A Composite Index Framework for Quantifying External Influences on Built Environment Planning and Decision Making

Abstract

Abstract Infrastructure systems are influenced by a wide range of external factors, including demographic changes, economic cycles, environmental stressors, and technological transitions. These factors affect infrastructure demand, operating conditions, and deterioration processes through nonlinear, delayed, and interacting pathways, causing gradual shifts that can accumulate and emerge as abrupt changes in system performance at higher planning levels. This multiscale behavior creates significant challenges for infrastructure planning, as agencies need to monitor large volumes of heterogeneous data and derive timely, actionable insights from indicators reported at different temporal and spatial scales. Traditional planning methods, which often rely on isolated indicators or static forecasts, struggle to detect emerging patterns and diagnose their underlying drivers across system hierarchies. To address these limitations, this study proposes a hierarchy-aligned composite index framework that integrates diverse external factors and performance measures to support continuous monitoring and adaptive decision-making. The framework combines causal inference methods and dynamic factor analysis to identify influential drivers, extract latent common trends, and organize information in a structure consistent with infrastructure system governance. Rather than merely simplifying interpretation, the composite index streamlines abundant, multidimensional information into interpretable and traceable signals. A case study of Florida’s multimodal transportation system demonstrates the framework’s ability to track and diagnose system behavior across multiple transportation modes and planning levels. Practitioner review with FDOT planners confirms the interpretability and practical relevance of the indices and highlights the framework’s value for adaptive infrastructure planning under uncertainty.