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.
Highlights
In case of a safety threat in a public place, the crowd has to be evacuated fast
While these behavioral deviations could be implemented in the social force model framework, we assume them to be small compared to the deterministic part of a moving crowd controlled by guides and approximated with the Gaussian random force term
We model the movement of a crowd consisting of passengers and guides with a modified social force model
Summary
In case of a safety threat in a public place, the crowd has to be evacuated fast. There are many studies where the optimal evacuation routes have been calculated [1]. Passenger terminals are characterized by high and fluctuating crowd flow, with a wide variety of people with different destinations to reach [8, 9] They typically have security staff that can respond fast and guides the evacuation if needed. In our recent study [7], the number of guides, their initial positions, and exit assignments needed to minimize the crowd evacuation time is solved in a single optimization problem. In a real-life evacuation, people can participate in various activities that do not immediately get them to the exit, which is a typical concern in evacuation modeling [3] While these behavioral deviations could be implemented in the social force model framework, we assume them to be small compared to the deterministic part of a moving crowd controlled by guides and approximated with the Gaussian random force term.
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