Behavior-Based Optimization of Emergency Shelter Siting: A TPB–NSGA-III Approach Applied to Hangzhou

Urban disasters pose severe, concentrated risks to dense populations, generating asymmetric and time-critical demands on emergency services and infrastructure. To address these challenges, we develop a behaviorally informed shelter siting framework that integrates a Theory of Planned Behavior (TPB)-based choice model with a Non-dominated Sorting Genetic Algorithm III (NSGA-III) multi-objective spatial optimization to simulate aggregation willingness and determine optimal shelter locations. The model explicitly represents symmetric and asymmetric spatial patterns and jointly optimizes population coverage and travel time under three demand scenarios (general, emergency, vulnerable). Comparative experiments show consistent, measurable gains: coverage increases of 0.19~6.9% and travel-time reductions of 14~18% are obtained, with improvements concentrated in high-need pockets. These results indicate that behaviorally informed, symmetry-aware optimization improves access, equity, and robustness while offering a modular tool for planners.