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. It 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. It 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 reproduction number 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.

Heavy goods transportation is responsible for around 27% of CO₂ emissions from road transport in the EU and for 5% of total CO₂ emissions in the EU. The decarbonization of long-distance transport in particular remains a major challenge. The combination of battery electric trucks (BETs) with on-route high-power charging (HPC) offers a promising solution. Planning and setting up the required infrastructure is a critical success factor here. We propose a methodology to evaluate the charging infrastructure needed to support the large-scale introduction of heavy-duty BETs in Germany, considering different levels of electrification, taking the European driving and rest time regulations into account. Our analysis employs MATSim, an activity-based multi-agent transport simulation, to assess potential bottlenecks in the charging infrastructure and to simulate the demand-based distribution of charging stations. The MATSim simulation is combined with an extensive pre-processing of transport-related data and a suitable post-processing. This approach allows for a detailed examination of the required charging infrastructure, considering the impacts of depot charging solutions and the dynamic nature of truck movements and charging needs. The results indicate a significant need to augment HPC with substantial low power overnight charging facilities and highlight the importance of strategic infrastructure development to accommodate the growing demand for chargers for BETs. By simulating various scenarios of electrification, we demonstrate the critical role of demand-oriented infrastructure planning in reducing emissions from the road freight sector until 2030. This study contributes to the ongoing discourse on sustainable transportation, offering insights into the infrastructure requirements and planning challenges associated with the transition to battery electric heavy-duty vehicles.

Ride‐hailing services have the potential to play a major role within future transport systems. While the operation as well as environmental, social and economic consequences are research subjects, the focus in the literature lies on system efficiency and profitability. In this study, vehicle relocation strategies are investigated with respect to service provision equity using an agent‐based simulation approach. The aim is to identify ways of operation that support social fairness while maintaining the profitability and effectiveness of the service. The results show that a typical demand‐anticipatory relocation strategy leads to an imbalance in the spatial distribution of mean wait times for the use case of Berlin. This can be significantly improved by applying different relocation strategies. As a consequence, the overall demand for the system rises while the ratio of empty vehicle kilometres increases slightly.

Shared pooled mobility has been hailed as a sustainable mobility solution that uses digital innovation to efficiently bundle rides. Multiple disciplines have started investigating and analyzing shared pooled mobility systems. However, there is a lack of cross-community communication making it hard to build upon knowledge from other fields or know which open questions may be of interest to other fields. Here, we identify and review 9 perspectives: transdisciplinary social sciences, social physics, transport simulations, urban and energy economics, psychology, climate change solutions, and the Global South research and provide a common terminology. We identify more than 25 000 papers, with more than 100 fold variation in terms of literature count between research perspectives. Our review demonstrates the intellectual attractivity of this as a novel perceived mode of transportation, but also highlights that real world economics may limit its viability, if not supported with concordant incentives and regulation. We then sketch out cross-disciplinary open questions centered around (1) optimal configuration of ride-pooling systems, (2) empirical studies, and (3) market drivers and implications for the economics of ride-pooling. We call for researchers of different disciplines to actively exchange results and views to advance a transdisciplinary research agenda.

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.