In this study, a noise internalization approach is presented and successfully applied to a real-world case study of the Greater Berlin area. The proposed approach uses an activity-based transport simulation to compute noise levels and population densities as well as to assign noise damages back to road segments and transport users. Iteratively, road segment and time dependent noise exposure tolls are computed to which transport users can react by adjusting their route choice decisions. Since tolls correspond to the transport user’s contribution to the overall noise exposures, the incentives are given to change individual travel behavior towards reduced noise exposure costs. Applying the internalization approach to the case study reveals that transport users shift from minor to major roads and take detours in order to avoid areas with high population densities. The contribution of the presented methodology is that the within day dynamics of varying population densities in different areas of the city are explicitly taken into account and affected people at work and places of education may be incorporated, which is both found to have a major impact on toll levels and network utilization. Depending on the time of day and depending on which population groups are considered, noise exposures are reduced by means of different traffic management strategies.