An improved social force model (ISFM)-based crowd evacuation simulation method in virtual reality with a subway fire as a case study

Crowd evacuation simulation using virtual reality(VR)is significant for digital emergency response construction.However,existing evacuation simulation studies suffer from poor adaptation to complex environments,inefficient evacuations,and poor simulation effects and do not fully consider the impacts of specific disaster environments on crowd evacuation.To more realistically express the crowd evacuation results obtained under the influence offire environments and the subjective consciousness of pedestrians in subway stations,we designed a dynamic pedestrian evacuation path planning method under multiple constraints,analysed the influences of an‘environmental role’and a‘subjective initiative’on crowd evacuation,established an improved social force model(ISFM)-based crowd evacuation simulation method in VR,developed a prototype system and conducted experimental analyses.The experimental results show that the crowd evacuation time of the ISFM is affected by the disaster severity.In simulation experiments without disaster scenarios,the improved model’s crowd evacuation efficiency improved by averages of 12.53%and 15.37%over the commercial Pathfinder software and the original social force model,respectively.The method described herein can effectively support real-time VR crowd evacuation simulation under multiexit and multifloor conditions and can provide technical support for emergency evacuation learning and management decision analyses involving subwayfires.

Deep reinforcement learning and 3D physical environments applied to crowd evacuation in congested scenarios

To avoid crowd evacuation simulations depending on 2D environments and real data,we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning(DRL)and 3D physical environments(3DPEs).In 3DPEs,we construct simulation scenarios from the aspects of geometry,semantics and physics,which include the environment,the agents and their interactions,and provide training samples for DRL.In DRL,we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy.With a unified configuration,we conduct evacuation simulations.In scenarios with one exit,we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics(number,mass and height)on evacuation.In scenarios with two exits and a uniform(nonuniform)distribution of agents,we explore the impact of exit characteristics(width and relative position)and agent characteristics(height,initial location and distribution)on agent exit selection and evacuation.Overall,interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation.