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

arXiv: https://arxiv.org/abs/2502.03396 · HTML: https://mlsanity.com/paper/2502.03396/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [The Paradigm of Digital Twin Communications](https://mlsanity.com/paper/2105.07182/) (2021)
- [Does Twinning Vehicular Networks Enhance Their Performance in Dense Areas?](https://mlsanity.com/paper/2402.10701/) (2024)
- [A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges](https://mlsanity.com/paper/1907.09454/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Lightweight DL Model for Smart Grid Power Forecasting with Feature and Resolution Mismatch](https://mlsanity.com/paper/2510.16911/) (2025)

## Abstract (arXiv)

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%.