  In literature, scientists describe human mobility in a range of granularities
by several different models. Using frameworks like MATSIM, VehiLux, or Sumo,
they often derive individual human movement indicators in their most detail.
However, such agent-based models tend to be difficult and require much
information and computational power to correctly predict the commutation
behavior of large mobility systems. Mobility information can be costly and
researchers often cannot acquire it dynamically over large areas, which leads
to a lack of adequate calibration parameters, rendering the easy and
spontaneous prediction of mobility in additional areas impossible. This paper
targets this problem and represents a concept that combines multiple
substantial mobility theorems formulated in recent years to reduce the amount
of required information compared to existing simulations. Our concept also
targets computational expenses and aims to reduce them to enable a global
prediction of mobility. Inspired by methods from other domains, the core idea
of the conceptional innovation can be compared to weather models, which predict
weather on a large scale, on an adequate level of regional information
(airspeed, air pressure, etc.), but without the detailed movement information
of each air atom and its particular simulation.
