Lennart Adenaw

In a response to the 2022 cost-of-living crisis in Europe, the German government implemented a three-month fuel excise tax cut and a public transport travel pass for 9 Euro per month valid on all local and regional services. Following this period, a public debate immediately emerged on a successor to the so-called "9-Euro-Ticket", leading to the political decision of introducing a similar ticket priced at 49 Euro per month in May 2023, the so-called "Deutschlandticket". We observe this introduction of the new public transport ticket with a sample of 818 participants using a smartphone-based travel diary with passive tracking and a two-wave survey. The sample comprises 510 remaining participants of our initial "9-Euro-Ticket study from 2022 and 308 participants recruited in March and early April 2023. In this report we report on the status of the panel before the introduction of the "Deutschlandticket".

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this fifth report, we present first analyses of the recorded tracking data. 910 participants completed the tracking until September, 30th. First, an overview over the socio-demographic characteristics of the participants within our tracking sample is given. We observe an adequate representation of female and male participants, a slight over-representation of young participants, and an income distribution similar to the one known from the "Mobilit\"at in Deutschland" survey. Most participants of the tracking study live in Munich, Germany. General transportation statistics are derived from the data for all phases of the natural experiment - prior, during, and after the 9 EUR-Ticket - to assess potential changes in the participants' travel behavior on an aggregated level. A significant impact of the 9 EUR-Ticket on modal shares can be seen. An analysis of the participants' mobility behavior considering trip purposes, age, and income sheds light on how the 9 EUR-Ticket impacts different social groups and activities. We find that age, income, and trip purpose significantly influence the impact of the 9 EUR-Ticket on the observed modal split.

In order to transform cities into more liveable, safe, and sustainable places, we must shift our mobility paradigms. As one auspicious concept amongst novel intelligent transportation systems, personal rapid transit (PRT) disaggregates urban transportation into small, electric vessels that are centrally operated on dedicated infrastructure, yielding the potential to make public transit more convenient, affordable, and sustainable all at once. In light of this, we examined the potential performance of PRT in a medium-sized German city. Utilizing the traffic simulator SUMO, as well as a specifically developed open source mobility scenario consisting of infrastructure and travel demand, we assessed the level of service and efficiency. We found that a fleet of 30 vehicles can serve the mobility demand of the chosen city while passenger waiting times are guaranteed to stay below three minutes. Vehicle occupancies can be doubled when coordinating vehicles between stations instead of letting them idle randomly. Furthermore, our results show that different combinations of system designs and operating strategies succeed in meeting typical performance requirements—for instance, an operating strategy where unoccupied vehicles idle randomly can effectively compensate for a reduced fleetsize. Depending on the preliminaries of specific cities, such as the availability of space, travel behavior, political background, or acceptable investment and operational costs, a matching transportation system can be designed around the quantitative findings obtained in this study.

Manifold applications in transportation system engineering rely on accurate modeling of human mobility demand. This demand is often represented by so-called mobility plans. Distinguished by their levels of aggregation, activity-based and trip-based models are the most prominent types of demand models in the literature. Macroscopic trip-based models are widely available but do not model mobility at the person level. In contrast, activity-based approaches simulate mobility microscopically but are complex and thus rarely available. The goal of this article is to present, apply, and validate an approach to generate activity-based mobility plans which microscopically reproduce real-world mobility demand but circumvent the complexity of activity-based approaches. To achieve this, existing trip-based models and mobility surveys are employed. Application results for car mobility in the city of Munich show that the obtained mobility plans are realistic on both a microscopic and a macroscopic level with regard to time, space, and activities. The presented approach can thus be considered appropriate for generating activity-based mobility plans whenever the development of a full-scale activity-based demand model is infeasible.

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15%. In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this second report, we update the information on study participation, provide first insights on the smartphone app usage as well as insights on the first wave results, particularly on the 9 EUR-ticket purchase intention.

The automotive sector’s transition to Battery Electric Vehicles (BEVs) requires extensive deployment of additional charging infrastructure. To determine optimal new locations, planners consider and rate a multitude of factors that influence the charging demand at candidate sites. Researchers have proposed a variety of placement criteria and methods to automate site selection. However, no common set of criteria has emerged. In addition, due to the lack of usage data, the applicability of existing criteria remains unclear. Therefore, the goals of this article are to extract the most relevant factors from literature and to evaluate their ability to characterize charging point usage. First, we review the literature base to collect, analyze, and cluster existing influencing factors and to analyze how they affect charging demand. Second, we conduct a case study using real-life charging station data from Hamburg, Germany. Based on the extracted influencing factors, we identify four clusters within Hamburg’s public charging infrastructure. While the mean performance indicators duration, daily transactions, and connection ratio hardly differ among these clusters, the temporal occupancy curves clearly show distinct charging behavior for each cluster. This work contributes to the state of the art by structuring the diverse landscape of charging station location placement criteria, by deriving a set of measurable influencing factors, and by analyzing their effect on a location’s charging demand, yielding an open source data set of charging point usage.

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

With electromobility on the rise, several re-searchers have acknowledged the charging station location problem as a major challenge for city planners and decision-makers. Especially in urban areas, where there is little room for privately owned charging infrastructure, public charging infrastructure is relevant to users of battery electric vehicles. This paper reviews current literature on the charging station location problem and proposes a four-step model concept for the placement and allocation of urban public charging sites based on the shortcomings of existing approaches. The proposed model combines the advantages of existing approaches and is exclusively built on socio-demographic and geospatial data that is readily available in all larger municipalities of the world. The model consists of a demand estimation based on socio-demographic and geospatial data, a candidate site selection based on a genetic algorithm, an agent-based infrastructure simulation to account for user behavior and system dynamics, and a heuristic repositioning approach to optimize charger allocation.