In the project, Soja will investigate under what circumstances people change their transport choices and what mental mechanisms are involved. Despite various initiatives and improved transport technologies, there is no decrease in CO2 emissions from human transport. The goal of URGENT is to expand the knowledge base so that more effective strategies can be developed.
The approach is interdisciplinary, and include elements from, e.g. psychology, sociology and machine learning. Data will be collected over 3 years via a semi-annual survey combined with case studies. Machine learning is used to identify which personal, social, technical or spatial factors are most relevant concerning behavioural changes.
In addition, so-called rebound effects (uninteded effects) of changed mobility behaviour, e.g., reduction of car use, choice of electric cars will be investigated—revealing how behavioural changes in one area (e.g. commuting) spread to others (e.g. air travel, food consumption).