Dynamic Ambulance Deployment to Reduce Ambulance Response Times Using Geographic Information Systems: A Case Study of Odunpazari District of Eskisehir Province, Turkey

Swalehe, Masoud; Aktaş, Semra Günay

doi:10.1016/j.proenv.2016.09.033

Cited by 21 publications

(10 citation statements)

References 13 publications

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“…There have been many case studies on ambulance stationing and routing [34,11,35,39,32,22,24], but the methodologies regarding discrete-event simulations vary widely. Some are theoretical and include no simulations, some use private softwares, while only a few provide open-source tools [7].…”

Section: Our Contributionsmentioning

confidence: 99%

“…They found they could reduce the number of ambulance locations by 81.6 % without reducing coverage. Swalehe et al [39] included a case study in Eskisehir Province, Turkey using GIS (Geogrpahic Information System) methods and found they could reduce the response time from 10 minutes to 5 minutes for 77.6 % of ambulance demand areas. McCormack et al [32] included a case study in London, England using genetic algorithms and testing the algorithm on a calibrated and validated simulation model.…”

Section: Past Workmentioning

confidence: 99%

“…To help EMS services achieve the best response time the operational research community has widely studied how ambulance stationing and routing can optimize response times. [7,6,27,34,11,35,39,32,22,24].…”

Section: Introductionmentioning

confidence: 99%

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OpenEMS: an open-source Package for Two-Stage Stochastic and Robust Optimization for Ambulance Location and Routing with Applications to Austin-Travis County EMS Data

Ong¹,

Kulpanowski²,

Xie³

et al. 2022

Preprint

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Emergency Medical Systems (EMS) provide crucial pre-hospital care and transportation. Faster EMS response time provides quicker pre-hospital care and thus increases survival rate. We reduce response time by providing optimal ambulance stationing and routing decisions by solving two stage stochastic and robust linear programs. Although operational research on ambulance systems is decades old, there is little open-source code and consistency in simulations. We begin to bridge this gap by publishing OpenEMS, in collaboration with the Austin-Travis County EMS (ATCEMS) in Texas, an end-to-end pipeline to optimize ambulance strategic decisions. It includes data handling, optimization, and a calibrated simulation. We hope this open source framework will foster future research with and for EMS. Finally, we provide a detailed case study on the city of Austin, Texas. We find that optimal stationing would increase response time by 88.02 seconds. Further, we design optimal strategies in the case where Austin EMS must permanently add or remove one ambulance from their fleet.

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Section: Our Contributionsmentioning

confidence: 99%

Section: Past Workmentioning

confidence: 99%

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OpenEMS: an open-source Package for Two-Stage Stochastic and Robust Optimization for Ambulance Location and Routing with Applications to Austin-Travis County EMS Data

Ong¹,

Kulpanowski²,

Xie³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The optimization method, which is the first approach, is preferred to solve problems by transferring real-life issues to a mathematical environment [8], [9], [19], [20]. As ambulance modelling studies are based on uncertainty, the use of stochastic, genetic and dynamic optimization models in ambulance location studies is widely used in the literature [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Ambulance Service for Hospital Selection: Optimization With Discrete-Event Simulation Application for Yozgat Province of Turkey

Atalan

2021

Mühendislik Bilimleri Ve Araştırmaları Dergisi

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This study aims to develop a new approach in the ambulance service to refer the patient not to the nearest hospital but to a suitable hospital that responds better to his/her illness. Due to the lack of staff and equipment in a hospital, patients are often very likely to be transferred to another hospital. A case study was performed with a discrete-event simulation method for an improved ambulance service flowchart in this research. The case study was conducted in Yozgat province of Turkey, including two hospitals (city and university), one emergency call center, and six ambulance stations. Each of them has one vehicle. The data used covers the one-year from January 2018 to January 2019. The method developed for this study resulted in a 21.14 % improvement in ambulance service. The most crucial point that was noteworthy among the findings was that referral of the patient to the most appropriate hospital, rather than the nearest hospital, was important in terms of both dispatching time and the life-threatening condition of the patient.

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“…Acil durum istasyonlarının yerini belirlemede çeşitli bilgi teknolojileri ve görüntüleme yöntemleri kullanılmaktadır. Son zamanlarda yapılan çalışmalara bakıldığında (Erkal ve Değerliyurt, 2013;Swalehe, 2016) Coğrafi Bilgi Sistemleri (CBS) tabanlı analizlerin kullanımının yaygınlaştığı ve yer belirlemede etkili bir araç olduğu görülmektedir (Sarhan, vd., 2015). Mevcut araştırmanın amacına hizmet etmesi için CBS'nin kullanılması uygun görülmüştür.…”

Section: Introductionunclassified

Erzincan İli 110 Acil Çağrılarının Coğrafi Bilgi Sistemleri ve Farklı İstatistiksel Analiz Yöntemleri ile Değerlendirilmesi

Gürel

Bayata

Bayrak

2020

Afet Ve Risk Dergisi

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The most important criterion for 110 emergency call is the time, which is between the arrival of the teams at the event location after the emergency call and the initiation of emergency response; and called as "response time". One of the most important problems affecting the intervention time is the problems arising from the station settlement. Today, advanced information technologies and imaging methods are widely used in the determination of the station settlement area. One of the methods used for this purpose is "Geographic Information Systems" (GIS). In the literature, various station settlement determination process in Turkey have been seen in the CBS. In this study, it is aimed to use time series and artificial neural networks to model the geographic information systems and calls to determine the coverage of the existing station locations based on the calls received by the 110 fire stations in Erzincan city center. The current research covers the neighborhoods of Erzincan center and the data were taken from the Erzincan Municipality Fire Department. SPSS (Statistical Package for the Social Sciences) package program, MATLAB program and ArcGIS program were used to analyze the data. In order to query the data in ArcGIS, basic geographical data (map) bases were created. Then, the data were digitized in the same coordinate system and transferred to the ArcGIS program. At the end of the research, the most appropriate the fire station were recommended with the locational analysis functions of GIS.

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Dynamic Ambulance Deployment to Reduce Ambulance Response Times Using Geographic Information Systems: A Case Study of Odunpazari District of Eskisehir Province, Turkey

Cited by 21 publications

References 13 publications

OpenEMS: an open-source Package for Two-Stage Stochastic and Robust Optimization for Ambulance Location and Routing with Applications to Austin-Travis County EMS Data

OpenEMS: an open-source Package for Two-Stage Stochastic and Robust Optimization for Ambulance Location and Routing with Applications to Austin-Travis County EMS Data

Ambulance Service for Hospital Selection: Optimization With Discrete-Event Simulation Application for Yozgat Province of Turkey

Erzincan İli 110 Acil Çağrılarının Coğrafi Bilgi Sistemleri ve Farklı İstatistiksel Analiz Yöntemleri ile Değerlendirilmesi

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