Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS Digital Twin
This work addresses a critical issue for healthcare authorities by improving real-time ambulance tracking in emergency scenarios, though it appears incremental as it applies existing AI methods to a specific domain problem.
The study tackled the problem of temporal misalignment in Healthcare Intelligent Transportation Systems (HITS) Digital Twins, which causes discrepancies in ambulance location tracking, by integrating AI predictive models (SVR and DNN) to anticipate vehicle locations, resulting in a reduction of the synchronization gap by approximately 88% to 93%.
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 track 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%.