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Predicting the behavior of crowds in complex environments is a keyrequirement in a multitude of application areas, including crowd and disastermanagement, architectural design, and urban planning. Given a crowd's immediatestate, current approaches simulate crowd movement to arrive at a future state.However, most applications require the ability to predict hundreds of possiblesimulation outcomes (e.g., under different environment and crowd situations) atreal-time rates, for which these approaches are prohibitively expensive.  In this paper, we propose an approach to instantly predict the long-term flowof crowds in arbitrarily large, realistic environments. Central to our approachis a novel CAGE representation consisting of Capacity, Agent, Goal, andEnvironment-oriented information, which efficiently encodes and decodes crowdscenarios into compact, fixed-size representations that are environmentallylossless. We present a framework to facilitate the accurate and efficientprediction of crowd flow in never-before-seen crowd scenarios. We conduct aseries of experiments to evaluate the efficacy of our approach and showcasepositive results.

Deep Crowd-Flow Prediction in Built Environments