For numerical simulations of crowd dynamics in an evacuation we need a computationally light environment, such as the cellular automaton model (CA). By choosing the right model parameters, different types of crowd behavior and collective effects can be produced. But the CA does not answer why, when, and how these different behaviors and collective effects occur. In this article, we present a model, where we couple a spatial evacuation game to the CA. In the game, an agent chooses its strategy by observing its neighbors' strategies. The game matrix changes with the distance to the exit as the evacuation conditions develop. In the resulting model, an agent's strategy choice alters the parameters that govern its behavior in the CA. Thus, with our model, we are able to simulate how evacuation conditions affect the behavior of the crowd. Also, we show that some of the collective effects observed in evacuations are a result of the simple game the agents play.

With self-driven particle models, like the social force model, most of the physics of moving crowds can be modeled. However, it has not been fully unraveled why large crowds evacuating through narrow bottlenecks often act against their self-interest. They form jams in front of the bottleneck, that slow down the evacuation, and fatal pressures build up in the crowd. Here, we take a novel approach, and model the local decision-making in an evacuating crowd as a spatial game. The game is coupled to the social force model, so that different strategies alter the physical parameters. With our integrated treatment of behavioral and physical aspects, we are able to simulate when, why and how typical phenomena of an evacuation through a bottleneck occur. Most importantly, we attain nonmonotonous speed and kinetic pressure patterns, in contrast to the monotonous patterns predicted by the pure social force model. This is a result of impatient agents in the back of the simulated crowd pushing and overtaking their way forward. Our findings give insight into the origin of crowd disasters, since the build-up of kinetic pressure has been related to the risk of falling and crowd turbulence.

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For web-based real-time safety analyses, we need computationally light simulation models. In this study, we develop an evacuation model, where the agents are equipped with simple decision-making abilities. As a starting point, a well-known cellular automaton (CA) evacuation model is used. In a CA, the agents move in a discrete square grid according to some transition probabilities. A recently introduced spatial game model is coupled to this CA. In the resulting model, the strategy choice of the agent determines his physical behavior in the CA. Thus, our model offers a game-theoretical interpretation to the agents' movement in the CA.

In an emergency situation, the evacuation of a large crowd from a complex building can become slow or even dangerous without a working evacuation plan. The use of rescue guides that lead the crowd out of the building can improve the evacuation efficiency. An important issue is how to choose the number, positions, and exit assignments of these guides to minimize the evacuation time of the crowd. Here, we model the evacuating crowd as a multi-agent system with the social force model and simple interaction rules for guides and their followers.We formulate the problem of minimizing the evacuation time using rescue guides as a stochastic control problem.Then, we solve it with a procedure combining numerical simulation and a genetic algorithm (GA). The GA iteratively searches for the optimal evacuation plan, while numerical simulations evaluate the evacuation time of the plans. We apply the procedure on a test case and on an evacuation of a fictional conference building. The procedure is able to solve the number of guides, their initial positions and exit assignments in a single although complicated optimization. The attained results show that the procedure converges to an optimal evacuation plan, which minimizes the evacuation time and mitigates congestion and the effect of random deviations in agents' motion.