A pedestrian ABM in complex evacuation environments based on Bayesian Nash Equilibrium
Keywords: Agent-based Modelling, Pedestrian Evacuation, Bayesian Nash Equilibrium, Crowd Simulation, Pedestrian Behaviours
Abstract. This research proposed an improved pedestrian evacuation ABM incorporating Bayesian Nash equilibrium (BNE) to provide more realistic simulations of evacuating behaviours in complex environments. BNE theory was introduced to improve the rationality of model simulations by quantifying individual decision-making process. Latest research put forward that BNE pedestrians (agents) were capable of evacuating faster and displayed more intelligent and representative evacuating behaviours. To further evaluate the role of BNE played in agents’ navigations in complex scenarios, this paper extends the above work by introducing impassable barriers with changeable sizes to realise the simulations in a more complex evacuation space with several narrow corridors. In order to match the demands of efficiently avoiding congestions and impassable areas, the decision-making rule of BNE agents when one patch was occupied by over 10 agents was improved from 100% best strategy to a multi-strategy combination: with 50% optimal strategy, 40% suboptimal strategy and 10% choosing one of the remaining options. It was found that compared with the agents following the other two traditional models, BNE agents could change their original exiting route after considering possible movements of the neighbouring agents and may evacuate through the corridors relatively further from the exit. A detailed introduction of the improved ABM is provided in this paper. Potential research directions are also identified.