A collaborative metaverse-digital twin system for traffic perception, reasoning, and resource scheduling

In highly dynamic and high-concurrency urban traffic environments, intelligent systems must be capable of real-time perception, accurate reasoning, and agile scheduling to effectively manage complex traffic situations. However, prevailing approaches often suffer from fragmented perception, shallow reasoning, and delayed scheduling, leading to a lack of coordination among critical system modules. This deficiency significantly hinders holistic intelligent decision-making and real-time regulation under rapidly changing conditions. To address these challenges, this paper proposes a collaborative framework that integrates digital twins with metaverse-based semantic modeling. A three-layer architecture is constructed, consisting of the physical infrastructure layer, virtual twin resource layer, and traffic situation awareness layer, thereby forming a closed-loop mechanism of perception–reasoning–scheduling. The proposed system leverages the immersive semantic environment provided by the metaverse to enable contextual interpretation and intent recognition of traffic data. This semantic understanding drives the dynamic evolution and causal reasoning of digital twin entities. Based on the inferred results, a cloud–edge–end collaborative scheduling strategy is triggered to allocate resources adaptively. In addition, an interactive feedback mechanism is incorporated to support the real-time verification and continuous optimization of scheduling outcomes. Within this framework, we design a metaverse-driven Mixture-of-Experts perception network that enables multi-level semantic recognition and prediction of global traffic trends, regional congestion, and local anomalies. Furthermore, we introduce a multi-agent scheduling mechanism that combines virtualized resource mapping with structure-aware transfer strategies, thereby enhancing the generalization capacity and dynamic responsiveness of scheduling policies across heterogeneous infrastructure environments. Extensive experimental evaluations demonstrate that the proposed approach outperforms existing mainstream methods across several key metrics, including task acceptance rate, long-term revenue, congestion mitigation effectiveness, and resource utilization efficiency. These results validate the proposed framework’s superior adaptability and system-level of intelligence in complex traffic scenarios.