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Methodology for creating dynamic emergency vehicle availability maps

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the main priorities of emergency services is to minimize the response time to calls. In the process of proper allocation of emergency vehicles, maps of emergency vehicle accessibility are found to be helpful. These maps represent areas within which emergency services can reach the specified location within a certain time. Calculating travel times requires taking into account the rapidly changing current road conditions. This paper presents a method for dynamically generating maps of emergency vehicle accessibility, considering network models and irregular computational grids.
Rocznik
Strony
24--37
Opis fizyczny
Bibliogr. 30 poz., mapy, rys.
Twórcy
autor
  • AGH University of Krakow, Kraków, Poland
  • AGH University of Krakow, Kraków, Poland
  • AGH University of Krakow, Kraków, Poland
  • University of Maria Curie-Skłodowska, Lublin, Poland
Bibliografia
  • Andersson, T., & Värbrand, P. (2007). Decision support tools for ambulance dispatch and relocation. Journal of the Operational Research Society, 58(2), 195-201. https://doi.org/10.1057/palgrave.jors.2602174
  • Apache Hadoop. (2023). https://hadoop.apache.org/
  • Apache Spark. (2023). https://spark.apache.org/
  • Azizan, M. H., Lim, C. S., Hatta, W. A. L. W. M., & Teoh, S. (2013). Simulation of emergency medical services delivery performance based on real map. International Journal of Engineering and Technology, 5(3), 2620-2627.
  • Branas, C. C., MacKenzie, E. J., & ReVelle, C. S. (2000). A trauma resource allocation model for ambulances and hospitals. Health Services Research, 35(2), 489.
  • Budge, S., Ingolfsson, A., & Zerom, D. (2010). Empirical analysis of ambulance travel times: the case of Calgary emergency medical services. Management Science, 56(4), 716-723. https://doi.org/10.1287/mnsc.1090.1142
  • Diller, G. P., Kempny, A., Piorkowski, A., Grübler, M., Swan, L., Baumgartner, H., Dimopoulos, K., & Gatzoulis, M. A. (2014). Choice and competition between adult congenital heart disease centers: evidence of considerable geographical disparities and association with clinical or academic results. Circ Cardiovasc Qual Outcomes, 7(2), 285-291. https://doi.org/10.1161/CIRCOUTCOMES.113.000555
  • Fisher, R., & Lassa, J. (2017). Interactive, open source, travel time scenario modelling: tools to facilitate participation in health service access analysis. International Journal of Health Geographics, 16(1), Article 13. https://doi.org/10.1186/s12942-017-0086-8
  • Geisberger, R., Sanders, P., Schultes, D., & Delling, D. (2008). Contraction hierarchies: faster and simpler hierarchical routing in road networks. In C. C. McGeoch. (Ed.), Experimental Algorithms. WEA 2008. Lecture Notes in Computer Science, 5038. Springer. https://doi.org/10.1007/978-3-540- 68552-4_24
  • GeoTools. (2023, July). GeoTools the open source Java GIS toolkit. https://geotools.org/
  • Ingolfsson, A. (2013). Ems planning and management. In G. S. Zaric (Ed.), Operations Research and Health Care Policy (pp. 105-128). Springer.
  • Karau, H., Konwinski, A., Wendell, P., & Zaharia, M. (2016). Poznajemy Sparka. Błyskawiczna analiza danych. PWN.
  • Kozieł, G. (2014). Algorytmy wyznaczania optymalnej trasy przejazdu. Logistyka, 3, 3206-3212.
  • Lee, E. (2014). Designing service coverage and measuring accessibility and serviceability of rural and small urban ambulance systems. Systems, 2(1), 34-53. https://doi.org/10.3390/systems2010034
  • Lewandowicz, E., & Flisek, P. (2017). Dostępność komunikacyjna w analizach sieciowych w przestrzeniach heterogenicznych (Communication availability in network analysis in heterogeneous spaces). Roczniki Geomatyki, 15(4(79)), 375-389.
  • Lupa, M., Chuchro, M., Sarlej, W., & Adamek, K. (2021). Emergency ambulance speed characteristics: a case study of Lesser Poland voivodeship, southern Poland. GeoInformatica, 25, 775-798. https://doi.org/10.1007/s10707-021-00447-w
  • Lupa, M., Szombara, S., Chuchro, M., & Chrobak, T. (2017). Limits of Colour Perception in the Context of Minimum Dimensions in Digital Cartography. International Journal of Geo-Information, 6(9), 276. https://doi.org/10.3390/ijgi6090276
  • Mitosz, M, Złomaniec, P., & Badurowicz, M. (2014). Modele matematyczne optymalizacji tras w transporcie medycznym [Mathematical models of route optimization in medical transport field]. Logistyka, 6, 7524-7533.
  • Myers, B., Fisher, R., Nelson, N., & Belton, S. (2015). Defining remoteness from health care: integrated research on accessing emergency maternal care in Indonesia. AIMS public health, 2(3), 257-273. https://doi.org/10.3934/publichealth.2015.3.257
  • OSRM. (2023, July). Open Source Routing Machine. github.com/Project-OSRM/osrm-backend/wiki/Running-OSRM
  • Peleg, K., & Pliskin, J. S. (2004). A geographic information system simulation model of EMS: reducing ambulance response time. The American journal of emergency medicine, 22(3), 164-170. https://doi.org/10.1016/j.ajem.2004.02.003
  • Piórkowski, A. (2018). Construction of a dynamic arrival time coverage map for emergency medical services. Open Geosciences, 10, 167-173. https://doi.org/10.1515/geo-2018-0013
  • Płokita, I., Piórkowski, A., & Lupa, M. (2016). Comparative analysis of algorithms for calculating arrival times of emergency vehicles. Geoinformatica Polonica, 15, 85-91. https://doi.org/10.4467/21995923GP.16.009.5485
  • Schmid, V. (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research, 219(3), 611-621. https://doi.org/10.1016/j.ejor.2011.10.043
  • Shuib, A., & Zaharudin, Z. A. (2010). Framework of tazopt model for ambulance location and allocation problem. World Academy of Science, Engineering and Technology, 70, 678-683.
  • Swalehe, M., & Aktas, S. G. (2016). Dynamic ambulance deployment to reduce ambulance response times using geographic information systems: A case study of Odunpazari District of Eskisehir Province, Turkey. Procedia Environmental Sciences, 36, 199-206. https://doi.org/10.1016/j.proenv.2016.09.033
  • Terzi, O., Sisman, A., Canbaz, S., Dündar, C., & Peksen, Y. (2013). A geographic information system-based analysis of ambulance station coverage area in Samsun, Turkey. Singapore Med J, 54(11), 653-658. http://dx.doi.org/10.11622/smedj.2013228
  • Vanderschuren, M., & McKune, D. (2015). Emergency care facility access in rural areas within the golden hour?: Western Cape case study. International Journal of Health Geographics, 14(1), 5. https://doi.org/10.1186/1476-072X-14-5
  • Wajid, S., Nezamuddin, N., & Unnikrishnan, A. (2020). Optimizing ambulance locations for coverage enhancement of accident sites in South Delhi. Transportation Research Procedia, 48, 280-289. https://doi.org/10.1016/j.trpro.2020.08.022
  • Westgate, B. S., Woodard, D. B., Matteson, D. S., & Henderson, S. G. (2016). Large-network travel time distribution estimation for ambulances. European Journal of Operational Research, 252(1), 322-333.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8145b5b4-caff-4a2b-a428-23407d8b48d8
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