KDEM: A Knowledge-Driven Exploration Model for Indoor Crowd Evacuation Simulation

Yuji Shen, Bohao Zhang, Chen Li, Changbo Wang, Gaoqi He

Abstract: Knowledge plays an important role in the indoor crowd evacuation. However, most evacuation simulation models assume that the agent is familiar with the simulation scene and does not apply the posterior knowledge to the simulation. This also makes it difficult for these models to judge the real situation of unfamiliar and complex scenes effectively. This study proposes a Knowledge-Driven Evacuation Model (KDEM), focusing on indoor crowd evacuation. In order to adapt the model to more complex scenarios, we refine the building structure knowledge and add bridge knowledge, and clarify the role of building object knowledge of safety signs and dangerous sources. The SEIR model is used to construct the function of knowledge dissemination and decay, and knowledge discovery is proposed as another way of knowledge acquisition. An exploration module is proposed, using Dijkstra and ORCA models, to help agents plan goals and paths and avoid obstacles in action. The experimental results show that the KDEM model conforms to the real situation and can provide practical guidance for public safety.