Choice, necessity or chance? Understanding behaviouR chanGE iN Transport

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.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. 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 and
(3) transport technology adoption.

Applying causal machine learning methods, the project will uncover which personal, social, technical or spatial factors are most relevant initiators of behaviour change and will specify the causal relations between involved factors, informed by the case studies. 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). URGENT applies a novel analytical strategy that cross-fertilizes concepts from psychology (behaviour change models), human geography (mobility biographies approach), sociology (mobility cultures) and machine learning (causal discovery and causal inference). 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.

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101044339).