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%.

翻译：为医疗智能交通系统（HITS）构建数字孪生（DT）是当前的研究热点，其核心目标在于提升HITS的管理效能，尤其在紧急情况下，救护车辆需准时抵达事故现场，其实时位置追踪对医疗管理机构至关重要。尽管现有系统声称具备实时表征能力，物理域与虚拟域之间仍存在时间错位，导致救护车位置表征出现偏差。本研究提出在构建的模拟DT数据管道框架中集成AI预测模型，特别是支持向量回归（SVR）和深度神经网络（DNN），以预测医疗车辆在虚拟世界中的下一位置。这些模型使虚拟表征与物理实体保持一致，即从隐喻意义上抵消了两个世界间的同步延迟。通过在历史地理空间数据集上进行精细训练，SVR和DNN在MATLAB和Python环境中展现出卓越的预测精度。通过多种测试场景，我们直观展示了所提方法的有效性，凸显了SVR和DNN在显著缩小HITS数字孪生内部观测差距方面的关键作用。这一变革性方法将紧急HITS中的实时同步能力提升了约88%至93%。