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...
Saved in:
Main Authors: | Reem Tluli, Ahmed Badawy, Saeed Salem, Mahmoud Barhamgi, Amr Mohamed |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Open Journal of Intelligent Transportation Systems |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10787208/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ROUTE FIRST-CLUSTER SECOND METHOD FOR PERSONAL SERVICE ROUTING PROBLEM
by: MELİKE KÜBRA EKİZ, et al.
Published: (2019-06-01) -
Retrospective analysis of trauma patients transported by dispatch monitored type B ambulances to Dhulikhel Hospital, Kavre, Nepal, 2019–2023
by: Maxwell L. Mantych, et al.
Published: (2025-01-01) -
ANALYSIS OF PUBLIC SAFETY CENTER 119 AMBULANCE SERVICES USING LEAN SIX SIGMA
by: Thasya Sabilla Putri Rojak, et al.
Published: (2024-12-01) -
Emergency department crowding in the Netherlands; evaluation of a real-time ambulance diversion dashboard
by: E. C. M. Baan-Kooman, et al.
Published: (2025-01-01) -
A two-stage robust optimization model for emergency service facilities location-allocation problem under demand uncertainty and sustainable development
by: Hongyan Li, et al.
Published: (2025-01-01)