Crowd Evacuation Under Real Data: A Crowd Congestion Control Method Based on Sensors and Knowledge Graph

Abstract

Crowd congestion is an important factor affecting evacuation efficiency, and reasonable regulation of crowd congestion in the evacuation process is one way to improve crowd evacuation efficiency. Since traditional crowd evacuation simulation methods are based on hypothetical scenarios and rules, crowd evacuation simulations lack realism. To solve this problem and reduce the scale of crowd congestion during evacuation, this paper proposes a crowd congestion control method based on sensors and knowledge graph. First, we model the evacuation scenario by extracting information from real scenes in a video to improve the realism of the evacuation simulation. Second, we construct a crowd congestion knowledge graph (CCKG) to represent the congestion information in evacuation scenes, which improves the model’s ability to characterize crowd information. Then, a gravity field is generated based on the information in the knowledge graph, and the crowd is guided by the gravity field for evacuation. Finally, we use the constructed CCKG to predict the next moment of congestion and regulate the crowd evacuation route in real-time to reduce crowd congestion. The experimental results show that using sensors to extract data from real scenes can improve the realism of crowd evacuation simulation. At the same time, the introduction of a knowledge graph can improve the characterization ability and prediction effect of the model, circumvent large-scale crowd congestion, and improve the efficiency of crowd evacuation.