Deep Crowd-Flow Prediction in Built Environments
This addresses the need for real-time crowd behavior prediction in applications like disaster management and urban planning, though it appears incremental as it builds on existing simulation approaches.
The paper tackles the problem of predicting long-term crowd flow in large, realistic environments, which is computationally expensive with existing simulation methods, and proposes a novel CAGE representation to enable instant predictions with positive results.
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.