Choice, necessity or chance? Understanding behaviouR chanGE iN Transport

Introduction

Despite various soft and hard measures as well as improved transport technologies, carbon emissions from transport are not decreasing. URGENT will provide the knowledge base for more effective intervention strategies. To this end, the project examines under which individual and contextual circumstances people change their mobility behaviour and what mental mechanisms are involved in these behaviour change processes.

Methodology

The project uses a holistic interdisciplinary approach based on a unique longitudinal dataset. Over a 3-year period, data will be collected via a bi-annual survey combined with case studies including sub-samples of survey participants.

Data Collection

Based on the survey, in-depth interviews, and an intelligent mobility diary app, the case studies examine how people adapt their mobility behaviour in prospect and response to:

1. Residential relocation
2. Anticipated vs. sudden life events
3. Transport technology adoption

Analysis

Applying causal machine learning methods, the project will uncover which personal, social, technical, or spatial factors are the most relevant initiators of behaviour change. It will also specify the causal relations between involved factors, informed by the case studies.

Rebound Effects

URGENT will additionally examine rebound effects of changed mobility behaviour (e.g., car use reduction, electric vehicle adoption) and reveal under which conditions, and to what extent, behaviour change in one area (e.g., commuting) positively or negatively spills over to other areas (e.g., air travel, food consumption).

Interdisciplinary Approach

URGENT applies a novel analytical strategy that cross-fertilizes concepts from:

• Psychology (behaviour change models)
• Human geography (mobility biographies approach)
• Sociology (mobility cultures)
• Machine learning (causal discovery and causal inference)

Conclusion

The project will not only fundamentally increase the understanding of behaviour change in transport but also bears the potential to lead to a breakthrough in studying causality in transport research at large.