Data-Driven Mixed-Methods Framework for Cybernetic Urban Mobility Governance

TL;DR

This paper presents a cybernetically inspired mixed-methods framework that combines qualitative surveys and quantitative data analysis to support sustainable urban mobility governance, demonstrated through a case study in Trondheim, Norway.

Contribution

It introduces a novel integrated framework that bridges policy and implementation via feedback analysis, combining qualitative and quantitative methods for mobility transition assessment.

Findings

Identified key mobility constraints and congestion points.

Revealed seasonal variations in mobility patterns.

Provided data-driven recommendations for sustainable mobility policies.

Abstract

This study develops a cybernetically inspired mixed-methods framework that bridges the gap between policy formation and implementation through feedback-driven analysis of mobility transitions. Using a major campus consolidation in Trondheim, Norway as a case study, we examine how this framework supports sustainable mobility through integrated analysis of mobility patterns, constraints, and transition impacts. The consolidation eliminates over 1,300 parking spaces while increasing daily population by 9,300 people. We employ a mixed-methods approach combining qualitative survey data (n=573) with quantitative big data analysis of public transit and crowd movement patterns. This integrates three analytical components and provides grounded insights into commuting flows, modes, durations, distances, and congestion points, while addressing the spatiotemporal mobility realities of affected populations. The analysis reveals complex mobility constraints, with 59.3% of respondents having children and private cars dominating in winter (49.4%). Though there is broad support for sustainable mobility goals, 86.0% identify increasing travel duration as primary difficulty. Quantitative analysis highlights peak usage patterns and congestion risks, with seasonal variations. This study demonstrates integrating qualitative and quantitative analysis to anticipate negative impacts and enable efficient sustainable mobility policies. The results inform practical recommendations for data-driven mobility interventions that align sustainability goals with lived realities.