The Flightschedule Profiler: An Attempt to Synthetise Visually an

Airport’s Flight Offer in Time and Space

Jean-Yves Blaise and Iwona Dudek

UMR CNRS/MCC 3495 MAP, Campus CNRS Joseph Aiguier, 31 chemin J. Aiguier, 13402, Marseille, France

Keywords: Information Visualisation, Time-oriented Data, Transport.

Abstract: Online route planners and travel reservations systems have become in the past years part of our everyday

lives. Such sites, originating from the airlines themselves or oriented on “search and compare” tasks, do

provide valuable services. But the very nature of the queries users formulate (ultimate result: one flight)

limits the type of information one can expect to retrieve, and in particular does not allow to get an overall

view of an airport’s flight offer over time and in space. In this contribution we introduce a proof-of-concept

visualisation that sums up in a synthetic way the [where to, when to] profile of an airport, its realm of

possibilities. The visualisation acts as an upstream service, independently of any actual reservation loop: its

main role is to help unveiling significant spatio-temporal patterns (densities and continuity over time for

instance). The prototype is implemented on a real life data set: the winter 2013/2014 schedule of the airport

in Nice. Ultimately, beyond a discussion on the issue, on the pluses and minuses of the prototype, this

position paper questions the way travel data is presented, and as such can promote debates over the potential

impact of information visualisation solutions in that context.

1 INTRODUCTION

Air transport has become in the last decades

something quite common: 842 million passengers in

the EU in 2013, according to Eurostat. The

following statement can be found in an Opinion of

the European Economic and Social Committee: “Air

transport has developed from a luxury to a mode of

mass transport” (EEC 2004). As a consequence,

along with a growing numbers of travellers, we have

witnessed the emergence of new flight routes, new

flight schedules, new airports, new airlines. Hence

one could assume that with this move came a need

to renew the way information about flights can be

communicated and visualised, that innovative

solutions have been introduced in order to allow

travellers to compare in a synthetic way alternative

routes and schedules, and to allow airport authorities

to post their offers in a readable, clear-cut manner.

To the best of our knowledge, however, visual

and comprehensive presentations of an airport’s

flight schedule (in time and space) are simply not

available at end-user level. There definitely is a lot

of information available on the net through for

instance airlines reservation systems, airports

destination maps, airline route maps, etc. But is that

space + time information really synthetic, easy to

read, efficiently presented? Would applying

information visualisation (Infovis) principles help

designing solutions that can help users get a global

view of what an airport can offer?

The above mentioned existing solutions, often

either form-based or map-based, hit three major

limits: (a) they do not allow for a consistent context

+ focus presentation of the information, (b) they do

not allow for a time + space visualisation of the

information, (c) they are stuck in a discrete time

model that is inherently space consuming in terms of

visualisation.

In this position paper we introduce a proof-of-

concept visualisation that sums up in a synthetic way

an airport‘s flight offer. The visualisation combines

spatial and temporal information, and thereby helps

unveiling spatio-temporal patterns by summarising

visually parameters such as destination, frequency,

schedule, seasonality (operating periods). It is

primarily used in a context view, in order to profile

the airport’s offer globally, thereby summing up

visually its specific “to and fro” profile in time, and

space. But the visualisation also allows for focus

views (day-by day reading, destination per

destination, etc.). Finally, it allows users to switch

from a visualisation in ordinal time (only the order is

known, not the exact time) where temporal densities

Blaise, J-Y. and Dudek, I.

The Flightschedule Proﬁler: An Attempt to Synthetise Visually an Airport’s Flight Offer in Time and Space.

DOI: 10.5220/0006081804070412

In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, pages 407-412

ISBN: 978-989-758-203-5

407

are assessed in a clear-cut manner to a visualisation

in discrete time where the exact time schedule of

each flight can be read.

The approach is tested on data concerning the

airport in Nice, the second largest in France (over 12

million passengers in 2015, according to the airport

authorities). Initially developed as a static graphic it

is now an online web prototype (Figure 1).

Figure 1: A screenshot of the online prototype, showing

(top) densities of flight per half-day over the week, (left)

user options, (centre) flight directions and densities in

ordinal time, (right) flight details and a cartography

(OSM).

It has to be said straightaway that what we are

presenting in this paper is closer to an experiment

than to a fully operational system: hence no claim

will be made on the potential impact of our

contribution. Yet beyond this limitation, and others

to be mentioned in the paper’s last sections, we think

the issue is worth discussing. The preliminary results

(and method) we report can act as food for thinking

(and this beyond Air transport as such - railway

transport for instance faces the same challenges).

The paper is structured as follows: section 2

narrows the questions this position paper wishes to

address. In section 3 we comment on pre-computer

era or contemporary designs that we consider as

inspiring. Section 4 presents our proof-of-concept

experiment in details, while section 5 summarises

the approach’s potential benefits, as well as its

limitations. Finally, section 6 sums up what we think

can be considered as fruitful feedbacks from this

study, and potential perspectives of development.

2 THE ISSUE

Flight reservations are today commonly done on the

Internet – 67% of the EU air passengers booked

online according to a 2016 Eurostat report (Eurostat

2016). A significant number of travel planning and

reservations systems have emerged in the past years

(either these of the airlines themselves, or of third

parties). They indeed provide a most valuable

service for users, but the very nature of the query

one fires on such systems (typically “find flights

from A to B on day D, order them by price / duration

etc.”) strongly impacts the type of information the

users will retrieve, and the way it will be displayed.

Shortly said, such queries end up on:

 sequential data series (one flight after the other,

page per page)

 verbose presentations (time slots and routes are

given as textual indications, temporal densities

are not clearly assessed)

 focus views (details on one flight at a time)

 the time parameter is present (departure and

arrival time, durations) but the space parameter

is limited to a textual list of airports.

Naturally this short list is an over-simplification

of the current offer: a number of flight planners do

propose significant improvements (including by

proposing alternative modes of transportation). The

introduction in a number of reservation systems of

services such as “show all week” or “find

neighbouring airports” shows the above issues are

taken into consideration. But because they are

included as an add-on in a sequential querying

process these new services are little more than a

band-aid solution if wanting to get a global overview

of a given airport’s flight offer profile.

The flightSchedule profiler prototype clearly

does not aim at replacing reservation systems, but

rather provides an upstream service : the possibility

to get a quick, visual, space + time overview of the

availability of flights to and from an airport, and

consequently to map visually its specific realm of

possibilities. So on what legacy, on what existing

solutions can one base on when wanting to achieve

this goal? In the next section we underline the

largely untapped potential of some examples

stemming from the history of Infovis.

3 RELATED WORKS

M. Friendly’ s research highlights how visualisation

has emerged over the years (over the centuries, in

fact) as a major challenge in fostering insight on data

sets. He mentions two major legacies - cartography

and statistics - that he considers as rooting the

development of data and information visualisation

(Friendly, 2006). And indeed a full range of

stunning examples can be quoted in the specific

context of “travel data”. E. J Marey’s 1885 train

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schedule (see Tufte, 2006) proposes a still

unchallenged context+focus visualisation of

relations between two railway stations (Figure 2). It

offers a global view, on a 24 hour slot, of all trains

from Paris to Lyon (and back), assessing not only

departure / arrival times but also speed on each

segment of the travel or duration of stopovers at a

glance. Although focusing on time (and cyclic time

in fact) a spatial information is present: distances

between stopovers on the graphic is proportional to

the real distances.

Figure 2: E.J. Marey’s train schedule (redrawn and

simplified). Time runs horizontally from left to right (24

hour slot, starting 6AM), stations are distributed vertically.

Oblique lines corresponds to trains connecting Paris to

Lyon and vice-versa. The angle (a) represents the speed of

train (the duration of the travel in fact). Note for instance

that it is straightforward to see that the two fastest trains,

highlighted in black, depart at the same time.

The 1933 map + schedule visualisation of the

Czechoslovakia Air Transport Company (Tufte,

2001) is another inspiring example: it combines in

one unique visualisation space (position of cities)

and time - departure / arrival times (Figure 3).

Figure 3: An extract of the 1933 Czech Airlines map and

schedule (redrawn from Tufte, 2001). Each circle

corresponds to a location in space, departure and arrival

times are shown inside the circles, lines with flight code

connect airports (the presence of an arrow differentiates

inbound from outbound flights).

But with the above examples what we basically wish

to pinpoint is there definitely is room for graphic

creativity in the context of travel data. The

omnipresence of reservation systems may have

introduced a de-facto standardisation (including of

expectations) that we think can be questioned. In

parallel, an emphasis is now often put at research

level on issues regarding the handling of massive

movement data sets like in (Klein et al., 2014) – our

concern in this paper is not about visualising

trajectories, or trails, but basically about synthetizing

end-user level information.

A broad look at time-oriented data visualisation

shows there are today indeed promising research

paths: the overview proposed by (Aigner et al.,

2011) introduces some inspiring solutions more or

less mixing time, space and quantities (ring maps,

flow maps, space-time cube, space-time Path, etc.).

In that context, the flightSchedule profiler prototype

we experiment aims at investigating the way travel

data can be displayed visually in a space + time

combination. It bases on three major choices:

 ordinal time model – in order to minimize the

amount of space needed to display all the infor-

mation. This model of time is used in visual

solutions like sparklines (Tufte, 2006b) or in

historical data sets (Blaise and Dudek 2012).

 a somehow stylized cartography that only

shows the essential: origin-destination vectors.

This choice is in line with for instance the

“Global map for accessibility” proposed in

(Nelson, 2008).

 a “details on demand” design, in line with the

Visual Information-Seeking Mantra, as worded

by (Shneiderman, 1996).

4 THE PROTOTYPE

In this section we first present, one by one, the main

information layers that are combined in the

visualisation. Specific information on one flight or

on one destination airport is available in the

visualisation through user-side interaction – we do

not detail that aspect at all since it is far from being a

breakthrough.

4.1 The Geographic Layer

Cartography is used as a background – a sort of

“mental image” in the sense of (Spence, 2001) – on

which we position origin-destination vectors

between Nice Airport and the airports it is connected

to. In other words, the visualisation underlines the

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orientation information, and highlights in which

geographic sectors the density of destinations is the

biggest. For the destination airports that are close

enough from Nice to be present on the map and

those that are beyond the map’s limits, the origin-

destination vector connects the origin point (Nice) to

a destination point projected on a circle that marks

the limits of the map (Figure 4).

Figure 4: The cartography shows vectors connecting Nice

to a destination point (represented here by small black

lines) along a circle marking the map’s limit.

Naturally, as can be observed on Figure 4, some

of the origin-destination vectors almost overlap one

another (typically, the vectors connecting Nice to the

various airports in London).

Figure 5: Dealing with overlapping vectors: -

redistribution of the destination points inside 16

geographic sectors (see top left lines connecting the

interior and external circles). Note that the visual

comparison of densities is preserved, with an

overwhelming proportion of northbound flights.

As an answer, we draw a second, larger, circle

and redistribute all destination points regularly

inside 16 geographic sectors: the solution still

assesses visually densities in the various sectors, but

a clear differentiation of each destination is made

(Figure 5). User-side interaction is then added that

allows the retrieval of additional pieces of

information (name / code of airport, country, etc.). A

filtering of national/international flights is also

possible, and the retrieval of a “real” geographic

map (online version - OpenStreetMap layer).

4.2 The Ordinal Time Layer

Once each destination airport is represented as a dot

on the external circle we switch from a representa-

tion of space to a representation of time. To each

black dot (i.e. destination airport) we attach one or

several coloured dots that each represent a flight

from Nice to that destination airport, in ordinal time.

Noticeably the information delivered corresponds to

one specific day inside a week. Flights that occur on

a regular basis (everyday monday over the period)

are differentiated from flights that are occasional, or

do not operate throughout the whole 6 months

period (Figure 6).

Figure 6: The ordinal time layer (Monday flights): each

coloured dot corresponds to one flight. Dark red dots -

flights that occur on a regular basis, light orange dots -

seasonal or occasional flights. Note the varying

proportion of non-regular flights between those heading

north-west and others. User-side interaction triggers the

displaying of textual and visual information about cities,

airport codes, plane types, departure and arrival times,

operating periods etc.

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4.3 The Discrete Time Layer

The prototype was initially designed, as shown in

Figure 6, in order to highlight densities in ordinal

time. However we did test a solution based on the

discrete time model, inspired by (Blaise and Dudek

2011). The result is shown on Figure 7: densities of

flights, hour per hour, and destination per

destination, are assessed visually – yet the usability

of such a visualisation does require further

investigation, and indeed a robust evaluation effort.

Figure 7: The discrete time layer (Monday flights):

concentric circles correspond to hours of a day (time runs

outwards). Each dot corresponds to a given flight, with the

same colour codes as in the ordinal time layer.

5 BENEFITS AND LIMITATIONS

The prototype was intended to allow for a visual

assessment of an airport’s flight offer. At this stage

what can be said is that it does help unveiling some

significant spatio-temporal patterns such as a

northbound flights trend, the predominance of non-

regular flights in that geographic area, and in

particular for UK flights, a majority of flights

operating after midday, a significant variability in

the number of flights depending on the day and on

the destination, etc.

The visualisation can be used to investigate the

offer for a given day, but also to allow for

comparative or cumulative reading of the data, as

illustrated in figure 8.

Figure 8: Allowing for comparisons: Monday flights (top):

vs. Tuesday flights (bottom). Note differences in

destinations, overall number of flights, and type of flights.

Yet, although we consider the result as providing

a somehow valuable service, the visualisation at this

stage has clear limitations:

 scalability: what can be seen as a credible

option for a middle size airport like Nice would

be irrelevant in the case of major hubs (or at

least would require severe data filtering steps

prior to the visualisation itself).

 Updating: at this stage the data is stored in an

RDBMS and the visualisation produced on the

fly – but no automatic feeding of the applica-

tion is implemented.

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 Connecting flights could be visualised but this

feature is not implemented at this stage.

 The discrete time visualisation is relatively

arduous to read: further development is needed

in order to embed more user-side interaction

(typically, brushing data items).

 Cumulative visualisations (several days shown

in one same space) are rather dense, in

particular in discrete time – yet they are useful

to compare the flight offer on one day to

possibilities on other days. This would require

a specific visual encoding effort.

Furthermore, we did conduct an informal round

of evaluation with a group of six testers – who were

only asked to decode the information - but are fully

aware that a robust evaluation effort remains to be

done both in terms of readability of the visualisation,

and of added-value. In short, what we present should

definitely be understood as an early proof-of-

concept prototype: we do acknowledge that there are

at this stage significant limitations that minor its

potential impact.

6 CONCLUSIONS

Online planners and travel reservations systems play

today a prominent role in the everyday life of

travellers, yet the very nature of the queries users

formulate (ultimate result: one flight) limits the type

of information one can expect to retrieve. We

introduce a proof-of-concept visualisation that sums

up in a synthetic way an airport’s [where to, when

to] profile and thereby allows users to get an overall

view of its flight offer over time and in space.

The visualisation’s role is not to replace the

above mentioned reservation systems, but provides

an upstream service, helping to unveil significant

spatio-temporal patterns in relation with a given

airport. It is implemented on a real life data set: the

winter 2013/2014 schedule of the airport in Nice

(circ. 200 flights per week). At this stage the

development still leaves a lot of room for

improvement, yet it already underlines the potential

benefit of a context + focus information visualisation

solution in renewing the way users portray an

airport’s flight offer. This experiment now needs to

be questioned through a robust evaluation effort, and

in terms of genericity (other transportation modes

for instance). Future works will primarily focus on

added-value assessment, user-side interaction and

visual encoding issues, but at the end of the day we

view this specific visualisation as one in many: it can

be seen as one element of a toolbox to come that

would include alternative solutions, suited to major

hubs, including isochrones, etc.

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