A nested-block scalable framework for efficient and equitable urban infrastructure management.

Area of Science:

• Urban Infrastructure Management
• Network Optimization
• Sustainable City Planning

Background:

• Cities face challenges in maintaining complex infrastructure networks due to budget and environmental constraints.
• Existing optimization methods are inadequate for large-scale, multi-year infrastructure planning.
• The complexity of urban systems requires novel approaches for efficient management.

Purpose of the Study:

• To develop a scalable and efficient optimization framework for city-wide infrastructure maintenance and renewal.
• To address the limitations of conventional methods in handling large numbers of assets and complex constraints.
• To enable coordinated planning across diverse infrastructure types and scenarios.

Main Methods:

• A nested-block optimization framework that decomposes city networks into coordinated sub-blocks.
• Transforming an intractable decision space into a solvable, parallelizable structure.
• Preserving budgetary and temporal constraints while managing millions of assets.

Main Results:

• The framework achieves rapid convergence and fully feasible plans for million-asset scale problems.
• Demonstrated superior efficiency and scalability compared to existing methods.
• Successfully unified diverse assets (roads, pipelines) and extended to energy/transit systems.

Conclusions:

• The framework offers a systematic tool for infrastructure management, identifying evolutionary patterns and investment pathways.
• Enables cross-asset and cross-scenario coordination, embedding equity and efficiency objectives.
• Provides practical pathways for more coordinated, just, and sustainable urban infrastructure.