# Ambulance Locations in a Tiered Emergency Medical System in a City  
[PERSON]  
1 Faculty of Management Science and Informatics, University of Zilina, Univerztima 1, 010 26 Zilina, Slovakia; Ludmila. This paper deals with tactical decisions about locating EMS stations in an urban area. We are trying to improve the existing EMS system. We respect the current number of stations but look to locate them in a better way, with the view to increase the availability of the service, especially for patients in critical conditions. We concentrate on the main goal of EMS--to provide a rapid response to medical emergencies. The costs associated with the opening of stations, or the costs of relocating them from their current positions, are not considered because it is beyond the scope of our research to look for potential locations where the stations can be sited and thus to estimate investment costs related to the candidate locations. Instead of considering the costs associated with every closed or open station, one can limit the number of stations that can be relocated under a given budget constraint. Currently, we suppose that no stations can be added, but there are no limitations regarding the number of relocated stations. In general, the costs of constructing EMS stations are hard to predict, so most studies omit them and focus on performance criteria. If the costs are taken into account (e.g., [7, 8]), then they are determined empirically without serious justification. The growing availability of demographic data and data on road networks enables us to better predict where people demanding EMS are located. Two principal approaches have been applied so far to model demand zones in a city. Either the city needs to be covered by a rectangular grid [9, 10, 11, 12, 13], with a grid element that corresponds to a demand zone; or, the demand zones need to be identified with urban administrative units, such as census areas [14], city zones [15], postal codes [6], and suburbs [16]. The modelling approach depends on the data availability. The studies using a grid structure usually identify the demand for EMS with historical calls archived by an information system. On the other hand, when demand zones correspond to census areas or other territorial units, then the demand usually corresponds to the population of the unit. This latter method allows for the incorporation of demographic characteristics, such as gender or age, into the demand prediction [14]. However, we are not aware of a comparative study that would recommend one or the other method. That is why we decided to use both methods in our research and assess them by means of computer simulation. For the grid model, we have used the LandScan data model that contains the average number of people over 24 h in cells of approximately 1 km\({}^{2}\). Instead of large census units, we investigate a more detailed demand model. We draw inspiration from the local administration of the city of Presov ([[https://egov.presov.sk/](https://egov.presov.sk/)]([https://egov.presov.sk/](https://egov.presov.sk/)), accessed on 18 December 2021) that publishes and updates, weekly, the number of citizens in every street in the town. A benefit of having such data is that every street may be regarded as a separate demand zone and the center of the street as an aggregated demand point. Such a microscopic approach enables the EMS system to reflect the urban development and to relocate resources closer to residents. The number of articles related to EMS planning has grown rapidly during the last two decades. This growth may be attributed to increases in the availability of data and in the power of decision-supporting software tools. Several survey articles have been published during this period. A recent survey of location models applied to healthcare facilities was presented by [PERSON] et al. [2]. The authors focused on the two most frequent types of problems, namely, the covering and median problems.