=Paper=
{{Paper
|id=None
|storemode=property
|title=People Go Out Together: a Neglected Context Factor in Pedestrian Assistance Systems
|pdfUrl=https://ceur-ws.org/Vol-786/paper4.pdf
|volume=Vol-786
}}
==People Go Out Together: a Neglected Context Factor in Pedestrian Assistance Systems==
https://ceur-ws.org/Vol-786/paper4.pdf
People go out together: a neglected context
factor in pedestrian assistance systems
Bjoern Zenker
Chair for Artificial Intelligence
Friedrich-Alexander-University Erlangen-Nuremberg
Haberstraße 2, D-91058 Erlangen
bjoern.zenker@cs.fau.de
Abstract. Pedestrian navigation systems (PNS) are currently not as
frequently used as navigation systems for cars. We think, this is due
to the PNS’s lack of considered context factors. In this paper we argue
that actually the social contacts of the users are an important context
factor for PNSs. Our argumentation is based on the results of two studies
which we have conducted concerning outgoing behavior of humans. The
results show that over 60% of all pedestrians in their leisure time go
out in groups. Thus, PNSs should incorporate this important factor. We
will present a novel PNS for groups (PNS4G), which allows a group of
individuals to get assistance in meeting and navigation to a common
goal, e.g. for going to the cinema.
1 Introduction
1.1 Motivation
Current PNSs are intended for single users only. They assist one user by helping
the user to go from a starting place to a destination. But our everyday knowledge
tells us, that this is often not sufficient, for example when you want to meet a
friend who is situated at a different place than you. In such a case a meeting
point must be found where your friend and you can meet and then proceed to
your common destination.
For this setting, we can roughly identify two phases for which assistance can
be provided. First, assistance can be given in the phase of making the appoint-
ment, e.g. settling on a time and destination. Second, assistance can be given for
the navigation of all people involved in the meeting to their common destination.
This includes finding appropriate meeting points and routes.
1.2 Outline
First, in Section 2, we will present the state of the art of PN for single users and
groups of users and inspect the problem of measuring group sizes of people.
In the following section we will present our own studies which measured group
sizes of pedestrians more accurate.
As most people go out in groups, we built a PNS which focuses on considering
this important contextual factor. This system is presented in Section 4.
Finally we conclude in Section 5 and make a forecast on future work.
2 State of the Art
Current pedestrian navigation systems help pedestrians to find their way to
their goals by giving turn-by-turn instructions. Today all pedestrian navigation
systems known to the authors are for single users only. Examples of such systems
are Google Maps Navigation, ovi maps, MobileNavigator, PECITAS [1], P-Tour
[2], RouteCheckr [3], COMPASS [4] and many more.
One exception to these single user PNS is a prototypical PNS from [5] which
helps people to meet. The system recommends a restaurant in an area which is
reachable by all participants in time and helps them navigating to the restau-
rant by displaying some maps. No detailed information about this system is
available in literature. For the special case of exactly two users the systems
MeetMe (http://aboutmeetme.com) and MeetWays (http://meetways.com)
recommend meeting points and routes. This is done by first calculating the
shortest path between the two users and second recommending places of interest
(POI) which are near to the middle of the calculated path.
While there exist numerous studies about animal group sizes, only few exist
for human group sizes. One of the first studies about human group sizes orig-
inates in the year 1951, when James measured group sizes in politics and in
public places, e.g. pedestrians, in department stores, playgrounds. In [6], James
determines a mean group size of 2.41 people in informal groups. A total of 7405
groups have been studied. 71, 07% of all groups consisted of two persons, the
other 28, 93% have been groups between three and seven persons. In this study,
the counting of individuals has not been taken into account. The subsequent pa-
per [7] declares a mean group size of 1, 46, if individuals are considered. James
also deduced a generalized model of pedestrian group sizes from this data, but
this model has been prooven incorrect by [8].
From James’ data we can calculate, that 34.46% of all groups (including
groups of size 1, namely individuals) are groups of two or more people. To a
different result comes [9], who observed, that up to 70% of pedestrians in a
commercial street walk in groups.
Note the difference between the latter two units of measurement. By con-
verting James’ result, we find that 55.14% of all pedestrians in James’ study are
in groups. Now one can easily see that the results of the two studies differ about
15%.
2.1 Crowding
There are various problems with measures of group sizes, as group sizes most
of the time do not follow a normal distribution. [10] Hence, only statistical
methods for non-normal distributed data may be used. Besides that, one has
to distinguish measurements between Insiders’ and Outsiders’ View as Jarman
noted in [11]. One finding which motivated this discrimination was that Jarman
showed, that average individuals (Outsiders’ View) live in groups bigger than
the average individual (Insiders’ View).1
The mean group size for the Insiders’ View conforms to the arithmetic mean.
Let S = {s1 , . . . , sn } be a tuple of the measured group sizes si ∈ S.
1X
s̄out = m̄arithm = s
n
s∈S
The mean group size for the Outsiders’ View, called Typical Group Size
according to Jarman or crowding according to [10], is calculated as the following.
P 2
s
s∈S
s̄in = c = P = L2 (S)
s
s∈S
In fact, this method of calculating a mean equals the second Lehmer mean
L2 . As Jarman invented the measure crowding based on sociologic reasoning
only, it is astonishing that he ended with this well know mathematical mean. To
the knowledge of the author, this hasn’t been noticed yet.
A short example will reveal the difference. Imagine that we have observed
one group of size two and one group of size five. The Insiders’ View group size
calculates to s̄in = 2+5
2 = 3.5. If we do not only take into account the measured
group sizes but the group sizes of all individuals in the groups, this conjures up
another image: In the Outsiders’ View, the average individual lives in a group
of size s̄out = 2·2+5·5
2+5 = 4, 14. This is an example which shows, that average
individuals (Outsiders’ View) live in groups bigger than the average individual
(Insiders’ View).
3 People go out together
As the studies of James are now more than half century old and not tailored to
our specific subject of research, we conducted two studies of group sizes recently.
The first study measured group sizes of people using means of public transporta-
tion. The second study measured group sizes of pedestrians and people in shop-
ping malls, restaurants and other places of public life. After the discussion of
these two studies we will compare and interpret their results, also with regard
to ’ findings.
3.1 Groups using Public Transportation
In a diary study about information needs of public transport users we asked the
participants questions about the group size in which they were travelling. 10
1
Except the case in which all groups have the same size.
participants completed a total of 188 rides with means of public transportation
in the area of the metropolitan area of Nürnberg.2 16.07% of all rides have been
conducted in their leisure time, 74.40% in their time for business and in 12.50%
people had to settle an affair. 80.95% of all participants rode alone and had no
meeting with other people planed, 7.14% rode alone and had a meeting planned,
5.36% rode in a group and had a meeting planned and 6.55% rode in a group
and had no meeting planned.
The average group size (Outsiders’ View) was 1.19 individuals. (1.06 individ-
uals per group travelling for business and 1.81 individuals per group for leisure.)
Considering only groups of two or more individuals, we get 2.60 individuals per
group (2.00 individuals per group travelling for business and 3.20 individuals per
group for leisure). Looking at the group size distribution of the leisure subgroup
one can see smaller and bigger groups occur more often than groups of middle
size. We think this is due to the limited amount of participants and recorded
rides.
88.10% of all participants were riding alone, 11.90% were riding in a group.
If we consider all people who were riding in a group or wanted to meet more
people, we come to the result that 19.05% of all people using public transport
are potential users for a PNS4Gs. Now, let us only examine rides for leisure.
There, 37.04% of all people traveled in a group and 62.96% traveled alone. From
the people traveling alone, 35, 29% wanted to meet other people. This results
in 59, 26% potential users in the leisure subgroup. (8, 80% in the business sub-
group.)
By reason of the limited number of participants and rides, this study has
only a small informative value.
3.2 Group sizes of pedestrians
We conducted a second study to measure group sizes of pedestrians and people
in public places like shopping malls and restaurants. This study was oriented at
the setting of [6] and measured the frequency of group sizes at different places in
the city of Erlangen, Germany (105.157 inhabitants). 992 observations of group
sizes were logged.
Only groups of people have been logged, which apparently did not pursue a
job task. People belonged to a group when they where talking or interacting with
each other or walking together. Small children, who seemed to be not able to walk
through the city alone, have not been counted. Observed frequencies have been
recorded with a specially developed application on a smartphone, see Figure 1.
That way, the observer could easily note frequencies while observing pedestrians
at the same time. The observations “restaurants and coffee shops”, “shopping
center 5pm” and “pedestrian zone” have been conducted on a Saturday in April,
while the other observations have been conducted on a Wednesday in April the
same week. In the future we want to conduct more observations in different
locations, on different days and times of the day.
2
Some rides have been logged by multiple participants who travelled together. These
rides are assessed in this analysis only once.
Fig. 1. Application for logging group size frequencies
Fig. 2. Observed group size frequencies, Insiders’ (m) and Outsiders’ (g) mean group
sizes. For easy observation a chart displays the group size frequencies.
Table 2 shows an overview of the different observed places and frequencies.
For means of comparison, also the data of the study from [7] and the study from
the last section is shown. This data was not included in calculating averages and
total sums.
We can see that on average 53.52% of all observed groups in this study
are groups consisting of 2 or more individuals. This means, that 60.01% of all
observed people have been in groups. In restaurant and coffee shops, the average
group sizes exceed group sizes in the other observed places. Bus stops and the
suburban street have the smallest average group sizes.
3.3 Comparison and Interpretation
The average group sizes for bus stop and the public transportation study seem
not to correlate. We interpret this as an indicator, that further studies with
greater samples have to be conducted. Especially the public transportation study
seems biased due to an unexpected high average group size. Our result is more
close to [9] who found, as mentioned, that up to 70% of all people are walking
in groups. Note that James came to a different result. This might be due to the
different places of observation used in both studies. Our study focused on places,
where people spend their free time, while James examined a broader variety of
places. Another explanation would be, that outgoing behavior has changed in
the last 57 years and today more people go out together.
The observation that over 60% of people go out in groups fortifies our assump-
tion, that this contextual factor is so important, that it should be incorporated
in PNSs. The next section shows how we built a PNS4G which considers this
and gives assistance to groups of people who want to go out.
Of course, measuring group sizes is not a proof that people meet. They could
be all families that have started as a group at home. But it is a good indication
that people meet. Group sizes in places like restaurants and shopping malls are
higher than group sizes in streets and means of transportation. Unless bigger
groups tend to use means transportation which have not been observed (like
cars), this is a strong indication, that people meet.
4 Integrated Appointment Assistance and Group
Pedestrian Navigation System
We have identified two main phases of going out. In a first phase, the appoint-
ment phase, the destination and the time to meet have to be agreed upon. In the
following meeting phase individuals proceed to the common destination. There
exist more phases, but we focus in this paper on the two presented. For appoint-
ment phase and meeting phase we built two assistance systems.
The meeting assistance system consists of two subsystems, the Appointment
Assistance System (AAS) and the Group Pedestrian Navigation System (GPNS).
AAS helps individuals in the appointment phase to agree on a destination. After
having settled on a destination, the meeting phase begins: routes for all individu-
als are calculated. These routes are not only routes directly from each individual
to the destination, but routes where individuals meet at intermediate points
on their way to the destination. Both systems are presented in the subsequent
subsections.
Currently, AAS and GPNS are coupled loosely. After the destination is settled
upon, the result is passed to GPNS as input. For more elaborate systems in the
future, characteristics of the group route (GR) should be considered also in the
appointment phase.
Both systems extends the PNS ROSE [12] and can be used using one single
client, currently running on mobile phones with Java Mobile Edition. The client
communicates with a J2EE server using JSON over HTTP. The server stores
user profiles, friends lists and events lists in a database. Also the server offers
services such as geocoding, route generation for pedestrians with support of
public transport and recommendation of events.
4.1 Appointment Assistance System
The AAS assists groups of individuals settle on a common destination. As we
cannot rely our design on experienced data we build a flexible system which
allows several task flows to be implemented. We then compared task flows to
find an appropriate task flow for our scenario. The task flow of the user to user
interaction can be modeled in different ways. We implemented different task
flows and evaluated them. By using this method, we examined which user to
user interaction work flow is best. A detailed description of this system is out of
the scope of this paper.
4.2 Group Pedestrian Navigation System
After a settlement is achieved our system helps the individuals to navigate to
the agreed destination. For this, the current positions of the participants and
the destinations are passed to the GPNS.
One can easily imagine, where the participants could meet: at the destination,
at one of the participants homes or at some dedicated locations in between.
Often, the latter is the case: People meet at intermediate locations to yield a
good compromise between detour and conjoint travel.
To model this behavior we reduced the problem of finding satisfying meeting
points to the Steiner Problem. Various versions of this problem exist, but the
essence of this problem is to “Find the shortest network spanning a set of given
points. . . ” [13]. In [14] we discuss the calculation of meeting points and routes
for multiple people based on two versions of the Steiner Problem in detail.
After calculation in the server, the routes are sent to the participants mobile
phones. There, routes are displayed and allow turn-by-turn navigation for each
user. Figure 3 shows the client of a user who is going to meet shortly another
user. To facilitate meeting, positions of users are updated on the screen almost in
real-time. Meeting points could be dynamically recalculated, but as this might be
confusing to the users, the current implementation does not employ this feature.
After meeting they will continue their journey to their destination conjoint. An
overview map of the routes of two users in the city of Berlin and their meeting
point (pink) can be seen in Figure 4.
Fig. 3. Screenshot: User (cross hairs) will meet his friend (user icon) soon at the meet-
ing point (red cross).
5 Conclusion and Future Work
There are several contextual factors which still have to be integrated in PNSs.
We identified the social contacts of the users as one important factor. This is due
to the finding, that over 60% of all pedestrians go out in groups together. Because
people visit most events and locations together, supporting groups of users will
become one major challenge for future developments of PNS. To consider this
finding in pedestrian navigation, we built an integrated appointment assistance
and group pedestrian navigation system.
Currently we are collecting more data for the two studies to ensure more
reliable results. We have also work ongoing, which allows not only to create
walking routes for groups, but also to take means of public transportation into
account. Furthermore we are developing an HTML client to allow more users
use the system and to carry out extensive user studies.
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