Minimization of mean-CVaR evacuation time of a crowd using rescue guides: a scenario-based approach

TL;DR

This paper develops a scenario-based optimization method to minimize evacuation time and risk in crowd evacuations using rescue guides, accounting for uncertainties in crowd movement and congestion.

Contribution

It introduces a bi-objective scenario optimization framework combining mean and CVaR of evacuation time, with a novel solution approach using simulation and genetic algorithms.

Findings

Guides positioned behind each agent group reduce evacuation time and risk.

The CVaR-optimal plan effectively mitigates worst-case congestion.

The approach balances average and worst-case evacuation performance.

Abstract

In case of a threat in a public space, the crowd in it should be moved to a shelter or evacuated without delays. Risk management and evacuation planning in public spaces should also take into account uncertainties in the traffic patterns of crowd flow. One way to account for the uncertainties is to make use of safety staff, or guides, that lead the crowd out of the building according to an evacuation plan. Nevertheless, solving the minimum time evacuation plan is a computationally demanding problem. In this paper, we model the evacuating crowd and guides as a multi-agent system with the social force model. To represent uncertainty, we construct probabilistic scenarios. The evacuation plan should work well both on average and also for the worst-performing scenarios. Thus, we formulate the problem as a bi-objective scenario optimization problem, where the mean and conditional value-at-risk (CVaR) of the evacuation time are objectives. A solution procedure combining numerical simulation and genetic algorithm is presented. We apply it to the evacuation of a fictional passenger terminal. In the mean-optimal solution, guides are assigned to lead the crowd to the nearest exits, whereas in the CVaR-optimal solution the focus is on solving the physical congestion occurring in the worst-case scenario. With one guide positioned behind each agent group near each exit, a plan that minimizes both objectives is obtained.