Incorporating genetic algorithm to optimise initial condition of pedestrian evacuation based on agent aggressiveness

In the study of pedestrian evacuation dynamics, pedestrian aggressiveness is important to consider as it potentially impacts evacuation efficiency. A representative example is the ‘faster-is-slower’ phenomenon. In reality, a crowd is a mixture of patient and impatient pedestrians, rather than a collection of like individuals. The impact of such heterogeneity has been reported in previous studies; however, these studies are all based on handcrafted solutions. In this study, we propose a heuristic approach for incorporating a genetic algorithm into the floor field cellular automata model to investigate the optimal initial pedestrian evacuation condition. Pedestrian aggressiveness is represented by two different approaches: the revised friction function and the persisting probability. To verify our genetic algorithm approach, we compared the results with the theoretical analysis and simulation results based on handcrafted solutions. The first contribution of this study is that the initial condition, a mixture of patient and impatient pedestrians, affects evacuation efficiency. Another contribution of this study is the heuristic approach incorporating a genetic algorithm. Our approach exerts the computational efficiency advantage of the floor field cellular automata model.