Detecting self-organising patterns in crowd motion: Effect of optimisation algorithms

TL;DR

This paper improves the detection of self-organizing crowd patterns by replacing the Nelder-Mead algorithm with Simulated Annealing and using a square wave model, validated through simulations and real experiments.

Contribution

It introduces a novel optimization approach combining Simulated Annealing with a square wave model for better pattern detection in crowd behavior analysis.

Findings

Enhanced pattern fitting accuracy validated statistically

Simulated Annealing outperforms previous algorithms

Applicable to diverse crowd management scenarios

Abstract

The escalating process of urbanization has raised concerns about incidents arising from overcrowding, necessitating a deep understanding of large human crowd behavior and the development of effective crowd management strategies. This study employs computational methods to analyze real-world crowd behaviors, emphasizing self-organizing patterns. Notably, the intersection of two streams of individuals triggers the spontaneous emergence of striped patterns, validated through both simulations and live human experiments. Addressing a gap in computational methods for studying these patterns, previous research utilized the pattern-matching technique, employing the Nelder-Mead Simplex algorithm for fitting a two-dimensional sinusoidal function to pedestrian coordinates. This paper advances the pattern-matching procedure by introducing Simulated Annealing as the optimization algorithm and employing a two-dimensional square wave for data fitting. The amalgamation of Simulated Annealing and the square wave significantly enhances pattern fitting quality, validated through statistical hypothesis tests. The study concludes by outlining potential applications of this method across diverse scenarios.