A Time-location-Based Itinerary Visualization

Florian Haag

, Thomas Schlegel

and Thomas Ertl

Institute for Visualization and Interactive Systems, University of Stuttgart,

Universit

atsstraße 38, 70569 Stuttgart, Germany

Junior Professorship Software Engineering of Ubiquitous Systems, Technische Universit

at Dresden,

othnitzer Straße 46, 01178 Dresden, Germany

Keywords:

Timetable Visualization, Itinerary Visualization, Transportation.

Abstract:

With the advent of linked data sources, transportation information systems are no longer limited to indicating

how to get from one location to another. They can suggest where to go shopping on the way or plan several

synchronized itineraries for groups of travelers. Along with these developments, information about stopovers

evolves from mere additional data to a crucial part of the itinerary. However, current time-based visualizations

of itineraries cannot adequately convey the stopovers contained in an itinerary. We propose a time-location-

based itinerary visualization that can be used when planning trips, which allows for the easy comparison of

itineraries with different routes, and for aligning itineraries of several travelers in collaborative scenarios. We

describe the visualization concept and report on a user study that conﬁrms the basic ideas and provides a

number of insights on how the visualization can be developed further.

1 INTRODUCTION

Automated travel planning systems for public trans-

portation have become commonplace in many coun-

tries. Instead of transportation company employees,

software searches for the appropriate itineraries based

upon the user’s wishes. In particular, such travel in-

formation systems are increasingly used on mobile

devices (Heimonen, 2009), and a multitude of efforts

to cater to mobile users of transportation information

systems have been undertaken (Arikawa et al., 2007;

Ferris et al., 2010).

With the increased integration of linked data into

services for general audiences, different kinds of in-

formation and data from several sources can be au-

tomatically combined (Gahleitner and W

oß, 2004;

Walther et al., 2009). This is particularly promising

in the ﬁeld of transportation, where the raw data from

travel itineraries can be cross-linked with information

about shopping opportunities or points of interest for

tourists (Alves et al., 2009; Husain and Dih, 2012).

An additional aspect to consider is that informa-

tion systems get integrated into and start supporting

our social lives (Khalil and Connelly, 2005). Systems

that support not only single users, but groups of users

in their mutual lives are being developed (Boti

cki

et al., 2011). Travelling is one of these social activi-

ties; groups of people join each other for common legs

of a journey or meet each other while underway (Gar-

cia et al., 2011).

As a result, trip planning systems of the future will

do more than just ﬁnd an itinerary that brings a trav-

eler from one place to another. They will suggest

an itinerary that lets users run errands without mak-

ing detours, or choose routes so they can meet friends

who are underway at roughly the same time. Conse-

quently, the current ways to display itineraries are not

sufﬁcient for that kind of trip planning, as they were

designed with single trips for single or homogenous

groups of travelers in mind. Instead, new visualiza-

tions are required to provide an overview of the avail-

able itineraries, with information about possible ac-

tivities at stopovers or about itineraries of other trav-

elers.

2 RELATED WORK

A very widespread approach for displaying itinerary

suggestions is a tabular display. This is offered by

systems such as the website of the German railroad

operator Deutsche Bahn AG (DB, 2014) and provides

a basic, text-based overview of the available trips.

Other services have enhanced such a table-based

view so that the vertical axis indicates time. Sev-

Haag F., Schlegel T. and Ertl T..

A Time-location-Based Itinerary Visualization.

DOI: 10.5220/0005199600770084

In Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP-2015), pages 77-84

ISBN: 978-989-758-088-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

eral alternative trip suggestions are placed next to

one another, similar to Gantt charts (overview by

Wilson (2003)) or the LifeLines approach (Plaisant

et al., 1996). Sometimes, detailed information can be

made available upon request (Batrinca et al., 2013).

On the other hand, displaying the relevant informa-

tion side by side allows for a direct comparison be-

tween timelines (Havre et al., 2000; Zhao et al., 2012).

This time-based approach has become widespread in

practice, in the context of passenger trafﬁc (Daimler,

2014; Schildbach, 2014) as well as transport logis-

tics (NETRONIC, 2013) or work scheduling (Chau

et al., 2004). Additional information such as the es-

timated walking time at stops (if travelers have to

bridge a distance to get to the connecting vehicle) can

be integrated into this kind of visualization. However,

the described timeline approaches do not provide an

easy way to get an idea of the different routes used by

the trip suggestions.

Map-based views are based upon the geographic

properties of the transportation network and dis-

play the temporal aspect only as an additional fac-

tor (Cherry et al., 2006) or in a separate view (Stewart

et al., 2013). While they provide a good overview

of the available routes and the whereabouts of the re-

spective stopovers, they do not allow for a quick com-

parison of departure and arrival times or durations of

waiting phases at stopovers. Related approaches pro-

vide chances for integrating additional information

into the maps, but do not propose a complete possi-

ble solution that displays the full itineraries (B

ottger

et al., 2008). When spatial and temporal informa-

tion are combined, visualizations tend to focus on the

exact representation of the spatial aspect without al-

lowing for a direct comparison of times and durations

in overlapping itineraries (Thudt et al., 2013; Ventura

and McGufﬁn, 2014). If both aspects are represented,

comparing itineraries at the same or at overlapping

locations becomes problematic (Hewagamage et al.,

1999), or—when using three dimensions—times can-

not always be clearly seen at a glance (Kraak, 2003).

Alternatively, place-time-charts have been pro-

posed as a visualization base rather than a geograph-

ical map. They are not only used for transportation

or logistics planning (VIA, 2013; Goverde and Meng,

2011; Regmi and Hanaoka, 2012), but have also been

proposed for passenger information, however without

integrating any extended information on the transfers

directly in the visualization (Masoodian and Budd,

2004).

A schematic graph-based and a matrix-based view

have been presented, as well (Keller et al., 2011).

While both provided an overview of the number of

routes and the travel time, the graph-based visualiza-

tion did not allow for an intuitive comparison of the

travel times, while the matrix-based visualization re-

quired memorizing the mapping of colors to various

aspects of the itineraries.

Therefore, none of the existing visualizations

gives a good idea of where several itineraries coincide

or differ in terms of location, while also conveying

an adequate comparison of the arrival and departure

times at each stop.

3 TIME-LOCATION-BASED

ITINERARY VISUALIZATION

We propose a timetable visualization for trip plan-

ning that provides an overview of both the time and

the location in the course of the journey, and that al-

lows for a side-by-side comparison of several alter-

native itineraries. Locations are abstracted to logi-

cal places, such as named stops in a public trans-

portation network, rather than the physical (geograph-

ical) location. The respective information can be re-

trieved from linked data sources that provide meta-

information on businesses and facilities near public

transportation stops (Ruta et al., 2012).

3.1 Basic Itinerary Layout

As a basis for our visualization, we chose a grid-

based approach, comparable to the small multiple

paradigm (Tufte, 1983, p. 48): Rows in the grid rep-

resent single stops that appear in any of the displayed

itineraries, while each itinerary occupies one column

in the grid. Each cell of the grid indicates a stop (ori-

gin, destination, or intermediate transfer).

Figure 1: Depiction of four grid cells in the time-location-

based visualization. They contain an excerpt of two

itineraries (columns) at two stops (rows). Three transfers

are shown (the different colors of the wide bars represent

different transportation lines). The left itinerary does not

require a transfer at the second shown stop.

Figure 1 shows four exemplary cells in the

itinerary grid. In keeping with the aforementioned

placement, the two columns belong to two distinct

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

itineraries, and the two rows indicate how two dif-

ferent locations are integrated in those two itineraries.

The lower left cell only shows a thin connecting line,

meaning that there is no transfer required at the given

location in that itinerary. The other cells each feature

a wide bar indicating—by its height—the time spent

at the location, as well as two tall bars symbolizing

the means of transportation used to get to and to de-

part from the location. The colors of these tall bars

match the line colors from the transit map for a good

visual correspondence here, though other color map-

pings are conceivable.

3.2 Transfer Information

The time spent at each location is represented by the

height of the wide white bar. At its upper and lower

edge, a thin bar represents the arrival and the depar-

ture time, respectively. These times are also written

as text above and below the according lines. Thus,

the vertical dimension represents time.

The same scaling is used across the whole grid.

Each grid row has its own time axis, so arrival and

departure times at one location can be compared vi-

sually across all itineraries.

Finally, the white area may partially be ﬁlled with

two semi-transparent rectangles. One rectangle ex-

tends from the arrival time downward and indicates

the estimated walking time at the location. The other

rectangle extends from the left border of the cell to

the right as an indicator for the walking distance—

the further the cell is ﬁlled horizontally, the closer the

walking distance gets to the maximum distance dis-

played in the bottom right corner of the screen (cf.

Figure 2).

Figure 2: A single grid cell displays some information on

a transfer. On the vertical time axis, the times of arrival

and departure at the location are shown. About half of the

area between the arrival and departure is ﬁlled with a grey

bar, implying that about half of the stopover time will be

required for walking. A yellow bar ﬁlls the area horizontally

by about

, which, with respect to the total distance of 90m

shown in the lower right corner, points to a walking distance

of approximately 60m.

3.3 Static Example

Figure 3 shows an exemplary timetable that displays

ﬁve itineraries to travel from the stop Hedelﬁngen

to the stop Ellental. It is evident at a single glance

that only itineraries 2 and 4 require a transfer in

Bietigheim-Bissingen. By comparing the relative ver-

tical placement of arrival and departure lines, users

can also recognize that all itineraries in this case take

about the same time, and that no two stopovers at any

of the stops overlap even by a short amount of time.

The stop at Central Station inevitably requires bridg-

ing a distance of about 100 meters, as the horizontal

walking distance indicator almost completely ﬁlls up

the stopover area at that location and the reference is

indicated as 110 meters in the lower right corner of

the screen.

Figure 3: A complete screen showing ﬁve itineraries with

the time-location-based approach. Two of the itineraries

require two intermediate transfers, the other three do not

require a transfer at stop Bietigheim-Bissingen. Three dif-

ferent colors are used for the vertical bars, which means

that three different transportation lines are used in these

itineraries (cf. Section 3.1).

3.4 Order of Locations

The order of the locations in the time-location-based

approach is not ﬁxed. As different itineraries might

include the same locations, but in opposite orders,

there is no single optimal ordering for all displayed

locations. Instead, the order of rows can be changed.

Figure 4 shows two itineraries, where locations

have been ordered based on Itinerary 1. For Itiner-

ary 2, the order of stopovers is still recognizeable: A

thin connection line links the colored tall bars in the

order the transfers happen, and moreover, that order

is reﬂected by the horizontal displacement of the tall

bars.

ATime-location-BasedItineraryVisualization

Figure 4: Two itineraries in the time-location-based visu-

alization that visit a bookstore and an electronics store in

different orders. The stops are sorted to match the order

found in Itinerary 1. The order of stops in Itinerary 2 is

still recognizeable both by the horizontal displacement of

the thick bars and by the thin blue connection lines.

4 USER STUDY

We have conducted a small user study to gather some

comments by possible users and gain some insight

into how they react to and use our visualization. Our

study had two goals: On the one hand, we wanted

to compare the performance of users in traditional

time-based timetable visualizations with that in our

time-location-based approach. On the other hand, we

wanted to test whether the additional information con-

tained in our timetable visualization approach would

be correctly recognized by users.

For the comparative goal, we chose to present

screenshots of the free mobile app

Ofﬁ (Schildbach,

2014), shown in Figure 5, which is widely used in

Germany and provides public transit data for many

European cities. It serves as a typical example for the

class of transportation information services that dis-

play itineraries with a standard time-based visualiza-

tion (Victor, 2012; Upbin, 2012; Voyages-sncf.com,

2014; Daimler, 2014).

4.1 Participants

Eleven participants between 20 and 30 years of age

took part in our user study. All participants thought

of themselves as frequent users of public transporta-

tion, though only four of them had used mobile ap-

plications to retrieve related information. Participants

were recruited with a public ad and were not afﬁliated

with the authors.

Figure 5: A screenshot of

Ofﬁ’s (Schildbach, 2014) time-

based visualization as shown in the user study.

4.2 Materials

Written descriptions of the featured visualizations

were prepared to warrant providing each participant

with the same initial information. Trial descriptions,

along with a brief presentation of the scenario, were

prepared on paper, as well.

The prototypical application, which was installed

and conﬁgured before the user study on a Samsung

Galaxy S4 Android smartphone (cf. Figure 6), in-

cludes two major features:

• It displays static screenshots of itineraries output

by the comparison application that were retrieved

from actual requests to the transportation informa-

tion system.

• It displays itineraries based upon our time-

location-based approach. The displayed itiner-

aries were hard-coded based on the task deﬁni-

tions for the study. Column headers could be

tapped to reorder stops based on the respective

itinerary (cf. Section 3.4).

Only actual itineraries from the public transit system

of the city and region of Stuttgart were used. This

use of real data ensured that the itineraries were re-

alistic. Moreover, none of the participants could rely

on extensive prior knowledge about Stuttgart’s transit

system as the study was conducted with inhabitants of

Dresden, about 400 kilometers away.

4.3 Design

The study was broken up into four tasks, each of

which consisted of three to six trials. Trials were al-

ways conducted in the same order to warrant compa-

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

Figure 6: The application used in the study, showing the

time-based (left) (Schildbach, 2014) and the time-location-

based (right) visualization on devices of the type used in

the study. Various itineraries with different stopovers can

be seen well on the right-hand device here.

rable results, and because tasks and trials would grad-

ually increase in difﬁculty.

4.3.1 General Information (Departure and

Arrival Times, Number of Routes ...)

The ﬁrst task aimed at a comparison of our visu-

alization with the traditional time-based approach.

Each participant was shown three sets of itineraries

with the time-based visualization and three sets of

itineraries with the time-location-based visualization.

Stop names were shown as locations. Based upon that

on-screen output, some general information about the

itineraries had to be determined—in detail, the origin

and destination of the itineraries, the earliest shown

departure time and the latest shown arrival time after

and before a given time, respectively, the minimum

and maximum number of vehicle changes, as well as

the displayed number of distinct routes.

4.3.2 Available Time at Stopovers, Walking

Distance

In the second task, three sets of itineraries were shown

in connection with a scenario where a user needs to

run several errands on their way to a destination. Lo-

cations were mapped to stop names; some stops were

marked to contain special facilities (such as particu-

lar stores). Users were asked to identify the itinerary

from each set that leaves a maximum amount of time

for the errands, and the itinerary that requires the least

amount of walking, based on the distance.

4.3.3 Order of Stopovers

For the third task, instead of concrete stops, the lo-

cations were labeled bank, supermarket, etc., and

did not necessarily refer to the physically same place

across all itineraries. Each itinerary would visit the

locations in a different order, and users were asked to

identify the itinerary in each set that visits the facili-

ties in a speciﬁc order (e.g. fetching money from the

bank before buying something in the supermarket).

4.3.4 Aligning with other Itinerary

The fourth task dealt with a short encounter between

two acquaintances at a stopover. Therefore, each of

three sets of itineraries was combined with an addi-

tional highlighted itinerary that had a different origin

and destination than the others (cf. Figure 6, right).

Users were asked to identify the itinerary that pro-

vided the best opportunity to meet the other person

(using the highlighted itinerary) underway.

4.4 Procedure

Participants took part in the study one at a time in a

closed room, with one supervisor of the study present.

The printed information was handed out to them.

Participants were provided with the mobile device

on which the application was running. They had some

time to get acquainted with both visualizations based

on two sample sets of itineraries before starting the

tasks. While it was made clear to them that the whole

study would be recorded on video, the time measure-

ment in task 1 was not mentioned in order to avoid

any feeling of time pressure.

After completing the tasks, participants were

asked to provide some comments on the two visual-

izations, which were noted down by the study super-

visor. Each participant concluded the study within 30

to 40 minutes and received a small ﬁnancial reward.

4.5 Results

While we were primarily interested in user comments

on the time-location-based itinerary visualization, we

did check the correctness of the responses. This was

done in order to determine whether the study partic-

ipants had correctly understood how to read the vi-

sualizations in question. Overall, participants could

answer most questions on both visualizations cor-

rectly, though error rates—and the distribution of er-

rors among participants—varied considerably. Most

answers were given in a timespan between 3 and 20

seconds for both visualizations. Table 1 provides an

overview of the correctness of answers.

ATime-location-BasedItineraryVisualization

Table 1: Correctness of responses in the user study: For

each task (T), the questions, the visualization (time-based

(t) or time-location-based (tl)) and the total number of re-

sponses is shown, along with the number of incorrect re-

sponses and the number of subjects who responded incor-

rectly to at least one question.

T Question Vis.

Total

Resp.

Incorrect

Resp. Subj.

origin/

destination

t 33 0 0

tl 33 13 6

earliest

departure

t 33 1 1

tl 33 0 0

latest

arrival

t 33 2 2

tl 33 6 3

no. of

changes

t 33 3 3

tl 33 12 5

number

of routes

t 33 12 10

tl 33 9 4

av. time

33 18 10

min. dist. 33 5 4

3 order 33 6 3

4 meeting 33 1 1

4.5.1 Task 1: General Information

Most of the questions about the general information

were correctly solved. Several users, however, had

some difﬁculties recognizing the destination depend-

ing on the order of locations in the time-location-

based visualization. Determining the number of ve-

hicle changes sometimes proved difﬁcult for a few

users especially in the time-location-based approach,

whereas this posed no problem to the other six partic-

ipants. Likewise, seven of the users could correctly

distinguish the numbers of routes among the dis-

played itineraries in the time-location-based model.

Using the time-based visualization, in contrast, ten

out of the eleven participants failed to determine the

number of different routes in one of the trials.

A few participants were discontented with aspects

of the time-location-based visualization such as the

reordering of locations, the coloring of the tall bars

(though there were also positive remarks about these

colors matching different lines in the transit system)

and the lack of any directly visible information on the

concrete lines to use (for instance, a bus number).

4.5.2 Task 2: Available Time at Stopovers,

Walking Distance

Exactly recognizing the available time at stopovers

proved difﬁcult for most participants, even though

eleven of the incorrect answers were still partially cor-

rect. In the question about walking distances, seven of

the participants did not commit any mistakes.

Several of the participants raised concerns about

the time necessary to get used to the time-location-

based visualization. Also, they found the time re-

quired for walking to be irrelevant. Other partici-

pants commented, however, that the displayed infor-

mation was overall important; that the time available

at stopovers was easily recognizable by the vertical

time scales and that the indication of the walking

distance was useful. Two participants explicitly re-

marked that they liked the large amount of informa-

tion shown at a time.

4.5.3 Tasks 3 & 4: Order of Stopovers, Aligning

with other Itinerary

As comments on the tasks focusing on the correct or-

der of stopovers and on aligning one’s itinerary with

that of an acquaintance were very similar, the results

will be presented together.

Tasks 3 and 4 were correctly answered by almost

all participants. Only three participants made any

mistakes at all in the order-related task. An additional

participant corrected their initial erroneous answers

for one of the trials in each task 3 and task 4.

Several participants stated that they liked the

scenarios presented in these tasks. Two partici-

pants spoke favorably of the option to sort the time-

location-based view based upon each itinerary. Three

participants remarked that they considered the thin

connection line across cells unclear especially when

locations were not sorted in the order of the exam-

ined itinerary, with one of the participants explicitly

describing the line as “necessary, but confusing”.

Further comments dealt with issues in our pro-

totypical implementation that were not inherent to

the visualization concept, such as a lack of feedback

about the current location sort order, the fact that the

highlighted reference itinerary in task 4 was not ﬁxed

to the viewport and would thus sometimes become in-

visible, and the missing support for zooming.

5 DISCUSSION

We have gathered a number of insights on what could

be considered successful and what still needs to be

changed for our time-location-based approach to be-

come useful. In general, the study participants were

able to solve the tasks by using the time-location-

based visualization. In tasks with high error rates

for the time-location-based visualization, all mistakes

were committed by about half of the participants,

while the others solved the respective task correctly.

Still, in the comparative task, users mostly performed

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

better in the time-based visualization. One exception

is the question about the number of different routes,

which reﬂects that routes cannot be compared in the

time-based visualization.

The basic grid layout appeared to be welcomed by

users. The side-by-side comparison of the itineraries

with different routes and the uniform time-scale

across the whole view were pointed out to be good

ideas. The same applies to the general amount of in-

formation, though differing statements by users im-

ply that some details—such as the estimated walk-

ing time—should be made optional. The possibility

to align one’s itinerary with that of another traveler

was positively noted, but it also evoked the question

whether the system could automatically determine the

best opportunity for a meeting. Travelers may, how-

ever, possess some additional knowledge about a par-

ticular stop, which is not available to the system and

thus could not be factored into such a decision.

The capability to reorder the locations was partly

praised, partly criticized. Much of the critique was

related to users being unsure about the current sort-

ing, which points to a lack of visible feedback in our

implementation. The observed difﬁculties in recog-

nizing the point of origin and the destination of the

trips could be mitigated by giving the respective cells

more visual distinctiveness compared to the stopover

cells. Moreover, using diagonal lines for the connec-

tion lines across cells could further enhance the visi-

bility of the current sorting order and make the con-

nection lines more dissimilar to the grid lines.

Some of the users made remarks about a lack of

information, such as the concrete bus or tram lines to

use, and expressed that they are used to time-based

visualizations. Therefore, a combination of the time-

location-based and the time-based visualizations ap-

pears to be promising, where the time-location-based

view is used during the planning phase and users are

free to switch to the time-based view when starting

their journey. This, along with explanatory interac-

tive clues displayed upon request, could also help

solve the perceived effort to learn how to use the time-

location-based visualization.

6 CONCLUSION AND FUTURE

WORK

We have discussed the state of the art for visually

comparing itineraries. Based upon related work and

the requirements resulting from multi-user scenarios,

we have deﬁned a novel way to visualize itineraries

that can be easily compared both based on tempo-

ral and local aspects, and that is suited for the syn-

chronization of trips across travelers. We have imple-

mented a prototypical mock-up of our visualization

and have conducted a small user study. Users could

solve most tasks correctly and various aspects of the

visualization were praised as useful ideas. However,

some users also committed a high number of mistakes

in some tasks, which have helped identify ﬂaws in

some aspects of our initial time-location-based visual-

ization concept. In the discussion of the study results,

we have assembled a list of suggestions for the further

development of the time-location-based visualization

and some open issues that need to be solved.

We are conﬁdent that the time-location-based

itinerary visualization is a promising approach that

should receive further attention. The optimizations

hinted at in the study results discussion, zooming fea-

tures for the time axis, cosmetic changes such as the

user suggestion to express certain locations with icons

rather than texts, and the integration of real-time in-

formation for the displayed itineraries will require

some further user experiments. Ultimately, the results

may be applicable to other ﬁelds as the time-location-

based visualization can be generalized for other types

of event-related information—event logs of software

systems that communicate and exchange data among

each other, as well as the planning of shared resources

that can be reserved and used by the staff of an enter-

prise come to mind.

ACKNOWLEDGEMENTS

We would like to thank Steffen Huber and Bianca

Zimmer of TGU interaktion.biz (unit of TTI GmbH)

for their ﬁne-tuning and practical execution of the

user study. Also, we would like to thank our stu-

dent research assistant Dang Huynh for his imple-

mentation of the Android prototype. This research

was funded through the IP-KOM-

OV project (Ger-

man Ministry of Economy and Technology (BMWi)

grant number 19P10003N).

REFERENCES

Alves, A. O., Pereira, F. C., Biderman, A., and Ratti, C.

(2009). Place enrichment by mining the web. In AmI

’09, volume 5859 of LNCS, pages 66–77, Heidelberg,

Germany. Springer.

Arikawa, M., Konomi, S., and Ohnishi, K. (2007). Nav-

itime: Supporting pedestrian navigation in the real world.

IEEE Pervasive Comput., 6(3):21–29.

Batrinca, L., Khan, M. T., Billman, D., Aydemir, B., and

Convertino, G. (2013). A timeline visualization for

ATime-location-BasedItineraryVisualization

multi-team collaborative planning. In EA CHI ’13, pages

157–162, New York, NY, USA. ACM.

Boti

cki, I., Looi, C.-K., and Wong, L. H. (2011). Supporting

mobile collaborative activities through scaffolded ﬂexi-

ble grouping. Educ. technol. soc., 14(3):190–202.

ottger, J., Brandes, U., Deussen, O., and Ziezold, H.

(2008). Map warping for the annotation of metro maps.

IEEE CG&A, 28(5):56–65.

Chau, K., Anson, M., and Zhang, J. (2004). Four-

dimensional visualization of construction scheduling and

site utilization. ASCE J. Constr. Eng. M., 130(4):598–

606.

Cherry, C., Hickman, M., and Garg, A. (2006). Design of

a map-based transit itinerary planner. Journal of Public

Transportation, 9(2):45–68.

Daimler AG (2014). moovel. My A to B. .

https://www.moovel.com.

DB Vertrieb GmbH (2014). DB Bahn: bahn.de. .

http://www.bahn.de.

Ferris, B., Watkins, K., and Borning, A. (2010). OneBus-

Away: Results from providing real-time arrival informa-

tion for public transit. In Proc. CHI ’10, pages 1807–

1816, New York, NY, USA. ACM.

Gahleitner, E. and W

oß, W. (2004). Enabling distribution

and reuse of ontology mapping information for semanti-

cally enriched communication services. In Proc. Work-

shop on DEXA 04, pages 116–121, Piscataway, NJ, USA.

IEEE.

Garcia, I., Sebastia, L., and Onaindia, E. (2011). On the

design of individual and group recommender systems for

tourism. Expert Syst. Appl., 38(6):7683–7692.

Goverde, R. M. and Meng, L. (2011). Advanced monitoring

and management information of railway operations. J.

Rail Transp. Plann. Manage., 1(2):69–79.

Havre, S., Hetzler, B., and Nowell, L. (2000). ThemeRiver:

Visualizing theme changes over time. In Proc. InfoVis

’00, pages 115–123, Piscataway, NJ, USA. IEEE.

Heimonen, T. (2009). Information needs and practices of

active mobile internet users. In Proc. Mobility ’09, pages

50:1–50:8, New York, NY, USA. ACM.

Hewagamage, K., Hirakawa, M., and Ichikawa, T. (1999).

Interactive visualization of spatiotemporal patterns using

spirals on a geographical map. In Proc. VL ’99, pages

296–303, Piscataway, NJ, USA. IEEE.

Husain, W. and Dih, L. Y. (2012). A framework of a person-

alized location-based traveler recommendation system in

mobile application. IJMUE, 7(3):11–18.

Keller, C., Korzetz, M., K

uhn, R., and Schlegel, T. (2011).

Nutzerorientierte Visualisierung von Fahrplaninforma-

tionen auf mobilen Ger

aten im

offentlichen Verkehr. In

Mensch & Computer 2011, Munich, Germany. Olden-

bourg.

Khalil, A. and Connelly, K. (2005). Improving cell phone

awareness by using calendar information. In INTERACT

2005, volume 3585 of LNCS, pages 588–600. Springer,

Heidelberg, Germany.

Kraak, M. (2003). The space-time cube revisited from a

geovisualization perspective. In Proc. ICC ’03, pages

1988–1996, Durban, South Africa. ICA.

Masoodian, M. and Budd, D. (2004). Visualization of travel

itinerary information on pdas. In Proc. AUIC ’04, pages

65–71, Darlinghurst, Australia. ACS.

NETRONIC Software GmbH (2013). Gantt Dia-

gramm – Visuelles Steuern von Produktion- Projekten-

Ressourcen. http://www.netronic.de/produkte/varchart-

xgantt/ueberblick.html.

Plaisant, C., Milash, B., Rose, A., Widoff, S., and Shnei-

derman, B. (1996). LifeLines: Visualizing personal his-

tories. In Proc. CHI ’96, pages 221–227, New York, NY,

USA. ACM.

Regmi, M. B. and Hanaoka, S. (2012). Assessment of in-

termodal transport corridors: Cases from north-east and

central asia. RTBM, 5(0):27–37. Intermodal Freight

Transport and Logistics.

Ruta, M., Scioscia, F., Ieva, S., Loseto, G., and Di Sciascio,

E. (2012). Semantic annotation of OpenStreetMap points

of interest for mobile discovery and navigation. In Proc.

MS ’12, pages 33–39, Piscataway, NJ, USA. IEEE.

Schildbach, A. (2014).

Ofﬁ. http://oefﬁ.schildbach.de.

Stewart, K., Fan, J., and White, E. (2013). Thinking about

space-time connections: Spatiotemporal scheduling of

individual activities. Trans. GIS, 17(6):791–807.

Thudt, A., Baur, D., and Carpendale, S. (2013). Visits: A

spatiotemporal visualization of location histories. In Eu-

roVis – Short Papers, pages 79–83, Leipzig, Germany.

Eurographics Association.

Tufte, E. R. (1983). The Visual Display of Quantitative In-

formation. Graphics Press, Cheshire, CT, USA.

Upbin, B. (2012). Why Hipmunk is the world’s best travel

site. .

http://www.forbes.com/sites/bruceupbin/2012/06/29/

why-hipmunk-is-the-worlds-best-travel-site/.

Ventura, Q. and McGufﬁn, M. J. (2014). Geo-topo Maps:

Hybrid visualization of movement data over building

ﬂoor plans and maps. In Proc. GI ’14, pages 159–166,

Toronto, Ont., Canada. CIPS.

VIA Consulting & Development GmbH (2013). LUKS

2.3.5 wurde im April 2013 ver

offentlicht. .

http://www.via-con.de/luks-2-3-5-released/1921.

Victor, B. (2012). BART Widget. .

http://worrydream.com/bartwidget/.

Voyages-sncf.com (2014). Mytripset – vos itin

eraires en

europe. http://mytripset.voyages-sncf.com.

Walther, M., Schuster, D., and Schill, A. (2009). Federated

product search with information enrichment using het-

erogeneous sources. In BIS 2009, volume 21 of LNBIP,

pages 73–84. Springer, Heidelberg, Germany.

Wilson, J. M. (2003). Gantt charts: A centenary apprecia-

tion. Eur. J. Oper. Res., 149(2):430–437.

Zhao, J., Drucker, S. M., Fisher, D., and Brinkman, D.

(2012). TimeSlice: Interactive faceted browsing of time-

line data. In Proc. AVI ’12, pages 433–436, New York,

NY, USA. ACM.

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications