
BeCoS Lab builds data-driven models to forecast mobility choices and technology adoption in cities. How: it uses AI, econometrics, and causal inference to analyze individual behavior and urban systems. Core methods include data-driven choice models, neurophysiological data modelling, and activity-based and system-level modelling. The work integrates built environment insights and urban analytics with adaptive infrastructure planning and policy support. Target users are urban planners, researchers, and technology developers seeking scalable, city-wide insights.

BeCoS Lab builds data-driven models to forecast mobility choices and technology adoption in cities. How: it uses AI, econometrics, and causal inference to analyze individual behavior and urban systems. Core methods include data-driven choice models, neurophysiological data modelling, and activity-based and system-level modelling. The work integrates built environment insights and urban analytics with adaptive infrastructure planning and policy support. Target users are urban planners, researchers, and technology developers seeking scalable, city-wide insights.