Abstract
Due to the interaction and external interference, the crowds will constantly and dynamically adjust their evacuation path in the evacuation process to achieve the purpose of rapid evacuation. The information from previous process can be used to modify the current evacuation control information to achieve a better evacuation effect, and iterative learning control can achieve an effective prediction of the expected path within a limited running time. In order to depict this process, the social force model is improved based on an iterative extended state observer so that the crowds can move along the optimal evacuation path. First, the objective function of the optimal evacuation path is established in the improved model, and an iterative extended state observer is designed to get the estimated value. Second, the above model is verified through simulation experiments. The results show that, as the number of iterations increases, the evacuation time shows a trend of first decreasing and then increasing.
Highlights
As a common continuous micromodel, the social force model is proposed on the basis of the concept of social force combined with classical Newtonian mechanics and has some advantages that discrete models such as cellular automaton do not have, e.g., arbitrary turning of crowds and different degrees of overlapping generated between crowds, which makes the crowds simulated by the social force model closer to reality
Liu [17] developed a social force model to study the crowd evacuation when a terrorist attack occurs in the public place, and the simulation results showed that the emergency exit choice strategy has an advantage over the ordinary exit choice strategy in daily life for reducing casualties. e more unbalanced the terrorists’ initial distribution around the exits is, the more noticeable this advantage will be
The optimal evacuation path is used as an unknown expectation, and the expected control is calculated by repeated iterations. e social force model is improved in this paper using an iterative extended state observer by defining the objective function of the optimal evacuation path
Summary
Zhang et al [21] proposed a modified two-layer social force model to simulate and reproduce a group gathering process based on low-density group organization patterns, and the experiment result showed that a stable group pattern and a suitable leader could decrease collision and allow a safer evacuation process. Such problems as arbitrary turning and overlapping of crowds have been solved, the social force model can effectively describe the “self-organization” and “arching” phenomena. The optimal evacuation path is used as an unknown expectation, and the expected control is calculated by repeated iterations. e social force model is improved in this paper using an iterative extended state observer by defining the objective function of the optimal evacuation path
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