This paper investigates the potential of Digital Twins (DTs) to enhance
network performance in densely populated urban areas, specifically focusing on
vehicular networks. The study comprises two phases. In Phase I, we utilize
traffic data and AI clustering to identify critical locations, particularly in
crowded urban areas with high accident rates. In Phase II, we evaluate the
advantages of twinning vehicular networks through three deployment scenarios:
edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis
demonstrates that twinning significantly reduces network delays, with virtual
twins outperforming physical networks. Virtual twins maintain low delays even
with increased vehicle density, such as 15.05 seconds for 300 vehicles.
Moreover, they exhibit faster computational speeds, with cloud-based twins
being 1.7 times faster than edge twins in certain scenarios. These findings
provide insights for efficient vehicular communication and underscore the
potential of virtual twins in enhancing vehicular networks in crowded areas
while emphasizing the importance of considering real-world factors when making
deployment decisions.

该研究探讨了数字孪生（DT）在提升密集人口城区中的网络性能方面的潜力，具体关注车辆网络。研究包括两个阶段，第一阶段利用交通数据和人工智能聚类来确定关键位置，尤其是在事故率高的拥挤城区。第二阶段评估了通过三种部署场景对车辆网络进行孪生的优势：基于边缘的孪生、基于云的孪生和混合孪生。我们的分析表明，孪生显著减少了网络延迟，虚拟孪生优于物理网络。虚拟孪生即使在车辆密度增加的情况下，如 300 辆车的情况下，仍保持较低的延迟，例如 15.05 秒。此外，在某些情境中，云孪生的计算速度较快，是边缘孪生的 1.7 倍。这些发现为高效车辆通信提供了见解，并强调在做出部署决策时考虑现实因素的重要性。