A Container-Based Software Architecture for Coupling Heterogeneous Agent-Based Evacuation Models using Machine Learning - Carvajal, Aracelly S. Balboa; Muñoz, Gabriel Astudillo; Noel, Rene (Anais do Congresso Ibero-Americano em Engenharia de Software (CIbSE), 2026)
Large-scale disaster simulations often require the integration of heterogeneous systems, such as indoor egress models and city-wide traffic simulators. However, coupling these systems presents significant interoperability and scalability challenges due to high computational costs. This work proposes a modular software architecture to bridge this gap. We developed ”Oráculo”, a containerized microservice system that uses Machine Learning (ML) surrogates to encapsulate the complexity of microscopic Agent-Based Simulations (ABS). The architecture, orchestrated via Docker, allows outdoor simulators to query indoor evacuation rates through a lightweight API, decoupling the internal logic from the external flow. Validation results demonstrate that the ML component achieves an R 2 of 0.87, proving that high-fidelity simulation dynamics can be effectively served as efficient software components.