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

TL;DR

The paper tackles the gap between policy formulation and implementation in urban mobility by proposing a cybernetically inspired mixed-methods framework that integrates qualitative survey insights with large-scale mobility data. Using NTNU's campus consolidation in Trondheim as a testbed, it combines a qualitative survey (n≈573) with big-data analyses from public transit, cellular routing, toll data, and TravelTime-based routing to map spatiotemporal mobility patterns, constraints, and potential impacts. Key contributions include a transferable methodology that yields grounded, data-driven policy recommendations and demonstrates how adaptive governance can treat outcomes as feedback for continuous learning. The study shows that structural constraints (families, health, seasonality) and infrastructure gaps shape mobility choices, and that a cybernetic, feedback-driven governance model can align sustainability goals with lived realities in complex socio-technical systems.

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.

Figures (18)

• Figure 1: Heatmap depicting the geographical distribution of bus stops nearest to the home of survey respondents. The inset map shows the bus lines available in the public transit dataset. Note: The blue marker indicates NTNU's Gløshaugen campus. Due to the wide geographical spread, some outlying stops are not visible within the map bounds.
• Figure 2: Overview of bus stops and major roads around Gløshaugen with available historical mobility data. Note: The blue marker indicates NTNU's Gløshaugen campus. Road segments with available historical peopleFlow data are highlighted in blue.
• Figure 3: Demographic breakdown of the 573 survey respondents by gender and age group, highlighting the nearly balanced gender distribution and the concentration of respondents in the 36--55 age range.
• Figure 4: Overview of mobility modes used by survey respondents during summer and winter, with bus users highlighted in red. Note: Point size indicates the number of bus transfers per trip (0--5); larger points represent more transfers.
• Figure 5: Potential changes to work start and end times among 97 respondents who anticipate adjusting their work patterns due to parking reduction. Note: Solid lines indicate current work hours; dashed lines indicate anticipated changes; and the shaded area represents the typical workday (08:00--15:45).