SOURCE-LINKED INTELLIGENCE

Choice, necessity or chance? Understanding behaviouR chanGE iN Transport

CORDIS · observation · Publication date unknown

amine 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 beha

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recordType

award

status

SIGNED

region

EU

value

1936613

unit

EUR