Choice, necessity or chance? 

The URGENT project aims to provide the knowledge base for more effective intervention strategies for behavioural change in transport. To this end, the project examines the individual and contextual circumstances under which people change their mobility behaviours and the mental mechanisms involved in this process. URGENT uses a holistic, interdisciplinary approach based on a unique longitudinal dataset. Over a three-year period, we are collecting data via a large biannual survey as well as a case study involving interviews and mobility diary apps with selected participants. With this threefold approach, we examine how people adapt their mobility behaviours in anticipation of and in response to:
· residential relocation;
· retirement (as a long-time anticipated life-event);
· crash involvement (as a very sudden event);
· electric vehicle adoption.
A special focus lies on the different effects of sudden versus anticipated/planned and desired versus undesired life events. We thus pursue 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 will uncover which personal, social, technical, or spatial factors are most relevant initiators of behavioural change and will specify the causal relations between the 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 aims to 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.