Emergency Service Stations Research Articles

Urban drone stations siting optimization based on hybrid algorithm of MILP and machine learning

Urban environments, characterized by high population density and intricate infrastructures, are susceptible to a range of emergencies such as fires and traffic accidents. Optimal placement and distribution of fire stations and ambulance centers are thus imperative for safeguarding both life and property. An investigation into the distribution inefficiencies of emergency service facilities in selected districts of Chengdu reveals that imbalanced distribution of these facilities results in suboptimal response times during critical incidents. To address this challenge, a two-stage clustering method, incorporating X-means and K-means algorithms, is employed to identify optimal number and locations for Unmanned Aerial Vehicle (UAV) fire stations and drone ambulance centers. A Mixed-Integer Linear Programming (MILP) model is subsequently constructed and solved using the Gurobi optimization platform. Bayesian optimization—a machine learning technique—is exploited to elucidate the interplay between response speed and service capacity of these UAV-based emergency service stations under an optimized layout. Results affirm that integration of MILP and machine learning provides a robust framework for solving complex problems related to the siting and allocation of emergency service facilities. The proposed hybrid algorithm demonstrates substantial potential for enhancing emergency preparedness and response in urban settings.

Factors influencing the implementation of severe acute malnutrition guidelines within the healthcare referral systems of rural subdistricts in North West Province, South Africa.

Severe acute malnutrition (SAM) is associated with 30.9% of South Africa's audited under-five children deaths regardless of available guidelines to reduce SAM at each level of a three tyre referral system. Existing research has explored and offered solutions for SAM guidelines implementation at each referral system level, but their connectedness in continuation of care is under-explored. Therefore, I examined implementation of SAM guidelines and factors influencing implementation within subdistrict referral systems. An explanatory qualitative case study design was used. The study was conducted in two subdistricts involving two district hospitals; three community health centres, four clinics, and two emergency service stations. Between February to July 2016 and 2018, data were collected using 39 in-depth interviews with clinical, emergency service and administrative personnel; 40 reviews of records of children younger than five years; appraisals of nine facilities involved in referrals and observations. Thematic content analysis was used to analyse all data except records which were aggregated to elicit whether required SAM guidelines' steps were administered per case reviewed. Record reviews revealed SAM diagnosis discrepancies demonstrated by incomplete anthropometric assessments; non-compliance to SAM management guidelines was noted through skipping some critical steps including therapeutic feeding at clinic level. Record reviews further revealed variations of referral mechanisms across subdistricts, contradictory documentation within records, and restricted continuation of care. Interviews, observations and facility appraisals revealed that factors influencing these practices included inadequate clinical skills; inconsistent supervision and monitoring; unavailability of subdistrict specific referral policies and operational structures; and suboptimal national policies on therapeutic food. SAM diagnosis, management, and referrals within subdistrict health systems need to be strengthened to curb preventable child deaths. Implementation of SAM guidelines needs to be accompanied by job aids and supervision with standardised tools; subdistrict-specific referral policies and suboptimal national policies to ensure availability and accessibility of therapeutic foods.

Incremental gains in response time with varying base location types for drone-delivered automated external defibrillators

IntroductionDrone-delivered automated external defibrillators (AEDs) may reduce delays to defibrillation for out-of-hospital cardiac arrests (OHCAs). We sought to determine how integration of drones and selection of drone bases between emergency service stations (i.e., paramedic, fire, police) would affect 9-1-1 call-to-arrival intervals. MethodsWe identified all treated OHCAs in southern Vancouver Island, British Columbia, Canada from Jan. 2014 to Dec. 2020. We developed mathematical models to select 1–5 optimal drone base locations from each of: paramedic stations, fire stations, police stations, or an unrestricted grid-based set of points to minimize drone travel time to OHCAs. We evaluated models on the estimated first response interval assuming that drones were integrated with existing OHCA response. We compared median response intervals with historical response, as well as across drone base locations. ResultsA total of 1610 OHCAs were included in the study with a historical median response interval of 6.4 minutes (IQR 5.0–8.6). All drone-integrated response systems significantly reduced the median response interval to 4.2–5.4 minutes (all P < 0.001), with grid-based stations using 5 drones resulting in the lowest response interval (4.2 minutes). Median response times between drone base location types differed by 6–16 seconds, all comparisons of which were statistically significant (all P < 0.02). ConclusionIntegrating drone-delivered AEDs into OHCA response may reduce first response intervals, even with a small quantity of drones. Implementing drone response with only one emergency service resulted in similar response metrics regardless of the emergency service hosting the drone base and was competitive with unrestricted drone base locations.

Rational Selection of Rail Transit Emergency Site Using Complex Network Topology and Genetic Algorithm

Rail transit network is a convenient and reliable transportation mode in urban areas. With every surge in transportation demand, the network undergoes operational changes like restructuring and making of multiline tracks. Urban rail transit network is a complex network and needs proper emergency services and controlling mechanisms. Unexpected mishaps in rail transit will cause more hazard and panic than the other transportation systems. Therefore, a reliable emergency service station is required to ensure the safety and security of passengers. In the proposed method, the basic statistical properties of complex network topology are considered to construct a P-center site selection model for urban rail transit emergency service stations. The P-center site selection model is solved by genetic algorithm. Validity and reasonableness of the model are demonstrated by implementing it in the Hangzhou emergency service stations for rail transportation. The results show that the P-center site selection model based on statistical properties of complex network topologies can better solve the urban rail transit emergency service station site selection problems. The model minimizes the number of emergency service stations while satisfying the optimal objective function and reduces the construction cost of emergency service stations. The approach has a significant effect on improving system reliability and reducing the risk of emergencies.

Abstract 9873: Comparing Base Locations for Drone-Delivered Defibrillators

Introduction: Drone-delivered defibrillators may improve response for out-of-hospital cardiac arrest (OHCA). Prior studies have assumed that drones may be stationed at any police, fire, or paramedic station; however, cross-service implementation may not be logistically feasible. We sought to compare estimated response times by drone base location type. Methods: We included OHCAs (Jan. 2014 to Dec. 2020) in southern Vancouver Island, British Columbia, Canada where OHCA response includes fire and paramedic services. We created four models with candidate drone base locations at: police stations, fire stations, paramedic stations, and on a grid with 1 km sides as an optimistic model. We used mathematical optimization to select 1-5 drone bases for each model. Assuming a drone system had been in place during the study period and accounting for drone availability, we estimated 9-1-1 call-to-defibrillator intervals (measured to either drone, paramedic, or fire arrival) and calculated the proportion of OHCAs where a drone would arrive prior to fire and paramedic for each model. Median response times were compared to historical response using one-sided sign tests. Results: We included 1,610 OHCAs with a median historical response time of 6.4 mins (IQR 5.0-8.6). We identified 21 police stations, 59 fire stations, 21 paramedic stations, and 7,008 grid locations in the study area. Median 9-1-1 call-to-defibrillator intervals ranged from 4.3-5.3 mins for police, 4.3-5.3 mins for fire, 4.5-5.4 mins for paramedic, and 4.2-5.4 mins for grid locations (all P&lt;0.001). Drones arrived prior to fire and paramedics in 36.6-65.4% of cases for police, 38.1-66.2% for fire, 37.3-63.2% for paramedic, and 35.7-66.8% for grid locations. Conclusion: Locating drone bases at different types of emergency service stations significantly decreases 9-1-1 call-to-defibrillator intervals, while resulting in similar response intervals to those achieved using optimistic grid-optimal locations.

The allocation scheme of distribution network’s emergency service stations based on feeder fault prediction results

In order to solve the problems of low efficiency and long time consuming to determine the location scattering of distribution network emergency service stations on account of geometric shortest path method, this paper proposes an optimal allocation scheme of distribution network emergency service stations based on feeder fault prediction results. In this method, the weight of feeder fault level is fully considered by constructing the optimization model of emergency service station in accordance with the prediction results of feeder fault, and the process parameters such as path congestion factor, path obstacle factor, feeder bending and twisting factor, weather influence factor and average speed of rush repair vehicles are applied to the location optimization model of emergency service station. Compared with the original method, this model can obtain the total time which significantly shortens the road time of rush repair, and leaves as much free time as possible for the subsequent fault repair stage.

Evaluation of time availability of the selected rescue service of a large city. A case study of Warsaw

Abstract This article presents the GIS analysis of time availability of the Voivodship Emergency Ambulance and Sanitary Transport Station operates an Independent Public Health Care Facility “Meditrans” in Warsaw. The research focuses on determining the level of service for residents in each district with the emergency service station. In addition, an attempt was made to improve accessibility on the example of three selected districts. The authors attempt to select methodology of planning the location of the emergency service station in order to optimize the level of service for residents.

BİR ÜNİVERSİTE HASTANESİ ACİL SERVİSİNE 112 İLE BAŞVURAN HASTALARIN ANALİZİ

INTRODUCTION: In Turkey, emergency diseases, natural disasters, accidents and injuries are frequently experienced and the number of patients per 112 emergency service station is increasing over years. Therefore, development and organization of the emergency healthcare services is a crucial issue. In this study, we aimed to examine the patients brought to emergency department by 112 Emergency Services, and to evaluate effect of demographic features of the presenting patients on the emergency service intensity of 112 ambulances when future planning of is made for emergency departments.MATERIAL &amp;amp; METHODS : This study is a cross-sectional study, and study population consisted of the persons brought to the emergency department (ED) of the XX University Hospital by 112 ambulance. Patients’ data were obtained from 112 emergency department ambulance form and hospital system. Sample of the study consisted of 1066 persons with complete data and who accepted to participate to the study. Data of the study were collected with a data collection form. Data were evaluated using SPSS statistical software. Descriptive statistics are expressed as median (min. – max.), mean ± standard deviation, frequency, and percentage. Analytic statistical analysis was performed using Kolmogorov Smirnov test, Chi-square test, Student’s t test, and Spearman correlation analysis. p&amp;lt; 0.05 values were considered statistically significant.RESULTS: 1066 persons included in the study, 52.3% were male and 47.7% were female. Median age was 52.0 (1.0–112.0). 44.3% of all patients were brought within working hours (09:01–16:00) The patients were most commonly brought on Saturday (15.4%). Of all patients, 0.4% were intubated and intravenous access was opened in 87.5% patients by 112 teams. Vital findings were examined in all patients. According to the preliminary diagnosis of 112 emergency ambulances, the most frequent patient group which brought by 112 ambulance was trauma patients (n=335,%31.44), second frequent group was cardiovascular system emergencies (n=169,% 15.9). There was a statistically significant, strong positive correlation between presumed diagnoses by 112, and definitive diagnoses decided after evaluation in the ED (r=0.621, p&amp;lt;0.001). 67.9% (n = 724) of the patients who were brought to the emergency department by 112 emergency ambulance were discharged after the first evaluation. CONCLUSION: The majority of the patients who brought by the 112 emergency ambulance were discharged. This suggests that 112 emergency ambulances are unnecessarily used. This problem can be solved by making the society aware of this issue. If it is considered that the most common diagnosis is trauma in patients who brought by 112 emergency ambulances, planning to increase the knowledge and experience of 112 emergency ambulance crew on prehospital trauma can be a good idea. While staffing the emergency services, appropriate planning can be made by considering the hours when 112 emergency ambulances are most frequently applied. The bed capacities of the departments where the most frequent service and intensive care hospitalizations are made should be observed.

Seismic vulnerability of lifeline buildings in Himalayan province of Uttarakhand in India

Seismic vulnerability of around 67, 60, 36, 23 and 18% buildings of Fire and Emergency Service, Police, hospital, school and local administration respectively in the earthquake prone Uttarakhand province located in Indian Himalaya has been assessed using Rapid Visual Screening (RVS) methodology. The study suggests that 71.86% of the surveyed local administration buildings, 64.58% schools, 62.08% Police stations, 56.25% Fire and Emergency Service stations and 52.86% hospitals together with 61.68% buildings of other departments would be put to disuse immediately after an earthquake that would pose challenges of various sort in mobilising resources for search, rescue and emergency healthcare together with relief and restoration as the remaining facilities would be highly overburdened. The study highlights poor quality of construction, lack of maintenance and non-compliance of safety standards as the main reasons enhancing vulnerability of the surveyed buildings. It is therefore recommended to undertake prioritised, planned and prompt demolition and reconstruction of Grade 5 buildings and detailed assessment and retrofitting of Grade 4 and Grade 3 buildings besides introducing measures for routine maintenance of public infrastructure and ensuring seismic safety provisions in these.

An iterative solution approach to a multi-objective facility location problem

This work presents a novel methodology for solving multi-objective facility location problems (mo-FLPs) with the focus on public emergency service stations. Our study is one of a few studies incorporating the objectives of three well-known problems, viz. the p-median problem (pMP), the maximal coverage location problem (MCLP) and the p-center problem (pCP). Aiming to find a set of Pareto optimal solutions and a compromise solution for all three objectives, we have developed an algorithm which solves each individual location problem sequentially. The proposed approach is mainly based on a combination of the branch & bound and iterative goal programming techniques. The performance of the algorithm is demonstrated with numerical examples.

A multicriteria spatial decision support system for solving emergency service station location problems

Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers’ preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran.

A taxonomy for emergency service station location problem

The emergency service station (ESS) location problem has been widely studied in the literature since 1970s. There has been a growing interest in the subject especially after 1990s. Various models with different objective functions and constraints have been proposed in the academic literature and efficient solution techniques have been developed to provide good solutions in reasonable times. However, there is not any study that systematically classifies different problem types and methodologies to address them. This paper presents a taxonomic framework for the ESS location problem using an operations research perspective. In this framework, we basically consider the type of the emergency, the objective function, constraints, model assumptions, modeling, and solution techniques. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions.

Optimal Locations of Expressways of Expressway Emergency Service Station: A Case Study of Korean Expressways

Optimal Locations of Expressways of Expressway Emergency Service Station: A Case Study of Korean Expressways

Goal Programming Model for Fire and Emergency Service Facilities Site Selection

The aim of this paper is to apply Goal Programming to select a site for a Fire and Emergency Service station in Sfax region in Tunisia. The decision making process involves incommensurable and conflicting objectives including proximity to industrial firms and response time. The concept of satisfaction function is utilized to explicitly introduce the Decision-Maker's preferences in the decision-making process.

An Information System for Risk-Vulnerability Assessment to Flood

An exhaustive knowledge of flood risk in different spatial locations is essential for developing an effective flood mitigation strategy for a watershed. In the present study, a riskvulnerability analysis to flood is performed. Four components of vulnerability to flood: 1) physical, 2) economic, 3) infrastructure and 4) social; are evaluated individually using a Geographic Information System (GIS) environment. The proposed methodology estimates the impact on infrastructure vulnerability due to inundation of critical facilities, emer gency service stations and bridges. The components of vulnerability are combined to determine an overall vulnerability to flood. The exposures of land use/land cover and soil type (permeability) to flood are also considered to include their effects on severity of flood. The values of probability of occurrence of flood, vulnerability to flood, and exposures of land use and soil type to flood are used to finally compute flood risk at different locations in a watershed. The proposed methodology is implemented for six major damage centers in the Upper Thames River watershed, located in the SouthWestern Ontario, Canada to assess the flood risk. An information system is developed for systematic presentation of the flood risk, probability of occurrence of flood, vulnerability to flood, and exposures of land use and soil type to flood by postal code regions or Forward Sortation Areas (FSAs). The flood information system is designed to provide support for different users, i.e., general public, decisionmakers and water management professionals. An interactive analysis tool is developed within the information system to assist in evaluation of the flood risk in response to a change in land use pattern.

The future of preclinical emergency medicine in Germany

Against the background of an ever-increasing shortage of financial support, closure of smaller hospitals and shortage of personnel in the medical branch, the necessity of the Federal emergency system is being increasingly called into question. In reality the number of missions which are relatively indicated are clearly increasing nationwide: an emergency doctor is not absolutely necessary in many situations. However, for complex emergency situations in Germany, an emergency medical system must remain an integral component of the preclinical care system in addition to the well-trained rescue service personnel. Hereby it is less important to have more emergency medical doctors, but more important to have a higher emergency medical qualification, possibly by a reduction in the density of emergency service stations. By the introduction of a ranked assistance system and the inclusion of "first responders", the time period before the arrival of the highly qualified emergency medical doctor can be bridged by qualified paramedics and general practitioners. The impulse of the legislators, assimilation of the rescue service acts, restructuring of rescue service catchment areas and the introduction of integrated demand-oriented control stations with a consequent quality management system as well as the implementation of a medical leader rescue system can reduce costs and further improve the quality of the emergency medical rescue system.

Thermal Power Plants for Emergency Service

A MAJOR interruption of the electric supply in a city of any importance is nowadays attended with disastrous consequences. The provision of some reserve source of power, capable of supplying the vital services at least, is therefore imperative when such failures can occur. The main causes of failures are breakdowns of transmission lines, strikes or enemy bombardment. A paper read by Mr. Hvisten-dahl on November 10 to the Institution of Electrical Engineers distinguishes between the different classes of service to be furnished by stand-by plants. Descriptions are given of automatic and semiautomatic stand-by plants used in towns in Norway and Sweden. Although unthinkable a few years ago, the bomb-proof emergency service station is now a grim reality. Sandbags provide some protection against fragments of stray shells or bombs; but they are little use against repeated attacks from the air, and a large steam-power station is an easily located target. This has led to the construction in several countries of the Continent of bomb-proof stand-by power plants for the essential services in towns or districts or for supplying the power requirements of munition factories. These plants are either of the surface or of the underground type. The plan of a station for an output of 10,000 kw. is shown located in a tunnel cut into the face of a cliff. The turbo-set, boiler and tanks containing sufficient fuel for 7-10 days' service, and the auxiliary starting set, are accommodated in a gallery about 150 ft. long and of about the section of a normal double-track railway tunnel. When ground conditions are not favourable to such a disposition, or the danger of flooding exists, a station of the surface type is adopted, with roof and walls of reinforced concrete about 4 ft. thick, having the appearance of a block of dwelling houses or made inconspicuous in some other way.