arXiv:2606.14157v1 Announce Type: cross Abstract: Cities deliver basic services through mixed public-private facility networks, including schools, clinics, transit providers, and subsidized service points. In these systems, planners often observe where households go, but not the latent cost function through which they trade off factors such as distance, price, and institutional access. We study this urban problem through school choice in the Philippines, where the country's largest national education subsidy is intended to redirect learners from congested public schools to participating privat
The increasing availability of large-scale urban mobility data and advancements in AI, specifically inverse optimal transport, enable a deeper analysis of complex urban systems now.
Understanding latent cost functions in urban access can significantly improve resource allocation, urban planning, and the effectiveness of public services for governments and citizens.
Traditional urban planning methods, which often rely on assumed cost functions, can now be informed by data-driven insights into actual human behavior and trade-offs.
- · Urban planners
- · Smart city initiatives
- · Government service providers
- · AI/ML application developers
- · Inefficient public service models
- · Urban planning based solely on intuition
More efficient and equitable distribution of urban services based on observed demand and inferred costs.
Improved policy design for infrastructure development and social welfare programs tailored to citizen preferences.
Enhanced resilience and sustainability of cities through better resource management and reduced congestion.
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Read at arXiv cs.AI