Description du projet
Examen des modifications de comportement en matière de mobilité
Le secteur de la mobilité applique une série de mesures douces et dures ainsi que des technologies de transport améliorées pour réduire ses émissions de carbone. Cependant, les résultats ne se sont pas révélés satisfaisants. Le projet URGENT, financé par le CER, génèrera des connaissances qui faciliteront la mise en œuvre de stratégies d’intervention plus efficaces en examinant la manière dont les individus adaptent leur comportement de mobilité et les mécanismes mentaux qui interviennent dans ce processus. Il s’appuie sur une approche interdisciplinaire complète fondée sur un ensemble de données longitudinales unique qui combine les informations collectées via une enquête biennale, des études de cas, des entretiens approfondis et une application de journal de la mobilité intelligente. URGENT appliquera des méthodes d’apprentissage automatique causal pour détecter les facteurs à l’origine des changements de comportement les plus pertinents, examinera les effets de rebond de cette modification de comportement en termes de mobilité ainsi que les interactions entre les changements de comportement dans différents domaines.
Objectif
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.
Champ scientifique
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régime de financement
ERC - Support for frontier research (ERC)Institution d’accueil
2800 Kongens Lyngby
Danemark