LIFE Project: Implementing a modelling framework for emergency vehicles advanced priority strategies

Given the aging demographics and rapid urbanisation, cities need to be equipped to respond to emergency (eg. 999 calls) more quickly. By 2050, over 25% of the UK’s population will be over 65. This has implications on the overall health services as well as the NHS Trust to cope with anticipated rise in ambulance call outs amidst worsening urban congestion. Ambulance services are required to reach 75% of emergency calls within 8 minutes. For this reason, there is a growing need to develop new and innovative applications for an even more intelligent use of the existing transport system that will support in real-time emergency vehicles to reach life threatening emergency cases quicker. this paper will discuss the methodology and the preliminary results of the modelling framework implementation of a “Life First Emergency Traffic Control” or “LiFE” system, a ITS implementation seeking to identify the best solution to reduce the time to respond to emergency calls, whilst operating a resilient service with a cost and fuelefficient fleet. Results of the application of a microsimulation model to replicate the behaviour of ambulances in urban area and how different reactions of general traffic can impact on the travel time of an ambulance are presented. The proposed microsimulation modelling framework has been developed with the final aim to understand and evaluate the impacts and the best scenarios to improve ambulance (or any Emergency vehicle) response time and gains in cost-saving, whilst assessing mitigation strategies to reduce other impacts such as residual congestion. The work is part of an Innovate UK collaborative funded project, namely Life First Emergency Traffic Control (LiFE) with the aim to develop an innovative application for an intelligent transport system that operates in realtime to enable ambulances to reach life threatening emergency cases quicker by integrating ambulance route finder applications with traffic management systems

Towards smart open dynamic fleets

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-33509-4_32Nowadays, vehicles of modern fleets are endowed with advanced devices that allow the operators of a control center to have global knowledge about fleet status, including existing incidents. Fleet management systems support real-time decision making at the control center so as to maximize fleet perform‐ ance. In this paper, setting out from our experience in dynamic coordination of fleet management systems, we focus on fleets that are open, dynamic and highly autonomous. Furthermore, we propose how to cope with the scalability problem as the number of vehicles grows. We present our proposed architecture for open fleet management systems and use the case of taxi services as example of our proposal.Work partially supported by Spanish Government through the projects iHAS (grant TIN2012-36586-C03) and SURF (grant TIN2015-65515-C4-X-R), the Autonomous Region of Madrid through grant S2013/ICE-3019 (“MOSI-AGIL-CM”, cofunded by EU Structural Funds FSE and FEDER) and URJC-Santander (30VCPIGI15).Billhardt, H.; Fernández, A.; Lujak, M.; Ossowski, S.; Julian Inglada, VJ.; Paz, JFD.; Hernández, JZ. (2016). Towards smart open dynamic fleets. En Multi-Agent Systems and Agreement Technologies. Springer. 410-424. https://doi.org/10.1007/978-3-319-33509-4_32S41042

A generic method to develop simulation models for ambulance systems

In this paper, we address the question of generic simulation models and their role in improving emergency care around the world. After reviewing the development of ambulance models and the contexts in which they have been applied, we report the construction of a reusable model for ambulance systems. Further, we describe the associated parameters, data sources, and performance measures, and report on the collection of information, as well as the use of optimisation to configure the service to best effect. Having developed the model, we have validated it using real data from the emergency medical system in a Brazilian city, Belo Horizonte. To illustrate the benefits of standardisation and reusability we apply the model to a UK context by exploring how different rules of engagement would change the performance of the system. Finally, we consider the impact that one might observe if such rules were adopted by the Brazilian system

Algoritmos de gestión de personal enfocados a la mejora del servicio al cliente. Aplicación a servicios de urgencias de atención primaria

En el presente artículo se desarrolla una metodología de planificación y organización de recursos en el contexto de las urgencias de un centro de salud que puede ser extrapolada a otro tipo de servicios. Dicha metodología desemboca en la realización de un programa de ordenador que resuelve de forma efectiva ciertas tareas de planificación. Planificar los servicios de urgencias de atención primaria (SUAP) para que mejoren su calidad y sean atrayentes para los pacientes puede descongestionar las urgencias hospitalarias incidiendo de manera integral en todo el servicio de urgencias sanitario. El tiempo de espera reducido es el principal factor que los usuarios identifican con la calidad del servicio, por ello actuar sobre este elemento supone una opción para lograr este fin. Se presenta el desarrollo de un algoritmo que, teniendo como objetivo no sobrepasar un tiempo de espera prefijado en un SUAP, obtenga como respuesta la distribución de personal a asignar optimizando además el consumo de recursos. Adicionalmente, se aporta un segundo desarrollo computacional que transforma la asignación teórica de personal anterior en otra que sea fácilmente aplicable en la práctica por los gestores de los servicios. De esta forma, se ha desarrollado un programa que calcula el número de facultativos necesarios en un SUAP, una vez que se ha fijado el tiempo máximo de espera deseable en dicho servicio. El programa constituye un instrumento de gestión y planificación del número de médicos y su actividad en un SUAP, dependiendo de los tiempos de espera deseados, que se adapta a las características dinámicas y cambiantes que dichos servicios presentan por su naturaleza, siendo de aplicación en un amplio abanico de sectores productivos. This paper presents a methodology for planning and organizing resources in the context of a medical clinic emergency. This methodology can be applied to other type of services and is implemented in software that effectively solves the planning of certain tasks. Planning the primary attention emergency services (to improve their quality and to be attractive for patients) can clear the hospital emergency rooms, influencing in an integral way the whole sanitary emergency service. The hospital emergency services receive a considerable proportion of patients who should be assisted in the emergency services for primary attention. A reduced wait time is the main factor that patients identify with quality of service. Therefore, optimizing this element is an option to reach this aim. This has led to the development of an algorithm which aims to not exceed a prearranged wait time in the emergency services of primary attention, and gets as a response the distribution of personnel to be assigned to optimize the consumption of resources. Additionally, it is given a second computational development which transforms the previous theoretical assignment of personnel into another one that is easily applicable in practice by the agents of the services. It has developed a plan that calculates the number of doctors needed in an emergency service of primary attention, once the maximum wait time desired of this service has been determined. The plan is a tool for the management and planning of the number of doctors and their activity in an emergency service of primary attention, depending on the desired wait time, which adapts to the dynamic and changing features which these services present due to their application in a wide variety of production sectors

Algoritmos de gestión de personal enfocados a la mejora del servicio al cliente. Aplicación a servicios de urgencias de atención primaria

En el presente artículo se desarrolla una metodología de planificación y organización de recursos en el contexto de las urgencias de un centro de salud que puede ser extrapolada a otro tipo de servicios. Dicha metodología desemboca en la realización de un programa de ordenador que resuelve de forma efectiva ciertas tareas de planificación. Planificar los servicios de urgencias de atención primaria (SUAP) para que mejoren su calidad y sean atrayentes para los pacientes puede descongestionar las urgencias hospitalarias incidiendo de manera integral en todo el servicio de urgencias sanitario. El tiempo de espera reducido es el principal factor que los usuarios identifican con la calidad del servicio, por ello actuar sobre este elemento supone una opción para lograr este fin. Se presenta el desarrollo de un algoritmo que, teniendo como objetivo no sobrepasar un tiempo de espera prefijado en un SUAP, obtenga como respuesta la distribución de personal a asignar optimizando además el consumo de recursos. Adicionalmente, se aporta un segundo desarrollo computacional que transforma la asignación teórica de personal anterior en otra que sea fácilmente aplicable en la práctica por los gestores de los servicios. De esta forma, se ha desarrollado un programa que calcula el número de facultativos necesarios en un SUAP, una vez que se ha fijado el tiempo máximo de espera deseable en dicho servicio. El programa constituye un instrumento de gestión y planificación del número de médicos y su actividad en un SUAP, dependiendo de los tiempos de espera deseados, que se adapta a las características dinámicas y cambiantes que dichos servicios presentan por su naturaleza, siendo de aplicación en un amplio abanico de sectores productivos. This paper presents a methodology for planning and organizing resources in the context of a medical clinic emergency. This methodology can be applied to other type of services and is implemented in software that effectively solves the planning of certain tasks. Planning the primary attention emergency services (to improve their quality and to be attractive for patients) can clear the hospital emergency rooms, influencing in an integral way the whole sanitary emergency service. The hospital emergency services receive a considerable proportion of patients who should be assisted in the emergency services for primary attention. A reduced wait time is the main factor that patients identify with quality of service. Therefore, optimizing this element is an option to reach this aim. This has led to the development of an algorithm which aims to not exceed a prearranged wait time in the emergency services of primary attention, and gets as a response the distribution of personnel to be assigned to optimize the consumption of resources. Additionally, it is given a second computational development which transforms the previous theoretical assignment of personnel into another one that is easily applicable in practice by the agents of the services. It has developed a plan that calculates the number of doctors needed in an emergency service of primary attention, once the maximum wait time desired of this service has been determined. The plan is a tool for the management and planning of the number of doctors and their activity in an emergency service of primary attention, depending on the desired wait time, which adapts to the dynamic and changing features which these services present due to their application in a wide variety of production sectors

Fire truck relocation during major incidents

The effectiveness of a fire department is largely determined by its ability to respond to incidents in a timely manner. To do so, fire departments typically have fire stations spread evenly across the region, and dispatch the closest truck(s) whenever a new incident occurs. However, large gaps in coverage may arise in the case of a major incident that requires many nearby fire trucks over a long period of time, substantially increasing response times for emergencies that occur subsequently. We propose a heuristic for relocating idle trucks during a major incident in order to retain good coverage. This is done by solving a mathematical program that takes into account the location of the available fire trucks and the historic spatial distribution of incidents. This heuristic allows the user to balance the coverage and the number of truck movements. Using extensive simulation experiments we test the heuristic for the operations of the Fire Department of Amsterdam‐Amstelland, and compare it against three other benchmark strategies in a simulation fitted using 10 years of historical data. We demonstrate substantial improvement over the current relocation policy, and show that not relocating during major incidents may lead to a significant decrease in performance

Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation

Providers of Emergency Medical Services (EMS) are typically concerned with keeping response times short. A powerful means to ensure this, is to dynamically redistribute the ambulances over the region, depending on the current state of the system. In this paper, we provide new insight into how to optimally (re)distribute ambulances. We study the impact of (1) the frequency of redeployment decision moments, (2) the inclusion of busy ambulances in the state description of the system, and (3) the performance criterion on the quality of the distribution strategy. In addition, we consider the influence of the EMS crew workload, such as (4) chain relocations and (5) time bounds, on the execution of an ambulance relocation. To this end, we use trace-driven simulations based on a real dataset from ambulance providers in the Netherlands. In doing so, we differentiate between rural and urban regions, which typically face different challenges when it comes to EMS. Our results show that: (1) taking the classical 0-1 performance criterion for assessing the fraction of late arrivals only differs slightly from related response time criteria for evaluating the performance as a function of the response time, (2) adding more relocation decision moments is highly beneficial, particularly for rural areas, (3) considering ambulances involved in dropping off patients available for newly coming incidents reduces relocation times only slightly, and (4) simulation experiments for assessing move-up policies are highly preferable to simple mathematical models

Equilibrium-Based Workload Balancing for Robust Emergency Response Operations in Metropolitan Areas

This thesis presents an equilibrium-based modeling framework for emergency response (ER) workload balancing to achieve robust operation in large-scale metropolitan areas. The problem is formulated as a non-linear mathematical program (NLP), which determines the optimal workload cutoff for each ER station such that the weighted sum of the area-wide expected response time and its variation are minimized. The concept of Marginal Cost of Uncertainty (MCU) is introduced to measure the impact of a station’s workload increase on the area-wide service performance. The solution of the NLP is proved to be equivalent to a state of equilibrium in which all stations have a minimum MCU. An iterative solution methodology is developed, which adopts a modified version of the Frank-Wolfe decomposition algorithm for convex optimization. The workload is iteratively shifted among adjacent stations until the state of equilibrium is achieved. At equilibrium, no station can reduce its MCU value by unilaterally shifting a part of its workload to any other station(s) in the area. The developed framework is applied to determine the optimal workload balancing strategy for 58 fire stations serving the City of Dallas. The framework is shown to enhance the robustness of the ER service especially under operation scenarios with imbalanced workloads

EVA: Emergency Vehicle Allocation

Emergency medicine plays a critical role in the development of a community, where the goal is to provide medical assistance in the shortest possible time. Consequently, the systems that support emergency operations need to be robust, efficient, and effective when managing the limited resources at their disposal. To achieve this, operators analyse historical data in search of patterns present in past occurrencesthat could help predict future call volume. This is a time consuming and very complex task that could be solved by the usage of machine learning solutions, which have been performed appropriately in the context of time series forecasting. Only after the future demands are known, the optimization of the distribution of available assets can be done, for the purpose of supporting high-density zones. The current works aim to propose an integrated system capable of supporting decision-making emergency operations in a real-time environment by allocating a set of available units within a service area based on hourly call volume predictions. The suggested system architecture employs a microservices approach along with event-based communications to enable real-time interactions between every component. This dissertation focuses on call volume forecasting and optimizing allocation components. A combination of traditional time series and deep learning models was used to model historical data from Virginal Beach emergency calls between the years 2010 and 2018, combined with several other features such as weather-related information. Deep learning solutions offered better error metrics, with WaveNet having an MAE value of 0.04. Regarding optimizing emergency vehicle location, the proposed solution is based on a Linear Programming problem to minimize the number of vehicles in each station, with a neighbour mechanism, entitled EVALP-NM, to add a buffer to stations near a high-density zone. This solution was also compared against a Genetic Algorithm that performed significantly worse in terms of execution time and outcomes. The performance of EVALP-NM was tested against simulations with different settings like the number of zones, stations, and ambulances.A medicina de emergência desempenha um papel fundamental no desenvolvimento da Sociedade, onde o objetivo é prestar assistência médica no menor tempo possível. Consequentemente, os sistemas que apoiam as operações de emergência precisam de ser robustos, eficientes e eficazes na gestão dos recursos limitados. Para isso, são analisados dados históricos no intuito de encontrar padrões em ocorrências passadas que possam ajudar a prever o volume futuro de chamadas. Esta é uma tarefa demorada e muito complexa que poderia ser resolvida com o uso de soluções de Machine Learning, que têm funcionado adequadamente no contexto da previsão de séries temporais. Só depois de conhecida a demanda futura poderá ser feita a otimização da distribuição dos recursos disponíveis, com o objetivo de suportar zonas de elevada densidade populacional. O presente trabalho tem como objetivo propor um sistema integrado capaz de apoiar a tomada de decisão em operações de emergência num ambiente de tempo real, atribuindo um conjunto de unidades disponíveis dentro de uma área de serviço com base em previsões volume de chamadas a cada hora. A arquitetura de sistema sugerida emprega uma abordagem de microserviços juntamente com comunicações baseadas em eventos para permitir interações em tempo real entre os componentes. Esta dissertação centra se nos componentes de previsão do volume de chamadas e otimização da atribuição. Foram usados modelos de séries temporais tradicionais e Deep Learning para modelar dados históricos de chamadas de emergência de Virginal Beach entre os anos de 2010 e 2018, combinadas com informações relacionadas ao clima. As soluções de Deep Learning ofereceram melhores métricas de erro, com WaveNet a ter um valor MAE de 0,04. No que diz respeito à otimização da localização dos veículos de emergência, a solução proposta baseia-se num problema de Programação Linear para minimizar o número de veículos em cada estação, com um mecanismo de vizinho, denominado EVALP-NM, para adicionar unidades adicionais às estações próximas de uma zona de alta densidade de chamadas. Esta solução foi comparada com um algoritmo genético que teve um desempenho significativamente pior em termos de tempo de execução e resultados. O desempenho do EVALP-NM foi testado em simulações com configurações diferentes, como número de zonas, estações e ambulâncias