Efficiency Of Emergency Response Research Articles

Lagrangian relaxation-based approaches for cooperative location and assignment of emergency responders

Efficient fire response and the coordination of various fire vehicles are paramount for delivering optimal services and conducting effective rescue operations. This study explores the challenges of cooperative station location and vehicle assignment, focusing on the synergistic dispatch of fire vehicles from multiple stations. It highlights that the response time of the last arriving vehicle, crucial for forming a complete and effective response unit, is a critical determinant in the overall success of fire rescue operations. The primary objective of the model is to minimize the total system cost, encompassing both the capital investment in fire stations and the potential losses from fire incidents. By employing a Lagrangian relaxation technique, the model effectively simplifies the optimization challenge into more manageable sub-problems, thereby enhancing solution accuracy. The robustness and flexibility of the model were tested through simulations in a hypothetical network and a real-world scenario, using data from urban fire services. The results demonstrate the model’s scalability and adaptability to different network configurations and operational constraints, showcasing its potential to substantially enhance emergency response efficiency and decision-making in urban fire management. This research makes a significant contribution to emergency response management by providing a validated framework that improves the coordination and effectiveness of fire rescue operations, thereby reducing fire damage and enhancing public safety.

Spatial Analysis of Advanced Air Mobility in Rural Healthcare Logistics

The transportation of patients in emergency medical situations, particularly in rural areas, often faces significant challenges due to long travel distances and limited access to healthcare facilities. These challenges can result in critical delays in medical care, adversely affecting patient outcomes. Addressing this issue is essential for improving survival rates and health outcomes in underserved regions. This study explored the potential of advanced air mobility to enhance emergency medical services by reducing patient transport times through the strategic placement of vertiports. Using North Dakota as a case study, the research developed a GIS-based optimization workflow to identify optimal vertiport locations that maximize time savings. The study highlighted the benefits of strategic vertiport placement at existing airports and hospital heliports to minimize community disruption and leverage underutilized infrastructure. A key finding was that the optimized mixed-mode routes could reduce patient transport times by up to 21.8 min compared with drive-only routes, significantly impacting emergency response efficiency. Additionally, the study revealed that more than 45% of the populated areas experienced reduced ground travel times due to the integration of vertiports, highlighting the strategic importance of vertiport placement in optimizing emergency medical services. The research also demonstrated the replicability of the GIS-based optimization model for other regions, offering valuable insights for policymakers and stakeholders in enhancing EMS through advanced air mobility solutions.

Integrating Inter-Professional Insights for Enhanced Disaster Response: A Cross-Sectional Analysis in Jubail's Royal Commission Hospital, Saudi Arabia.

This study aimed to evaluate disaster preparedness and management among an inter-professional team at the Royal Commission Hospital (RCH) in Jubail, Saudi Arabia. Conducted between May and July 2023, this cross-sectional study involved healthcare providers in both patient-facing and non-patient-facing roles. Participants responded to a comprehensive online questionnaire comprising 22 questions across seven sections covering aspects of emergency response, disaster management, and infection control. The study targeted a minimum sample size of 500 participants, successfully garnering responses from 512 individuals. Of the 512 participants, 59.9% (n=312) were healthcare providers in patient-facing roles, and 40.1% (n=209) were in non-patient-facing roles. The results revealed notable disparities in awareness and preparedness between these two groups. Healthcare providers demonstrated higher awareness levels compared to their non-patient-facing counterparts. For instance, 76.9% of healthcare providers were aware of the hospital's emergency response plan compared to 56.2% of non-healthcare providers (χ² = 52.165, p < 0.001). Similar disparities were observed in understanding the term "disaster" (86.5% vs 54.1%, χ² = 27.931, p < 0.001), and awareness of a command center (73.4% vs 45.2%, χ² = 42.934, p < 0.001). These findings underscore the critical need for enhancing awareness, education, and preparedness within healthcare facilities, emphasizing an integrated approach that includes both healthcare and non-healthcare staff. By addressing these gaps, healthcare facilities can significantly improve their emergency response efficiency, disaster management capabilities, and infection control measures, thereby enhancing the overall safety and quality of patient care.

Examining fire service coverage and potential sites for fire station locations in Kathmandu, Nepal

In the densely populated Kathmandu Metropolitan City, inadequate fire service coverage poses a significant fire risk. With the demand for fire services increasing due to rapid growth in population and infrastructure development, the limited number of fire stations, insufficient human and physical resources, poor street connectivity, narrow streets, and delays in response to calls for emergency services have resulted in significant property loss, environmental impact, serious injuries, and even loss of life. Although a substantial body of research exists on understanding fire patterns and fire station site selection in developed countries, there is a noticeable lack of similar research in developing countries, which can be attributed to a lack of available data and interest. This study utilises ward-level urban fire incident data in Kathmandu from July 2019 to February 2023 to examine the spatial pattern of fire risk, fire service coverage and identify potential sites for fire stations. Results reveal a significantly higher fire rate in the central part of the city. Fire rescue service cannot reach most areas in Kathmandu within 5 min, with some areas exceeding 10 min response time. The findings from this study will enable fire agencies to enhance the operational efficiency of emergency response and help develop policies and programs related to fire risk prevention and mitigation. This includes targeted education and awareness campaigns and the allocation of finite fire service resources, such as the strategic placement of fire stations.

AcciSense – A Real-Time Accident Detection and Emergency Response System

The AcciSense project pioneers advanced technology and intelligent algorithms for real-time accident detection and emergency response optimization on highways. Its core objective is swift accident identification and automated hospital notifications to improve road safety and ensure timely medical aid. By integrating these technologies, AcciSense aims to revolutionize highway emergency response, addressing the need for faster and more effective medical services. A standout feature of AcciSense is its automated ambulance dispatch, reducing response times crucial for saving lives during emergencies. Seamless integration with hospital systems enhances response coordination, ensuring prompt medical assistance at accident sites. This approach highlights technology's transformative role in bolstering road safety and emergency response efficiency. AcciSense focus on intelligent algorithms and cutting-edge technology reflects a commitment to innovation and effectiveness in emergency response and road safety challenges. It promises to enhance highway transportation safety through proactive accident detection and technology-driven response coordination, emphasizing technology's critical role in improving emergency services and saving lives on highways.

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

Research on Application Solutions of Blockchain Technology in Multi-Party Collaborative Emergency Logistics System

With the continuous development of global trade and logistics business, the construction of emergency logistics system has become an important part to ensure the efficient operation of logistics chain. However, in the face of emergencies, such as natural disasters, traffic accidents or public health crises, the traditional emergency logistics system is vulnerable to information asymmetry, data tampering and other problems, resulting in low emergency response efficiency. Blockchain technology, with its decentralized, immutable, transparent and traceable characteristics, offers new possibilities for solving these problems. This paper aims to discuss the solution of blockchain technology applied in the field of emergency logistics from the perspective of supply chain, in order to build a multi-agent collaborative emergency logistics “digital platform” network, and improve the response ability and toughness of the urban emergency logistics system that “combines emergency and peace”.

Application and Assessment of GIS Technology in Flash Flood Risk Management

This paper aims to explore the application of Geographic Information Systems (GIS) technology in the risk management of flash flood disasters and its role in safety assessment. Initially, the article briefly introduces the basic principles and functionalities of GIS technology, as well as how it can be effectively used in the monitoring, analysis, and prediction of flash flood disasters. Subsequently, the paper delves into the key applications of GIS in the risk assessment of flash floods, including the identification of disaster-prone areas, classification of risk levels, and planning of response strategies. The article also emphasizes the advantages of GIS technology in integrating multi-source data, achieving dynamic monitoring, and enhancing the efficiency of emergency response. Finally, through an analysis of existing research, the paper points out the potential and future directions of GIS in the field of safety evaluation of flash flood disasters. This study aims to provide more effective technical support and decision-making references for the risk management of flash flood disasters.

Methods for Addressing Pavement Defects Based on Digital Twin Technology--A Case Study of Snow and Water Accumulation on Road Surface

This paper explores the utilization of digital twin technology for the management of urban road infrastructure challenges, specifically addressing issues related to snow and water accumulation. The study emphasizes the constraints associated with traditional road maintenance methods and proposes the implementation of a digital twin city framework to augment real-time monitoring and enhance emergency response efficiency. Through the integration of real-time data from sensors, traffic, and weather sources, the digital twin model replicates road conditions and forecasts potential issues, thereby enabling early warning systems. Furthermore, the research delves into the application of conductive asphalt concrete and superhydrophobic materials to mitigate road-related challenges. The results indicate that digital twin technology significantly enhances economic and operational efficiency in urban road infrastructure management when compared to conventional approaches.

Emergency Decision Making for Electric Power Personal Accidents Based on Ontology and Case-Based Reasoning

Improvements in the emergency response efficiency and management level of power construction sites are conducive to reducing the construction safety risk of power infrastructure projects and then achieving the sustainability of construction site safety. Therefore, this paper proposes an emergency decision-making method for electric power personal accidents, which applies ontology and case-based reasoning to electric power emergency decision making. Firstly, ontology technology is used to structurally represent power accident case knowledge and clarify concepts and their relationships. Then, a power accident knowledge ontology hierarchy is designed, and a powerful personal accident case library is established. Secondly, by calculating cases’ conceptual similarity, attribute similarity, and structural similarity, a global power accident case similarity calculation method is proposed, and case matching is performed based on the calculation results to achieve case knowledge retrieval and reuse. Finally, the results of the example-based study show that the method effectively achieves the accurate retrieval of electric power accident cases, improves the efficiency of the emergency decision response to electric power construction site accidents, and then provides support for emergency decision making for electric power construction site accidents.

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

The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.

An integrated EDIB model for probabilistic risk analysis of natural gas pipeline leakage accidents

Natural gas pipeline construction is developing rapidly worldwide to meet the needs of international and domestic energy transportation. Meanwhile, leakage accidents occur to natural gas pipelines frequently due to mechanical failure, personal operation errors, etc., and induce huge economic property loss, environmental damages, and even casualties. However, few models have been developed to describe the evolution process of natural gas pipeline leakage accidents (NGPLA) and assess their corresponding consequences and influencing factors quantitatively. Therefore, this study aims to propose a comprehensive risk analysis model, named EDIB (ET-DEMATEL-ISM-BN) model, which can be employed to analyze the accident evolution process of NGPLA and conduct probabilistic risk assessments of NGPLA with the consideration of multiple influencing factors. In the proposed integrated model, event tree analysis (ET) is employed to analyze the evolution process of NGPLA before the influencing factors of accident evolution can be identified with the help of accident reports. Then, the combination of DEMATEL (Decision-making Trial and Evaluation Laboratory) and ISM (Interpretative Structural Modeling) is used to determine the relationship among accident evolution events of NGPLA and obtain a hierarchical network, which can be employed to support the construction of a Bayesian network (BN) model. The prior conditional probabilities of the BN model were determined based on the data analysis of 773 accident reports or expert judgment with the help of the Dempster-Shafer evidence theory. Finally, the developed BN model was used to conduct accident evolution scenario analysis and influencing factor sensitivity analysis with respect to secondary accidents (fire, vapor cloud explosion, and asphyxia or poisoning). The results show that ignition is the most critical influencing factor leading to secondary accidents. The occurrence time and occurrence location of NGPLA mainly affect the efficiency of emergency response and further influence the accident consequence. Meanwhile, the weight ranking of economic loss, environmental influence, and casualties on social influence is determined with respect to NGPLAs.

Petri-net based cooperation modeling and time analysis of emergency response in the context of domino effect prevention in process industries

Emergency response is an important way to reduce losses after a major industrial accident occurs and mitigation measures should be arranged and analyzed in preparedness. The cooperation in emergency response actions which make up an emergency response process has a significant impact on the efficiency and success rate (or reliability) of emergency response, and improper arrangement of actions may reduce emergency response efficiency. As emergency response is characterized by rapid response, this work studies the success of emergency response based on time analysis. In this paper, cooperation modes of emergency response actions and their time characteristics are analyzed. A timed colored Petri-net (TCPN) based approach is proposed to model the cooperation of the actions and perform time analysis. The proposed approach is illustrated by an example of fire brigades’ response to a tank fire. Simulations are performed and the probabilities of preventing fire escalation under different cooperation modes are analyzed. TCPN based modeling and analysis of emergency response actions are helpful for planning the necessary actions in the preparation of an emergency situation.

A Threat Intelligence Analysis Method Based on Feature Weighting and BERT-BiGRU for Industrial Internet of Things

The combination of 5G technology and the industrial Internet of things (IIoT) makes it possible to realize the interconnection of all things. Still, it also increases the risk of attacks such as large-scale DDoS attacks and IP spoofing attacks. Threat intelligence is a collection of information causing potential and nonpotential harm to the industrial Internet. Extracting network security entities and their relationships from threat intelligence text and constructing structured threat intelligence information are particularly important for IIoT security protection. However, threat intelligence is mostly text reports, which means the value information needs to be extracted manually by security analysts, and it is highly dependent on personnel experience. Therefore, this study proposes an IIoT threat intelligence analysis method based on feature weighting and BERT-BiGRU. In this method, BERT-BiGRU is used to classify attack behavior and attack strategy. Then, the attack behavior is weighted to make the classified result more accurate according to the relationship between attack strategy and attack behavior in ATT&amp;CK for ICS knowledge. Finally, the possibility of attack and the harm degree of attack are calculated to form the threat value of the attack. The security analysts can judge the emergency response sequence by the threat value to improve the accuracy and efficiency of emergency response. The results indicate that the proposed method in this study is more accurate than the other standard methods and is more suitable for the unstructured threat intelligence analysis of IIoT.

Context Aware Combat COA Recommendation using Preference Learning

In the daily combat readiness duty, faced with emergencies, time window left for decision-making is limited, far from enough to research and design a totally new COA (Course of Actions). However, using the state-of-the-art AI technologies, it is yet unable to autonomously generate COAs of trust. From the perspective of practicality, it is effective to improve emergency response efficiency, by making use of historically accumulated COAs. Similar problems have similar solutions, which is especially suitable for fields with incomplete knowledge systems and limited data accumulation. An intelligent method of COA recommendation is proposed, which can accurately recommend appropriate COAs based on current mission requirements, battlefield situation, and preferences. Based on the recommended COAs, user can form a new COA more efficiently, faced with emergencies. As a solution for AI technology supported combat decision making closer to practical, the method has certain reference value.

Construction and spatio-temporal derivation of hazardous chemical leakage disaster chain

ABSTRACT Hazardous chemicals are indispensable substances for the development of modern industries. However, improper storage, transportation, or handling of hazardous chemicals can cause chain disasters such as leakage and explosion, seriously threatening the safety of people’s lives, society, economy, and natural environment. In this study, we analysed the occurrence and evolution process of various disaster events caused by hazardous chemical leakage based on the disaster system theory, and constructed a hazardous chemical leakage disaster chain from the aspects of disaster-causing factors, disaster-pregnant environment, hazard-affected bodies, and post-disaster losses. Taking a methane leakage accident as a case study, we conducted numerical simulation and spatio-temporal dynamic derivation on the hazardous chemical leakage disaster chain in a three-dimensional (3-D) geographic scene. This study can provide support on handling hazardous chemical leakage disasters and assist informed decision-making, including monitoring, pre-warning, disaster forecast and prevention, emergency preparedness, and post-disaster response, further improving the efficiency of emergency response and urban safety management.

A BIM-Based Safety Management Framework for Operation and Maintenance in Water Diversion Projects

Timely assessment of the structural safety status of water diversion projects and taking necessary preventive measures are the primary conditions to ensure the safe operation of large-scale diversion structures. This paper proposes an integrated visualization framework based on the Building Information Model (BIM) to support the safety management of water diversion projects. The framework integrates data collection, data analysis, warning issuance, and emergency disposal into an integrated platform, which improves the automation level of safety management and the efficiency of emergency response. On this basis, a prototype system is developed and implemented in a water diversion project in Henan Province, China. The prototype of the system can automatically evaluate the structural safety status of water diversion structures, issue corresponding warnings to managers, and provide visual prompts to support decision-making. The system prototype and its demonstration verify the applicability and effectiveness of the framework.

Fine-Grained Ontology Reconstruction for Crisis Knowledge Based on Integrated Analysis of Temporal-Spatial Factors

Previous studies on crisis know­ledge organization mostly focused on the categorization of crisis know­ledge without regarding its dynamic trend and temporal-spatial features. In order to emphasize the dynamic factors of crisis collaboration, a fine-grained crisis know­ledge model is proposed by integrating temporal-spatial analysis based on ontology, which is one of the commonly used methods for know­ledge organization. The reconstruction of ontology-based crisis know­ledge will be implemented through three steps: analyzing temporal-spatial features of crisis know­ledge, reconstructing crisis know­ledge ontology, and verifying the temporal-spatial ontology. In the process of ontology reconstruction, the main classes and properties of the domain will be identified by investigating the crisis information resources. Meanwhile the fine-grained crisis ontology will be achieved at the level of characteristic representation of crisis know­ledge including temporal relationship, spatial relationship, and semantic relationship. Finally, we conducted case addition and system implementation to verify our crisis know­ledge model. This ontology-based know­ledge organization method theoretically optimizes the static organizational structure of crisis know­ledge, improving the flexibility of know­ledge organization and efficiency of emergency response. In practice, the proposed fine-grained ontology is supposed to be more in line with the real situation of emergency collaboration and management. Moreover, it will also provide the know­ledge base for decision-making during rescue process.

Open-channel landslide hazard assessment based on AHP and fuzzy comprehensive evaluation

Abstract Cross-regional water diversion projects usually face risks of hazards, in particular high risks of sudden landslides on the slopes of water supply open channels. Determining the hazard level rapidly after a hazard occurs plays a critical role in improving the efficiency of emergency response and recovering the water supply capacity of the projects. Through in-depth analysis of the factors that affect the severity of open-channel landslides, an indicator system for hazard assessment of open-channel landslides was established and the criteria for evaluation of different hazard levels were identified. Based on the hierarchy of the indicator system, a two-level fuzzy comprehensive evaluation model was established to assess the impact of indicators in the model on the hazard level of open-channel landslides, and the weight of each indicator was determined by the analysis hierarchy process. The linear function was used to calculate the membership degree of each indicator to determine the hazard level. A hazard scenario was created with the water diversion project from the Irtysh River to Urumqi as an example, and the two-level fuzzy comprehensive evaluation model was applied to conduct hazard assessment. The proposed method was confirmed to be reasonable and practical.

Risk assessment of an oil depot using the improved multi-sensor fusion approach based on the cloud model and the belief Jensen-Shannon divergence

At present, enterprises have introduced the Internet of Things (IoT) technology to monitor and evaluate the safety status of oil depots, allowing for the collection of a substantial amount of multi-source monitoring data from factories. However, sensor monitoring data is often inaccurate and fuzzy. To improve the reliability of risk prevention and control based on multi-source sensor data, this study proposed a CM-BJS-DS model based on the cloud model (CM), the Belief Jensen-Shannon (BJS) divergence and Dempster-Shafer(D-S) evidence theory. First, the relevant evaluation factors of the accident and their threshold intervals of different risk levels were determined, and the fuzzy cloud membership functions (FCMFs) corresponding to different risk levels were constructed. Then, the sensor monitoring data were processed using the correlation measurement of the FCMF, and basic probability assignments (BPAs) were generated under the risk assessment frame of discernment. Finally, the BPAs were pre-processed by the improved evidence fusion model and the accident risk level was evaluated. Based on the monitoring data, a case study was performed to assess the risk level of vapor cloud explosion (VCE) accidents due to liquid petroleum gas (LPG) tank leaks. The results show that the proposed method presents the following characteristics: (i) The BPAs were constructed based on the monitoring data, which reduced the subjectivity of the construction process; (ii) Compared with single sensors, the multiple sensor fusion evaluation yielded more specific results; (iii) When dealing with highly conflicting evidence, the evaluation results of the proposed method exhibited a higher belief degree. This method can be used as a decision-making tool to detect potential risks and identify critical risk spots to improve the specificity and efficiency of emergency response.