A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation
In the realm of Emergency Medical Services (EMS), the integration of Machine Learning (ML) techniques has emerged as a catalyst for revolutionizing ambulance operations. ML algorithms could play a pivotal role in dynamically allocating resources, devising efficient routes, and predicting demand patt...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10787208/ |
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| author | Reem Tluli Ahmed Badawy Saeed Salem Mahmoud Barhamgi Amr Mohamed |
| author_facet | Reem Tluli Ahmed Badawy Saeed Salem Mahmoud Barhamgi Amr Mohamed |
| author_sort | Reem Tluli |
| collection | DOAJ |
| description | In the realm of Emergency Medical Services (EMS), the integration of Machine Learning (ML) techniques has emerged as a catalyst for revolutionizing ambulance operations. ML algorithms could play a pivotal role in dynamically allocating resources, devising efficient routes, and predicting demand patterns. By thoroughly reviewing the existing literature and methodologies, this paper provides a comprehensive overview of the approaches used in ambulance allocation, routing, demand estimation and simulation models. We discuss the challenges faced by these methods, emphasizing the need for innovative solutions that can adapt to real-time data and changing emergency patterns. Through this survey, we aim to offer valuable insights into the current state of research and practices, shedding light on potential areas for future exploration and development. The findings presented in this paper serve as a foundation for researchers and practitioners working towards enhancing the efficiency of ambulance deployment in EMS. |
| format | Article |
| id | doaj-art-855d29a5c5f44556afe6d7f60e8cda0d |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-855d29a5c5f44556afe6d7f60e8cda0d2025-01-24T00:02:44ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01584287210.1109/OJITS.2024.351487110787208A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand EstimationReem Tluli0https://orcid.org/0000-0001-8038-331XAhmed Badawy1https://orcid.org/0000-0002-5515-6542Saeed Salem2https://orcid.org/0000-0001-6478-4674Mahmoud Barhamgi3Amr Mohamed4https://orcid.org/0000-0002-1583-7503Computer Science and Engineering Department, Qatar University, Doha, QatarComputer Science and Engineering Department, Qatar University, Doha, QatarComputer Science and Engineering Department, Qatar University, Doha, QatarComputer Science and Engineering Department, Qatar University, Doha, QatarComputer Science and Engineering Department, Qatar University, Doha, QatarIn the realm of Emergency Medical Services (EMS), the integration of Machine Learning (ML) techniques has emerged as a catalyst for revolutionizing ambulance operations. ML algorithms could play a pivotal role in dynamically allocating resources, devising efficient routes, and predicting demand patterns. By thoroughly reviewing the existing literature and methodologies, this paper provides a comprehensive overview of the approaches used in ambulance allocation, routing, demand estimation and simulation models. We discuss the challenges faced by these methods, emphasizing the need for innovative solutions that can adapt to real-time data and changing emergency patterns. Through this survey, we aim to offer valuable insights into the current state of research and practices, shedding light on potential areas for future exploration and development. The findings presented in this paper serve as a foundation for researchers and practitioners working towards enhancing the efficiency of ambulance deployment in EMS.https://ieeexplore.ieee.org/document/10787208/Emergency medical services (EMS)ambulance servicesmachine learning (ml)allocation optimizationvehicle routing strategiesdemand estimation |
| spellingShingle | Reem Tluli Ahmed Badawy Saeed Salem Mahmoud Barhamgi Amr Mohamed A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation IEEE Open Journal of Intelligent Transportation Systems Emergency medical services (EMS) ambulance services machine learning (ml) allocation optimization vehicle routing strategies demand estimation |
| title | A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation |
| title_full | A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation |
| title_fullStr | A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation |
| title_full_unstemmed | A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation |
| title_short | A Survey of Machine Learning Innovations in Ambulance Services: Allocation, Routing, and Demand Estimation |
| title_sort | survey of machine learning innovations in ambulance services allocation routing and demand estimation |
| topic | Emergency medical services (EMS) ambulance services machine learning (ml) allocation optimization vehicle routing strategies demand estimation |
| url | https://ieeexplore.ieee.org/document/10787208/ |
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