Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS’s Digital Twin

Al-Shareeda, Sarah; Celik, Yasar; Bilgili, Bilge; Al-Dubai, Ahmed; Canberk, Berk

Abstract

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time, and tracking their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance’s location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle’s next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN’s key role in significantly reducing the witnessed gap within the HITS’s DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%.

Citation

Al-Shareeda, S., Celik, Y., Bilgili, B., Al-Dubai, A., & Canberk, B. (2025, February). Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS’s Digital Twin. Presented at 2025 5th IEEE Middle East and North Africa Communications Conference (MENACOMM), Byblos, Lebanon

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2025 5th IEEE Middle East and North Africa Communications Conference (MENACOMM)
• Start Date: Feb 20, 2025
• End Date: Feb 22, 2025
• Acceptance Date: Dec 31, 2024
• Online Publication Date: Mar 13, 2025
• Publication Date: Feb 20, 2025
• Deposit Date: Apr 2, 2025
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Series Number: 2837-4894
• ISBN: 9798331519964
• DOI: https://doi.org/10.1109/MENACOMM62946.2025.10910975
• Public URL: http://researchrepository.napier.ac.uk/Output/4231071
• External URL: https://menacomm.org/