Static floor field construction and fine discrete cellular automaton model: Algorithms, simulations and insights

Abstract

While cellular automaton (CA) models have a broad range of applications in the field of pedestrian dynamics, there are still three significant issues (the efficient and accurate construction of the static floor field (SFF), the insufficient simulation accuracy, and the discretization effect) that have not been resolved or fully clarified yet. This work aims to tackle these challenges. First, a novel approximate algorithm that resolves the dilemma between accuracy and efficiency is proposed to construct the SFF, based on which the static navigation field is created by the proposed method. Then, a novel fine discrete CA model based on the desired direction is developed to capture the locomotion movement behaviour. The novel SFF construction algorithm and locomotion movement model are (theoretically and/or numerically) validated and compared to the state-of-the-art approaches in manifold scenarios. Later on, systematic simulation analyses are conducted to investigate the discretization effect at the micro and macro levels. The assessment metrics show that the performance of the proposed algorithm and model is superior over that of the state-of-the-art approaches. The proposed model can resolve the well-known diagonal movement artefact in the existing CA models and reproduce crowd movement and self-organized lane formation observed empirically. It is found that the non-isotropic SFF results in significantly biased and unrealistic evacuation movement. Moreover, the discretization degree is found to significantly affect the individual and crowd movement simulation outcomes, and the magnitude and direction of its effect depend on the used model and the setup of the scenarios. The proposed algorithm and model are promising tools in pedestrian modelling and simulation. The findings provide fresh insights into the development and application of CA models.