Exploring the links between values, attitudes and mode choice in the context of Munich, Germany

Despite growing concerns about climate change, the transport sector has achieved limited emission reductions, partly because car-reduction policies vary in effectiveness and often overlook psychological drivers of travel behavior. Linking values, attitudes, and mode choice remains underexplored, even though values shape norms, perceptions, and ultimately mobility decisions. To address this gap, this study examines how values shape mode-specific attitudes and travel behavior using survey data from Munich. We estimate a value–attitude–behavior hierarchy with structural equation modeling (SEM) and apply four machine learning (ML) algorithms to assess the relative importance of psychological and socio-demographic predictors. SEM results show that openness to change increases cycling through positive cycling attitudes, whereas conservation values strengthen pro-car attitudes and private car use; no value-attitude link emerges for public transport. ML models identify age, income, and conservation as key predictors of car use, while perceiving a mode as relaxing strongly predicts both car and public transport use. Convenience and pleasure are the strongest attitudinal predictors of cycling. These findings provide concrete policy guidance, suggesting that cycling campaigns should emphasize convenience and enjoyment, public transport marketing should highlight relaxation, and car-reduction measures should anticipate resistance among individuals with strong conservation values. Future studies should examine how psychological factors can support targeted, value- and identity-driven sustainable mobility marketing.