Guideline for Crowd Evacuation Simulation

Validation of a Pedestrian Simulator with RiMEA Test Scenarios

Yayun Zhou, Wolfram Klein and Hermann Georg Mayer

Siemens AG, Corporate Technology, CT RTC AUC, Otto-Hahn-Ring 6, 81739 Munich, Germany

Keywords:

Crowd Evacuation, Microscopic Pedestrian Simulation, Simulation Guideline.

Abstract:

This paper introduces a way to verify a microscopic pedestrian simulator. The microscopic pedestrian sim-

ulator tested in this paper is developed by our group, which can be used to guide the crowd evacuation and

prepare respond plans for emergent situations as reference to city council and law enforcement agency. It is

important that the simulation results reveal the true behavior of pedestrian, for certain precaution actions can

be taken in order to guarantee the safety of the crowd. Therefore, the simulator has to been tested and veriﬁed

using different test scenarios. In this paper, we documented the performance of our simulator tested with

all 14 scenarios proposed by the RiMEA (Richtlinie f

ur Mikroskopische Entﬂuchtungs-Analysen) guideline.

The test results show that our simulator passes all the tests. Moreover, our pedestrian simulator constantly

improves its performance by cooperating with construction companies and government departments running

on-site tests with ﬁrst-hand data. Now it covers emergency scenarios such as ﬁre / smoke and ﬂoods.

1 INTRODUCTION

Nowadays, more and more people are moving from

small villages to big cities forming metropolises

worldwide. The increasing population density and

limited living space make metropolises top targets for

terror attacks and make them vulnerable to natural

disasters. A key task for smart city is to make the

city safer to its residents. Central stations, skyscrap-

ers and sport stadiums should be designed to reduce

risk by removing congestion, reducing crowd density

at hot spots, minimizing evacuation times or maxi-

mizing throughput. In those cases, a simulation soft-

ware which simulates the crowd movement, predicts

the evacuation time and identiﬁes the possible bottle-

neck is very helpful to design smart buildings. As for

existing non-smart-buildings, the pedestrian simula-

tor can provide insight in evacuation time and aids to

prepare respond plans.

Because of its various applications and convincing

results, adopting pedestrian ﬂow simulation is becom-

ing a popular topic in designing public transport sys-

tems and large infrastructure facilities (Kneidl et al.,

2012) (Gilg et al., 2014) (L

ammel et al., 2008). There

are two major types of models adopted by pedestrian

behavior simulation: microscopic models and macro-

scopic models. The so-called microscopic models

simulate the behavior of a single pedestrian while

the macroscopic models consider ﬂows of pedestrian

entities. The macroscopic models are often used to

study large scale behavior and typically use network-

based models (e.g. (Hamacher and Tjandra, 2002)) or

ﬂuid dynamics models (e.g. (Helbing, 1992)). The

microscopic models on the other hand are often in-

terested in local small scale behavior of pedestrian

ﬂows. The microscopic models can be further dis-

tinguished into force models (e.g. (Burstedde et al.,

2001)), discrete-choice models (e.g. (Antonini et al.,

2006)) and agent-based models (e.g. (Ronald et al.,

2007)).

Despite of all the advantages and numerous re-

searches done before, the features mentioned above

can only be exploited if the simulation tool fulﬁlls

several technical demands: First of all, the simula-

tor should provide results with a well deﬁned accu-

racy: The simulation results should be investigated

and compared with real measurements. For certain

cases, the simulation model and parameter should

be calibrated in order to achieve better performance.

A second critical limitation is the simulation speed,

i.e. time needed until a simulation result is available.

Moreover, the usability of the software also affects

its acceptance. After all, the potential users are most

likely to be architects or government ofﬁcer who are

neither familiar with mathematical modeling nor have

programing experiences. Therefore, the abstract re-

Zhou Y., Klein W. and Mayer H..

Guideline for Crowd Evacuation Simulation - Validation of a Pedestrian Simulator with RiMEA Test Scenarios.

DOI: 10.5220/0005436700350042

In Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS-2015), pages 35-42

ISBN: 978-989-758-105-2

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

sults should be properly visualized to help users link

simulated scenarios to real situations.

Based on these prerequisites, our group developed

a pedestrian simulation environment named Crowd

Control, which has already been used for several

evacuation projects in the ﬁeld of trains and build-

ings (K

oster et al., 2010)(Davidich and K

oster, 2012).

In the newly released version of our simulator, more

scenario-models such as ﬁre / smoke, and ﬂood are

integrated, which allow our simulator to meet various

simulation demands. In order to ensure its accuracy

after the extension, certain tests must be performed.

In this paper, we test our newly released simulator

with the test scenarios proposed by the RiMEA guide-

lines (RiMEA e.V., 2009) and document the test re-

sults. With the user-friendly graphical interface, we

are able to create the test scenario by simple mouse-

clicks. The test results show that our simulator passes

all the tests within short simulation time. Besides, the

2D and 3D visualization kits of our simulator allow

us to check the simulation results; a detailed analysis

can be done by exporting the results to Excel, e.g..

The remaining of the paper is organized as fol-

lows: In Section 2 the theoretical background and the

simulation model are brieﬂy introduced; In Section 3

the software architecture is sketched; In Section 4 the

test scenarios and test results are documented; In Sec-

tion 5 the performance of the simulator is evaluated

and a conclusion is drawn based on the simulation re-

sults.

2 SIMULATION MODEL

The simulator tested in this paper is developed based

on a discrete, microscopic force model. The simu-

lation model is a cellular automaton on a hexagonal

grid (Fig. 1). At each time step each cell has a certain

state: It is either empty or occupied by a pedestrian

or a ﬁxed obstacle. The positions are updated accord-

ing to a set of rules (K

oster et al., 2010). In com-

parison to continuous simulation, a discrete model is

much faster and does not suffer from artifacts such

as instability factors like non realistic oscillatory be-

havior. In addition, it is easier to include interactions

of the pedestrian with dynamic events like closing or

opening gates or catastrophic events like ﬁre spread,

since calculations can be restricted to discrete events

in time. While macroscopic pedestrian simulation is

predominantly focused on producing statistical data

(like ﬂows and densities), microscopic simulation can

help to identify even the behavior of single pedestri-

ans. Of course one has to be aware that the behav-

ior of a speciﬁc single individual can never be antic-

Figure 1: Schematic sketch of a cellular automaton simulat-

ing microscopic pedestrian dynamics.

ipated, but the interaction of a larger number of in-

dividuals can be emulated by microscopic simulation

very well (Gilg et al., 2014).

In the simulation model, each pedestrian is ini-

tially assigned with an individual velocity, which is

considered as a normally distributed variable, whose

average and variance are given parameters. In each

time step, pedestrians adjust their speed during the

simulation as responses to the change of situation.

For instance, a pedestrian slows down his movement

when the crowd density is too high (for example con-

gestion). The observation area is divided in hexagon

cells and each cell is in a certain state for each time

step: It is as mentioned above either empty or occu-

pied and an occupied cell cannot be entered. In ev-

ery time step a person can move one cell forward or

stay at the current cell, but cannot move farther than

one cell. The update scheme of the cellular automa-

ton has to guarantee, that pedestrians with a higher

speed are allowed to move more often than pedestri-

ans with a lower speed. Thus a slow person might

not move at each time step compared to faster mov-

ing person. Once the set of persons which are allowed

to move is determined, they are updated sequentially.

The movement rule is similar to the so-called static

ﬂoor ﬁeld cellular automaton (Burstedde et al., 2001),

in which the movement is determined by a potential

f . In the simplest case, the movement rule for a sin-

gle pedestrian is purely deterministic: Find the un-

occupied neighboring cell with the minimal total po-

tential value f

total

= f

navigation

(x;y) + f

pedestrian

(x;y).

In this equation, the f

pedestrian

(x;y) represents the

force that the pedestrians repel each other, while the

navigation

(x;y) represents the force that the pedestri-

ans are attracted by the target. For more details about

the simulation model, please refer to (K

oster et al.,

2010).

SMARTGREENS2015-4thInternationalConferenceonSmartCitiesandGreenICTSystems

3 SOFTWARE ARCHITECTURE

In this section, we will introduce the architecture of

our simulator. Figure 2 shows the main components

of the software and its data ﬂow. The software com-

prises a simulation kernel, a graphical user interface,

a tool for statistical evaluation and a 3D visualiza-

tion. The graphical user interface enables the user to

create speciﬁc scenarios using tools provided by the

software. Besides, the scenarios also can be extracted

from architectural drawings (Mayer et al., 2014) or

data bases. In addition, it is possible to couple the

simulation with third party tools, such as Microsoft

Excel, which is very useful for processing results and

analyzing data. Furthermore, the tool has been cali-

brated with many real measurements and recently ex-

tended by adding more items like ﬁre model, ﬂood

model to extend its functionality.

Figure 2: Software Architecture.

Figure 3 shows the graphical user interface, in

which user can create a scenario by adding source,

destination, walls, obstacles and staircases. Besides,

it provides the user possibilities to create more vivid

scenarios by add 3D elements, such as trees. The re-

cent version also allows user to add extra dynamic

models such as ﬁre spots to simulate the pedestrian

behavior under certain emergency situations. All the

objects can be conﬁgured by adjusting the parameters.

For instance, the user can assign a source (the place

where a group of pedestrians are generated) with a

pedestrian velocity proﬁle. In this way, sources rep-

resenting pedestrians with different ages can be gen-

erated. In this paper, all the test scenarios are created

by this graphical user interface.

The created scenarios and the parameter of the

simulator are saved as XML ﬁles, which contain the

topology and model relevant information. Those in-

put ﬁles are passed to the computation kernel, which

updates the position of persons for each time step.

The simulator generates two types of output ﬁles.

One type of output ﬁle contains the geometry and per-

son position information, which is used to visualize

Figure 3: Graphical User Interface.

the crowd movement combined with a 2D or 3D visu-

alization engine. The other type of output ﬁle contains

the density, velocity and evacuation time information,

which can be exported to external software, such as

Excel to analyze the results. In 2D visualization, each

pedestrian is represented as a colored dot. The color

of the dot is determined by the person density, which

is shown via a very intuitive coloring scheme green

yellow - red for a small, medium or high density. In

this way, critical spots can be easily identiﬁed by the

presence of red color. The 3D visualization is realized

by an interface to the 3D-Game-Engine Irrlicht. The

computed output ﬁle of the simulation storing the ge-

ometry and all persons positions at all time steps is di-

rectly taken over by the Irrlicht-Engine. The 3D visu-

alization is capable of displaying stairs, doors, narrow

corridors, passages, ramps and pillars. It helps the

user interpret and analyze the results directly. Similar

to the 2D-visualization, crowds or congestion can be

seen and discussed directly by that coloring scheme.

Figure 4 shows an example of a 3D visualization.

The user can follow the movement of the persons in

a familiar, well known 3D-environment without any

delay. The control panel on the right side allows the

user to navigate and move in the 3D-Game-Engine

to gain a better observation perspective. The zoom

functionality enables the user to analyze details of the

scenarios. The animation control allows the user to

Figure 4: Example of a 3D Visualization.

GuidelineforCrowdEvacuationSimulation-ValidationofaPedestrianSimulatorwithRiMEATestScenarios

review the simulation or analyze the situation of a spe-

ciﬁc time. Using menus under the control panel, the

user can even identify and walk as one speciﬁc person

through the scenario. All those functions are devel-

oped to increase the usability, which as mentioned in

the ﬁrst section is very crucial for the acceptance of a

simulator.

4 RiMEA TEST SCENARIOS AND

TEST RESULTS

As mentioned in the ﬁrst section, a pedestrian sim-

ulation tool must fulﬁll several technical demands

and certain guidelines, such as the IMO (Interna-

tional Maritime Organization) (IMO, 2014) guide-

lines and the RiMEA (Richtlinie f

ur Mikroskopische

Entﬂuchtungs-Analysen) guidelines (RiMEA e.V.,

2009). According to the Rimea guideline, 14 scenar-

ios should be tested in order to check the performance

of a person stream simulator and verify its function-

ality. The test scenarios in this paper are generated

according to the Rimea-Standard V.2.2.1. The test re-

sults and their corresponding parameters for the con-

crete application to the Crowd Control simulation tool

from Siemens are documented in this paper. Due to

the special grid property of our simulator, in addition

to the standard RiMEA tests, the tests are repeated

several times in different rotation angle and shifted

horizontally and vertically to prove the robustness of

the simulator. Besides that, the scenarios are tested

with different grid sizes.

There are some important assumptions in the

RiMEA guideline. The ﬁrst assumption is the ve-

locity of person group with different ages on the

ground. The velocities are deﬁned in the following

table 1 (Weidmann, 1992):

Table 1: Velocities on the ground according to Weidmann.

Person Group(Age)

Velocities on the Ground (m/s)

Minimum Maximum

under 30 0.58 1.61

30 to 50 1.41 1.54

Over 50 0.68 1.41

Person with limited mobility 0.46 0.76

The second assumption is the velocity of person

group with different ages climbing up and down the

staircases. The velocities are deﬁned in the following

table 2 (Fruin, 1971):

All tests are repeated several times in order to

identify the inﬂuence of the basic grid to the simu-

lation results. The tests are repeated in the following

orders:

• by rotating the topology in different angles

Table 2: Velocities on the staircase according to Fruin.

Person Group(Age)

Velocities on the Staricase (m/s)

Climbing Down Climbing Up

Indoor Outdoor Indoor Outdoor

under 30 0.76 0.81 0.55 0.58

30 to 50 0.65 0.78 0.50 0.58

Over 50 0.55 0.59 0.42 0.42

Person with limited mobility 0.42 0.32

• by shifting the topology vertically

• by shifting the topology horizontally

• by using different grid sizes for the basic hexago-

nal grid

The ﬁrst 3 tests are single pedestrian tests. In the

ﬁrst test, it should be proven, that a person with a

given velocity (1.33m/s) runs through a 2 m wide,

40 m long corridor within the given time. The ex-

pected time is between 26 to 34 seconds. Inaccuracy

tolerance of the person position is 0.4 m (body size,

1 second reaction time). Inaccuracy tolerance of ve-

locity is 5%. In the second test, the person retains

the same given velocity as the ﬁrst test climbing up a

staircase. It should be proven, that a person climbs up

a 2 m wide, 10 m long (the slope length) stair with a

deﬁned velocity in a given time range. In the third

test, the person retains the given velocity climbing

down a staircase. It should be proven, that a person

climbs down a 2 m wide, 10 m long (the slope length)

stair with a deﬁned velocity in a given time range.

Table refres:test1-3 shows the test results of the ﬁrst

3 test scenarios. From the table, we can see that all

the results of repeated tests (shift, rotate topology and

change grid size) lie in the given time range.

Table 3: Test Results of RiMEA Test 1-3.

RiMEA Test Number

Tested Time of Simulator (s) Allowed Time Range (s)

Minimum Maximum Minimum Maximum

Test 1 30.27 32.72 26 34

Test 2 11.63 12.89 10.84 14.17

Test 3 8.69 9.25 7.22 9.44

RiMEA test 4 is to check the relationship of a spe-

cial ﬂow with the person density. The special ﬂow

with respect to the person density is given:

s,max

= ρ · 1.34 · (1 − e

−1.913·(

−

t·5.4

)

) (1)

In order to realize the test, a ring is generated with

internal radius 70 meter and external radius 74 meter

(Fig. 5(a). 8 person blocks each generates 5000 per-

sons and the persons walk in circles. The ring radius

remains constant, but the number of persons increases

as time goes by. The test results are plotted in the di-

agram (Fig. 5(b)). We can see that the test results ﬁt

the Weidmann diagram very well.

RiMEA test 5 is to check the reaction time of each

person. In this test, 10 persons stand in an 8 m×5 m

SMARTGREENS2015-4thInternationalConferenceonSmartCitiesandGreenICTSystems

(a) Topology (b) Result

Figure 5: RiMEA Test 4.

room with a 1 m wide exit in the middle of the 5 m

long wall. The reaction time is to be set uniformly

distributed in the interval of 10 s and 100s. We need

to verify that each reaction time corresponds to a per-

son. In this test, our simulator generates 10 persons

in the interval [10,100], since the ﬁrst person is not

generated at 10 second, the time interval between two

persons reaction time is 90/11. We can compare the

reaction time of each person with the expected value

10 + i · d, where i is the index of the person and d is

the time interval between two persons reaction time.

The simulation results conﬁrm the conclusion.

In RiMEA test 6, 20 pedestrians walk along a 2 m

x 10 m corridor and turn left, go further along a 2 m x

10 m corridor. There should be no person go though

the walls. To carry out this test, we set three areas out-

side the corridor (three rectangular areas: outside top

left, outside top right and outside bottom) to check if

the number of person in each area equals to zero. We

also repeated this test to test the robustness of the sim-

ulator and all the tests fulﬁll the requirement. Fig 6

shows one of these tests.

Figure 6: RiMEA Test 6.

In RiMEA test 7, we choose a group of 50 persons

and assign the pedestrian velocity according to table 1

listed above. The task is to check if the simulated

velocity corresponds to the velocity listed in the table.

Fig 7 shows the test results. From this ﬁgure, we can

see that the minimal velocity and maximal velocity of

each group lie in the given range listed in table 1.

Figure 7: Result of RiMEA Test 7.

In RiMEA test 8, a test topology with three ﬂoors

is given (Fig. 8). Each room contains 4 persons and

has a 1 meter wide door. This topology is adopted to

test the inﬂuence of parameter in each person on the

total evacuation time. If the parameter of each per-

son is changed, the total evacuation time should also

change correspondingly. In this test, the persons are

assigned with different velocities for each simulation

run. We need to check if the evacuation time is within

the given tolerance. We also repeat the tests with dif-

ferent topology orientation and location, the evacua-

tion time variation is under the given tolerance.

Figure 8: Topology of RiMEA Test 8.

In RiMEA test 9, 1000 persons with same ﬁgure

size and no reaction delay are uniformly distributed

in a public room with four exits. The topology of the

test room is shown in the following picture (Fig. 9).

Assume that people in this group are all adults and

their velocities are set according to table 1. In the

ﬁrst test, the time of the last people leaves the room is

documented. Then two exits are closed and the test is

repeated to check the time of the last people leaves the

room again. Then we calculate the ratio of the evac-

uation time with four doors open and the evacuation

time with two doors open. If the ratio lies between

0.4 and 0.6, the simulator passes the test. In our test,

the ratio lines between 0.49 and 0.56.

For RiMEA test 10, we construct a test topology

(Fig. 10) with a corridor, 12 rooms each with a 0.9 m

door are located along the corridor. 23 adults with no

reaction delay and deﬁned velocity according to the

Table 1 are scattered in rooms. The people in room

1,2,3,4,7,8, 9 and 10 are assigned to use the main

GuidelineforCrowdEvacuationSimulation-ValidationofaPedestrianSimulatorwithRiMEATestScenarios

Figure 9: RiMEA Test 9 with 4 Doors Open.

exit and the remaining people are assigned to use the

second exit. The expected result is that the people

behave just like the assignment and leave the region

using the assigned exit. We set a tripwire in the corri-

dor. The tripwire is located between room 5 and room

11, which separates two exits. If the number of per-

sons who cross this line is 0, it means that persons use

the assigned exit to leave the room. In all the tests, no

person crosses the tripwire.

Figure 10: Test Topology for RiMEA Test 10.

RiMEA test 11 is about the choice of emergency

route. The test topology is a public room with two

exits: exit 1 and exit 2 (Fig. 11). 1000 adults with no

reaction delay are gathered on the left side of the room

(achieve the maximal person density). The velocities

of the people are set according to the Table 1. The

people should leave the room through two exits. The

expected result is that the people prefer to leave the

room through a nearer exit (in this case, exit 1), but if

the exit 1 is over-crowded, people should choose the

alternative exit (exit 2) to leave the room.

In this test two tripwires are deﬁned to count the

number of persons crossing these lines. The number

of the persons who cross the tripwire 1 stands for the

number of persons who leave the room with exit 1,

and the number of the persons who cross the tripwire

2 stands for the number of the persons who leave the

Figure 11: Test Topology for RiMEA Test 11.

room with exit 2. The test criteria is to check the num-

ber of persons using exit 1 and exit 2 respectively. The

simulator passes the test if two criteria are satisﬁed:

1. More persons use exit 1 than exit 2;

2. At least 20% of the persons use exit 2;

In all the repeated tests, the ratio of the persons using

the exit 2 lies between 22.6% and 42.4%, the simula-

tor passes this test as well.

The test topology of RiMEA test 12 is two rooms

connected by a narrow corridor (Fig. 12). 150 adults

with no reaction delay are gathered on the left side

of room 1 and room 2 has an exit. These 150 people

in room should leave the room through exit in room

2. Since the corridor is very narrow, the person ﬂow

is limited. The expected result is that the crowded

condition occurs only in room 1 but not in room 2.

For this test, we select two areas (one is in room 1

right before the narrow corridor and the other is in

room 2). The person density of these two areas are

checked to see where the crowded condition occurs.

The simulation results show that the maximal person

density in region 2 lies between 1.40 to 1.96, while

the average person density in region 1 lies between

3.33 to 4.63, which conﬁrms the expectation that the

crowded condition occurs only in room 1 but not in

room 2.

Figure 12: Test Topology for RiMEA Test 12.

The test topology of RiMEA test 13 is a room

connected to a staircase toward upstairs by a corri-

SMARTGREENS2015-4thInternationalConferenceonSmartCitiesandGreenICTSystems

dor. 150 adults with no reaction delay and velocity

according to Table 2 are gathered in the room. These

people leave the room by taking the staircase. The

expected result is that the crowded condition occurs

at the exit of the room to the corridor. Besides, peo-

ple are crowded in front of the staircase and the sit-

uation becomes worse as the time goes by, since the

person ﬂow over the staircase is smaller than the cor-

ridor. The test topology is constructed in two levels:

at the ﬁrst level, there is a room, source, a corridor and

a staircase; at the second level, there is a staircase and

a target area (Fig. 13). For this test, we deﬁne two ar-

eas in the test topology. Area 1 is located at the room

exit and area 2 is located in front of the staircase. We

count the person density of these two areas to check if

the maximal person density is larger than 3 (deﬁnition

of crowded situation). The test results show that the

maximal person density at the room exit lies between

3.24 and 4.28. The maximal person density in front

of the staircase lies between 3.78 and 4.72. The per-

son density of both areas exceeds the threshold of the

deﬁnition of crowded situation.

(a) Level 1 (b) Level 2

Figure 13: Test Topology of RiMEA Test 13.

The last RiMEA test refers to the simulator con-

ﬁguration. The test topology is shown below (Fig.

14). The start area and the targeted area are located

at the second ﬂoor and marked with different colors.

The start area is marked as red and the targeted area is

marked as green. They are further connected by two

staircases and a long corridor on the ground ﬂoor. The

task is to check how a pedestrian chooses his route: he

chooses a long route which is on the same ﬂoor or a

short route though the staircases and the corridor on

the ground ﬂoor. For different simulators, different

choices can be made (short, long, mixed or conﬁg-

urable). Our simulator supports the version ”short”.

That means all persons select the shortest path. In this

test, we construct a two levels topology: the ﬁrst level

has two staircases and no other obstacles; the source

and target areas are located at the second ﬂoor. Be-

sides the staircases to downstairs, there is a corridor

connecting these two areas at the same level (Fig. 15).

The simulator passes the test if no person crosses the

tripwire set in the second ﬂoor corridor. The tests are

repeated by rotating, shifting the topology as well as

changing the cell size to verify the robustness of the

simulator. In all tests, the number of persons who

cross the tripwire equals to zero.

Figure 14: Test Topology for RiMEA Test 14.

Figure 15: Constructed Topology for RiMEA Test 14.

5 CONCLUSIONS

In this paper, all tests listed in the RiMEA guideline

are implemented and used to test the Crowd Con-

trol Simulator Version 2.2. The tests are repeated

by rotating, shifting the topology as well as chang-

ing the cell size to verify the robustness of the simu-

lator. The test results show that the simulator passes

all the tests, which indicates its reliability and robust-

ness. All of the mentioned RiMEA test cases are part

of a larger test suite containing additional internal test

cases, which compares the simulation results auto-

matically with given result values. These test cases

should be checked after each software update.

Our simulator offers architectures, city planners,

authorities and other potential users a powerful tool to

get precise and valuable simulations results, in order

to improve the infrastructure planning and the safety

of passengers. This new version of the simulator not

only allows the user to create a test scenario by us-

ing its graphical user interface, but also interacts with

BIM (Building Information Modeling) model to im-

prove the performance of the building (Mayer et al.,

2014). With the newly integrated emission model

(such as ﬁre, smoke) and ﬂood model, the simula-

tor can calculate an optimal escape route for emergent

situation provided that the building is equipped with

sensors and detectors. There are enough applications

for a robust reliable pedestrian behavior simulation

tool like our Crowd Control Simulator. It is highly de-

sirable to complement the RiMEA tests with scenar-

GuidelineforCrowdEvacuationSimulation-ValidationofaPedestrianSimulatorwithRiMEATestScenarios

ios based on measured data. However, collecting this

data and ﬁnding appropriate measures for qualitative

assessment of simulation results is still an open ﬁeld

of research. It is also the interests of the authors to

collect more test scenarios, on which different mod-

els can be tested, compared and calibrated.

REFERENCES

Antonini, G., Bierlaire, M., and M.Weber (2006). Discrete

choice models of pedestrian walking behavior. Re-

search Part B: Methodological, 40.

Burstedde, C., Kaluck, K., A.Schadschneider, and Zittartzl,

A. (2001). Simulation of pedestrian dynamics using

a two dimensional cellular automation. Physica A:

Statistical Mechanics and its Applications, 295.

Davidich, M. and K

oster, G. (2012). Towards automatic and

robust adjustment of human behavioral parameters in

a pedestrian stream model to measured data. Safety

Science, 50.

Fruin, J. (1971). Pedestrian planning and design.

Metropolitan Association of Urban Designers and En-

vironmental Planners.

Gilg, A., Klein, W., Mayer, H., and Witte, F. (2014). Intel-

ligent crowd control, the various use cases in public

mass transit. In International Conference on Railway

Engineering 2014.

Hamacher, H. and Tjandra, S. A. (2002). Mathematical

modelling of evacuation problems: A state of the art.

Pedestrian and Evacuation Dynamics.

Helbing, D. (1992). A ﬂuid-dynamic model for the move-

ment of pedestrians. Complex Systems, 6.

IMO (2014). Guidelines of the international mar-

itime organisation imo. http://www.imo.org/Pages/

home.aspx.

Kneidl, A., Hartmann, D., Mayer, H., and Borrmann, A.

(2012). A holistic multi-scale approach for simulation

o pedestrian. In Proceedings of the 6th International

Conference on Pedestrian and Evacuation Dynamics.

oster, G., Hartmann, D., and W.Klein (2010). Micro-

scopic pedestrian simulations: From passenger ex-

change times to regional evacuation. In Operations

Research Proceedings.

ammel, G., Rieser, M., and Nagel, K. (2008). Large scale

microscopic evacuation simulation. In Pedestrian and

evacuation dynamics 2008.

Mayer, H., Klein, W., Frey, C., Daum, S., Kielar, P., and

Borrmann, A. (2014). Proc. of the ashrae/ibpsa-usa

pedestrian simulation based on bim data. In Building

Simulation Conference.

RiMEA e.V. (2009). Rimea guidline on evacuation scenar-

ios. http://www.rimea.de/ﬁleadmin/ﬁles/dok/analyse/

Analyse PedGo 20060609.pdf.

Ronald, N., Sterling, L., and M.Kirleyr (2007). An agent-

based approach to modelling pedestrian behaviour. In

Lecture Notes in Computer Sciencel.

Weidmann, U. (1992). Transporttechnik der fussg

anger.

Schriftenreihe des Institut f

ur Verkehrsplanung,

Transporttechnik, Strassen- und Eisenbahnbau, 90.

SMARTGREENS2015-4thInternationalConferenceonSmartCitiesandGreenICTSystems