A Two-Phase and Bi-Level Spatial Configuration Methodology of Shelters Based on a Circular Assignment Model and Evacuation Traffic Flow Allocation - Yujia Zhang; Wei Chen; Guangchun Zhong; Guofang Zhai; Wei Zhai (ISPRS International Journal of Geo-Information)
With the continued recognition of the devastating effects of natural hazards, the construction of shelters has become essential in urban disaster preparedness planning systems. After analyzing the deficiency of the conventional spatial allocation model of shelters and the hierarchy of evacuation assignments, this study proposes a bi-level and two-phase spatial configuration methodology of shelters. The first hierarchy aims to evacuate refugees from demand blocks to both emergency shelters and resident emergency congregate shelters. The second hierarchy aims to transfer refugees from selected shelters in the first hierarchy to resident emergency congregate shelters. Each hierarchy contains two phases of optimizing calculations. The optimization objects for the first phase and second phase are minimizing the number of new shelters and the evacuation time, respectively. A genetic algorithm and exhaustive approach are programmed to determine the solution of the model in the first and second phases, respectively. The evacuation assignment rule is proposed based on the gravity model, which distributes evacuees proportionally to nearby shelters. This study uses the deterministic user equilibrium problem to present the evacuation traffic flow allocation, which improves the scientificity of the location model of shelters. The refuge demands differentiate the population between daytime and nighttime through mobile signaling data and improve the accuracy from the plot scale to the building scale. Using mobile signaling data to differentiate refuge demands between day and night populations enhances the model’s precision. Finally, to validate the proposed methodology, this study selected the main area of Changshu City, Jiangsu Province, China, which has a population of 1.6 million, as a case study area.