用AI预测救护车位置，让数字孪生系统实时同步
Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS Digital Twin
- 用SVR和DNN模型预测救护车下一位置，弥补虚实延迟
- 在测试中使医疗交通系统数字孪生同步率提升88%~93%
- 适合智能急救、交通数字孪生方向的研究者参考
构建医疗智能交通系统（HITS）的数字孪生（DT）是当前热点，尤其在紧急情况下，确保救护车准时到达现场并实时追踪其位置对医疗部门至关重要。尽管强调实时呈现，物理世界与虚拟世界之间仍存在时间错位，导致位置信息不一致。本文提出在模拟的数字孪生数据流框架中集成支持向量回归（SVR）和深度神经网络（DNN）等人工智能预测模型，以预判医疗车辆在虚拟空间中的下一位置，从而实现虚拟表征与实际状态的对齐，相当于消除两世界间的同步延迟。模型基于历史地理空间数据精心训练，在MATLAB与Python环境中表现出色。通过多种测试场景验证，结果表明SVR与DNN能显著缩小HITS数字孪生中的位置差距。该方法使应急医疗交通系统的实时同步性提升约88%至93%，实现关键性突破。
原文摘要 · Abstract (English)
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%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。