The missing contribution of the transport sector to the reduction of greenhouse gas emissions raises questions about our highly mobile, carbon-intense, and car-dependent lifestyles. To reach sustainability goals, significant transformations in transport behaviours and systems are urgently required. The URGENT project aims to provide the knowledge base for more effective intervention strategies to achieve such transformations. To this end, the project examines the individual and contextual circumstances under which people change their mobility behaviour and the mental mechanisms involved in this change process.
The project relies on a holistic, interdisciplinary approach and utilises a unique longitudinal dataset: Over a three-year period, data will be collected via a large biannual survey, complemented by qualitative interviews and a mobility diary app for a sub-sample of participants. With this threefold approach, URGENT examines how people adapt their mobility behaviours in anticipation of and in response to:
· residential relocation;
· retirement;
· crash involvement;
· electric vehicle adoption.
A special focus lies on the different effects of sudden versus planned/anticipated and desired versus undesired life events. URGENT thus pursues the question of whether and to what extent travel behaviour change is subject to choice, necessity, or chance.
Applying causal machine learning methods, the project further aims to uncover which personal, social, technical, or spatial factors are the most relevant initiators of behaviour change and will specify the causal relations between the involved factors. URGENT will additionally examine rebound effects of changed mobility behaviour 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’s analytical strategy 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 aims to increase the understanding of behaviour change in transport, and also bears the potential to lead to a breakthrough in studying causality in transport research at large.
