  Predicting the behavior of crowds in complex environments is a key
requirement in a multitude of application areas, including crowd and disaster
management, architectural design, and urban planning. Given a crowd's immediate
state, current approaches simulate crowd movement to arrive at a future state.
However, most applications require the ability to predict hundreds of possible
simulation outcomes (e.g., under different environment and crowd situations) at
real-time rates, for which these approaches are prohibitively expensive.
  In this paper, we propose an approach to instantly predict the long-term flow
of crowds in arbitrarily large, realistic environments. Central to our approach
is a novel CAGE representation consisting of Capacity, Agent, Goal, and
Environment-oriented information, which efficiently encodes and decodes crowd
scenarios into compact, fixed-size representations that are environmentally
lossless. We present a framework to facilitate the accurate and efficient
prediction of crowd flow in never-before-seen crowd scenarios. We conduct a
series of experiments to evaluate the efficacy of our approach and showcase
positive results.
