(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
The convergence of digital twin technology and advanced video representation learning offers a transformative paradigm for modeling crowd dynamics in complex urban and infrastructural environments. This paper presents a systematic framework for constructing digital twin-oriented spatio-temporal models that leverage hierarchical multi-stream video representations to capture the multi-scale, multi-modal nature of human movement. The proposed architecture integrates high-level semantic reasoning with low-level motion encodings, enabling robust and scalable simulation of crowd behaviors for applications in smart city management, event security, and transportation planning. We examine the structural trade-offs between model granularity, computational efficiency, and predictive accuracy, and discuss implications for system governance, data fairness, and infrastructure sustainability. A critical analysis of deployment strategies reveals the need for adaptive streaming pipelines and federated learning mechanisms to ensure real-time responsiveness and privacy compliance. The paper further considers the role of policy frameworks in governing the use of crowd models, particularly with respect to bias mitigation, accountability, and ethical boundaries. By situating the technical contributions within a broader socio-technical context, we argue that hierarchical multi-stream video representations are not merely a computational improvement but a foundational component for trustworthy and responsible digital twin ecosystems. The study draws on recent advances in motion encoding, trajectory prediction, and large-scale video understanding, and proposes a roadmap for future research that balances innovation with societal resilience.