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A major part of urban private vehicles needs to be electrified in order to meet the German climate protection goals in the transport sector. Therefore, charging strategies must be developed and the geographical distribution of infrastructure has to be planned accordingly. We propose a new method to combine a multi agent traffic simulation (MATSim) with real-world vehicle distribution and measured charging behavior. The results of our simulation were analyzed for the exemplary use case of Berlin’s private passenger cars. Three different charging strategies were developed and analyzed: charging at home, at work and during leisure activities. Charging at home was suitable to serve all agents’ charging demand with low charging power (7.4 kW). Charging at work only served 56.23 % of the agents with a charging power of 11 kW, but required fewer charging points. Charging during leisure activities served from 46.36 % to 81.92 % of the agents, depending on the chosen charging power and user behavior.

App-based ride-hailing services have expanded rapidly in recent years. In this study, simulation experiments are carried out for two regions in Germany: the metropolitan Berlin area and the rural area of the district Vulkaneifel (Volcanic Eifel). Transport users’ travel behavior is fixed and the operator is enabled to adjust the ride-hailing vehicle fleet size to keep 90% of all waiting times below 10 minutes. The results show that the trip density has a major impact on the required ride-hailing fleet size as well as the resulting service (e.g. operation hours, vehicle-kilometers and pooling rate). The simulation results reveal a nonlinear relationship of trip density and ride-hailing service parameters. A linear upscaling of simulation results for a small population sample may yield an overestimation of the fleet size, operating hours and vehicle kilometers. For low trip densities, the effect of pooling (ride-sharing) is disproportionately smaller compared to larger trip densities.

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19.This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. The model is validated against the infection dynamics in Berlin (Germany).The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reinfection rate R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%.Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.Author summaryEvidently, there is an interest in models that are able to predict the effect of interventions in the face of pandemic diseases. The so-called compartmental models have difficulties to include effects that stem from spatial, demographic or temporal inhomongeneities. Person-centric models, often using social contact matrices, are difficult and time-consuming to build up. In the present paper, we describe how we built a largely data-driven person-centric infection model within less than a month when COVID-19 took hold in Germany. The model is based on our extensive experience with mobility modelling, and a synthetic data pipeline that starts with mobile phone data, while taking the infection dynamics and the disease progression from the literature. The approach makes the model portable to all places that have similar so-called activity-based models of travel in place, which are many places world-wide, and the number is continuously increasing. The model has been used since its inception to regularly advise the German government on expected consequences of interventions.