  One crucial information for a pedestrian crowd simulation is the number of
agents moving from an origin to a certain target. While this setup has a large
impact on the simulation, it is in most setups challenging to find the number
of agents that should be spawned at a source in the simulation. Often, number
are chosen based on surveys and experience of modelers and event organizers.
These approaches are important and useful but reach their limits when we want
to perform real-time predictions. In this case, a static information about the
inflow is not sufficient. Instead, we need a dynamic information that can be
retrieved each time the prediction is started. Nowadays, sensor data such as
video footage or GPS tracks of a crowd are often available. If we can estimate
the number of pedestrians who stem from a certain origin from this sensor data,
we can dynamically initialize the simulation. In this study, we use density
heatmaps that can be derived from sensor data as input for a random forest
regressor to predict the origin distributions. We study three different
datasets: A simulated dataset, experimental data, and a hybrid approach with
both experimental and simulated data. In the hybrid setup, the model is trained
with simulated data and then tested on experimental data. The results
demonstrate that the random forest model is able to predict the origin
distribution based on a single density heatmap for all three configurations.
This is especially promising for applying the approach on real data since there
is often only a limited amount of data available.
