Estimating the Demand for Ambulances in Traffic Accidents
Background: Effective Emergency medical service (EMS) delivery in road traffic accidents requires accurate resource planning that relies on operational, tactical and strategic demand forecasts. This study aims to estimate the demand for ambulances in traffic accidents using time series modeling tech...
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| Format: | Article |
| Language: | English |
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Negah Institute for Scientific Communication
2025-07-01
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| Series: | Health in Emergencies & Disasters Quarterly |
| Subjects: | |
| Online Access: | http://hdq.uswr.ac.ir/article-1-641-en.pdf |
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| _version_ | 1849471271267467264 |
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| author | Manoochehr Babanezhad Hassan Khorsha Ali Mohajervatan Ali Choori |
| author_facet | Manoochehr Babanezhad Hassan Khorsha Ali Mohajervatan Ali Choori |
| author_sort | Manoochehr Babanezhad |
| collection | DOAJ |
| description | Background: Effective Emergency medical service (EMS) delivery in road traffic accidents requires accurate resource planning that relies on operational, tactical and strategic demand forecasts. This study aims to estimate the demand for ambulances in traffic accidents using time series modeling techniques.
Materials and Methods: We conducted a retrospective cohort analysis of ambulance demands related to traffic incidents in Golestan Province, Iran. The analysis of individual time series was utilized for demand prediction. Then, we applied statistical methods to present the performance indicators.
Results: This research examined 37409 calls that led to ambulance dispatch from March 2021 to March 2023. According to the examination of traffic collision data, the demand rate is greater during the daytime compared to nighttime. Nonetheless, ambulance responses to deadly accidents take place more frequently at night compared to daytime. Our analysis indicates that demand will vary between 2400 and 800 with a 90% confidence level. Additionally, at an 80% confidence level, the demand range is expected to be between 300 and 2800.
Conclusion: By analyzing the historical data, we have identified a trend and seasonal patterns in the data, which suggests an increase in demand during the summer months. Forecasting the course of service recipients in the prehospital emergency service can increase situational awareness and help manage the challenges caused by overcrowding. By anticipating the surge in demand for services during peak periods, it is possible to plan and allocate resources effectively and minimize delays. |
| format | Article |
| id | doaj-art-63e677351f5c4ccea3591d4c1616d45e |
| institution | Kabale University |
| issn | 2345-4210 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Negah Institute for Scientific Communication |
| record_format | Article |
| series | Health in Emergencies & Disasters Quarterly |
| spelling | doaj-art-63e677351f5c4ccea3591d4c1616d45e2025-08-20T03:24:53ZengNegah Institute for Scientific CommunicationHealth in Emergencies & Disasters Quarterly2345-42102025-07-01104247258Estimating the Demand for Ambulances in Traffic AccidentsManoochehr Babanezhad0Hassan Khorsha1Ali Mohajervatan2Ali Choori3 Department of Statistics, Faculty of Sciences, Golestan University, Gorgan, Iran. Department of Management of Statistics and Information Technology, Golestan University of Medical Sciences, Gorgan, Iran. Department of Anesthesia and Prehospital Emergency Care, School of Paramedical Sciences, Golestan University of Medical Sciences, Gorgan, Iran. Department of Humanities and Sport Science, Faculty of Sport Sciences, University of Gonbad Kavous, Gonbad, Iran. Background: Effective Emergency medical service (EMS) delivery in road traffic accidents requires accurate resource planning that relies on operational, tactical and strategic demand forecasts. This study aims to estimate the demand for ambulances in traffic accidents using time series modeling techniques. Materials and Methods: We conducted a retrospective cohort analysis of ambulance demands related to traffic incidents in Golestan Province, Iran. The analysis of individual time series was utilized for demand prediction. Then, we applied statistical methods to present the performance indicators. Results: This research examined 37409 calls that led to ambulance dispatch from March 2021 to March 2023. According to the examination of traffic collision data, the demand rate is greater during the daytime compared to nighttime. Nonetheless, ambulance responses to deadly accidents take place more frequently at night compared to daytime. Our analysis indicates that demand will vary between 2400 and 800 with a 90% confidence level. Additionally, at an 80% confidence level, the demand range is expected to be between 300 and 2800. Conclusion: By analyzing the historical data, we have identified a trend and seasonal patterns in the data, which suggests an increase in demand during the summer months. Forecasting the course of service recipients in the prehospital emergency service can increase situational awareness and help manage the challenges caused by overcrowding. By anticipating the surge in demand for services during peak periods, it is possible to plan and allocate resources effectively and minimize delays.http://hdq.uswr.ac.ir/article-1-641-en.pdftime seriesestimatingtraffic accidentsemergency medical service (ems)ambulance |
| spellingShingle | Manoochehr Babanezhad Hassan Khorsha Ali Mohajervatan Ali Choori Estimating the Demand for Ambulances in Traffic Accidents Health in Emergencies & Disasters Quarterly time series estimating traffic accidents emergency medical service (ems) ambulance |
| title | Estimating the Demand for Ambulances in Traffic Accidents |
| title_full | Estimating the Demand for Ambulances in Traffic Accidents |
| title_fullStr | Estimating the Demand for Ambulances in Traffic Accidents |
| title_full_unstemmed | Estimating the Demand for Ambulances in Traffic Accidents |
| title_short | Estimating the Demand for Ambulances in Traffic Accidents |
| title_sort | estimating the demand for ambulances in traffic accidents |
| topic | time series estimating traffic accidents emergency medical service (ems) ambulance |
| url | http://hdq.uswr.ac.ir/article-1-641-en.pdf |
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