Modelling the impact of automated driving

The paper presents projections for the impact of private autonomous vehicles on destination choice, mode choice and thus overall travel demand for Germany and the USA. These results were obtained by combining a vehicle technology diffusion model and a travel demand model

Learning a Generative Transition Model for Uncertainty-Aware Robotic Manipulation

Peer reviewedPreprin

Self-Supervised Learning for Precise Pick-and-Place Without Object Model

ACKNOWLEDGEMENT We would like to thank Tamim Asfour for his helpful suggestions and discussions.Peer reviewedPostprin

User-specific and Dynamic Internalization of Road Traffic Noise Exposures

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

An activity-based and dynamic approach to calculate road traffic noise damages

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

CO2-Emissionen im Personenverkehr: Einfluss von Soziodemographie, Wohnort und Einkommen

Für verschiedene Bevölkerungsgruppen wurde untersucht, welchen Einfluss Soziodemografie, Wohnort und Einkommen auf die durchschnittlichen jährlichen CO2-Emissionen haben. Die Analysen zeigen, dass die durchschnittlichen CO2-Emissionen von Stadt- und Landbevölkerung nahezu identisch sind, jedoch mit dem Haushaltseinkommen ansteigen. Datengrundlage der detaillierten Analysen zu den CO2-Emissionen im Personenverkehr ist ein Datensatz, der die Gesamtmobilität der in Deutschland lebenden Bevölkerung umfasst, d. h. alle Verkehrsmodi sowie Wege und Fahrten im In- und Ausland