This study presents an approach to collect and classify usage data of public charging infrastructure in order to predict usage based on socio-demographic data within a city. The approach comprises data acquisition and a two-step machine learning approach, classifying and predicting usage behavior. Data is acquired by gathering information on charging points from publicly available sources. The first machine learning step identifies four relevant usage patterns from the gathered data using an agglomerative clustering approach. The second step utilizes a Random Forest Classification to predict usage patterns from socio-demographic factors in a spatial context. This approach allows to predict usage behavior at locations for potential new charging points. Applying the presented approach to Munich, a large city in Germany, results confirm the adaptability in complex urban environments. Visualizing the spatial distribution of the predicted usage patterns shows the prevalence of different patterns throughout the city. The presented approach helps municipalities and charging infrastructure operators to identify areas with certain usage patterns and, hence different technical requirements, to optimize the charging infrastructure in order to help meeting the increasing demand of electric mobility.

While scooters, often powered by 2-stroke engines, are quite popular in the southern parts of Europe they are rather scarce in the rest of Europe. Over the last years, electric-powered two-wheelers debuted on European markets. Since this type of vehicle can considerably improve traffic, reduce shortage of parking, and cut local emissions in cities, its low distribution and propagation in Europe seems rather inconvenient. This article presents the results of an escooter pilot study conducted in Munich, Germany, in order to broaden the understanding of the low distribution and of the field of application, usage, and constraints this kind of vehicle is facing in a European city. Therefore this article gives a summary about the subject of low-powered two-wheelers. In order to analyze the gathered data, a method to minimize irregularities in completion of a trip diary is presented. Final results of this pilot study show that escooters are particularly utilizable for commuting and leisure trips and can thereby substitute up to 64% of average daily distances. Additionally, weather conditions as constraints for escooter usage are examined. Even with weather conditions prevailing in Central Europe, escooters can be used at 58% of days per year without weather adjusted gear.

Carsharing (CS) services enable users not to have to depend on a privately owned vehicle by supplying cars for spontaneous rental. Optimized service, higher system availability and a more efficient urban transportation system can be obtained by analyzing a wide usage behavior and diurnal patterns within such a system. In order to gain a deeper understanding of the spatio-temporal behavior of such a service, it is vital to distinguish different demand patterns occurring within urban areas. We therefore analyze the demand for carsharing in the city of Munich, Germany. Based on data of a free-floating carsharing (FFCS) service provider we analyze rentals, drop-offs, and resulting availability of vehicles within different urban areas as well as develop diurnal demand patterns and cluster these by incorporating an incremental cross-correlation clustering. The compiled pattern clusters reveal notable distinctions in terms of demand, returns, and vehicle availability between the examined areas. By recognizing and analyzing these distinctions, FFCS will be able to adapt to new business areas, service operation, and pricing strategies in order to optimize their service.

In this study, a framework regarding park and ride facilities is presented and demonstrated to evaluate different approaches of charging concepts. The innovation in this study is that the framework can be used to evaluate arbitrary conductive charging concepts on a detailed level and on the basis of real usage data. Thus, the results can be broken down to the level of individual charging events and charging points. Among other factors, the study considers the expected growth in electric vehicles, the construction and operating costs for the investigated charging infrastructure, and the impact of heterogeneous electric vehicle fleets with different vehicle-specific charging powers. Since both technological and economic perspectives are considered in the framework, the study is relevant for all decision makers involved in the development and operation of charging infrastructure. The results in the investigated case of park and ride facilities show a high potential for cost-efficient low-power charging concepts. Thus, significantly higher energy volumes could be transmitted and better economic results could be achieved by the investigated low-power approaches. Especially for heterogeneous electric vehicle fleets, the number of available charging points appears to be more important than the charging power of the individual charging points in this case.

This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.