  Evacuation plans have been historically used as a safety measure for the
construction of buildings. The existing crowd simulators require fully-modeled
3D environments and enough time to prepare and simulate scenarios, where the
distribution and behavior of the crowd needs to be controlled. In addition, its
population, routes or even doors and passages may change, so the 3D model and
configurations have to be updated accordingly. This is a time-consuming task
that commonly has to be addressed within the crowd simulators. With that in
mind, we present a novel approach to estimate the resulting data of a given
evacuation scenario without actually simulating it. For such, we divide the
environment into smaller modular rooms with different configurations, in a
divide-and-conquer fashion. Next, we train an artificial neural network to
estimate all required data regarding the evacuation of a single room. After
collecting the estimated data from each room, we develop a heuristic capable of
aggregating per-room information so the full environment can be properly
evaluated. Our method presents an average error of 5% when compared to
evacuation time in a real-life environment. Our crowd estimator approach has
several advantages, such as not requiring to model the 3D environment, nor
learning how to use and configure a crowd simulator, which means any user can
easily use it. Furthermore, the computational time to estimate evacuation data
(inference time) is virtually zero, which is much better even when compared to
the best-case scenario in a real-time crowd simulator.
