Optimized leaders strategies for crowd evacuation in unknown environments with multiple exits

TL;DR

This paper develops and tests optimized leader strategies to improve crowd evacuation times in unknown environments with multiple exits, using a combination of microscopic, mesoscopic models and metaheuristic optimization.

Contribution

It introduces a novel combination of agent-based and mean-field models with Monte-Carlo simulations and metaheuristic optimization to enhance evacuation strategies in complex environments.

Findings

Optimized leader strategies significantly reduce evacuation time.

Metaheuristic algorithms effectively identify optimal control parameters.

Models accurately predict crowd dynamics and evacuation outcomes.

Abstract

In this chapter, we discuss the mathematical modeling of egressing pedestrians in an unknown environment with multiple exits. We investigate different control problems to enhance the evacuation time of a crowd of agents, by few informed individuals, named leaders. Leaders are not recognizable as such and consist of two groups: a set of unaware leaders moving selfishly toward a fixed target, whereas the rest is coordinated to improve the evacuation time introducing different performance measures. Follower-leader dynamics is initially described microscopically by an agent-based model, subsequently a mean-field type model is introduced to approximate the large crowd of followers. The mesoscopic scale is efficiently solved by a class of numerical schemes based on direct simulation Monte-Carlo methods. Optimization of leader strategies is performed by a modified compass search method in the spirit of metaheuristic approaches. Finally, several virtual experiments are studied for various control settings and environments.