The document describes a simulation of evacuation from a building using an agent-based model. Agents represent individuals, groups, or people with communication devices. The simulation analyzes how information spreads during evacuation and compares results between open and restricted geometries. Statistical analysis methods are applied to detect phases or transitions in the system. The impacts of different communication technologies and evacuation strategies are also studied. The goal is to define requirements for communication networks and sensors to optimize the evacuation process based on the simulation results.

1. regime I:
regime II:
regime III:
Mirko Kämpf and Jan W. Kantelhardt
Abstract Our evacuation simulation tool utilizes
established algorithms for the emotional and intelligence driven
motion of human beings in addition to a simple lattice gas
simulation. We analyse the spread of information inside a
restricted geometry of a real building and compare these results
with the data from a simulation in the free space. We apply the
DFA and the RIS statistic to our simulation dataset to detect
phases or phase transitions of the whole system. We study the
impact of communication technology by comparison of different
update algorithms and exit strategies. These results help us to
define basic functional requirements to the underlying
communication technology and network topology as well as to
the needed sensors.
Information Spread in the Context of
Evacuation Optimization Institut für Physik, Fachgruppe Theoretische Physik,
Martin-Luther-Universität Halle-Wittenberg, 06099 Halle (Saale), Germany
Acknowledgement
We thank the EU for funding.
Motivation There are many approaches for an
optimization of the process of evacuation of large buildings or
areas. One of them is to use signals, sometimes dynamical
controlled from outside, for routing the people to the next exit or
cars to the next free lane on a street. A much better strategy
could be, to use information about the neighborhood of a person
or a car. Lots of sensors and „intelligent“ devices are already
available. They can measure properties of the real world. Other
devices can transmit this data to other persons.
But what rules for the routing of persons and that rules for the
best decision of a person are the best in the context of the whole
system?
How can we describe the system, with physical properties and
how can we find transitions in the state of such a system? To
support the development of such a toolset in the SOCIONICAL
project we do numerical simulations.
Analysis of the Influence of AmI Technologie on the
Agents Motion and on Information Flow [2,3]
Rules for Routing and Quality of Information
Information Spread in Open vs. Restricted Geometry [1]
Model & Simulation Techniques
DY
10.30
References
[1] K. Kloch et al.; Ad-hoc Information Spread Between Mobile Devices ...; Proceedings of ARCS 2010, Vol. 5974 of Lecture Notes in Computer Science, 101–112. Springer, 2010.
[2] R. Holzer et al.; Quantitative Modeling of Self-Organizing Properties; In Proc. of 4th Int’l. Workshop on Self-Organizing Systems, Zurich, Dec. 9-11 2009, Springer, LNCS.
[3] C. Auer et al.; A Method to Derive Local Interaction Strategies for Improving Cooperation in Self-Organizing Systems; In Proc. of 3rd IWSOS, Vienna, Dec. 10-12 2008. Springer, LNCS.
[4] P. Gawronsky et al.; Evacuation in the Social Force Model is not stationary; (submitted, arXiv:1103.0403).
[5] D. Chowdhury et al.; Statistical Physics of Vehicular Traffic and some Related Systems; Physics Reports, 329 (2000) 199-329.
Outlook
• Can we detect phase transitions of the whole system by looking only at the fluctuation properties of several values?
• Are there any agents with some kind of synchronization, with or without direct radio links between them?
• Does the communication flow affect the total evacuation time and how is this dependency defined?
• Can we define communication rules for an optimization of the total evacuation process?
Contact mirko.kaempf@physik.uni-halle.de
Statistical Time Series Analysis (DFA, RIS) [4,5]
But how can an agent determine
how reliable such an information
really is?
Every time an existing map is
merged with a received one, a
special weight has to be given
before the calculation of the
average data.
Depending on the time since a fact
was recognized the information is
less relevant. So we can define a
current information map in every
time step.
d
During the evacuation of the the
agent its speed and its average
speed in a single sector of its way
are determined. This information is
put into a map for each way the
agent passed. In the cross sections
a routing decision tells the agent
which way to go. If there is some
information about the properties of
the way, the decision is easier.
In this way an agent can find an
optimized way if he has a map with
the current properties of the flow
through the building.
The numbers on the edges show the time an agent will need to
pass this way. Based on this information, if it was received, the
agent is able to determine the best direction to go.
The sketch shows a part of a building with moving agents. Every
agent has information about the fields he was before. By using
radio technology it can share this information with other agents. If
the radio range is larger then distance between the floors d, the
agents in the upper floors can use this information for routing.
2
37
corridor with x boxes stairway with y boxes
prefered exit

Agents have a simple structure
and represent
(i) individual persons or
(ii) persons with AmI device,
(iii) groups of persons.

They behave similar as
molecules of gas, with few
additional rules

They are reflected by other
agents or walls and have to wait
if there is a jammed path

We aim at studying phase
transitions
AmI based
information spread
Lattice gas model with
1. ‘stupid’ agents (like gas)
2. different roles (speed /
types like staff, rescue
worker, normal people)
Input: simulation geometry
(symmetry) (AGH)
Simplified
analytical
(mean field)
theory
compare
Simulation of lattice gas
model with agents
representing (i) person
(ii) person & AmI device
(iii) group of people
integrate
Emotion spread &
group formation
Input: Strategies and
parameters for
information spread
(U Passau)
Input: Parameters from
agent models
(VU Amsterdam)
#of floors #of floors
0200040006000
#of agents
0200040006000
#of agents
t
40006000800010000
tMW
2000300040005000
Results of the numerical simulations
total evacuation time mean evacuation time
Scaling Theory: Generalizing the Numerical Results
We find three regimes for the
evacuation behavior:
)1(Low density – not all floors
are occupied
)2(Medium density – smooth
evacuation
)3(High density – jammed
staircases
The curves from different simu-
lations can be scaled on top of
each other if the quantities are
divided by the system size
#of agents / # of floors
(2( )3)
Include emotion / information spread in scaling theory
Ratio of infected agents Infection rates determined
in different regimes
Results from Detrended Fluctuation Analysis
Results from Return Interval Statistics
In a cooperation with the University of Passau some quantitative statistical measures are applied to the system for
optimization of the local strategies. We want to analyze the best communication strategy and the best routing strategy for
the agents. What is better, to rely only on local data with an assumed good quality or is it more efficient to spread the
data through the whole system? What data do the agents need in what situation? How many agents do need the
information?
Measure of Target Orientation
For a calculation of the measure of target orientation of a system we have to define a value, which represents the
optimized target of the process. In our scenario, the target is reached if all agents are evacuated. The simulation results
are calculated based on different rules and parameter sets. By an analysis of the target orientation depending on the
rules and parameters the best rules an the optimized parameter sets can be detected.
Resilience with respect to an external event
The measure of resilience shows, how ressistent a system is in respect to undetermined external events like the
breakdown of the stair case or a blocked exit in our evacuation scenario. Like in the case of the measure of target
orientation special rules and parameter sets are analyzed: What rule and what parameter set are most efficient in the
context of what kind of external event?
Measure of Emergence of communication patterns
During the exchange of information between agents, specific patterns can emerge. Such dependencies or
communication patterns can be detected and analyzed by calculating the value of emergence.
a) b)
But because of the memory of the system and its dynamical
properties an agent will never have fully correct data.
In contrast, a theoretical external instance has always the correct
data for every point in the map. So we can calculate the difference
between the real value and the value an agent assumes by using
its map. This gives us a criterion for the optimization of the
information exchange algorithms and radio properties in our
simulation.
Fig a) shows the „Fundamental Diagramm“ for measured speed and density values on a highway. Each Point shows a 15 min average.
Based on known rules for phase definitions the points are colored green for free flow, blue for synchronized flow and red for traffic jam.
For each part all time series with a constant phase, the fluctuation function (fig b) was calculated. If the slope of F(s) is about 0.5 the
data is uncorrelated. If the slope is higher, like in the red curve, there is a correlation in the data.
Although we do not have as many data
points as in a traffic scenario, we want to
study such a „Fundamental Diagramm“ for
pedestrians movement during the
evacuation process.
The aim is, to identify typical properties to
describe the systems state based in the
measurement on individuals or special
points.
One aim is, to use an analytical model to predict global properties of large-scale
information technology systems from the parameters of simple local interactions. The
first example is intended as a step towards using complex systems modeling methods
to control self-organization in organic systems. It is motivated by a concrete
application scenario of information distribution in emergency situations, but is relevant
to other domains such as malware spread or social interactions. Specifically, it was
shown how the spread of information through ad-hoc interactions between mobile
devices depends on simple local interaction rules and parameters such as user
mobility and physical interaction range (radio range R). As a first result three
qualitatively different regimes of information `infection rate‚ K can be analytically
derived and validate the model in extensive simulations. One special property of this
model is the unrestricted geometry in which the agents can move.
In our model, the geometry is defined by the building. How does this affect the flow of
information or the infection rate? Can we find comparable analytical descriptions of
the information flow depending on the local rules and strategies?
Infection ratio K plotted versus radio range
R for N = 512, L = 2000 and 2 ≤ R ≤ 75 in
part (a). Vertical lines c1 and c2 mark the
separations of the three regimes.
Scaled simulation results for various N
compared with the analytical value of K
computed according for the first regime is
shown in part (b).
First results for simulations in a restricted
geometry show comparable result to the
previous simulations in a free space.
The infection rate depends obviously on the
radio range. This simulations have to be
repeated with other rules for the movement
of the agents and with other geometrical
properties.
So we can study what parameters and what
strategies influences the information spread
in which way.
R1 R2
We plan to apply two statistical methods which were used in
previous work with comparable datasets to the simulation results.
F(s)
We analyzed the time lag between the exit of two following persons
during an evacuation simulation based on the social force model. The
Distributions P(r) (fig. a) and the scaled distributions R · P(r/R) (fig b) of
the time lags r with mean R a given number of persons remaining in the
evacuated room are plotted. The unscaled distributions in (a) show that
the nearly Gaussian peak for short time lags is hardly changing, while
the exponential tail is decaying with decreasing n.
The scaled distributions in (b) show that the mean R characterizes the
exponential peak fairly well.
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