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Free-floating sharing with e-mopeds and e-scooters is an upcoming mobility trend. The main problem in shared micromobility is uneven fleet distribution. One option to correct this asymmetry is user-based redistribution. This is especially challenging in free-floating, as the business area must be subdivided. While much research exists for car sharing and station-based bike sharing, the formation and evaluation of sub-areas in free-floating micromobility has barely been analyzed. In this work, we investigate the generation of sub-areas by irregular forms using the Voronoi diagram. The booking data of a free-floating e-moped sharing service in Stuttgart, Germany, serve as basis. Four different generation methods are compared: k-means, density-based spatial clustering of applications with noise (DBSCAN), grid-based clustering, and the use of the underlying road network. The results of the tessellation are evaluated on the basis of geometric properties, data distribution, and adaptability. The k-means algorithm efficiently generates cells in a uniform way, while DBSCAN can adapt well to different demand patterns based on density distribution. Grid-based clustering allows for the consideration of the radius. Metrics for the clusters and polygons were used to compare the methods. It can be seen that k-means (k = 122) performs better in both comparisons. Grid-based ended up in the middle and delivers polygons similar in shape to k-means, while DBSCAN had difficulty adapting to the data. The polygons from the street network generate many polygons, the number and characteristics of which are difficult to influence compared to the other methods.

Free driving with electrically powered micromobility vehicles such as e-mopeds and e-scooters is an emerging mobility trend. This trend has also been visible in Germany since the ordinance on the use of electric microvehicles on public roads came into force in 2019. Car sharing and station-based bike sharing have been scientifically studied more often than the usage patterns and behavior of e-moped and e-scooter customers. Insights into usage patterns and customer behavior can be used to improve customer satisfaction and the business model, for example, to increase the utilization rate or distribution of e-mopeds or to offer customers more targeted incentives to perform operational activities. Similar to the existing scientific work, the data set of an e-moped supplier in Stuttgart, Germany is analyzed. Data were analyzed according to the (CRISP-DM) and a (RFM) analysis was performed. The data were clustered using different clustering configurations depending on the model used and the number of clusters. Clusters resulting from the highest performing configuration according to Calinski–Harabasz index (k-Means with 4 clusters) were further analyzed. The resulting clustering allows conclusions to be drawn about how customer usage patterns and behavior have changed compared to previous analyses in the literature. Examples of further findings are that one in five customers abandoned the registration process, or that early adopters were between 40 and 55 years old.

Nowadays, managing electric energy is an important task to ensure stabilization of the power grid. The growth of renewable energy resources and the reduction of fossil energy resources made this task more crucial, as more energy fluctuations need to be balanced. The growth of electric vehicles (EVs) enables the potential to support grid balancing, as their batteries can be used as energy buffer for vehicle-to-grid (V2G). However, when merging a certain number of EVs to an EV fleet for such services, a challenging task is to calculate the usable energy (flexibility) of the fleet that can be used for V2G services. There are approaches dealing with flexibility, but there exist gaps such as missing combination of different influence factors resulting from detailed statistical EV usage behaviour or missing the consideration of relevant parameters necessary for a flexibility calculation. This paper proposes a simulation approach considering several relevant influence factors to calculate negative and positive flexibility of an EV fleet. The model contains the simulation of single EV users with statistical mobility and plugin behaviour and the simulation of the EVs including charging processes, charging behaviour and energy consumption. The results show that the approach calculates valid flexibilities of an EV fleet validated by charging data.

Nowadays, shared micromobility has become a trend in cities as an alternative to conventional automotive vehicles, especially for short-distance travel. It also plays an important role in the reduction of the number of automotive vehicles which results in a decrease of air pollution and traffic congestion. Shared micromobility is, however, influenced by the terrain characteristics. Varying elevation within a fleet operational area can cause imbalances in the use of micromobility stations if a steep terrainlies between stations. It also impacts the energy consumption of electric micromobility vehicles such as e-bicycles and e-scooters. Therefore, to simulate the state of charge (SOC) of traction batteries for micromobility close to reality, it is essential to include elevation data into the simulation model. This paper proposes a workflow for Simulation of Urban MObility (SUMO) comprising several steps with concrete implementation and validation in order to prepare and define the simulation model with micromobility stations and the integration of elevation data using a REST API. The integration of elevation and bike station data is validated with a defined vehicle type following a routein the hilly part of Stuttgart, Germany. A comparison of micromobility trips, with and without elevation data, was performed through a simulation by recording changes in energy consumption and driven altitude differences. The proposed workflow provides a basis for more complex use cases such as analysing micromobility business areas, improving vehicle distributions and developing incentive strategies for hilly and steep terrains.

Road traffic significantly increased in the last decades. The avoidance of accidents and the continuity of traffic flow are significant challenges for road mobility. In this paper, a real-time Internet of Things (IoT) architecture is presented for these current road traffic challenges. The presented architecture takes advantage of the emerging 5G mobile technology and relies on infrastructure components so that the presented architecture can be integrated into city infrastructure to become smarter and provide benefits to citizens. A collision warning service is being implemented to warn road users of imminent collisions. The parallel traffic flow optimization will also enable the prioritization of highlighted traffic participants, such as emergency vehicles. By prioritizing pedestrians, bicycles, e-bikes and buses, for example, it will also be possible to achieve specific traffic and climate policy goals. An according smartphone app allows the resulting advantages to be used in the short term. Furthermore, additional actuators such as displays and signal transmitters will integrate all road users, regardless of the specific app usage. By using 5G, these components will also be implemented quickly, flexibly and reliably as infrastructure IoT devices. The paper concludes with a presentation of a research project in which these solutions are currently being implemented.

The ongoing centralization of platform markets cannot be explained by the progressive business models of the providers only but has three main reasons. First, the use of digital technologies reduces transaction costs and disrupts traditional markets. Second, network-, scaling- and lock-in- effects imply a strong tendency toward monopolization of digital platform markets and benefit first-to-market strategies. Third, the rise of digital platforms is closely related to the collection, extraction and analysis of personal data and has thus a negative impact on data privacy. This paper introduces an approach to a decentralized infrastructure for the mobility market which gives high priority to data privacy by combining established and arising distributed technologies in a novel way.

Die Bedeutung von vernetzten IT-Systemen ist in den letzten Jahren im Zuge der digitalen Transformation durch Technologien wie Cloud, Künstliche Intelligenz und dem Internet der Dinge stark gestiegen. Ungeachtet ihrer zahlreichen Vorteile weisen vernetzte IT-Systeme eine hohe Komplexität auf und erfüllen zunehmend Kernfunktionen von Unternehmen und Produkten. Ein sicherer und störungsfreier Betrieb ist daher unabdingbar. Die vorliegende Marktstudie bietet einen Überblick über den Stand der Technik und Praxis für die Überwachung und Störungserkennung in vernetzten Systemen. Neben einer Marktuntersuchung wurden dafür 28 Experten befragt. Als Ergebnis zeigt die Studie Handlungsoptionen auf, stellt aktuell am Markt verfügbare technische Lösungen systematisch vor und beschreibt praktische Erfahrungen aus der Anbieter- und der Anwenderperspektive.

Over the last decades the necessity for processing and storing huge amounts of data has increased enormously, especially in the fundamental research area. Beside the management of large volumes of data, research projects are facing additional fundamental challenges in terms of data velocity, data variety and data veracity to create meaningful data value. In order to cope with these challenges solutions exist. However, they often show shortcomings in adaptability, usability or have high licence fees. Thus, this paper proposes a scalable and modular architecture based on open source technologies using micro-services which are deployed using Docker. The proposed architecture has been adopted, deployed and tested within a current research project. In addition, the deployment and handling is compared with another technology. The results show an overcoming of the fundamental challenges of processing huge amounts of data and the handling of Big Data in research projects.

Business success of companies heavily depends on the availability and performance of their client applications. Due to modern development paradigms such as DevOps and microservice architectural styles, applications are decoupled into services with complex interactions and dependencies. Although these paradigms enable individual development cycles with reduced delivery times, they cause several challenges to manage the services in distributed systems. One major challenge is to observe and monitor such distributed systems. This paper provides a qualitative study to understand the challenges and good practices in the field of observability and monitoring of distributed systems. In 28 semi-structured interviews with software professionals we discovered increasing complexity and dynamics in that field. Especially observability becomes an essential prerequisite to ensure stable services and further development of client applications. However, the participants mentioned a discrepancy in the awareness regarding the importance of the topic, both from the management as well as from the developer perspective. Besides technical challenges, we identified a strong need for an organizational concept including strategy, roles and responsibilities. Our results support practitioners in developing and implementing systematic observability and monitoring for distributed systems.

During the last decade, more and more cities follow the trend towards smart cities caused by today's rising urbanization and its accompanying challenges such as rising traffic or air pollution. Thus, many cities use technical solutions to collect and store data from an installed sensor network and analyze it to provide useful information to several city fields. However, smart city solutions show shortcomings, especially in its applied technology with respect to missing flexibility caused by their complexities and sizes. This paper describes a demo of a flexible solution for smart cities addressing these challenges using a modular design, open standards and technologies.