Data Taxonomy Towards the Applicability of the Digital Twin Conceptual Framework in Disaster Management

TL;DR

The paper addresses the vulnerability of Digital Twins in disaster management due to data-source interruptions by introducing a Data Source Taxonomy and a similarity/vulnerability assessment, plus a data-source replacement mechanism integrated into the Digital Twin framework. It develops two core research questions (DS1: data-source similarity and DS2: data-source vulnerability) and embeds them within the building-blocks approach of a DT, extending Data Ingress and Scenario Management with streaming and batch processing and a Data Source Manager. A traffic data case study demonstrates how the taxonomy and replacement algorithm identify similar and complementary data sources to maintain DT functionality when primary sources fail. The findings show improved robustness and adaptability of DT-based disaster response systems, enabling rapid, informed decision-making under crisis conditions and guiding future standardization and ontology integration efforts.

Abstract

The Digital Twin (DT) offers a novel approach to the management of critical infrastructures, including energy, water, traffic, public health, and communication systems, which are indispensable for the functioning of modern societies. The increasing complexity and interconnectedness of these infrastructures necessitate the development of robust disaster response and management strategies. During crises and disasters, data source availability for critical infrastructure may be severely constrained due to physical damage to communication networks, power outages, overwhelmed systems, sensor failure or intentional disruptions, hampering the ability to effectively monitor, manage, and respond to emergencies. This research introduces a taxonomy and similarity function for comparing data sources based on their features and vulnerability to crisis events. This assessment enables the identification of similar, complementary, and alternative data sources and rapid adaptation when primary sources fail. The paper outlines a data source manager as an additional component for existing DT frameworks, specifically the data ingress and scenario mangement. A case study for traffic data sources in an urban scenario demonstrates the proposed methodology and its effectiveness. This approach enhances the robustness and adaptability of DTs in disaster management applications, contributing to improved decision-making and response capabilities in critical situations.

Figures (9)

• Figure 1: General architecture of a Digital Twin to provide added value Brucherseifer2021. The twinning is an essential function of the DT, connecting physical and real space in real-time. The virtual replica consists of models and data.
• Figure 2: DT Conceptual Model for the operation of infrastructures to improve resilience in crisis situations, as introduced in Brucherseifer2021
• Figure 3: Classification of data source properties. The classification is divided into two main categories, Data Features and Source Vulnerability, each of which contains a number of descriptive characteristics.
• Figure 4: Sequence of steps taken to evaluate the attributes of the data sources.
• Figure 5: Sequence of steps taken to identify a data source replacement.