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The freight traffic plays an important role in the agent-based transport simulation models. Compared to the commuter traffic, the freight traffic receives relatively less attention. This study aims to bring freight traffic into the spotlight of the traffic simulation. In this study, the long-haul freight traffic for Germany is generated based on open-source data in the context of an agent-based model. The outcome of this study, which is also open-sourced, can be applied to any of the simulation scenarios within Germany. In addition, it can also be used as a full scenario for studies on logistics and sustainable solutions for freight transport. [ABSTRACT FROM AUTHOR]

As climate change continues to become a bigger threat, greenhouse gas (GHG) emission reductions are becoming more and more urgent. About 7% of all GHG emissions in Germany are caused by trucks. Up to now, GHG calculations for trucks were limited in accuracy due to the lack of a matching database. For this work, a suitable database has been produced. It is applied to an existing simulation scenario. The simulation consists of "no-tax" and a "high-tax" (300 ϵ per ton of CO 2 eq) scenario. In both scenarios, there are conventional (diesel) and battery electric trucks available. The fleet composition results from a tour planning algorithm. The results of the emissions calculations show a marked decrease in exhaust-related emissions between the two scenarios. CO 2 emissions decreased by about 48% which is in line with the reduction of diesel trucks mileage to approx. half. NO x also lessened by about 51%. Non-exhaust emissions, however, showed no such decrease. Since most PM emissions do not stem from exhaust gasses, but instead consist mainly of road- and tire-wear, they only decreased slightly. This suggests that, while a GHG tax will be effective in decreasing GHG and other exhaust emissions, it will not lead to any reduction in urban air pollution for other (non-exhaust) pollutants. [ABSTRACT FROM AUTHOR]

From the transport economic literature, it is known that optimal pricing of (environmental) externalities improves the urban system. In contrast to theory-based optimal pricing strategies, real-world policy setting often follows so-called "backcasting" approaches where certain targets are set, and policy measures are implemented in order to reach those targets. An example for the latter approach is the EU goal to reduce global greenhouse gas emissions in the transport sector by 20% until 2020 with respect to 1990 levels. This article aims to (i) compare optimal pricing and backcasting approaches for a specific case study in a simulation environment by identifying the contribution of each approach in EU's 2020 emission reduction target, and (ii) to determine the costs required to reach the desired targets. For this purpose, an optimal emission pricing strategy is applied to a real-world scenario of the Munich metropolitan area in Germany. The highly differentiated tolls relate to individual exhaust emissions, i.e. they are calculated using damage cost estimates from the literature and vary over time of day, with traffic situation, and with vehicle type. The results indicate that the desired reduction in CO2 emissions is not reached for optimal pricing approach, and that the initial damage costs estimates need to be multiplied by a factor of 5 in order to reach the target, yielding a price of 350 CO2. When aiming at a decrease of the overall emission costs by 20% (CO2 and local pollutants), the initial cost estimates need to be multiplied by a factor of 10. Furthermore, it is shown that the major contribution to the overall emission reduction stems from behavioral changes of commuters and reverse commuters rather than from urban travelers; under some circumstances, urban travelers even increase their CO2 emission levels. Hence, the study rises awareness that conflicting trends for different types of pollutants and different types of individuals are very likely: an increase in non-methane hydrocarbon levels for urban travelers and freight depicts that pricing emissions does not necessarily result in a reduction of all pollutants or of the emissions levels of all travelers. It is shown how agent-based simulations can be used to provide valuable insights and decision support in such possibly counter-intuitive situations. [ABSTRACT FROM AUTHOR]

This paper provides an introduction to multiagent traffic simulation. It includes some description of where we are with respect to the implementation of a real-world Berlin scenario. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the reunification, are considerably larger than in previous scenarios that we have treated. [ABSTRACT FROM AUTHOR]

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., Competing Interests: MB and KN own shares of the commercial company senozon. MB and AN are employees of senozon. None of this alters our adherence to PLOS ONE policies on sharing data and materials.