Towards resilient cities: A hybrid simulation framework for risk mitigation through data-driven decision making

Providing a comprehensive view of the city operation and offering useful metrics for decision-making is a well-known challenge for urban risk-analysis systems. Existing systems are, in many cases, generalizations of previous domain specific tools/methodologies that may not cover all urban interdependencies and makes it difficult to have homogeneous indicators. In order to overcome this limitation while seeking for effective support to decision makers, this article introduces a novel hybrid simulation framework for risk mitigation. The framework is built on a proposed city concept that considers the urban space as a Complex Adaptive System composed by interconnected Critical Infrastructures. In this concept, a Social System, which models daily patterns and social interactions of the citizens in the Urban Landscape, drives the CIs demand to configure the full city picture. The framework's hybrid design integrates agent-based and network-based modeling by breaking down city agents into system-dependent subagents, to enable both inter and intra-system interaction simulation, respectively. A layered structure of indicators at different aggregation levels is also developed, to ensure that decisions are not only data-driven but also explainable. Therefore, the proposed simulation framework can serve as a DSS tool that allows the quantitative analysis of the impact of threats at different levels. First, system-level metrics can be used to get a broad view on the city resilience. Then, agent-level metrics back those figures and provide better explainability. On implementation, the proposed framework enables component reusability (for eased coding), simulation federation (enabling the integration of existing system-oriented simulators), discrete simulation in accelerated time (for rapid scenario simulation) and decision-oriented visualization (for informed outputs). The system built under the proposed approach facilitates to simulate various risk mitigation strategies for a scenario under analysis, allowing decision-makers to foresee potential outcomes. A case study has been deployed on a framework prototype to demonstrate how the DSS can be used in real-world situations, specifically combining cyber hazards over health and traffic infrastructures. The proposal aims at pushing the boundaries of urban city simulation towards more real, intelligent, and automated frameworks.