Detecting aggressive agents in egress process by using conflict data in cellular automaton model

Abstract

Aggressive behaviors at exits cause conflicts and increase egress times. To deter agents from aggressive behaviors, we first need to know who the aggressive ones are. Therefore, we developed a method for detecting aggressive agents. We focused on only egress simulations with a cellular-automata model in this article since we would like to deeply investigate theoretical characteristics of our method. There are two types of agents, which are normal agents and aggressive agents in the simulations. Aggressive agents tend to push out others in conflicts and try to move to their target cell aggressively. We considered all the possible combinations of agent types, labeled them, and computed the joint probabilities of the labels from the conflict data obtained from the egress simulations. The label which achieved the maximum joint probability was regarded as the predicted label. Our detecting method succeeded in detecting the aggressive agents perfectly with the reasonable number of observations. Moreover, there were no false accusations. We have also investigated how the restriction of the usage of the conflict data affect the results. By only using the conflict data of successes in solving conflicts, the accuracy failed to achieve 1.0 when there are many aggressive agents. However, if there are a few very aggressive agents, the progress rate of the accuracy increases by the restriction of the usage of the conflict data. We elucidated this counterintuitive phenomenon theoretically by exploiting a simple probabilistic calculation.