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dc.contributor.authorSwalehe, Masoud
dc.contributor.authorAktaş, Semra Günay
dc.contributor.editorLai, PC
dc.contributor.editorLow, CT
dc.contributor.editorWong, PPY
dc.date.accessioned2019-10-23T17:56:17Z
dc.date.available2019-10-23T17:56:17Z
dc.date.issued2016
dc.identifier.issn1878-0296
dc.identifier.urihttps://dx.doi.org/10.1016/j.proenv.2016.09.033
dc.identifier.urihttps://hdl.handle.net/11421/22904
dc.descriptionInternational Conference on Geographies of Health and Living in Cities - Making Cities Healthy for All (H-Cities) -- JUN 21-24, 2016 -- Univ Hong Kong, Hong Kong, PEOPLES R CHINAen_US
dc.descriptionWOS: 000387507400032en_US
dc.description.abstractBackground and Objective: Ambulances should always reach patients in the shortest time possible whenever they are called upon so as to increase patient survival chances especially in cardiac related medical cases. The placement of ambulances directly affects the time ambulances reach patients. The objective of the study was to find optimal stations to deploy ambulances so as to reduce ambulance response times and increase patient survival chances as a result. Data and Methods: To reduce ambulance response times for Odunpazari district, the study employed system status management technique and maximal coverage location problem optimization model, to deploy ambulances according to ambulance demand and ensure maximum ambulance demand coverage is realized with a small ambulance fleet size, respectively. ArcGIS network analyst location allocation tool was used to find optimal ambulance stations from where ambulance demand areas can be reached within 5 minutes of drive time. Four different ambulance deployment plans were modeled for periods ranging from 00:00 to 06:00 hrs, 06:00 to 12:00 hrs, 12:00 to 18:00 hrs and 18:00 to 24:00 hrs. The study used a total of 20,260 ambulance demand calls' data for Odunpazari district collected from January 1st to December 31st 2014. Results:The Odunpazari district fleet of 17 ambulances was deployed differently for every six hours; between 00:00 to 06:00 hrs, 06:00 to 12:00 hrs, 12:00 to 18:00 hrs and 18:00 to 24:00 hrs to match ambulance demand and as a result, 77.6% of ambulance demand areas were located within 5 minutes of drive time from the nearest ambulance station. Conclusion:The study found out that moving ambulances closer to ambulance demand areas reduces ambulance response times and dynamic ambulance deployment is by far a more effective ambulance deployment strategy than static ambulance deployment.en_US
dc.description.sponsorshipUniv Hong Kong, Dept Geography, HKU Geograph Informat Syst Res Ctr, Hong Kong Geograph Informat Syst Assoc, Australian Res Council Ctr Excellence Climate Syst Sci, Int Geospatial Health Res Networken_US
dc.language.isoengen_US
dc.publisherElsevier Science BVen_US
dc.relation.ispartofseriesProcedia Environmental Sciences
dc.relation.isversionof10.1016/j.proenv.2016.09.033en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAmbulance Response Timesen_US
dc.subjectDynamic Ambulance Deploymenten_US
dc.subjectMaximal Coverage Location Problemen_US
dc.titleDynamic Ambulance Deployment to Reduce Ambulance Response Times using Geographic Information Systems: A Case Study of Odunpazari District of Eskisehir Province, Turkeyen_US
dc.typeconferenceObjecten_US
dc.relation.journalInternational Conference On Geographies of Health and Living in Cities: Making Cities Healthy For Allen_US
dc.contributor.departmentAnadolu Üniversitesi, Yer ve Uzay Bilimleri Enstitüsüen_US
dc.identifier.volume36en_US
dc.identifier.startpage199en_US
dc.identifier.endpage206en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US]


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