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

2025-02-05

AI Summary

To address the ambulance positioning inaccuracy caused by virtual–physical synchronization latency in digital twin (DT)-enabled healthcare intelligent transportation systems (HITS), this paper proposes a predictive virtual–physical coordination framework. The method innovatively integrates support vector regression (SVR) and deep neural networks (DNN) into the DT data pipeline, leveraging multi-source historical geospatial data to predict the next-time-location of emergency vehicles—replacing conventional reactive synchronization mechanisms. Experimental evaluation across diverse operational scenarios demonstrates that the proposed approach improves DT real-time synchronization accuracy to 88%–93%, significantly reducing virtual–physical localization latency. Consequently, it enhances both the timeliness of emergency response and the reliability of dispatch decision-making.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Application Domains: Transportation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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%.

Problem

• AI-driven emergency vehicle tracking
• Digital Twin synchronization in healthcare ITS
• Predictive models for real-time location accuracy

Innovation

• AI predictive models integration
• Support Vector Regression usage
• Deep Neural Networks application