Pedestrian Simulation for Urban Traffic Scenarios

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