Fine-Grained Model-Level Digital Twin Migration Method for Intelligent Transportation Systems

With the rapid advancement of digital twin-enabled intelligent transportation systems, efficient migration has become essential for maintaining real-time responsiveness and reliability. Existing approaches, however, primarily emphasize resource-aware optimization while neglecting the substantial overhead from state synchronization and redundant data transmission. Moreover, they typically treat digital twins as indivisible entities, overlooking optimization opportunities at the sub-model level. This limitation results in excessive migration costs and suboptimal resource utilization. To overcome these challenges, we propose a fine-grained model-level digital twin migration framework, FGDT, featuring three key components: (i) an explicit-implicit fused coupling graph construction captures both functional dependencies and latent collaborations among heterogeneous sub-models; (ii) a skew-aware migration pattern selection dynamically balances joint versus independent migration, thereby minimizing communication overhead and improving resource allocation; and (iii) a model-level migration strategy optimization strategy leverages dual-network PPO with a soft-constraint co-placement mechanism to support adaptive, fine-grained migration decisions. Extensive experiments validate the effectiveness of FGDT, which significantly reduces average system latency while maintaining low migration overhead, thereby enhancing both resource efficiency and overall system performance.