Coupled simulation-optimization model for pedestrian evacuation guidance planning

Abstract

Effective evacuation guidance can guarantee people's safety by facilitating their swiftly exit hazardous areas during an emergency. However, pre-determined guidance plans based solely on distance comparisons to exits may not always be the most effective due to unstable accessibility conditions and uneven crowd distribution. Therefore, it is imperative to incorporate real-time optimal guidance information in the plan. Coupling simplified CTM (Cell Transmission Model)-based simulation, this study proposed a computationally efficient DRF (Directed Rooted Forest)-encoded planning for developing evacuation guidance plan. Taking them as a holistic model, the simulator predicts evacuation dynamics at a constant computational cost regardless of crowd size, while the planning module optimizes the guidance plan directionally by leveraging the simulation's intermediate and final outputs. Numerical tests have demonstrated that the tight coupling between optimization and simulation module has substantially enhanced the model's capacity to fine-tune the guidance plan and optimization efficiency. The proposed model may serve as the foundation for developing real-time evacuation guidance plans for large-scale crowded buildings, either on the premise of accelerated simulation or as an efficient generator of training data for machine learning models.