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

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.


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.


Towards user-centric transport in Europe – Challenges, solutions and collaborations

The EU projects Mobility4EU ( and MIND-SETS ( jointly organized the event “Towards user-centric transport in Europe – Challenges, solutions and collaborations” in Brussels in May. The aim was to bring together stakeholders from different areas to discuss innovations in transport and mobility addressing future challenges for the European transport system ( One part of this event were several interactive sessions in which the participants discussed various issues relating to the improvement of current transport practices. The partners of DATASET2050 acted as facilitator of the session “Enhancing multi-modal collaboration between service providers”, in close collaboration with the EU project PASSME.

Applying the “World Café” approach, this session focused on the better integration of different service providers in order to facilitate a seamless journey for the passenger (see the figure below for examples of existing and future intermodal concepts, these will be addressed in more detail in upcoming deliverables of DATASET2050). Within different groups, simulating a round table in a relaxed atmosphere, participants of the workshop discussed a variety of approaches that reduce friction along the journey. One aspect which had been discussed extensively is the establishment of a legal framework accompanying the closer collaboration of service providers. Legal issues concern the liabilities in terms of delays, lost luggage or reimbursements. Furthermore, data sharing both between providers as well as between providers and passengers had been highlighted as being a main contributor to a seamless journey. Passengers receiving real-time information as well as alternative travel options along the journey have to be enabled by a shared data pool all providers have access to. This, however, requires a clearly defined set of rules and responsibilities regarding data handling and passenger privacy issues in order for all parties to participate. In terms of passengers’ data privacy concerns, studies have shown that passengers agree to share their personal data with service providers if both data security can be guaranteed and their journey and travel experience is improved.

In terms of collaboration, the “last mile”, which covers the distance between the final destination and the last stop of e.g. public transport, often constitutes a bottleneck within the seamless journey. Therefore, ideas within the workshop circled around passengers pooling their demand and sharing a taxi in order to get to the airport or the train station. Or autonomous cars providing an option to cover the last segment of the passenger journey by efficiently assigning resources to the time and location they are needed. Overall, participants argued that there is no single solution which fits all passenger needs but that customization and differentiation across distinct groups have to take place.


inter-modal conecepts


9292 /
Airbus /
Airportbus Munich /
Air-Rail /
Air-Train /
Ally /
Allygator /
Amtrak /
Bike Train Bike /
Blacklane /
Boring Company /
Bus and Fly /
Cabify /
Cambio /
Car2Go /
Chu Kong Shipping Enterprises /
Citybike /
Citymapper /
Clipper /
Cotai Waterjet /
Deutsche Bahn /
DriveNow /
Drivy /
Ehang /
Emmy /
Ferrara Bus & Fly /
Flinkster /
Flightcar /
Fraport /
Free2Move /
Gett /
GoEuro /
Hannovermobil /
Hyperloop One /
Iberia /
Kobe – Kansai Bay Shuttle /
Lyft /
Memobility /
MobilityMixx /
Moovel /
Mozio /
Mycicero /
Mydriver /
Network Rail /
Octopuscard /
Oystercard /
Rallybus /
Rome2Rio /
Stadtmobil /
Swiss Helicopter /
Tamyca /
Taiwan High Speed Rail /
Thalys /
Touch & Travel /
Turo /
Tuup /
Uber /
Union Station Denver /
Urbanpulse /
Velib /
Voom /
VW (Sedric) /
Wideroe /
Wmata /
Yugo /
Zee Aero /

Augmented reality and data visualization (in aviation)

Present-day technology is so powerful that the perception of reality can be easily and realistically modified with IT tools, providing users withan experience beyond “simple” reality. This is achievable by mixing real-world environment elements supplemented and/or augmented by computer-generated inputs. The current post unpacks this topic, focusing specifically on the data visualization aspects. In brief, augmented reality can take two approaches:

  • First, inventing totally new scenarios, in which the user becomes part of a “parallel universe”. Supplementing the real-world environment with an unreal one; either a virtual place (video game) or a different location (i.e. another real location). This is the case of futuristic 90’s and early 2000’s alike head-mounted displays with users’ eyes looking at full screens recreating other places. The ergonomics aspects are usually modest for most of the applications due to the head-mounted displays weight and size.


  • The second, and closer to “data visualization” area is the so called “mediated reality”. The real-world environment enhanced by virtual elements displayed in glasses, windscreens etc. In them, additional information/data is provided. The real challenges is to decide what, how and when to display the information, without requiring users to look away from their usual viewpoints, while providing extra value. The integration and user experience is much more natural and enjoyable than the fully immersive systems.
Research project Augmented Reality - contact-analogue Head-Up Display (10/2011)

Research project Augmented Reality – contact-analogue Head-Up Display (10/2011)

In this context, one of the very early examples of head-up displays can be found precisely in aviation, almost 80 years ago. In 1937, the German ReviC12/A fighter aircraft included a basic reflector sight indicating some basic aircraft magnitudes such as speed and turn rate, to reduce the (visual) workload of pilots in case of extreme maneuvering
Nowadays virtually all modern fighters (F18, F16, Eurofighter) use head-up displays. The most modern versions (F35) do not have head-up displays, and instead include helmet mounted displays, ensuring the proper orientation of the user’s head, for all circumstances.
One of the trending topics of augmented reality within aviation is its usage in air traffic control (ATC), particularly in Tower environments. Below are two common approaches:

  • Visual information is enhanced to ease identification and tracking of aircraft. This includes tools similar to head-up displays and/or helmets-displays that enhance the information (providing for instance, aircraft ID, scheduled times, etc). This approach could be extremely useful in low visibility conditions by facilitating the tower ATCOs tasks. It also avoids dividing attention between the primary visual field (the window) and the auxiliary tools (surface radar, strips etc).

Screen Shot 2017-02-23 at 15.05.29maxresdefault

  • The extreme version is a complete virtual control tower, the so called “remote tower”. ATC would have remote control rooms with video-sensors on-site, including augmented reality enhancements. The synthetic augmentation of vision increases the situational awareness at the airport, especially during poor visibility conditions, or blocked line-of-sight areas due to airport geometry. It additionally provides benefits in terms of cost saving (no need to build and maintain control tower facilities) and a more efficient use of human resources (potentially serving multiple airports with low traffic events from a centralised location). Research in this field started in FP6 project “ART” and is now being progressed by SESAR WP6. In fact, Örnsköldsvik/Gideå airport is the first on the world deployment of remote tower, in late 2015, by the Swedish LFV. In US, Fort Collins-Loveland Municipal Airport was the first approved and tested airport with a remote tower in 2016.

Screen Shot 2017-02-23 at 15.08.47
For the air passenger and mobility context, augmented reality and the wide range of solutions providing additional real-time information to passengers is taking off as well. (No pun intended.)
These technological innovations include indoor location tracking, real-time information on boarding gates, real-time updates on flight delays, and information on airport facilities and shops. This is also being expanded to knowing the number and location of available parking spaces to facilitate the passenger experience in the (sometimes not so easy) airport processes. For example, Copenhagen airport, in collaboration with SITA, created the very first augmented reality indoor app in 2012. Now there is an endless list of both airlines and airports with similar apps.
Do you think augmented reality together with innovative data visualization can have a significant impact in future aviation?
What are its challenges and potential benefits?
We’re interested in hearing your thoughts and ideas.

On maps

How are “mobility” and trips visualized and represented? Well, the most direct, intuitive way of doing so, is using maps. Representations, converting the 3-dimensional earth (*sphere*) to a flat  2-dimensional surface. This post is about maps, map properties, map distortion and curious maps. We hope you enjoy it!

Mapping the earth, or parts of it, is a classic, well-studied problem. For hundreds / thousands of years, cartographers and mathematicians have come up with different methods to map the curved surface of the earth to a flat plane. The main problem is that you cannot do this perfectly, (Theorema Egregium). The shape, area, distances and directions of the surface cannot be represented properly at the same time on a map.


Shape: If a map preserves shape, then feature outlines (like country boundaries or the coast lines) look the same on the map as they do on the earth. A map that preserves shape is conformal. The amount of distortion, however, is regular along some lines in the map. For example, features lying on the 20th parallel are equally distorted, features on the 40th parallel are equally distorted (but differently from those on the 20th parallel), and so on. The Mercator projection is one of the most famous and well-used shape-preserving maps:


Area: If a map preserves area, then the size of a feature on a map is the same relative to its size on the earth. For example, on an equal-area world map, Spain takes up the same percentage of map space that the actual Spain takes up on the earth. In an equal-area map, the shapes of most features are distorted. No map can preserve both shape and area for the whole world, although some come close over sizeable regions. Sinusoidal projection is an area-preserving projection:


Distance: If a line from a to b on a map is the same distance (accounting for scale) that it is on the earth, then the map line has true scale. No map has true scale everywhere, but most maps have at least one or two lines of true scale. For instance, in the Casini projection, the distances perpendicular to central meridian are preserved:


Direction: Direction, or azimuth, is measured in degrees of angle from north. On the earth, this means that the direction from a to b is the angle between the meridian on which a lies and the great circle arc connecting a to b. If the azimuth value from a to b is the same on a map as on the earth, then the map preserves direction from a to b. No map has true direction everywhere.

Finding the compromise: Most of the maps used are compromise solutions, partially preserving some of the above mentioned properties. The most used one (by far) is the one you can find in Google Maps, OpenStreetMaps etc. called Web Mercator, Google Web Mercator, WGS 84 Web Mercator or WGS 84/Pseudo-Mercator. It is a variation of the Mercator projection, ignoring the ellipticity of Earth for faster computation:


The Winkel tripel projection. “Triple” stands for trying to minimize errors in three properties at the same time: area, direction, and distance. The Winkel tripel is the arithmetic mean of different projections (equi-rectangular, area and shape preserving)


There is even a whole family called Myriahedral projections. These consider the earth *sphere* to be a polyhedron with a very large number of faces, a “myriahedron”. This myriahedron is cut open into small pieces and unfolded. The resulting maps have a large number of subareas that are (almost) conformal and that (almost) conserve areas. The location of the map interruptions can be “selected” (oftenly using sea areas etc)

7 8









Some ingenious representations mix the approach from Myriahedral projections and other property-preserving projections. e.g. the Goode Homolosineprojection:


All the previous projections provoke distorsion. There is no perfect projection. In the nineteenth century, Nicolas Auguste Tissot developed a simple method for analysing map-projection distortion. An infinitely small circle on the earth’s surface will be projected as an infinitely small ellipse on any given map projection. The resulting ellipse of distortion, or “indicatrix”, shows the amount and type of distortion at the location of the ellipse. Some examples for the most-used projections are given below.


If all the previous was not enough, it just leaves the door open to other projections that represent additional variables in maps, such as socio-demographic or technical indicators.

A map with country size proportional to population:


Proportional to number of immigrants:


Proportional to the number of tourists (Spain the biggest country in the world!):


Or even proportional to the total number of flights (this is one of my favourites!):


Some references and further reading on the topic:

How do you catch a plane?

How do you catch a plane? More interestingly: what are the stages of your door-to-door journey when an airline flight is involved? They’re most likely not all the same as anyone else’s.

There are a myriad ways of getting from your starting point (home, office, hotel) to the airport – from “door” to “kerb”. It could be by private car – whether “kiss-and-fly” (driven by family or friend), a ride-share with another passenger/co-worker, a taxi/minicab or their modern app-based equivalents, or we can just drive ourselves and park in the long-term or short-term car park (or maybe an off-site car park which then took you to your terminal by shuttle bus). It could be by bicycle or motorbike. It could be by bus, coach, tram, train, metro, or a combination of these – but how did you get to the stop/station for first one; walking, taxi, driving, “kiss-and-ride”, cycling – and where did the last one drop you off: the terminal, the airport transport hub? You may have driven, and then had to return, a hire car (if this is a return leg or your trip) and then took the hire company’s shuttle. If you left from an airport hotel, you most probably took their shuttle.


Getting through the airport (from “kerb” to “gate”) also involves many options. Did you check in online or do you have to do it at the terminal? Do you have bags to check in? Do you have to go through passport control? How long is the queue at the security check? How much buffer time did you leave, that you can now spend browsing around the shops? How many miles do you have to walk to get to your gate? Is your flight delayed?

Your answers to these questions (and their equivalents for the “gate” to “kerb” and “kerb” to “door” legs once your flight has landed), as well as the process for the actual flight(s) from gate-to-gate (including any transfers), have a bearing on how long your total journey will take. To be able to determine where there is room for reduction in this journey-time, and where research must be directed to initiate this reduction, in order to meet the ACARE goal of 4 hours door-to-door for intra-EU journeys, the DATASET 2050 team have analysed the component times of the current air journey in detail. This work is presented in the project’s deliverable 4.1 – Current Supply Profile.

Data for such analysis is hard to come by. Much of it is proprietary and, if it’s available at all, is sold at a high price – too high for this project. That which is available generally concerns all passengers, rather than just those on intra-EU travel; are people really likely to ride on one of the scheduled overnight coaches from Edinburgh to Heathrow to take a short-haul flight?

Making use of tools such as those provided by Google Maps, DATASET2050 researchers have been able to see the time taken to access airports by car, bicycle and public transport for a selection of airports.

2-2 2-3 2-4
 Berlin Tegel access-egress times by (L-R) bicycle, public transport, and car

(NB: scales are different, see them full size below)

These results and the many others included in D4.1 will help colleagues working on the next steps in the DATASET2050 project determine which parts of the different segments of your door-to-door journey can be speeded up, and where research and development is needed to further reduce our journey times.






A new generation of business traveller

The DATASET2050 project does not only examine current European passenger profiles but also looks at possible passenger types in 2035 and 2050. To develop future demand profiles, current ones are either adjusted (see Current European PAX profiles), or new profiles are developed. As there is still a lot of uncertainty regarding how we are going to live and travel in the future, and since the project follows a data-driven approach, only passenger characteristics that can be supported by data are taken into consideration. Examples for developments supported by data are the ageing population in Europe, the increase in single households and the tendency to have fewer children per household.

For 2035, six future passenger profiles for the EU28 and EFTA countries are developed. Among these, the Digital Native Business Traveller was identified as one of the main passenger types in Europe. This group takes a journey mainly for occupational reasons and it can be seen as the new generation of business traveller. However, due to the high usage of technological devices one can assume that this passenger type is constantly connected and always online in continuous digital exchange with the private life, friends and family. He or she will be in the typically age of the working population of around 24 to 64 years, which today represents the digital savvy Generation Y and Generation Z. The income level and amount for transport expenditure will be medium to high. 0.5 to 1.5 trips per capita per year are taken, either alone or accompanied by another person. A large share of this passenger type will be female as the increase in female tertiary education enrolments might lead to an increase in working women within higher professions and hence an increase in women travelling for business purposes. Finally, he or she does not mind checking in luggage but takes only hand luggage when going on short trips. Public transport, taxi or car sharing are the preferred airport access mode choices.


Figure: The new generation of business traveller is digital savvy and constantly connected, enabled by emerging technology and new innovations to come.

This is one example of how a typical passenger group in 2035 could possibly look like. The outcome of all passenger demand profiles will be put in contrast with coming work packages (i.e. future supply profile), enabling this way a complete assessment on the European door-to-door mobility in the future. More information about the remaining passenger types, the methodology and databases can be found in the report on future passenger profiles.

Current European PAX Profiles

Have you ever wondered about all the different people at the airport? Almost all of us have already flown: for going on holidays, visiting friends and family or going on a business trip. Likewise, many have been sitting at the airport, waiting at the gate and watching different passengers walking past. An airport is a melting pot where people of all ages, backgrounds, income levels and interests come together. As part of the DATASET2050 project, passenger characteristics are examined and six general passenger profiles (PAX profiles) are generated to gain an understanding of what distinguishes current European air travellers.

These PAX profiles are derived using existing passenger studies as well as data on demographical, geographical, socio-economic and behavioural aspects. At first, profiles are distinguished by travel purpose, i.e. whether passenger travel for personal or for business reasons. Since the amount of passengers travelling for private reasons exceeds that of passengers travelling for business reasons (on average across all EU28 + EFTA countries ten per cent business trips), there are four groups describing leisure passengers and two groups describing business travellers, as can be seen in the figure below. Following, passenger groups are assigned to pre-defined age intervals taken from an analysis of European countries as well as respective average travel activity within the particular age group.


Figure: PAX profiles according to travel purpose and age intervals with example profile information for “Executives”, “Family and Holiday Traveller” and “Best Agers” (own depiction based on PAX profile analysis)

All six passenger groups also differ by their income level. “Executives” have a high income; “Youngsters” have a low income and the remaining passenger groups have a medium income. Income alone has a great impact on travel budget and consequently on travel behaviour, i.e. how often someone is travelling or which transport mode is used to access the airport. Furthermore, the use of technical devices throughout the entire journey depends on age groups. Hence, all six passenger groups differ by the level of frequency in regard to mobile phone and internet usage. This translates to their booking and travelling behaviour as well. “Youngsters” and “Executives” are the two passenger groups using information and communication technologies (ICT) with a high frequency. “Youngsters”, for example, are digitally savvy and more likely to complete travel related tasks online compared to the group of “Best Agers”. Such processes along the journey could be online check-in or generating a boarding card on a mobile device.

The value of time also influences travel behaviour as passengers who value time a lot tend to save time along their journey and vice versa. Among all six PAX profiles, “Executives” and “Price-conscious Business Traveller” value time the most which is reflected, for instance, by their time-saving choice of hand luggage only. In contrast, “Youngsters” are young, often students or apprentices, and time rich but money poor. To compensate their low income, they tend to use public transport (often the longer access mode choice) to save money as they do not mind the additional time spent in public transport. “Family and Holiday Traveller” and “Best Agers” also have a rather low value of time.

The six passenger groups also differ by their length of stay. The trip length in terms of nights staying is another parameter influencing the amount of luggage a particular passenger is taking along the journey. The amount of nights spent at a particular destination differs both by travel purpose and by type of journey conducted. Business travellers tend to spend fewer nights per trip than leisure passengers. And “Youngsters” visiting friends in urban centres spend less nights than “Family and Holiday Traveller” on their summer vacation. In turn, this may influence the access mode selected, the time spent in luggage check-in processes, or during luggage collection at the destination airport. For instance, in order to minimize time and effort accrued to respective handling processes, business passengers reduce the amount of luggage taken along. Finally, it is important to mention that one person can be assigned to several PAX profiles. A manager of an international company can travel for business purposes (being assigned to the group of “Executives”) and in private life being a dad and flying with his wife and two children into the summer vacation (being a “Family and Holiday Traveller”).

More information on the PAX profiles and the analysis can be found in the DATASET2050 report “Data driven approach for a Seamless Efficient Travelling in 2050”.

DATASET2050 presentation at Data Science in Aviation Workshop (EASA, Cologne-Germany)

The annual event exploring Data Science in Aviation (ComplexWorld funded; organized by Innaxis) has recently celebrated its fourth edition this past September 8th 2016. The event was hosted on the EASA premises in Cologne, Germany . This year it highlighted a presentation of the DATASET2050 project, “Data Science for Mobility”, by project coordinator Samuel Cristobal (Innaxis).




On the Data Science In Aviation event:

Previous editions of the Data Science in Aviation event were hosted in Madrid 2013, Paris 2014 and Brussels 2015. The popular event usually draws attendance from more than 80 individuals from top European and worldwide aviation entities (including Airbus, Eurocontrol, Boeing, EASA, Airlines, Airports, ANSPs, SESAR , Universities, etc) along with ICT and data-related entities (including CERN researchers, Fraunhofer, Infrastructure-related, and various universities). Notable presenters from the 2016 edition included EASA, Innaxis, NATS, Eurocontrol, Boeing, ENAC and Fraunhofer.

In terms of the event agenda and content, the presentations has traditionally outlined how data science is understood as a useful set of fundamental principles that support and guide the principled extraction of information and knowledge from aviation data. Furthermore, the discipline leans on well-known data-mining techniques, and goes far beyond these techniques with successful data-science paradigms which provide specific applications in various air transport areas (safety, performance, mobility etc).


On DATASET2050 Samuel’s presentation:

The event also highlighted a key presentation from Innaxis project coordinator, Samuel Cristobal. Samuel presented five different points on data science in aviation.

  • First, he explained how some of the data science tools, techniques and concepts have been used in the mobility context, specifically using the DATASET2050 project as a case example.
  • Second, Samuel explained the different door to door phases under analysis (door-kerb; kerb-gate; gate-gate; kerb-door), which helps to delve deeper in the different data science components within aviation phases.
  • Third, Samuel outlined the different links between project objectives and overall Flightpath2050 goals.
  • The fourth point explored mobility data in Europe, and the value of the DATASET approach in this context.
  • The presentation concluded with a fifth and final point announcing the next communication actions. The full presentation can be accessed here:


In sum, the fourth edition of the Data Science in Aviation event was an excellent opportunity for dissemination of DATASET2050. This was in conjunction with a fruitful exchange of ideas with other aviation data scientists, some of whom working with similar tools in other sub-areas far from mobility. We hope to continue this momentum of knowledge exchange and look forward to a potential fifth edition of the popular event.


You can watch DATASET2050 Samuel’s presentation here, and the rest of the event videos at Innaxis’ Vimeo channel.

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