Multi-objective optimization of EMS facilities using multi-source data: A case study in Dangtu, China

In recent decades, rapid urbanization has introduced significant challenges to urban planning, exacerbated by ongoing urban expansion and population growth. Among these challenges is the critical need to enhance public safety and the living environment, driven by principles of human-centered design and sustainable urban development. To address this, this paper introduces a novel multi-objective optimization framework for Emergency Medical Services (EMS) facility layout, integrating multi-source data to support urban planning and manage public safety risks effectively. This framework uses corroborative multi-source data to analyze current EMS facilities and suggests improvements through preservation, expansion, and new facility introduction, carefully considering construction costs, resident usage efficiency, and access equity. A multi-objective evolutionary algorithm calculates Pareto optimal solutions for site selection, allowing for a balanced consideration of conflicting EMS siting objectives. Further solution set clustering enables decision-makers to quickly identify and refine strategies aligned with their preferences. We demonstrate the applicability of our framework through a quantitative and qualitative case study in Dangtu, China. The results reveal that our approach not only aids urban planners in making informed decisions that improve EMS facility accessibility but also ensures equitable use and enhances public safety in alignment with sustainable urban development goals.