An automated method for macro evacuation network modeling and visualization of micro‐level behavior based on macro simulation

The topological networks with evacuation information of most macro evacuation models are created manually, which is a repetitive and time-consuming work. Meanwhile, micro-evacuation simulation software often pre-inputs path networks to reduce computational costs. To address these issues, this study developed a semantic segmentation model to recognize doors and rooms from building floor plans. Then, a series of morphological image processing techniques is proposed to further improve the accuracy of the results. Various types of functional nodes in topological network are extracted from the results, and improved A-star algorithm is adopted to find out the interconnectivity, that is, the lines among functional nodes. The topological network with evacuation information is generated. The proposed method achieves fast and automated generation of macro evacuation network models from building floor plans and finds possible congestion nodes in the building. Additionally, this study proposes a micro-level visualization method for abstract macro-simulation results. In the case study presented, the generated macro evacuation network model and the simplified micro-level simulation results transforming method both demonstrate excellent performance.