Nico Kuehnel

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.

Since 2023, the on-demand ridepooling service MOIA operates, as part of Hamburg's public transport, an integrated wheelchair-accessible service and offers subsidized fares for individuals with severe disabilities. This study analyzes one year of demand patterns in Hamburg following these enhancements, utilizing data analyses to assess their impact on service usage and accessibility. Findings reveal insights into distinct spatio-temporal user behavior, offering valuable guidance for the development of large-scale urban mobility solutions.

Agent-based simulations are powerful tools for simulating emergent mobility modes, but they often require significant memory and computing power. To address this issue, researchers have previously used sampling techniques, where only a fraction of agents are explicitly simulated while others are simulated through teleportation. However, recent studies have highlighted the challenges of scaling ride-pooling simulations because of their reliance on demand density, which does not scale linearly. In this study, we introduce a new methodology for simulating dynamic ride-pooling services called flow-inflated selective sampling (FISS). Unlike previous approaches, FISS considers all agents but—for selected modes—only explicitly simulates a fraction of their trips while simulating the remaining trips through teleportation. Here, we explicitly simulate all public transport and ride-pooling trips and sample private car trips. The capacity consumption of explicitly simulated cars is scaled up to obtain realistic traffic flows, rather than adjusting the network capacity as in previous approaches. We implement FISS in the MATSim simulation environment for a large scenario in Munich, Germany, and show that it preserves traffic flows while keeping key performance indicators of ride-pooling services stable and unbiased. Mode choice decisions based on FISS also remain stable, and runtimes of the assignment can be almost halved. Overall, FISS is a simple yet effective approach that can significantly reduce the computational burden of agent-based simulations while maintaining the accuracy of the results. It can be particularly useful for simulating ride-pooling services, which can be challenging to scale because of their dependence on demand density.

Data play an indispensable role in transport modelling. The availability of data from non-conventional sources, such as mobile phones, social media, and public transport smart cards, changes the way we conduct mobility analyses and travel forecasting. Existing studies have demonstrated the multitude and varied applications of these emerging data in transport modelling. The transferability of current research and further endeavours depend mostly on the availability of these data. Therefore, the openness or public availability of the prominent data for transport modelling needs to be adequately investigated. Such a discussion should also encompass these data’s application aspects to provide a holistic overview. This paper defines a typology for the data classification based on a set of availability or openness attributes from the existing literature. Subsequently, we use the developed typology to classify the prominent transport data into four categories: (i) Commercial data, (ii) Inaccessible data, (iii) Gratis and accessible data with restricted use, and (iv) Open data. Using this typology, we conclude that the public data, which refer to the data that are accessible and free of cost, are a superset of open data. Further, we discuss the applications and limitations of the selected data in transport modelling and highlight in which task(s) certain data excel. Lastly, we synthesise our review using a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis to bring out the aspects relevant to data owners and data consumers. Public availability of data can help in various modelling steps such as trip generation, accessibility, destination choice, route choice, network modelling. Complementary datasets such as General Transit Feed Specification (GTFS) and Volunteered Geographic Information (VGI) increase the usability of other data. Thus, modellers can gain from the positive cascade effect by prioritising these data. There is also a potential for data owners to release proprietary data, such as mobile phone data, with restricted-use licenses after addressing privacy risks. Our study contributes by dealing with two problems at the same time. On the one hand, the paper analyses existing data based on their potential for mobility studies. On the other hand, we classify them based on how open they are. Hence, we identify the most promising public data for developing the next generation of transport models.

In this article, we simulate and evaluate the operational challenges of non-autonomous ridepooling systems through driver shifts and breaks and compare their capacity and efficiency to automated on-demand services. We introduce shift and break schedules and a new hub return logic to perform the respective tasks at different types of vehicle hubs. This way, currently operating on-demand services are modelled more realistically and the efficiency gains of such services through autonomous vehicles are quantified. The results suggest that operational challenges substantially limit the ride-pooling capacity in terms of served rides with a given number of vehicles. While results largely depend on the chosen shift plan, the presented operational factors should be considered for the assessment of current operational real-world services. The contribution of this study is threefold-from a technical perspective, it is shown that the explicit simulation of operational constraints of current services is crucial to assess ride-pooling services. From a policy perspective, the study shows the potential of future autonomous services in direct comparison with non-autonomous services. Lastly, the paper adds to the literature a realistic ride-pooling simulation use case based on observed real-world demand and shift data.

Road traffic is a common source of negative environmental externalities such as noise and air pollution. While existing transport models are capable of accurately representing environmental stressors of road traffic, this is less true for integrated land-use/transport models. So-called land-use-transport-environment models aim to integrate environmental impacts. However, the environmental implications are often analyzed as an output of the model only, even though research suggests that the environment itself can have an impact on land use. The few existing models that actually introduce a feedback between land-use and environment fall back on aggregated zonal values. This paper presents a proof of concept for an integrated, microscopic and agent-based approach for a feedback loop between transport-related noise emissions and land-use. The results show that the microscopic link between the submodels is operational and fine-grained analysis by different types of agents is possible. It is shown that high-income households react differently to noise exposure when compared low-income households. The presented approach opens new possibilities for analyzing and understanding noise abatement policies as well as issues of environmental equity. The methodology can be transferred to include air pollutant emissions in the future.