Urban mobility systems in European cities face climate, health, and spatial challenges that call for policies to reduce private car use while increasing access by public transport and active modes. Road space reallocation shows promise, but there is limited understanding of how citywide implementation affects travel behavior over time. This research addresses this gap by conceptualizing the effects of citywide road space reallocation on travel behavior and developing a quantitative System Dynamics (SD) model. The method is combined with Scenario Discovery to evaluate the role of uncertainties on the effectiveness of the policy, and expert interviews are conducted to support the validation of the model. Applying the model to a single case study with ongoing road space reallocation indicates that the policy can trigger significant modal shift to cycling for mid-distance trips (1.5–6 km), while modal shift from the car to public transport for long-distance trips (7–10 km) is slower and more uncertain. The level of change depends on uncertainties, including the future availability of bike parking and the uptake of e-bikes. The research contributes a data-light SD model that quantifies existing conceptual models and provides strategic insights for designing robust road space reallocation policies. Next modeling steps should prioritize the inclusion of bike-and-ride, and future empirical research should examine bike adoption over time in cities with improving cycling networks. Recommendations for policy makers include accelerating bike adoption through targeted initiatives, improving monitoring and target-setting, and collaborating across municipalities to address car dependence in suburban areas.