Summary and Conclusion

This chapter gives a concise summary of various ideas, conceptual arguments, machine-learning algorithms, theory-based learning, and implementation procedures for the incorporation of urban dynamics and complexity into digital twins of cities for the effective and efficient modeling, simulation, prediction, and management of our ever-evolving cities. It also proposes directions for future research along the line of elaborating and strengthening the theoretical underpinnings of the approaches in the proposed framework and experimentally verifying and validating them in synthetic simulations and reallife applications; further developing methods to learn the low-dimensional manifold of the extremely high-dimensional data to achieve parsimony beneficial to digital-twin construction and learning; facilitating our multiple stakeholder decision making; and designing operational systems for the simulation, prediction, and management of large-scale urban systems with continuous knowledge and data flows among the physical city entities, the corresponding digital twins and the data hubs in a synchronised and timely manner. It is envisioned that it will advance a new frontier of modeling geographical systems in general and urban systems, together with their digital twins, in particular. It will also break new grounds in interdisciplinary research involving academia, industry, government, and practicing professionals on human and physical systems that are ever evolving in space and time.