Data-driven cybernetic framework for adaptive urban mobility governance

Adaptive governance provides the institutional framework for iterative policy learning in mobility transitions, but often struggles to keep up with contemporary big data paradigms. This study develops a data-driven cybernetic framework as a high-frequency, feedback-oriented complement to adaptive governance. Using campus consolidation in Trondheim, Norway, we examine how it links policy intentions to commuting realities through integrated mobility analysis. We use a spatially-linked multi-source quantitative approach combining survey data (n=573) with public transit and crowd movement data. The framework integrates three analytical components to characterise commuting flows, modes, durations, distances, route concentration, and the spatiotemporal constraints shaping daily travel. The analysis reveals unevenly distributed mobility constraints, with 59.3% of respondents having children and private cars dominating in winter (49.4%). Although support for sustainable mobility goals is broad, 86.0% identify longer travel duration as the main difficulty. Baseline transit and crowd movement analyses highlight peak usage patterns and concentration risks as signals for recalibration. The study shows how multi-source quantitative analysis can establish pre-intervention baselines, identify system vulnerabilities under policy change, and support more responsive mobility governance. Interpreted through a cybernetic governance lens, the results inform context-sensitive interventions that align sustainability goals with lived realities. • Cybernetic governance applied as high-frequency complement to adaptive governance. • Survey and big mobility data spatially linked for multi-source quantitative analysis. • Pre-intervention baselines and system vulnerabilities identified under policy change. • Feedback architecture connects mobility telemetry to policy recalibration signals. • Replicable approach for evidence-based monitoring of urban mobility policy impacts.