Pedestrian Simulation for Urban Traffic Scenarios

Jörg Dallmeyer
Goethe University Frankfurt
P.O. Box 11 19 32
60054 Frankfurt, Germany
dallmeyer@cs.uni-frankfurt.de
Pedestrian Simulation for Urban Traffic Scenarios
Keywords: Pedestrian Simulation, Traffic Simulation, Urban
Scenarios, GIS
Abstract
Simulations are widely used for modeling, analysis, planning,
and optimization of traffic flows and phenomena. Every human
moving in a city participates at least to some extent as
a pedestrian in urban traffic. Nevertheless, pedestrians usually
are not part of traffic simulations. This work presents a
model for pedestrian movement, taking into account interactions
with other road users and among pedestrians on pedestrian
crossings. The components of the model are evaluated
separately and in a city scenario with an accumulated road
length of about 550km. Experimental results indicate an influence
of pedestrians on urban traffic. This leads to the finding,
that the consideration of pedestrians in urban traffic simulation
may lead to a gain of knowledge.
1 INTRODUCTION
A continuously growing amount of road users leads to the
need for traffic simulation in order to understand phenomena
of traffic jam formation and to find ways to increase traffic
flow. Traffic is well understood for freeway scenarios.
Urban traffic comprehends more heterogeneous road users
like, e.g., cars, bicycles and pedestrians. Pedestrian simulation
has gained more attention in the last years. There are high
fidelity models like the social force model [7], macroscopic
models like [8] or microscopic models built on cellular automatons
like [1].
Different types of models are used for different scenarios,
e.g., optimization of traffic light green times for pedestrians,
regulation of pedestrian flows at railway stations, or evacuation
and panic scenarios for pedestrian safety.
The focus of this work is the integration of pedestrians into
the simulation system MAINS 2 IM (MultimodAl INnercity
SIMulation) [3, 12] in order to study the impact of pedestrians
on urban traffic. Thus, a model taking account of realistic
pedestrian velocities and road crossing behavior has to
be found. The model needs to be computationally efficient in
time and space in order to simulate a whole city. To the best
of our knowledge, such a model does not exist.
The paper is structured as follows: Section 2 describes the
traffic simulation system for urban multimodal traffic. Section
Andreas D. Lattner
Goethe University Frankfurt
P.O. Box 11 19 32
60054 Frankfurt, Germany
lattner@cs.uni-frankfurt.de
Ingo J. Timm
University of Trier
54296 Trier, Germany
ingo.timm@uni-trier.de
3 discusses related works, dealing with pedestrian behavior in
order to build a new model (section 4). The model is examined
in section 5. Section 6 discusses the influence of pedestrians
on urban traffic in the simulation system. This work
concludes with a short summary and a discussion of potential
future investigations in section 7.
2 SIMULATION SYSTEM
The pedestrian model developed in this work is part of the
traffic simulation system MAINS 2 IM for urban traffic scenarios
under consideration of multimodal traffic. The system
deals with cartographical material from OpenStreetMap 1 . A
user-defined area can be extracted from an OpenStreetMap
(.osm) file. Then, the geoinformation is split into several layers
in order to group the information logically. The system
generates a simulation graph with roads represented by objects
of the data structure EdgeInformation (EI), connected
by objects of NodeInformation (NI). This is done
with help of a geographical information system (GIS) on basis
of GeoTools 2 . A number of analysis steps (e.g., finding
roundabouts, bus routes, pedestrian crossings, traffic lights
and much more) and correction steps (e.g., putting NIs at intersections
and consider bridges and tunnels) are performed
afterwards in order to compute a valid graph.
Multimodal traffic simulation is done with help of three basic
traffic models for cars (passenger cars, trucks, and buses),
bicycles and pedestrians. The models are continuous in space
and discrete in time with a time step of one second per simulation
iteration. A control module calls the update methods
of each simulated traffic participant once per simulation iteration,
i.e., once per simulated second.
The simulation system is written in Java and runs on a PC.
More detailed descriptions of the construction of the simulation
system and the developed models for cars and bicycles
can be found in [3, 4] and on http://www.mainsim.eu.
This work deals with the developed model for pedestrian
movement. Therefore, an introduction to real world pedestrian
behavior and pedestrian simulation is provided in the
next section.
1 http://www.openstreetmap.org
2 http://www.geotools.org

3 PEDESTRIANS IN URBAN TRAFFIC
Pedestrian behavior with regard to the interplay between
road users and pedestrians is dependent on walking velocities
of pedestrians and the road crossing behavior. This section
discusses relevant literature in this field.
The modeling of pedestrians for simulation of road networks
has been discussed in [10], proposing a model based
on the vehicle model of VISSIM [15]. The work has the focus
of creating a model with realistic flow-velocity-density
relationships. The shortcoming of this approach is the lack of
two-way traffic, which is important for pedestrians.
Different studies on pedestrian walking velocities have
been performed. A review is provided in [11]; in the following
we briefly summarize the most important insights. A
pedestrian p chooses velocity vp with respect to its physical
constitution and whether p walks on a sidewalk or crosses
a road. Crossing velocity is dependent on whether p is crossing
with or without right of way. Additionally, aggressiveness
increases velocity. This is modeled with help of an aggressiveness
factor AF. These major findings have been derived
from several studies monitoring different inner city crossings
at different places [11].
When p attempts to cross a road and is not able to do so due
to road traffic, aggressiveness of p increases. Aggressiveness
affects the walking velocity increasingly, when p finally is
able to cross the road. Referring to [9], this may result in a
maximum velocity increment of 0.5m · s −1 .
The lowest velocities are walked on sidewalks. When
crossing a road, pedestrians tend to speed up. The more hazardous,
the faster. When crossing with right of way, pedestrians
are slower than without right of way. The slowest crossing
velocities are walked on crosswalks. At pedestrian lights, the
waiting time until p is able to cross, increases aggressiveness
and thus velocity.
Each of these cases has to be separated with respect to
the physical constitution of p. Men are slightly faster than
women. The elderly are slower than adults and children. Finally,
different localities lead to different velocities [11].
When a pedestrian p decides to cross a road, p determines
the estimated crossing time [13]
ECT = w
+ AF (1)
vp
where w is the width of the road. The distribution of ECT
among pedestrians has been investigated in [2]. A mean ECT
of ECT ≈ 7s at a road with w = 9.1m and a mean velocity
of v = 1.2m · s −1 leads to a mean AF of AF ≈ −0.58s. This
means, that pedestrians tend to underestimate the time needed
to cross a road.
These findings build the basis for our model of pedestrian
movements.
Figure 1. Positioning of pedestrian way objects on intersections.
4 PEDESTRIAN MODEL
The interaction between road users and pedestrians is one
of the central aspects of this work. At first, pedestrians need
to find their way through the simulation graph (subsection
4.1). Interaction with road users will only take place, when
pedestrians cross roads. Thus, a mesoscopic simulation model
for pedestrians is introduced in this section. It consists of one
model for sidewalk movements (subsection 4.2) and another
model for crossing roads at pedestrian crossings (subsection
4.3).
4.1 Routing
The calculation of fastest ways in a graph data structure is a
well known problem. In this work, precalculated routes from
each NIa to each NIb can be used as well as online calculated
routes with help of the A* search algorithm and a probabilitybased
routing mechanism [3]. The result of each algorithm is
a list of EIs and a list of NIs, defining the route to be walked.
In the beginning, it is randomly chosen, which side of the
road a pedestrian stands on. Then, a heuristic algorithm calculates
where to cross the roads during the walk through the
calculated route. Crossing can be done on EIs if there is a sufficient
gap. Additionally, NIs store pedestrian ways, allowing
for crossing and pedestrian interaction. Figure 1 shows an intersection.
NI is connected to three EIs : EI1...EI3. Each
dashed line shows the position of a pedestrian way.
A pedestrian has to cross a road, e.g., whenever the following
EI is connected to the current EI into a direction which
is oppositely to the side of the road of the pedestrian. The
pedestrian plan of road crossings privileges pedestrian crossings
and traffic lights (both stored in NIs) over crossing the
road without right of way. Additionally, NIs are used to cross
a road, when crossing on an EI was planned, but not possible.
The models for movement and crossing on EIs and NIs are
different. The following sections 4.2 and 4.3 discuss both.
4.2 Sidewalk Movement
With regard to computational efficiency, pedestrians are
simulated with a very simple model when walking on sidewalks.
Let N b a (µ,σ) = min(max(N (µ,σ),a),b) be Gaussian
distributed random number with µ and σ bounded to the
interval [a···b]. Each pedestrian distinguishes his base velocities
for sidewalks α, for crossing with right of way β and

Figure 2. Pedestrian road crossing behavior.
Figure 3. Pedestrian way: Measures and lanes.
without right of way χ in � m · s −1� :
α = N 1.75
0.5 (1.33, 0.25) (2)
β = N 1.5·α
α (1.5, 0.5) (3)
χ = N 2·α
α (1.8, 0.5) (4)
These values correspond to the literature review given in [11].
Assuming that smaller interactions between pedestrians on
sidewalks average in time and space, pedestrians move linearly
without interaction with other pedestrians. For determining
ECT , pedestrians are modeled with a base AF of
AF = N +3
−3 (−0.5, 1.0) (5)
In order to cross a road, pedestrian p calculates ECT and enters
the road, when no road user passes the current road position
of p, before p leaves the road adding a safety distance
of two seconds, as shown in Figure 2. In this example, the
pedestrian will not cross the road, because the car on the right
side will reach its position before the crossing is done with
the safety distance of 2s.
Crossing a road is done at right angle to the course of the
EI. This is also done in linear movement, because it is unlikely
that interaction with other pedestrians will occur in
these situations. When a pedestrian has started crossing the
road, he will not abort it. When ECT was too low (e.g., because
of a negative AF), road users need to brake in order to
avoid an accident. In these cases, cars are interfered by pedestrians.
4.3 Pedestrian Interaction at Crossings
Crossings are the places where pedestrians may interact
with each other, e.g., because several pedestrians may use a
traffic light at once. Thus, a microscopic model for pedestrian
movement is used to model pedestrian ways, which are placed
at NIs. Figure 3 shows the composition of pedestrian ways.
1 function update(iteration)
2 if iteration �= iterationOld then
3 iterationOld ← iteration;
4 checkQueue();
5 foreach Pedestrian p do
6 determineUsableLanes())
7 foreach Pedestrian p do
8 chooseLane()
9 foreach Pedestrian p do
10 provideLane()
11 foreach Pedestrian p do
12 calcV()
13 foreach Pedestrian p do
14 f .pos ← f .pos + v;
15 if f .pos ≥ l then
16 f .crossingNI←false;
17 remove(p);
Figure 4. update()-method of way-objects.
The pedestrian way is separated into w lanes (width of the
crossing) and has a length of l (width of the EI to cross).
The proposed model is a space continuous adaptation of the
cellular automaton based model, described in [1], permitting
individual pedestrian velocities, that are not covered by [1].
The model reproduces the interaction between pedestrians,
walking in both directions on the way object. The basic algorithm
is shown in Figure 4.
Whenever a pedestrian p is on a way object w, it forwards
the update call to w. Lines 2 and 3 assure that the way is only
updated once per iteration.
Each pedestrian starting a crossing action on a way chooses
its lane at first. If no free lane is available, one lane will be
chosen randomly and the pedestrian will be put into a queue
for waiting pedestrians. Line 4 checks for pedestrians, waiting
to enter the way and adds them when their lane provides a free
entry position.
Each pedestrian p calculates the magnitude of usable lanes
in the neighborhood N lane of its current lane as shown in
equation 6.
N lane = {p.lane − 1, p.lane, p.lane + 1} ∩ {1,··· ,w} (6)
If possible sidestep positions of different pedestrians interfere
with each other, only one of them may use the corresponding
lane. Due to the avoidance of asynchronisms, the lane choosing
process is divided into an update (line 8) and a provide
(line 10) step.
In line 8, each pedestrian p calculates the gaps g(l) to the
next pedestrian for each lane l ∈ N lane in the direction of

travel of p. A rating for the lanes ϑ(l) is calculated. It is identical
to g(l) and is halved if the next pedestrian is opposing
traffic. ϑ(l) is limited to a maximum visual range of 10m.
A usage probability of upl is assigned to each lane l ∈
N lane . If the current lane p.lane of p has the maximum ϑ(l)
in all lanes l ∈ N lane , its probability up p.lane will be set to
95% and the residual 5% are split for the rest of lanes in
N lane . When the current lane is not with maximum rating,
the probabilities will be computed using equation 7.
pl = ϑ(l)
⎛
⎞
⎝ ∑
l∈Nlane
ϑ(l) ⎠
−1
∀ l ∈ N lane
Each pedestrian randomly selects his new lane with the calculated
probability distribution. Pedestrians thus choose the
lane, promising to be the fastest with respect to the gaps and
opposing pedestrians. This also leads to the formation of trails
in both directions. In line 10, the lane changes are performed.
Each pedestrian calculates his new velocity in line 12 after
equation 8.
v = min(ϑ(p.lane),vmax) (8)
The velocity vmax is the desired velocity of the corresponding
pedestrian. Equation 8 thus leads to desired velocities
bounded to the given gaps on the corresponding lanes. If no
movement is possible because of opposing traffic, the two affected
pedestrians will come to pass with probability ppass =
0.15. The velocity then becomes vpass = 0.25 · vmax.
Lines 14 to 17 perform the movement of each pedestrian
on the way and will remove it when he has reached the end of
the way. The crossingNI-flag of the corresponding pedestrian
is set to false and it will proceed on the other side of road or
new EI in the next iteration.
5 MODEL EVALUATION
This section evaluates the presented model for pedestrian
movement. Unless otherwise specified, experiments are done
in the map extract shown in Figure 5. It is a whole city with
about 89,000 inhabitants.
The following subsections 5.1 to 5.3 discuss different parameters
of the pedestrian model.
5.1 Pedestrian Velocity
This subsection discusses pedestrian velocities during simulation.
Each pedestrian is enhanced by an analysis module
which protocols velocities and other parameters. The velocities
are separated for different incidents. The average velocity
v over the whole walk of each pedestrian is recorded. The
velocities on sidewalks vs, during road crossing with vr and
without vr right of way are recorded as well.
The simulation experiment is done with a warm-up phase
of 2,500 iterations, followed by a measurement phase of
(7)
Figure 6. Distribution of walked velocities.
quantity
quantity
Figure 7. Distribution of aggressiveness.
100,000 iterations. The composition of road users is 50%
cars, 15% bicycles and 25% pedestrians. The amount of traffic
participants is 3,000 which is kept constantly during the
simulation time. Whenever a traffic participant finishes its
travel, a new one will be generated.
As shown in figure 6, the basic effects discussed in section
3 are reproduced by the simulation model (vs < vr < vr). The
measured velocities correspond well to field studies (e.g., [6,
9, 14]).
5.2 Pedestrian Aggressiveness
The distribution of aggressiveness of all pedestrians is
recorded for the whole simulation. Figure 7 shows a histogram
of the measured data with its probability density function
f and a boxplot.
The shown distribution fits the claimed characteristics in
section 3 and [2, 5]. Figure 8 illustrates the process of becoming
aggressive. In this example, a pedestrian initially has

1224m MAINS
²
Multimodal
Innercity
Simulation
IM
Multimodale Innerstädtische Straßenverkehrssimulation
Figure 5. Map extract of the city Hanau, Germany with an accumulated length of roads of 548km, amount of EIs: 5,400 and
amount of NIs: 3,878.
Figure 8. Change of aggressiveness over time.
a basic AF of about +0.4, leading to a behavior which is overcautious.
From point a to c, the pedestrian has to wait for a
sufficient gap in order to cross a road. His aggressiveness increases
with step size 0.1 · s −1 . This value is not documented
in literature but shows the basic principle. Finally, the crossing
action takes place and the aggressiveness recovers.
5.3 Pedestrian Crossings
Pedestrian crossings can be analyzed with help of the well
known density (ρ) - mean velocity (v) diagram. The traffic
density of a pedestrian way is calculated as stated in equation
9.
ρ = #pedestrians
C
(9)
The capacity of a pedestrian way is C = w·l ·(0.457) −1 . This
definition is according to [1] which defines cells with width
of 0.457m being fully occupied by one pedestrian.
The ρ - v relation has to be divided according to the amount
of pedestrians walking in both directions, because different
compositions lead to different ρ - v relations.
In order to analyze the relation, a single pedestrian way
object is used for simulation. It has five lanes and a length of
l = 10 · 0.457m and thus leads to a realistic pedestrian crossing.
The amount of pedestrians is set to one for the first run
and is then increased by 1, until 75 runs have been made.
The results represent average values of 10 replications for
each setting. After a settlement phase of 500 iterations, 5,000
iterations are used for measurements of the average velocity
v of all pedestrians on the way. This procedure is performed
for different compositions of directions of travel with
50%,60%,··· ,100% pedestrians walking into one direction
and the rest into the other direction. Figure 9 shows the resulting
diagram.
As can be seen, the maximum density differs in relation to
the composition of walking directions. This results from the
queuing process of the algorithm, shown in figure 4. Pedestrians
walk faster, when there is no opposing traffic and therefore
the distances between two subsequent pedestrians increase.
Figure 9 shows that the more balanced the distribution of
walking directions is, the higher the maximum values of ρ
get and the lower the mean velocity v. This is in line with the
results presented in [1] with the difference that only realistic
densities for urban traffic road crossings are generated.
It seems to be likely that only parts of the shown figure 9
occur in urban traffic. Hence, another experiment comes back

100
90
80 7060
50
Figure 9. Density velocity relation for a pedestrian way.
Figure 10. Density velocity relation for all pedestrian ways
in Hanau with relative frequencies fi (ρ).
to the scenario of the city Hanau, shown in figure 5. Every
pedestrian way in the simulation records pairs of data for ρ
and v, whenever at least one pedestrian uses it. The measured
values of ρ are grouped into ρ-intervals of width 0.005. This
is done because the huge amount of metered values would
lead to point clouds not enabling to see the underlying effects.
Intervals with less than 11 values are dropped, because
of the risk of overvaluing outliers. The composition of walking
directions is not distinguished for this experiment. Figure
10 presents the ρ - v plot for the urban simulation.
Additionally, the maximum velocity vmax for each group
of ρ and the relative frequencies fi (ρ) are plotted. The curves
300 350 400 450 500
averaged travel time (cars)
0 1.000 2.000 3.000 4.000
#pedestrians
Figure 11. Influence of pedestrians on urban car traffic:
Comparison of travel times of cars with varying amounts of
pedestrians. Each boxplot represents 1,000 values.
for v and vmax result from regression analysis. The plot
shows that most results have been generated at low densities.
The values of vmax decrease rapidly with increasing ρ,
because fast pedestrians are hindered by slower ones. A maximum
density of ρmax = 0.16 though suggests that pedestrians
could have an influence on urban road traffic, because of
roads being blocked by pedestrians notably times.
6 INFLUENCE ON URBAN TRAFFIC
The previous section has shown that the developed model
for pedestrian movement reproduces the basic effects and coherences
as reported previously in literature. This section investigates
the influence of simulated pedestrians on urban
traffic. Simulating pedestrians leads to additional computational
effort and therefore should only be done if a significant
effect on urban traffic can be shown (or if pedestrians are in
focus of the corresponding study).
The map excerpt presented in figure 5 is used for another
experiment. 2,000 simulated road users (95% cars, 5% bicycles)
are created. After a settlement phase of 1,000 iterations,
the travel times of each car leaving the simulation are
recorded. This is done for 10,000 iterations and afterwards
averaged to the value t. The amount of road users is kept
constantly through the simulation by adding a new road user,
whenever another one has left the simulation.
The experiment is repeated with identical road users complemented
with 0, 1,000, 2,000, 3,000 and 4,000 pedestrians.
The whole procedure is repeated 1,000 times for all five settings.
Figure 11 shows the simulation results.
A tendency to increasing travel times of cars for increasing
amounts of pedestrians can be observed. The jitter of the
simulation results also increases, due to the probabilistic sim-

#pedestrians t [s]
0 410.9319
1,000 416.3292
2,000 419.8662
3,000 423.2393
4,000 426.7230
Table 1. Comparison of average travel times of cars t [s]
with different amounts of pedestrians. Averaged over 1,000
simulation runs each.
Hypothesis p p < α *
H a 0 3,157 · 10 −8 �
H b 0 1,478 · 10 −4 �
H c 0 5,37 · 10 −4 �
H d 0 2,803 · 10 −4 �
Table 2. p-value comparisons for H a 0 ···Hd 0 .
ulation model. Table 1 compares the average travel times, extracted
from the simulation results.
The results show an increasing travel time for cars with increasing
amounts of pedestrians. Let tt (x) be the averaged
travel time obtained with x · 10 3 pedestrians. In order to examine
if an increasing amount of pedestrians results in increasing
travel times for cars in the simulation, the hypotheses
shown in equations 10 to 13 are proposed.
H a 0 : tt (0) ≥ tt (1) (10)
H b 0 : tt (1) ≥ tt (2) (11)
H c 0 : tt (2) ≥ tt (3) (12)
H d 0 : tt (3) ≥ tt (4) (13)
The free statistics software R-project 3 is used to run t-tests
in order to find out if significant differences are given in the
measured data. This is done via multiple testing. Each hypothesis
includes at least one simulation result control sample
which is used twice for testing. The error level α = 0.05 thus
has to be adjusted after the method of Bonferroni to:
α * = α
= 0.025 (14)
2
The results of the t-tests are shown in table 2. All results
of the statistical tests are significant for the selected error
level and therefore indicate that a lower number of pedestrians
leads to lower travel times on average:
tt (0) < tt (1) < tt (2) < tt (3) < tt (4) (15)
The results indicate that pedestrians do actually have an influence
on car traffic: Pedestrians slow down car traffic in urban
scenarios in the built simulation system MAINS 2 IM.
3 http://www.r-project.org/
7 SUMMARY AND PERSPECTIVES
The behavior of pedestrians in urban traffic has been discussed.
A bi-modular model for pedestrian movement has
been developed, taking account of interaction on pedestrian
crossings. It could be shown that the model can replicate observations
with respect to field studies. The correlation of traffic
density and mean velocity on pedestrian crossings could
be reproduced. The presented model for pedestrian movement
deals with individual velocities based on field studies.
The traffic of whole cities can still be simulated under usage
of the presented model.
Simulation results indicate that pedestrians slow down road
traffic in a significant range. In future work it should be examined
if an analysis of simulation results leads to an understanding,
how often and how intense these influences take
place in relation to different areas of a simulated city. After
this step, only the effects (cars need to brake, because of
pedestrians using traffic lights, pedestrian crossings or crossing
roads without right of way), but not the originators (the
pedestrians) would be needed to be simulated.
So far, only the influences of pedestrian behavior on cars
has been evaluated. It would be interesting to see how different
intervention actions on urban traffic, e.g., different velocity
restrictions or new traffic-free environments affect pedestrian
travels.
The simulation system is able to readout bus routes from
OpenStreetMap. When a bus stops in real world, several
pedestrians enter and leave the bus and thus strong effects in
road traffic could emerge, especially for high traffic densities.
Currently buses are implemented, but pedestrians do net yet
enter or leave them.
Traffic engineers only have two main possibilities to enable
pedestrians to cross roads: (a) traffic lights and (b) pedestrian
crossings. These are only two actions. An optimization
method, e.g., Simulated Annealing could be used to minimize
travel times for road users under optimization of interchanging
(a) and (b) at the places, one of them is set. This could
also be done in the face of minimization of fuel consumption
and CO2 emissions for whole cities.
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BIOGRAPHY
Jörg Dallmeyer received the bachelor degree (2008) as
well as the master degree (2009) in computer science at the
Goethe University of Frankfurt am Main, Germany. Since
December 2009, he is a PhD student at the chair for “Information
Systems and Simulation” at the Goethe University
Frankfurt. His research interests are actor based simulation
for the field of traffic simulation under consideration of multimodal
traffic and the building of simulation systems from
geographical information.
Andreas D. Lattner received the Diploma degree in computer
science at the University of Bremen, Germany in 2000.
From December 2000 to March 2007 he was working as research
scientist at the Center for Computing Technologies
(TZI) at the University of Bremen. In 2007 he obtained the
doctor’s degree for his thesis on “Temporal Pattern Mining
in Dynamic Environments”. He works now as a postdoctoral
researcher at the chair for “Information Systems and Simulation”
at the Goethe University Frankfurt. His research interests
include knowledge discovery in simulation experiments,
temporal pattern mining, and multi-agent systems.
Ingo J. Timm received Diploma degree (1997), PhD
(2004) and venia legendi (2006) in computer science from
University of Bremen. From 1998 to 2006, he has been PhD
student, research assistant, visiting and senior researcher, and
managing director at University of Bremen, Technical University
Ilmenau, and Indiana University Purdue University
- Indianapolis. In 2006, Ingo Timm was appointed full professor
for Information Systems and Simulation at Goethe-
University Frankfurt. Since 2010, he holds a chair for Business
Informatics at University of Trier. He works on information
systems, knowledge-based systems in logistics and
medicine. His special interests lie on strategic management
of autonomous software systems, actor-based simulation and
knowledge-based support to simulation systems.
Acknowledgement This work was made possible by
the MainCampus scholarship of the Stiftung Polytechnische
Gesellschaft Frankfurt am Main.