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

TL;DR

This paper introduces AI models like SVR and DNN to improve real-time location tracking of emergency vehicles in healthcare transportation systems by reducing synchronization delays in digital twins, thus enhancing emergency response accuracy.

Contribution

It presents a novel integration of AI predictive models within a digital twin framework to align virtual and physical vehicle locations in healthcare ITS.

Findings

AI models significantly reduce location discrepancy by 88-93%

SVR and DNN achieve high prediction accuracy in simulation environments

Enhanced real-time synchronization improves emergency response effectiveness

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 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%.