{"id":3476,"date":"2026-04-11T16:37:04","date_gmt":"2026-04-11T08:37:04","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3476"},"modified":"2026-06-03T14:06:33","modified_gmt":"2026-06-03T06:06:33","slug":"predicting-demand-for-emergency-ambulance-services-a-comparative-approach","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=predicting-demand-for-emergency-ambulance-services-a-comparative-approach","title":{"rendered":"Predicting Demand for Emergency Ambulance Services: A Comparative Approach"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 10<\/a><\/p>\n<\/div>\n\n\n\n

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Tanissara Butsingkorn1<\/sup>, Arthit Apichottanakul1,2<\/sup>, and Sirawadee Arunyanart1<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>Supply Chain and Logistics System Research Unit, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand<\/p>\n\n\n\n

2<\/sup>Department of Production System Technology and Industrial Management, Faculty of Technology, Khon Kaen University<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0October 19, 2023
Accepted:\u00a0November 17, 2023
Publication Date:\u00a0April 11, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Comparison of EMS demand volume curves for the five forecasting models with actual volume in the test data for the Mueang Khon Kaen dataset.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202410_27(10).0011<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Accurate forecasting of demand for emergency medical services (EMS) is crucial for effective healthcare management, contributing to improved response times and cost control during emergencies. Additionally, it facilitates resource allocation and the implementation of knowledge-based policies, ultimately enhancing patient care and services. This study focuses on forecasting EMS demand related to patient transportation from 25 sub-hospitals in Khon Kaen, Thailand, to the central medical center hospital for the purpose of receiving necessary medical treatment. To improve the precision of demand forecasting, we evaluated various forecasting approaches. The results indicate that ANN outperforms other models. This can be attributed to the ANN’s ability to identify complex relationships and efficiently learn from observed data through nonlinear mapping. These findings underscore the potential applications of the ANN model for addressing this problem.<\/p>\n\n\n\n

Keywords:\u00a0<\/em>Machine learning, artificial neural networks, autoregressive integrated moving averages, simple moving averages, nonlinear autoregressive, wavelet nonlinear autoregressive<\/p>\n\n\n\n

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