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The aim of the project led by the Transport and Mobility Laboratory is to formulate a dynamic vehicle choice forecasting framework enriched by market, institutional and behavioural variables.
Predicting vehicle choice evolution in the future, particularly the acceptance of alternative drive trains and low emission vehicles, is a central concern of both the automotive industry and public authorities. Collectively optimal consumer choices will play a decisive role in reducing energy consumption and CO2 emissions associated with the private transportation sector, hence contributing towards satisfying public mobility targets. On the side of manufacturers, the competitiveness and inventiveness of this vital sector hinges crucially on forecasting how the public will react to innovative technologies.
Traditional static vehicle forecasting models are increasingly under strain given the need to account for the complex interdependence between consumer preferences, institutional interventions and vehicle market dynamics. Numerous factors, such as uncertainty of new vehicle technologies and models offered, multiple incentives and institutional interventions in the market, economic fluctuation, changing household structure and evolutions in personal motivations and vehicle-perceptions call for research into dynamic model development.
This forms the motivation for this research collaboration between the Transport and Mobility Laboratory and Groupe PSA. The aim of the project led by Professor Bierlaire is to formulate a dynamic vehicle forecasting framework enriched by market, institutional and behavioural variables. Drawing on a large-scale, longitudinal dataset, a system of models accounting for aggregate transition probabilities, vehicle ownership duration and disaggregate demand determinants are developed. The central contribution with regard to existing vehicle demand models is to account for the inherently dynamic nature of vehicle decisions.
The findings will aid the prediction of consumer behaviour in response to critical policy variables, such as incentives, and complex market dynamics, like the appearance and diffusion of novel vehicle powertrains. Reliable demand forecasting is an essential factor to facilitate smart and affordable long-run transportation policies as well as efficacious industrial strategies promoting the uptake of new vehicle models and technologies.
|Principal investigator||Prof. Michel Bierlaire|
|Project managers||Dr. Amanda Stathopoulos / Dr. Michaël Thémans|