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In this paper, an activity-based and dynamic approach is presented to analyze population exposures to road traffic noise. The contribution of this innovative approach is that (1) affected people at the workplace and places of education are incorporated and (2) the within day dynamics of varying population densities in different areas of the city is explicitly taken into account. The proposed methodology is applied to a real-world case study of the Greater Berlin area. The results demonstrate the need to account for the spatial and temporal variation in the population since the use of static resident numbers would result in an overestimation of residential noise damages. Going beyond residential exposures, the inclusion of further activity types is found to have a substantial effect on the results. Assuming individuals at work or education to be additionally affected by noise, population exposures in the central business districts are much larger than in residential areas. The proposed approach may be seen as a first step towards improved noise mapping standards to provide better recommendations for policy makers and ensure a more efficient use of noise control strategies.

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Autonomous vehicles (AV) create new opportunities to traffic planners and policymakers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator's profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator's side, the routing-and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users' side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city's transport system.