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.

1

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:

2

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:

3

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:

4

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:

5

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)

6

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:

9

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.

10

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:

11

Proportional to number of immigrants:

12

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

13

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

14

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.

2-1

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.

 

 

2-2

2-3

2-4

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.

3-1

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.

4-1

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).

5-1

 

 

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: https://www.dropbox.com/s/91julyl8gsij2k9/DATASET_SC_v1.pdf?dl=0

 

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.

Passengers’ environmental awareness and travel behaviour

Passengers’ travel behaviour can be influenced by various factors, such as disposable income, travel purpose, age group or technological affinity (see also #blogpost7 Passengers expectations: door-to-door travel and beyond). One of these influential factors is the environmental awareness of passengers and in which way it impacts – or even alters – travel behaviour.

Air transportation emits greenhouse gases and hence has a potential harmful effect on our environment in the form of CO2 emissions, for example. Passengers contribute to this by their choice of means of transport, their choice of holiday destinations and kilometres travelled (Cohen and Higham, 2011; Brouwer et al., 2008).

Overall, air travel passengers seem to have a basic understanding of the environmental impact and many also have pro-environmental values. However, according to several studies it does not result into behaviour changes of passengers yet. It is hence not a factor influencing their holiday planning, the choice of a destination and the type of transportation (Hares et al., 2009; Böhler et al., 2006). Research also reveals that the willingness of passengers to pay for carbon offsetting schemes, one possibility to neutralize emissions generated by one’s own journey without compromising the means of transport or influencing the decision on holiday destinations, is low as well (Eijgelaar, 2009; Mair, 2011).

The three main barriers towards pro-environmental behaviour change are a lack of alternative transport systems, the high value of holidays with the freedom to travel to every destination one wants, and the lack of feeling personal responsibility for climate change (Hares et al., 2009; Böhler et al., 2006). However, within some recent studies, evidence emerged showing an increasingly pro-environmental awareness in passengers’ mind-set and a willingness to actually change air travel behaviour in the future (Cohen and Higham, 2011; Gössling et al., 2009).

To sum up, environmental awareness among passengers seems to be already present but does not lead to current behaviour changes. This, among other factors, will be explored within DATASET2050 and it will be modelled how such drivers influence the travel demand of air transport passengers in the future.

6-1

References

  • Böhler, S., Grischkat, S., Haustein, S. and Hunecke, M., 2006. Encouraging environmentally sustainable holiday travel. Transportation Research Part A: Policy and Practice, 40(8), 652-670.
  • Brouwer, R., Brander, L. and Van Beukering, P., 2008, “A convenient truth”: air travel passengers‟ willingness to pay to offset their CO2 emissions, Climatic Change, 90(3), 299-313.
  • Cohen, S.A. and Higham, J.E., 2011, Eyes wide shut? UK consumer perceptions on aviation climate impacts and travel decisions to New Zealand, Current Issues in Tourism, 14(4), 323-335.
  • Eijgelaar, E., 2009, Voluntary carbon offsets a solution for reducing tourism emissions? Assessment of communication aspects and mitigation potential, Transport and Tourism: Challenges, Issues and Conflicts, 46-64.
  • Gössling, S., Haglund, L., Kallgren, H., Revahl, M. and Hultman, J., 2009, Swedish air travellers and voluntary carbon offsets: towards the co-creation of environmental value?, Current Issues in Tourism, 12(1), 1-19.
  • Hares, A., Dickinson, J. and Wilkes, K., 2009, Climate change and the air travel decisions of UK tourists. Journal of Transport Geography, 18(3), 466-473.
  • Mair, J., 2011, Exploring air travellers‟ voluntary carbon-offsetting behavior, Journal of Sustainable Tourism, 19(2), 215-230.

EU Door-to-Door Mobility Workshop: 12th July 2016

We’re pleased to host and coordinate the first workshop examining EU door-to-door mobility.
An outline of sessions can be found below (abstracts are here).

Date: 12th July 2016. 10:30 – 17:00 (approx)

Location: 309 Regent Street, London W1B 2HW University of Westminster, UK

10:00 refreshments on arrival

Welcome and introduction – University of Westminster (PDF)

  • Session 1. Challenges of a data-driven model

The DATASET2050 model

The current state of mobility in EuropeUniversity of Westminster (PDF)
Which journeys are in scope when measuring the 4-hour door-to-door target?
What data sources are available for the current and future models?
Meeting the passenger’s demand: current and futureBauhaus Luftfahrt (PDF)
Challenges ahead: how will we model 2035 and 2050?
Assessing current supply and demand profiles.
Developing a new model for European mobility Innaxis (PDF)
What new metrics (and segmentations) do we need, apart from simply measuring average journey times?
Analytical approach – what metrics are needed?
What is the current status of such journeys – latest progress with the model.

  • Session 2. Further exploring the journey process phase by phase – where are the efficiency gains?

Door-to-kerbKai Nagel, Technical University of Berlin (PDF)
Improved airport accessibility: intermodal mobility; efficiencies of different modes (e.g. better utilisation of road-based modes – fuller cars/taxis; prioritisation schemes); modal shift; integration and passenger confidence.
Kerb-to-gateGenovefa Kefalidou, Horizon 2020 PASSME project (PDF)
Reducing door-to-door airport travel time for passengers in Europe.
Providing passengers with real-time information on predicted demand for airport services.
Improving the airport experience for passengers.
Gate-to-gateSteve Williams, NATS (PDF)
The impact of new SESAR solutions aimed at improving gate-to-gate operations, including free-routing, business trajectories, functional airspace blocks and ATM performance targets.
The role of wider EU policies such as Regulation 261/2004.

  • Session 3. Looking ahead to 2035 and 2050

Futures near and farChristoph Schneider, Munich Airport (PDF)
Evolution of demand – market maturities, new technologies and travel patterns.
From where will the key performance improvements come? – panel discussion (PDF)
Major improvements and barriers. Is the 4-hour target achievable – at what price? What should be the role of regulation and policy?
Close and wrap-upUniversity of Westminster (PDF)

Registration: Attendance is free of charge, however the number of places are limited.
Dynamic conversations and exchanges of views are encouraged at the workshop.

You’re cordially invited: EU Door-to-Door Mobility Workshop hosted by DATASET2050

The 4-hour door-to-door challenge in Europe – are we heading in the right direction?

The DATASET2050 project is pleased announce a one-day workshop in central London on Tuesday 12 July 2016 focusing on the 4-hour door-to-door challenge. The event will be hosted by University of Westminister.

The workshop will focus on the challenges facing the Flightpath 2050 4-hour door-to-door target, with presentations from the project team (Innaxis, University of Westminster, EUROCONTROL and Bauhaus), along with guest speakers from Heathrow and Munich Airports, NATS and the PASSME project. Sessions will consider the current journey process and where efficiency gains may come from, data sources, new metrics, and a look ahead to 2035 and 2050.

Attendance is free of charge, however the number of places are limited!

Register today at: http://www.dataset2050.com/eumobilityworkshop. A preliminary draft of the agenda is also available.

DATASET2050 is a Coordination and Support Action funded by the European Commission under the Horizon 2020 Mobility for Growth topic “Support to European Aviation Research and Innovation Policy” (MG.1.7-2014).

For additional questions, please contact Hector at hu@innaxis.org.

Mobility datasets exploration tool

Within the project, we have recently listed the sources of EU door-to-door mobility datasets, reports and papers. That information is crucial for us to build the subsequent data-driven tasks (including the model). On top of that, they could be extremely useful to anyone doing research or simply interested in the mobility topic.

Having this in mind, the consortium has developed a visual, interactive tool that provides all the information in a simple, attractive way.  By using a dynamic D3.js , it includes information about data sources together to their temporal data coverage, authors, description and availability

How it works? Click here: http://visual.innaxis.org/mobilityDataSETs/. The datasets have been categorized in 9 families, all of them relevant within mobility context.

  • Demographic
  • Passenger demand
  • Passenger type
  • Passenger behaviour
  • Door-to-kerb
  • Kerb-to-gate
  • Gate-to-kerb
  • Airside capacity
  • Competing services

By clicking in each of them (the text, right side), all the data sources available within that family are displayed. Doing a mouseover on each of them (right side), detailed information is given in a tool tip about the data coverage, sources etc. In the cases too many sources are available, scrolling is the way to see them all 🙂 Clicking on the [x] at the top brings you back to the main page.

enjoy!

7-1

 

 

http://visual.innaxis.org/mobilityDataSETs/

 

April’s post: Mobility performance, KPAs

There are many performance targets for the European (air) transport system. It is clear that performance-based frameworks are needed and utilised, especially when decision makers need to act on legislative packages or when operational managers need to make procedural changes or decisions regarding technology. This overarching model of operations proves that any costly decision must ultimately result in an increase in performance.

Different performance frameworks look into different aspects of the European mobility framework, with varying goals that are not necessarily compatible or aligned in the same direction. To illustrate, ‘Flightpath 2050’ envisions an air transport system that improves safety levels but also guarantees time-related performance for the future passengers of Europe; up to four hours maximum door-to-door travel time for 90% of travellers using air as a mode. This number is not arbitrary, as it corresponds to the type of experience high-level experts envision for European passengers. However, punctuality and efficiency metrics are mostly flight-centric. Passengers are rarely considered in performance schemes and therefore very little is known about the actual door-to-door time performance from the passenger perspective. Decisions such as ‘when’ or ‘where’ to act in achieving this goal have proven to be more challenging than initially expected.

The European Commission Single European Sky Unit is working on ‘Reference Period 3’, which delves deeper into the performance scheme for air navigation service and network functions from 2020. This performance framework is very detailed, but unfortunately does not yet include provisions for passenger punctuality. Due to the complexity of different, non-interchangeable metrics, the KPAs and the different performance goals do not necessarily match.

SESAR and CleanSky have detailed, technical performance goals. By looking into specific technology developments or procedures, it is clear that their technologies will surely improve the performance of many concrete operational elements (e.g. runway performance or environmental impact in terminal areas, to mention two of them) – however it is yet unclear how much those programmes will contribute to passenger mobility.

In addition, traditionally, passengers have been categorised as ‘business’ and ‘leisure’ travellers. However, these traditional distinctions have become less distinct over recent years and will continue to do so in the future. This is driven by various developments such as newly emerging markets and cultural backgrounds, an ageing society, and increasing digitalisation within private and business life. Resulting passenger needs and expectations during their journey can thus differ to a great extent. This is reflected in their willingness to pay for extra services and time savings during their stay at the airport, for example. Therefore, the initial passenger group classification is not sufficient any more to properly address and integrate passenger requirements across the different transport modes. (D3.1 on passenger profiling 2.0 to be delivered soon!)

See you in the next blog post!

Connect with us!