Aircraft, network, and zoology

It is well known that the problem of building a schedule plan for an airline is a difficult one. The core difficulty is indeed to take into accounts the multiple constraints of aircraft, crew, maintenance, passenger correspondence etc, while trying to capture as much market as possible, all with minimum expenses. It is similar to riding a bike... except you do not know who is riding, where the wheels are, where you are supposed to go and if you should buy a car instead.

One of the most important constraints is the aircraft, since:

  • it is impossible to fly without it (rockets are quite unsafe to land at airports),
  • it is quite expensive (I've been told).

Let's imagine that, as an airline, you roughly know what cities you want to connect and how many passengers should travel with you. Where should your existing aircraft fly? Should you buy one? Do you have different strategies if you are a low-cost carriers or a traditional one? This is roughly the answers that our agents are trying to answer in the second block of our Vista model, the "schedule mapper". Of course, since our model simulates all the airlines in Europe, we cannot dedicate as much time (real and computational) as airlines do in reality to their schedule plan. But, like for the other parts of Vista, we are trying to catch to main behaviours of the system.

As usual, we start from what we can observe from data. For instance, it is common to say that aircraft usually go back and forth, and that some of them do sometimes triangular flights. Is that true? To investigate this, we take a three days time window where we track the itineraries of aircraft in terms of airports, defined as "patterns", using DDR data. What kind of patterns 'live' in this environment? How to classify them?

First, like taxonomists do not care about the specifics of a single individual to make a classification, we should not take into account the details of the patterns to classify them (in fact, that's the definition of a classification...). So for instance Rome - Paris - Rome has the same pattern than Frankfurt - London - Frankfurt, which can be rewritten 1 - 2 - 1 for instance. If a specific sequence is an individual in zoology, a pattern is thus akin to a taxon.

We can roughly divide these taxons into two "reigns": the ones which are closed (more explicitly have at least one closed loop), and the rest. For instance, an aircraft doing Paris - Frankfurt - Rome - Paris - Rome - Paris in three days has a closed pattern, whereas an aircraft doing Rome - Madrid - Barcelona is open. Of course, in the long run, most of aircraft do at least one full loop, but in three days some of them cannot make it. However, when counted in number of flights, most of them are closed in 3 days already, as shown in the figure below. In the following, we focus only on these closed taxons. Pretty much like one could focus on a study on mammals for instance, except that in this case, the mammals represent most of the animal kingdom.

Among them, some are more elemental than others, in the sense that they cannot be constructed from their peers. These are the ones which have exactly one closed loops. The ones present in the data are represented in the figure below, with their frequency of appearance (the number n corresponds to the number of airports in the loop). Most of them are single returns (1 - 2 - 1), triangular flights (1 - 2 - 3 - 1), and rectangular flights (1 - 2 - 3 - 4 - 1), and we focus on these three ones in the following. Note that rectangular flights seem more frequent than triangular ones, perhaps contrary to the popular belief.

All the other patterns can be constructed from these elementary ones, and we name them 'combined' patterns. For instance, (1 - 2 - 1 - 2 - 1) is composed of two single back and forth. In terms of zoology, it is a bit like saying that an elephant can be obtained by gluing a snake to a hippopotamus. Or that a giraffe is really nothing more that a horse with a periscope in the throat, which personally I believe very much. In any case, it easy to plot the frequency of appearance of these combined taxons, as shown in the figure below. Since all of them are coming from three taxons, we use notation the (X, Y, Z), where e.g. (2, 0, 0) represents two returns, (1, 1, 1), a return, a triangular flight and a rectangular one, etc. Some very rare patterns have been omitted in the figure. As expected from the previous figure, most of the aircraft goes back and forth during the three days. It is interesting to see that triangular flights are very under-represented, and that it is more frequent to have a rectangular flights every now and then, in combination with returns. Note that when a pattern features several returns, it is not necessarily between the same airports (e.g. Warsaw - Oslo - Warsaw - Vienna - Warsaw). In fact, we found that most of the combined patterns are 'impure', i.e. they are composed of elementary patterns with different airports (like gluing two birds of different colours for instance).

What does Vista do with this freak zoo? Well, the way the airlines choose implicitly the different patterns is a complex procedure, driven by the different constraints cited above. So the idea is that the best patterns should be selected for their efficiency, much like some taxons are selected by evolution based on their fitness in the given environment. Each taxon has also some particularities. For instance, flights using the taxon (4, 0, 1) mainly departs (from their first airport) in the early morning,  whereas taxons (2, 0, 0) are used by flights departing more frequently in the late morning, and sometimes in the evening, as shown in the figure below. Other regularities can be found in terms of average turn-around times for instance.

In the model, we use all these data to build reasonable schedules by resampling the different taxons for each airline. This will be described in a later blog post. And no more weird animal crossings, we swear!

How long?

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

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

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

Time taken for each phase journey:

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

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

AUTHOR: GRAHAM TANNER

Jet-bridges: The gateway to time-wasting?

Author: Pete Hullah

So you walked for what seems like miles to get to your gate. You've just queued for an age to have your boarding-card scanned and your passport checked. "Bon voyage" says the attendant. Welcome to the jet bridge, or Passenger Boarding Bridge (PBB) in the jargon. A claustrophobic metal box, often not air-conditioned, where you can now stand in another queue, at the front of which a business-class passenger is slowly trying to place too much luggage into the overhead bin while simultaneously talking into a telephone, apparently oblivious to the world behind. When you land, you'll have another interminable walk from your gate to passport-control/luggage-reclaim/exit.

And ever was it so, and ever will it be so.

But why is it like that? This walking and queuing is a constraint on mobility and on the EU goal of having 90% of intra-EU32 air passengers undertaking their journey in less than four hour, door-to-door. There must be something we can do to reduce this.

Why do we walk so far in airports?

In the early days of civil aviation, passengers were led on foot from the terminal to the plane and climbed a mobile staircase to board it; the reverse process applied upon landing. This is still the case in some smaller airports. As the number of flights increased, it became difficult to park planes close to the terminal, thus the walk (sometimes in the rain!) lengthened and more staff were needed to marshal the passengers. From the early 1960s, airports started installing piers and PBBs that made marshalling easy and kept passengers dry. With the development of the hub, PBBs helped passengers transfer between flights within a short time.

But PBBs meant that the gates at an airport have to be spaced far-enough apart to allow aircraft to park at them safely; at least an aircraft wingspan between them therefore. (Some airports reduce the distance a bit by curving the piers of having circular satellites - Paris CDG Terminal 2F and CDG Terminal 1 are examples of this).

Aircraft wingspans can range from some 25m for regional jets like the E170 and CRJ and around 35m for B737s and A320s, to more than 65m for B777s and B747s and even nearly 80m for A380s. The spacing, or combination of spacings, used at a given airport depends on that airport's traffic but it is fair to consider an average 40m walk or travellator from one gate to the next. With a gate either side of the pier we have an average of 20m walk per gate - plus any additional walk (usually a shopping mall) from security/border-control/etc. to the first gate.

Why so much queuing?

160 passengers or so have to wait at the gate while an A320 is prepared to accommodate them and they can board, generally through just one PBB attached to the front door. In order to improve the time it take to actually seat passengers on the plane, airlines often request people to pass through the gate as a function of the "zone", or group of rows, of the aircraft they're seated in - generally starting from the rows at the back. However, it is impossible to impose such a sequence and additionally, business class passengers (seated at the front) are generally advised that they can "board at [their] convenience" thereby blocking other passengers while they stow their cabin luggage. If passengers boarded in the correct sequence boarding time could be massively reduced.

So why not scrap the jet bridge?

Scrapping the jet bridge could be a solution to both these problems and enable a real reduction in time wasted. Isn't it time we re-thought about buses?

Buses are already a feature of airports.

  • Because airlines are charged more for parking at the gate and for using PBBs than for parking further away and using buses some, particularly low-fare airlines, tend to prefer this solution. This is especially the case if the plane has overnighted.
  • A plane could have docked at the wrong section of the airport - an incoming international flight parked at the international terminal will be a domestic outbound whose gate is at the domestic terminal; the domestic passengers are bussed to the airside of the international gate.
  • At Washington Dulles, "mobile lounges" that rise to the aircraft door take international passengers directly to immigration thereby saving time and heightening security by avoiding "losing" passengers on the air-side of the controls.

Now if you ask anyone about buses or mobile lounges at an airport they will cringe! But given that they are uncomfortable - most passengers have to stand in them - that there's no distinction between business and economy classes, and that airport policy seems to be to cram as many passengers as possible into one of them before it moves off, that dislike is understandable. The reason for this overcrowding is mostly economic - why employ 3 drivers when you can squeeze all of the passengers into 2 buses. With the advent of automated transport, this argument is removed.

If aircraft parked at stands by runways, less taxiing would be needed than for getting back to a terminal (especially from the new runways at Frankfurt or Schiphol, for example) and there would be no need for taxiing aircraft to cross runways, which brings a risk of runway incursion accident. Buses use much less fuel to carry the same number of passengers (and they could be electric) and they can use simple tunnels to cross runways.

Airport buses v2

If bus loading concourses were designed like a train station under the pier with several buses per flight lined up perpendicular to the pier, the entire width of bus-train, pavement and escalator would be some 5-6 metres per A320. 30 gates would therefore require 180m as opposed to the 600m required today.

Using multiple buses allows embarkation and disembarkation from both the front and rear doors. This can therefore speed up these processes, provided the departing passengers have been assigned to buses according to their seat zone on the plane (rarely the case today). Access to each bus of a bus-train could be controlled by automatic gates opened by scanning a boarding card, thus ensuring correct zoning of the passengers; the business-class bus could be more comfortable than the economy-class ones. Additionally, the buses could be available well before the plane was ready for boarding, taking the place of waiting areas - no pier seating required - and enable the gate process to be executed smoothly at the passenger's convenience and finished on time. Additional seating could be placed on an upper floor in the shopping area.

Once the bus-train has arrived at the aircraft, doors can be opened in sequence to allow passengers to board smoothly. As with the mobile lounges at Dulles, buses can take passengers directly to where they need to be - the central immigration/transfer/luggage-reclaim area - rather than their having to walk down long piers.

A well-designed bus transfer system could reduce walking, boarding and taxiing time at an airport and considerably help reach Europe's 4-hour door-to-door target.

Mobility metrics and indicators rethought

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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

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)

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

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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).

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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).

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

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The EU projects Mobility4EU (http://www.mobility4eu.eu/) and MIND-SETS (http://www.mind-sets.eu/) 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 (http://www.mobility4eu.eu/midtermevent/). 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

References

9292 / https://9292.nl/
Airbus / http://www.airbus.com/de/
Airportbus Munich / https://www.airportbus-muenchen.de/
Air-Rail / http://www.air-rail.org/index.php?lang=EN
Air-Train / https://www.globalairrail.com/awards/24-awards/awards-2016/216-airtrain-city-marketing
Ally / https://www.ally.com/
Allygator / https://www.allygatorshuttle.com/
Amtrak / https://www.amtrak.com/home
Bike Train Bike / http://www.bitibi.eu/
Blacklane / https://www.blacklane.com/de
Boring Company / https://www.boringcompany.com/
Bus and Fly / http://busandfly.com/de/page.php
Cabify / https://cabify.com/
Cambio / https://www.cambio-carsharing.de/
Car2Go / https://www.car2go.com/DE/de/
Chu Kong Shipping Enterprises / http://www.hulutrip.com/product/41617.html
Citybike / https://www.citybikewien.at/de/
Citymapper / https://citymapper.com/rhineruhr
Clipper / http://clippercard.com
Cotai Waterjet / http://www.cotaiwaterjet.com/airport-ferry-services.html
Deutsche Bahn / https://www.bahn.de/p/view/service/fahrrad/call_a_bike.shtml
DriveNow / https://www.drive-now.com/de
Drivy / https://www.drivy.de/
Ehang / http://www.ehang.com/
Emmy / https://emmy-sharing.de/
Ferrara Bus & Fly / http://www.ferrarabusandfly.it/
Flinkster / https://www.flinkster.de/
Flightcar / http://farewell.flightcar.com/
Fraport / http://www.fraport.de/de.html
Free2Move / https://de.free2move.com/about
Gett / https://gett.com/
GoEuro / https://www.goeuro.com/trains/
Hannovermobil / https://www.uestra.de/mobilitaetsshop/hannovermobil/
Hyperloop One / https://hyperloop-one.com/
Iberia / http://www.iberia.com/de
Kobe – Kansai Bay Shuttle / https://www.kobe-access.jp/eng/
Lyft / https://www.lyft.com/
Memobility / http://www.memobility.de/
MobilityMixx / https://mobilitymixx.nl/home.html
Moovel / https://www.moovel.com/de/de
Mozio / https://www.mozio.com/de-de/
Mycicero / http://www.mycicero.it/
Mydriver / https://www.mydriver.com/de/
Network Rail / https://www.networkrail.co.uk/
Octopuscard / http://www.octopus.com.hk/home/en/index.html
ÖBB / https://www.oebb.at/de/
Oystercard / https://oyster.tfl.gov.uk/oyster/entry.do
Rallybus / http://rallybus.net/
Rome2Rio / https://www.rome2rio.com/de
SBB / https://www.sbb.ch/de/home.html
Stadtmobil / https://www.stadtmobil.de/
Swiss Helicopter / http://www.swisshelicopter.ch/DE/
Tamyca / https://www.tamyca.de/
Taiwan High Speed Rail / https://www.thsrc.com.tw/index_en.html
Thalys / https://www.thalys.com/de/de/
Touch & Travel / http://www.7mobile.de/handy-news/touch-travel-bahnticketkauf-mit-dem-smartphone-moeglich.htm
Turo / https://turo.com/
Tuup / http://tuup.fi/
Uber / https://www.uber.com/
Union Station Denver / http://unionstationindenver.com/
Urbanpulse / http://www.urbanpulse.fr/
Velib / http://www.velib.paris/
Voom / https://www.voom.flights/
VW (Sedric) / http://www.discover-sedric.com/de/
Wideroe / http://www.wideroe.no/en
Wmata / https://www.wmata.com/
Yugo / https://getyugo.com/barcelona
Zee Aero / http://www.zee.aero/

How do you catch a plane?

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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.

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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.

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 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.

 

 

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