Minimization of Mean-CVaR Evacuation Time of a Crowd using Rescue Guides: a Scenario-based Approach


  • Anton von Schantz School of Science, Aalto University, Espoo, Finland
  • Harri Ehtamo School of Science, Aalto University, Espoo, Finland
  • Simo Hostikka School of Engineering, Aalto University, Espoo, Finland



evacuation, rescue guides, multi-agent system, scenario-based approach, bi-objective optimization, numerical simulation, genetic algorithm


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.


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How to Cite

von Schantz, A., Ehtamo, H., & Hostikka, S. (2021). Minimization of Mean-CVaR Evacuation Time of a Crowd using Rescue Guides: a Scenario-based Approach. Collective Dynamics, 6, 1–29.