Dynamic Urban Digital Twins: Physics-Coherent Traffic Video Synthesis with Spatiotemporal 3D Semantic Constraints
Keywords:
urban digital twins, traffic video synthesis, physics-coherent generation, spatiotemporal 3D semantic constraints, neural rendering, infrastructure governanceAbstract
The proliferation of urban digital twins has opened new frontiers for simulating and managing complex metropolitan systems, yet the generation of realistic traffic video streams within these environments remains a fundamental challenge. Existing video synthesis approaches often produce visually plausible outputs that violate physical laws of traffic flow, object dynamics, or temporal continuity, thereby limiting their utility for infrastructure planning, autonomous vehicle testing, and policy analysis. This paper presents a comprehensive framework for dynamic urban digital twins that achieve physics-coherent traffic video synthesis by integrating spatiotemporal three-dimensional semantic constraints. The proposed architecture leverages a hierarchical representation of urban scenes, coupling neural rendering with physics-based vehicle behavior models and semantically annotated 3D point clouds. A key innovation is the incorporation of real-world traffic flow data and geometric reasoning to enforce lane adherence, collision avoidance, and velocity consistency across consecutive frames. We discuss the structural trade-offs inherent in balancing perceptual fidelity with physical plausibility, and examine the computational infrastructure required for real-time deployment across large-scale city models. Governance and policy implications are addressed, particularly concerning the use of synthetic data for equitable infrastructure design, bias mitigation in training datasets, and the ethical boundaries of simulating public urban spaces. The framework is evaluated through both quantitative metrics—such as Fréchet Video Distance and physics compliance scores—and qualitative case studies drawn from metropolitan traffic corridors. Results demonstrate a significant improvement in maintaining long-term temporal coherence and physical realism compared to conventional generative approaches. This work contributes a scalable blueprint for constructing urban digital twins that not only represent static geometry but also faithfully emulate the dynamic, physics-governed behaviors of real-world traffic, thereby advancing the state of the art in simulation-driven urban science and policy.
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