giving a good balance between the measuralibity and its concetpual proximity with the initial KPA, this indicator is the one which has been selected as key performance indicator for seamlessness in DATASET2050.

In DATASET2050, we have gone through the exercise of finding the right indicator for all of the KPAs defined by ICAO, including safety, flexilibity, efficiency, etc. These concepts are sometimes too vast and need to be broken down into sub-KPAs, called “Mobility Focus Areas”. For all of them, several indicators have been defined, but we selected only one final KPI in the end per KPA. For instance, the KPA “flexibility” has been subdivided into “diversity of destinations”, “multimodality”, and “resilience”. Only on key indicator has been selected in the end, weighting the travel options by the distance between the potential destinations. All this work can be found in the public deliverable 5.1 of DATASET2050, soon available here

To conclude, the choice of a good indicator is thus dictated by the balance between the measurability of the metrics and its relationship with the overall concept. This is an important issue, as the indicators are then used by the policy makers to drive the system is a certain direction. And the quality of the indicator decides whether it is the right one or not.

Author: Gérald Gurtner (University of Westminster) as part of DATASET2050 post series

10 years later… and so much to come!

This year marks Innaxis’ 10th Anniversary. A most remarkable date that we are very happy to celebrate and share with you. This decade -and the 30 projects developed so far- have provided us the opportunity of creating solid relationships with trusted partners and strengthening those links through successful collaborative stories. We consider you as part of this trusted network of partners, colleagues and friends and we feel very grateful for it.

As you surely know, Innaxis was founded with the objective of finding applications of Complexity Science to address problems of real socio-technical systems. From that (quite abstract) idea, we have done our (exciting and challenging) way to become a reference research organization at the confluence of Complexity Theory, Data Science and Societal Challenges, mainly in the Aviation and Mobility sector. This rapid evolution has been possible, and even more stimulating, thanks to people like you and organizations like yours, who have accompanied us in this journey.

Addressing real-life problems through breakthrough innovation requires a clear focus on applied research and a close collaboration with end-users to ensure the solutions meet users´ expectations and help in solving their needs. To effectively apply some of the research results obtained, we launched some time ago a new venture called Tadorea as a spin-off of Innaxis. Tadorea focuses on applying Knowledge Discovery and Machine Learning solutions to the aviation sector, leveraging on massive data analytics. We strongly believe on the potential of this promising area, and so David Pérez has been appointed as General Manager of Tadorea to take the lead of our spin-off efforts. David will nevertheless stay very well connected to Innaxis by being nominated to its Board of Trustees.

And it is also time to give new responsibilities to people who have been with us for a long time and have shown an outstanding capacity and performance, combined with personal styles which are quite unique. Both Paula López-Catalá as Programme Director and Samuel Cristóbal as Science & Technology Director, are newly appointed to these most relevant functions. Together with them, David, Arantxa Villar as Finance Director and Carlos Álvarez Pereira as President, will integrate the Management Team to pursue our -even more- ambitious goals in this new era which we are much willing to share, explore and enjoy with you.

Workshop: Digital for Sustainability – In Need of a Disruptive Research Agenda

"Digital Transformation" is the buzz phrase of the day. Since the 1980s an explosive growth has happened in Information and Communication Technologies (ICT), and its become pervasive, bringing a perception of tremendous acceleration in technological innovation. There are also high expectations for the role of ICT in sustainable development. Concepts such as disruption, dematerialization and zero marginal costs contribute to the (up to now) false belief that becoming increasingly digital will lead to low resource consumption. However, research shows that the ICT sector itself is not environmentally friendly; it is the fastest growing contributor to emissions, it consumes large amounts of energy, water and critical resources, and produces equally vast amounts of harmful waste with minimal recycling.

To address the generic claim of ICT as contributing to a better and “green” world, there should be mutual recognition and cooperation between digital tech and sustainable development, especially to understand the significant effort needed to harness the power of ICT for human advancement. Digital technologies and sustainability have rarely been analysed together in a rigorous manner. The scientific literature about the nexus of these topics is, up to now worryingly thin, and in many aspects not yet addressing the right questions, much less the responses.

This issue demands a rigorous inquiry of issues at stake and the foundation of a research agenda that builds strong synergies aimed to act beyond current hyped assumptions.

Considering this, Innaxis would like to invite you to the “Digital for Sustainability – In Need of a Disruptive Research Agenda” workshop. This event will be organised during the World Resources Forum on Tuesday 24th October 2017 in Geneva.

The goal of this workshop is to ignite a community of interested parties, who work on interdisciplinary research and action agendas, and to enable the alignment of digital technologies with the goals of sustainable development.

Speakers: Carlos Alvarez Pereira, Ladeja Godina Košir 
Workshop Organisers: Innaxis Research Institute and Texelia AG
Workshop Co-Organiser: Circular Change
Workshop Chairs: Soumaya El Kadiri (Texelia AG) and Joséphine von Mitschke-Collande (Innaxis Research Institute)

Date and time: Tuesday 24th October 2017, 16h30 – 18h30

Venue:
Centre International de Conferences (CICG)
Rue de Varembé 17
1211 Genève - Switzerland

 

Vista tactical model – Mercury: because passengers matter

Over the next decades, EU mobility is expected to progressively evolve from the gate-to-gate focus currently prevalent in the aviation and ATM industry towards a seamless and efficient door-to-door-orientated vision.  The paradigm shift from gate-to-gate (hence aircraft centered) to door-to-door (passenger-oriented) is present at virtually all strategic research documents and agendas. The paradigm shift is here to stay. From a passenger perspective, which of the following scenarios create more impact?:

  • Scenario A): a 8 minute delay in an aircraft arrival time with no connecting passengers
  • Scenario B): a 5 minute one that prevents a significant number of passengers doing a connection in that airport and subsequently expand their door-to-door trip in more than 10 hours

How can that impact be predicted in terms of time and cost? One of the very first research exercises was the POEM project (SESAR 1- WPE) etc. This project was the original seed of Mercury. Mercury has been afterwards improved, validated and completed in other reseach initiatives for SESAR and European Commission, reaching its current door-to-door status.

What is mercury?

Mercury is a modelling and simulator tool - a framework capable of measuring the performance of the air transport network. It provides a wide range of performance and mobility metrics, capable of describing in detail different air transport scenarios.

Mercury draws on extensive data, drawn from a wide range of industry sources, including airlines, airports and air navigation service providers. Mercury's data models have been demonstrated through over 5 years of research and development, plus industry consultation.

How passenger matter in mercury?

Mercury is the first air transportation network simulator that puts passengers in the centre. Each day of simulation the itineraries of more than 3 million passengers are reproduced. Each passenger has its individual profile, ticket and decisions to make. According to EU regulation 261/2004 passengers are compensated by delay and cancellations. Extended delays, aborted journeys, overnight stays there are all part of the Mercury simulator.

Of course airlines play a major role as well, Mercury incorporates costs models for canonical airline categories. Each of the airline decision of waiting for certain passengers, cancel a flight or even board the passengers and send a ready message even when a ATFCM slot was assigned is taken according to each airline rational cost model.

The secret ingedient: a spice of randomness

There is no way one could develop a simulator like Mercury taking into account every detail in the air transportation system. Some process are just too complex or simply put we do not understand yet. Whilst others are just exogenous factors far beyond the reach of the air transportation system. 

But what if we could use a different approach. In Mercury each day of operations is repeated, introducing small variations representing everyday uncertainty and exogenous factors.

Ultimately, small changes lead to completely different day of operations, delays and cancellations. Just similarly to what happens with some chaotic systems, the sensitivity to the initial conditions allow to explore overall trends and stable status, in some cases called emergence.

Interested in reading further info about Mercury? Click here to visit the website.

Author: Samuel Cristóbal (Innaxis)

Entry level/Junior Data Scientist or Data Engineer

Innaxis is currently seeking for Data Scientists and Engineers to join its research and development team based in Madrid, Spain. Talented and highly motivated individuals who want to pursue and lead a career outside of the more mainstream, conventional alternatives. Individuals with a great dose of imagination, problem solving skills, flexibility and passion are encouraged to apply.

  • As a Data Engineer, you will help the team to design and integrate complete solutions for Big Data architectures; from data acquisition and ETL processes until storage and delivery for analysis, using the latest technologies and solutions for the ultimate performance.
  • 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, despite technical limitations and committing with regulatory requirements.

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 ones. New projects and initiatives are evaluated continuously and open to new opportunities and ideas proposed within the team.

The Innaxis 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.

Skills wanted

Our team work very closely on a daily basis, so a broader knowledge means a much better coordination. Therefore, there is a unique list of skills ideally wanted for both positions. Those skills would be then weighted/assessed as requirements or “bonus points” according to the candidate’s position of interest, i.e. Data Scientist or Data Engineer.

  • University degree, MSc or PhD on Data Science or Computer Science, or related field provided all other requirements are met.
  • 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 or 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.
  • Continuous interest for the latest technologies and developments, e.g. blockchain, Terraform,
  • Excellent English communication skills. It is the working language at Innaxis.
  • And of course, great doses of imagination, problem solving skills, flexibility and passion.

Benefits

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

  • Being part of a young, dynamic, highly qualified, collaborative and heterogeneous international team.
  • Great flexibility in many aspects -including working hours, compatibilities and location- and most excellent working conditions.
  • A horizontal hierarchy, all researchers’ opinions matter.
  • 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 with continuous revision.
  • 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: academics, private companies and consulting.
  • No outsourcing whatsoever, all tasks will be performed at Innaxis offices.
  • An agile working methodology; Innaxis recently implemented JIRA/Scrum and all the research is done on a collaborative wiki/Confluence.

Apply

Interested candidates should send their CV, a research interest letter (around 400 words) and any other relevant information supporting their application to recruitment@innaxis.org You will be contacted further and a personal selection process will start.

 

FDM Raw Data: Why Binary Data and How to Decode It?

Authors: Lukas Hahndorf & Javensius Sembiring (TU Munich)

SafeClouds.eu gathers 16 partners for research collaboration with a wide and diverse group of users, including air navigation services providers, airlines and safety agencies. SafeClouds.eu encourages active involvement from users, as the project aims to apply data science techniques to improve aviation safety. SafeClouds.eu is unique as it involves data combination and collaboration from ANSPs, airlines and authorities in order to improve our knowledge on safety risks, all while maintaining the confidentiality of the data. This safety analysis requires comprehensive understanding of various data sources, and supports the use case analysis as selected by the users.
The basics of the FDM data, as one of the main data sources for the project, is outlined in this post.

Onboard Recording

A large amount of data is recorded during civil aircraft flights. Apart from the “Flight Data Recorder” that is mainly used for accident investigations (widely known as “Black Box”), there are also recorders for regular operations. These recorders are often called “Quick Access Recorders” (QAR). QAR data is analysed in terms of safety, efficiency and other aspects in Flight Data Monitoring activities for airlines and is furthermore an integral part of the research project SafeClouds.eu.
image2017-7-14 17_54_24

Figure 1: Example for a QAR (Source: https://www.safran-electronics-defense.com/aerospace/commercial-aircraft/information-system/aircraft-condition-monitoring-system-acms)

Aircraft are very complex systems with a large number of sensors constantly recording measurements. Important parameters regarding the aircraft state, including position, altitude, speed, engine characteristics and many others are recorded by the QAR. Depending on the aircraft type and airline, the number of recorded parameters can reach several thousand.

As a digital device, the recording uses binary format. In other words, if we look at the QAR data we would only see a bit stream, i.e. a sequence of 0 and 1. In order to use the data and investigate, for example the aircraft position, two additional components are necessary. First, logic is needed to determine how the data is written into the bit stream. This is given by an ARINC standard and two versions are presently used: ARINC 717 standard is used for older aircraft types and the ARINC 767 is used for newer aircraft types. Second, a detailed description of the location of any considered parameter in the bit stream is needed. This is given by a “dataframe” which is a text document of up to several hundreds of pages.

image2017-7-14 17_54_34

Figure 2: Overview (Source: “Flight Data Decoding used for Generating En-Route Information based on Binary Quick Access Recorder Data”, Master thesis, Nils Mohr, Technical University of Munich)

File Formats

One of the advantages of data stored in binary format is storage efficiency. The size of the same flight data file stored in binary format compared to being stored in engineering values (e.g. in a CSV file) might be ten times smaller. Considering the research project SafeClouds.eu or the shared framework for flight data such as ASIAS of the FAA, FDX of IATA or Data4Safety of EASA which collects millions of flight data, an efficient storage is obviously needed.

However, storing flight data in binary format then requires an efficient way to transfer the binary data into engineering values. Considering the bit stream logic, two parts are necessary. First, the bit stream logic (provided by the ARINC standard) needs to be represented in a decoding algorithm. Second, the dataframe information, i.e. which parameter can be found in which part of the bit stream needs to be accessible to the decoding algorithm.

Decoding

Recorded parameters have different characteristics. For example, they can be numeric, alphanumeric or characters. Depending on these characteristics, different decoding rules have to be applied. As an example, a temperature recording of 36.5 °C with a linear conversion rule is considered in the following figure.

image2017-7-14 17_54_46

Figure 3: Simple Decoding Example (Source: “Flight Data Decoding used for Generating En-Route Information based on Binary Quick Access Recorder Data”, Master thesis, Nils Mohr, Technical University of Munich)

Starting from the bit stream, just specific binary values are relevant for the temperature recording. As mentioned above, this information can be found in the dataframe. The combination of all bits leads to a number in the binary system, which can then be transferred into the associated decimal value. Applying the conversion rule for linear parameters gives the result 36.5. Information about these rules as well as the unit, in this case degree Celsius, can be found in the dataframe.

Summary

The data that is recorded by civilian aircraft in their daily operation contains valuable information that can be used for airline safety analyses. Due to the nature of the recording, the data is generated in binary format. To make the data accessible and readable for the analysts, a decoding algorithm is applied. For the development of this algorithm, information about the recording logic and for all the considered parameters must be available.

Author: Lukas Höhndorf (TU Munich)

VISTA: priorities and building a credible model

Setting priorities and building a credible model

In Vista, capturing the level of development of the ATM system in the 2035 and 2050 horizons is critical, and we need to ensure that the most relevant scenarios for stakeholders are prioritised during the project. A consultation with relevant expert stakeholders has been conducted to help us with these tasks. The consultation focused on obtaining the experts’ view on key aspects of the project, namely: identification of potential missing metrics for the different stakeholders; prioritisation of the metrics generated by the model; identification of potentially missing factors and possible values considered for them; ranking of foreground factors (see previous blog) by relevance; ensuring that none of the factors identified as background factors should instead be considered as foreground; prioritisation of background scenarios and identification of the level of maturity of the system for 2035 and 2050 and, finally, understanding which particular results produced by Vista would be of particular interest to experts and stakeholders. The consultation questionnaire comprised twelve detailed questions and was targeted at high-profile experts in the ATM field.

The result of this activity allowed us to prioritise the metrics and scenarios that will be modelled and ensured that we had not missed any relevant source for regulations or technical evolution of the system. A second consultation is planned in order to review the firsts results obtained with the model. With these consultations, Vista maximises its impact on the community, addressing the topics that are relevant to stakeholders and validating the results obtained.

Another strength of Vista is the inclusion of key stakeholders, not just as consultation body, but as core partners in the project. Vista benefits from such partnership with airlines (SWISS, Norwegian and Icelandair), a FABEC ANSP (Belgocontrol) and airport experts (EUROCONTROL). Dedicated site visits have been carried out in Reykjavik, Oslo and Zurich to further understand the airlines’ business models, needs and projected system evolution. These visits also allowed the modelling team of Vista to have first-hand access to the strategic, pre-tactical and tactical management of airlines’ operations. This access ensures that the model captures the impact of the different factors as closely as possible to reality. Moreover, the airlines’ involvement in the project provides crucial data and validation of preliminary results. Similarly, planned meetings in Brussels and London with Belgocontrol and EUROCONTROL will ensure that the vision of ANSPs and airports are properly considered in the model.

ECTL_logo      belgocontrol_logo    Icelandair_NO

 

 640px-Swiss_International_Air_Lines_Logo_2011.svg   norwegian-logo

Moving the people to the terminal? Why not move the terminal to the people?

The question of ground access to airports is the object of many studies. How do we get people to the airport quickly, efficiently and sustainably? A previous blog post touched on the many different means people use to accomplish this part of their journey.

One of the major options that is often pursued is the creation of a train line joining the airport to its host city. However, while this city is often the most frequent origin/destination of travellers using the airport, it by no means accounts for the majority of surface access/egress journeys.

For example, fewer than 54% of access/egress journeys to/from London Heathrow (LHR) come from the whole of Greater London, much less from the central part London that is served by the Underground and the Heathrow Express train. In fact, the Express trains between them only account for 13% of terminating passengers which, while certainly not negligible, leaves seven out of every eight passengers to take a different mode of transport. 61% of passengers to LHR use private transport. After all: who want’s to join all of the congestion travelling into the centre of a big city from the suburbs (or beyond) where they live, just to get the train out to the airport?

So what’s the solution? Heathrow Airport Ltd (HAL) is pursuing a plan as part of its “Heathrow 2.0” initiative to ensure that the 100 largest cities in the UK are linked to LHR by train with no more than one connection. More rail access will be available when the “Crossrail” line that will run between Brentwood and Reading through London has been completed.

We can also make it easier to park, reducing time to do so and the walking time needed to catch the shuttle to the airport. Stanley Robotics, a French startup, is proposing a robotic valet that will pick your car up at the entrance to the car park and park it for you, fetching it for you when you return.

Enter the toast-rack.

Airports like LHR are moving to the “toast-rack” layout. With the creation of Terminal 5 (T5), satellite terminals (5B and 5C) are placed between, and perpendicular to the runways, fed from the main terminal (5A) at the end. An air-side underground train takes passengers from the check-in and security in 5A to the satellites.

 Annotated LHR

The new “Queen’s Terminal” will eventually have the same design.

But when T5 was built both the Underground Piccadilly line and the Heathrow Express were extended – parallel to the air-side underground train serving the satellites – to bring passengers land-side to 5A. Move them west to move them back east – not very efficient!

Why not move the terminal, instead of the people?

Isn’t it time we started re-thinking how we design our airports? Is there any reason why the terminal needs to be where the runways are? Previously, it has been possible to check your bags in at a railway terminal before boarding your train/bus to the airport (at London Victoria for Gatwick, for example) but this is not really moving the terminal to the people.

Crossrail

With Crossrail, a new underground branch line is being built from the London-Reading line (access from London only) to LHR, bringing more land-side passengers (but only some of them – many will still come by car) – but inconveniently not serving T5. Now if Terminal 5 check-in, baggage claim, security, etc. had been constructed on the London-Reading line, the same investment could have paid for an air-side line that would link in with the T5 air-side line and carry every passenger to 5A, 5B, 5C and beyond.

Imagine a terminal only a few kilometres from the runways, where the train line and the motorway access already exists. For LHR, this could have been at Iver, in an area served directly by the existing train line (and the same two motorways as LHR is) but with enough room for all of the airport’s needs with space for a complete airport business, hotel and shopping city without anything being bounded by runways, taxiways, gates, service areas etc. The car parks could have been right next to the terminal instead of, as is the case with T5, being so far away that the airport has had to spend £30m to provide personal transport “Pods” to transfer people to and from the terminal.
Annotated Iver

This air-side line could even be extended to a second or third terminal, closer to other access points to the city. In the case of a multi-airport city like London, one could even envisage an air-side railway linking all of the air-sides (Gatwick, Heathrow, Luton and Stansted), and their displaced terminals, enabling passengers to use the terminal nearest to them and to fly from the runway of their airline’s choice.

Complete separation

Having only air-side activities where the runways are and leaving the land-side activities where the people are is a much cleaner solution that reduces airport access time, and provides more space for land-side activities. And once the concept of separating the land-side from the air-side is accepted, the air-side/runways can be located somewhere where fewer people will be annoyed by the noise. If staff and passengers have to use a land-side terminal access miles from the air-side, the pressure to live near the airport for easy access would move away from the runways, causing less encroachment into noise-impacted areas.

For Heathrow, it’s too late to think of implementing such a system now; the investment in Heathrow Express, the Crossrail link, T5, the Queen’s Terminal, the Pods, etc. has already been made.

But when the next new airport is built, perhaps it would be worthwhile to think that, instead of annoying both travellers and residents by building terminals and runways together, it would be much better to put the terminals close to the people and the runways far away from them.

Author: Pete Hullah (EUROCONTROL) as part of DATASET2050 project

European door-to-door mobility workshop! 20th Sept, Madrid

European door-to-door mobility workshop, took place 20th September 2017, 10:30-16:00

The event, hosted at Madrid Campus (Google Space) mixed active debate and participation from all the workshop attendees with presentations from top entities in the field. See presentations below, some pictures will be available in the following days: agenda :

Videos here soon!

 

 

Note: The DATASET2050 workshop was organized planned in tandem with the  the ACARE WG1 meeting (restricted to ACARE members - 19th afternoon), and the CATER open event (19th morning), both  in ISDEFE premises (Madrid)

 

-Further logistics info (hotels, transport) available here: Download our complete guide.

 

Are regulatory and business changes aligned with key ATM objectives? (Introducing the Vista project).

How will different regulatory and business changes affect the KPIs of ATM stakeholders in the 2035 and 2050 horizon? Are the various foreseen changes aligned to obtain improvements in key indicators? Will trade-offs emerge from different policies to be implemented? What is the impact of technology changes on different economic developments?

Vista considers these questions and examines the effects of conflicting market forces on European performance in ATM, through the evaluation of impact metrics on four key stakeholders (airlines, passenger, airports and ANSPs), and the environment. The project comprises a systematic, impact trade-off analysis using classical and complexity metrics, encompassing both fully monetised and quasi-cost impact measures. Vista will model the current, 2035 and 2050 timeframes based on various factors and their potential evolution.

The factors modelled in Vista influence the stakeholders’ choices: prices of commodities and services, regulations from national and supranational entities, and new technologies are all part of a complex socio-economic system that results in evolving business models, passenger choices, etc. These factors are divided between regulatory and business factors. Business factors may affect technology uptake and economic changes. Regulations, on their turn, may act as enablers of the technological and operational changes, e.g., the Single European Sky regulatory framework, or may directly affect the performance of some stakeholders, such as air passenger rights.

The different factors considered have been obtained from a literature review of regulations, projects, technological and operational changes. Concerning the regulations, the different areas of the ATM network and regulations applying to them have been reviewed. Communications and strategies laid down by, or foreseen by, regulatory trends have been used to identify the possible evolution of these regulations. The main source for the business factors are the SESAR projects, in particular, the high-level goals of SESAR described in its Master Plan (Ed. 2015), as well as more precise information related to the SESAR workpackages. The expected impacts of operational sub-packages in SESAR will be used to identify the impact of these on the evolution of KPIs. Some more long-term R&D research activities are also considered, in particular to be used in the 2050 scenarios of Vista. Other business factors include the price of fuel, the business models of the airlines, and changes in demand linked to the socio-economic development of Europe. Regarding the latter, many factors will be considered as closely linked and the diverse possibilities of development will be significantly influenced by outputs such as the STATFOR forecasts.

From_forces_to_scenarios_v2

In the Vista model, regulatory and business factors are classified between foreground and background factors. Background factors are grouped to generate background scenarios onto which the foreground factors will be tested. These background scenarios, identified below, define different possible evolutions for the 2035 and 2050 horizons and have been defined to identify the impact of the technology on different economic development scenarios.

Period Name Technology development Economic development
Current Current Current Current
2035 L35: Low economic, Low Techno Trajectory-based performance as defined in SESAR Low economic development
M35: High economic, Low Techno Medium economic development
H35: High economic, High Techno Performance-based performance as defined in SESAR
2050 L50: Low economic, Low Techno
M50: High economic, Low Techno High economic development with an increment on environmental-friendly passengers
H50: High economic, High Techno Enhanced performance-based performances as defined in SESAR

Examples of foreground factors, the impact of which will be individually assessed, include: regulatory changes on passenger provision schemes, fuel charges, or the introduction of smart ticketing. Foreground factors can also be grouped in higher-level categories to identify the impact of different policies on the scenarios: environmental mitigation policies (e.g., emission scheme and noise pollution regulation), regional infrastructure usage (e.g., airport access, regional infrastructure development), passenger focus modifications (e.g., passenger provision schemes and reacommodation tools) and Single European Sky (e.g., 4D trajectory management, traffic synchronisation, airspace charges). The qualitative impact of the factors, both foreground and background, on each part of the model has been identified.

VISTA_architecture_pre

Vista will necessarily model all ATM phases: strategic, pre-tactical and tactical. Factors will have different impacts on these time scales. The Vista model has been created following these temporal layers. A scenario, defined as an instantiation of foreground and background factors, will be executed by the model. The strategic layer, will use an economic model to balance the strategic demand and capacity of the different elements in the ATM network and schedules will be provided to the pre-tactical layer. The pre-tactical layer will generate flight plans, passenger itineraries and ATFM regulations. These flights and passenger itineraries will be executed tactically using the Mercury model . Mercury model has been developed on previous projects (POEM , ComplexityCosts) and allow the simulation of flights and passengers itineraries obtaining not only traditional flight-centric metric but also passengers focus ones. See our next blog (August) for more information regarding Vista tactical layer. Being a stochastic model the output of the different layers will be consolidated to analyse the results and understand the horizontal and vertical trade-offs identified. Finally, a learning loop will be used to give feedback to the strategic layer on the metrics obtained and, based on the initial expectations of the model, to adjust strategic behaviour. This will ensure that, after several iterations, a stable and realistic realisation of the scenario is obtained.

In order to capture the impact of the different factors on the model and the evolution of the system, dedicated site visits and consultation with experts have been performed (see next blog entry for more details). The Vista approach and methodology was been presented at the 2017 World ATM Congress (7-9 March 2017, Madrid) and at the 2017 ART Workshop (26 April 2017, London).

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