Methodology for creating dynamic emergency vehicle availability maps

Lupa, Michał; Naziemiec, Weronika; Adamek, Katarzyna; Zawadzki, Mateusz

doi:10.2478/pcr-2023-0003

Abstract

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.

References

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
Search in Google Scholar Back to article
Apache Hadoop. (2023). https://hadoop.apache.org/
Search in Google Scholar Back to article
Apache Spark. (2023). https://spark.apache.org/
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
GeoTools. (2023, July). GeoTools the open source Java GIS toolkit. https://geotools.org/
Search in Google Scholar Back to article
Ingolfsson, A. (2013). Ems planning and management. In G. S. Zaric (Ed.), Operations Research and Health Care Policy (pp. 105–128). Springer.
Search in Google Scholar Back to article
Karau, H., Konwinski, A., Wendell, P., & Zaharia, M. (2016). Poznajemy Sparka. Błyskawiczna analiza danych. PWN.
Search in Google Scholar Back to article
Kozieł, G. (2014). Algorytmy wyznaczania optymalnej trasy przejazdu. Logistyka, 3, 3206–3212.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
OSRM. (2023, July). Open Source Routing Machine. github.com/Project-OSRM/osrm-backend/wiki/Running-OSRM
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article

Methodology for creating dynamic emergency vehicle availability maps

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