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Using the same vehicles for both passenger and freight transport, to increase vehicle occupancy and decrease their number, is an idea that drives transport planners and is also being addressed by manufacturers. This paper proposes a methodology to simulate the behavior of such vehicles within an urban traffic system and evaluate their performance. The aim is to investigate the impacts of resignation from fleet ownership by a transport service company (TSC) operating on a city-wide scale. In the simulation, the service provider hires private autonomous cars for tour performance. Based on assumptions concerning the operation of such vehicles and TSCs, the software Multi-Agent Transport Simulation (MATSim) is extended to model vehicle and operator behavior. The proposed framework is applied to a case study of a parcel delivery service in Berlin serving a synthetic parcel demand. Results suggest that the vehicle miles traveled for freight purposes increase because of additional access and egress trips. Moreover, the number of vehicles en route is higher throughout the day. The lowering of driver costs can reduce the costs of the operator by approximately 74.5%. If the service provider additionally considers the resignation from fleet ownership, it might lower the operation cost by another 10%, not taking into account the costs of system transfer or risks like vehicle non-availability. From an economic perspective, the reduction of the overall number of vehicles in the system seems to be beneficial.

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.

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 person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. Results show that in Berlin (Germany), behavioral changes of the population mostly happened before the government-initiated so-called contact ban came into effect. Also, the model is used to determine differentiated changes to the reinfection rate for different interventions such as reductions in activity participation, the wearing of masks, or contact tracing followed by quarantine-at-home. One important result is that successful contact tracing reduces the reinfection rate by about 30 to 40%, and that if contact tracing becomes overwhelmed then infection rates immediately jump up accordingly, making rather strong lockdown measures necessary to bring the reinfection rate back to below one.

Parking is a major constraint for car users and therefore an important factor in mode choice decisions. In this paper we introduce a model to simulate parking search behavior for cars within a multi-agent transport simulation, including full simulation of all steps of parking search, such as walking to and from the vehicle. This is combined with the capabilities of privately owned autonomous vehicles (AVs), which may park automatically, often in other locations than conventional cars, once they are not in use. Three different strategies for AVs to park are developed: (1) Conventional parking search, (2) parking at a designated AV lot, and (3) empty cruising, where vehicles do not use any parking space, but keep on driving. We apply the simulation model to a residential neighborhood in central Berlin, where parking pressure is generally high and apply different shares of AV usage to the synthetic population used. This allows a detailed evaluation of effects for both AV and conventional vehicle owners. Results suggest that the usage of designated parking lots may be the most beneficial solution for most users, with both vehicle wait times and parking search durations being the lowest.