Quantifying Functional Criticality of Lifelines Through Mobility-Derived Population-Facility Dependence for Human-Centered Resilience

2025-12-18

AI Summary

Traditional infrastructure criticality assessments rely on physical attributes or topological metrics, failing to capture real-world societal impacts—especially in underserved communities with limited service alternatives. This study introduces the “functional criticality” paradigm, quantifying residents’ actual behavioral dependence on lifeline facilities (e.g., hospitals, supermarkets) using 1.02 million anonymized mobile trajectories. We innovatively integrate human mobility patterns with hazard exposure to establish a human-centered resilience assessment framework. Results identify “super-critical facilities”—only 2.8% of supermarkets and 14.8% of hospitals accommodate disproportionately high visitation loads. Under flood scenarios, population-weighted accessibility vulnerability surges by 67.6%. We further propose an origin-specific substitutability metric and a probabilistic coupling analysis method. Collectively, these advances support equitable, adaptive infrastructure resilience planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: AI for Accessibility
• Reasoning under Uncertainty: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Lifeline infrastructure underpins the continuity of daily life, yet conventional criticality assessments remain largely asset-centric, inferring importance from physical capacity or network topology rather than actual behavioral reliance. This disconnect frequently obscures the true societal cost of disruption, particularly in underserved communities where residents lack service alternatives. This study bridges the gap between engineering risk analysis and human mobility analysis by introducing functional criticality, a human-centered metric that quantifies the behavioral indispensability of specific facilities based on large-scale visitation patterns. Leveraging 1.02 million anonymized mobility records for Harris County, Texas, we operationalize lifeline criticality as a function of visitation intensity, catchment breadth, and origin-specific substitutability. Results reveal that dependence is highly concentrated: a small subset of super-critical facilities (2.8% of grocery stores and 14.8% of hospitals) supports a disproportionate share of routine access. By coupling these behavioral scores with probabilistic flood hazard models for 2020 and 2060, we demonstrate a pronounced risk-multiplier effect. While physical flood depths increase only moderately under future climate scenarios, the population-weighted vulnerability of access networks surges by 67.6%. This sharp divergence establishes that future hazards will disproportionately intersect with the specific nodes communities rely on most. The proposed framework advances resilience assessment by embedding human behavior directly into the definition of infrastructure importance, providing a scalable foundation for equitable, adaptive, and human-centered resilience planning.

Problem

• Develops human-centered metric to quantify behavioral reliance on critical facilities
• Identifies disproportionate dependence on few super-critical facilities in communities
• Reveals future climate hazards will disproportionately impact most relied-upon infrastructure

Innovation

• Human-centered metric using large-scale mobility data
• Coupling behavioral scores with probabilistic hazard models
• Scalable framework for equitable resilience planning