Individual and household life events are interdependent and influence mobility-related decisions at different levels over time. This paper developed an integrated dynamic model to capture the interdependences among life events, with a special focus on vehicle transactions. Particular attention was paid to the inclusion of vehicles’ characteristics such as the age, fuel type, and size of cars, which are pertinent to emission forecast. A dynamic Bayesian network (DBN), containing individual and household characteristics and latent attitudes toward car ownership and use alongside life events, was employed to study the interdependences. The temporal relationships among life events and lead-lag effects were also captured in the DBN. The longitudinal survey data “the Netherlands Mobility Panel (MPN)” from 2013 to 2018 was used to train and test the DBN. The analysis results confirm the dynamic interdependences between vehicle transactions and other life events and reveal noticeable associations between attitudes and purchase decisions. It is found that several life events (e.g., “Birth of a baby”, “Marital status change”) have concurrent or varied lag-effects on vehicle transaction decisions. The validation indicates that the proposed DBN approach has a high predictive accuracy of vehicle transaction decisions and other life events.