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Register a new userMeasuring the rationality in evacuation behavior with deep learning
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The bounded rationality is a crucial component in human behaviors. It plays a key role in the typical collective behavior of evacuation, in which the heterogeneous information leads to the deviation of rational choices. In this study, we propose a deep learning framework to extract the quantitative deviation which emerges in a cellular automaton (CA) model describing the evacuation. The well-trained deep convolutional neural networks (CNNs) accurately predict the rational factors from multi-frame images generated by the CA model. In addition, it should be noted that the performance of this machine is robust to the incomplete images corresponding to global information loss. Moreover, this framework provides us with a playground in which the rationality is measured in evacuation and the scheme could also be generalized to other well-designed virtual experiments.
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Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with massive pedestrians. We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability, and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: 1) varying pedestrian distribution ratios, 2) varying exit width ratios, and 3) varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to the high efficiency of exit utilization.
In todays terrorism-prone and security-focused world, evacuation emergencies, drills, and false alarms are becoming more and more common. Compliance to an evacuation order made by an authority in case of emergency can play a key role in the outcome of an emergency. In case an evacuee experiences repeated emergency scenarios which may be a false alarm (e.g., an evacuation drill, a false bomb threat, etc.) or an actual threat, the Aesops cry wolf effect (repeated false alarms decrease order compliance) can severely affect his/her likelihood to evacuate. To analyse this key unsolved issue of evacuation research, a game-theoretic approach is proposed. Game theory is used to explore mutual best responses of an evacuee and an authority. In the proposed model the authority obtains a signal of whether there is a threat or not and decides whether to order an evacuation or not. The evacuee, after receiving an evacuation order, subsequently decides whether to stay or leave based on posterior beliefs that have been updated in response to the authoritys action. Best-responses are derived and Sequential equilibrium and Perfect Bayesian Equilibrium are used as solution concepts (refining equilibria with the intuitive criterion). Model results highlight the benefits of announced evacuation drills and suggest that improving the accuracy of threat detection can prevent large inefficiencies associated with the cry wolf effect.
In this paper, the impact of escaping in couples on the evacuation dynamics has been investigated via experiments and modeling. Two sets of experiments have been implemented, in which pedestrians are asked to escape either in individual or in couples. The experiments show that escaping in couples can decrease the average evacuation time. Moreover, it is found that the average evacuation time gap is essentially constant, which means that the evacuation speed essentially does not depend on the number of pedestrians that have not yet escaped. To model the evacuation dynamics, an improved social force model has been proposed, in which it is assumed that the driving force of a pedestrian cannot be fulfilled when the composition of physical forces exceeds a threshold because the pedestrian cannot keep his/her body balance under this circumstance. To model the effect of escaping in couples, attraction force has been introduced between the partners. Simulation results are in good agreement with the experimental ones.
In real world, individual rationality varies for the sake of the diversity of peoples individuality. In order to investigate how diversity of agents rationality affects the evolution of cooperation, we introduce the individual rationality proportional to the $beta$th power of the each agents degree. Simulation results on heterogeneous scale-free network show that the dynamic process is greatly affected by the diversity of rationality. Both promotion and inhibition of cooperative behavior can be observed at different region of parameter $beta$. We present explanation to these results by quantitative and qualitative analysis. The nodes with middle degree value are found to play a critical role in the evolutionary processes. The inspiration from our work may provide us a deeper comprehension towards some social phenomenon.
This paper examines different evacuation strategies for systems where several rooms evacuate trough the same means of egress, using microscopic pedestrian simulation.As a case study, a medium-rise office building is considered. It was found that the standard strategy, whereby the simultaneous evacuation of all levels is performed, can be improved by a sequential evacuation, beginning with the lowest floor and continuing successively with each one of the upper floors after a certain delay. The importance of the present research is that it provides the basis for the design and implementation of new evacuation strategies and alarm systems that could significantly improve the evacuation of multiple rooms trough a common means of escape.
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