Towards a digital twin for supporting multi-agency incident management in a smart city

Abstract

Cost-effective on-demand computing resources can help to process the increasing number of large, diverse datasets generated from smart internet-enabled technology, such as sensors, CCTV cameras, and mobile devices, with high temporal resolution. Category 1 emergency services (Ambulance, Fire and Rescue, and Police) can benefit from access to (near) real-time traffic- and weather data to coordinate multiple services, such as reassessing a route on the transport network affected by flooding or road incidents. However, there is a tendency not to utilise available smart city data sources, due to the heterogeneous data landscape, lack of real-time information, and communication inefficiencies. Using a systems engineering approach, we identify the current challenges faced by stakeholders involved in incident response and formulate future requirements for an improved system. Based on these initial findings, we develop a use case using Microsoft Azure cloud computing technology for analytical functionalities that can better support stakeholders in their response to an incident. Our prototype allows stakeholders to view available resources, send automatic updates and integrate location-based real-time weather and traffic data. We anticipate our study will provide a foundation for the future design of a data ontology for multi-agency incident response in smart cities of the future.

Research on Digital Twin Dynamic Modeling Method for Transmission Line Deicing System

“…Twin geometric models are the focus of twin systems [21][22][23][24]. This section of the paper proposes a new process for creating a dynamic geometric twin model that precisely represents the actual power line and guarantees swift and stable data transmission from collection to application, distinct from existing physical simulation models.…”

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

“…Disaster response is one of the most critical applications of the Internet of Emergency Services (IoES) [74,75,132]. The IoES can help responders quickly and effectively manage disasters, reduce response times, and save lives [25,139].…”

“…The Smart Transportation scenario in the Internet of Emergency Services (IoES) refers to the use of emerging technologies and data-driven approaches to enhance the safety and efficiency of transportation systems during emergency situations [48,68,132,148]. This scenario involves the integration of various components such as sensors, communication networks, data analytics, and decision support systems to facilitate the real-time monitoring and coordination of emergency responses in the transportation sector.…”

Semantic agent framework for automated flood assessment using dynamic knowledge graphs

“…The performance of flood forecast models is evaluated regularly and continuous model recalibration cycles based on validation datasets from a series of past flood events ensure current, meaningful, and accurate flood forecasts. While the exact methodology remains undisclosed, in part due to the exclusive access to more detailed data granted to emergency responders, the public warnings data provided is highly trusted by authorities and public services (Met Office and Environment Agency, 2022) as well as academia, both as data source (Barker and Macleod, 2019; Wolf et al, 2022) and benchmark (Smith et al, 2009). As the focus of our work lies on the methodology of integrating flood-related data from various domains and sources, the interested reader is referred to Pathak and Eastaff (2014) for more details about models as well as validation and data workflows deployed by the Environment Agency.…”