Deep Crowd-Flow Prediction in Built Environments

TL;DR

This paper introduces a novel method for real-time long-term crowd flow prediction in large environments using a compact CAGE representation, enabling efficient and accurate scenario forecasting.

Contribution

It presents a new CAGE encoding scheme and a framework for instant, accurate crowd flow prediction in unseen environments, improving over simulation-based methods.

Findings

Effective prediction of crowd flow in large environments

CAGE representation encodes scenarios efficiently and losslessly

Framework achieves real-time predictions with positive experimental results

Abstract

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.