Tilmann Schlenther

The largest on-demand ridepooling (DRT; demand-responsive transport) service in a single European city has officially been part of Hamburg’s public transit system since 2023. Policy makers, practitioners, and planners aim to provide a holistic mobility offering and by doing so, reduce the dependency on private car usage. Against this background, an agent-based simulation is presented and deployed to investigate how various pricing schemes influence the ridership of DRT service, with a particular focus on connections to/from traditional public transit (PT). This involves a novel process that addresses a common problem of service overcrowding while simulating fixed-size DRT fleets with a low mode share in agent-based transport models. The results suggest that a DRT discount for PT season ticket holders significantly increased overall ridership, whereas the number of intermodal trips remained constant. Similar results were observed for PT-quality-dependent pricing schemes, in which DRT was surcharged if parallel PT connections were relatively good, or discounted if they were relatively poor or nonexistent. In contrast, a direct discount for intermodal trips increased the share and the absolute number of intermodal trips, which tended to replace direct DRT trips. Most importantly, the results indicated a tradeoff between operator revenue (or the need for subsidies) and the share of intermodal trips.

Under a 2021 amendment to German law, the KelRide project became the first public on-demand service operating electric autonomous vehicles (AVs) without fixed routes on public roads. This paper addresses two notable gaps in the literature by (1) conducting an ex post evaluation of demand predictions for a non-infrastructure (Mobility-on-Demand (MoD)) project and (2) using real-world data to analyze how demand responds to key Autonomous Mobility-on-Demand (AMoD) system parameters in a rural context. Earlier simulation-based demand forecasts are compared to observed booking data, and the recalibrated model is used to investigate the sensitivity of passenger numbers to vehicle speed, fleet size, service area, operating hours, and idle vehicle positioning. Results show that increasing vehicle speed leads to a superlinear rise in passenger numbers—especially at small fleet sizes—while demand saturates at large fleet sizes. A linear increase in demand is observed with expanding service areas, provided fleet size is sufficient. Extending operating hours from 9 a.m.–4 p.m. to full-day service increases demand by a factor of two to four. Passengers numbers also vary notably depending on the positioning of idle vehicles. Consistent with empirical findings, the analysis underscores that raising AV speed is essential for ensuring the long-term viability of autonomous mobility services.

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.

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.

As part of this work, a citizens’ assembly was set up to obtain feedback on measures to decarbonize Berlin’s transport sector. The general goal of decarbonization was widely supported. Based on existing studies, discussions were held for private passenger transport, commercial passenger transport, freight transport, and all other transport modes. Agreement could be found on the decarbonization paths for all segments, with the exception of private passenger transport. For the latter, pull measures, like improvements of public transport or bike lanes, are generally accepted, going along with a general desire to switch to non-fossil fuels and to make the transport system in Berlin less car-oriented. Simulations show that pull measures will be far from sufficient to decarbonize private passenger transport. However, more effective push measures, such as higher prices or ban on fossil-fueled vehicles, did not yield clear majorities for this segment.

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.

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.

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.