Toward Sustainable and Inclusive Cities: Graph Neural Network-Enhanced Optimization for Disability-Inclusive Emergency Evacuation in High-Rise Buildings

Emergency evacuation planning in high-rise buildings presents complex optimization challenges critical to achieving sustainable and inclusive urban development. Traditional evacuation models inadequately address vulnerable groups’ needs—particularly persons with disabilities—while neglecting fire spread dynamics, congestion effects, and real-time risk assessment. This neglect undermines both human safety and social equity—core dimensions of sustainable communities. Sustainable cities must integrate inclusive design and emergency preparedness into high-rise development. This paper develops a comprehensive mathematical optimization framework for disability-inclusive emergency evacuation that integrates dynamic fire spread modeling, congestion-aware routing mechanisms, and explicit accessibility constraints within a unified formulation. The proposed approach balances evacuation efficiency, safety, and fairness across diverse population groups through a multi-objective optimization model that incorporates time-varying risk assessments, elevator priority systems for wheelchair users, and group-specific mobility coefficients. To address the computational scalability challenges inherent in large-scale mixed-integer nonlinear programming problems, we introduce an innovative solution methodology that combines Graph Neural Networks (GNN) with Proximal Policy Optimization (PPO) algorithms. The graph neural network component captures spatial-temporal feature representations of building geometry, occupant distributions, and hazard dynamics, while the reinforcement learning algorithm develops adaptive routing policies that respond to evolving emergency conditions. Experimental results on a representative high-rise building scenario demonstrate that the proposed GNN-PPO method achieves substantial improvements in safety, efficiency, and equity. The dynamic policy successfully prioritizes vulnerable populations, utilizes elevator systems effectively for persons with disabilities, and adapts to real-time emergency conditions, providing a robust framework for inclusive emergency evacuation planning in complex building environments. This work demonstrates how advanced computational methods can advance sustainability objectives by ensuring equitable safety outcomes across diverse populations—a prerequisite for truly sustainable cities.