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.
1.1
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 su cient, for example when you want to meet a friend who is situated at a di erent 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 appointment, 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 nding appropriate meeting points and routes. 1.2
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
Current pedestrian navigation systems help pedestrians to nd 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 [
One exception to these single user PNS is a prototypical PNS from [
While there exist numerous studies about animal group sizes, only few exist
for human group sizes. One of the rst studies about human group sizes
originates in the year 1951, when James measured group sizes in politics and in
public places, e.g. pedestrians, in department stores, playgrounds. In [
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
di erent result comes [
Note the di erence between the latter two units of measurement. By converting James' result, we nd that 55:14% of all pedestrians in James' study are in groups. Now one can easily see that the results of the two studies di er about 15%. 2.1
There are various problems with measures of group sizes, as group sizes most
of the time do not follow a normal distribution. [
The mean group size for the Insiders' View conforms to the arithmetic mean. Let S = fs1; : : : ; sng be a tuple of the measured group sizes si 2 S.
The mean group size for the Outsiders' View, called Typical Group Size
according to Jarman or crowding according to [
s2S
P s2 sin = c = s2S
P s s2S = L2(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 di erence. Imagine that we have observed one group of size two and one group of size ve. The Insiders' View group size calculates to sin = 2+5 = 3:5. If we do not only take into account the measured 2 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 sout = 2 2+5 5 = 4; 14. This is an example which shows, that average 2+5 individuals (Outsiders' View) live in groups bigger than the average individual (Insiders' View). 3
As the studies of James are now more than half century old and not tailored to our speci c subject of research, we conducted two studies of group sizes recently. The rst study measured group sizes of people using means of public transportation. The second study measured group sizes of pedestrians and people in shopping 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 ' ndings. 3.1
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 Nurnberg.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 a air. 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 individuals 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 subgroup.)
By reason of the limited number of participants and rides, this study has only a small informative value. 3.2
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 [
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 co ee 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 di erent
locations, on di erent 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.
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 [
The observation that over 60% of people go out in groups forti es our assumption, 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
We have identi ed two main phases of going out. In a rst phase, the appointment 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 appointment 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 individuals 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 [
The AAS assists groups of individuals settle on a common destination. As we cannot rely our design on experienced data we build a exible system which allows several task ows to be implemented. We then compared task ows to nd an appropriate task ow for our scenario. The task ow of the user to user interaction can be modeled in di erent ways. We implemented di erent task ows and evaluated them. By using this method, we examined which user to user interaction work ow is best. A detailed description of this system is out of the scope of this paper. 4.2
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 nding 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. There are several contextual factors which still have to be integrated in PNSs. We identi ed the social contacts of the users as one important factor. This is due to the nding, 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 nding 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. 13. Winter, P.: Steiner problem in networks: a survey. Networks 17(2) (1987) 129{167 14. Zenker, Bjoern; Muench, A.: Calculating Meeting Points for Multi User Pedestrian Navigation Systems. KI 2011, LNAI 7006 (2011) 347{356