Bike-sharing is considered one of the most sustainable forms of urban transportation Abouelela (Trans. Res. Part A: Policy Pract. 169:103602, 2023) and, therefore, understanding what determines the efficiency of a bike-sharing system (BSS) is crucial. Various factors from different domains, like built environment or behavioral studies, have been demonstrated influential in single BSSs and small-scale datasets (from a single or few BSS). However, the multitude of potential factors requires rigorous analysis and application of statistical models on large datasets. Due to the unavailability of large-scale datasets comprising hundreds of BSSs and unified databases containing variables, interactions are only known to a limited extent. Here we show the usage patterns of different types of BSSs, and draw statistically valid conclusions on the relevance of factors from different domains by gathering over 43 Mio km open data from 267 BSSs and 108 predictors, as well as applying time-series clustering, building predictive classification models, and fitting a stepwise regressive Ordinary Least Squares (OLS) model. Results show that operational factors like operating time, design factors like operational area and share of electric bikes, sociodemographic factors like age and gender, built environment like cycling infrastructure, and economic factors like overnight stays all play an important role. Descriptive statistics reveal significant room for improvement in the operations e-bike-sharing system (electric BSS) and show how climate influences operational organizations. We anticipate this publication and the associated dataset, which we make openly available, to spark discussions about the design and operations of BSS and to be a starting point for further analyses.

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.

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.

This study quantifies the reduction in external costs and changes in travel times resulting from the shift from car use to intermodal travel in Munich, based on real-world tracking data. By re-routing all car trips and calculating the travel times, distances, and externalities of alternative modes, we demonstrate that without increasing travel time, external costs can be reduced by 21 %, and by up to 77 % if a doubling of travel time is accepted. These findings highlight the significant benefits of intermodal mobility for urban transportation planning and underline its importance as a viable alternative to the private car.