Cross-Disciplinary Deep Learning Framework for Urban Sustainability: Linking Transport Efficiency, Material Durability, and Environmental Health

Abstract

Transport efficiency, material durability, and environmental health are essential for urban sustainability. However, deeper temporal, spatial, and causal connections among these domains remain underexplored. Deep learning can augment information from traditional empirical, deductive, and a priori techniques. A cross-disciplinary educational framework rooted in urban planning, systems engineering, machine learning, and education theory can nurture the required competencies. The proposed approach is structured along three pillars: data architectures, scalable deep learning methods, and practical applications for a specific context or combination of contexts. Evidence from the transport domain, where the model is most mature and where synergies with the material durability domain are clearest, supports three conclusions. Spatiotemporal modeling of urban mobility based on deep learning can enrich transport outcomes, detect mobility-related disruptions, and inform preventive measures. The development is guided by neural network architectures tailored for infrastructure networks, which capture and exploit hidden or explicit relationships. Finally, appropriate reinforcement learning formulations facilitate the definition of dynamic transport optimization problems that emerge naturally from the referenced spatiotemporal modeling.

Underlying deep-learning methods for material durability and lifecycle assessment are predictive models of material longevity and surrogate models for lifecycle assessment. The predictive models incorporate inputs from internal or external non-destructive inspection signals that detect and quantify degradation processes and uncertainties associated with their inference. Validation and calibration rely on field data. The surrogate models combine physics-based and data-driven components for efficient yet accurate computation. Evidence from the environmental health domain provides additional utilities for transport and material developments. Urban exposure models for short-term concentrations draw on low-cost sensors and exploited spatiotemporal redundancy to manage data sparsity. Information fusion approaches support joint estimation for chemical-species mixtures. Spatiotemporal exposure-response relationships allow risk mapping and uncertainty characterization.